Manufacturing Operations Management Talk

The Urgent Need to Align Business and Smart Manufacturing Strategy

There has been a rising interest in the pursuit of productivity improvements through the implementation of Smart Manufacturing (SM) technologies and CESMII, the U.S. Smart Manufacturing institute, has a national mandate to support and accelerate the adoption of these technologies and techniques. In this article we share key recommendations from the CESMII ecosystem of professionals engaged with manufacturing leaders in their journeys.

A big eye-opener has been that organizations achieving the greatest benefits are treating the Smart Manufacturing initiative as a business transformation journey—a transformation to a data-driven, collaborative, synchronized, agile and innovative organization. These results underscore the need for executives to align the business and technology adoption strategies. However, many are delegating the initiative to their IT team and viewing it primarily as a technology implementation project. Instead, we are recommending the following strategic steps.

The first step for executives is to clearly define and communicate the strategic business goals to the leadership team. The goals should not be a long laundry list or something like “transform operations management by adopting additional technology”. There should be two or three goals in business terms that consider customers and revenue streams. For example: double the production rate, increase market share by 20%, or introduce a new product line. They should characterize where the business wants to be five years from now. The strategic business goals establish a “true north” for the leadership team and a framework for the discussion of investment priorities.

The second step is to enlist the leadership team to coach a mindset and culture that drives the desired behavioral outcomes. The Lean Mindset cultivated over the last decades has driven efficiency across many organizations by eliminating waste and focusing on customer value. However, the fast pace of innovation in products, supply chain, and customer services is creating new challenges that are beyond the manual Lean methods of the past. There is a need to embrace a new Digital Mindset without abandoning the Lean Mindset. In fact, studies have documented that when digital techniques are applied along with Lean tactics the projects yield over thirty percent greater improvement. [1]

A Digital Mindset means that employees understand why the organization needs real-time data to stay competitive and deliver enhanced service to their customers. When employees see paper forms they wonder if there is a more efficient paperless way to collect the data. If it takes days to investigate a customer complaint because employees are gathering data manually, they push for having digital data readily available, so root-cause analysis takes hours instead of days.

The bigger benefits of Smart Manufacturing are realized when data flows in real-time throughout the organization as things are happening at the factory. Leaders from Quality, Inventory, Maintenance, IT, and Supplier Management should be part of the leadership team driving the initiative and the desired level of synchronization across all the functions that support production. If they are involved from the beginning, they are more likely to be aligned with the goals and drive the desired outcomes in their respective areas. As the team discusses the potential top initiatives, a clear link to the strategic business goals drives the investment priorities.

The third step is the development of a technology-enabled strategic roadmap. When leadership follows a strategic approach, they avoid two common risks of a strictly continuous improvement approach. One risk is that uncoordinated technology implementation projects achieve performance improvement but do not achieve the process reengineering required to move the business closer to its strategic goals.

We can illustrate the second risk with an example. A project spearheaded by the Maintenance department implements a new maintenance management system that monitors equipment status, maintenance schedules, and downtime. Another project is spearheaded by Operations to implement a new system to schedule production orders and purchase of materials. If there is no thought put into how data will be shared among these different systems, production orders are sometimes scheduled during the times that machines are down for maintenance and the related customer orders are delivered late to schedule. This simplified example illustrates the risk of creating additional disconnected data silos and suboptimization of business processes when the overall strategy is not coordinated.

A strategic roadmap defines a few key strategic improvement programs that implement the solutions and processes needed to accomplish the business goals. The organization can work on some continuous improvement projects in parallel, but it needs to allocate budget and time for the strategic initiatives that are going to move the business competitively forward.

The roadmap discussions require grounding the team on the business goals and a common understanding of the current state of operational processes, systems, workforce, and supply chain. CESMII’s systematic approach to roadmap development is being used by manufacturers to align their team, explore solutions, and prioritize the technical capabilities needed to achieve the desired future state and business outcomes.

Even though most manufacturers are in the early stages of their SM journey, we do not recommend that organizations delay their efforts. Many are looking to invest in the next few years and establish a good foundation of systems and training to help their workforce implement process improvements.  Delaying the SM initiative can put the organization at a competitive disadvantage. Many OEMs are striving for a highly connected ecosystem to improve their products and customer service. They are looking for partners and suppliers that are digitally ready to support those ecosystems.

To learn more about the many resources available from CESMII including the SM Acceleration Roadmap process and ecosystem visit the CESMII.org website.

References

[1] Digital lean manufacturing – Industry 4.0 technologies transform lean processes to advance the enterprise, S. Laaper, B. Kiefe, Deloitte, 2020

 

Originally published at: https://engineersoutlook.com/the-urgent-need-to-align-business-and-smart-manufacturing-strategy/

 

Digital Lean with Smart Manufacturing

Introduction to Digital Lean Manufacturing

Lean has played a significant role for the past few decades in driving efficiency across manufacturing organizations by eliminating waste and focusing on customer value. Lean techniques have promoted simple intuitive visual and analytical approaches to decision making, problem-solving and continuous improvement.

However, today’s advanced manufacturing technology and complex product landscape is creating new challenges for manufacturers that are sometimes beyond the simple manual methods. Thankfully, digital technologies have also become more practical for manufacturers and can be used to implement Lean principles in this complex landscape.

Smart Manufacturing and Industry 4.0 technologies including IIoT, cloud computing, AI and robotics make it practical to implement and integrate digital solutions for new levels of insight, accuracy, speed, cohesion, automation, and flexibility.

Digital alone is not a magic bullet. Nor is it a replacement for traditional lean. The key to driving down production costs while increasing performance is to find the right combination of digital and traditional lean—one that fits the organization’s current situation and desired end state.

The term “Digital Lean” is often used to identify this new era of Lean practices that stays true to the Lean principles while embracing real-time data-centric techniques from Smart Manufacturing for enhanced decision-making, problem-solving and continuous improvement. Lean can become part of the culture and mindset for an organization that aims to create more value for customers while reducing effort and resource waste in production activities. A culture that remains open to changing processes and continuous improvement of productivity and quality.

 

 

Industry analysts Deloitte, Bain and BCG have studied manufacturers over recent years and documented that the cost reduction among manufacturers who implemented digital technologies along with Lean techniques was 30-40% higher than among those strictly using traditional Lean techniques. [1, 2, 3] Below is a list of examples of how digital Smart Manufacturing technologies are being used to enable those higher levels of productivity and cost reduction.  

 

 

Improved Decision-Making

Data Collection

Collecting more data in a timely manner is the backbone of Digital Lean techniques. The integration of OT (control systems, industrial networks, and systems) and IT (enterprise information systems) in Smart Manufacturing brings timely plant and production insights to the entire team managing and supporting production operations.   

Data is collected in real-time via sensors, gateways, and smart platforms that make data available to production systems, analytics and AI-based algorithms that turn data into insights for better decision-making and trigger automated alerts to the right personnel to handle non-routine situations. Data is more transparent and accessible, making it easier to display, share, and analyze among multiple stakeholders.

New sensors, data gateways and software not only make it practical to collect data directly from machines, but also make it possible for machines to automatically identify products as they show up at the workstation. For example, using auto-ID technologies like RFID, a machine can load the appropriate program and tools without manual intervention. This type of automated changeover makes it easier to increase the product mix without affecting efficiency and allows the operators to focus on more value-added activities.

Manufacturing systems of record are often maintained via manual methods yet many value stream analysis forget to include the time wasted on inefficient paperwork and wait time introduced by paper-based problem handling procedures. Much wasted time and effort can be eliminated by applying digital techniques to these record keeping processes. 

Metrics and Key Performance Indicators

An important step for the success of Digital Lean initiatives is to establish an agreement on Key Performance Indicators (KPIs) for the organization that encourage Lean behaviors. Standardizing metrics used in the organization helps to align the team on common goals and understanding of business priorities. Common KPIs for Lean include on-time delivery rate, cycle time, cycle time efficiency, asset downtime, cost of poor quality, and surplus inventory.

In traditional Lean, data for these KPIs would be collected over time, then later compiled, and manually analyzed in spreadsheets. With smart technology, this data can be collected automatically and continuously, allowing KPIs to be monitored in real-time or near real-time. This allows workplaces to quickly implement changes and adjustments to improve KPIs, as well as spot problems and waste early as it starts to happen.

For measurement of KPIs to be effective, the data must be correct, relevant, and consistent.  Ensuring this usually means standardizing processes, making sure that the information coming in via smart technology is the most relevant, and that employees are correctly using the smart technology. Metrics work best if employees buy-in, trust the data, and actively participate at all levels of an organization.

Visualization and Display Tools

Once an organization has defined its metrics and collected data to measure them, the next step is to make the information visible and accessible to the multiple stakeholders in the enterprise including management and the team at the shopfloor. It is important for all stakeholders to understand how production performance impacts the customer and the business goals for the entire enterprise.

Traditionally data is gathered manually and recorded in spreadsheets for after-the-fact analysis. With smart technologies, dashboards are configured to automatically use collected data to generate charts, diagrams, and other displays that support everyday decision-making processes.

Old school Andon boards are replaced by digital Andon boards with dashboards updated in real-time at monitors mounted at hallways around the plant, as well as accessed through mobile devices and computers at any business office that needs access to the data.   

Improved Resource Productivity

Resource Optimization  

Simulation techniques can be used for what-if analysis to make decisions about what actions to take to optimize the use of resources and help achieve cost, quality, and schedule targets.

Centerlining is an approach to reduce process variability and increase machine efficiency in manufacturing. Manual centerlining processes include standardizing setup and quality practices, such as regular measurements by operators or inline measurements. With digital tools, operational data is collected directly from sensors or mobile devices and the corresponding machine or process adjustment actions can be performed quicker. AI-based techniques can automatically derive adjustments using historical data.

