What is the factory of the future?
Daniel J. Power
Ciara Heavin and Shashidhar Kaparthi
The "Factory of the Future" (FoF) is a vision of automated production. In some descriptions, raw materials would arrive at the input end of an autonomous production machine and then at the output end a fully operational car, truck, TV, or toaster would appear with no use of human labor ready for delivery. It seems plausible that in the near future, smart industrial robots with a variety of sensors, effectors, payload capacities and capabilities will produce much of what is consumed. The availability of a constant stream of data as a result of sensors monitored by sophisticated Artificial Intelligence (AI) is central to the future factory. Tomorrow's factory will be interconnected, data-driven, and adaptive. Creating a smart factory requires vision, patience, experimentation, and technology progress.
Fast-changing disruptive technologies and new business models, an aging population, safety concerns, and sustainability issues are transforming manufacturing. Both information technology and production technology are experiencing disruptive innovations. The convergence of Artificial Intelligence (AI) and manufacturing innovation is creating a paradigm shift. Smart automation is a vision for how manufacturers can and should enhance production by making improvements in three dimensions: 1) plant organization, management, and structure, 2) plant digitization, and 3) plant processes (Boston Consulting Group, 2016). The focused factory of Skinner (1974) must be designed using Intelligent Automation to rapidly adapt. An extensive network of partners including R&D collaborations with universities and research institutes is also required.
Disruptive innovation in product design is enabling manufacturing automation by simplifying manufacturing engineering. Product digitization is replacing hardware with software and making manufacturing easier by reducing mechanical content. For example, the amount of software-defined functionality is much higher in an Apple Watch than in a Rolex watch. Automation of production processes for products with a higher degree of software-defined functionality is easier. Digitization over time has increased software-defined functionality. Digitization and increases in software-defined functionality have disrupted imaging, music, and video products. More recently, software-defined functionality is on the increase in automobiles and is impacting automation in this industry.
Let’s briefly examine two companies, Amazon and Tesla, on the leading edge of intelligent automation. Amazon has more than 100,000 robots in its warehouses. Also, Amazon has installed machines in several of its US warehouses that scan and box items to be sent to customers. At Amazon robotic automation has taken over certain duties, such as carrying pods of inventory and transporting pallets through buildings. Amazon is providing warehouse workers with utility belts that ward off robots.
Tesla's factory in Fremont, California is one of the world's most advanced automotive plants. Elon Musk, Tesla CEO, attempted to build an intelligent, fully automated factory, but limitations with vision systems required reintroduction of human workers on the assembly line. Supposedly that problem has been corrected. The Tesla assembly line is built with a combination of AI software and automation. The manufacturing process for the Tesla Model S uses more than 160 specialist robots. Intelligent automation systems in the Tesla factory sense and produce very large amounts of data used to automate entire processes, make decisions, and guide robots. The Tesla Fremont facility is pioneering automated, vertically integrated manufacturing.
Industry analysts and staff of technology vendors assert that intelligent automation (IA) and data and analytics (D&A) are transformative and strategically and tactically important for organizations. Both technologies, especially when coordinated, are likely to change how organizations operate and how people work. Intelligent Automation or Intelligent Process Automation (IPA) integrates AI, machine learning, natural language processing, and automation to redesign how business processes could work. There are real concerns that adopting IPA will result in job losses, problems with invasive bots, loss of privacy, and poor ROI because of technology obsolescence. However, many commentators assert that AI and IA are creating new opportunities to promote innovation and creativity among employees. For example, Davenport and Kirby (2015) refer to the need for employees to "Step Up" "There will always be jobs for people who are capable of more big-picture thinking and a higher level of abstraction than computers are" (p. 60).
In 1958, James Bright, a Harvard Professor of Business Administration, wrote a book titled Automation and Management. Bright investigated whether automation in manufacturing was creating new and different problems for management. Based upon field interviews, he concluded that automation resulted in a higher degree of dependency between elements of the production system (p. 225); that rapid transmission of data and information is essential; that automation implies modifications of a larger, more intricate machine so the cost and time required for changes in automated systems increases; that an automated production line is a major commitment to increased rigidity of a firm's capabilities; and that automation implies integration and consolidation of production facilities. He argued that inevitably integration and consolidation would lead to greater vulnerability and that it is a mistake to try to do everything automatically. He notes "There are many spots where a man is a more efficient, reliable, and cheaper solution to a manufacturing problem than an intricate mechanism." Amazon uses both automated systems and people in its warehouses. At Tesla, Musk still hopes to create a fully automated factory.
