Filling the Worker Gap for Manufacturing Careers

A lot of people talk about how new technological advancements in automation, 3D printing, and AI are going to improve manufacturing but lead to a new kind of worker being required to fill manufacturing jobs. However, few people address where this “new kind of worker” is going to come from. According to the article “For the Manufacturing Industry, School is in Session” by Adina Solomon, there is an expected need for 3.5 million manufacturing workers in 2025 but only 1.5 million of those jobs are projected to be filled because of an anticipated proficiency gap in the labor market of about 2 million jobs. Below are examples of how two groups of people are working today to try and address this problem and prepare the next generation to fill these new manufacturing roles that are much different from those of the past.

In Indiana, Seymour High School has started a business called OWL Manufacturing (based on the school’s mascot) which is run by students at the school. Students working at the business elect to take this as a course and spend their time working in a manufacturing environment for school credit. The purpose of the business was to give students a hands-on learning environment where they can learn about how the manufacturing jobs of today are different from the days of their parents, teach them valuable skills to be used in a manufacturing role, and build excitement for a career in manufacturing. Since its launch in 2016, they have gone from 17 to 43 students working in all sorts of roles and many of the graduates who went through this program either went straight into a manufacturing role or are attending secondary schooling with the intent of getting a career in manufacturing. While this program is currently unique to the state it would not be surprising to see more pop-up. This is because the Governor of Indiana, Eric Holcomb, signed an executive order in 2017 to create the Office of Work-Based Learning and Apprenticeship whose goal is to help support and create programs like OWL Manufacturing to educate and raise interest for the manufacturing jobs of the future.

More information on OWL Manufacturing can be found here:

More information on the Office of Work-Based Learning and Apprenticeship can be found here:

In North Carolina, businesses have been stepping up to the plate instead of schools. A consortium of companies from the state have come together to form the Guilford Apprenticeship Partners (GAP). The goal of GAP is to recruit high school students into an apprenticeship while they are in high school and then, once the students graduate, provide them tuition at a local two-year college where they can get their associates degree while working full-time in a manufacturing environment. Throughout the entire four year program the student works in a manufacturing environment giving them hands-on experience in the career for which they are getting an education to pursue. The other primary purpose of GAP is to educate current students and their parents about how jobs in manufacturing are changing in order to counteract some of the stereotypes that manufacturing jobs are “dirty and physically difficult”. The other benefit of this program is that it offers students who want a career in manufacturing a cheaper route than completing a four-year degree and accumulating student loan debt.

More information on GAP can be found here:





  1. What are other states doing to try and tackle this issue? Are there other programs similar to these out there right now?
  2. Will these types of programs be enough to cover the 2 million job gap that is currently expected? Are these programs and ones like it making a significant enough impact?

How will apprenticeship programs like GAP change the way younger generations view secondary education? Will we see less enrollment in the standard four-year degree and an increase in trade schools or associate degrees?

How will manufacturing progress in 2019?

As manufacturers are continuing to run their operations as lean and efficient as possible technology is continuing to drive change industry. Decision Analyst, on behalf of IQMS, conducted a survey of 151 North American Manufacturers about technologies that they are using to transform their operations. Louis Columbus wrote about the results in his article “Ten Manufacturing Technology Predications for 2019” where he summarizes what the key technological advancements will be that transform manufacturing as we enter the New Year.