Optimization techniques that leverage real-time data include the use of artificial intelligence (AI) and machine learning (ML) algorithms to detect unusual patterns and trends that might be causing the use of more effort, resources and energy than usually required to perform the task.

Reduced Equipment Downtime

Unplanned downtime can cause big delays and time wasted waiting while equipment is repaired, and production is rescheduled to work around equipment issues.

Preventive maintenance is usually performed on machines based on a fixed frequency schedule based on maintenance manual recommendations. Advanced sensors for conditions such as vibration, force and temperature can be installed on machines, monitored in real-time, and analyzed by AI-based predictive maintenance algorithms to trigger maintenance only when needed based on machine usage and performance instead of a fixed schedule. The increased accuracy of methods using real-time machine data reduces the amount of downtime and maintenance cost.

Smart machines with embedded sensors can monitor their own internal conditions and are able to immediately alert workers of breakdowns, tool wear, or malfunction.

Improved Quality

Work Standardization and Worker Guidance

When workers understand the standard, they know the best way to do the specific job and avoid the waste that would result from deviations and errors.

Smart Manufacturing techniques strive to have the right data available to the worker at the right time (when it is needed) and in the right form to minimize manual search and reformatting processes.  Data that includes the work instructions for complex production tasks and any data collected from machines that helps the operator make the right decision about next steps or adjustments needed to the process.

Virtual Reality (VR) techniques are becoming more popular to train workers on complex tasks without risking the impact of learner mistakes on the actual product, especially when the potential mistakes would be on high-cost items.

Digital work instructions are easier to maintain up to date than paper instructions. They can be turned into interactive tools to guide the worker as they perform the production job. Employees equipped with mobile devices, like smart glasses, can receive step-by-step instructions with illustrations or Augmented Reality (AR) visuals superimposed over the physical view of the production process.

Head-up displays that provide work guidance can also provide visual warnings when AI-based assistance software detects any anomaly in the process.  These tools provide additional confidence for workers to take on more responsibility for self-inspection.

AI-based assistance can not only warn about quality concerns but can also warn the worker about safety concerns when handling hazardous materials or detecting hazardous conditions such as proximity to certain equipment like moving cranes or vehicles.

Reducing Errors and Process Variation

Digital Lean techniques can help with the quality challenge presented by the increased speed of change and complexity in today’s products. The cost of poor quality and reduction of defects are common types of waste tackled by lean techniques.

One of the techniques to increase quality is to add more in-process checks through either worker self-inspection or automated process monitoring with techniques like computer vision and AI that triggers warnings in real-time when anomalies are detected. This type of process monitoring, and warning system is equivalent to a digital poka-yoke technique.

Software solutions can track inspection and test samples to reduce the effort related to checks performed during the production process. For example, a smart screwdriver that captures torques values can be connected real-time to the production system, to not only let the operator know when to stop at the correct torque value, but also to automatically collect the data as part of the inspection records for the product.

When problems do happen, the data collected through smart systems allows for faster and better root-cause analysis. Mobile devices can be used by employees to submit error reports, observations, or suggestions for improvement from the shop floor.

The Lean technique of Jidoka refers to the ability to stop a process when an issue is found and immediately alert the supervisor and support personnel that can assist in solving the problem. Old school Andon lights used to indicate that the line was stopped are replaced by event triggered alerts that are displayed at dashboards and broadcasted via email to the respective support department to assist in clearing the issue as soon as possible.

Software solutions can help expedite the response to quality issues as soon as they are detected to quickly identify the root-cause and minimize the impact of the problem. The greater level of detail of data collected by smart systems makes it easier to find the root causes of problems and reduce waste by analyzing the relationships and patterns found in historical data. AI-based techniques can also be used to help identify potential root causes of failures.

Improved Flow

Production Flow, Line Balancing and Scheduling

The idea of maintaining a consistent flow in the plant and production process is one of the important principles in Lean manufacturing.  The concept behind the lean technique of Heijunka is to not only level production effort throughout the plant but also to set the production pace to match customer demand patterns and reduce manufacturing waste by leveling the type and quantity of production output.

When the company makes products in smaller batches and maintains smaller inventory buffers there is greater flexibility to meet changing customer demand patterns. Digital production systems make it easier to track smaller product batches, change production schedules to match changing customer demand, and make schedule adjustments to work around issues causing unplanned bottlenecks and affecting the rhythm of operations.  

Traditionally, the size of production runs for Heijunka are not recalculated often due to the complicated nature of calculating the ideal run size for each Heijunka wheel iteration. The use of data from integrated production systems makes it easier to recalculate the run size more often to match customer demand and account for current resource availability, production time, and quality expectations. This is especially true in a high mix production environment.  

Demand Pull and Just-In-Time Inventory

Lean manufacturing encourages switching from planning and receiving materials and parts based on schedules to just-in-time methods that establish smaller buffers and signal demand pull based on actual inventory consumption. The manufacturer is purchasing materials based on actual versus forecasted needs. However, manual ways to signal demand can cause delays and errors.

Digital techniques that monitor inventory levels and automatically signal demand for replenishment based on actual inventory levels and lead times can improve accuracy and avoid delays while also maintaining low inventory levels.

Kanban is a visual cue mechanism used to signal, as material is pulled from inventory, that the stock level has reached the replenishment level and an order needs to be triggered.  Digital Kanban can be implemented and automated or operator-driven triggers from the line can immediately send digital requests for replenishment of materials. Auto-ID technology such as RFID sensors can be used to track the consumption of parts in real-time and trigger replenishment automatically. Smart systems can also analyze usage trends to recommend changes to the replenishment levels based on actual parts usage and scheduled product orders. 

A “control tower” system can collect data and direct material movement inside and outside the factory and integrated value chain. The production plans for each day can be adjusted by an AI-based scheduling algorithm based on the latest information on current factory conditions, orders, customer priorities, capacity, inventories, and supplier lead times.  

Digital lean techniques can go further and enable efficient make-to-order processes to avoid the building of goods that sometimes end up on the shelf and not being required by any customer.

Kaizen or Hoshin Kanri?  

We have discussed examples of how Smart Manufacturing technologies are helping manufacturers take Lean to a new level—Digital Lean. The next natural question is: where do we start? There are some general guidelines like start small, focus on return on investment (ROI), and look for “low hanging fruit” to achieve early successes. We can apply Kaizen processes to start quick with an aggressive view of continuous process improvement.

However, there is something additional to consider with Digital Lean initiatives. It is also worthwhile to consider a more strategic Hoshin Kanri approach to deciding priorities. The organization will eventually want to scale implementation of digital process improvements and integrate the new data sources across the enterprise. This is where a Smart Manufacturing strategy for the organization comes into play. Kaizen thinking alone can drive a culture of solving problems in isolation and could end up creating new silos of information if there is no concerted effort to create a platform to share information in the enterprise.

Many companies have started digital initiatives and stalled because they skipped the important step of linking the Smart Manufacturing initiatives to the business strategy. Hoshin Kanri is a method for ensuring that a company's strategic goals drive progress and action at every level within that company. Business goals provide the framework that holds the initiatives together. Continuous-improvement tactics are the muscles to achieve the business goals. Both are important.

If you do value stream mapping as part of your Kaizen or Hoshin Kanri analysis, make sure to include all the steps related to managing the paperwork and reporting that goes along with production operations. You might be surprised at how much time is wasted in the value stream due to the inefficiency of paper-based procedures.

Additional Resources

CESMII, the US Smart Manufacturing Institute, has established a set of tools to help manufacturers jump start their Digital Lean and Smart Manufacturing journey through alignment of business strategy and technology strategy for increased operational performance, customer service, and competitive edge. For more information on CESMII and the strategic roadmap building tools, you can visit: https://www.cesmii.org/education/roadmap-tools/

References:

[1] When Lean Meets Industry 4.0 -  The Next Level of Operational Excellence, D. Küpper, A. Heidemann, J. Ströhle, C. Knizek, D. Spindelndreier, BCG, 2017

https://www.bcg.com/publications/2017/lean-meets-industry-4.0

[2] Digital Lean – A Guide to Manufacturing Excellence, P. Serlenga, I. Leppavuori, I. Moraes, M. Forlini, Bain & Company, 2019 

https://www.bain.com/contentassets/47b06ba77050462caa1aa70050b37c5a/digital-lean-playbook_v5_final.pdf

[3] Digital lean manufacturing - Industry 4.0 technologies transform lean processes to advance the enterprise, S. Laaper, B. Kiefe, Deloitte, 2020

https://www2.deloitte.com/us/en/insights/focus/industry-4-0/digital-lean-manufacturing.html

 

Challenges To Overcome for Manufacturing Breakthrough Performance

The Opportunity

The manufacturing sector has an opportunity to grow significantly in the years ahead. Manufacturing companies that create products from raw materials or components, either by machine or by hand, in plants, factories, and mills are a significant driver of the U.S. economy.

One of the encouraging trends is reshoring. There are several reasons for the shift from offshoring to reshoring.

• Economies in many go-to offshoring countries have grown stronger, resulting in increased wages for their residents.
• In the locations where labor is still inexpensive, the infrastructures often cannot handle complex manufacturing operations.
• During the pandemic, many companies realized the fragility of their supply chains and they are now looking to reshore suppliers in critical medical and food supply chains.

As an industry, manufacturing is undergoing a digital transformation. The recent proliferation of technologies like IIoT, artificial intelligence, 3D printing, robotics and Smart Manufacturing are disrupting the sector so much that many manufacturers are upgrading the way they perform and manage production and are even rethinking their business models and supply chains—all of which are elevating the bar for manufacturing operation management and excellence.