The possibilities for automation of both tangible goods and delivery of less tangible services have increased greatly in the 65 years since Bright examined automation at 13 U.S. companies. Now most companies have some automation ranging from supporting isolated production tasks or semi-independent work processes to more comprehensive systems. Many of Bright's conclusions remain useful to consider and investigate.
For example, Bright concluded the problems of automation are proportional to the degree of uniqueness rather than the amount, degree and sophistication of automation. He asserted that automation was easier and less risky when there was a high degree of process and product stability. If managers had the attitude that "change" is "customary" and normal, then their attitude toward automation was more positive. He concluded that automation was full of surprises and the surprises were not always unpleasant. In the mid-1950s, management generally tended to make automation decisions largely on the quantification of a few benefits, notably cost reduction and capacity increases.
Also, Bright concluded the automated plant is much more of a design problem than is building the conventional plant. The automated line must fit its human environment. The automated plant is intimately dependent on successful technology, successful adaptation to the marketing problems, and social acceptance. Managers must become technologists and automation designers. Management must anticipate the rate at which technical evolution will catch up with both its automated production system and its product. Management must be prepared fo disappointments because automation is an experiment on a large scale.
A major concern in the 1950s was the impact of automation on employment and labor. Bright argued important advantages of automation for the employees should be explained. For example, he found that automation led to 1) greater job security through competitive strength, 2) safer, easier working conditions, 3) a longer working life, and 4) better pay (p. 233). All of those advantages and benefits for workers may not result from building the factory of the future, but other benefits for labor and society, in general, may result.
Top management's task in implementing automation is to make everyone aware that there truly is a need for patience and help from all concerned (Bright (1958), p. 234). Also, management must be looking far ahead and be as sensitive as possible in detecting the need for adaptation and change. Anticipatory, collaborative decision making is important because the complexities of relationships are much greater and more significant with automation. All employees of an organization must be intimately conscious of what is happening in other parts of the business. With automation, it becomes an important job of management to create superior teamwork.
Intelligent Automation is the robotic process automation (RPA) platform frontier. RPA includes workflow orchestration, mobile data capture, analytics, and eSignature. Information technologies, like machine learning, motion sensor, optimization algorithms, and robotic vision are disrupting many traditional manufacturing and retail industries and creating new dominant competitors.
More intelligent business processes and manufacturing automation may lead to higher productivity, more flexible production systems, and more stable profitability. Intelligent automation includes decision automation using Artificial Intelligence (AI) with sensors to control and manage complete manufacturing systems and production processes.
Automation is an experiment in advanced production concepts and philosophy. The advanced automated plant of just a few years ago is no longer an advanced plant. Technology obsolescence is occurring rapidly.
We can and should anticipate the factory of the future in retail, traditional manufacturing, e.g. automobile assembly, electronics, and life sciences devices. Adoption of intelligent automation, however, requires more sophisticated governance and control than earlier robotic process automation. Senior managers must be proactive in defining policies, procedures, and monitoring changes. Managers must create a broad governance program for Intelligent Automation that assesses, develops, enforces and monitors policies and procedures prior to implementation. After implementation, the governance program must be agile, ethical and responsive to social, economic, and technological change.
References
Boston Consulting Group, "The Factory of the Future," Dec. 6, 2016 at URL https://www.bcg.com/en-us/publications/2016/leaning-manufacturing-operations-factory-of-future.aspx
Bright, J. R., Automation and Management, Boston, MA: Division of Research, Graduate School of Business Administration, Harvard University, 1958.
Davenport, T. H., & Kirby, J. (2015). Beyond automation. Harvard Business Review, 94(6), 59-65.
Skinner, W., "The Focused Factory," Harvard Business Review, May 1974 at URL https://hbr.org/1974/05/the-focused-factory.
Last update: 2020-04-17 01:01
Author: Daniel Power
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