Manufacturing Operations Management Talk

Hidden Treasures In Plain Sight - at the Manufacturer’s Shelf

There are hidden treasures at the manufacturer’s shelf and we are not talking about the inventory shelf. We are talking in this article about the IT shelf of already owned software that is not being used. More specifically about the Manufacturing Operations Management (MOM) or Manufacturing Execution System (MES) software the company purchased a few years back and hasn’t been deployed to its full capabilities.

Gartner has recently published some interesting survey results validating this premise. [1] Figure 1 shows that even though most companies have achieved the expected return on investment (ROI), they still believe that there is more value to capture with their MES.  

It seems that it is easy to achieve the initial benefits and then move attention to other things. Does management even know about these potential benefits? Has the organization bothered to present these additional areas of improvement as possible phase 2 or phase 3 projects with their own ROI?

I have seen these types of stories often. The next steps are perceived as harder work. We took care of the “low hanging fruit” in that first phase but the next phase is going to take harder integration work. However, the potential benefits can also be much bigger.

In fact, MES is a foundational enabler to the Smart Manufacturing strategy and it was probably not positioned that way in its first implementation. MES is often implemented and justified based on the benefits of eliminating paper-based processes in production. However, as illustrated in Figure 2, Gartner has documented that companies that fully embrace the MES as an enabler to more process improvement and business transformation are achieving three to ten times the initial benefit in the next three to five years. [2]

The fact that the MES is not fully rolled out to all facilities and programs might be obvious, but the fact that there is more functionality and integration potential left on the table might be a little less obvious.

Typical areas of process improvement post initial implementation of the MES include:

• Integration of in-process quality management processes, material review board (MRB), rework specifications, and corrective action management.
• Integration of automated factory equipment like parts placement and inspection equipment that collected a lot of data that could be pumped directly into the MES.
• Integration of engineering data directly from the PLM system including 3D CAD as the basis for 3D visuals for work instructions and integration of specifications in PMI directly into the MES inspection verification requirements.
• Integration of the supply chain management processes including supplier quality management.

If your company has forgotten to pursue these opportunities, you are not alone. But the good news is that you have already done the hard work of implementing phase one of the MES. It is time to fully leverage that initial investment and start planning the next phase of MES at your organization.

If you have access to MESA International or Gartner research papers, I encourage you to read these reports for more insights on how to get the most value out of your MES investment.

References

[1] Survey Analysis: More MES Value to Be Captured from Supply Chain Collaboration and IIoT, Franzosa and Jacobson, Gartner, 2019

[2] Get the Most Out of Digital Manufacturing by Extending the Value of Your Manufacturing Execution System, Franzosa, Gartner, 2016

 

 

Manufacturing Operations Management Takes Center Stage for Industry 4

Many new technologies are being introduced these days into manufacturing, yet the Manufacturing Operations Management (MOM) or Manufacturing Execution Systems (MES)—which has been around for decades—is viewed as a fundamental enabler for the Smart Manufacturing and Industry 4.0 digital transformation. Technologies like augmented reality, artificial intelligence, cloud and edge computing, mobile, and auto-ID are being integrated into MOM solutions. Likewise, smart machines, sensors, and IIoT platforms are expanding capabilities to integrate to enterprise systems like MOM.

Regardless of rumors about the potential demise of MOM solutions, the facts are that many of the functions and features provided by MOM cannot be replaced by new IIoT platforms, even with analytics and apps. Alone, IIoT devices and platforms do not offer many new possibilities to manufacturers because they cannot provide operational context for data, cannot trigger actions in response to data, and are not designed to orchestrate processes across the factory and value chain. Many production processes need the human in the loop and MOM provides that platform.

MOM does not deliver Smart Manufacturing by itself. It needs to be integrated into the organization’s digital platform and “system of systems” including engineering, business, and automation systems. MOM does provide these important features required to integrate Operations into the Industry 4.0 digital transformation strategy:

• Intelligent Insights.Raw data captured from machines and workers during production must be turned into information by adding context and organization. Context includes data about the product specifications, process step, people, and equipment involved in the activity when the datum—like a dimensional measurement or environmental condition—is captured. Organization of stored data is also important to enable views that joins data from multiple activities for correlation and root-cause analysis. The organization of data makes it easier to aggregate data into significant metrics and key performance indicators (KPIs). It helps put the human in the loop to make better decisions quickly. 
• Data Stewardship and Governance.A proliferation of custom apps in production can lead to a multitude of data silos that are not coordinated to facilitate integration and aggregation. The MOM database provides de-facto governance, through its production-centric data model and integration interfaces, maintaining data clean, normalized, and organized to facilitate joins and searches across data in the enterprise.
• Standardization and Control. The MOM manages and enforces every aspect of the work steps including equipment, materials, and data collection for automated and manual processes. MOM enforces the policies and rules to make sure people and equipment are operating as they’re supposed to. The standardization makes production processes repeatable with consistent performance and quality.
• Response Action. MOM functions include methods to handle exception-driven events during the production process. Events or incidents like a suspect defect or process anomaly. MOM is the natural system to receive these types of notifications and alerts from an IIoT analytical platform. In MOM, these triggers can initiate procedures to record a discrepancy, change the equipment settings or operator instructions based on an automatically detected condition.
• Connection and Digital Thread. MOM provides an integration bridge between the plant and key enterprise systems including engineering’s PLM system, and procurement, inventory and cost management’s ERP system. This integration can be orchestrated to provide a digital thread linking the product’s engineering design specification to the product’s realization process, and even the product’s sustainment services in a way that facilitates the maintenance of a digital twin for each physical product unit.  The digital thread and digital twin can provide many benefits including better data exchange across the enterprise, and capability to analyze the product design against actual performance for continuous improvement. The digital thread accelerates the introduction of new products and variations by making it easier to implement design changes into downstream production definition elements like inspection requirements, machining programs, and work instructions.

The need for more real-time factory data and enterprise-wide connectivity is refueling the interest in MOM and MES. IIoT technologies are a great addition to the manufacturing IT landscape however, for the reasons listed above, MOM is needed more than ever as a foundational piece to the Smart Manufacturing and Industry 4.0 strategy.

 

Shaping Skills and Culture for the Factory of the Future

The “factory of the future” is not just about implementing new technologies like smart machines, cloud platforms, edge devices, robots, and artificial intelligence. It is about a business transformation that requires a shift in mindset and organizational culture to engage with customers, rethink outcomes, and execute highly connected processes.   

Traditional organizational structures are becoming inadequate for changing business needs and many jobs are changing or being replaced by automation. The composition and definition of jobs in the factory are rapidly changing. The reliance on digital technology is increasing in every job.

Organizational culture can move people to act or inhibit them from acting. Culture is built over time by design or default. Leaders can shape culture intentionally and harvest the higher performance attained through organizational culture linked to business strategy. Shaping organizational culture is more than a nice-to-have, it is a business imperative that has a tangible, meaningful impact on the bottom line.

Organizational Culture – the formal or informal, agreed upon, attitudes and behaviors that are encouraged, rewarded, and corrected inside an organization.

There is a need for organizations to reinvent themselves and promote a culture with higher levels of digital dexterity.  Gartner analysts surveyed manufacturing organizations [1] and found that:

• 56% indicated existing employees struggle to embed digital into their day-to-day work
• 52% lacked skilled workers to support digitalization plans
• 50% had no formal skills development plans for existing employee

According to the National Association of Manufacturer’s second quarter 2018 Manufacturer’s Outlook Survey, “For the third straight survey, the inability to attract and retain quality workforce was the top business challenge for manufacturers, cited by 76.7% of respondents.” [5]

Manufacturers cannot sit back and wait for the workforce to develop the required skills at their own pace.  If your company is not doing anything about it, you are in the minority. According to the NAM survey, two thirds of respondents say they plan to increase apprenticeships, training and mentoring programs in the next year. [5]

Lead the Transformation

Shaping culture is a discipline and demands the same time, rigor, and conscious attention as any business improvement endeavor. There are many tools available to shape culture and develop new skills in the organization, but a good transformation starts with good leadership. Leaders play an important role in not only promoting and communicating the need for a culture change, but also in modeling the right behaviors. When leadership, engagement and tools are combined, great success is achieved and people nourish what they’ve jointly created.

Leaders create the vision and narratives that resonate with a company and its employees. The corporate business narrative should highlight the importance of developing new skills whenever appropriate. A narrative is a story that explains how and why the company is pursuing the evolution to a more digitally integrated business. It sets the tone on why it is important for all employees to become digitally dexterous.  

Digital Dexterity – ability and practice needed to leverage and manipulate media, information and technology for better personal and business outcomes including the ability to participate across virtual and physical systems to communicate and collaborate in agile networks of data exchanges.

Leaders can model the desired skills and culture themselves and be seen actively engaged in the new skills development programs. For example, a manager in a meeting with multiple department heads can point to a department that is underperforming on a specific metric and either choose to address the issue constructively and offer to help the department, or choose to demand an improvement from the department in a month without offering help. The leader can build trust among team members by modeling the importance of transparency and the need for collaboration or can decide to use the availability of new data as a tool to increase fear among supervisors. Which behavior will yield better results?

Assess Capabilities and Concerns

The increasing level of digitization in business is changing the velocity at which organizations and people collaborate in digital manufacturing ecosystems. Automation and robotics will replace the need for people in some repetitive manual tasks but will also increase the need for new skills in other people-driven tasks.

Organizations will embrace technologies that require skills with higher levels of complex problem solving, creative thinking, and cognitive flexibility. For example, field technicians will be equipped with new capabilities like augmented reality work instructions and data input via voice commands.

The development of digital skills in the workforce is an important part of developing the culture for the factory of the future. It is important to start with an idea of existing capabilities compared to the skills required to properly execute the corporate digital strategy.

In fact, Gartner found that the top three challenges for Chief Data Officers all had to do with concerns about the required culture and skills to achieve their goals. [2] (See Figure1) 

Figure 1 – Top internal roadblocks to Chief Digital Officer initiatives

Are employees struggling with new digital technologies and processes in their day-to-day work? Some probably do and it is probably affecting the overall business performance. Those struggles may include learning new competencies, adapting to new levels of collaboration among departments, working with new human-machine interaction, and losing their intuitive response actions due to radical change in business processes and organizational structure. 

Are employees concerned about the increased level of automation and machine-human interaction? Do they feel that their jobs are in jeopardy because they do not have a clear picture of the future state and how they are part of it? It is important that employees understand that they will have a part of the company’s future state if they do their part and develop the skills required for their future job.  

One way to know if the organization needs to tackle new levels of digital dexterity and literacy is by pointing out symptoms of organizations with issues in these areas. The following are potential symptoms in organizations that need more digital dexterity:

• Using the wrong type of chart to clearly present a particular dataset and correlation
• Not understanding the correct context around a data trend or specific data deviation
• Not clearly stating the assumptions behind the presentation of analysis
• Using the word “median” and “average” interchangeably without understanding the difference
• Requesting “give me all the data and I will figure out later what to do with it”
• Cherry-picking data and dimensions to highlight at staff meetings in order to bias a business decision rather than uncovering different perspectives on the data
• Not agreeing on common definition of metrics across the organization

Cultivate and Promote Adaptability

Several technology advances are expected to have a beneficial transformational impact over the next few years on the daily tasks of the workforce. Technologies like speech recognition, content collaboration platforms, and mobile role-based apps are already improving performance and workflows for many workers. In two to five years, human augmentation and remote expert guidance are expected to help workers perform a wider range of capabilities with less formal training. Natural language interfaces and bots will get more sophisticated and will become part of many workers activities. It is important that the workforce becomes ready and comfortable with these types of assistive technologies.

Employees need to demonstrate an openness to new digital rich and interactive ways of working not only with peers but also with technology and machines integrated into the business processes. For example, tools like collaboration platforms that leverage shared knowledge and social networking create a new level of transparency in the organization. Personnel must trust that management will use this new level of data transparency for the benefit of optimizing the overall business process, and not for micromanaging employees’ time spent at the restroom. Otherwise, employees will not fully embrace these new technologies and a more “collaborative” culture.

When we embed digital technologies into manufacturing processes we create the need for new job profiles like robotics specialist, IIoT data scientist, machine learning programmer, and augmented reality author.  Where are these skills going to come from?

Methods of tackling the skills gap and culture development include:

• Recruiting skills from the outside as permanent or contract personnel
• Education and skills development programs
• Knowledge management process to capture the undocumented knowledge from current subject matter experts.
• Mentorship programs between more experienced and newer employees
• Behavior reinforcement programs

 

Develop Digital Dexterity and Literacy

Even though tools like advanced analytics are becoming more readily available through easier to use tools and economical cloud services, the lack of employee data literacy can be a constraint to its widespread use throughout the enterprise or can lead to misuse of the data or technology.

Enlist the HR team to emphasize the need for new skills like data literacy explicitly in hiring, onboarding and employee development activities.

Data Literacy - the ability to read, write and communicate data in context. This includes an understanding of data sources and constructs, analytical methods and techniques applied, and the ability to describe the use case, the application and resulting value. [6]

Speaking data is no longer a language reserved for data modelers. Data and business intelligence is getting democratized as newer tools make it easier for employees to access structured data and create custom reports to show the correlations they need to highlight and identify specific issues or opportunities in the business. However, even though tools are making it easier to access and mine data, these new capabilities require that employees attain higher skills in data literacy.

This need becomes more evident when we consider that humans are interacting with machines more in everyday business and that machines speak “data” as their first language. Machines are getting smarter, but their communication skills are still limited and designed to be efficient in providing data, not so much on making data understandable to humans. Personnel working with these machines need to understand what the robots are saying and how to respond appropriately.

There will be different levels and specialties of data literacy required depending on how close each job is to the data or machine versus how close the job is to working with other people, but in general, everyone in the organization will need higher data literacy skills. For example, the data competency will be different for jobs like information architecture, data asset management, data science, customer data service, and workflow optimization analysis. It is important for organizations to hire on more personnel with these skills—hires that will share the new knowledge with others through organized knowledge transfer programs.

People have to be given a chance to learn in order for a culture to thrive. The organization should not only provide the tools needed to be successful in the new corporate business model; they must also provide the training required to use the tools and promote the desired collaborative behaviors.

Approach teaching data literacy by using examples from employees’ personal lives. Use examples like online shopping, home finance management, GPS maps and personal health monitoring. This creates a unifying experience from executives to workers, from data scientist to those who struggle with a spreadsheet. With the basis of personal life examples, it is then easier to transition to the business scenarios.

The organization can leverage this transformational time as a great opportunity for exchange of knowledge between employees with seniority that understand well the business processes and the newer workers that bring external knowledge of new technology into the organization. Programs could be crafted to promote a different type of knowledge exchange relation, beyond the typical mentor program, that benefits both the senior and new employee.

Knowledge from experienced personnel could also be captured into digital standard operating procedures (SOP). The team can prioritize areas that need to be documented. Areas where the company might be of risk of losing valuable skills, and areas that have a big variance in quality and rework tied to  experience level. The team needs a methodology to capture the knowledge. This is usually done through illustrated work instructions authored in a Manufacturing Operations Management (MOM) or Manufacturing Execution System (MES). Some organizations might even adopt 3D augmented reality to assist mechanics in their day to day jobs. 

Third party resources including commercial training sites, assessment guidance, expansion courses by universities and colleges, are generally fragmented for this new digital manufacturing landscape. Pioneers should try to collaborate in these early stages with industry consortia and manufacturing consulting  groups that are trying to fill the gap in current educational programs.

It is important to see these development efforts as more than a one-time training exercise. Organizations need an enablement program that allows the workforce to stay up-to-date with skills and train themselves through exploration and on-demand training. This is critical to a sustainable skills management model.  

Enable more self-service technology like access to self-service query and reporting tools. Empower more non-IT employees to participate in the digital integration of the enterprise. Of course, IT has to be setting some governance and rules to ensure that the organization is marching to an overall architecture plan. But IT moves from being perceived as a constraint to perceived as an enabler for progress. 

IT should establish programs for data governance that establish data security and quality protocols, general data models and metadata management, data integration, and business intelligence tools. For example, centralized data and metrics glossaries, can help create a common data language in the organization.

Emotional and behavioral training might also be needed to facilitate the new levels of communication, transparency, and automated oversight. People are not automatically ready for these new levels of openness, collaboration and supervision. Without training, employees can get frustrated, anxious and experience higher levels of stress in the workplace.

Embrace Organizational Collaboration

Employees need to demonstrate an awareness of the internal and external business context and effectively collaborate with people of diverse perspectives and experience levels.

Management should support more transparent and collaborative ways of working among internal departments. This means that metrics are not used as a whip to extract higher performance from specific departments or employees. Instead, metrics are used to diagnose systemic issues and potential process improvements in the organization.  It means that we focus on the performance of teams and less on the performance of specific employees.

The enterprise should elevate more suppliers to collaborative partners in the future value chain. This requires a different culture for managing suppliers. We move from reactive and cost cutting at all cost to a more win-win collaboration where the best way to service the end-customer at a reasonable price is openly discussed among all stakeholders in the value chain. Behaviors are reinforced among partners with financial rewards instead of penalties.

