Manufacturing Operations Management Talk

Do We Want to Make Manufacturing Great “Again”?

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

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

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

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

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

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

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

There are plenty of manufacturing jobs going unfulfilled.

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

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

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

What skills are needed for these manufacturing jobs?

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

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

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

What are manufacturers doing about the skills shortage?

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

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

What can the federal government do to help manufacturing?

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

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

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

What can states do to attract more manufacturers?

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[12] Manufacturing Production Technician Career, MyMajors, 2017

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

The Role of Government Supporting Smart Manufacturing

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

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

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

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

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

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

Transportation Infrastructure - The government supports nation-wide infrastructure

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

 

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

 

 

 

Notes from the Smart Manufacturing and IIoT Round Table Discussion

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

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

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

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

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

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

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

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

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

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

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

Q: Did you do ROI analysis?

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

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

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

Another company started by blueprinting their as-is processes.

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

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

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

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

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

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

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

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

Q: How can MESA help you with your initiatives?

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

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

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

 

Six Innovation Areas Leading to the Model-Based MRO Enterprise

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

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

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

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

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

 

3D Printing of Spare Parts

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

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

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

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

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

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

Automation with Robotics and UAVs

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

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

 

 

 

 

 

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

 

  

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

 

 

  

Asset Sensor and Diagnostic Connectivity via the IoT

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

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

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

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

Digital Thread and Digital Twin

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

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

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

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

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

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

Progress towards a full digital thread:

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

 

Advanced Analytical Capabilities

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

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

Descriptive Analytics

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

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

Diagnostic Analytics

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

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

Predictive Analytics

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

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

Prescriptive Analytics

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

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

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

Advanced Guidance with Augmented Reality and Natural Language

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

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

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

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

 

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

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

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

References

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

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

 

Reducing Cost of Quality with Integrated MOM and EQMS

– Using Manufacturing Operations Management Software to Reduce Cost of Quality

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

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

Breaking Down the Cost of Quality

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

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

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

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

 

 

 

The Relationship between Prevention and Failure Costs

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

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

 

  

 

 

Six-Sigma Projects Reduce the Number of Defects

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

 

 

 

 

 

 The Effect of Improving the QMS Processes

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

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

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

Reducing the Cost of Quality Failure Prevention

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

Work Instructions and Process Change Management 

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

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

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

Verifying Personnel Training and Certification 

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

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

Verifying Equipment Calibration 

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

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

Reducing the Cost of Quality Appraisal

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

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

Is 100% Inspection Needed?

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

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

Automated Inspection and Statistical Process Control (SPC)

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

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

Automated Quality Metrics 

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

Process Audits

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

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

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

Reducing the Cost of Quality Failures

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

Discrepancy Documentation

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

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

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

Workflow Driven Routing of Tasks

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

Rework, Re-inspection, and Re-testing

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

Corrective and Preventive Actions (CAPA)

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

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

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

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

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

The Cost of External Quality Failures

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

Conclusion

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

References

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

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

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

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

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

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

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Your Next Enterprise App? Survey Says: Manufacturing Operations Management

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

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

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

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

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

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

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

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

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

Data Stream Processing and Streaming Analytics

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

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

Data in the Smart Manufacturing and IIoT IT Framework

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

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

 

 

 

 

 

  

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

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

 

 

 

 References:

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

 

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

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

 

Unraveling the Hype on Data Lakes for Industrial IoT

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

First, what are data lakes?

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

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

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

Second, what are the misconceptions about data lakes?

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

Myth: A data lake strategy does not include ETL tools

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

Myth: A data lake has to be Hadoop

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

Myth: Data lakes are inexpensive to implement

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

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

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

Myth: A data lake creates a single source of truth

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

Myth: Anyone can use a data lake

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

Myth: A data lake is a data integration method

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

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

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

 

References

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

How “Smart” Are We in Manufacturing Today?

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

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

 

Full article link:

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

 

Demystifying the Digital Thread and Digital Twin Concepts

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

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

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

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

Expected Benefits

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

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

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

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

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

As-Is and To-Be Scenarios

The As-Is handover between departments:

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

The desired To-Be scenario with a digital thread:

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

Interesting Use Cases  

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

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

Potential Challenges

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

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

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

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

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

References

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

 

The Smart Manufacturing Elevator Pitch – Take Two

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

 

Finally a Clear Picture of US Manufacturing Comeback!

Thank you Supply Chain Digest [1] for this table with clear numbers. Depending on what case an article wants to make, they might use one column or the other. So it is great to see both the differences since 2009 and the differences since the peaks years earlier in a consolidated table.

I want to make a few observations from this clear tabulation.

#1.  US Manufacturing output is much better now than what it was in 2009. Except for a couple of exceptions, most manufacturing areas have big gains.

#2. Several manufacturing areas seem to be as strong or stronger now than ever. These area have trends that look very promising.

• Semiconductors
• Food
• Computer and Electronics
• Aerospace Equipment
• Ship and Boat Building
• Motor Vehicle Parts
• Machinery
• Consumer Durables
• Plastic and Rubber Products

#3. Some manufacturing areas might never return to their old peaks in the US. I have a hypothesis that if these areas have not recovered to anywhere close to their old peaks that perhaps these are the areas that have truly left the US and might not come back any time soon. Many of these had peaks in the US before 2000.

• Textiles
• Apparel
• Primary Metals
• Hardware
• Paper, Paperboard
• Electrical Equipment
• Household Appliances

The rest of the manufacturing areas might still have hope to  return to their peaks of 2006-2008. I encourage you to read the full Supply Chain Digest article at the reference below.

Reference

[1] “Supply Chain News: US Manufacturing Recovery by Key Sectors”, Supply Chain Digest, 2016 February

 

Eliminating the Hateful Eight Types of Disconnectivity in Smart Manufacturing

“For hundreds of years, humans and machines have worked together. But something peculiar has happened recently: the machines are now speaking to us—and to one another”
                                                              -- Booz Allen, Vice President at Sedar LaBarre [1]

That would be a great quote from Booz Allen, if it weren’t slightly ahead of its time. New machines want to speak to us and want to be part of orchestrated processes in the factory, but we are not there yet. The mission ahead for Smart Manufacturing initiatives is to make it happen and realize the enormous potential from new levels of synchronization spanning from product design to order, realization, and delivery.