  1. More attainable lights-out production courtesy of affordable Smart Machines that are able to run unattended for two or more shifts.
  2. Real-time monitoring with Wi-Fi enabled shop floors and IoT enabled smart machines to improve scheduling accuracy, inventory control, plan performance, and greater flexibility in managing production lines.
  3. Greater adoption of analytics and BI to capitalize on data streams and improve capacity through better resource planning and scale their businesses.
  4. Mobile ERP and quality management applications will become mainstream thanks to advances in integration, usability and high-speed cellular networks and help companies improve data accuracy and operational efficiencies and reduce operational delays.
  5. Digitally-driven transformation with a customer focus by utilizing the above to offer short-notice production runs and achieve greater supplier collaboration.
  6. Replace old legacy machines with cheaper smart machines helping small and mid-tier manufacturers pursue new digital business models.
  7. There will be a major shift to fast-tracking of smart, connected products to avoid price wars and premature commoditization so that within two-years at least two –thirds of product portfolios will be connected thanks to IoT and other technological innovations.
  8. Spreading of the security perimeter thanks to a proliferation of IoT endpoints and an increasing amount of threats to operations from new sources.
  9. Utilizing IIoT to increase productivity by helping improve the inconsistent, inflexible legacy data structures form the shop floor to the top floor.
  10. Greater revenue streams from those manufacturers who were early adopters of IoT will widen the gap between those who adopted IoT early and those who did not.



  1. What will happen to manufacturers who don’t embrace these changes? Will they be able to catch up or will they soon become irrelevant?
  2. What will be the major challenges faced by manufacturers who try to adopt these changes in their operations? How quickly will they see the results from these changes?
  3. Looking beyond 2019, how will the manufacturing space continue to grow as newer technologies come out?


Improving Food Manufacturing

When it comes to increasing profits in the food manufacturing industry the name of the game is efficiency. This means finding ways to cut costs and improving operations while maintaining good quality so that margins can improve and facilities can produce more. The article “Six tips for Improving manufacturing efficiency” by Megan Ray Nichols outlines six different ways that companies can improve their facilities to become more efficient and see increased profits. These tips revolve around three main ideas: mitigating risks, reducing operating costs through more conservative operations, and embracing the use of technology in facilities. By following these six simple tips, a food or beverage operation can reduce costs, increase productivity, and ultimately be a more profitable operation.

Her fist tip is to embrace the inherent risks of a food manufacturing operation and plan for them. Ready your plant for the risks by investing in additional backup generators and control systems as well as business loss insurance to mitigate the risk and cost of lost inventory in the event of a mishap. Along with this, reduce contamination risks to prevent recalls from happening. Recalls waste time, harm productivity, and most importantly are extremely expensive. Be proactive and do everything possible to prevent contamination in the plant.  Second, become more energy efficient. Nearly 60% of food manufacturer’s energy bill comes from refrigeration. Therefore one of the easiest ways to lower cost is to make these units more efficient which can be done by simply placing them in as far away from heat-generating equipment and avoiding the use of incandescent light bulbs, both of which force cooling units to work harder. Along with becoming more energy efficient, conserve water. The food manufacturing industry uses more water than most other industries. Water recycling programs, reuse systems, and flow restrictions can significantly decrease operating costs and provide savings which can be reinvested elsewhere to improve production efficiency. Finally, take advantage of new technological advancements by embracing preventative maintenance and increase the use of automation and integration. Use preventative maintenance to track and plan when equipment needs to be fixed. This proactive approach can save time and money, as the beer brewer New Belgium demonstrated when it was able to cut downtime by 50% when implanting a preventative maintenance system. Using automation and integrating your processes so that they can “talk to one-another” allows your system to run more efficiently because each section can instantly react to what happens in another.

Questions to think about:

  1. Besides moving refrigerators and installing less light bulbs, how else can food manufactures reduce their energy usage to cut costs?
  2. Are there any alternatives to food storage that can reduce the risks of losing large amounts of inventory in the event of a power failure?
  3. Do food manufacturers really need to maintain high inventory levels or is there a better way to plan production and delivery so that less inventory can be held at facilities?


Process Mining – Big Data Working for Manufacturers

In the article Why Manufacturers Need Process Mining – A New Type of Data Analytics, the author is extolling the benefits of what he calls a new type of data analytics – process mining.  Process mining can be used to reduce inventory costs, identify production bottlenecks, improve on-time delivery, and optimize logistics between production sites, distribution centers, and end clients.  It’s hard to justify  that process mining is actually a new type of data analytics that can be used for manufacturers, but process mining does follow the a few of the key rules for successful big data usage.  First, as the name implies, it concentrates on one process.  Implementation of successful big data projects normally requires concentration on one area for improvement.  The specificity of big data projects usually allows them to be successful, and the article looks at why process mining, and big data analytics on the whole, can be successful in saving manufacturers money.