A way to promote collaboration while also increasing skills is to use a work shadowing program between internal departments. When implementing a shadowing program to promote collaboration between internal departments, it is better to focus the program on learning the workflow between departments versus learning the inner workings of each department. By focusing on the inputs, outputs and the data exchanges between departments, employees will learn how collaboration helps the overall business processes. Focus the program on this type of systems thinking. The end goal is to learn the importance of better communication, transparency and workflow practices.

Broker more connections with experts outside of the organization. The organization is not going to learn new skills unless they are exposed to more outside expertise through trade organizations, technical conferences, and training services.

Reinforce the New Culture

Management can cultivate the importance of digital dexterity, not just as a business skill, but also as an important life skill as we will continue to see more digital technology affecting our daily lives.

Choosing behaviors that will be rewarded sends a clear message to the organization. Both formal rewards (via company performance management systems) and informal rewards (via a more personal approach by leadership) are key to reinforcing the behaviors that collectively shape the culture.

It is important to recognize that not everyone will come around for this journey. Not every job will remain in the future state of the organization and new jobs will become part of the landscape. Some will embrace the change, some might not, but all should have the opportunity to learn new skills and find a place in the future organization.

References

[1] “Let’s Get Digital: Findings from Gartner’s Factory Digitization Study”, Jacobson, 2018 Gartner Supply Chain Summit

[2] Executive Guidance: Digital Dexterity at Work, Gartner, 2018

https://www.gartner.com/en/executive-guidance/digital-dexterity

[3] Digital Transformation and the Workforce, IndustryWeek Survey, 2018

[4] Gartner Says Too Few Organizations Have the Digital Dexterity to Adopt New Ways of Work Solutions, Gartner, 2018

https://www.gartner.com/newsroom/id/3879581

[5] “Manufacturing the Next Generation”, Quality Magazine, 2018

[6] “Getting Started With Data Literacy and Information as a Second Language: A Gartner Trend Insight Report”, Logan/Duncan, Gartner, 2018 

 

Manufacturing Technology and IT Trends Update - Spring 2018

I was fortunate to participate at several industry conferences this spring listening and speaking with many leaders and practitioners working on innovation initiatives for their manufacturing companies under the general theme of Smart and Digital Manufacturing transformation. A few noteworthy observations are summarized below.

The digital transformation trend that started a few years ago continues stronger than ever. The digital business strategy for these companies has two main goals: (i) digital business optimization with goals of improved customer experience and improved productivity, and (ii) digital business transformation with goals of new business models and increased revenue through product and service offerings that leverage new levels of digital product data in this era of IoT.

Gartner states that around 65% of manufacturers surveyed are working on their digital business strategy and roadmap. These manufacturers expect that 50% of their production data and information will be automated by 2020.

MESA International’s survey, performed with Industry Week, shows that among U.S. manufacturers, the preferred term for this digital transformation is Smart Manufacturing (around 50%) followed by Connected Enterprise, Digital Manufacturing, and Industry 4.0 (each around 10-15%).

MESA also reports that 62% of manufacturers have already started with projects towards Smart Manufacturing. [1] Their survey is very consistent with the Gartner survey. Both reports are good benchmarks on industry-wide progress towards Smart Manufacturing goals.

 

There are a lot of new technologies we can apply to help us realize the Smart Manufacturing vision and manufacturers are currently trying many of them. Connected supply chain, Manufacturing Execution Systems (MES), and Robotics were on the top of manufacturers list of current projects related to Smart Manufacturing.

Gartner shows us the latest on their hype-cycle for these technologies. It is worthwhile noting that IIoT, cloud manufacturing systems and predictive analytics are heading down into the “trough of disillusionment” on the cycle which means that manufacturers have experimented with these technologies and are now resetting their expectations of value and practical use cases.

The Gartner survey also points out that manufacturers are currently focused on getting the foundational pieces in place including master data management, supply chain collaboration, and manufacturing execution system (MES) before they start implementing the next wave of innovation with IIoT, big data, and advanced analytics.

Edge computing devices are helping connect manufacturing equipment to the cloud. They can handle high frequency data acquisition on one side with analog, digital or serial input, they can perform computations and translations using controllers like Arduino, Beaglebone, or Raspberry Pi, and communicate on the other side with enterprise systems using standards understood by enterprise systems like MTConnect or OPC UA on XML or JSON.

In a recent report [2], Gartner warns that manufacturers are not paying enough attention to the intersection of plant operations management systems and supply chain management systems. 71% of manufacturers are working these initiatives independently in parallel and 85% report that integration of plant and supply chain is seen as a current challenge. Gartner predicts that manufacturers tackling this connection will likely see big benefits as new Smart Manufacturing ecosystems evolve in the next few years.

We are finally talking about robots and artificial intelligence (AI) beyond the simple use cases for replacing humans on brute strength, repetitive, or unsafe tasks. We are starting to see more use cases where robots and AI are working side by side collaborating and assisting the workforce with functions like feature detection, image analysis, natural language interfaces, and smart advisors.

One of the areas of AI assistance is computer vision that will monitor the work area and automatically guide or warn the technician by performing (a) visual inspection on things like cracks or scratches, (b) parts tracking that detects correct or incorrect parts as they are installed, (c) process monitoring that can detect out of sequence steps or detect safe practices.  

There is progress with machine learning (ML) tools where humans assist in training the machine and can adjust for bias and unusual scenarios as they come up in the collected data. We are still in experimentation stages with deep learning where machines can train their own neural networks based on image recognition and historical data.

Yet, much of the AI talk is still aspirational. Examples of what visionaries are looking for include the following types of tasks:

• Scheduling production based on maximum machine performance and least impactful changeovers;
• Assigning available staff with the greatest efficacy for a process and product;
• Managing maintenance schedules for least downtime impact;
• Forecasting and optimizing material inventory based on the latest actual completion dates;
• Coordinating delivery routings with awareness of truck locations, schedule, and traffic conditions;
• Identifying sales trends that haven’t yet been identified by humans and recommend production volume changes

Indeed, we are seeing a lot of exciting technology advances but how do we put it all together and thread it into a new Smart Manufacturing enterprise? MESA International reports that many have started pilots and initiatives tied to trying out these technologies but Gartner reports that 2/3 of manufacturers are working out their strategies for Smart Manufacturing. There is still much to define to get from vision to reality. 

Manufacturers need tools to help them navigate the ocean of technologies, systems, platforms and connection alternatives. One recent assessment tool is the Singapore Smart Industry Readiness Index [6] which helps companies assess at a high level their readiness in dimensions of process, technology and organizational structure. MESA International has several tools for assessment and is working on more of these types of tools to help manufacturing IT staff. Their current tools include: MESA Manufacturing Operations Management (MOM) Capability Maturity Model [7], Metrics Maturity Framework [8], and the MESA Smart Manufacturing Page [10] which is periodically updated with new resources and many are open to non-members. I encourage you to check out the resource references listed below for more information on advancing your Smart Manufacturing initiatives.

References:

[1] Research Report: Seeking Common Ground for Smart Manufacturing, MESA International, 2018

https://services.mesa.org/ResourceLibrary/ShowResource/7b029261-c659-4d14-9799-9918cf1f3711

[2] Harvest the Value of Smart Manufacturing in the Supply Chain - Not the Factory, S. Jacobson, Gartner, 2018

https://www.gartner.com/document/3874477?ref=solrAll&refval=205017407&qid=f538c5f5cc9d6de268c42dd554d1bd1a

[3] Four Best Practices to Manage the Strategic Vision for the Internet of Things in Manufacturing, S. Jacobson, Gartner, 2016

https://www.gartner.com/document/code/307794?ref=grbody&refval=3843663

[4] Webcast: Smart Manufacturing: Continuous Improvement or Strategic Transformation, MESA International, 2018

https://services.mesa.org/ResourceLibrary/ShowResource/16a26f39-7e0a-4cdb-b2f5-71af93941b1c

[5] Managing Disruption Requires Supply Chains to Foster Innovation and Scale the Digital Supply Chain: A Gartner Trend Insight Report, M. Griswold, Gartner, 2018

https://www.gartner.com/document/3870491?ref=solrAll&refval=205017548&qid=f538c5f5cc9d6de268c42dd554d1bd1a

[6] Singapore Smart Industry Readiness Index, Singapore Economic Development Board, 2018

https://www.edb.gov.sg/en/news-and-resources/news/advanced-manufacturing-release.html

[7] MESA Manufacturing Operations Management (MOM) Capability Maturity Model, MESA International, 2016 (available to MESA members only)

https://services.mesa.org/ResourceLibrary/ShowResource/a4fcb3cc-bc28-4f87-84cb-3da7432cc3b2

[8] Metrics Maturity Framework - A Guide to Assessment and a Roadmap to Increased Performance, MESA International, 2016  (available to MESA members only)

https://services.mesa.org/ResourceLibrary/ShowResource/4c5956c7-c7c3-4ed7-9689-bbfadafba3be

[9] Seeking Common Ground for Smart Manufacturing – Research Report, MESA International, 2018

https://services.mesa.org/ResourceLibrary/ShowResource/7b029261-c659-4d14-9799-9918cf1f3711

[10] MESA’s Smart Manufacturing Page (periodically updated with new resources and many for non-members)

http://www.mesa.org/en/the-road-to-smart-manufacturing.asp

[11] Smart Manufacturing: Continuous Improvement or Strategic Transformation? – Recorded Webcast, MESA International, 2018

https://services.mesa.org/ResourceLibrary/ShowResource/16a26f39-7e0a-4cdb-b2f5-71af93941b1c

 

The Third Dimension of Smart Manufacturing—Value Chain Management

As companies make progress on their smart manufacturing initiatives, the competitive intensity of new products, services and ecosystems raises the stakes for all manufacturers. There is a need to think differently about business models, ecosystems and ways to outflank the competition. The smart manufacturing strategy isn’t just about incremental change or cost savings; it’s about innovating products and services and incorporating innovation at a much higher pace than ever before.

Smart manufacturing is an endeavor to achieve higher levels of intelligence, orchestration and optimization within our factories and across the manufacturing value chain. This endeavor requires synchronization along three key dimensions: smart factory or Industrial Internet of Things (IIoT), product lifecycle and value chain management.

Many companies seem to be investing more on the product lifecycle and smart factory dimensions than on the value chain dimension. However, manufacturers focused on optimizing ecosystems offering new value and service levels to customers might very well end up being the true winners of the smart manufacturing race.

It is important to note that there are exceptions to the pattern of internal vs. ecosystem focus on smart manufacturing. For example, the food manufacturing industry is prioritizing more on the supply chain side. The industry is motivated to achieve higher levels of traceability in the food supply chain to meet customer demand for more information and guarantees about what they are consuming.

It is easy to be enticed by cool technology like highly automated machines and robots, and the local optimization of specific processes in the factory. The benefits can be realized quicker and the process change landscape is completely within the company’s authority to make it happen. However, the goals of the smart manufacturing vision will not be achieved through strict accumulation of gains from continuous improvement projects within each plant. The value chain dimension will need to be tackled with a combination of strategic alliances and new business processes that link the value chain with new levels of digital data traveling along with physical materials, components and products. This type of initiative will require executive-level strategic vision. These projects might not show short-term payback and will require work outside the company walls to achieve new levels of collaboration with partners and suppliers.

The goal of the value chain management dimension is to minimize resources and access value at each stakeholder function along the chain, resulting in optimal process integration, decreased inventories, better products and enhanced customer satisfaction. The scope spans from managing suppliers of materials and parts to managing the handover of information through internal departments, including the production shop floor, and all the way to managing the delivery of the product to the end customer. It encompasses the procedures, forms and data handoffs that link these organizational entities into a value chain that delivers a final product and services for that product to the end customer.

The standardization of IT practices that enterprise resource planning (ERP) started decades ago for cash-to-order processes within the organization—covering activities like contracts, procurement, receiving, invoicing, purchase orders, delivery and payment—must be extended now across the entire value chain with an emphasis on open data exchange standards that enable publish/subscribe connections across the Internet and across each partner’s manufacturing operations management system.

The value chain dimension in smart manufacturing requires as much or more attention than the smart factory dimension. Working on value chain innovation will yield much higher payback by positioning the company as a valuable partner in future manufacturing ecosystems.

There will be some technical challenges along the way to create the smart manufacturing connected enterprise, but the biggest challenges ahead are probably cultural. Companies that figure out how to establish and embrace new business models, new connected processes and new levels of transparency among partners in the ecosystem will be the leaders of future smart manufacturing ecosystems.

 

This article appeared at Automation World blog April 2018: The Third Dimension of Smart Manufacturing—Value Chain Management

 

Smart Manufacturing - Six Reasons to Not Delay the Digital Transformation

In the past, it was perfectly rational to observe technology innovation as it went through the adoption cycle and wait for early adopters to incur the cost of experimentation of the first wave of adoption. In this article, we discuss why wait-and-see is not a wise approach towards today’s Smart Manufacturing transformation.

Industry analysts including McKinsey & Company [1] and the World Economic Forum [3] have documented the current undisputable momentum building up in industries including manufacturing towards a new digital enterprise reality. Has your organization embraced this enterprise digital transformation? Figure 1 shows the results from a recent survey by Industry Week [4] and it reveals that indeed most surveyed manufacturers have intent to move forward with a Smart/Digital Manufacturing initiative. However, when asked about the roadmap and timing of these efforts, manufacturers have significantly less enthusiastic answers.

Manufacturers are often held back by the lack of a concrete roadmap, the perception of security risks, and the overwhelming number of approaches and technologies being talked about under the heading of Smart Manufacturing. Given the confusing landscape, is it better to wait a little and wait for early adopters to incur the cost of experimentation with the first wave of adoption of these new processes and technologies?

This is a strategic question that each organization should ask itself—very soon; because the clock is ticking, and surveys indicate that competitors are moving forward. Each organization should evaluate the disruptive potential of Smart Manufacturing in their corner of the market. The executive team should strategically discuss questions like these:

• How will Smart Manufacturing disrupt our industry in the next five to ten years, and what new ecosystems and competitors will emerge?
• How would a digital enterprise and real-time data help our business achieve strategic goals? Where is the value for our company, and how can we maximize it?
• What new business value can we bring to our customers based on real-time data, higher levels of interaction, and the ability to deliver digital data with the product?
• What new capabilities, skills, and mind-sets will we need in our organization? How will we identify, recruit, and retain the right new talent?
• What should we pilot now to start capturing value and accelerate our journey? Do we understand how to establish an IT infrastructure that enables Smart Manufacturing?

As the intensity of global competitors developing new products, services and ecosystems raises the stakes, the pressure is on manufacturers to think differently about business models; cultivating additional revenue streams and finding novel ways to outflank the competition. The Smart Manufacturing strategy isn't just about incremental change or cost of savings, it's about innovating and incorporating innovation at a much higher pace than ever before.

Reasons to not delay the Smart Manufacturing initiative

Continuous improvement strategies that depend on Kaizen events and small incremental changes might have worked for organizations in the past few decades but may not work now to achieve the transformation required for Smart Manufacturing. At least not without a planned roadmap leading to a future state that establishes the required systems infrastructure to connect the enterprise and enable the business to participate in new manufacturing ecosystems with OEMs and peer suppliers.

#1. New entrants will disrupt markets

The Smart Manufacturing revolution is expected to disrupt markets and change the competitive landscape. Methods of measuring market share might require update and the organization might not detect market changes until it is too late. It is natural to maintain defensive focus against the same usual competitors while new ones might go unnoticed. New market entrants with innovative products and service offerings can start to take revenue away from the traditional market before the organization and industry analysts notice the changes. By then, the organization could have lost key contracts and lost market position.

#2. Innovation leaders will dominate redefined markets

History shows that companies leading disruption of markets enjoy an ongoing advantage in those markets for many years. Especially when the resulting market lowers costs and pricing for end customers. Competitors that implement Smart Manufacturing sooner can drive prices down due to their increased levels of efficiency and productivity. Your organization might be left with lower profit margins and less money to invest on catching up and implementing the required technology to quickly join the redefined market ecosystem. Will your old customer see you as a legacy supplier or see you as a supplier that is innovative and evolving with the market?

 #3. New ecosystems will evolve quickly

A big part of the intent fueling the Smart Manufacturing revolution is the desire to create ecosystems tying multi-tier suppliers into new value chains that efficiently deliver products with high customer configurability, products with high traceability of components, and products sold as services to end customers—products that go beyond the physical unit with a digital footprint that travels along with the product during its lifecycle. If your organization is not ready to join these ecosystems as they are forming, it could be harder to join them later. Especially if competitors that join early establish a good reputation. New ecosystems will not want to introduce risk into processes and supply chains that are working. Opportunities might be limited to filling in gaps and replacing unreliable suppliers. Quality, reliability, speed, and the ability to participate in the chain of required data exchanges will be more critical than pricing to join new ecosystems focused on offering premium customer service. Your organization could soon be facing new ways of conducting business with old customers.

#4. Talented resources could become scarce

It is already difficult to acquire the required new talent into manufacturing companies. The manufacturing skills gap is well documented. [5] [6] A latecomer to Smart Manufacturing might find it even harder to find talented resources if the best resources were hired into the early adopters as job seekers perceived them to be the innovative leaders and better workplace choices.