How Smart Manufacturing complements Lean Manufacturing efforts

Smart Manufacturing can be viewed as a process improvement movement complementary to the Lean Manufacturing movement of the past two decades. Lean Manufacturing initiatives design out the overburden, inconsistencies and waste to achieve enhanced processes that deliver products smoothly and consistently. In contrast, Smart Manufacturing initiatives are bringing the physical and digital worlds together designing in connectivity and orchestration to achieve enhanced processes that deliver products and their data smoothly and consistently to customers. Going forward, both Lean and Smart Manufacturing will be interrelated improvement efforts in the manufacturing value chain.

Eliminating the Hateful Eight Types of Disconnectivity

There are eight types of disconnectivity in the manufacturing value chain that need to be eliminated as organizations embark on the Smart Manufacturing journey to synchronize, automate and optimize the physical and digital processes.
Each of the communication and process participants listed in this slideshow need to be systematically connected to achieve the desired Smart Manufacturing goals, which we discussed in the prior article titled: “On the Journey to a Smart Manufacturing Revolution.” [2]

#1 Equipment

Machines and equipment are getting smarter with more onboard computer processing power and more connectivity to the Internet. This advancement in connectivity is referred to as the Internet of Things (IoT). For example, at home, I can set up my printer to directly order ink when it is low and have it shipped to my house. At the factory, we refer to this type of internet connectivity direct from machines as the Industrial Internet of Things (IIoT).

Equipment, machines, robots and advanced sensors can be connected to systems that automatically adjust or switch operations based on sensing the product, diagnostic or environmental conditions. IIoT connectivity needs to be orchestrated in the overall Smart Manufacturing architecture to, for example, avoid excessive downtime at the factory, because each of our machines has auto-requested maintenance a few days apart instead of coordinating a single service day of downtime for the entire line.

According to a research report from the analyst firm Berg Insight, the installed base of wireless IIoT devices reached 10 million in 2014 and will grow at an annual growth rate of 27% to reach over 43 million by 2020. [3] Yet, many organizations are struggling to reap efficiencies and insights from the connectivity and data because they haven’t created the proper technical and decision making structures to organize connected processes and analyze data for real business value.

#2 Workforce

Yes, people are an integral part of Smart Manufacturing.

People are resources that need to be connected to the processes orchestrated and data produced by the Smart Manufacturing system. We might appreciate disconnectivty when we are on vacation, but definitely not at work. We want to know what is happening at our operations as soon as possible. We want to be able to see when customers raise issues, when the production line is not running smoothly, and when problems are not being resolved in a timely manner.

Technicians, inspectors and material movers need function-specific applications that dispatch them to prioritized tasks and enable them to update status and document issues immediately. Some functions are performed at a desktop, other functions are performed on-the-go on a mobile device—function specific applications connect people at the place where they need to perform the function.
Some workforce resources might be external to the organization. As an example, smaller manufacturers can’t afford to keep every specialized skill as a full-time employee, and might need to route tasks to third-party contract technicians.

Managers need dashboards that display status, trends and alerts to focus their attention on where it is needed most. However, the old passive information model of aggregating data and providing metrics to managers to digest and take action is not enough to achieve new levels of efficiency.
New active manufacturing systems need to automate routine decisions and trigger action based exceptions and unpredictable patterns that require experts to manually analyze for root-cause. There are always plenty of exceptions to manage in daily operations, so don’t worry, people will always be needed.

Legacy paper-based procedures promoted departmental silos by creating paperwork between people in different disciplines, departments and companies in the manufacturing value chain. Social communication and collaborative work tools can connect teams and communities in new ways breaking down old silo boundaries and creating new levels of efficiency in processing and trouble shooting procedures.

As the manufacturing enterprise becomes more connected, it creates the need for a new breed of talent—one that embraces computer skills as much as mechanical skills. The next generation of manufacturing talent must be able to collaborate across multidisciplinary walls, connect digital factories, interpret results from analytics, and protect supply chains from cyber intrusions. Luckily, millennials are tech savvy, learn fast and embrace teamwork. Manufacturers will face strong competition from high-tech companies in recruiting this talent and will need to elevate the importance of strategies to recruit, develop and retain the workforce of tomorrow.

#3 Design

There are currently many manual steps and translations between the virtual product and process designs modeled in 3D and the programming and configuration of physical manufacturing and inspection processes. This type of connectivity is referred to as the “digital thread” spanning from design into supply chain, manufacturing, inspection, and product service.

One of the first applications of the digital thread is between product design and 3D printing processes. A few more of the enhanced processes targeted for the digital thread include: (a) simulation of the virtual production processes and validation of designs in collaboration with partners before physical processes are ever changed, (b) flow of revised specifications into the supply chain to ensure subassemblies are received to the correct design revision level, and (c) automation of translations between design specifications and instructions loaded into automated production and inspection machines.

#4 Parts

One of the goals of Smart Manufacturing is to create flexible manufacturing processes that can adapt to build different products and support mass customization business models. Connected auto-identified parts and materials can help achieve that type of flexibility.

Accuracy of product genealogy becomes more difficult to achieve as product variances and changes increase in a multi-tier supply chain. Accuracy can only be guaranteed if identifiers are read directly from components and materials with automatic identification (auto-ID) tags, like RFID, either embedded in the component or in the packaging. Common applications for RFID include tracking goods in the supply chain, reusable containers, high-value tools assets, parts on inventory shelves, and parts issued to manufacturing.

Flexible manufacturing processes need to (a) recognize the configuration of each product as it traverses the line, (b) load the right parts and programs to machines, and (c) compare component parts as they are loaded into machines and assembled into product in order to verify the correct bill of material configuration.

RFID tags can contain more than part and serial numbers, they can have information about hazardous warnings, expiration date or storage requirements that could trigger special handling processes at the shop floor. For example, automated processes could alert staff if material has expired due to the time it spent outside of the required refrigeration temperature.

#5 Processes

Business processes are chains of tasks that produce a specific service or product for specific customers. Tasks in a business process can span across organizational departments and system boundaries. In order to minimize delays throughout the business process, task outputs need to be connected as inputs to successor tasks. Communication and data processing among tasks should avoid manual data input and translation errors whenever possible.