The Challenges to Overcome

However, the sector could miss out on much of this opportunity if several key challenges are not addressed for manufacturers.  

• Need for Higher Productivity
• Complexity of Products and Technology
• Systems and Data Integration  
• Supply Chain Resiliency
• Lack of Skilled Talent

Need for Higher Productivity

For U.S. manufacturers to be competitive in the global marketplace they must achieve higher levels of productivity through adoption of advanced technologies, smart manufacturing techniques, and a higher skilled workforce.

The resurgence in reshoring will lead to an increase in U.S. made products in the future. However, to sustain the reshoring trend, manufacturers must achieve higher performance using new technologies like smart manufacturing and robotics to automate many of the processes that used to require intense human labor.

Labor is not the only expensive resource for manufacturers, the smart manufacturing innovation needed includes the technologies that contribute to reduce the industry’s energy usage, carbon footprint, and use of nonrenewable resources (like oil, natural gas, and coal).

Complexity of Products and Technology

One thing is certain: change and progress are inevitable. The pace of technology innovation continues to increase and product, production processes and supply chains continue to get more complex. Manufacturers have a long history of embracing technology to drive efficiency. Now they must also use technology to manage the complexity of new products and processes.  

From sensors to automation, artificial intelligence, 3D printing, robotics and cloud computing, a key challenge for many manufacturers is keeping up with the rapid pace of technology innovation. Not only to use it in their products but also to leverage it in their processes and customer services.

We have been hearing about the promised value of Industry 4.0 technologies for several years and many manufacturers agree on this potential. However, the industry productivity has been flat for the last decade. Many manufacturers have started their digital transformation initiatives but a big percentage of them have stalled.

Systems and Data Integration  

Many manufacturers understand the important role technology plays, yet many also feel they are not providing their team the best tools for bottom-line success. There is often a disconnect between understanding the need for new technology and actual implementation and adoption.

Manufacturers have been enhancing their production systems and implementing newer industrial automation equipment that incorporates sensors, controllers, computers, and network connectors. The use of robotics including robots working alongside humans is expected to triple or quadruple over the next five years. Yet, the cost and complexity of systems and data integration remains a barrier to wider adoption of Smart Manufacturing technologies and techniques that allow enhanced data-driven operations in a secure, open, and scalable way.

The good news is that new Smart Manufacturing solutions are entering the market for low-cost, scalable infrastructure that enables a highly connected and data rich operating environment— solutions that not only improve productivity but also augment the workforce and supply chain.

 Supply Chain Resiliency

Shifts in sourcing and shipping logistics are constantly evolving throughout the manufacturing industry, but it was especially true as a result of the COVID pandemic. Other global factors like trade wars, military conflicts, and climate change are causing uncertainties and risk concerns for manufacturers. Manufacturing supply chains will need to be more resilient in adapting to potential future disruptions.

Product designs are also getting more intricate with an increasing number of electronic components, microprocessors, and embedded software. Not only are the number of components increasing, but parts are much smaller, more technologically advanced, and sourced from multiple vendors adding more complexity to the product and its supply chain.

Modern supply chains are dynamic with a lot of change taking place, a lot of stakeholders involved, and increasing requirements for data exchange for higher collaboration, transparency, and risk management. Organizations need information systems to help with these complexities in the product lifecycle starting with design, through supply chain and manufacturing operations.

Manufacturers must increase their efforts toward digital supply chain projects that build agility and scalability to help to manage risk during disrupted and uncertain times. Manufacturers must focus not only on improving cost and quality, but also on delivering the best customer experience and competing in a future market landscape of highly integrated manufacturing and service ecosystems.

Lack of Skilled Talent   

Today’s manufacturers need workers with more advanced technical skills than ever before. Unfortunately, there aren’t enough workers with these skills to fill the many roles available today, creating what is known throughout industry as the “skills gap.”

During the past several years, the skills gap has been top of mind for U.S. manufacturers. The data shows that the demand for jobs in the manufacturing sector has been and continues to grow. These are good paying jobs with a median salary of $46,000. Jobs that have a significant economic impact on the economy for the country.

As reshoring trends continue and more baby boomers retire, many manufacturing jobs will continue to go unfilled over the next decade. In this age of automation and robotics, skilled workers are still needed to operate and maintain complex machinery, integrate technology and processes, fill managerial positions, and perform decision-making and problem-solving tasks. To fill the widening skills gap, manufacturers will need to not only attract new workers, but also train existing workers on new skills and technologies.

Many of the software technology required for Smart Manufacturing solutions are becoming more affordable, yet the time, complexity, cost and knowledge required for implementation and integration continue to be a barriers to adoption for the average manufacturer.

Smart Manufacturing solutions for interoperability and enhanced insights are valuable tools, however to truly mine that potential value, manufacturers also need (i) a talent pool ready to implement and sustain the solutions, and (ii) a workforce ready to leverage the insights and embrace a culture that values transparency and collaboration.

Smart Manufacturing education is needed to teach the new workforce how to implement and leverage the many practical technical options available. Solutions that make it easy to integrate data collection from production assets and processes without constraining a manufacturer to a single vendor.

Solutions are needed for both (i) education at universities and colleges, and (ii) training at trade schools, community colleges and high schools. Manufacturers are asking universities and colleges to help get the Smart Manufacturing education down to high school level, so it does not require a four-year degree to get the skills.

Manufacturers also need training to upskill the workforce. There is a need for continual learning because the skills for new methods and technologies need to be refreshed every four or five years.

One way to expand the talent pool is by attracting more underrepresented groups such as women and ethnic minorities to manufacturing careers and education. To help attract this talent pool, education options need to be more affordable and modular to make it easier to acquire the skills while working. Micro-credentials can be a solution if that can be standardized for the industry so they are credible to manufacturers and valuable on the resume of job seekers. 

In addition, manufacturing education has to be cool with engaging hands-on exercises with technologies like robotics and 3D printers. Education organizations can help attract more students to manufacturing and win over parents, so they encourage their kids into manufacturing careers. Today’s Smart Manufacturing solutions are not only increasing productivity for manufacturers but are also making an impact on climate change. Students should be able to simply scan a QR, download an app to their phone and realize that today’s manufacturing is not their grandfather’s manufacturing.

It is encouraging that the U.S. government is investing to help manufacturers tackle these challenges. To learn more about how CESMII, the Smart Manufacturing Institute, is helping tackle these challenges through ecosystem, technology and education solutions visit www.cesmii.org.

The Evolving Smart Manufacturing Supply Chain

As more manufacturers adopt Smart Manufacturing and manufacturing operations management methods increasing connectivity and data availability within their organizations, they are also realizing that a connected supply chain becomes a strategic competitive advantage in the marketplace. A report from Gartner predicts that in five years, 50% of large organizations will compete as collaborative digital ecosystems rather than discrete firms, sharing inputs, assets, and innovations. [1] A new degree of collaboration and integration in new manufacturing ecosystems enables not only enhanced visibility, but also increased speed and resiliency.

The rapid adoption of digital technologies keeps fueling disruptive change in the marketplace. Computer technology, so small it fits comfortably into our mobile phones, has become nearly completely embedded in consumers’ lives and is making its way into industrial equipment, enabling information sharing, communication, and operational analysis in real time.  Internet access and Wi-Fi has become widely available at an affordable cost. Consumers use mobile devices to shop and order goods using real time information about inventory availability and receive same day delivery of their orders. 

Speed and data are the currency of today’s supply chain. Companies that learn to coordinate a supply chain in real time are becoming better options for their customers, quicker to see new opportunities in the market, and quicker to respond to disruptive changes in their markets.

The supply chain woes that resulted in weeks of empty shelves and missed deliveries during the COVID-19 pandemic highlighted the need to enhance collaboration features in the supply chain to become more resilient and improve the ability to absorb, adapt and recover from a disaster or disruptive event. In fact, according to Accenture [2], companies are increasingly prioritizing restructuring their supply chains and approaches to production to counteract disruptions.  

Manufacturers are not only enhancing traditional supplier management features like procurement and issue management, but they are also implementing enhanced features like demand-capability matching, dynamic fulfillment, and product data services. The new ecosystems favor suppliers with specialized modular capabilities and services that can be recombined and scaled as required to accommodate market and supply chain changes.

In this journey to a highly connected ecosystem, small and medium manufacturers (SMMs) can take a low-risk incremental approach as long as they establish and follow a strategic Smart Manufacturing roadmap. Practical tools to establish such a roadmap are accessible to manufacturers through CESMII – the U.S. Smart Manufacturing Institute [3].

Manufacturers can first establish the internal technology-enabled, insight-driven infrastructure and culture required for transparency and collaboration. Cloud services and B2B integration can help them take that collaboration to a new level in the supply chain. Examples of the incremental approach are found in a guidebook from MESA International [4].  One example describes how a manufacturer rolled out modules in multiple stages:

• Production Monitoring – real-time visibility into the performance of production activities
• Quality Tracking – incorporating smart digital attachments and measurement devices (e.g., calipers, gauges) to wirelessly transfer the measured values to a tablet SM app for the inspector
• Material Requirement Tracking − real-time material availability status updates to the shop and ERP to proactively avoid machine starving time due to material non-availability

The solutions were implemented gradually in one plant with an initial investment of around $20K before they were rolled out to four more plants. The benefits realized included improvement of data accuracy, product quality, reduction of material loss and rejected parts yielding savings of $32K within 18 months. Another example describes how a manufacturer extended these smart methods into their supplier chain to improve the visibility of component inventory commitments and remove the uncertainties that were causing delays in their production and client deliveries.

Check out the CESMII resources and the MESA guidebook for more examples on how your company can get started on the Smart Manufacturing journey.

