Focusing on purpose, practice, pursuit, and perfection can help organizations shape and deliver their future factory vision. By Sath Rao
The factory of the future will bring transformational changes and opportunities for those who are prepared for the intelligent manufacturing journey. Success will be tied to the ability to navigate unchartered territory, where outcomes and customer experiences will be the true north, and new technologies will be key enablers to manage business model changes and complexity. The real change we need will be in the new approaches to integrating humans, machines and data for greater innovation.
In a previous article for the Manufacturing Leadership Journal, “Vision 2030: A Framework for Future Factories”, I focused primarily on the convergence of trends and technologies, highlighting innovations such as intelligent design, new services, cognitive manufacturing platforms, and human-to-machine convergence.1 Ambient intelligence, including advances in cloud solutions and ubiquitous connectivity, are creating new insights and solving problems on an exponential scale. At the same time, business model innovations are setting the stage for new possibilities for creating, capturing, and disseminating value for products and services, all enhancing the customer experience. These trends are reshaping manufacturing in new and exciting ways. What’s important is to be aware that the mega forces are converging, and this convergence will necessitate a reevaluation of our approaches to preparing for the future.
Looking ahead at what’s next and what we must do now, four essential approaches are needed to shape and realize that factories of the future vision: purpose, practice, pursuit and perfection.
An organization’s purpose is its reason for existence. The forces of Manufacturing 4.0 are redefining industry boundaries, and there is a need to reevaluate original intent and establish a version that recognizes the need to take bold bets or at least be willing to test options to overcome the innovator’s dilemma – potentially disruptive ideas versus focusing on today’s customer needs. Purpose distills the essence of what an organization wants to accomplish, and the values that it will espouse to achieve the end goal. Although technology can play an important role in helping accelerate transformation, redefining the purpose first (that is, revisiting it in the context of Manufacturing 4.0) is fundamental to success and supporting an organization’s philosophy, culture and people, and how they relate to the changing business landscape.
“The forces of Manufacturing 4.0 are redefining industry boundaries.”
According to media reports, in 2009, Ford was sticking to its product vision, developing higher fuel economy, more small vehicles, and a full range of cars, crossovers and trucks. The consistency-of-purpose mantra preached by the CEO at the time was taking root and was welcomed by analysts as being “in the right direction”, given the significant amount of debt Ford had taken on. Fast-forward to 2019, Ford has evolved into two distinct organizational divisions: the automaker’s global legacy business, designing, building and selling trucks, SUVs and cars; and the new group developing automated vehicles, mobility services, and other interests including a transportation insights platform for cities. The legacy business generates the cash that is now being consumed by this new mobility business.
Revisiting purpose, pivoting, and the ability to drive data agility to support business agility make up the success ingredients for the factory of the future. Tesla experiments and pivots with advanced robotics and rapid scale-up, supported by human insights and finesse, to drive a better finished product and record deliveries, while Ford has been retooling for a pivot into the all-electric and smart mobility future.
Organizations should consider the following three points when they revisit their purpose. These should be considered as defining transformation imperatives.
Create a Long-Term Philosophy
In his 1982 book, Out of the Crisis, renowned professor, engineer, and management consultant Dr. W. Edwards Deming offered 14 key principles for management to follow to significantly improve the effectiveness of a business or organization. His first point focuses on creating a constancy of purpose toward the improvement of product and service, with the aim to become competitive, and to stay in business, and to provide jobs. 2
“The establishment of constancy of purpose means acceptance of obligations like the following,” says Deming. “Innovate: allocate resources for long-term planning. Put resources into research and education. And constantly improve design of product and service. This obligation never ceases.” 3
While his first point is about the power of purpose, Deming is careful to state that his points are all interrelated and part of a system. For most organizations, achieving a profit is part of that mission, but as the definition suggests, long-term success requires more than profit. It requires profit with purpose. For example, some organizations have incorporated social and environmental benefits into their mission, such as developing and implementing technologies to help society.