Legacy business processes where designed around the sequential handover of paper documents between different departments. Modern business processes can be reinvented around new cyber-physical paradigms that promote real-time response, collaborative teams and 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 to avoid damages, 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 and Inventory Control.

Automated processes are standardized processes, and standardization promotes best practices and reduces overhead tied to maintenance of multiple ways of doing similar functions in the organization.

#6 Suppliers

To optimize today’s highly outsourced manufacturing value chain, supply chain management has to switch off the traditional procurement function managed by paper documents, emails and phone calls. Suppliers need to connect digitally with the companies they supply via web portals, supply chain hubs, and business-to-business (B2B) communication exchanges.

A demand driven supply chain can reduce inventories and schedule performance. Automated demand triggers can be achieved to replenishment and move material based on sensing part installation or empty shelves.

The orchestration of multi-tier supply chains for complex products like aircraft or medical devices require additional attention to connected change management processes for product design information.

#7 Customers

Manufacturers have traditionally faced challenges in collecting reliable data to understand and adjust to changing customer needs. Data collected via surveys is unreliable, expensive and burdens the customer.

Today’s data analytics capabilities represent a new frontier for customer satisfaction. The ability to collect and analyze customer usage data in real time allows manufacturers to make quicker adjustments. For example, remotely collecting usage data on farms from deployed windmills can reveal shifting wind conditions and immediately allow a remote operator to optimize the degree of effort that specific windmills are exerting within those farms. This type of data visibility direct from customers allows manufacturers to not only fix current products, but also anticipate the need for new features before customers ask for them.

Customer involvement in the middle of some business processes can lead to better customer experiences. Customers want more visibility of progress on long projects, and online processes to coordinate orders, deliveries, audits, warranty claims and returns. Customers should be able to remotely approve changes like deviations from specifications, late deliveries, and corrective actions.

#8 Records

As more machines are connected, it would be easy to fall into data hoarding practices. Data should be stored for a specific period of time as part of a connected system strategy, and not just collected because it might be useful one day.

Data hoarding practices can be expensive and make it hard to find real productivity improvement areas among all the noise.
Different documentation and data records have varying degrees of precision, control and time persistence requirements in support of different business processes in the organization. Some data can be of a temporary nature and only needed during the execution of a specific business process, but some data has persistence requirements.

It is important that systems of record are well connected as sources and destinations for data in processes orchestrated by Smart Manufacturing systems. Regulated industries require detailed production history records. As an example, the FDA's quality system regulation (21 CFR Part 820) requires medical device manufacturers to establish and maintain device history records (DHRs) for each batch, lot and unit they produce. The DHR must include as-built history that can be validated against the respective product design (as-designed) and applicable processing and labeling standards.

Pulling it Together With Security and Communication Standards

Security and communication standards are two important considerations that apply to each of the eight areas of connectivity for smart manufacturing systems.

The connected nature of future Smart Manufacturing systems provide great opportunities for optimization but also increase the exposure to potential cyber threats. A strong cybersecurity program spanning people, processes and technologies is necessary to protect information assets and the trusted relationships with suppliers and customers.

A key missing capability to achieve Smart Manufacturing is the ability to practically and affordably knit together a threaded orchestrated business processes across varied equipment, systems, suppliers and customers.

Today, vendors provide custom one-off solutions that are complicated, expensive, require enormous amounts of time to implement, and are built on proprietary communications. To achieve affordable plug-and-play capabilities, next-generation hardware and software technologies need to work together through common standardized communications.

The list of disconnectivity areas above serves as a roadmap for identifying gaps and opportunities towards new levels of connectivity for future Smart Manufacturing systems. They are all of equal importance on the journey to future Smart Manufacturing excellence.

References
[1] “Capitalizing on Connectivity: The Top 5 Priorities for Industrial Manufacturers”, Booz Allen Hamilton Inc., January 2015

[2] “On the Journey to a Smart Manufacturing Revolution”, Conrad Leiva, IndustryWeek IdeaExchange, December 2015.

[3] “The installed base of wireless IoT devices in Industrial Automation reached 10.3 million in 2014”, Berg Insight blog post, May 2015

[4] “5 Signs you are a Big Data Hoarder”, Bernard Marr, Data-Informed.com, August 2015

[5] “MESA White Paper #52: Smart Manufacturing – The Landscape Explained”, Smart Manufacturing Working Group, MESA.org, January 2016

This article was originally published as "Eliminating the Eight Types of Disconnectivity in Smart Manufacturing" at IndustryWeek.com, Feb 2016

 

What is Smart Manufacturing? What is Ahead?

This presentation recording from MESA.org provides an overview of insights assembled by the MESA Smart Manufacturing Working Group.

Smart Manufacturing is an initiative to bring about a revolution in manufacturing business strategy, turning traditional factories from cost centers into profitable innovation centers. Smart Manufacturing is fueled by a convergence of technologies and process improvement initiatives with the potential to radically improve the way manufacturers thread processes and systems in the entire product value chain in order to deliver more options and new service models to customers. Technologies fueling Smart Manufacturing include the integration of smart machines, Industrial Internet of Things (IIoT), and information technology (IT) including cloud services, 3D models, mobile computing, intelligence, analytics and integration platforms.

"Smart Manufacturing – The intelligent, real time orchestration and optimization of business, physical and digital processes within factories and across the entire value chain.”   MESA Smart Manufacturing Working Group

This presentation discusses the drivers, goals, building blocks and roadmap to Smart Manufacturing. Link to recorded presentation:

"Smart Manufacturing - The Journey Ahead"

 

 

On the Journey to a Smart Manufacturing Revolution

In the 1970s and 80s we witnessed a revolutionary wave of productivity improvements in manufacturing. New technologies, including personal computers (PCs), numerically controlled (NC) machines, programmable logic controllers (PLCs), and computer aided manufacturing (CAM), combined with new process improvement ideas like Total Quality Management (TQM), Just-In-Time, and Six Sigma yielded new levels of productivity and efficiency in many manufacturing industries.  

In this decade, we are seeing a convergence of technologies and process improvement initiatives of a similar scale with the potential to radically improve the way manufacturers thread processes and systems in the enterprise and the way they deliver customization and services to customers.