References

[1] Gartner Predicts 2022 Supply Chain Strategy, S. Bailey, N. Sandrome, Gartner, 2021

https://www.shippeo.com/en/resources/gartner-predict-2022-supply-chain-strategy

[2] Why supply chain innovation paves to road to resilience, M. Reiss, Accenture, 2021

https://www.supplychain247.com/article/accenture_on_operations_why_supply_chain_innovation_paves_the_road_to_resil

[3] Smart Manufacturing Business Transformation Tools, CESMII – The Smart Manufacturing Institute, 2022

https://www.cesmii.org/sm-acceleration-toolkit/

[4] A Low-Risk, Incremental Approach to Smart Manufacturing for Small & Medium Manufacturers, A. Seshan, C. Leiva, S. Zippel, R. Spurr, M. Ford, J. Zhu, J. Winter, MESA International, 2022

https://www.pathlms.com/mesa/courses/39290

 

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.

 

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.

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 

 

 

 

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

 

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

 

Vision 2020 for Quality in the Extended Value Chain

– 20/20 visual acuity into the supply chain by year 2020

Many manufacturing companies depend heavily on their supply chains to deliver a quality product on-time to the end customer. It is typical to have more than 50% of a product’s components provided by suppliers. Hence, visibility and quality management into the supply chain has become essential to the delivery of a quality product. As a data point, Gartner surveyed supply chain leaders in 2014 and found that improving quality of supplied components was a No. 1 priority [2].

20/20 is a measure of “perfect” visual acuity or clarity of vision. 20/20=1 represents 100% vision. If we were creating a scale of visual acuity into our supply chains what would our current vision be? 20/40=50%, 20/100=20%? Gartner’s survey results show that very few manufacturers would claim visual acuity near 20/20. In fact only 22% reported to have quality management functions integrated into their supply chain management and many of those only have visibility into their first tier of suppliers. It’s no wonder that over half of the high-profile quality failures of the past decade, ranging from flawed product launches, customer and patient safety incidents, and product recalls, are rooted in the extended value chain [2]—failures and recalls with a lingering negative effect on the product brand.

Many companies are focusing their quality efforts internally and are treating the supply chain separately. It is typical to see quality managed for internal operations with a combination of multiple Manufacturing Execution Systems (MES) or Manufacturing Operations Management (MOM) software and silos of quality managed for suppliers via multiple separate quality and ERP software.  However, quality management would be better done in unison with an integrated MOM, EQMS and supply chain management software solution, so accountability and points of verification are optimized throughout the value chain. For future competitiveness, it will not be enough to focus on schedule and cost reduction, it is necessary to increase visual acuity on the quality side. Gartner consultants are recommending that manufacturers set their goals to improve supply chain visual acuity with regards to quality by 2017 [2]. Manufacturing companies can follow Gartner’s advice and set their plans for 2017 or at least shoot for the year 2020. That should be easy to remember: 20/20 Quality by 2020 : )

The last big era of progress in quality management was in the 1980’s with the rise of Total Quality Management, the publishing of the ISO9001:1987 standard for quality management systems, the refinement of the Plan-Do-Check-Act (PDCA) cycle, and the development of the Six Sigma methodology. The current renewed interest in quality management into the supply chain and the publishing of the latest ISO9001:2015 revision could be milestones marking a new renaissance for quality management—an opportunity for big progress. ISO9001:2015 renews emphasis on risk and supply chain management.

What does 20/20 vision include for quality management? The following is a list of visibility requirements for quality in the value chain as it relates to the continuous PDCA cycle which is at the heart of the ISO9001 standard.

PLAN

• Supplier Network Map. Mapping of critical suppliers in the multitier supplier network. Suppliers handling unique IP (intellectual property), trade secret, equipment or tooling critical to production.
• Verification Requirements. Definition of inspection and verification requirements that need to flow down to suppliers. Especially those critical-to-quality characteristics that are tied to design engineering specifications in revision controlled 3D models and PMI (product manufacturing information). Confirmation and verification of qualification of suppliers in lower tiers handling critical-to-quality components.
• Performance Goals. Clear expectation of quality and schedule measures and goals for each program and supplier tier.

DO

• Progress Reports. Visibility of manufacturing progress to scheduled dates on long cycle time made-to-order components. Perhaps even in-process verification of critical-to-quality characteristics as the components are manufactured at the supplier site.
• Performance Measures. Reporting of standard simple metrics that the entire supply chain can use to communicate performance to schedule, quality and collaboration goals.
• Change Control. Management of engineering change notice (ECN) processes to ensure that the respective supplier has received the specification changes and is incorporating the change into the desired effectivity including units in process.

CHECK

• Verification Results. The earlier there is visibility of any issue with components the better. It is desirable to move more inspection and verification to the supplier site whenever possible. Inspection results should be available electronically along with product delivery date updates. Some critical characteristics might even be verified in-process. In-process and delivered product inspection results can be provided by the supplier via supplier portals or transmitted electronically via B2B (business-to-business) interfaces up the supply chain. Inspection results reported by suppliers and gathered through source and receiving inspection allows calculation of the real capabilities of each supplier to hold the required tolerance levels in specifications.
• Nonconformance Documentation. Whether defects are found before the supplier ships a component, at receiving inspection, or at production, it is necessary to have visibility of defects, non-conformances, and the root-causes identified for those issues. Containment and corrective actions can be initiated with specific suppliers to avoid further shipment of units with critical problems until they are resolved.
• Supplier Audits. Suppliers must be reassessed periodically via supplier audits for compliance to regulatory and contractual requirements. Companies need to maintain visibility of supplier audit history and ongoing audit scheduling arrangements.

ACT  

• Corrective Actions. Whether tied to poor performance, specific product defects or audit findings, supplier management must maintain visibility of all corrective actions open with suppliers, their respective root-cause investigation, and resolution implementation plans.

Regulated industries including food, drugs, medical devices, and aerospace have additional accountability interest into their supply chains. These industries have been (a) adding these type of requirements to the industry specific ISO9001 extensions including ISO13485 and AS9100, and (b) participating in the development of standards for communicating quality requirements and results in the supply chain within standards groups like OAGi (Open Applications Group).

Manufacturers like Boeing and Land-O-Lakes, organizations like MESA and NIST, and software vendors like Oracle, e2open and iBASEt, are collaborating within OAGi to (a) develop B2B integration use cases to help manufacturers understand what message standards are available and how to use them to orchestrate a business process with B2B interactions, (b) add message standards to on-board suppliers including sync parties, and (c) add message standards for quality management including inspection requirements, inspection orders, certificate of analysis, and inspection results.

The above efforts by ISO and OAGi, and referenced publications from Gartner are some examples of the renewed interest in quality and the focus on moving quality management processes into the supply chain. There are more examples out there and it is important that organizations are engaged in these types of efforts to improve quality acuity into the supply chain by 2020. Companies should review the assignment of quality functions and governance in their organizational structures, and refresh and integrate the quality management systems across the enterprise to facilitate enhanced quality management processes into the multi-tier supply chain.

The next era of connected quality management into the extended enterprise is coming. Will your organization be ready?

The efforts to extend and adopt standards for the exchange of quality information in the supply chain are far from complete. It is a good time for industry leaders to engage in these activities to ensure their needs are represented and that standards continue to evolve. Contact OAGI.org for more information.

References

[1] “Improve Multitier Supplier Quality by Leveraging Visibility”, Ray Barger, Simon Jacobson, and Christian Titze, Garter, 2015

[2] “Supply Chain is Missing the Mark on Quality”, Jacobson, Suleski, Barger, Chadwick, Stevens and Dorman, Gartner, 2015

[3] “The Evolution of Six Sigma”, Jim Folaron, J.P. Morgan Chase & Co., www.asq.org, 2003

[4] OAGI - Quality Content, B2B Network Collaboration, and Smart Manufacturing Working Groups, http://www.oagi.org/dnn2/OurCommunity/WorkingGroups.aspx

 

Clarifying the New Manufacturing IT Lingo

- Smart Manufacturing vs Industrie 4 vs Digital Manufacturing vs IIoT

I had the pleasure of facilitating a workshop on Smart Manufacturing at the Industry Week conference with over 120 professionals involved in different aspects of manufacturing information technology (IT) in a wide range of industries. After we briefly introduced Smart Manufacturing and shared a few results from a MESA-SCMWorld survey, I casually surveyed the group on clarity
among the new terms in manufacturing IT and adoption for the “plug and play” paradigms promoted by Industrial Internet of Things (IIoT), Smart Manufacturing, Digital Manufacturing, and Industrie 4.0 initiatives. Some of my observations from this interesting discussion follow.

QUESTION: Third or Fourth Industrial Revolution?

SURVEY SAYS: 4TH Industrial Revolution. The audience was pretty confident with this response. There was an overwhelming show of hands in favor of 4th versus 3rd.

Some consulting groups have favored calling this era the start of the third industrial revolution and others are calling it the fourth industrial revolution. Everyone agrees that the era of mass production is behind us. An era that was enabled by revolutionary advances like the assembly line and electrical standards. But many groups closer to manufacturing automation want to recognize that there was a third industrial revolution that started in the 1970’s fueled by advances in industrial automation and software for personal computers (PCs).

There is agreement that the next industrial revolution is starting now, and it is fueled by advances in model-based manufacturing, additive manufacturing, robotics, and the Internet of Things (IoT). New technologies are allowing manufacturers to move from mass production make-to-stock practices towards make-to-order mass customization business models. Based on popular opinion, we should stick to calling this the 4th Industrial Revolution.

QUESTION: IoT and IIoT side by side or one under the other?

Do we view the Industrial Internet of Things (IIoT) as a subset of the Internet of Things (IoT) with similar standards and methods of connecting manufacturing, home and office devices to cloud applications and smart phones? Or do we see IoT and IIoT as having similar goals but using different standards and infrastructure?