Process mining specifically looks at a very important factor in manufacturer’s processes, KPIs or key performance indicators.  KPIs are exactly what they sound like, the main factors that measure performance and the overall successfulness of process or project.  Process mining’s value lies in the fact that it initially makes one look at KPIs of a process.  It also challenges the manufacturer to validate if they are measuring the right KPIs and understand if the data they are gathering can be related back to KPIs.  Process mining, as all big data does, uses software do the difficult work of visualizing the processes and highlighting specific variances impacting KPIs.  One example used in the article is the examination of throughput times and the ability to identify that specific vendors are not meeting their lead-time commitments.  These types of analyses and results is exactly what big data projects are meant to do and achieve.

Another point that the article points out is that process mining encourages manufacturers to identify inefficiencies and problems within the process.  It encourages companies to embrace their issues.  This ideology is absolutely necessary for continuous improvement, and it is a key to any big data project.  Embracing issues can be difficult, especially for older, engrained processes, but it is the only way to eliminate them.  It certainly is not the easiest thing to do, and requires a humble mindset entering an improvement project.  Process mining has all the key factors of a successful big data software, and it could be very useful for manufacturers that want to embrace the big data revolution.


Do you believe addressing KPIs first is the best way to approach a big data project?

Do you think process mining is actually a new or different type of big data analytics or just a rebranding of basic big data?

Do you think that some manufacturers are reluctant to implement big data projects because they do not want to know their inefficiencies?

Looking for the Signal in the Noise

In the article A Signal in the Noise: How to Best Manage Big Data, the author advocates some ways to improve the use of data for manufacturers as well as his idea for the best way to approach the use of big data analytics and data management.  The author equivocates big data as sometimes “a needle in the haystack” when it comes to finding and using it in the manufacturing environment.  With so much data produced by even small firms, the management, organization, and analyzation of the data can become overwhelming if not impossible.  Industrial Internet of Things-based systems are estimated to create $4 trillion to $11 trillion in new economic value for manufacturers by 2025, according to McKinsey Global Institute.  With such large value to be had, ignoring or underutilizing data could be a catastrophic mistake for manufacturers.

In a study of manufacturers conducted by the MPI Group, 76% of respondents reported plans to increase their use of smart devices in the next two years while 66% plan to increase their investment in IIoT-enabled products over the same time period.  With many firms taking the necessary steps to collect and use data, a strategic approach is necessary.  The author suggests a number of steps to jump-start the transformation to using data analytics.  Recognizing human limits and the burden of isolation is the first suggested step.  Here the author is advocating that firms understand that human teams are just not capable getting the insights that technology is capable of.  The next step is forgetting the traditional supply chain cycle, and embrace the complexity of modern supply chains.  With the IIoT, data can be captured all along the supply chain which can offer useful insights not previously possible.

Finally, the author advocates understanding four different measures before taking on new data management and analytics initiatives.  Find the actual problem, the “signal in the noise” is the first and foremost issue at hand.  Unless there is direction to the project, the project will almost certainly fail.  Next is understanding the business case.  One has to understand the strategic advantage behind any data management implementation.  Lastly, finding where the optimization is most desperately needed and identifying the experts that are best suited to handle the data being generated is key.  Overall, the biggest pitfall in bringing on new technology is the belief that good things will just happen.  An understanding of the problem, the right people, and a tailored process will allow the technology to do the work it is supposed to.

Do you believe that understanding big data is really about finding the proverbial needle in a haystack?

Do you believe the increase in smart device usage automatically translates to more useful big data, or just more data in general?

As with most strategic IT plays, projects start off to gain a strategic advantage but quickly become part of the IT infrastructure.  Do you think we are in the middle stage between big data being a strategic advantage and a necessary IT infrastructure requirement for manufacturers?