Invest in People
An organization that has clearly defined its purpose can remain focused on continuous improvement and innovation but can also look beyond profitability alone to drive retooling, reskilling and other long-term imperatives. Collective private-public partnerships and investments will be critical as industry looks to hire the next wave of human capital.
In manufacturing today, there are more than half a million open jobs. According to a study by the Manufacturing Institute and Deloitte, 2.4 million jobs could go unfilled over the next decade, leading to about $2.5 trillion in GDP that could be at risk.4 A deficit of this magnitude will certainly affect nearly every industry. To address this risk, organizations must define a constancy of purpose when it comes to human-capital that looks beyond short-term tactical imperatives. They need to provide a meaningful opportunity for the next generation to join the organization and continue to learn and develop. In The Toyota Way, author Jeffrey K. Liker emphasizes the importance of investing in people, by respecting them, challenging them and fostering their professional growth.
Invest in the Ecosystem
Just as it is important to invest in people, it is also imperative for manufacturers to look beyond individuals and invest in the partner and supplier ecosystem so that they are contributing to their success and improve their business agility.
Rather than pressuring business partners for profits and reducing their margins, an organization can share what it has learned, focusing on problem-solving, continuous improvement and education. Open innovation platforms help pull innovation from across geo-boundaries and adjacent industries.
Manufacturing practice is rapidly evolving, as organizations reimagine the relationships between people and machines and the value they can gain from data. As artificial intelligence (AI) and analytics gain maturity, manufacturers gain opportunities to build on new insights that can power outcomes that were impossible just a few years ago. Here are three critical issues to consider regarding practice from a future factories’ perspective.
Embrace a Paradigm Shift
Factories have historically been built around human labor and mechanical technology. In the 20th century, factories began enlisting robotics to automate and drive productivity gains, freeing up humans for other tasks. Those early factory modernization initiatives often focused on human-to-machine interfaces, and the know-how to get manufacturing right.
In the factory of the future, new practices will add data to the human-machine equation. These practices will be built not only on technology, but on understanding how to make everything work together. Economies of scale will transition to economies of learning, with an increased focus on humans, machines, the data that is generated during the process, and its ultimate use. In this new paradigm, knowing how to do something will be important, but more important will be to learn what new approaches can drive transformative change.
Tame the Data Tsunami
In our increasingly connected economy, a tremendous volume of data is being generated. Yet, surprisingly, only a small portion of that data is being used. According to the World Economic Forum, approximately 70% of the captured production data that is generated goes unused.5 Manufacturers need new practices that enable them to manage exponential data growth, learn from that data and derive new insights from it. In other words, although the data being generated has incredible potential for transforming the business, until it is accessible, understood, and applied to business outcomes, its potential will remain untapped.
Manufacturing transformation also requires an evolution in thinking, from a risk-averse mindset and incremental innovation, to one of bold big bets. Although organizations must always consider short-term business concerns, they must also keep a constant watch on how disruptors are upending the rules within their industry. They must capture and use more of the data at their disposal to better understand their business and channel future energies toward the decisions that offer the most benefit for their organization.
Look Beyond the Data
Gaining insight based on data is fundamental to success. Taiichi Ohno, a Japanese industrial engineer known as the Father of the Toyota Production System (TPS), understood this concept well. Ohno developed the concept of the Ohno Circle, in which a member of a plant’s operations or process improvement team would be asked to stand in a chalk circle on the plant floor and narrate what they had observed. The individual’s only instruction before the work-shift was: “watch.” Hours later, Ohno would return and ask the individual to describe what they had observed.
Ohno realized that data, although valuable, will always be one step removed from an actual process. Truly understanding a process requires attention to details, close observation over time, and a genuine understanding of context. Without firsthand facts and context, data alone provides a limited view. As Ohno put it: “Data is of course important in manufacturing, but I place the greatest emphasis on facts.”6
Paying such detailed attention to manufacturing practices is therefore an essential step in setting the stage for transformative outcomes.
“Manufacturing is moving from a know-how approach, based on humans and machines with traditional quality control approaches, toward a know-what approach, building on humans, machines and data together.”