#5. Evolution is a valid path

Creating a new division or acquiring a newer company are valid paths into Smart Manufacturing, but they are not the only paths. Organizations can evolve their practices with different approaches including piloting new techniques at new green-field programs and implementing technology that makes it practical to bridge older equipment at brown-field sites into newer Information Technology (IT) infrastructures and processes. When organizations implement digital transformation, they are not just enabling new business models, they are also making the old models work more efficiently by improving visibility, control, velocity, and customer service. These type of systems and process enhancements  strengthen relations with existing customers and puts the organization in a better position to tackle new initiatives knowing there is a reliable revenue stream from an existing customer base.

#6. Internal shadow IT is probably not waiting

Departmental managers around your organization will continue to do process improvement projects and some will entail the development of apps to tackle specific problems within their area of responsibility. After all, it is easy these days for non-IT personnel to develop apps and deploy them on their own smart phones. Shadow IT is a symptom of an organization that is not updating IT systems and infrastructure fast enough to keep up with the internal needs for process improvement.

However, shadow IT efforts can create more problems than they solve and will not get the organization to the required Digital Manufacturing platform. There needs to be a plan in place orchestrated by the IT department providing guidance and governance on security, data models, standardized enterprise systems, and data exchange practices. Otherwise, we can end up with a mishmash of tools and apps that don’t play well together, need to be updated separately, and cannot be efficiently sustained in the long term. Shadow IT is not a game you want to play in the high stakes arena of Smart Manufacturing.  Even if the use of apps continues to be prevalent in the Digital Manufacturing landscape, the IT department should be empowered to architect them as part of an integrated enterprise systems landscape. The longer the organization waits to institutionalize Smart Manufacturing, the bigger the effort will be to move departments off their custom independent solutions and into enterprise integrated processes.

In Summary - Do not wait

Organizations should move forward without delay, define their end-state strategic goals, and their roadmap with progressive milestones toward their Smart Manufacturing vision. To advance the initiative effectively, it is important to get specific about the organization’s digital transformation roadmap.  

The industry is going through an exciting revolution and these are great times to be working in manufacturing. Companies with highly connected and adaptable systems will have a big advantage in future markets.

References

[1] “Digital manufacturing: The Revolution will be Virtualized”, Hartmann, King, Narayanan ; McKinsey & Company; 2018

[2] “Why Digital Strategies Fail”, Bughin, Catlin, Hirt, Willmott; McKinsey & Company; 2018

[3] “Digital Transformation of Industries”, World Economic Forum and Accenture; 2016

[4] “Manufacturing’s Digital Transformation: Is Your Company Leading the Way or Falling Behind?”, IndustryWeek, 2018

[5] “The Skills Gap in US Manufacturing 2015 and Beyond”, Deloitte, Manufacturing Institute, 2015

[6] “Out of Inventory: Skills shortage threatens growth for U.S. Manufacturing”, Accenture, The Manufacturing Institute, 2014

 

This article appeared in IndustryToday, March 2018, "Digital Manufacturing: Reasons to Not Dawdle" 

 

Related article:

Eight Potential Barriers To Strategic Manufacturing Process Improvements

 

 

 

 

Three Dimensions Converge on Smart Manufacturing

- Smart Factory, Digital Thread, and Value Chain Management

There is a myriad of new technologies coming into the manufacturing arena, each with tempting value propositions. How does an organization know that they are investing in the right areas to stay competitive? If your organization is confused about where to start or where to focus investments on the journey to a future highly connected, orchestrated, and optimized Smart Manufacturing enterprise, you are not alone.

A clear roadmap to Smart Manufacturing is of the utmost importance for each organization, but not easily realized because of the complexity of different organizational perspectives, data models, and business processes that converge at the manufacturing shop floor—processes that get products designed, outsourced, built, tested, packaged, and delivered to the customer in a consistent manner.

This article discusses how to organize the convergence of processes supporting Smart Manufacturing into three dimensions or perspectives: (i) smart factory, (ii) digital thread, and (iii) value chain management. These different dimensions relate to different perspectives and systems coming from engineering, operations, and business management disciplines and help explain why the Smart Manufacturing endeavor requires collaboration among many different stakeholders in the organization.

This three-dimensional model also explains the intersection of several manufacturing improvement initiatives included under the scope of Smart Manufacturing: IIoT (Industrial Internet of Things), Model-Based Manufacturing, and Connected Enterprise. The Digital Thread dimension aligns with the goals of Model-Based Manufacturing, he Smart Factory dimension aligns with the goals of IIoT, and the Value Chain Management dimension aligns with the goals of orchestrating and optimizing the entire value chain in the Connected Enterprise.

The Smart Factory Dimension

From a Smart Factory perspective, we are interested in connecting equipment, resources and personnel in order to acquire real-time data through automated methods, analyze it, and leverage that information to (a) provide complete real-time visibility of factory processes, (b) optimize process control, and (c) provide insights to where we can further improve performance.

For example, an assembly line with Smart Manufacturing automated and semi-automated processes may do the following:

• Monitor production flow in real-time to eliminate constraints, dispatch automated material handling, and eliminate wasted idle time
• Auto-identify parts going down the line to automatically load programs and materials for each different product configuration
• Automatically aggregate product data, analyze and identify constraints and required adjustments or improvements
• Manage equipment remotely using sensors to conserve energy, reduce downtime and trigger preventive maintenance

To achieve these levels of automation, through which products, parts, and equipment interact among themselves with enhanced communication mechanisms, we will need resources and industrial automation equipment with communication standards to acquire and publish data to higher levels of processes in the Smart Factory stack including operations management and intelligence applications.

 

Figure 1: The Smart Factory Dimension

The Smart Factory dimension illustrated in Figure 1 includes the following connected processes and systems flowing from equipment and resources up to higher levels of process control, analytics, and intelligence.

• Smart Machines, Sensors, Tooling, and Workforce interact with each other via structured communications and integrated systems providing real-time data about their status and the processes they are executing
• Smart Apps, Controllers, OT-IT Bridges like the Manufacturing Service Bus provide the communication bridge between Operations Technology (OT) exchanging data directly with machines and tooling, and Information Technology (IT) systems and apps where personnel interface to execute supervision, production, inspection, and maintenance tasks at the shop floor
• Operations Management System optimizes the flow of products through production processes and orchestrates the allocation of resources
• Connected Enterprise Systems including PLM, ERP and SCM are maintained in real-time sync through A2A data exchanges with production process status, resources used, and products produced
• Business Intelligence System receives periodic updates of aggregated data for performance analysis and business metrics

The Smart Factory dimension is aligned with the goals of the IIoT (Industrial Internet of Things). The IIoT takes the concepts of ease of equipment connectivity, data acquisition and advanced analysis via cloud services from the Internet of Things (IoT) initiative in consumer markets and applies them to the next generation of automation for the factory floor.

An enabler behind the IIoT is that it is becoming easier to connect and mine data directly from smarter machines. The IIoT can monitor, collect, exchange, analyze, and deliver valuable new insights. Mined data is more accurate, consistent, near real-time, and enables organizations to sense inefficiencies and problems sooner, saving time, money, and driving smarter, faster business decisions for industrial companies.

A major issue slowing down the IIoT is interoperability between older devices and machines that use different protocols and have different architectures. In contrast to the automation achieved in the last few decades, the connectivity methods targeted under IIoT and Smart Manufacturing need to be open, standards based, and able to facilitate publish-subscribe methods over the internet.

In the past, organizations depended on custom integration, vendor-proprietary interfaces and separate network protocols for integration and automation at the factory. Moving forward with IIoT, organizations want to embrace open standards and Internet protocols to facilitate an easier swap and mix of multi-vendor equipment and software, which might be on-premise or in the cloud.

A big promoter of the IIoT is the Industrial Internet Consortium (IIC) which adopted the term, and promotes the move from older automation protocols to newer Internet-enabled IIoT protocols for industrial equipment.

The Operations Management system optimizes the flow of products through production processes and orchestrates the allocation of resources. It executes programs for processes like cutting, machining or 3D printing equipment, and collects data from operators or directly out of equipment for inspection, test, pick-and-place, or packaging processes. Data is collected in a structured form that allows distribution to multiple subscribing functions in the Smart Manufacturing system like quality verification and parts traceability.

Enterprise Systems manage all kinds of business processes in the organization from receiving orders from customers to scheduling production, planning deliveries, ordering materials, invoicing, receiving payment, and paying suppliers. The timely performance of these activities depends on real-time data from the connected Operations Management system through A2A (application-to-application) data exchanges

Business Intelligence systems aggregates and organize data into actionable metrics and Key Performance Indicators (KPIs) the represent the organization’s strategic goals. In the digitally connected Smart Manufacturing organization, management is automatically alerted of areas not performing to plans and expectations. Management must be able to drill down from metrics into causal analysis, and depending on analytical capabilities, systems might even be able to suggest areas for improvement.

The Digital Thread Dimension 

The Digital Thread dimension of Smart Manufacturing starts with the engineering design definition of the product and follows the product lifecycle through its sourcing, production and service life ensuring that the digital definition of each product unit is aligned with the physical product. The digital data for each product includes every incorporated revision to the engineering definition and any deviations from the design specifications approved and executed on the product during its lifecycle. The flow of processes in the Digital Thread dimension is depicted in Figure 2 including:

• Specifications Management for design of product and processes including definition of 3D models and recipes, product variations and configurations, and engineering change management practices
• Operations Management which includes production and verification processes including programs and work instructions for automated 3D printing, machining, and verification against engineering specifications
• Product Services Management for maintenance of the product during its service life with data collected on product performance, modifications, and replacement of components.

Figure 2: The Digital Thread Dimension

In discrete manufacturing, the Digital Thread perspective is aligned with the goals of Model-Based Manufacturing and Model-Based Enterprise initiatives. The Digital Thread initiative aims for seamless threads of structured communications and data exchanges throughout the value chain that are accessible to all stakeholders across the extended ecosystem to ensure complete visibility and traceability of the digital and physical product from design through sourcing, production, and ultimately to the end user or customer.

The Digital Thread begins with a 3D model-based or recipe-based definition of the product from design teams flows into the Operations Management and the Supply Chain Management via standards of integration for pervasive distribution of the data throughout the connected Smart Manufacturing enterprise. Examples of communications in the Digital Thread include product and process specifications, test results, conformance issues, asset maintenance requirements, and details and approvals for deviations from standard specifications.

Digitally connected model-based design and manufacturing help connect previously disconnected functional departments through a logical thread of integrated data and processes which aids in (a) faster design revision distribution and new product introduction (NPI), (b) accurate translation of product to process specifications, (c) smarter business decisions with visibility of product performance during its lifecycle, and (d) quicker resolution of issues requiring engineering design changes.

The product design engineer states the materials, form, and fit requirements for the components in 3D models for discrete manufacturing, or the chemistry and physical transformations in a recipe for process industries. The manufacturability of a product is dependent on the particulars of design parameters and tolerances. The production and inspection process definition is a repeatable structured means of conveying the engineering intent to Operations Management. There should also be a design feedback loop between the design of the product and the design of the manufacturing processes, tooling design and inspection capabilities. There is a need for better ways to communicate specifications, capture the relation to actual measurements, and leverage that information for resolving issues on non-conformance to the requirements in a well-defined formal way.

As an example, in discrete manufacturing today there is a lot of manual interpretation, transformation, and translation of data between engineering and manufacturing systems. In addition to being inefficient, each time data is manually converted from one format to another, it introduces a chance for misinterpretation and error. For example, in current processes, CAD models need to be manually converted to (a) computer numerical control (CNC) programs for machining, (b) coordinate measurement machine (CMM) programs for inspection, and (c) manufacturing execution system (MES) illustrated work instructions for assembly. During these manual processes, the associativity to objects in the CAD model is usually lost. When a CAD model revision comes down the pipe, the engineers and programmers must do a thorough review of the entire model to avoid missing anything instead of concentrating with confidence on a few highlighted revised areas. In future processes, with structured digital handoffs, systems will be able to easily highlight revisions, do impact analysis on downstream programs and instructions, and facilitate the automated incorporation of changes.

The Digital Thread will provide a formal framework for the controlled and automated interplay of authoritative technical and as-built data with the ability to access, integrate, transform and analyze data among disparate systems throughout the product lifecycle. In the Digital Thread, the product’s data “travels” along with the physical product and evolves through data collected at each step of its manufacturing process. By “travel” we mean that the data needs to be easily accessible at any time during production and referenceable to each product’s lot or serial number. The scope of data includes as-designed requirements, validation and inspection records, as-built records with part genealogy traceability, and as-tested data. The Digital Thread needs to be able to deliver the digital product data along with the physical product to the end customer. For some products, the thread of the product data will continue into Product Service to maintain the product during its entire service life.

The Value Chain Management Dimension

An important dimension to achieving a fully connected extended enterprise in Smart Manufacturing is the Value Chain Management perspective. Value Chain Management focuses on minimizing resources and accessing value at each stakeholder function along the chain, resulting in optimal process integration, decreased inventories, better products, and enhanced customer satisfaction.

The scope of Value Chain Management spans from managing suppliers of materials and parts, to managing the handover of information through internal departments including the production shop floor, and all the way to managing the delivery of the product to the end customer. It encompasses the procedures, forms, and data handoffs that link these organizational entities into a value chain that delivers a final product and services for that product to the end customer.

The standardization of IT practices that ERP started decades ago for cash-to-order processes within the organization—covering activities like contracts, procurement, receiving, invoicing, purchase orders, delivery, and payment—must be extended now across the entire value chain with an emphasis on open data exchange standards that enable publish/subscribe connections across the internet and cloud services. Configurable repeatable patterns of orchestrated activities across the value chain will enable highly automated, efficient, and agile business processes.

As illustrated in Figure 3, the Value Chain Management dimension includes processes for:

• Customer Management with online interaction with customers for quicker custom product configurations, order in-process visibility, and approvals for changes, deviations or delays
• Compliance Management maintaining organizational guidelines, coordinates audits and monitors compliance performance with internal departments and external regulatory agencies
• Operations Management delivering real-time information from production processes to other business management functions and orchestrates activities into the supply chain to make sure that materials, parts, and subassemblies arrive at the right place at the right time
• Resource Management of personnel and equipment required to make the product, provide product services, and maintain the equipment up and running with the required capabilities and certifications
• Supplier Management with functions from identifying and establishing the supply chain with the right partners to monitoring, synchronizing, and maintaining the required quality levels.

Figure 3: The Value Chain Management Dimension

The new Smart Manufacturing ecosystem aims to create closer relations and interactions with customers in processes and services. Customer Management includes functions for customizing orders to customer preferences, providing more visibility to in-process order status, coordination of deliveries, download of data for each product shipment, known issue alerts for purchased products, warranty claims and issue resolution, approval for changes and deviations to contract specifications, and coordination of service subscriptions and service orders. Some organizations have a Program Management function that closely works to achieve program goals with the customer organization and the supply chain.

Since the Value Chain Management dimension encompasses procedures that link the enterprise departments into a connected value chain, it is necessary to have a Compliance Management function which maintains organizational guidelines, coordinates audits, monitors compliance among internal departments, and coordinates with external industry and government regulatory agencies. The Compliance Management function maintains the brand’s quality reputation.

Compliance Management functions include the business processes that (a) document and control standard practices, (b) record history for reporting and auditing purposes, (b) handle resolution of issues and tracking of corrective action and continuous improvement efforts. Compliance Management ensures that corporate procedures are aligned with industry regulations for safety, environmental protection, risk mitigation, and quality management as documented industry standards such as ISO9001, ISO14000, ISO45001, and ISO31000.

Operations Management touches every dimension in Smart Manufacturing performing a very critical coordination function. Operations Management orchestrates activities into the supply chain to make sure that materials, parts, and subassemblies arrive at the right place at the right time. It provides demand signals for resources and delivers real-time information from production processes that includes the context of orders, specifications, and resources. Good data from Operations Management enables confident decision-making in all parts of an organization including production, quality, maintenance, sales, and engineering. This information is essential to allow management to drill down from corporate Key Performance Indicators (KPIs) into causal analysis to uncover areas for improvement.

To ensure the operational outcomes desired we must properly train personnel on required skills and keep track of the required certifications for specialty jobs. Resource Management includes Workforce, Facilities, and Equipment Management. Equipment Management includes the care of the plant, environmental controls, machines, equipment and tools handling trouble-call tasks as well as scheduled preventive maintenance and calibration service tasks.

Workforce Management includes maintaining the right level of workforce with the right level of skills and certifications to perform the required production and inspection tasks. It includes tracking attendance and labor costs in concert with the Operations Management system. Human Resources has the challenge to attract the next generation of the Smart Manufacturing workforce with the required new skills for the job.

Supplier Management includes the activities for sourcing materials and components to suppliers, coordinating the proper production of those components at the supplier site including supplier qualifying and auditing, negotiating contracts, scheduling deliveries, managing warehouse and stockroom, receiving and inspecting incoming materials and parts, and handling of warranty issues, returns, and corrective actions with suppliers. Product design changes must be carefully coordinated with impacted suppliers. In the connected smart supply chain, digital data about materials and components must be delivered by suppliers along with the physical units in a way that allows easy roll up of the data to higher levels of assembly by the Operations Management system for full parts genealogy traceability. Communications into the supply chain is performed via the internet through B2B (business-to-business) data exechanges.