Organizations that bring together manufacturers, technologies and information systems, like the Manufacturing Enterprise Systems Association (MESA), the Industrial Internet Consortium (IIC), the Digital Manufacturing and Design Innovation Institute (DMDII), and the Smart Manufacturing Leadership Coalition (SMLC), are working on initiatives including the Industrial Internet of Things (IIoT) and Smart Manufacturing to coordinate this convergence of technologies and realize the process improvement potential sooner rather than later.

Smart Manufacturing is an initiative to bring about a revolution in manufacturing business strategy, turning traditional factories from cost centers into profitable innovation centers, through the integration of industrial automation, IIoT, and information technology (IT) including cloud services, 3D models, mobile computing, intelligence, and integration platforms. Smart Manufacturing initiatives include the following goals:

• Achieve new levels of efficiency to support new services and business models including mass customization (highly configured products) and product-as-a-service.
• Ability to receive published data from equipment using secure open standards, analyze and aggregate the data.
• Provide data from connected manufacturing equipment and processes directly into new analytics and event triggering capabilities into systems of record and process workflows that can loop back and trigger programming, tuning or maintenance changes on connected equipment.
• Enable more autonomous and distributed decision support at the plant floor level as smart machines are equipped with their own processing abilities and connectivity to enterprise systems.
• Promote the use of machine-to-machine (M2M) and application-to-application (A2A) connectivity standards in order to make these advanced capabilities accessible to manufacturers of all sizes and in all industry sectors, at acceptable levels of cost and implementation complexity.

 To achieve the above goals for the Smart Manufacturing revolution we are counting on the following building blocks:

• Smart machines and advanced robotics – Smart machines communicate with manufacturing systems and display a high level of autonomy. These machines recognize product configurations and diagnostic information, and make decisions and solve problems without human intervention. Robots with enhanced sensors, dexterity and intelligence can perform tasks without being pre-programmed as they can learn from experience. Sensors make them aware of the environment and safer for the people around them.

• Industrial Internet of Things (IIoT) – Manufacturing devices with network and internet connectivity– from mobile tablets to smart shelves to sensors embedded in automation controls to smart machines – are all active participants in event-driven self-tuning manufacturing processes integrated with open standards that support connectivity via the internet.
• Enterprise integration platforms - Enterprise integration platforms, like Enterprise Service Buses (ESBs), Manufacturing Service Buses (MSBs), and API Managers, have the ability to receive data broadcasted from equipment via secure open standards, analyze and aggregate the data, and trigger process controls and business processes across the enterprise.  Internet cloud services enable new connection capabilities across the enterprise and into the supply chain.

As an example, imagine a process where (a) RFID tags embedded in products broadcast the product configuration information to machines, (b) automated parts placement, assembly and inspection machines switch programs based on the received product information from each unit, (c ) defective product is routed out of the regular assembly line to a rework station, (d) defect information is messaged to the integrated enterprise quality system, and (e ) materials are ordered based on consumption reported by machines and delivered to the machine via automated material handling equipment.

However, there are some challenges on the journey and organizations are working on Smart Manufacturing initiatives because we are still falling short in some areas to achieve the desired levels of connectivity in manufacturing processes. If we want these solutions to be broadly available to small and medium size manufacturing companies, we will need to work on the following areas:

• Broad adoption of machine-to-machine (M2M), application-to-application (A2A), and business-to-business (B2B) integration standards that will enable multi-vendor hardware and software plug and play solutions with open integration platforms to the internet. Organizations like ISO, IEC, NIST, and OAGi play a key role in establishing and promoting standards for connectivity.

• Data messaging standards that create a digital thread of communications from product definition in engineering systems to manufacturing and inspection processes. Standards that not only distribute the product 3D definition but also communicate changes and record production history for traceability and archival purposes.

• New workforce skills will be required for the smart factory. Workers will need to learn how to configure and maintain smart machines and robots. IT personnel will need to learn about manufacturing systems, protocols for equipment integration, and how manufacturing data flows into business intelligence and corporate metrics.

To accelerate progress on the Smart Manufacturing revolution, organizations can work towards resolving the above gaps. To be on the forefront of adoption and a step ahead of the competition, organizations can get involved with some of the initiatives and organizations listed in this article.

This article was originally published on IndustryWeek IdeaXchange (industryweek.com) on Dec 31, 2015.

For more information on Smart Manufacturing, if you are a MESA.org member,  check out the white paper "Smart Manufacturing - The Landscape Explained".

 

My New Year Wish – Less Hype for Big Data Analytics, More Buzz for Smart Manufacturing

I know of several good use cases for big data related technology in manufacturing operations management and I am sure there are a few more out there but sorry to break it to you…it is not a magic bullet for manufacturing improvement!

Instead, I hope that Smart Manufacturing gets the attention in 2016 that it deserves as a new way to look at connectivity, manufacturing platforms and linkages in the manufacturing enterprise and extended product value chain.

The real value of connectivity of manufacturing equipment and systems is not found by trying to mine a stream of sensor data emanating from machines in the plant in the hope of finding some pearl of wisdom. Wisdom that must have been escaping our shop floor managers’ imagination for years because it was buried in the data. I don’t think so.

The real value of connectivity is found in being able to orchestrate and streamline business processes from desktops to machines, across department walls, across tiers of manufacturing operations management, and across tiers of suppliers— layers of production support processes that have been relegated to email, spreadsheet and other manual cumbersome and error prone methods far too long. Of course, orchestration requires data and metrics, but manufacturing processes and systems need to be designed to acquire certain data with the intention of doing something with it. It is planned data usage, transformation, integration and aggregation driving real innovation. The mining of all machine data in hopes of finding meaningful patterns in the future is not a solid innovation strategy.

In one article Gartner research mentions that 70% of captured manufacturing data goes unused today. [1] The question should not be “What can I do with this data?” the question should be “Why am I capturing data I don’t need”. The solution to that problem is to stop capturing data that does not have a designed use as part of a monitoring or control process.