Pursuit is the next key tenet in realizing the factory of the future. Pursuit defines how organizations respond to changes in the marketplace as well as challenges within their own organizations and processes. The following three points can help organizations to pursue their vision for their factory of the future.
Adapting Fast with Flexibility
One important aspect of pursuit is a focus on flexibility, so organizations can adapt as needed for the disruptions of the future. Manufacturing systems need to be agile, focusing equally on both running and transforming the business.
People play a vital role in enabling this flexibility and business agility. Just as they do when considering purpose, organizations must think of people as long-term assets, continually investing in their skills. Unlike machines, which constantly depreciate as time passes, the value of people can appreciate if the organization invests in training them and helping them develop new skills. By doing so, the organization gains the flexibility to keep pace with challenges, which can change by the day.
Build Fixes into the Process
The Japanese manufacturing tradition of jidoka — fixing problems as they occur — is one of the primary pillars in the Toyota Way. Simply put, if an employee spots a problem, they shouldn’t overlook it as an isolated incident and continue to work as though nothing is wrong. Instead, the employee should intervene in the process to fix the problem immediately. Jidoka is about implementing a system in which errors do not perpetuate into all products because quality is built into the process.
The Toyota Way refers to the incorporation of fixes deep into the fabric of a system as poka-yoke — fool-proofing the system so that errors don’t replicate. In a broader sense, the term can also refer to any limits or constraints designed into a process to prevent incorrect operation by the user. Poka-yoke is a strong example of the importance of close integration between machines and humans, using human ingenuity to go a step beyond merely automating tasks.
Keep Humans in the Loop
As organizations pursue their new initiatives to respond to market changes and challenges, it’s important to maintain the human connection across the supply chain. Organizations that are pioneering novel solutions to new problems can be tempted to run after the latest cutting-edge technology, but that’s not necessarily the best plan.
For example, Tesla intended to rely on the latest technology to dramatically increase the production volume for battery packs. However, the technology wasn’t entirely ready, and production fell behind. Instead, human ingenuity was needed to fill the gap. The employees at the factory were able to step in and figure out how to bridge the gap until the technology was in place. 7
“Unlike machines, which constantly depreciate as time passes, the value of people can appreciate if the organization invests in training them and helping them develop new skills.”
Microsoft CEO Satya Nadella has also discussed the application of computer-vision customizations on an assembly line to detect defects.8 Nadella emphasized that it requires a tool chain, a combination of edge-to-cloud technologies, to enrich data and convert that technology into an effective business tool. The customization of generic artificial intelligence APIs such as these to help with competitive differentiation, will become critical to success. Ultimately, the democratization of AI is about making insights available to enable humans and machines to work together for the greater good.
As James Wilson, author of Human + Machine9, noted: “We find in our research that companies that focus on human and machine collaboration create outcomes that are two to more than six times better than those that focus on machine or human alone.”
Attaining perfection is an evolving process. Manufacturing is moving from a know-how approach, based on humans and machines with traditional quality control approaches, toward a know-what approach, building on humans, machines and data together. The new paradigm is based on continuous improvement plus a series of transformations that occur when humans, machines and data integrate to continuously perfect the manufacturing environment. Here are three things to consider.
Gain Insights to Add New Value
The Go to Gemba (workplace) approach has become a tenet of lean transformation. This approach is all about following the action, understanding that value is created where things actually happen, and advising leaders to go there to observe and learn firsthand.
Ohno referred to this as genchi genbutsu, or “real location, real thing.” While data is important, it remains one step removed from the process and can at best be an indicator of what is going on out on the shop floor. Ohno advised readers to go there with an open mind, observe, and determine the actual facts.
Insights are also gained from the collection and analysis of data from industrial IoT (IIoT). In the evolving product-as-a-service continuum, the definition of Gemba should include the data from the operational phase. Product life cycle management will include creating digital twins of the product, impacting outcomes and customer experiences, and learning from the treasure trove of data for product and service improvements. In other words, an innovative manufacturer like Tesla is not only focused on making electric vehicles, but also on collecting and adding value to the data generated from autonomous vehicles and applying learnings to improve the user experience and the next generation of the product.