McKinsey Study Shows Big Data is Affecting Numerous Industries Including Manufacturing

In the article How McKinsey’s 2016 Analytics Study Defines the Future of Machine Learning, the investigates a McKinsey study released in January of this year that shows how far big data and analytics have come in the previous 5 years as well as what to expect in the very near future.  The main insights gleaned show that the use of big data is growing in numerous industries, and that the key realizing the value of big data is integration and adopting from the top down.  For manufacturing specifically, machine learning leading to preventative maintenance and predictive analytics is one of the most powerful advantages that has emerged.  Unfortunately, just about every industry examined in the paper has still has not captured more than 50% of the potential value from data and analytics.

The original paper released by McKinsey released in 2011 speculated that manufacturing would find the greatest potential value in data and analytics from possible drops of 50% in product development cost, 25% lower operating cost, and 30% gross margin increases.  As of 2017, the new McKinsey paper shows that manufacturing has only captured 20-30% of this total value with the main issues being siloed data in legacy IT systems along with skeptical and sometimes unsupportive leadership.  With regard to one of big data’s most powerful uses, machine learning, manufacturing stands to realize significant value from machine learning.  In the areas of real-time optimization, predictive maintenance, and forecasting, there is definitely some value to be had in regard to machine learning.  In the areas of strategic optimization, predictive analytics, and understanding data trends, there is substantial value to be had.

Design-to-value, supply chain management and after-sales support are three areas where analytics are making a financial contribution in manufacturing.  Unfortunately, manufacturing as a whole has lagged in adoption with a few advanced players capturing a significant amount of the value.  Digging deeper to see exactly where the money has been saved, analytics has benefited the supply chain and production aspects of manufacturers, but only the small groups that have adopted the technology.  McKinsey estimates that only 5-10% of manufacturers have used analytics for financial savings in the supply chain and production aspects.  In the supply chain, the cost savings has come in the form of 10-15% reduction in product cost, and in production side, the cost savings has come in the form of 10-15% reduction in operating costs.  Overall, the main hindrances to more big data implementation is senior management involvement and designing and implementing the appropriate organization structure to support data and analytics.

Do you believe big data analytics adoption accelerate over the coming years?

Do you believe the results of the study are completely accurate?

How far away are we from a time where if manufacturers are not using analytics, they will not be able to compete in the market?

Manufacturing Recognizes the value in Big Data Analytics, Lack Implementation

In the article Big data Analytics is Transforming Manufacturing, the author reviews a study done by Alan Turing Institute and Warwick Analytics regarding how big data analytics is finding its way into manufacturing.  The main points of the study indicate that many manufactures understand the usefulness of big data analytics, but not many are actually not using big data to its full potential.  The study indicates 7 key points where big data could offer benefits to manufacturers, and it also reveals some interesting findings.

The study itself gleaned its findings from views of 50 senior industrial executives, most of whom are with global manufacturing companies in the UK.  Its noted that the greatest value of big data analytics is in its predictive powers, giving companies the ability to extract meaningful insights on processes, production, and maintenance, to name a few of the important manufacturing functions.  Three of the seven key areas addressed in the study were improving yield, predicating and/or preventing maintenance, and improving supply chain operations – all areas where manufacturers could extract value through big data analytics.

The findings of the study paint an interesting picture of big data at this current point in time.  Just over 40% of businesses are in the experimental stages of using big data analytics, but surprisingly, 50% didn’t know the difference between big data and previous business intelligence tools.  With a knowledge gap present, it’s not surprising that more widespread adoption of big data analytics isn’t happening.  Without an understanding of how big data will improve a corporation, it’s hard to get big data programs sponsored and rolled out.  Though these issues are certainly present, there are few absolutes.  One of them is that the data is being collected, in huge amounts, and that this data is normally being used to some extent.  The issue lies in that the proper analytics are not begin done so the possible benefits are not being realized.


What will it take for more manufacturers sponsor big data programs?

What are the differences between big data analytics and previous business intelligence?

A reoccurring question seems to be – are we collecting too much data?  If so, how do we begin to collect only the pertinent, necessary data?