An LNS article in 2015 titled “Analytics: The New Frontier in Manufacturing Operations and Business Performance” boasts analytics as the new frontier for manufacturing systems. [2] However, looking at the data in that article we can come to a totally different conclusion. LNS found that manufacturer’s top objectives were:

1. Ensuring consistent quality of products
2. Responsiveness to customer order demands
3. Increasing production capacity and capabilities
4. Getting new products to market faster

Then LNS finds that the top challenges for meeting the above are:

1. Lack of collaboration across different departments
2. Disparate systems and data sources
3. ROI justifications for improvement investments
4. Difficulty coordinating across the supply and demand chains

And, finally, the LNS survey lists the following as the most critical areas of software for manufacturers:

1. Production Execution Management
2. Quality Management
3. Real-time Performance Visibility
4. Data Collection and Reporting
5. Supply and Demand Response
6. Overall Equipment Effectiveness
7. Analytics

Analytics was listed as #7, yet it somehow got the headline of the article. Does that make sense? Instead note what did get a lot of attention: “lack of collaboration across departments” and “difficulty coordinating across supply and demand chain”. Those are the real opportunities for improvement in manufacturing business processes. Instead of more articles pushing the hype agenda for big data and analytics, in 2016, it would be great to see more emphasis on manufacturing process improvement enabled by integration standards that connect machines, processes, systems  and the entire Smart Manufacturing value chain of the future.

There is hope in the horizon, one article from Gartner in 2015 suggested that Big Data might be passing the “peak of inflated expectations” and heading for the “trough of disillusionment” in the hype cycle. As manufacturers discover the real use cases for big data technologies they will be able to properly prioritize the importance of its usage within innovation initiatives and stop looking at the technology itself as the innovation.

More on Smart Manufacturing in this article: On the Journey to aSmart Manufacturing Revolution

References:

[1] "Predicts 2016: Opportunities Abound for the Factory to Reach Its Potential", Gartner, 2015 

[2] "Analytics: The New Frontier in Manufacturing Operations and Business Performance", LNS Research, 2015

[3] "Hype Cycles for 2015: Five Megatrends Shift Computing Landscape", Gartner, 2015

 

 

Drivers, Challenges and Potential for a Smart Manufacturing Future

Last week I had the pleasure of being interviewed on the topic of Smart Manufacturing and digitalization by Dr. Rebecca Silveston-Keith for a study being performed by the University of Industry Demonstration Partnership (UIDP). I am sharing in this post some highlights from that discussion.

Rebecca: What are the main drivers for implementing digitalization in manufacturing?

Conrad: Before I answer your question, I would like to clarify that I fold “digitalization” under a bigger Smart Manufacturing umbrella to explain the relationship between the multiple manufacturing technology advances that are converging to fuel the next big industrial revolution, the Fourth Industrial Revolution. 

Back to your question… One of the global main drivers behind investment in manufacturing technology and infrastructure is a recognition by governments of the role of manufacturing in economic recovery and growth.

In addition, many US manufacturing corporations have recognized the need to advance their entire supply chain in order to achieve new levels of productivity and quality, and have been sponsoring initiatives and groups like the Industrial Internet Consortium (IIC) and the Smart Manufacturing Leadership Coalition (SMLC) where industry and academia work together to advance and leverage the new digital technologies ad platforms.

Rebecca: What are the major challenges or bottlenecks ahead?

Conrad: In order to encourage manufacturing growth within the country, it is clear that there needs to be investment in the digital infrastructure. Industry, government and academia have recognized that there are gaps in the virtual infrastructure with respect to how systems interconnect so that the digital message fidelity/accuracy is maintained from product design down through the supply chain fabrication and assembly processes. These gaps prevent smaller manufacturers from reaching the benefits of highly automated manufacturing and prevents the creation of a highly integrated manufacturing supply chains.

Connectivity and standards are a challenge in manufacturing. Without standardization, the solutions are customized for each manufacturing operation which is slow and costly. Standardization would lower the cost of entry for small and medium manufacturers to implement more digitalization in their operations. Right now there are way too many different proprietary and custom integration methods. This is partly because big vendors would like to have monopolies and develop customers that become dependent on their proprietary solutions. No single vendor has been able to dominate and establish defacto standards either. Manufacturers are using different vendors for different aspects of digital manufacturing (the vendor that supplies the CAD tools is not the same as the one that makes the NC or CMM machines). Some vendor companies are starting to understand the potential of reaching a larger midmarket and are joining the alliance initiatives led by industry or government to provide these types of solutions to manufacturers. The Digital Manufacturing and Design Innovation Institute (DMDII) is an example of a government led initiative in the US.

Cyber security is a challenge. As more of the cloud technology and internet connectivity is leveraged and IoT (the Internet of Things) moves from consumer markets into manufacturing, (Industrial IoT or IIoT) it becomes imperative to assure the identity of the “things”, like machines, in order to have secure exchanges of information. The IT-OT (Information Technology to Operations Technology) integration issue is solvable but needs secured standards of communication to leverage the internet as the main gateway.

The right skills mix for the worker is a challenge. In order to put all this together, manufacturers need a different mix of skills. It is not just a matter of hiring people into manufacturing and IT jobs and performing on-the-job training, since they will need to understand mechanical, electrical, industrial systems, material science aspects as well as computer science in order to implement the new manufacturing infrastructure successfully. Blue collar repetitive jobs will be replaced by automation. However, the need for more sophisticated jobs will emerge as even the maintenance worker needs to teach robots what to do as well as configure, analyze, diagnose and trouble shoot issues. Clerical white collar jobs will also be automated but the engineering needs will probably go up and demand greater multi-discipline collaboration.

New employment models will, and have emerged as the new norm where manufacturers will rely on contract workers with specialized skills for everything from design to maintenance instead of hiring permanent employees. There is a need for very specialized skills like configuration, integration and maintenance of very sophisticated equipment and many organizations will not be able to retain these skills full time.

Adding to the skill requirement challenge is the perception that manufacturing jobs are dirty and declining.  A new message needs to be reinforced for the next generation of manufacturing workers that there will be jobs and they will be more sophisticated and interesting.

Intellectual property (IP) protection is a challenge in an increasingly distributed global manufacturing ecosystem. Engineers and manufacturer operators are no longer under one roof but in different physical locations or countries. How is the security and IP guaranteed? The process of black boxing the IP will be the norm in the manufacturing ecosystem, so that no one entity has total exposure to the full process (think Boeing who integrates parts from multiple suppliers around the world for its Dreamliner). IP for design is also an issue with the emergence of 3D print shops. New rules and technology for protecting the IP of the design need to be developed. For example, we might need a way to distribute a 3D definition that is not only encrypted and protected by a password but also restricted to single print by a 3D printer or expiration date. Similar to schemes we use to protect the distribution of movies.