“AI-enabled intelligent insights and edge-to-cloud data agility are
instrumental to realizing the vision of the factory of the future.”
Manufacturers have grappled with islands of automation and traditional monolithic applications as systems of record. IIoT and advanced data analytics will provide the missing intelligent insights that can power data-driven agility. AI-enabled intelligent insights and edge-to-cloud data agility are instrumental to realizing the vision of the factory of the future.
Reveal the Big Picture
Without visibility across the manufacturing environment, it is possible to focus on the wrong problems or to miss opportunities to identify the right problem. In The Case of the Missing Insights,10 I emphasized the need to solve problems, deriving value from data using AI and machine learning. Sometimes machine learning changes the paradigm and presents new insights.By moving one step beyond the focus on continual improvements to solving problems through human-and-machine integration, new opportunities can reveal themselves. A smart analytics architecture is foundational to providing this visibility.
A smart analytics architecture helps manufacturers make sense of improvement opportunities and, based on organizational transformation priorities, it helps them prioritize investments and avoid the need to rip and replace major automation and IT systems. Use cases can be prioritized into a portfolio for a multiyear roadmap that aligns with the run and transform cycle, which in turn will help side-step proof-of-concept (PoC) purgatory. The factory of the future does not have to be a greenfield endeavor.
Create a Roadmap for Intelligent Manufacturing
Intelligent manufacturing is not a destination, but a continuous journey. Not all companies are going to invest in new plants. Instead, these organizations need a strategy that enables them to take the old with the new, while remaining in a position to benefit from transformative technologies.
Technology is still a key building block, but softer issues will be at the core of initiatives: humans and machines working together, collaborating, and leveraging digital kaizen. Kaizen is all about good change, and the digital approach is creating good change, and fast. Digital kaizen is based on the right data being available, at the right time and in the right place, a concept known today as DataOps. Perfection comes from doing and taking the first step, and that first step is developing a data agility strategy that focuses on return-on-data.
Although it is important to consider new technologies, organizations also need the ability to take old architectures and modernize them. Data agility has to be central to competing in an evolving marketplace that places a high premium on innovation.
The Path to the Future
Ultimately, the factory of the future is about the journey. This means developing new approaches to integrate humans, machines and data while implementing the four principles of purpose, practice, pursuit and perfection to help smooth the path. The following three steps may help your organization to get started:
First: develop a use case roadmap, with a focus on DataOps, selecting the right set of use cases between the run and transform cycles.
Second: as data increases, organizations need to be in a position to draw inferences. An AI-enabled smart analytics architecture enables data agility, from the edge to the cloud, with machine learning and AI as core enablers.
Third: focus on a digital approach to lean manufacturing, building on the convergence of man, material, machine and method. The WEF’s Lighthouse manufacturers are already resetting industry benchmarks, according to McKinsey. Its analysis suggests that front-runners in AI adoption can anticipate a cumulative 122% cash flow change, while followers will see a significantly lower impact of only 10% cash flow change. 11
Organizations can use this approach to help drive business and manufacturing agility. The 4Ps then become key touchpoints to help companies realize their vision of what future factories can become in manufacturing’s digital era. M
1 Sath Rao, “Vision 2030, A Framework for Future Factories, Manufacturing Leadership Journal, February 2017, http://www.mljournal-digital.com/meleadershipjournal/
3 W. Edwards Deming, Out of the Crisis, The MIT Press, 1982.
5 “Technology and Innovation for the Future of Production: Accelerating Value Creation,” World Economic Forum, 2017.
6 Samuel Obara, Darril Wilburn Toyota By Toyota: Reflections from the Inside Leaders on the Techniques That Revolutionized the Industry (CRC Press, 2012)
10 Sath Rao, “The Case of the Missing Insights,” ML Journal Oct, 2019 https://www.manufacturingleadershipcouncil.com/2019/10/04/the-case-of-the-missing-insights/
11 Enno de Boer, Helena Leurent, Adrian Widmer, “‘Lighthouse’ manufacturers lead the way—can the rest of the world keep up?”, McKinsey & Company, 2019.