A Convergence on Smart Manufacturing

Smart Manufacturing strives for higher levels of connectivity, orchestration, and optimization of processes in the manufacturing value chain. To reach this goal, the organization must (i) align the intersection of the different organizational perspectives into a cohesive set of functional roles, data models, and business processes that converge to get products designed, outsourced, built, tested, packaged, and delivered to the customer in a consistent manner, and (ii) figure out how to leverage the many technology building blocks available into a flexible architecture of systems that helps automate the activities as much as possible, pass data seamlessly, and enable new levels of optimization, predictive, and prescriptive analysis in enterprise processes.

The organization can start trying out new technology but cannot complete the analysis of the required technology building blocks until the functional requirements to support the new connected enterprise are fully understood.  The three-dimensional model for Smart Manufacturing in Figure 4 provides a functional framework to start laying out business processes in the industrial ecosystem, the interactions required between activities, and the data exchanges required to support those interactions. Operations Management is a common central function in these three dimensions and has the critical role of coordinating the convergence of the digital, physical, and business process dimensions.

Figure 4: Three Dimensions Meet for Smart Manufacturing

The flow of information in the typical legacy manufacturing environment is, at best, full of manual information handoffs with a lot of human data interpretation and transformation along the way. Legacy processes were commonly designed around the sequential handover of paper documents between different departments. Within major enterprise systems such as ERP or PLM most processes are based or focused on departmental issues; the processes are rarely cross-functional. 

Modern processes can be reinvented around new cyber-physical paradigms that promote real-time response, collaborative teams, and more parallel tasks across production and supply chain. Consider the benefits of processes where utilities auto adjust based on environmental sensor data, where machines take corrective action and request maintenance to avoid costly damage, where part shelves report usage and are automatically replenished by suppliers, where correction tasks for non-conformances are routed in parallel to multiple departments including Engineering, Procurement, Inventory Control, and into the supply chain.

To minimize delays and communication errors among intra-departmental processes, process outputs need to be connected as inputs to successor processes. Communication and data processing among activities should avoid manual data input and translation errors whenever possible. Publish and subscribe data services must connect enterprise systems, web applications, mobile devices, and cloud services in a system of systems to ensure the pervasive distribution of the data.

There will be some technical challenges along the way to create the Smart Manufacturing connected enterprise. For example, data exchange standards will need to evolve and be adopted by hardware and software vendors, and security concerns will need to be addressed at all levels of enterprise communications. But the biggest challenges ahead are cultural. How do all involved embrace new business models, new processes, and new levels of transparency among departments and partners in the ecosystem?

Organizations will soon overcome these barriers and realize a network of connected partners, systems, and resources that will result in the transformation of conventional value chains and the emergence of new manufacturing practices and business models that leverage the higher levels of connectivity to achieve new levels of orchestration, optimization, and customer service.

Wishing for more Clarity in Manufacturing Information Technology for 2018

I can start to see the fog lifting in 2018 from over blown value claims and concerns bolstered over the last two years about implementing new information technology in manufacturing. I believe there is good reason to be excited about the potential of implementing new highly efficient practices, but I am hoping to see more clarity and better understanding of true scenarios, value, and effort to leverage applicable technologies that have become practical to implement in recent years. I am especially optimistic on more clarity in the following five areas:

• Security and governance concerns will not to be insurmountable
  • IT will start pushing back against the unmanageable wave of custom apps appearing at the shop floor. It is easy to build an app with the tools available. But what about security? What about architecting connectivity and data sharing? The current wave of apps is similar to the wave of spreadsheets introduced in the ‘70s when the PC first hit the shop floor. We are creating a myriad of data silos and custom apps that might help for some local optimization but do not necessarily advance the global optimization of the enterprise. IT governance and guidance is required. 
  • Standard approaches to bridging OT and IT networks will emerge. Many stakeholders are working on these types of solutions and the necessary security schemes to connect plant floor operational technology (OT) equipment to enterprise IT systems.
• Manufacturing Apps will be seen as complementary and not as replacement for MES
  • We will see a renaissance of MES. As late 2017 polls indicate, MES is at the top of manufacturers list for implementation because the success stories are abundant, and the word gets out. Do you need a Super MES or MES 4.0? Not really, all you need is a good MES for your specific industry.
  • There will be new UIs coming out and many MES will get a facelift thanks to the responsive and adaptive UI platforms available. Augmented Reality will become part of the UI landscape as AR instructions authoring becomes more practical for specific use cases.
• Hype over IoT/IIoT Platforms will be replaced by better understanding of good use cases.
  • The practical availability of connected sensors and machines coupled with platforms that can collect, visualize, distribute, and analyze this information is a great opportunity for manufacturing. However, it doesn’t mean we throw everything away and start over either. The new connectivity and platforms enable better and quicker data to be leveraged into improved business processes enabled by new cloud services and integrated enterprise systems including ERP, MES and PLM.
• Hype about AI/Analytics/Machine Learning will lead to better understanding of the different technologies under this category and their different use cases
  • There will be a higher demand for people that understand manufacturing data structures and have data science skills
  • There will be more realistic expectations for the use of unstructured data in manufacturing.
  • Manufacturers will uncover that the great value of higher connectivity is not in pursuing better intelligence, it is in pursuing better orchestration and process optimization that leads to reduced cycle time and increased flexibility. A higher level of business intelligence is a great side effect. 
• Fear of Robots will be replaced by sensible views on robots as helpers at the shop floor
  • Instead of taking over all manufacturing jobs, robot use will increase for work that is highly repetitive, high precision, and unsafe.
  • In many instances, robots will be helping the human worker instead of completely replacing the worker.
  • We will start to see more robots in managerial functions. Helping managers as sidekicks providing the pre-processing of the enormous amount of data that the factory will generate.

Better understanding of the value and use of these technology building blocks in a future Smart Manufacturing architecture will help accelerate their adoption. The initial hype has opened minds to the potential, but the momentum can fade if we don’t quickly follow with practical examples and guidance on how to thread these technologies together for high impact changes.

What is “Smart” about Smart Manufacturing?

A few decades back we might have asked “What is lean about Lean Manufacturing? But nowadays everyone knows what Lean Manufacturing means and what the guiding principles are. For Smart Manufacturing, we are just starting to educate on the principles and how to tell if projects and initiatives are making progress towards a future “Smart Manufacturing Enterprise”. I am confident that a few years from now, everyone will know what it means.

Smart Manufacturing is an endeavor to elevate the connectivity and orchestration of manufacturing processes across the entire value chain to new heights. In Smart Manufacturing, the digital information about each part or product, a.k.a. its digital twin, follows along with the physical part or product accumulating more data as it makes its way through production and inspection processes, supply chain processes, assembly and test processes, and eventually handed over to the end customer as a bundled physical and digital product or service.

Today’s manufacturing reality is far from this digitally connected dream so Smart Manufacturing is an endeavor that will take many years. However, industry leaders are positioning now to be the market disruptive innovators and are making strides to be among the first wave of companies ready to join these new smart manufacturing ecosystems as they are established.

What should manufacturers be doing now to get ready? Companies need to create their own roadmap for the journey and start investing in projects and initiatives that get the company closer to a digitally connected real-time enterprise. The following are some guiding principles to keep in mind for your Smart Manufacturing initiatives:

• Minimize manual data entry, translation or transformation of information at each process step.
• Processes should hand over structured parsable data (like XML or JSON web services) as output to successor processes to facilitate publish/subscribe mechanisms to systems within the company and into the value chain.
• Establish distributed nodes of autonomous diagnostic and decision support at the machine, factory, and enterprise levels. These nodes should roll up transaction information for each machine, factory, and each product unit as needed
• Allow ubiquitous use of mined information throughout the product value chain including end-to-end value chain visibility for each product line connecting manufacturer to customers and supplier network.
• Automate routine tasks and decisions but include people in the process loop wherever needed to handle nonroutine situations, manual adjustments, and complex decisions assisted by the analytical insights.
• Implement optimization schemes that leverage acquired data, advanced analytics, and machine learning algorithms to recommend process adjustments at different levels of the enterprise from controls, to operations, to value chain.
• Adopt machine-to-machine (M2M), application-to-application (A2A), and business-to-business (B2B) integrations standards that will enable multi-vendor hardware and software plug and play solutions with open integrations platforms to the internet. Learn about organizations including ISO, IEC, NIST, OAGi, MESA, IIC, DMDII, and CESMII which are establishing and promoting standards for data exchanges among assets, production processes, and supply chain.
• Develop a collaborative culture and new workforce skills that encourage projects that cross old functional boundaries like engineering, production, automation, and information technology (IT). Manufacturing automation personnel needs to understand IT and vice versa. Workers will need to learn to configure and maintain smarter machines and robots.

Smart Manufacturing is an evolution to new levels of connectivity over the next few years. Revolutionary productivity gains are expected from the resulting new integrated value chain processes. These are exciting times to be in manufacturing. Make sure you are not a spectator on the sidelines watching others make progress because it could be hard to jump in later and catch up if you wait too long to get started.

I encourage everyone to explore more Smart Manufacturing related resources at www.mesa.org.

This article was originally published at AutomationWorld’s blog on automationworld.com on Oct 19, 2017.

Manufacturing Operations Management Software ROI Fuels New Initiatives

Manufacturing Operations Management (MOM) software remains on the top of the list of prioritized manufacturing investments in surveys by both Gartner [4] and LNS Research [2]. Not only is it on the top of the list, LNS found that 68% of manufacturers were viewing the MOM as part of their enterprise systems strategy.

Why are MOM investments critical to manufacturers?

“MES (aka. MOM) applications boost manufacturing reliability by digitally enforcing processes, methods and procedures. Because MES applications also provide genealogy and traceability information required for compliance, they serve as a strategic nucleus for broader manufacturing architectures. MES data is also an important resource for determining discrete product quality. When scoped, implemented, and integrated properly with other processes for product supply, MES applications can support growth goals of the business while removing costs and complexity, driving process optimization, improving product quality, and directly impacting the bottom-line.”

Quote from Hype Cycle for Discrete Manufacturing and PLM – 2016, Gartner [4]

 

Leading companies are sharing their MOM success stories in surveys, at conferences and press articles, and the word is getting out. An LNS report [5] shows that implementers of MOM systems have improved Total Cost Per unit by 22.5%, Net Profit Margins by 19.4%, and On-Time Delivery by 22%. Improvements for MOM users were generally double the average among all respondents.

Specific improvements listed by manufacturers include:

• Common user interface across the enterprise
• Elimination of duplication of input
• Integrated interdepartmental information
• Platform of interdepartmental collaboration and workflow
• Closed-loop quality management

 

A similar survey performed by Gartner and MESA International [1] showed that most manufacturers implementing MOM/MES had achieved significant improvements within 12 months of implementation. Over a third of the surveyed manufacturers saw results within three months.

Because the MOM serves as a critical link in the information value chain 

MOM offers immediate benefits that are easy to quantify for the investment payback. However, manufacturers that stop their ROI analysis here risk missing the larger savings and productivity gains that impact strategic business outcomes. For many companies, MOM has served as a critical technology linking manufacturing operations with customer and supply chain processes.

 

 

As Gartner found out in their analysis [3], the largest benefits of MOM come from capitalizing on the visibility it provides into manufacturing performance and capabilities across the organization and supplier network. Participants in a 2015 Gartner study on the value of MOM reported that they were investing in MOM to drive continuous improvement, eradicate variability (that is, improve quality and compliance) and reduce costs. However, they also realized many more tangible and intangible benefits from the MOM implementation the following years.

Although these companies pointed out that the MOM couldn't be solely credited for all the performance improvements, they highlighted its pivotal role in creating an information environment that accomplished the following:

• Supported integrative and sustainable improvement initiatives by providing continuous performance feedback through real-time information visibility
• Provided the stakeholder visibility needed to manage and streamline the end-to-end supply chain
• Created a sense of ownership and performance consciousness among operations and supporting functional personnel
• Standardized processes across multiple production sites, allowing for resource sharing and flexibility

References

[1] 2016 MESA/Gartner Business Value of MES Survey, Meyer/ Jacobson/Franzosa, Gartner, 2017

 [2] The Global State of Manufacturing Operations Management Software - Weaving the Digital Thread Across Industrial Value Chains, Goodwin, LNS Research, 2015

 [3] Get the Most Out of Digital Manufacturing by Extending the Value of Your Manufacturing Execution System, Franzosa, Gartner, 2016

[4] Hype Cycle for Discrete Manufacturing and PLM - 2016, Halpern/Jacobson/Suleski/Franzosa, Gartner, 2016

[5] Metrics that Matter and the ROI of MES/MOM, Schwarz, MESA.org, 2016

[6] Has MES Come of Age? (recorded webcast), Fraser/Franzosa, MESA.org with IYNO and Gartner, 2014

 

 

Do We Want to Make Manufacturing Great “Again”?

A strong US economy and middle class needs a strong manufacturing sector so I would not expect any arguing about wanting manufacturing to be great. However, when we add “again” to the end of that phrase, we need to be clear on what we mean by “again”. If we are referring to making manufacturing popular again then I totally agree with the idea, but if we are referring to taking manufacturing back to older practices then it sounds like a bad idea. Manufacturing practices are much better now than they were 20 or 40 years ago and will continue to get even better going forward as we implement more Smart Manufacturing practices.   

Can we bring the old manufacturing jobs back? The straight answer is No.

We can’t because the old manufacturing jobs are gone and not just gone to other countries. The incentive of offshoring to pursue lower labor cost has dissipated over the last few years. [1, 3, 4, 5, 6, 7] Some manufacturers have even been reshoring factories over the last few years. However, it seems that most of the reshoring is also behind us. The manufacturing performed in other countries is usually there to make products sold to those markets. The old manufacturing jobs are gone because the manufacturing industry has evolved and the new processes and equipment require fewer personnel with higher skills.

The chart from MIT Technical Review contrasts the loss of manufacturing jobs against the increased manufacturing output as proof that the loss of manufacturing jobs over the past few decades has been related to huge productivity increases. While manufacturing output has been increasing, production employment has been decreasing, and that trend is not changing. We just do not make things like we used to. [1, 2]

Manufacturing has become a high-tech industry and the related jobs have evolved. There is no going back. Even schemes to tax robot labor will not bring those jobs back. Just like we are not going back to printing blueprints or faxing purchase orders, we are not going back to older labor intensive, unsafe, and error prone manufacturing methods. 

Let’s face it, some of those old manufacturing jobs were not that great. The pay was okay, but many of those jobs were dirty and unsafe requiring handling of hazardous materials and dangerous equipment. I remember, during my first manufacturing job, meeting several people with missing fingers due to accidents with older unsafe machines.

The fact that manufacturing jobs have changed should be viewed as a positive and the sector is growing in the U.S. thanks to the use of modern technology. The higher level of productivity of smarter factories allows manufacturers to compete against lower labor cost countries that might be using older machines and processes. Smart factories can produce higher quality products quicker through highly flexible and innovative equipment and systems. We should view this manufacturing evolution as a great opportunity to develop a strong highly skilled workforce.

There are plenty of manufacturing jobs going unfulfilled.

Based on a Manufacturing Institute’s 2015 report, [9] if we continue current trends over the next decade nearly 3.5 million jobs will be needed and 2 million would go unfilled due to the skill gap. This is based on a combination of baby boomers retiring and expected growth in demand for high-tech manufactured goods. If the gap becomes that significant, manufacturers will have to look for alternate places to fulfill the gap. How can we keep those jobs in the US? What skills do we need to develop for the required workforce?

The manufacturing shop of the future is more like Iron Man’s workshop and less like the old factory with smokestacks and assembly lines where mechanics turned the same wrench and the same five lug nuts over and over all day.

We need to figure out how to get more people interested and trained in these new manufacturing jobs. We can no longer bring new people into factories and put them to work without training. Should government help manufacturers with the cost burdens related to training workers on highly specialized equipment?

What skills are needed for these manufacturing jobs?

Technical, computer, math, and problem-solving skills top the list of requirements for modern manufacturing jobs which are becoming increasingly more technical in nature due to the advanced equipment and the integration of digital computer-driven processes in the smart factory.  [9, 11, 12, 13]

A recent study by Accenture [10] estimated that a U.S. manufacturer is losing on average 11 percent of their annual earnings (EBITDA) or $3000 per existing employee due to the talent shortage. Other reports estimate that it could be as high as $14,000 per employee. These additional costs are related to longer cycle times, higher overtime pay expense, higher downtime, and higher personnel training costs.

Will higher pay attract more people to manufacturing? Four out of five U.S. manufacturing companies surveyed [9] are willing to pay more than current market rates to hire and retain skilled workers to tackle talent shortage. An average manufacturing worker in the U.S. earned $77,506 in 2013 – 20 percent higher than what an average worker earned in other industries. However, while paying higher wages helps to attract talented candidates, it likely isn’t enough to solve the shortage.

What are manufacturers doing about the skills shortage?