Rebecca: You answered a few more of the questions on readiness and skills needed with that answer so I will skip ahead a bit… Are companies implementing digitalization?  Are small companies adopting digitalization?

Conrad: Most manufacturing organizations are aware of the potential of digitalization. The large, or more solvent, manufacturers are leading the early adoption and putting the infrastructure in place. They would like to see more of their supply chain adopting the new infrastructure, platforms, and communications standards and that is why they are sponsoring groups like IIC and SMLC. If through those types of efforts, we manage to put more plug-and-play standards in place, smaller businesses will be able to join the new manufacturing ecosystems that will evolve.  

Rebecca: How is digitalization influencing value creation within business? E.g. are there new business models emerging, innovative products, services and processes, value chains, R&D processes

Conrad: New business models enabled by digitalization include: mass customization, product-as-a- service and manufacturing-as-a-service. These new models change the way manufacturers view and interact with the suppliers and customers. New business models make sense for some types of manufactures that are producing high value customized products (think Invisalign braces) as opposed to those producing widgets or products destined for the grocery store shelf. 

These new business models are not for every type of product or process, but the possibility of a business model where manufacturing is the core competency is a bigger business model opportunity. 

However some solution providers might be under-representing the opportunity ahead and the resulting confusion from the wave of new IT terminology and recommendations can cause manufacturers to miss the bigger picture among the hype. For example, there is a misunderstanding about the role of big data and predictive analytics in Smart Manufacturing and IIoT. Big Data and predictive analytics should not be viewed as the end goal of Smart Manufacturing or IIoT initiatives.  They should be viewed as tools that might be useful for some manufacturers within their Smart Manufacturing strategy. Better business intelligence and analytics are a nice side effect of structured data communications throughout the organization. However, more important to most manufacturers will be the new levels of automation, process controls and process integration across the enterprise and product value chain. For example, being able to tell whether your product meets the design specifications after assembling the many parts received from a multi-tier supply chain might be more important to an organization than having an accurate view of machine down time.

Rebecca: Beyond company boundaries, how is industrial renewal and digitalization influencing different parts of the value chain / value network?

Conrad: Manufacturers need their suppliers to be up to speed as well because, for many, 50-70% of their product are made outside of their factory. The goal is to empower the small companies in order to drive costs down and improve quality through improved fidelity to specifications traveling in structured communications. Smaller companies in the supply chain will not be able to implement digitalization without standards in place that make it as easy as plugging a machine into the electrical outlet.

Rebecca: What else can labor unions, state agencies, vendors, customers, academic institutions, local, state, and federal agencies, suppliers/vendors do to stimulate industrial renewal?

Conrad: The types of initiatives I mentioned earlier are all good. In addition, perhaps more educational programs that focus on developing multifaceted engineers that understand mechanical, industrial, electrical, materials as well as systems and computer science are needed.

Rebecca: What does the US do or can do better than other countries?

Conrad: The US venture capitalistic driven economy stimulates entrepreneurship and tolerates mistakes along the way to success. The US is good at agile iteration, is more experimental and tends to try things faster. Time will tell whether this approach will result in faster implementation than the more methodical approach of some other countries.

The cowboy mentality in the US also means that industry does not necessarily embrace government participation even for things that they should like issues that span entire industries.  The partisan US government system might be a little slower in its ability to help implement and promote a new communication infrastructure and new standards than some other countries. We’ll have to see what happens.

 

I hope you enjoyed some of these highlights as much as I enjoyed the interview with Rebecca. I am looking forward to the results of this UIDP study.

Professor Jari Kuusisto of the University of Vaasa from Finland is carrying out joint research with international partners from Austria, Germany and the United States on industrial renewal and digitization. The aim is to produce insights and country comparisons that can be invaluable for businesses and policy makers in addressing industrial renewal related challenges. The key focus areas of the research surveys include understanding the drivers for industrial renewal phenomena, business, research and policy perspectives, and the skills, technologies and other related capabilities needed to be successful in participant countries. In the United States the University of Vaasa partnered with the University of Industry Demonstration Partnership, an organization focuses on developing strategies to overcome barriers slowing industry and academia research partnerships for the betterment of the US economy. Dr. Rebecca Silveston-Keith, representing the UIDP, is leading a series of 55 interviews capturing insights along the value chain from suppliers to fortune 500 companies, and the local state and federal agencies involved in manufacturing renewal.

 

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

 

You Say You Want a Revolution – A Fourth Industrial Revolution

The era of mass production is behind us. Ahead we are looking at a new era of manufacturing supporting mass customization and products sold as a service. Industry analysts and visionaries have identified this era as a new industrial revolution. We will dive into the developments leading to this current industrial revolution, but first, we will review the prior ones for some historical context.        

The First Industrial Revolution occurred between the late 18th century and early 19th century and it changed the way the world produced goods. Advances in materials, manufacturing processes, transportation and communications facilitated this industrial revolution.  Processes were developed to mass produce iron and steel to make everything from appliances, tools, and machines, to buildings and ships. The power loom revolutionized textiles and the assembly line revolutionized manufacturing. The steam engine enhanced transportation and the telegraph improved communications across the ocean.

The Second Industrial Revolution starts in the late 19th century, continues into the early 20th century and is nicknamed the Electrical Revolution. It includes the development of the internal combustion engine, and advances in fuel, infrastructure, standardization and mass production through interchangeable parts technology. Infrastructure advances include large growth in railroad, gas, water, electricity, telegraph and telephone networks.  Governments step in and establish standards for a range of services that facilitate this industrial revolution 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 cost of machines and equipment.