SURVEY SAYS: IIoT should be subset of IoT. The majority of manufacturers want to see industrial automation use similar standards and mechanism to home and office equipment integration. They would like to see apps on their phones with the ability to view, interact and control the shop just like they have apps today controlling their home or car. IIoT standards and methods should be a subset of IoT methods and standards.

One automation vendor warned that this view of IIoT and IoT might be an oversimplified view on the subject. Point taken, but the survey was more about confirming the expectations of these initiatives and the desires of the end users—similar type of “plug and play” capabilities at the factory and at home.

Another voice wanted to claim that many companies are already doing IIoT at their plants. We went ahead and tested this claim with the audience.

QUESTION: How many companies in this audience currently have integration from
machines to enterprise systems?

SURVEY SAYS: Around 80% of hands were up.

QUESTION: How many of these companies are using open standards for integration
and could simply swap out one automation or software vendor for another?

SURVEY SAYS: Only one timid person in the audience raised their hand half way
up.

These answers point to a big difference between IIoT and prior manufacturing automation integration methods. For the last few decades we depended on custom integration, proprietary interfaces and separate protocols for integration and automation at the factory. Moving forward with IIoT we want to embrace open standards and internet protocols so we can easily swap and mix multi-vendor equipment and software which might be on-premise or in the cloud.

QUESTION: Are Smart Manufacturing and Industrie 4.0 the same thing?

SURVEY SAYS: Yes. The consensus was that in general they are close enough in scope and purpose. However, Industrie 4.0 is perceived as a very specific German initiative so the majority of participants preferred the term Smart Manufacturing.

It is important to note that if initiatives from different countries do not coordinate among each other, we could end up with different standards supported by different countries. Similar to how we ended up with slightly different electrical standards in different countries. Nevertheless, better off with a few standards than none at all and we can always use converters to plug equipment to
different standards.

I noticed that the general voting participation was lower on this topic so I paused the survey for a few clarifications.

QUESTION: Would you like more clarification on the scope of Smart Manufacturing?

SURVEY SAYS: Yes.

Smart Manufacturing is about creating smart products, smart factories, smart manufacturing processes and smart enterprise procedures that link the entire product value chain with a digital thread.

Smart factories are the foundation of Smart Manufacturing. Smart machines and robots in smart factories are capable of managing complexity, are less prone to disruption and are able to manufacture goods more efficiently. In the smart factory, human beings, machines and resources communicate with each other as naturally as in a social network.

Smart products know about their configuration, details of how they were manufactured including critical components and how they are intended to be used. They actively support the anufacturing process, answering questions such as “which parameters should be used to process me?”, “when was I made?”, “for which customer?”, and “when do I need to be serviced?”

QUESTION: Should we view Digital Manufacturing as part of Smart Manufacturing or should we view Digital and Smart as separate initiatives?

SURVEY SAYS: Not clear. So instead we further discussed the difference and relationship between smart and digital manufacturing.

The digital thread that starts with the 3D model-based definition of the product from engineering flows to smart manufacturing and to the smart supply chain linked via standard integration interfaces that connect to web applications, mobile devices and cloud services. The network of connected devices, resources systems, partners and suppliers will result in the transformation of
conventional value chains and the emergence of new business models.

Several industries have used the term Model-Based Enterprise for the initiative of creating a continuous digital thread from design 3D models and specifications (a.k.a. Model-Based Engineering) flowing downstream into the supply chain, manufacturing, inspection and aftermarket services of the product.

As stated earlier, Smart Manufacturing includes smart products and smart manufacturing processes. The digital thread must be interpreted by smart product and smart manufacturing processes in order to build to the right product specifications and configuration. Therefore, we see the digital thread as a requirement for smart manufacturing. We also agree that these two initiatives could be worked side by side if the intersection areas and integration is coordinated among the two initiatives and well defined.

QUESTION: Are IIoT and Smart Manufacturing hype or substance?

SURVEY SAYS: Hype with opportunity behind it. The participants believe there is a bit of hype in the media right now but they also believe there is opportunity for big improvements in manufacturing based on the adoption of new technologies and standards into new manufacturing business models. The intent of these initiatives resonate with the audience.

Did I clarify things? Are we smarter now? (Trick question)

Thanks to everyone that participated in the workshop, especially to those who “volunteered” to participate : )

Are Your Manufacturing Operations Ready for Transparency?

What does transparency mean in the context of manufacturing operations? Literally it means we can see through the manufacturing organization into its internal workings. Practically it means full availability and access to information required for collaboration and collective management decision making. It means full disclosure of detailed historical records for root-cause investigation purposes to uncover areas for continuous improvement. It means being open to share our best practices along with our past mistakes so they serve as learning experiences.

Transparency is an essential condition for a free and open exchange and evaluation of priorities for process improvements. It is essential to creating a culture of trust and teamwork in the organization.

Transparency through metrics

The deployment of a performance measurement system with standardized manufacturing metrics is a key ingredient to providing unbiased visibility and transparency within the organization.

In addition to providing a mechanism for transparency, carefully selected metrics can help an organization achieve its strategic goals. Work groups can become more motivated and engaged when their performance is measured according to well selected numeric metrics. Personnel must understand the relation of their metrics to the corporate goals and feel that the targets are achievable. Work group level metrics are considered a better practice than individual employee goals to promote teamwork in the organization. Wouldn’t it be great if all employees understood the corporate goals and how they could make a difference?

Personnel must trust the accuracy of the data and the calculation of the metrics. Manually calculated metrics in spreadsheets can lead to introduction of bias, manipulation of the data and general distrust of the reported numbers. Having a manufacturing system that automatically collects data and rolls it up into metrics avoids these types of concerns and greatly improves accuracy.

Metrics selection

The selection of good metrics and the involvement of key stakeholders can greatly help with the trust and adoption of the standardized measurement system.

Generally we should be able to relate the metric to (a) strategic corporate goals, and (b) to desired outcomes for the customer. If the relation between the metric and a corporate goal or key performance indicator (KPI) is indirect, we should explain and publish this relation so it is very clear to all stakeholders.

It should be fairly straightforward to understand how the metric is derived. This will make it easier for everyone to visualize, trust and relate to the metric.

The consequences of bad performance of the metric should be easy to see on the shop floor. For example, for longer than planned cycle times we should start seeing work orders and planned inventory queuing up somewhere in the work cell. For an increase in cost of poor quality, we should see an increase of rework orders at the shop floor.

The metric should be actionable. It should be easy to derive the types of actions that could be taken to improve performance in relation to the metric. For example, a metric should not combine too many measures into one number because the possible corrective action becomes very unclear.

Metrics should be consistent and independent of subjective bias. A good metric is consistent in what it measures. The source of data, the equations used, and the weighting criteria applied should be consistent but might also evolve over time. When it does change, it should be communicated to all stakeholders. It is good practice to avoid measures where a subjective evaluation is introduced in the calculation. Different people might apply the subjective criteria differently leading to inconsistent results for that metric in the organization.

Accountability and fairness

The organization can set goals to achieve specific target levels on key metrics within a certain timeframe to promote motivation, create urgency, and establish commitments for the team. Accountability in the organization is driven by how performance to the goals is tied to a performance reward system and the actions taken by the management team about poor performance.

For accountability to work, personnel must understand and trust the metrics, perceive the targets to be achievable, and feel that they are empowered to improve performance on the metrics selected for goals. For example, manufacturing throughput rates are constrained by the number of purchase orders brought in from Sales. Increasing the number of purchase orders is out of the control of Operations so it would be better to set performance goals for Operations on something they have more control on like improving cycle times.

In order to successfully set goals and reward systems based on metrics, they must be fair across the organization. But some types of work are harder and more prone to error than others. To be fair with metrics used to compare and benchmark across the organization, it is important to have a method to weigh the difference in difficulty for achieving similar results across the different types of work centers. An example of a weighted metric is the Six Sigma metric, Defects Per Million Opportunities (DPMO). Since DPMO weighs defects by the number of opportunities for defects in each manufacturing operation, we can use the metric to compare performance across different work centers.

Constructive scrutiny

Are you ready for peers looking over your shoulder? Questioning your numbers? Are you used to constructive feedback? The executive management team must be open to bad news and reward the messenger of bad news. Instead of punishing the manager that comes forward, the organization should provide additional budget to that department to help fix the problems. Other managers will get the idea and embrace honest reporting as part of the organization’s continuous improvement processes.

I heard Allan Mulally a few years ago (CEO at Ford at the time) talking about establishing a culture of teamwork and rewarding bad news in management meetings.  Alan celebrated finding improvement opportunities. When he started at Ford, he would ask “How can all the lights in the dashboards be all green when we just announced big losses?” He applauded the manager that brought that first issue up in a meeting. Instead of frustration, he showed enthusiasm and asked, "How can we help you solve that problem?" The following week, he was presented with a rainbow of colors in the charts.

The anxiety can be high in management meetings when there is so much visibility and transparency. It is important to keep the meetings positive with a spirit to collectively address problems and improve the overall organization.

Team buy in

Often it is easier to select a set of metrics than to get employees to buy into the performance measurement system. We listed some important considerations above to improve stakeholder acceptance and trust. Personnel that is not used to being evaluated based on metrics will offer a natural resistance to this new process. The executive management team must express a compelling case for how this new system is linked to the organization achieving its strategic goals.

A few key departments can be selected to pilot the new system. Change champions should be enlisted in these departments and early success in those departments with the new system should have a high visibility celebration.

Evolution over time

Just as a company’s goals and objectives evolve over time so should the set of performance metrics be revisited periodically and changed over time.