Manufacturers realize they need to improve the perception of the industry as being clean, safe and high-tech rather than dirty and dangerous. [14] These perceptions are improving according to a more recent study but mostly among people closer to manufacturing and more aware of the great technology strides in the industry. [15] Programs like Manufacturing Day are helping change the awareness and attitudes towards manufacturing careers. According to the MfgDay.com website, an estimated 225,000 students had their attitudes toward manufacturing improved in 2016 thanks to over 1,600 Mfg Day events. [16]

Manufacturers are developing more internal training programs, recommending external certification programs, and working with local schools and community colleges to develop the training needed. They recognize the need for an integrated training approach that will depend on these partnerships. They are also more open to using some resources on contract for very specialized jobs like maintaining or programming sophisticated production equipment.

What can the federal government do to help manufacturing?

Manufacturing is of interest around the globe and is an important part of government agenda in many countries because it creates high paying jobs, drives technological innovation, and generates more economic activity than any other sector.

The competition to attract manufacturers is a global one and initiatives to promote and advance manufacturing from other governments include Germany’s “Industrie 4”, France’s “Industrie du Futur”, and China’s “Made in China 2025”. The U.S. should not fall behind in manufacturing technology leadership and the federal government has a role to play. The U.S. strategy to maintain and attract manufacturing needs to includes programs to help train the new manufacturing workforce.

In addition to helping with education, research and development programs, the federal government has an important role in maintaining and enhancing the infrastructure required by the manufacturing industry. See the related article on this site with the title “The Role of Government Supporting Smart Manufacturing” [19] for more information on how the federal government supports the manufacturing ecosystem.

What can states do to attract more manufacturers?

A state’s ability to attract and maintain manufacturers not only depends on the availability of resources including skilled labor, a rich infrastructure of universities, educational and research centers, and a strong network of suppliers of critical components like electronics. But also on the cost of doing business in the state which includes wages (which are tied to cost of living), utilities, taxation policies, and state and local regulations. [17]

Don’t rule out expensive areas. Sometimes cost of living can be offset by quality of life when trying to attract talent. Access to a good transportation infrastructure of ports, rail and highways is also important to help goods in from the supply and out to the distribution network. States should strive to develop areas like Silicon Valley in California with an abundance of suppliers in high-tech electronic components and software developers for embedded systems.

Some states are providing additional incentives to attract business including tax breaks for plant construction and expedited regulatory processes. Additional tax incentives can be provided as a proportion of business activity in the state including wages paid and sales tax generated. The states can also help with the cost of workforce training on the new skills required.

The U.S. is producing a lower percentage of college graduates with advanced degrees than Europe and Asia. The industry needs these higher skills as part of the workforce to support smart manufacturing factories. States with higher graduates in these advanced areas could have an edge.

In addition to the traditional Manufacturing Engineering, Industrial Engineering, and Quality Engineering degrees, more manufacturing certificate programs are needed in skills like CAD Drafting, Tool Design, Industrial Practices, Manufacturing Operations Management, and Quality Control Technology.

Industry-endorsed certifications can also be part of the training mix. These are third-party assessments based on standards that reflect the knowledge and skills required to do the work in a modern manufacturing workplace. For example, the American Society for Quality (ASQ) offers 16 different certifications including Quality Technician, Quality Inspector, and Quality Engineer. These do not substitute for, but rather complement, traditional education credentials. [18]

States can also work with industry to promote more “Learn and Earn” programs, more internships, and mentorships to align higher education with industry competency and skill requirements.

In summary, we want US manufacturing to have a great future and we want to continue the growth trends of recent years. However, growth will be hugely handicapped without development of the skilled workforce required for future smart manufacturing. Government, industry, and academia need to continue working together on solutions to really make US manufacturing’s future a great future.

Government’s role goes beyond helping with education of the new workforce. It also requires investment in infrastructure including smart grid, internet, transportation, communication standards, and research and development institutes to keep America competitive in this new global manufacturing landscape.

References

 [1] “What it takes to bring back a manufacturing job”, S. Ben-Achour, Marketplace.org, 2016

https://www.marketplace.org/2016/09/26/world/what-it-takes-bring-back-manufacturing-job

[2] “Here's how -- and where -- U.S. manufacturing is coming back”, C. Schouten, Market Watch, 2017

http://www.cbsnews.com/news/america-manufacturing-hope-the-national-context-how-where-u-s-industry-is-coming-back/

 [3] “Reshoring Gaining Strength Among Large Manufacturers, BCG Survey Finds”, IndustryWeek, 2015

http://www.industryweek.com/competitiveness/reshoring-gaining-strength-among-large-manufacturers-bcg-survey-finds

[4] “Why Donald Trump is Wrong about Manufacturing Jobs and China”, J. Rothfeder, The NewYorker, 2016

http://www.newyorker.com/business/currency/why-donald-trump-is-wrong-about-manufacturing-jobs-and-china

[5] “Manufacturers bringing the most jobs back to America”, M. B. Sauter and S. Stebbins, USA Today, 2016

https://www.usatoday.com/story/money/business/2016/04/23/24-7-wallst-economy-manufacturers-jobs-outsourcing/83406518/

[6] “Record number of manufacturing jobs returning to America”, A. Cheng, Market Watch, 2015

http://www.marketwatch.com/story/us-flips-the-script-on-jobs-reshoring-finally-outpaced-offshoring-in-2014-2015-05-01

[7] “Reshoring — An increasing trend in American manufacturing”, J. M. Kirschner, Hartford Business Journal, 2016

http://www.hartfordbusiness.com/article/20160523/PRINTEDITION/305199870

[8] “Americans Believe Manufacturing Industry Critical to Country’s Prosperity”, Industry Week, 2017

http://m.industryweek.com/competitiveness/americans-believe-manufacturing-industry-critical-country-s-prosperity

[9] “The Skills Gap in US Manufacturing 2015 and Beyond”, Deloitte, Manufacturing Institute, 2015

http://www.themanufacturinginstitute.org/~/media/827DBC76533942679A15EF7067A704CD.ashx

[10] “Out of Inventory: Skills shortage threatens growth for U.S. Manufacturing”, Accenture, The Manufacturing Institute, 2014

http://www.themanufacturinginstitute.org/Research/Skills-and-Training-Study/~/media/70965D0C4A944329894C96E0316DF336.ashx

 [11] Why America Has a Shortage of Skilled Workers, Michael Collins, IndustryWeek, 2015

http://www.industryweek.com/skilled-workers

[12] Manufacturing Production Technician Career, MyMajors, 2017

https://www.mymajors.com/career/manufacturing-production-technicians/skills/

[13] Got Skills? Think Manufacturing, Elka Torpey,US Bureau Labor of Statistics, 2014

https://www.bls.gov/careeroutlook/2014/article/manufacturing.htm

[14] 2015 Manufacturing Perception Study, Deloitte, Manufacturing Institute, 2015

[15] Manufacturing matters: The public’s view of US manufacturing, Deloitte, Manufacturing Institute, 2017

https://www2.deloitte.com/us/en/pages/manufacturing/articles/public-perception-of-the-manufacturing-industry.html

[16] Mfg Day’s Effects on the Public Perception of Manufacturing, Blog post on mfgday.com, 2017

https://www.mfgday.com/blog/mfg-day%E2%80%99s-effects-public-perception-manufacturing

[17] Aerospace State’s Incentives to Attract the Industry, R.M. Moller PhD, California Research Bureau, 2008

https://www.library.ca.gov/crb/08/08-005.pdf

[18] Roadmap for Manufacturing Education, The Manufacturing Institute, 2012

http://www.themanufacturinginstitute.org/~/media/DDB4265AC2F243FB97AEBA2A56CC7523.ashx

[19] The Role of Government Supporting Smart Manufacturing, manufacturing-operations-management.com, 2017

http://www.manufacturing-operations-management.com/manufacturing/2017/07/the-role-of-government-supporting-smart-manufacturing.html

 

The Role of Government Supporting Smart Manufacturing

Manufacturing is an important part of the government agenda in many countries because it creates high paying jobs, drives technological innovation, and generates more economic activity than any other sector. The competition for a strong manufacturing industry is a global one and initiatives to promote and advance manufacturing from other governments include Germany’s “Industrie 4.0”, France’s “Industrie du Futur”, and China’s “Made in China 2025”. The U.S. should not fall behind in manufacturing technology leadership and the federal government has a role to play.

The U.S. strategy to stimulate, maintain and attract manufacturers has many dimensions including promoting research and development, improving the business tax code, training the manufacturing workforce, and establishing favorable trade policies that open global markets and cut down trade barriers. The environment and proposition for establishing manufacturing plants in the U.S. needs to be competitive with other areas in the world. The country should maintain the tradition of a strong manufacturing industry—built upon a foundation of hard work, determination, and innovation.

Research and Development - The government plays a role in research and development of new manufacturing technologies

One of the ways that government stimulates manufacturing is through research and development (R&D) programs where academia, industry and government collaborate to move the entire industry forward and share the risk and rewards of innovative technology research. R&D activity is vital because economies that have consistent levels of innovation, also tend to have high levels of economic growth [3].  The business sector accounts for most of R&D investment, but the public sector is responsible for approximately 40% of R&D investment in the U.S. [4]

The U.S. federal government’s “Manufacturing USA” initiative has created a network of over a dozen manufacturing innovation institutes around the country including the following:

• America Makes - America Makes is a national accelerator and the nation's leading collaborative partner for technology research, discovery, creation, and innovation in additive manufacturing and 3D printing.
• ARM (Advanced Robotics Manufacturing) - The ARM Institute’s mission is to create and then deploy robotic technology by integrating the diverse collection of industry practices and institutional knowledge across many disciplines – sensor technologies, end-effector development, software and artificial intelligence, materials science, human and machine behavior modeling, and quality assurance – to realize the promises of a robust manufacturing innovation ecosystem.
• CESMII (Clean Energy Smart Manufacturing Innovation Institute) - Smart Manufacturing works to spur advances in smart sensors and digital process controls that can radically improve the efficiency of U.S. advanced manufacturing.
• DMDII (The Digital Manufacturing and Design Innovation Institute) - DMDII encourages factories across the United States to deploy digital manufacturing and design technologies, so those factories can become more efficient and cost-competitive.
• IACMI (The Institute for Advanced Composites Manufacturing Innovation) - IACMI is committed to accelerating development and adoption of cutting-edge manufacturing technologies for low-cost, energy-efficient manufacturing of advanced polymer composites for vehicles, wind turbines, and compressed gas storage.

Transportation Infrastructure - The government supports nation-wide infrastructure

In addition to helping with education, research and development programs, the federal government has an important role in maintaining and enhancing the infrastructure for utilities and transportation required by the manufacturing industry.

The federal government, working with state and local government, has played a central role in the development of our nation’s transportation infrastructure. In the 19^th and early 20^th century, the federal government invested in roads, locks, inland waterways, and harbors, and that investment paid off allowing the U.S. to grow into the strongest economy in the world.

In 2002, U.S. freight carriers moved over 19 billion tons of freight valued at more than $13 trillion, and traveled over 4.4 trillion ton-miles over our transportation network. The U.S. Department of Transportation (DoT) estimates that by 2035, the volume of freight shipped on the U.S. intermodal transportation system will increase to 33.7 billion metric tons, worth more than $38 trillion—an increase of more than 48 percent. [1]

Advances in logistics have turned the nation’s roadways into virtual warehouses thanks to “just in time delivery” but the growth in congestion on the nation’s roadways threatens these efficiency gains.

The nation’s rail network has also experienced significant freight growth, and the U.S. DoT estimates that demand for rail freight tonnage will increase by 88 percent by 2035.

Ports and waterways continue to serve as vital links to trade. The U.S. is the world’s largest trading nation, accounting for nearly 20 percent of the world’s total ocean borne trade. 95 percent of all U.S. foreign trade tonnage is shipped by sea. Over the next 20 years, the DoT expects U.S. foreign ocean borne trade to double.

Air transportation is another key part of the transportation system. The federal government has long been involved in promoting and developing the nation’s air service and infrastructure. This role continues today with the Federal Aviation Administration’s (FAA) operation of the air traffic control system and oversight of safety of airlines, pilots and airplanes. The FAA also provides grants to airports to develop their infrastructure through the Airport Improvement program.

Many more manufacturers are dependent on shipment services via airplane—not only from suppliers but also for delivery of products to customers. Airport congestion causes billions of hours of travel delay that result in additional billions of gallons of fuel being used while shippers, travelers, and commuters are stranded in traffic and not moving. This congestion is also increasing logistics costs. Transportation accounts for 60% of the total logistics costs for manufacturers.

Many aspects of the nation’s transportation network are currently operating at or near capacity. With future trade volumes expected to more than double across all modes, new strategies are needed to fund and allocate the resources required to further develop these systems and meet the needs. Insufficient investment, and the resulting inefficiency in the roadway system, is having a significant impact on the nation’s economy.

Other countries including Brazil, China, India, as well as many European countries, are investing in their transportation infrastructure. The U. S. government could provide more visibility on these issues to the public and explain the importance of these types of investments to the future of the manufacturing industry.

Energy and Utilities Infrastructure – The government facilitates the new Smart Grid

Between the late 19^th and early 20^th century advances in infrastructure, standardization and mass production methods fueled the second industrial revolution.  Infrastructure advances included large growth in railroad, gas, water, electricity, telegraph, and telephone networks.  Government stepped in and established standards for a range of services including railroad gauges, electricity voltages, layout of typewriter keyboards, and rules of the road for automobiles. This period was marked by large economies of scales created by the new infrastructure and a corresponding reduction in setup and indirect cost to manufacturers.

The U.S. electric grid turned out an engineering marvel with more than 9,200 electric generating units having more than 1 million megawatts of generating capacity connected to more than 600,000 miles of transmission lines. The electric grid is more than a network of electrical plants and wires, it is an ecosystem of asset owners, manufacturers, service providers, and government officials at federal, state, and local levels, all working together to run one of the most reliable electrical grids in the world.

The electric infrastructure is now aging and it is being pushed to do more than it was originally designed to do. The grid needs to be modernized making it “smarter” and more resilient using innovative technologies, equipment, and controls that communicate and work together to deliver electricity more reliably and efficiently. An improved grid can greatly reduce the frequency and duration of power outages, reduce storm impacts, and restore service faster when outages occur.

The “smart grid” is a proposed upgrade to the electrical infrastructure with smarter sensors, controls, digital meters, batteries, and systems that can improve: storage of electricity when we have excess, re-routing of resources based on demand, handling of faults, and consumer control enabled by APIs for business and home equipment. A new smart grid can integrate consumers and suppliers, provide improved security, reduced peak loads, increased integration of renewables, and lower operational costs.

Communications Infrastructure - The government helps to establish rules and guidelines for integrating the ecosystem

It might be counterintuitive because standards are difficult to develop and take time to adopt in industry, but standards support innovation and growth in many ways:

• Standards permit the sharing of investment and risks related to implementation of the standard across companies in an industry
• Standards help the exploitation of innovative ideas that depends on communication and integration standards as a framework for collaboration in the manufacturing ecosystem
• Standards allow innovation by combination of techniques that involve different standards across different domains.
• Standards avoid redundant efforts for many companies of “re-inventing the wheel” when a standard practice already exists.
• Standards create a more competitive environment by allowing smaller product and service companies to participate with less cost and risk in a more open multi-vendor manufacturing ecosystem.

Throughout the 19th century the American, British, and French militaries were sponsors and advocates of interchangeability and standardization. The standards developed had a positive impact on industry for many years but eventually became too many and a burden on defense procurement processes. In the U.S., defense standards including MIL-SPEC and MIL-STD had grown to over 30,000 in 1990. In 1994, the DoD decided to reform the practices and move from government developed standards to commercial standards developed by non-military standards groups.

Examples of organizations working on standards are ISO (an International Federation of the National Standardizing Associations), and the IEEE (Institute of Electrical and Electronics Engineers). These are technical professional associations with membership composed of engineers, scientists, and professionals from industry, academia and government working together to develop and evolve standards for specific applications and industries. Government now endorses and promotes these standards and uses them in their procurement processes.

Government was instrumental in the development of the Internet which evolved from early government networks like ARPANET and CSNET. In 1974, the IEEE published the TCP/IP standard which became the foundation for networking for the Internet. However, the foundational model for that standard was developed by DARPA (Defense Advanced Research Projects Agency) in the 1960s.  In 1982, the DoD declared TCP/IP the standard for all military computer networking. Afterwards IBM, AT&T and DEC adopted TCP/IP despite having competing protocols. The Internet has been embraced as a platform for eCommerce applications and data exchange with suppliers via integration standards like EDI (Electronic Data Interchange) and OAGIS (Open Applications Group Integration Specification).

The biggest use of the Internet is yet to come with the Internet-of Things (IoT) and the convergence of digital and physical worlds in Smart Manufacturing. Gartner estimates that connected pieces of equipment in manufacturing will rise 30% every year to over 500 million by 2020. [7] It would be impossible to be looking forward to the IIoT (Industrial Internet of Things) without standards like TCP/IP, OPC-UA, MTConnect, EDI, and OAGIS helping us connect equipment and systems from the manufacturing factory into the supply chains.

