Manufacturing Operations Management, Quality and Aerospace 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

 

 

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.