For example, for the last two years the strategic goals might have been to reduce cost by 10% and metrics were selected to reduce cost of poor quality and improve labor efficiency. This year the corporation might have a growth strategy and it wants to focus on reducing cycle time and time to introduce engineering changes into production.

Deploying a standard performance measurement system is no simple task but it is a tool to help your organization move up to a new level of business process maturity. It can become a differentiator for your business, and lead to major improvements to the bottom line.

 

Do We Have Manufacturing Problems Looking for Smart Solutions?

If you are responsible for manufacturing operations improvements, you can probably dream of a few solutions that would make some hard production problems go away or take your operations to a new level of efficiency. When you hear titles like “Smart Manufacturing”, it is easy to hope that these initiatives will yield some of those dream solutions.

I was in brainstorming workshops a few weeks ago discussing the next generation of platforms and integrated systems for Smart Manufacturing. The discussions around potential new enhanced processes were very fructiferous, so I am sharing a few ideas I personally found very interesting.

• Manufacturing machines could set up themselves and select the right configuration files based on auto identifying the product’s configuration from an RFID tag. 
• More defects could be detected in-process through robotic CMM that read the product configuration RFID, read the latest product definition directly out of CAD files, and automatically measure critical dimensions as the material is cut or product is assembled. 
• Processes that depend on final product testing to ensure product quality can cause a lot of scrap waste when the final product does not meet specifications. It would be more efficient to have improved sensors that would monitor the product recipe as ingredients are added and transformed with feedback processes that would adjust the process automatically to account for material, process or environment variables. 
• Variation within control specs/limits of raw material properties causes shifts in final product quality. We want to auto adjust our process or recipe to accommodate variations in materials instead of having to demand costly higher accuracy in material specifications. Material would come with actual measured properties instead of us assuming that they meet a specification. 
• Employee safety could be improved by using sensors and automating test procedures involving hazardous materials or processes.

It is easy to see that these types of processes would yield many benefits including:

• Quality consistency of realized products with less waste/scrap and rework
• Less labor of non-value added tasks like setup, testing, and fixing 
• Lower cost for manufacturing one custom configured piece at a time
• Improved personnel safety

But is it possible to reduce the cost of achieving these levels of automation? Will “Smart Manufacturing” ideas and new technologies get us there? Or will it always be easier to design human sensing and decision making into these processes? Some of the challenges
to achieving these levels of automation include:

• High cost of sensor and analysis technology 
• Lack of integration standards between hardware, network and software tiers 
• Lack of models that link available measurements/inputs to desired output properties
• Lack of trained resources in the factory to implement new technology

Can we can make these technologies more affordable and easier to implement. If we can, what problems would you want to address in your factory with the new generation of Smart Manufacturing technologies and integration standards?

 

Four Good Trends for Manufacturing Execution System Adoption

The Gartner research group and MESA (Manufacturing Enterprise Systems Association) recently completed their annual survey of manufacturers (over 100) on their usage of manufacturing systems. Thanks to several years of data, they are able to look at trends in the manufacturing industry and it was generally good news in 2014 for the Manufacturing Execution System (MES) market—for both users and vendors.

#1. MES is finally Accepted as part of Enterprise IT Landscape

MES projects have a proven track record of benefits and are now viewed at the same strategic and enterprise level of importance than PLM and ERP projects. Most companies are not putting MES projects in queue behind PLM and ERP projects anymore. Manufacturing competency is recognized as an important competitive differentiator.

#2. MES Benefits Expectations focused on Improving Product Quality

More companies view quality as an integral part of their manufacturing systems strategy and are seeing the benefits of integrating quality versus keeping quality as a separate process and system.

#3. MES Barriers Move from Credibility to Agreement

As MES is now viewed as critical part of the enterprise systems platform, the entire enterprise needs to participate in the requirements definition, selection process and rollout plan. The shift from local to corporate level project can slow down projects and the achievement of early benefits.

#4. MES Implementations Move Away from Heavy Customization

The increased availability of industry specific mature MES solutions means that companies are looking at more out-of-the-box implementations and should expect less need for expensive custom extensions to commercial products. To benefit from OOB functionality, it is important that companies do their homework and look for MES products that fit their needs instead of assuming that customization is a required part of a project with any MES product. In today’s market, heavy customization is usually a symptom of selecting a product that does not fit the industry or manufacturing type.

There are many more valuable insights in the recorded webcast than the four listed above, so if you are interested in these subjects, I recommend the whole webcast and presentation which is found for members at MESA.org (see References below). I also recommend joining MESA.org for full access to this and other valuable reference resources.

Reference:

“Has MES Come of Age?”, Recorded webcast by Julie Fraser of IYNO and Rick Franzosa of Gartner, MESA.org, 2014   https://services.mesa.org/ResourceLibrary/ShowResource/47eff093-b4f9-417e-9aa1-1ddbf3e82757

 

Eight Potential Barriers to Strategic Manufacturing Process Improvements

The tune at leading companies has changed from cost cutting to product and service innovation. The need to incorporate technology innovation faster into product lines is fueling an executive push for higher levels of process improvements. Can we scale our old practices to handle higher levels of product and process change? Probably not. We probably need to rethink processes at a strategic level to enable new business paradigms and increase speed for product updates and new product
introduction.

Engineers are using the latest CAD technology to make better 3D models faster with more reusable components, more product and manufacturing information (PMI). Model-Based Manufacturing practices are leveraging 3D models to simulate and accelerate production process innovation, develop shop floor work instructions, and create automated inspection and test procedures. So what is holding back our organizations from these types of process innovations? Why are some organizations still working with 2D drawings, spreadsheets, and paper work instructions at the shop floor?

Is IT investment cost holding us back?

U.S. aerospace and defense companies have been generating large amounts of cash reserves for quite some time. The largest aerospace and defense contractors have tens of billions of dollars in cash available to them. [2] There is still uncertainty that the U.S. will pass a defense budget in the
near future but companies have to move forward with process innovation or be left behind by the competitors that are gearing up with strategic investments. The cash reserves are there in many companies to make the required investments. However, the tight budgeting processes, that organizations put in place to control the wallet over the last few years, could be holding the organization back from large strategic investments.

Is commercial software maturity holding us back?

Based on Gartner’s 2014 Hype Cycle for Discrete Manufacturing and PLM information technology [3], commercial applications for Manufacturing Execution Systems (MES) and Enterprise Quality Management (EQMS) are in the “slope of enlightenment” moving to a stable maturity state. Applications for Model-Based Manufacturing (MBM), Supplier Quality Management, Manufacturing Integration Standards, and Operations Intelligence are beyond the hype peak for early adopters and are expected to fully mature within 5 to 10 years in the market.

Even companies that are industry followers versus leaders and very conservative in technology adoption, should probably be investing in these technologies which are ready for the pragmatic majority of the market. Some technologies like Model-based Manufacturing can be implemented in a pilot area to gear up organizations for the next program or product initiative.

Are we complacent with small continuous improvements?

Organizations with a proven record of continuous process improvement through localized short term Kaizen and Six Sigma efforts might have a hard time tackling the bigger strategic projects. “Good enough” can be the enemy of excellence.

Kaizen and Six Sigma efforts look to identify and eliminate areas of waste and variability. But
focusing solely on those efforts can lead to organizations overlooking initiatives like Model-Based
Manufacturing that can enable new business paradigms and revenue opportunities. The organization can fall behind in the changing competitive landscape.

It is not always possible to achieve higher levels of performance through small incremental
localized efforts—efforts that slightly improve on old business processes. That is why some
organizations follow the Lean practice of an annual Hoshin Kanri (business strategy improvement)
effort in which the organization makes an assessment of what resources and capabilities it needs to
stay competitive, win new business and grow.

What else could be holding us back?

Significant strategic process re-engineering can only happen when organizations face head on the
following potential barriers.

Dependence on Manual Processes

With the technology available today, the organization should not be relying on paper-based or spreadsheet-based processes at the shop floor to collect data, track progress, and prepare product or service documents for the customer. Yet, a recent survey [4] by IYNO Advisors found that over 50% of the companies surveyed were depending on homegrown or spreadsheet systems for their production systems. Many other companies are still depending today on paper-based processes at the shop floor.

Even traditional Lean Manufacturing practitioners like Toyota are now embracing information technology to raise the organization’s performance to a new level. This was the topic of a presentation by Toyota US CIO, Tim Platt, who spoke about "Lean's High Tech Makeover" [1] at the 2014 Industry Week conference. If an organization like Toyota can move beyond the manual Lean processes they promoted for years, your organization can change too and embrace automated Lean processes supported by information technology.

Poorly Defined Business Processes

It can be difficult to see the opportunities for improvement right in front of us if we don’t have a
good understanding of the current business processes. I have been lucky to participate in very eye
opening value stream mapping activities with experts from production, quality and engineering
departments. I have seen how different departments can often be so focused on their own daily duties that they do not give much thought to how their work is affecting downstream activities. We expect someone else in the organization is looking at the performance of the entire system. But the reality is that many organizations do not dedicate a team to enterprise level improvement and do not spend enough time cross training personnel on business processes that cross departmental walls. Many times enterprise level processes are not documented and often very few employees understand the overall value chain across the organization. These type of value mapping exercises across departments can uncover hidden gold.

If we don’t have a good understanding of key business processes across the organization documented, it is difficult to perform a proper evaluation and optimization of the entire value stream. Tools like value stream maps, business process activity diagrams, BPMN diagrams or SIPOC diagrams for each department are very useful tools in identifying processes that might not scale to handle a lot of innovation volume and speed in our product lines.

Insufficient Accurate Information 

To justify bigger investments in process, product or equipment changes we need good data backing up the justification. We need better data to justify a new MES system which we need in order to collect accurate data to make better informed decisions in the future. Are we are caught in a Catch-22 situation?