The work of the manufacturing institutes listed above includes evolving, developing, and promoting the communications standards needed for future digital and Smart Manufacturing ecosystems through documented integration practices and test bed results. After all, the digital highway needs rules of the road too. A government agency helping with standards is the National Institute of Standards and Technology (NIST). The NIST paper, “Current Standards Landscape for Smart Manufacturing Systems”, [5] is a great resource for learning more about the evolving role of standards in manufacturing. NIST has also published an important Cybersecurity Framework [6] which many organizations are using as a reference model for reviewing security in manufacturing systems.

Organizations like NIST have a role to develop the framework of infrastructure, models and standards for information management of the smart grid including how the different layers for configuration, operation, informatics and security will connect and exchange data.

In summary, manufacturing is a vital part of a healthy economy and the competition to attract manufacturers is a global one. Other countries, including Brazil, China, France, Germany, and India are investing in R&D, and infrastructure to improve their ability to attract, support, and maintain the manufacturing industry. The U.S. should not fall behind in manufacturing leadership and the government has multiple important support roles to play. Without government help, manufacturers would end up with less efficient infrastructure and higher indirect costs. Industry, academia, and government should strive to make “Made in America” a shared pride for all Americans. More visibility on the role of government and constraints to a competitive environment can help the public understand the importance of these types of investment for the future of the U.S. manufacturing industry.

References

 [1] The Government’s Role in Ensuring a Strong Transportation Infrastructure, U.S. Rep. James L. Oberstar, 2007

http://www.cts.umn.edu/events/oberstar/2007/speechoberstar

[2] Issue Background - Scope of the U.S. Highway Network, ARTBA, 2017

http://www.artba.org/government-affairs/policy-statements/highways-policy/

[3] Research & Development, Innovation and the Science and Engineering Workforce, National Science Foundation, 2012

[4] Digital Prosperity: Understanding the Economic Benefits of the Information Technology Revolution, Atkinson/McKay, The ITI Foundation, 2007

[5] NISTIR 8107 - Current Standards Landscape for Smart Manufacturing Systems, Lu/ Morris/Frechette, NIST, 2016

http://nvlpubs.nist.gov/nistpubs/ir/2016/NIST.IR.8107.pdf

[6] NIST Cybersecurity Network, https://www.nist.gov/cyberframework

[7] “The Emperor’s New Clothes and the Factory of the Future,” Simon Jacobson, Gartner Supply Chain Conference, 2015

 

This article was also published at: The Role of Government in Manufacturing Infrastructure Support, IndustryWeek, 2017 

 

 

 

Notes from the Smart Manufacturing and IIoT Round Table Discussion

I am sharing below some of my favorite highlights from the round table discussions on Smart Manufacturing at the 2017 Manufacturing & Technology Conference at Cleveland. MESA International organized this forum and there were many common discussion threads and questions among manufacturers moving forward with Smart Manufacturing and IIoT initiatives in their respective companies.

After going around the table with introductions we decided to frame the discussion around a repeating story line:  A manufacturer has multiple plants. They have a mix of plants running different Manufacturing Execution Systems (MES) plus some plants running on paper and spreadsheets. Some plants had over the years moved from spreadsheets to custom apps built on top of desktop database software and ended up with a few custom built MES solutions.

The manufacturer has just finished implementing ERP. The focus of the ERP project had been to standardize accounting practices and lower inventory costs. JIT initiatives drove the implementation of ERP a few years ago and they finally finished.

The manufacturer is now looking to connect manufacturing processes using a more standard solution across plants. 

Why? They have a new manager and he wants to be able to compare plants and prioritize the issues he is going to tackle. Where does he start? He needs visibility of metrics across the plants and needs numbers that he can use to compare. For real transparency, standard ways are needed to collect data and turn that data into metrics that are consistent across the plants.

There is also a push to lower the maintenance cost of the various custom MES solutions. The various MES solutions are slow to be enhanced and are not keeping up with OS and platform changes.

We agreed that this was a typical scenario that everyone could relate to. We continued with more in depth questions and examples to figure out approaches to better tackle next steps by learning from each other’s experience.

Q: The ERP vendor says they can handle the shop floor also. Should we go with the ERP vendor or go with an MES vendor? Should we get guidance from vendors or from a system integrator? Consultants are expensive resources.

A: The group had seen all the above approaches. The recommended practice is to select an advisor and vendor with experience in the specific industry. Interview and survey a few and select one you are comfortable with. This approach seems to yield the best guidance and results based on project experiences presented at the conference and documented by industry analysts.

Q: What are typical first steps that companies are taking on the journey to a future smart factory? What are companies doing now to get ready for the future?

A: One company started grabbing numbers from machines and putting them into a database. Managers liked it and wanted it in more areas. They said “Just Do It” .

Q: Did you do ROI analysis?

A: Justified first project as productivity improvement. Second project justified to better fulfill traceability requirements.

Q: Is it an all or nothing strategy? Is your plan to connect all machines or do you prioritize among machines and work centers?

A: One company decided to start with new work centers and new equipment first because the equipment had better support for connectivity and APIs. However, they are hoping to connect legacy equipment in the future.

Another company started by blueprinting their as-is processes.

Q: Did you apply Value Stream Mapping (VSM) to your as-is analysis? Some of the things we are working on within MESA is to create examples of applying VSM to manufacturing automation and IT projects. We would like to create templates and examples on how to justify and prioritize these types of projects using VSM type of methods.

A: We did not use something formal like VSM, but our management likes Lean so it would probably be a good idea.

Q: Many vendors pitch: give me your data and we can analyze it. But “getting the data” is the hard part. We are being asked to be “world class”. We are getting world class machines, but we are still chasing the process with clipboards and paper. A lot of manufacturing data on paper. Any ideas on best practices?

A: First, get your network in place. The right grade of network, cooling closets, etc. Then you need actionable data—not just any data. Spend time defining the problems you want to solve and the data needed to do the respective analysis. Be ready to shift gears as you do your analysis and find that your initial hypothesis was wrong. You don’t see correlation where you expected. You might need to start collecting different data. With the right infrastructure, it is easier to shift and add new types of sensors in different places.

Q: Management has seen the price tag to do the rest of plant and they asked us to take a step back and make sure we are doing the right thing. For example, should we keep working with this system integrator, with this vendor, or should we build it ourselves?

A: We use the OT/Automation group to do these projects. We do not wait around for IT. If we could only marry the IT team’s smarts of architecture and coding practices with the OT team’s smarts on the automation needs.

Q: Have you had to deal a loss of the initial drive to connect things?

A: No. We have had a steady increase of motivation. We found that having a “poster child” deployment helped a lot. We put a focused effort into making that project very successful. Now we have developed faith with management. They believe that if we connect the machines and get data, it will lead to more savings. They have seen the results of our champion deployments. Now everyone wants to replicate that success. They want it everywhere!

Q: How can MESA help you with your initiatives?

A: We find these roundtable discussions useful. We need more forums like this. People are more willing to share in these smaller groups among peers.

I did not capture the entire discussion in these notes—just my personal favorite highlights. Let us know if you feel any of these topics deserve further elaboration in future articles. If you find these types of discussions interesting, your team should consider joining MESA International. For more information visit www.mesa.org.

Karen Field from IndustryWeek magazine captured other perspectives on these discussions in her article: “MESA Turns to Crowd Sourcing to Tackle Manufacturers' Biggest Challenges”.

 

Six Innovation Areas Leading to the Model-Based MRO Enterprise

Maintenance, Repair and Overhaul (MRO) operations are facing a rapidly changing highly competitive environment. In the past, MRO shops could pass the cost of inefficiencies as a “cost of doing business” to the customer, but in the face of higher competitive pressures, MRO shops must look to modernize practices and leverage digital technologies to take operations to new levels of productivity.

In this article, we discuss the convergence of technologies enabled by a new Model-Based Enterprise (MBE) philosophy that leverages the engineering 3D models throughout MRO processes including spares management, inspection and work execution. In this new digital environment, MRO shops can leverage technologies like IIoT connected product and equipment, optimized 3D illustrated work packages, inspection drones, augmented reality, and improved intelligence and analytics to create the platform for those new productivity levels.

In a recent article [2], Airbus discussed a vision for the MRO hangar of the future. In this future vision, 3D models are leveraged in many ways as foundational data for a myriad of technologies like autonomous robotic inspectors and mechanics equipped with smart glasses for hands-free computer interaction.

In this article, we further discuss six innovation areas that are driving progress towards a future MBE at leading edge MRO hangars:

• 3D Printing of Spare Parts
• Automation with Robotics and UAVs
• Asset Sensor and Diagnostic Connectivity via the IoT
• Digital Thread and Digital Twin
• Advanced Analytical Capabilities
• Advanced Guidance with Augmented Reality and Natural Language

 

3D Printing of Spare Parts

The most obvious use of a 3D model is to produce the input file for a 3D printer. 3D printing is becoming a “must have” for manufacturers rather than a luxury R&D project. Especially in the aerospace and defense (A&D) industry where companies like Boeing and Airbus have been using the 3D printing process to manufacture components for more than two years, with Airbus recently printing 1,000 parts to meet delivery deadlines for the A350.

While only 7% of companies use additive manufacturing to produce their end products today, 31% use it for prototyping, and it’s predicted that 42% will rely on 3D printing for mass manufacturing in the next few years. This means that more and more CAD and CAM software systems offer capabilities to handle 3D printing.

3D printing will not only revolutionize prototyping and production, but will also transform spare parts inventory practices for service management. Manufacturers of spare parts will store the digital data to 3D print parts and components in CAD software rather than storing the physical parts on shelves. In other words, inventory will be produced only when necessary. This will be of special interest to produce hard-to-find spare parts or components that aren’t manufactured anymore.

The growth in 3D printing methods will probably come from the larger OEM and MRO companies because they can cover the upfront investment to design, build, and test the multiple prototypes it takes to finally develop a part to the desired quality level for a customer.  

The companies capable of developing almost instant spare parts and completing maintenance and repair tasks within hours will have a market advantage. Imagine an airline looking for an MRO able to repair an airplane that has broken down unexpectedly. Instead of waiting days or even weeks for parts to arrive, an MRO could manufacture the parts needed outright and fix the airplane within several hours.

The ability to manufacture parts on demand will affect inventory and logistics. MROs will no longer have to keep on-hand dozens of “just-in-case” replacement components until the right client comes along. Whenever they need a part, they can just make it. This will probably cause a collapse of the entire supply chain, making some part suppliers and even manufacturers vulnerable to extinction.

Automation with Robotics and UAVs

The rapidly expanding capabilities of autonomous equipment such as UAVs and robots are being leveraged in MRO shops and some of the first applications are in automated inspection.

Examples include the use of 3D scanners mounted on robots and UAV (Unmanned Aerial Vehicle) to inspect fuselage and wings for hail, lightning, or bird strike damage. UAVs can complete detailed aircraft checks, by comparing the images captured against the respective 3D models, and quickly report any damage requiring repairs. Checks that now take 4-6 hours can be performed in less than an hour.

 

 

 

 

 

For now, operators control the UAVs, either guiding the inspection or setting a predetermined pattern. The UAVs fly around aircraft, providing images of skin with the same detail as visual inspections. Engineers assess damage as they are presented images. The software continues to be enhanced to rapidly focus on possible problems areas and even suggest actions based on prior recommendations. Once the UAV learns the aircraft it is to inspect leveraging its 3D model, it can recognize a known point on the airframe, and fly a predetermined inspection pattern.

 

  

LHT’s Mobile Robot for Fuselage Inspection (Morfi) is automating inspection of metal fuselages for thermographic crack detection. Morfi moves over preprogrammed points on fuselages rapidly. Two coils heat the points with electric pulses and changes are recorded on infrared cameras, revealing cracks as shallow as 1 millimeter. Vacuum pads on the robot’s feet allow it to reach vertical and overhanging sections of the fuselage. Up to four areas can be inspected and results saved in less than 30 seconds.

 

 

  

Asset Sensor and Diagnostic Connectivity via the IoT

It is easier to embed micro computers in many devices and assets. The device will also be expected to easily connect and exchange data with other systems and centralized data repositories that will focus on analysis of exception and aggregate data in different dimensions including dimensions relating to the asset’s 3D model. By relating the intelligence gathered to the different 3D model areas, engineers can use performance data, not only to diagnose issues, but also to enhance future designs.

Today a simple engine designed for industrial use has hundreds of sensors recording thousands of measurements, along with enough memory to store months or even years of data. Until recently, the primary use of these embedded measurements and associated low level diagnostics was to help identify component failure to facilitate the identification of areas requiring repair.

Next in the evolution to a more digitally connected future is the ability to easily connect each device or asset to centralized or remote data processing capabilities using common internet networking protocols. This type of device connectivity is referred to as the Internet of Things (IoT). Will we be able to improve product service if we can remotely monitor the product and apply advanced analytics to predict potential issues before they happen? Can we optimize maintenance based on sensor and usage statistics versus prescribed intervals?

The increased capacity and lower cost of data storage makes it possible to store huge amounts of data. But these streams of data from sensors and diagnostic feeds need to be managed. Platforms for data management includes strategies for how much to store, locally versus centrally, raw versus aggregated, and for how long for different process orchestration, record keeping and analytical purposes.

Digital Thread and Digital Twin

In addition to the asset usage data collected via IoT or other techniques, MROs need to collect as-maintained data and cross reference the respective as-designed specifications. The flow of information between engineering specifications, requirements and actual service realization records is covered under the label of the “digital thread”.

The concepts of the digital thread and digital twin have been spearheaded by the military aircraft industry and their desire to improve the performance of future programs by applying lessons learned through these digital technologies in current and upcoming programs.

The digital twin refers to a digital model of a particular asset that includes design specifications and engineering models describing its geometry, materials, components and behavior, but more importantly it also includes the as-built and operational data unique to the specific physical asset which it represents. For example, for an aircraft, the digital twin would be identified to the physical product unit identifier which is referred to as the tail number.  The data in the digital twin of an aircraft includes things like tail number specific geometry extracted from aircraft 3D models, aerodynamic models, engineering changes cut in during the production cycle, material properties, inspection, operation and maintenance data, aerodynamic models, and any deviations from the original design specifications approved due to issues and work arounds on the specific product unit.

The digital thread refers to the communication framework that allows a connected data flow and integrated view of the asset’s data throughout its lifecycle across traditionally siloed functional perspectives. The digital thread concept raises the bar for delivering “the right information to the right place at the right time”.

A typical as-is condition for many MROs without a digital thread:

• Design 3D models are usually not shared between OEMs and asset owners
• Some drawings might be included with OEM maintenance manuals and delivered via PDF files
• Maintenance requirements are documented within the maintenance manuals to be parsed manually by asset owner
• Asset owner creates their own maintenance requirements based on OEM maintenance manuals and refer to specific maintenance manual sections
• MRO creates maintenance task cards based on asset owner maintenance requirements and maintenance manuals
• As-maintained records are archived by MRO and delivered to asset owner as PDF package. Both asset owner and MRO must maintain these records archived for auditing purposes

Progress towards a full digital thread:

• Asset’s 3D models are shared by OEM with asset owner by 3D technical packages and standards like 3D PDF and STEP.
• Maintenance manuals are shared using standards like S1000D for easy parsing into downstream systems.
• Maintenance requirements are managed online and cross referenced to sections of maintenance manuals
• Maintenance task instructions are developed leveraging links to maintenance manuals and 3D models for illustrations.
• Asset’s as-maintained data is delivered in parsable data format to asset owner along with finished asset

 

Advanced Analytical Capabilities

Humans will manage the exceptions and will soon only need to be involved when something goes awry. Integration between operational systems, business process management systems and analytics will become the norm. There are four general levels of analytic capabilities:

• Descriptive Analytics: “What has happened?”
• Diagnostic Analytics: “Why did it happen?”
• Predictive Analytics: “What is likely to happen next?”
• Prescriptive Analytics: “How it can be encouraged or prevented?”

Descriptive Analytics

Descriptive analytics collect and report on the changing state and diagnostic properties of devices over time. This data can be used to determine stability, trends, and patterns in those properties.

Descriptive analytics also allow comparative analysis between the performance of a specific asset and the performance of similar assets in order to identify unique deviations from the asset population. 

Diagnostic Analytics

Diagnostic analytics are just a step beyond purely descriptive numbers. One big problem for the IoT is the massive number of alerts that it can generate. Alerts are generally intended to get humans to pay attention, but “alert fatigue” can set in fast if there are too many of them – as there are already coming out of today’s aircraft.

Diagnostic analytics can determine whether alerts really need attention and what is causing them. It involves correlating multiple properties and looking for escalation based on the grouping of multiple conditions.

Predictive Analytics

Predictive analytics is where we start seeing bigger payoffs from diagnostic data connectivity and processing. The idea is to analyze historical data patterns that are leading to device failures, find the leading indicators, and start using those patterns to predict failure before it happens again on the same device or other similar devices.

The more data we have from similar devices or assets, the more accurate the prediction algorithms are going to be. OEMs and asset owners are creating ways to accumulate the needed data from their assets as they go into service.

Prescriptive Analytics

The initial steps for prescriptive analytics, are systems that use artificial intelligence and expert system techniques to continuously learn from human maintenance prescriptions under different circumstances, to become an automated “second opinion” for the human that proposes several options based on analyzed past decisions.