The Third Industrial Revolution, called the Electronics or Internet Revolution, starts in the late 20th century. Electronic technology becomes widespread. From telephones, to television, to satellites, to computers—electronics are everywhere. Closed government and private networks give way to an open network called the World Wide Web (a.k.a. the Internet). Numerical Controlled (NC) machines and Programmable Logic Controllers (PLC) are developed for manufacturing automation. Personal computers (PCs) are adopted at the factory floor as part of automation and tracking systems based on spreadsheets, custom database applications, and the first wave of commercial manufacturing software including Computer-Aided Manufacturing (CAM) and Computer-Integrated Manufacturing (CIM). Lean Manufacturing practices, automation, and information systems lead to big manufacturing productivity improvements in this era. Distributed information networks loosely connect manufacturing enterprise software for procurement, inventory control, scheduling, operations management, and financial management. During the latter part of this revolution, the Internet is embraced for eCommerce applications and data exchange with suppliers via standards like EDI. Service Oriented Architecture (SOA) concepts are developed for integration among enterprise applications, but due to a lack of standardization, integration mechanisms do not reach the desired level of plug-and-play integration.

The Fourth Industrial Revolution, dubbed the Digital or Cyber-Physical Revolution, is starting now in the 21st century. Many industry analyst and consulting groups are forecasting this industrial revolution. IDC predicts that enterprises will invest $120 billion next year to connect operations, buildings, equipment and mobile devices, and that the new Smart Manufacturing IT platform (which they call the 3rd IT platform) will drive innovation and growth in the IT industry for the next twenty years. Gartner estimates that connected pieces of equipment in manufacturing will rise 30% every year to over 500 million by 2020.

This industrial revolution is fueled by the convergence of multiple game changing technologies including the proliferation of personal use smart phones, Internet-based services and home automation, and advances in manufacturing including 3D model-based engineering, 3D printing, robotics, mobile tablet computers, and cloud computing. Advances in computer and network speed and volume capabilities provide enhanced platforms for commerce, industrial and social exchanges. “Smart” devices with huge computing power and Internet connectivity are not confined to personal use, they are starting to invade the manufacturing shop floor. Cyber-physical systems are digital representations of physical systems and are used to communicate status and properties of the physical system to other cyber-physical systems and applications in the Smart Factory. This revolution enables new business models for manufacturing shifting from mass production to mass customization of products, and selling of more products as an annual service versus the traditional buy-it-once sale.

The Fourth Industrial Revolution is also the “Smart Manufacturing” revolution. The new Smart Factory is a combination of smart facilities, machines and equipment with built-in sensors, self-diagnostics and connection to manufacturing systems. Smart Manufacturing goes beyond smart machines, IIoT and the Smart Factory, recognizing that manufacturing processes in the 21st century go beyond the plant floor and must integrate the entire value chain that creates the final product. Smarter digital threads of product and process definitions, and smarter connected manufacturing machines need to come together with smarter manufacturing business processes to achieve the Smarter Manufacturing enterprise. Production processes in the smart factory can be optimized for use of manpower, equipment and energy resources through simulation with digital representations and models. Smart Manufacturing brings under its umbrella multiple government and industry initiatives including IoT, Digital Manufacturing, Model-Based Enterprise and Industrie 4.0, and will need hardware and software vendors to embrace new standards for integration to enable a new level of productivity and plug-and-play among manufacturing equipment, facilities, and enterprise systems. Governments and industry have created initiatives to accelerate the needed standardization.

Examples of current government investments and initiatives in manufacturing technologies and standardization include “Industrie 4.0” (www.bmbf.de/en/19955.php) in Germany, DMDII (dmdii.uilabs.org) in the United States, and “Industry of the Future”  (www.economie.gouv.fr/files/files/PDF/pk_industry-of-future.pdf) in France. Industry is also taking on its own initiatives to establish standards in groups like the Industrial Internet Consortium (www.industrialinternetconsortium.org), the Smart Manufacturing Leadership Coalition (www.smartmanufacturingcoalition.org), and the Manufacturing Enterprise Systems Association (www.mesa.org).

The current industrial revolution has ramifications for manufacturing strategy and systems governance.  Available data from connected smart devices will be driving step changes in data processing and analytical capabilities. Operational efficiencies will be improved by the automation of decision and direct integration of operations to the value chain extending from suppliers to end customers.  People, systems, assets are now able to communicate and collaborate with each other in ways that were previously not possible.  Smart Manufacturing enabled systems will transform businesses, enabling them to shift from capacity to capability focus, and further boost productivity, improve quality, and bring products and services to market faster.

References:

[1] “The Future of Manufacturing,” Pierfrancesco Manenti, Lorenzo Veronesi and William Lee, IDC Manufacturing Insights, 2014

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

[3] “Industrial Internet: Pushing the Boundaries of Minds and Machines,” Peter Evans and Marco Annunziata, General Electric White Paper, 2012

 

MOM Cures Three Legacy Ailments in Operations

Manufacturers are picking up the pace of new product introductions to satisfy the customer demand for products that incorporate the latest technology innovations. The need for faster innovation adoption into product lines fuels a need for more efficient change management procedures throughout the organization. Many are asking themselves, "Is it possible to handle higher levels of product and process change with small patches to old practices?" Probably not. Strategic level process improvements are needed to increase the capability of the organization to handle higher rates of product updates and new product introduction.

Engineers are using the latest CAD technology to make better, more accurate,  3D models faster leveraging reusable components. 3D models include more product and manufacturing information (PMI) for use downstream in Model-Based Manufacturing, to simulate and accelerate production process innovation, in Manufacturing Operations Management (MOM) to develop shop floor for work instructions, and create automated inspection and test procedures. So why are some organizations still working with 2D drawings, spreadsheets, and paper work instructions at the shop floor? We have found three ailments that hold back many organizations from significant improvement in their change management speed and we have one prescription that helps all three.

Significant strategic process re-engineering can only happen when organizations face head on the following ailments brought upon the organization by a legacy of paper-based practices.

Dependence on Manual Processes is Holding Manufacturers Back

With the technology available today, any manufacturing 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 many companies still depend on paper today. 

Even traditional Lean Manufacturing practitioners like Toyota are embracing information technology to raise the organization’s performance to a new level. Toyota US CIO, Tim Platt, 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, other organizations can change too and embrace automated Lean processes supported by information technology.