It is important to know that we are delivering on time and paying the bills, but we also need to know more details about engineering and production processes if we want to improve those processes. It is important to know our delivered product quality, inspection results and defect counts but also more details about defect and cause classification to help us tackle the root cause of the problems.

Without the detail data, it is easy to always end up finding someone to blame for the problem
instead of taking a more methodical view to problem solving and finding the true system weaknesses. If we find that we keep blaming all problems on (a) lack of training, (b) personnel carelessness, and (c) human error, we should pause and figure out if we are jumping to conclusions because we do not have good data to identify other sources of potential issues. We want data that will point to any of the potential resources causing the problem which could be personnel related but could also be related to equipment, processes or product design.

Information maintained manually in different departmental databases or spreadsheets (information silos) is likely not consistent and could possibly lead to different conclusions and priorities in different departments. This information inconsistency can be tackled by implementing MES/EQMS information systems and integration standards that keep information synchronized between these production systems and other enterprise systems including PLM and ERP.

Lack of Flexibility to Change Processes

Are we evaluating software solutions based on our organization’s needs for growth and improvement or based on “fit” to our old legacy processes? It would be natural for the IT department to have a bias towards maintaining and improving the old custom systems that they developed versus looking to adopt new commercial software solutions. Commercial software benefits from the input and investment of many customers into one solution. If instead of looking at commercial solutions, the team keeps developing custom IT solutions and justifying the ongoing investment because the company is “different” than the competition, it might be good to get a second opinion from a neutral third party consultant.

It is also natural for engineering, manufacturing engineering, production control and quality
departments to be comfortable with the processes they have used for decades. It is good to expose
the team periodically to new ideas through trade conferences where they can see how other companies are innovating their business processes.

The cost of rebuilding custom integrations for new systems can also be a constraint. Integration
interfaces between enterprise applications based on integration standards like OAGIS make it easier
to replace functional modules in the enterprise system and stay up-to-date with best-in-class
functionality for different departments.

Fear of Too Much Change At One Time

Organizations might have a concern with making big changes all at once that will end up adversely
affecting production cost and schedule for even a few months. The risk associated with this learning
curve on new technology and business processes can be mitigated by implementing first in a pilot
area or “teaching plant” that can serve as an education platform for the rest of the organization
and proof point on the benefits and gains of implementing the new business processes.

Summary

The reality for most organizations include a mix of several of the barriers listed above. It is
important not to get put off by these challenges. Focus on how investments in new information
technology and new business processes can contribute to more contract wins, margin gains, and the
development of new agile, flexible and lean production operations. Expect production operations
remodeled based on the insights gained in your pilot implementation programs.

References:

[1] “Toyota Talks about Lean Manufacturing’s High-Tech Makeover”, manufacturing-operations-
management.com, 2014
http://www.manufacturing-operations-management.com/manufacturing/2014/05/toyota-talks-about-lean-manufacturings-high-tech-makeover.html

[2] “Aerospace and Defense Industry: Navigating a Global Future”, Trade & Industry Development, Jim Kemp, 2014
http://www.tradeandindustrydev.com/industry/aerospace-defense/aerospace-and-defense-industry-navigating-global-f-9187

[3] “Hype Cycle for Discrete Manufacturing and PLM, 2014”, Gartner, Halpern/Jacobson/Suleski, 2014
https://www.gartner.com/doc/2808322/hype-cycle-discrete-manufacturing-plm

[4] “Profitable Projects Webinar: Transforming Project-based Operations”, IYNO and Deltek,
Fraser/Kestel/Leiva/White, 2014
http://more.deltek.com/LP=3958?sourceid=49&utm_source=deltek-com-generic&utm_medium=deltek- com&utm_campaign=generic-Deltek-com&cmp=deltek-com_deltek-com-generic_generic-Deltek-com

 

Smart Manufacturing needs a Real-time Integrated Enterprise

The smart manufacturing factory employs 21st century technology to fabricate and assemble a mix of configurable products in response to immediate customer demands. It implements advanced processes that can incorporate technology innovation as fast as scientists and engineers develop it. It marries information, technology and human ingenuity to bring about a rapid revolution in product design, development, manufacturing, distribution, sales and service. It delivers these process improvements while also improving worker safety, minimizing environmental
impact, and improving product quality.

The smart manufacturing factory is at the intersection of a few technology initiatives including the development of smart manufacturing devices that are part of “The Internet of Things” (IoT), 3D model based manufacturing, and a re-architecting of enterprise systems around services and integration standards to improve communication speed and accuracy not only within the organization, but also with the entire partner and supplier network.

Smart Manufacturing Automation Devices

Manufacturing machines are getting smarter and are equipped with their own computers that control and coordinate a myriad of microprocessor chips on every sensor, motor, and actuator. Robots have been working behind fences automating many of the dirty, dangerous and dull work in the factory, and work requiring high levels of precision and consistent repetition. A new generation of easily configured and set up robots are being developed to assist human personnel side by side. For example, a prototype robot named Baxter (by Rethink Robotics, http://www.rethinkrobotics.com/baxter/) is taught his task through a demonstration by the operator on how to move its arm and hands to accomplish the task.

In the past, each advanced manufacturing center was managed as a process island within the plant operated by a few experts with its own special procedures and controls. The new generation of smart manufacturing equipment connects to the wireless network as easily as our cell phones and
computerized glasses (wearable technology in general), and will be ready to receive input and broadcast feedback. This creates an opportunity to standardize on new equipment and application data exchanges—machine-2-machine (M2M) and machine-2-application (M2A) —that eliminate the information silos within the plant.

For example, our espresso machines can already broadcast when they need coffee beans and when they need to be cleaned via a display at the machine. What if the machine also broadcasted that information to our home computer? An application on our computer could be tracking the inventory level of coffee in the pantry, the weekly usage pattern from our coffee machine, and could use this information to forecast when we would run out of coffee and even automatically put coffee on our shopping list. These are some of the promised concepts at home from the “Internet of Things” (IoT) (http://www.iot-a.eu/).

IoT concepts extend to the factory floor and the next generation of smart machines. There are efforts for a Common Industrial Protocol (CIP) to facilitate these type of data exchanges in the factory between machines, computers and personal devices.

The German government refers to this next generation of connected manufacturing machines, robots and information systems as “Industry 4.0” (http://www.bmbf.de/en/19955.php) and looks at it as the fourth industrial revolution. But to make Industry 4.0 a reality, equipment manufacturers will need to embed intelligence and communication capabilities into their products and we will need broadly accepted standards for communicating, collecting data and interacting with the equipment.

In the United States, NIST’s Smart Manufacturing Operations Planning and Control Program (http://www.nist.gov/el/msid/syseng/smopc.cfm) aims to enhance U.S. innovation and industrial competitiveness by facilitating the adoption of smart manufacturing systems (fully-integrated, collaborative manufacturing systems that respond in real time to meet changing demands and conditions in the factory, in the supply network, and in customer needs). This program will enable smart manufacturing based on efficient networked sensing and control, prognostics and health management (including diagnostics and maintenance), integrated wireless platforms, industrial control security, efficient information exchange and interoperability of system components.

Real-time Integrated Processes and Information Systems

IoT deployments will generate large quantities of data that need to be processed and analyzed as close to real time as possible. If a system can’t keep up with the volume of data, it will increasingly fall behind and its analysis and processing of old data will quickly be obsolete and of no value.

The recent trends in enterprise information technology (IT) have been to centralize applications to reduce costs. However, organizations will need to rethink that IT strategy or risk creating a bottleneck in an IoT world. The processing of data streams from smart devices will need to be
distributed and layers of systems will aggregate and filter the data from lower level systems close to machines, to layers of systems managing business processes (internally and into the supply chain) and all the way up to enterprise systems with centralized financial information for the business.

This might sound similar to having a SCADA or historian system collect machine information and pass filtered and aggregate data up to a Manufacturing Operations Management (MOM) or ERP system, but it is different because the smart machines can do much more processing and
filtering of the data themselves and will be communicating with the same protocols that our cell phones and TVs use. For these smarter devices, we can probably skip the traditional layers 0-2 in the old ISA95 and ISA88 models. The machines might broadcast straight to a cloud service which in turn can broadcast to our personal device which has been registered via the company’s portal to receive specific types of messages.

From the product design side, especially in Aerospace and Defense industry, the “Model-Based Enterprise” (MBE) (http://model-based-enterprise.org/) is redefining how manufacturers work with engineering and suppliers in the digital 3D world. Product definitions in 3D formats need to be communicated through the supply chain and through our own internal systems for procurement, production and quality control. Change management practices in the 3D world set the bar higher than what could ever be achieved via 2D drawings and paper forms. The goal is full associativity between engineering, inspection, manufacturing, and service definitions to facilitate change and configuration management practices among multiple tiers in the supply chain.

NIST has a digital thread for Smart Manufacturing that picks up the work from MBE and takes it downstream and links it to the Smart Manufacturing infrastructure (http://www.manufacturing.gov/docs/DMDI_overview.pdf)

Over 50% of the actual manufacturing of a product happens in the supply chain and over 50% of the data exchanged with partners and suppliers still travels today over email, phone and fax rather than flowing directly between business applications via B2B (business-2-business) integration in
structured XML. This low adoption level of B2B integration is surprising given that the first EDI systems were introduced four decades ago and XML standards have been around for decades also. There has been a lack of a concerted effort to tackle this integration arena. Perhaps because
organizations have been plenty busy trying to orchestrate A2A (application-2-application) integrations within their four walls. However, with such a big percent of the success of the company riding on the reliability of these communications, it is time to start improving B2B
integration techniques with our supplier network. 