The next prescriptive level would be for the machine to start making recommendations for actions directly, with high confidence levels, from analyzing the input data. For example, an airline pilot could be told, “Shut down engine number three now, before it overheats.”

Predictive and prescriptive analysis have the potential to radically improve how maintenance activities are planned in the future. Today, most maintenance activities are planned based on a combination of elapsed time and asset usage frequency. Today, we are probably doing a lot of maintenance that is not really needed or perhaps not enough maintenance in some cases. For example, for an automobile, the dealer prescribes the frequency of brake service based on miles driven. However, the mechanic checks the brake pad usage to verify if service is really needed because the real need depends on multiple variables like driving style and the number of hills in the area. We might have taken the car in for brake service too early and would probably go ahead and get it done to avoid an additional service visit. Enhanced sensors and analytics could feed future applications that will prescribe the ideal time to take a car in for service and group multiple needed services into fewer visits. If we can see the value of this type of app for our car, imagine the value of this type of analysis to optimize and minimize service downtime for airplanes.

Advanced Guidance with Augmented Reality and Natural Language

Augmented reality (AR) or mixed reality is the ability to layer digital 3D images and virtual objects on top of the real-world images when seen through technologies such as smart glasses or hand-held tablet computers.

Multiple companies are experimenting with the use of AR for support on inspections and repairs. AR may be able to recognize the asset a user looks at, overlay the points where service is needed, and integrate with a tablet that helps the user know what work needs to be done and instructions needed. Such capabilities could improve service efficiency; lowering the cost of labor; and reducing errors.

For example, some of the automated inspection methodologies mentioned above could be further enhanced with augmented reality technology by projecting the results, color coded for severity, on top of the physical surface so inspectors and repair personnel can quickly focus to the specific problem areas.

The two major technological trends for AR display today are (1) display on mobile tablets that requires the user to hold the device, (2) display on a head-mounted display like smart glasses. Smart glasses are getting closer to the form factor of safety glasses which is needed to really make this technology appealing at the shop floor.

 

The traditional way to interact with smart glasses has been with simple voice commands like Next, Yes, No, Check. But in parallel to the AR technology, there is much progress on natural language capabilities for easier voice driven interaction with these devices. Chat bot technology, like seen in Siri or Alexa for home use, is being married to AR capabilities to create a new generation of hands-free easier user interaction with the advanced AR display where the user can chat with the device in a more natural manner. For example, “Glasses, show me the illustration for the next step.” The system would know what to display based on the recognized asset, the completed prior work, the work assigned to the user, and the authored work instructions.

Everyday work cards and checklists can easily be performed through these wearable devices. Steps may be signed-off as they are performed and all information systems are updated with real-time information via Wi-Fi directly from the AR device.

The above technologies and innovations are examples of how leading MROs are paving the way to the fully model-based future of MRO operations. MRO shops need to continue this innovation path leveraging 3D models as they connect and optimize processes across engineering product specifications, maintenance requirements, and maintenance execution techniques.

References

[1] Model-Based Enterprise: An Innovative Technology Enabled Contract Management Approach, Mitzi Whittenburg, Journal of Contract Management, 2012

[2] MRO Hangar of the Future, Airbusgroup.com, 2016

 

Reducing Cost of Quality with Integrated MOM and EQMS

– Using Manufacturing Operations Management Software to Reduce Cost of Quality

Tackling the price of quality goes beyond reducing the number of defects. It involves evaluating the entire quality management system. Following are some ideas to help manufacturing organizations reduce cost while improving quality levels through smarter use of integrated information systems.

Quality guru Philip Crosby wanted us to see quality management as an embedded part of everyday practice; instead of seeing quality as a separate complex process of verification, layered on top of production as overhead. Crosby described quality costs as the price of making things right the first time plus the price of making things right when they are not. Doing things right the first time, every time; is not as easy as it sounds, because many production processes have sources of variance and margins of error. To minimize the issues and defects that arise from those margins of error, we need a quality management system (QMS). Guidelines for a full QMS have been developed and documented over the last few decades by industry standards like ISO9001, AS9100 or ISO13485. 

Breaking Down the Cost of Quality

The most widely accepted method for measuring and classifying quality costs is the prevention, appraisal, and failure (PAF) model which divides quality costs into the four categories in Figure 1.

The PAF model is a combination of ideas developed in the 1950’s by multiple quality gurus including Juran [1] and Feigenbaum. [2] Their concepts emphasized that it is better to have more upfront preventive investment in quality management to minimize the costs of quality failures later in the product lifecycle. 

Research shows that the cost of poor quality (including rework, returns, and reduced repeat sales) can range from 15%-40% of business costs. [3] Feigenbaum referred to these costs as the “hidden factory”—the part of the factory dedicated to fixing bad work. [2][4]

See Figure 2 for examples of the breakdown of cost of quality.  

 

 

 

The Relationship between Prevention and Failure Costs

The graph in Figure 3 shows the relationship between prevention costs and failure costs. There is a natural cost tradeoff between how much an organization spends on prevention versus how much it spends on fixing failures.

If factors affecting cost of failures and cost of prevention are static, then the only way to reduce cost it to lower the number of defects. To radically change that equation, an organization must tackle reducing the costs of the quality management system itself at the same time they are trying to reduce the number of defects.

 

  

 

 

Six-Sigma Projects Reduce the Number of Defects

A traditional way to tackle reducing the cost of quality is to reduce the number of defects. This is the focus of most Six-Sigma projects. Figure 4 shows how improving the sigma level would reduce defects and reduce the costs of the related failures up to a point. At some point the cost of quality will go up to achieve higher quality levels unless the cost of quality is tackled directly.

 

 

 

 

 

 The Effect of Improving the QMS Processes

A different way to reduce the cost of quality is to make the processes for handling prevention and failures more efficient. Figure 5 shows how the cost curves would change when the focus is on improving the quality management system itself.

Additionally, when improving the efficiency of the quality management processes and reducing the cost of failures (cost of processing a non-conformance, cost of inspection through automation), the savings can be reinvested into better prevention methods, such as more accurate machines, better tooling, and more training, which would lead to even higher levels of quality.

The use of a Manufacturing Operations Management (MOM) or Manufacturing Execution System (MES) can make a big difference to the efficiency of quality management processes. The following are a few specific examples of how to reduce the cost of quality with improved integrated quality management processes.

Reducing the Cost of Quality Failure Prevention

Prevention costs are incurred to prevent or avoid quality problems. These costs are associated with the design, implementation, and maintenance of the quality management system. They are planned and incurred before actual assembly of the product at the shop floor. The prevention costs include specifications and inspection requirements for incoming materials, processes, and finished products, worker training and gauge calibration. 

Work Instructions and Process Change Management 

The manufacturing of a complex product (such as an aircraft or satellite) involves the management of a continuous stream of engineering changes directed at work in process.

On a shop floor with a paper system, changes to work instructions are written into the margins with red ink and stamped by a liaison planner. A copy of the redlined document is routed back to a process planner to get the changes incorporated into future releases of the work instructions.  

An MES system can combine (1) the redlining of the work instructions on the shop floor, and (2) the update of the template work instructions maintained by MES. 

Verifying Personnel Training and Certification 

Personnel must be certified as competent based on education, training, skills, and experience. Personnel qualification processes must be standardized and documented. 

On paper, certification of employee for the task is left for the supervisor to verify. An MES can validate each employee’s skills and certification against the latest training records before they sign on to a job.

Verifying Equipment Calibration 

Equipment resources must be maintained to assure their optimal performance to capabilities, especially measurement equipment used to verify the product. The maintenance and calibration processes for equipment and tools must be standardized and documented.

An MES can verify calibration status for equipment and gauges specified to be used for data collection. Tools can be calibrated more efficiently based on actual usage instead of using the traditional date driven expiration scheme which ends up in tools being calibrated more often than is truly needed.

Reducing the Cost of Quality Appraisal

Quality appraisal activities are the most conventional quality practices and the cost of these activities are a very visible expenses because it is easy to see the cost of inspectors, testers and their equipment in the balance sheet.

Appraisal is an expensive and unreliable way of achieving quality. Appraisal in its best form is verification that the production processes and preventive measures are working. Appraisal in its least productive form, is separating the good from the bad product, counting defects, scrapping and calculating yield. While these activities are a necessary part of the quality program, it is important to move towards more preventive methods to reduce the cost of failures—the cost of poor quality.

Is 100% Inspection Needed?

Ideally the preference is to move away from 100% inspection and towards more inspection by production personnel; leaving only a small percentage of random over inspection for quality management personnel. An MES can monitor the results from inspections and the history of defects. Based on that history, process improvement efforts can focus on areas where problems are found instead of blanket improvement campaigns spread among all areas of production.

Another way to reduce inspection levels below the 100% inspection, 100% of the time, is to only trigger comprehensive inspection based on production process validation (PPV) or first article inspection (FAI) rules. In other words, when there is (a) a change in design, affecting fit, form or function of the product, or (b) a change of manufacturing sources, processes, inspection methods, location, tooling or materials, that can affect fit, form or function.

Automated Inspection and Statistical Process Control (SPC)

Where inspection is needed, and a lot of data needs to be collected, there might be possibility of reducing the clerical cost of data collection using automated inspection methods such as CMM (Coordinate Measurement Machines) or visual inspection machines.

Inspection and test results coming out of these machines can be imported directly into the MES. Critical measures and results can be tied to data collection points and to SPC run charts to monitor control levels. The X-bar and R run charts produced could also trigger alerts automatically for out of control conditions. Automation and integration may yield new levels of efficiency over the traditional processes of inspectors running spreadsheets and separate SPC software processes on the side.

Automated Quality Metrics 

How many people are dedicated to putting spreadsheets and charts together for weekly meetings. This is clerical non-value added time that can be eliminated. In addition to automating SPC, systems can automate the calculation of all quality metrics off the information collected by MES software.

Process Audits

Process audits are used to confirm that the quality management system is functioning correctly. The organization can perform internal audits and external audits to suppliers as part of their periodic assessment.

Through an audit, an organization can identify a system’s ineffectiveness, take corrective action, and ultimately support continuous improvement. Process audits provide a form of assurance to management as well as regulators that your entity is following contractual and industry regulations.

Audit scheduling and check lists are an important part of the audit planning system. Audit findings should be documented and prioritized per risk and management objectives.

Reducing the Cost of Quality Failures

During inspections, it is possible to find some aspects of products, materials and components to be nonconforming to specifications. These nonconformances could lead to rework, scraping, returns and recalls all of which should be documented in nonconformance or discrepancy reports and classified in a database in such a way that the organization can use the data to determine costs and areas for improvement.

Discrepancy Documentation

The cost of filling out a nonconformance report on manufactured or purchased parts includes the cost of:

• Problem Identification
• Containment Actions - activities to ensure nonconforming material is controlled and prevented from improper usage
• Correction and Disposition Documentation – instructions to correct and bring the product back to conformance, and instructions to handle component scrap or repair if needed

The use standard codes and descriptions for symptom, defect and cause types, can simplify and speed up the documentation process and provide more consistent data for analysis.

Workflow Driven Routing of Tasks

The costs of routing the discrepancy for disposition through a workflow process across multiple departments can be controlled by limiting the participants in each discrepancy to those who are needed instead of including the entire multi-discipline Material Review Board (MRB) in every review.

Rework, Re-inspection, and Re-testing

One of the advantages of using an MES is the handling of rework instructions to correct an issue. The same process planning tools used for authoring regular work instructions are used for authoring rework instructions and either append work to the original work order or edit the instructions for the affected units only. The resulting rework jobs should show on the same job list along with the planned work so the mechanics do not have to learn a different process for rework, re-inspection and re-testing. However, these activities do need to get classified, and segregated in reporting to the financial system as cost of poor quality.

Corrective and Preventive Actions (CAPA)

When failures do happen, the team should assess if adjustments are needed to the preventive measures to avoid recurrence for the same type of problem. This is done through the Corrective and Preventive Action (CAPA) process.

The cost of processing a corrective action includes the following activities:

• Problem Identification including recurrence data, cost impact and risk classification
• Analysis for root cause by a team of subject matter experts
• Implementation of an action plan to correct the root causes identified
• Verification tasks to make sure that the plan was implemented and effective

Do not flag every little problem for a full CAPA process. Select only problems that seems to be recurring and have a significant impact. This can be figured out by ranking issues based on a standardized risk assessment methodology.

It is also important to check history for similar problems and see if there have been solutions that have worked in other areas.

The Cost of External Quality Failures

The intangible costs of external quality failures, including customer dissatisfaction, loss of reputation and loss of future sales, might be hard to calculate but are not hard to picture as having a huge negative impact on the future of the business. The best way to reduce external failure cost is to not have it at all! The best way to avoid external quality failure costs is to focus on improving the other three costs of quality.

Conclusion

There is a natural cost tradeoff between how much an organization spends on prevention versus how much it spends on fixing failures. In addition to the traditional way of reducing the cost of quality by reducing the number of defects, it is possible to tackle the efficiency of the quality management system itself to further reduce cost of quality. The use of MES software leads to more embedded quality control throughout the entire manufacturing process, more ownership of quality by production personnel, and more smiles and fewer tears for everyone in the organization.

References

[1] “Quality Control Handbook – 1^st Edition”, Book by J. M. Juran, McGraw Hill, 1951

[2] “Total Quality Control”, Book by A.V. Feigenbaum, Harvard Business Review, 1956

[3] “A Review of Research on Cost of Quality Models and Best Practices”, Whitepaper by A. Schiffauerova and V. Thomson, International Journal of Quality and Reliability Management, Vol.23 No.4, 2006

[4] “Dr. Armand Feigenbaum on the Cost of Quality and the Hidden Factory”, Article by Tim Stevens, Industry Week, 1994

[5] “The Six Sigma Revolution”, Book by George Eckes, John Wiley & Sons, 2001

[6] “Strategic Initiative Guidebook on Quality and Regulatory Compliance”, Guidebook by MESA.org, 2013

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Your Next Enterprise App? Survey Says: Manufacturing Operations Management

An Industrial IoT Architecture Needs Data Distribution, Consolidation and Stream Processing

It is easy to come out of a presentation on “big data” these days thinking that the future of manufacturing operations management and manufacturing IT is a combination of three things: connected machines, data stored in a “data lake”, and an analytical engine. However, those closer to manufacturing systems management know that the reality of data storage and processing needs are vastly oversimplified by drawing a big data lake circle in the center of a diagram. In a prior article, “Unraveling The Hype On Data Lakes for Industrial IoT” [1], we discussed some of the myths around data lakes. In this article, we dig deeper into data processing and examine new and older technologies under the lens of the requirements to: distribute data from its source to departmental functions, consolidate views of data for multiple organizational levels, along with new processing opportunities presented by data streaming technology.

 The traditional data landscape across an enterprise is a myriad of islands of information with varied data structures and indexing schemes controlled by each department’s applications for functions including Procurement, Inventory, Production, Quality, Human Resources, Sales, Marketing, and Finances. Rather than trying to create a single mega system to manage the enterprise–one designed from the ground up with a consistent and integrated data model–organizations need ways to integrate, exchange and manage data across the departmental functions.  There are different methods and technologies available to perform data distribution, processing and consolidation tasks including:

• Enterprise Application Integration (EAI)
• Extract, Transform and Load (ETL)
• Enterprise Information Integration (EII)
• Data Stream Processing and Streaming Analytics

These techniques should be viewed as complementary with different functions in an overall IT technology stack. In fact, many vendors are combining multiple of these technologies into unified platforms in recognition of how they complement each other in different applications.

Enterprise Application Integration (EAI) - Application-to-application integration via messaged-based, transaction oriented, brokering and data transformation. Focuses on integrating business-level processes and reacting to events that occur during those business processes with transaction data messages for handoff to other business processes or systems. EAI integration mapping needs to be updated when applications change their integration interfaces. It can have simpler results than other methods for the end user because data is accessed via their main departmental application and integration is handled in the background by the IT department. Hub and spoke methods using middleware like Enterprise Service Bus (ESB) products facilitate message brokering between multiple applications.

Extract, Transform and Load (ETL) – Set-oriented point-in-time transformation of data for migration, consolidation and data warehousing. It can process very large amounts of data during non-peak use times and deliver data optimized for reporting, while freeing up transactional databases from the computational burden of complex queries. ETL scripts need to be updated when applications change their query web services or transactional data structures.

Enterprise Information Integration (EII) – Transparent data access and transformation layer providing a virtual consolidated view of data across multiple enterprise data sources. In other words, EII provides federated data queries across multiple data sources via one meta model. EII models can be placed on top of data warehouse models and join structured and unstructured data. It can provide real-time read and write access and manage data placement for performance, accuracy and availability. EII can be hard to implement when key fields and data types do not match across source data systems. EII can impact network bandwidth and have high demand on source systems during peak hours. Caching schemes improve performance by extracting data out of data source systems for reuse by multiple EII queries. The Hadoop toolkit is an example of a technology stack that enables EII techniques.

EII techniques are usually leveraged by data scientists for ad-hoc analysis while ETL techniques turn data into simpler structures for every day reporting needs of users that don’t need to understand the complicated data structures of source systems.