With a manufacturing operations management (MOM) system the organization moves into a completely paperless system for managing operations. Before MOM, operations would work on piles of paper work orders filled and assembled manually into massive build books. Many labor hours wasted on moving paper, organizing paper, fixing missing information and mistakes. When work orders, instructions and records are digital the paper handling waste is eliminated and processes are hugely streamlined. The mechanic does not have to search through paper or online drawings or books to find the information he needs. The online work instructions will have quick shortcuts and links to documents

Poorly Defined Business Processes are Hiding Inefficiency

It is very difficult to see opportunities for improvement without a good understanding of current business processes. The implementation of a MOM system forces the organization to a better understanding of the as-is and to-be business processes. Many times these processes are not documented and very few employees understand the overall value chain across the organization. Value stream mapping exercises across functional departments like engineering, production, and quality can be very eye opening. Departments are usually focused on their daily routines and do not spend much time thinking about how their work is affecting downstream activities.

Even if procedures are defined in a book it is often difficult to enforce policies at the shop floor. A MOM system installs a work, documentation and change management discipline on operations. It is much easier to implement best practices.

Inaccurate Delayed Information Leads to Sub-optimal Decisions

In order to make good decisions, management needs good data. It is important to know that products are delivered on time and bills are paid, but management also needs to know details about engineering and production to improve those processes. Data is needed beyond delivered product quality, inspection results and defect counts, details are needed about defect and cause classification to tackle the root cause of problems.

Without the proper data, it is difficult to do proper root-cause analysis and easy to always end up finding someone to blame for the problem instead of finding the true system weakness. In order to go beyond the typical blames of: (a) lack of training, (b) personnel carelessness, and (c) human error, organizations need data that will point to any of the potential resource causing problems including personnel, equipment, processes or product design. Information maintained manually in different departmental databases or spreadsheets (information silos) is likely not consistent and possibly leading to different conclusions and priorities in different departments.

With MOM, real-time dashboards provide status of in-process work access to quality data on any work order with the single press of a button. Quality management can be more proactive, focusing on process improvement instead of focusing on manual documentation. There is complete visibility and accountability of who is performing each task, what resources are causing constraints, and the level of engineering changes impacting operations. MOM systems can also provide manufacturing intelligence and scorecards capabilities to monitor the organization’s achievement of performance goals and help quickly identify areas for improvement.

In Summary

Strategic investments in new information technology and new business processes can look like big cultural and budget challenges, but strategic investments can also lead to big contract wins, big margin gains, and the development of new agile, flexible and lean production operation.

A MOM system helps to eliminate some legacy ailments in operations by providing (a) a platform for agile paperless business processes, (b) a systematic control of manufacturing business practices, and (c) an accurate real-time information system for better performance and continuous improvement management.

References

[1] “Toyota’s Lean High-Tech Makeover”, a presentation by Tim Platt, VP Information Systems, Toyota US, May 2014  

 

Manufacturing’s Evolution to the Digital Business World is Well on Its Way

-  What I learned at the 2015 Gartner Supply Chain Conference

There is no denial that the manufacturing industry is in a period of evolution to support a new digital business world. New companies are popping up with new business models, some companies will slowly be left behind, and a few companies will figure out how to transform and adapt to take advantage of the new markets and business opportunities. Companies that do not evolve will not be able to compete against new business models like mass customization and product-as-a-service. Examples of companies on this evolutionary path were presented at this year’s Gartner Supply Chain Conference in Phoenix, Arizona.

In this new digital manufacturing world, a Manufacturing Execution System (MES) is a requirement, not an option. An integrated MES system is needed for a digital thread into the plant floor and the supply chain in order to streamline and automate procedures to support use cases like single unit work orders where each order in the sequence builds a different custom product configuration. MES users are reporting more than cost and quality improvements. They are experiencing improvement in employee engagement and flexibility.

 The Industrial Internet of Things (IIoT) is real, not only within plants but also into the supply chain. Gartner is predicting 30% more connected devices every year with more open standards. Current IIoT examples are merely providing a glimpse into the future potential of the Smart Factory. The roadmap for the Internet of Everything (IoE) in the connected supply chain includes increased visibility, proactive issue resolution, enhanced customer experience, data anytime and anywhere, and multi-tier value chain collaboration. 

 

The complexity of mass customization requires tighter integration of Product Lifecycle Management (PLM), MES and Supply Chain Management (SCM) systems. Starting with product design and manufacturing simulations, taking advantage of 3D printing and robotics and smart manufacturing machines and processes that can setup themselves to each product’s custom configuration. The factory of the future will be focused on capability rather than capacity. The next level of integration in the digital thread will require more CAD vendor openness and support for standards like ISO10303.

Many examples were given showing great strides in the connected supply chain for some industries for demand sensing and inventory visibility, but Quality is currently a missing link in supply chain management. There is a great opportunity to improve the digital thread for managing quality, not just for transmitting requirements and data collection but also for handling issues and corrective actions more efficiently in a multi-tier supply chain network.

Companies that want to transform will need more “Mode 2” versus “Mode 1” type of business investments.

* Mode 1: Reduce cost, increase capacity and utilization, plan driven, setup for batch production, proprietary integrations
* Mode 2: Focused on agility, capability, demand driven, order qty 1, integration of IT and OT  

 

 

 

 Organizations must appoint change champions and start working on the following types of initiatives:

• Transforming the workforce to embrace learning new skills, flexible work skills and performance transparency across departments and divisions of the organization and partner network
• Moving from custom and proprietary integration to open standards to support the Industrial Internet of Things and Smart Machines
• Convergence of IT and OT (Operations Technology) integrating plant floor, enterprise systems, mobile, cloud and social technologies
• Smart products that can store and exchange information with smart machines and smart manufacturing processes about their configuration and state
• Taking business and manufacturing intelligence to the next level developing predictive and prescriptive analytic capabilities

If you have access to the Gartner presentations, the following is a list of my favorite presentations from the conference:

• “Operational Strategies for a Digital Business World,” Dwight Klappich, William McNeil “Product Innovation Platforms: Managing Complex Product Life Cycles,” Marc Halpern
• “The Emperor’s New Clothes and the Factory of the Future,” Simon Jacobson
• “How to Avoid the Pitfalls of MES Implementations,” Rick Franzosa
• “Quality: The Missing Link in Your Supply Chain Strategy”, Simon Jacobson, Gartner SCC 2015 Conference Presentation
• “Supplier Quality 2017: The Multitier Quality Frontier,” Ray Barger
• “Leading Supply Chain in a Digital Business,” Mike Burkett
• “Internet of Things in Supply Chain: What’s Real Now?” Mike Burkett