MESA (http://www.mesa.org) and the Open Applications Group (OAGi, http://www.oagi.org/) have recently signed a collaboration agreement. OAGi is a standards organization managing XML integration standards for A2A and B2B communications and is widely used in the discrete manufacturing arena.

APICS has recently joined with the Supply Chain Council (https://www.gartner.com/doc/2728128/supply-chain-councilapics-merger- help) and they have the SCOR framework for supply chain management.

The SCOR model addresses business practices but do not translate to B2B integration requirements. OAGi is working on mapping SCOR to B2B integration standards. NIST is also working with OAGi on integration standards that span into the supply chain.

Manufacturing organizations that want to lead the next generation of supply chain communications should join these organizations and participate in these efforts.

Benefits of a new real-time integrated enterprise platform for smart manufacturing include:
• Accelerate time to market for innovative products
• Future-proof the IT investment with flexibility to easily switch best-in-class functional modules
• Reduce total cost of ownership with simplified management across the platform by leveraging plug and play integration
• Ensure tight integration between enterprise applications and operational systems at the shop floor
• Increase speed and accuracy throughout the extended value chain
• Gain insight you can act on with metrics acquired directly from transacting the business process model

 

 

Are We Keeping Our Machines Happy?

I love this story. "What happens if Voyager runs into a planet? Who will fix it if it breaks down?"A five-year-old boy has questions for Astronaut Cmdr Chris Hadfield. He was worried about the Voyager alone in space.

 

 

 

Audio: http://www.cbc.ca/player/News/Canada/Audio/ID/2530849070/

Makes me wonder if we are keeping the machines at the shop floor busy and happy? What about the next generation of smart machines and robots? Will they be happy? Will we be as interested in our smart machines as the boy with Voyager?

Four Lessons Learned from Early Cloud SaaS Experiences in Aerospace Manufacturing

Gartner analysts (http://www.gartner.com/) recently published a paper “Lessons Learned From Cloud in Manufacturing Industries” sharing experiences from companies in Aerospace & Defense (A&D), Medical Devices, and Automotive industries that have been piloting solutions for product lifecycle management (PLM), manufacturing operations management (MOM) or supply chain management (SCM) in the cloud or  SaaS (Software as a Service). Some of the lessons most applicable to information technology initiatives for regulated industries like A&D and Medical Devices are listed below.

Gartner analysts point out that the cloud and SaaS offerings have a lot of potential benefits in the near future as they enable new collaboration paradigms inside the company and with partners, but they warn that “cloud for cloud’s sake” approaches will probably not yield much benefit. They expect cloud technology to further mature over the next two years and conquer some of the current constraints.

Four of the lessons learned and risks identified in their research:

#1 – Higher Costs
Many users of cloud solutions expect reduced costs, but do not necessarily get it. Cloud strategies can end up costing more than in-house solutions. For example, applications that perform a lot of
calculations can end up increasing infrastructure as a service (IaaS) costs—especially during high demand periods.

#2 – Integration and Workflow Constraints
Many multitenant applications offer one-size-fits-all integration and workflow options that might not suffice for your organization. Integration interfaces and workflow rules required between
enterprise applications (including CAD, PLM, MOM, QA, ERP, and SCM) to support your organization’s business processes might not be supported by the current crop of cloud offerings.
Just because an application is moved to the cloud does not guarantee that collaboration options are automatically improved. Applications have to be redesigned to take advantage of new collaboration paradigms enabled by the cloud.

#3 – Security Concerns
Most security risks with cloud-based solutions are overhyped. There were no reported breaches among the surveyed companies and the data is secured behind firewalls as it would be with an in-house server strategy.

However, current cloud solutions (a) lack ITAR (International Traffic in Arms Regulations) support inhibiting use by many A&D manufacturers, and (b) require 25%-50% higher implementation costs to meet GxP compliance requirements for additional logging, auditing, archiving, and nonrepudiation controls.

Some manufacturers have expressed a desire to keep data assets behind their own firewalls and use the cloud as a collaboration platform. This requires re-architecture of business processes and
applications.

#4 – No Quicker Startup Time
Even though the application is quickly available and users can quickly be added, the implementation of a new cloud solution still needs upfront planning and many of the same steps required in any implementation project. Implementation projects, regardless of cloud or on-premise architecture, require the same level of project management to be successful. Organizations receive the most benefits when they can re-engineering business processes to take advantage of a new solution, and these efforts will take considerable time and resources.

If you have access to Gartner reports, I encourage you to download and read the full report.

References:
“Lessons Learned From Cloud in Manufacturing Industries”, Hagemeyer, Koslowski, Halpern, Scheibenreif, and Shanler, Gartner, 2014

 

Managing Risk and Improvement in the Extended Enterprise via Corrective Action

Both continuous improvement and risk management are considered minimum compliance process requirements in the latest versions of Quality Management System (QMS) regulatory standards including ISO9001, AS9100, and ISO13485. Corrective and Preventive Action (CAPA) projects can be used to manage both risk mitigation and process improvement types of projects. 

When we look at processes in the entire product lifecycle starting at the supplier, continuing through manufacturing into aftermarket maintenance and repair services on the product, we find the following sources for potential CAPA projects:
• Audit Findings
• Inspection Findings
• Discrepancy Reports
• Equipment Maintenance Trouble Calls
• Product Warranty Claims
• Customer Complaints
• Performance Metrics Analysis

Whether a project is needed to mitigate risk or improve performance, many of the steps we follow are common and merit a similar discipline. Lean Manufacturing and Six Sigma practitioners use slightly different terminology and methodologies for process improvement, but they are really more similar than different.

Six Sigma practitioners use the DMAIC process to systematically reduce variability in a manufacturing process. Lean practitioners refer to the Toyota problem solving methodology and to kaizen events.

For diving down into specific urgent issues once they are uncovered, the Toyota Problem Solving process and the 8-Discipline (8D) process are very similar. The 8-Disciplines (8D) process is often used because it adds steps to contain the problem.

The Toyota Problem-Solving process and the 8D process have a lot of similarities but in many ways do not exactly align. The 8D process has an emphasis on corrective and preventive actions which are more appropriate to obvious quality issues, and the Toyota Problem-Solving process has more emphasis on defining the problem which might not be obvious on productivity issues.

Form the Team – The 8D process recognizes the importance of assembling the right team and places it as the first step. A small team is needed with the right mix of skills, experience and authority to resolve the problem and implement solutions. Ensure that everyone on the team has the time and inclination to work on the problem together. Formal meetings should initiate the effort and monitor progress. The company’s Corrective Action Database should be used to document findings, decisions and progress.

Describe the Problem – It is critical to define a clear problem. The problem statement provides the starting point for investigation. Terms used in the description must be understood by the entire team. Problem description should include the following:
(a) Who – Who is affected by the problem? Who first observed the problem? To whom was the problem reported to?
(b) What – What type of problem? What has the problem? What is or isn’t happening? What is the physical evidence?
(c) When – When did it first happen? When was it first observed? Is it a recurring problem? How often? What is the trend? (random, cyclical, continuous) Has it been observed before?
(d) How Much – What is the magnitude of the problem? How many units or components are affected? How much cost (labor and material) is associated to this problem?
(e) Why – Why is it a problem? What is the impact? What processes are affected? Is production stopped completely or partially? Is there a work-around? Are customer delivery dates impacted?

Some of the questions above might also be viewed as assessing the risk and impact of a potential problem recurrence. The risk can be given a risk score by multiplying a Likelihood Rating, Severity Rating and a Detectability Difficulty Rating.

Contain the Problem – Identify interim containment actions to prevent further cost impact from the problem. Is the problem tied to a machine? Is it tied to a supplier component? Are more lots affected by this problem? Suppliers must be informed as soon as possible about issues to prevent further shipments with problems. Inventory parts which might be affected must be identified and inspected. If problem has a life- threatening consequence, products in the field have to be identified for potential recall.

Identify the Root cause – An investigation and failure analysis is performed to determine the root cause of the problem. It is not enough to identify the first-level or intermediate-level causes of the problem. The true root cause has to be uncovered and corrected or a similar problem will eventually manifest itself.

Root cause analysis may start with brainstorming sessions among team members and move on to data analysis that further explores multiple potential causes. Different kinds of charts and diagrams are used in data analysis including histograms, Pareto charts, scatter charts, and concentration and affinity diagrams.

It is important to document the analysis discussion, findings and conclusions using cause-and-effect charts and fault tree diagrams. A five-whys type of analysis is also useful to understand all the intermediate-level causes between the root cause and the problem manifestation.

Corrective Actions – This discipline entails identifying all possible corrective actions to address the root cause of the problem. The owners of the corrective actions, the target dates for completion and the rationale behind each should be documented. It is sometimes a good idea to have a preliminary evaluation of the corrective action to test its effectiveness before wasting time and money on fully implementing an incorrect solution.

Corrective action includes further containment of potentially affected similar production or inventory based on the root cause identified. If problem has a life-threatening consequence, products in the field have to be identified and notifications issued for recall or repair.

Permanent Correction – Once the corrective actions have been defined and tested, the next step it to implement them and verify their effectiveness. If the corrective actions are found to have deficient effectiveness it might be necessary to return to the prior step and create new corrective actions.

Preventive Actions – Lessons learned from the above investigation and corrective actions might be able to benefit other processes or areas with similar vulnerabilities even if not affected under the current situation. Preventive measures should be outlined if it is possible to prevent similar problems in the future. Preventive actions, like corrective actions, require owners and target dates.

Acknowledge Success – The last step of the 8D process is management’s formal recognition of the accomplishments of the team. This demonstrates the commitment of the management team to the continuous improvement process and is good internal publicity for the effectiveness of these initiatives.