Data Stream Processing and Streaming Analytics

In contrast to the traditional database model where data is stored, indexed and subsequently queried, stream processing takes the inbound data while it is in-flight real-time through an EAI process and is able to look for alert conditions and patterns in the data stream or in-memory data aggregation—the “real-time data mart”.

There are some evolving combinations of the above techniques and technology stacks which use data stream processing techniques as a pre-processor during the extract phase of an ETL process before data ends up transformed and stored at the data warehouse.

Data in the Smart Manufacturing and IIoT IT Framework

Evolving Smart Manufacturing and IIoT systems and applications leverage these different data handling techniques to improve real-time analysis where needed, as well as provide improved availability of structured and unstructured data for different types of reporting and analysis where real-time data is not needed or even desired. For example, the weekly review by the management team of Key Performance Indicators (KPIs) for the organization should be based on data mined at a fixed time—not data mined real-time at different random times yielding different results for every query. We don’t want each manager showing up to the weekly meeting with a different set of data in their hands. In contrast, we want to know of a production machine about to go down as soon as possible especially if it will stop the production line.

Figure 2  shows an example of how we could put all these techniques to work together in an IT architecture for the enterprise connecting different types of data for different types of data processing applications. The reality for this picture at any one organization might have more additional parallel paths but this simplified view illustrates the general idea of how it can all work together.

 

 

 

 

 

  

The picture gets even more interesting in Figure 3 when we add the fact that we want to connect the entire value chain for a product including enterprise data from multiple manufacturing sites and operational data from multiple tiers of suppliers across the globe.

As we look forward to cloud integration we expect the data services to support integration to multi-tenant cloud applications like Salesforce and support for REST and JSON APIs.

 

 

 

 References:

“Unraveling The Hype On Data Lakes For Industrial IoT”, manufacturing-operations-management.com, 2016

 

This article was first published at IndustryWeek magazine September 27, 2016:

“A Smart Manufacturing IT Architecture Needs Data Distribution, Consolidation and Stream Processing”, Industry Week, 2016

 

Unraveling the Hype on Data Lakes for Industrial IoT

The hype about data lakes, big data and all the great data generated by the IIoT (Industrial Internet of Things) keeps growing. There are valid use cases for data lakes in manufacturing operations management and the Smart Manufacturing IT framework but companies should know more about the realities in this arena. Gartner recently published a report titled “Three Architecture Styles for a Useful Data Lake” [1] which mentions these concerns. We need to make sure they don’t stay buried in that report and instead bring them to the front of the conversation. 

First, what are data lakes?

A data lake is a collection of storage instances of various data assets stored redundantly in a near-exact copy of the source format. The general idea is to store data separate from source domains for future processing and analysis by different applications in the enterprise.

A data lake is a concept, not a technology. There are multiple types and purposes for data lakes. There could be more than one data lake in an IT architecture. Data lakes may add contextual metadata to the raw data. They may have a short or long shelf life for the data depending on the purpose for each data lake.

Examples of the use of data lake concepts range from self-service intelligence to ad-hoc data science analysis to advanced analytical alerts on new data trends. However, the data lake is not the new data warehouse. It is a place to search for and discover additional insights, it is not optimized for daily or weekly reporting of metrics to the organization.

Second, what are the misconceptions about data lakes?

There are many valid reasons to consider data lakes in the information technology landscape for future manufacturing systems. However, they should not be viewed as a silver bullet answer to the complexity of architecting and integrating enterprise platforms. They should be viewed as another tool in the IT arsenal, to be used where appropriate, and approached with an understanding of the following myths.  

Myth: A data lake strategy does not include ETL tools

Key to the data lake concept is the separation of independent data storage and computing software. The methods of moving data into and out of the data lake can include tools like ETL (extract, transform, load) and ESB (enterprise service bus).

Myth: A data lake has to be Hadoop

Hadoop is the most popular platform for data lakes. However, it is possible to use other types of relational, NoSQL and object data storage tools. Some of the factors to consider in this decision include data volume, velocity, variety, and scalability.

Myth: Data lakes are inexpensive to implement

Even though data storage cost continues to decrease, the costs of networks, integration, backup, security and support can add up very quickly and significantly. Open source software and cloud storage options can help with cost but they should not be viewed as free of cost.

Myth: Get all the data you can into the data lake. No need to worry about data modeling.

Gartner predicts that by 2018, 90% of data lakes will be rendered useless as they become overwhelmed by a vastness of data captured for uncertain use. The concept of garbage-in-garbage-out applies to the data lake. Data hoarded without a preplanned purpose or proper contextual or lineage information will create a lot of noise that can obstruct finding important signals in the data.

Myth: A data lake creates a single source of truth

Data collected in a data lake is usually schema-on-read which means that the structure is determined through discovery at the time it is analyzed. The data lake must remain flexible to be inclusive of new data sources. Data lakes have soft governance approaches to avoid anarchy in the data lake, but they don’t have hard governance. The data lake should not be considered a system of record.

Myth: Anyone can use a data lake

It is not trivial to do data discovery and model development in the data lake. Organizations will need staff skilled in statistics, machine learning and data science to reap benefits from data lake efforts.

Myth: A data lake is a data integration method

The data pipelines in a data lake scheme should be viewed as additional-to and not in-lieu-of data integration schemes linking the organization’s business systems. It is possible to leverage data integration messages as one of the sources for a data lake, but the data lake purposes should always be secondary, and should not interfere with the queuing and synchronization required for system integration purposes.

Myth: If we build a data lake, people will use it

The implementation of technology should always have specific use cases and a business purpose in mind. If we start with a need, the system will be used. If we start with technology and the need is not clear, there is a big risk that no one will fish in that data lake.

 

References

[1] “Three Architecture Styles for a Useful Data Lake”, S. Sicular, Gartner, 2016

How “Smart” Are We in Manufacturing Today?

To make the Smart Manufacturing vision a reality, manufacturers, equipment vendors and software vendors must all embrace standards-based messaging for connecting the resources, applications and partner ecosystem. This need was confirmed in the recent article “Smart Manufacturing Isn’t So Smart Without Standards” by Frechette, Morris, and Lu from NIST.org.

In this article we review the reality of adoption of integration standards in manufacturing based on a recent MESA and IDC Manufacturing Insights survey.

 

Full article link:

http://blog.mesa.org/2016/07/how-smart-are-we-in-manufacturing-today.html

 

Demystifying the Digital Thread and Digital Twin Concepts

The concepts of the digital thread and digital twin have been spearheaded by the military aircraft industry and their desire to improve the performance of future programs by applying lessons learned through these digital technologies in current and upcoming programs. However, these concepts and technologies are gaining interest beyond the aerospace and defense industry and are converging with the digital manufacturing and cyber-physical system goals of Industrie 4.0 and Smart Manufacturing.

In the Industrie 4.0 roadmap, a cyber-physical system refers to the digital representation of a physical system used to communicate properties and live status to other cyber-physical systems and applications in future smart factories. Smart Manufacturing strives to orchestrate and optimize business, digital and physical processes across smart factories and the entire product value chain. As system architects evaluate these initiatives, it makes sense that these similar digital connectivity goals start converging into a single framework strategy for manufacturing systems.

The digital twin refers to a digital model of a particular asset that includes design specifications and engineering models describing its geometry, materials, components and behavior, but more importantly it also includes the as-built and operational data unique to the specific physical asset which it represents. For example, for an aircraft, the digital twin would be identified to the physical product unit identifier which is referred to as the tail number.  The data in the digital twin of an aircraft includes things like tail number specific geometry extracted from aircraft 3D models, aerodynamic models, engineering changes cut in during the production cycle, material properties, inspection, operation and maintenance data, aerodynamic models, and any deviations from the original design specifications approved due to issues and work arounds on the specific product unit.

The digital thread refers to the communication framework that allows a connected data flow and integrated view of the asset’s data throughout its lifecycle across traditionally siloed functional perspectives. The digital thread concept raises the bar for delivering “the right information to the right place at the right time”.

Expected Benefits

The benefits expected from having a digital twin for each product unit include:

• Assessment of a system’s current and future capabilities during its lifecycle
• Early discovery of system performance deficiencies by simulating results way before physical processes and product are developed
• Optimization of operability, manufacturability, inspectability, and sustainability leveraging models and simulations applied during the entire lifecycle of each tail number
• Continuous refinement of designs and models through data captured and easily crossed referenced to design details

A Digital Thread is expected have the following benefits for manufacturers:

• Improve product quality by avoiding mistakes in manual translations of engineering specifications along the product value chain
• Improve the velocity of new product introductions (NPI) and the communication of engineering changes along the product value chain
• Increase the efficiency of digitally capturing and analyze data related to product manufacturing
• Allow manufacturers to deliver new services to customers along with physical product leveraging the digital data now available on the product

The digital thread should provide a formal framework for controlled interplay of authoritative technical and as-built data with the ability to access, integrate, transform and analyze data from disparate systems throughout the product lifecycle into actionable information. The product lifecycle includes: Design, Procurement, Test & Evaluation, Production, Field Operation, and Sustainment Services. The digital thread and digital twin include as-designed requirements, validation and inspection records, as-built data, as-flown data, and as-maintained data.

As-Is and To-Be Scenarios

The As-Is handover between departments:

• Design 3D model thrown over the wall from Product Engineering to Manufacturing Engineering
• Manufacturing Engineering creates 2D drawings from 3D models and hands over to Quality and Suppliers
• Conformance requirements are thrown over the wall from Quality to Production
• The product unit is thrown over the wall from Production to customer and sustainment cycle without any data on the specific unit beyond the original design
• Production archives as-built to records for auditing purposes

The desired To-Be scenario with a digital thread:

• 3D model linked to visuals for production process instructions are created by Product Engineers working together with Manufacturing Engineers
• Product characteristics are linked to 3D models and extracted directly out of designs into conformance requirements
• Conformance requirements are linked to manufacturing process and inspection instructions
• As-built data is delivered by Production along with product unit to customer and available for sustainment services to continue evolving the unit’s data during operation and maintenance services
• Product design changes follow the same data flow and automatically update downstream models, references and instructions.

Interesting Use Cases  

Today we must take the experts to the physical product unit to help troubleshoot issues where they are found. Tomorrow, with a digital twin, we could have experts in different locations looking at the same digital representation of the actual product unit. The Material Review Board (MRB) processes for nonconformance and deviations could be improved by involving more experts remotely and leveraging simulations to figure out the impact to the overall product performance before rework deviations are approved.

During sustainment services, maintenance technicians can receive direct support with the latest and most accurate information on the state and condition of the equipment. Data from smart connected equipment would be aggregated, analyzed and transformed into actionable information for the maintenance staff. Technicians can focus on trouble-shooting the issues at hand rather than performing lengthy diagnostics processes at every service visit.

Potential Challenges

Can people responsible for upstream lifecycle functions understand and accommodate the needs of people in downstream functions for the overall better outcome to the entire program?  Will feedback mechanisms be put in place to ensure we continue to improve the upstream processes that serve the downstream processes?

We have already seen concepts like design-for-manufacturing well adopted by industry. The concepts of the digital twin can be viewed as evolutionary and a natural progression on that type of thinking beyond the manufacturing process.

Can vendors see beyond their desire to hold a segment of the market hostage to their formats and products? Could they endorse open data exchange standards where needed to facilitate multi-vendor participation in the digital thread?

Can manufacturers, hardware, and software vendors move forward when many are overwhelmed trying to meet short term improvement goals promised to customers and shareholders?

The digital thread and digital twin concepts have been proposed as incremental steps and leading companies are moving forward with these initiatives. Only time will tell but the potential benefits of the digital thread and digital twin seem obvious. The vendors that endorse and provide these types of connectivity techniques could end up being favored by the market in the long term over vendors with proprietary closed solutions.

References

[1] Global Horizons Final Report: United States Air Force Global Science and Technology Vision - AF/ST TR 13-01, United States Air Force, 2013

 

The Smart Manufacturing Elevator Pitch – Take Two

In a prior MESA blog post [1], Rik Geerts explained how MESA International came up with an abbreviated definition for Smart Manufacturing and how he tried it out with a colleague in the food processing industry. I want to share some of the back story on the Working Group side of the story and my own experience trying out the elevator pitch in a totally different industry -- aerospace and defense.

As Chair of the MESA Smart Manufacturing Working Group, when I first saw the email from Rik requesting that we boil down the definition to a single sentence, I thought it would be an impossible task. After all, we had just finished a white paper [2] explaining the many technologies and initiatives that are converging into Smart Manufacturing and had trouble summarizing the goals for Smart Manufacturing to a half page. But we went ahead and forwarded the idea to the working group and it started a very interesting and productive string of emails and ideas.

After discussing a few key words back and forth and explaining the importance for each word in the definition, we converged on a sentence that allowed us to encompass the different dimensions and differentiators. Not a catchy phrase but a short sentence that can be used to start the conversation and make some very important points to distinguish Smart Manufacturing efforts from automation efforts of the past two decades. Smart Manufacturing sets a new bar for connectivity and integration.

A couple of weeks later, Rik shared with us his experience using the one liner and it gave me the idea to try it out too. I was at lunch at a user conference and I approached an attendee sitting by himself. Doug is a project manager who had just presented his team’s experience implementing a Manufacturing Execution System (MES) at an aerospace manufacturing company. Could I use the same abbreviated definition in a totally different industry?

Conrad: Doug, now that you completed this project, what are you going to work on next?

Doug: I haven’t really given it much thought. I have been so involved putting this solution in place... But now that we are done… I guess we should start thinking of what is next. Why do you ask? Do you have something in mind?

Conrad: As a matter of fact, I do. Smart Manufacturing.

Doug: I have seen some articles on that and on the Industrial Internet of Things (IIoT), but I really haven’t spent much time researching it. Why do you feel I should be interested?

Conrad: Because Smart Manufacturing is the next wave of innovation and connectivity. Smart Manufacturing is the intelligent, real-time orchestration and optimization of business, physical, and digital processes within factories and across the entire value chain.

Doug: Sounds impressive but it also sounds like the project we just completed at our company when we implemented MES?

Conrad: That project gets you started in this direction, but let me drill down a bit more to point out the differences.

Doug: Okay. I’m a bit curious. What do you mean by “orchestration”, “optimization” and “value chain”? We have some time before the next session starts.

Conrad: We stress “intelligent” because we want to contrast the active decision making of automated processes against the passive data collection and reporting of your current systems. Not that reports and metrics aren’t important, but we are moving beyond that to an era where systems are proactively helping us manage operations—active versus passive.  

Doug: So robots will be replacing workers AND managers too?

Conrad: Not exactly. Robots are being used for repetitive and precision jobs. They will also be making routine decisions through programmed criteria and algorithms. But people will always be needed. People will need to handle the more complicated tasks like configuring, setting up and maintaining the robots. Managers will be focusing on the daily non-routine issues that come up at the plant. In fact, Smart Manufacturing is about “orchestration” of “business, physical, and digital processes” and we cannot run the business processes without a smart workforce.

Doug: Can you give me an example of orchestrating processes across business, physical and digital?

Conrad:  We were talking about Model-Based Enterprise and the Digital Thread earlier today. Those concepts are also part of Smart Manufacturing. We want to take that 3D model coming out of product design, link and translate it directly into the programming of an inspection machine, like a CMM (Coordinate Measurement Machine)—linking the digital definition of the product to the programming of the physical machine. We would link that data transmission and translation into the entire business process managing engineering changes.

Changes are then synchronized across all equipment and all people involved in the manufacturing and inspection process. This orchestration is not limited to the plant, we could be talking about a part that is made at a supplier. We want this type of integration throughout the entire “value chain” producing the product. From design engineering to production in the supply chain, assembly and MRO (maintenance, repair and overhaul) operations.

Doug: Sounds like a pipe dream. Have you seen that working anywhere?

Conrad: Not completely yet. There are some gaps today. But we are working with some companies on these types of solutions and we would like to work with your company on getting closer to this vision.

Doug: Maybe I’ll wait and see what others come up with.

Conrad: You could take that position. But I think that your team does not want to sit on the side lines and watch your competitors get ahead. Because we are not just talking about optimization within their plants. We are talking about “optimization” of the entire value chain—entire supply chains creating new business models and new value for customers. The companies that start working together with a digital thread and connected systems will raise the bar and create new expectations from customers. For example, we are starting to see interest from the DoD on adding these types of requirements to future bids.

Doug: We have to get back into the session, but I think we need to keep talking about this latter. It sounds very interesting.

Doug and I continued that conversation later and his team is currently doing research into Smart Manufacturing and IIoT looking for opportunities to start some pilot projects.

So far, Rik and I have tested out the short Smart Manufacturing definition in two totally different industries, food processing and aerospace, and it seems to work. I think you should try it out in your industry and share your experience with us.

Was the idea understood? Did you have to adjust the message? Did it create interest in starting some initiatives at your company? We want to keep refining the message and expanding the list of examples moving Smart Manufacturing forward.

For more information on the MESA Smart Manufacturing Working Group visit this page.

References

[1] “The Smart Manufacturing Elevator Pitch – Literally”, Rik Geerts, MESA Blog, March 2016

[2] “White Paper #52 – Smart Manufacturing – The Landscape Explained”, MESA International, January 2016