Rethink the organization’s structure
Cyber-physical environments will change what managers do.
According to a 2019 white paper by the World Economic Forum (WEC) and McKinsey & Co., manufacturers adopting Industry 4.0 can scale their businesses two ways:
- Though operational excellence and production system innovations; or
- By entering new markets.
This thesis is supported by other McKinsey research. According to a 2016 study, nearly 90 percent of surveyed companies believe that Industry 4.0 innovations would help them improve their competitive positions and operational effectiveness. Eighty percent of U.S. companies think Industry 4.0 would allow new competitors from other industries to enter their markets.
What is Industry 4.0
The term “Industry 4.0”—or the Fourth Industrial Revolution—refers to self-optimizing cyber-physical industrial environments. In these environments, systems collect data, create analytical models, make decisions and optimize production. The result may shift the socioeconomic fabric much as did preceding industrial revolutions.
The first industrial revolution, for example, was born of innovations relating to steam power, leading to rapid factory development and associated production efficiencies. This productivity gain supported growing consumerism, urbanization, education, employment and, in short, capitalism.
The second industrial revolution reflected widespread industrialization driven by mass production, steel and iron works, electrification and widespread rail transport adoption. These innovations lead to modern business management practices and integrated supply chains, increasing division of labor into skilled and unskilled categories, and for better or worse, widespread adoption of tariffs to protect national economies.
The third industrial revolution— the digital revolution—followed advances in semiconductor technologies that enabled personal computing, digital record keeping, cellular phone technologies and the internet. The result was interconnectedness, globalization and business models such as outsourcing and e-commerce.
Building upon its predecessors, the latest, fourth revolutionary iteration is catalyzed by cloud computing, the Industrial Internet of Things (IIoT), and Big Data. Cloud enables economical storage of large datasets. Analytics and artificial intelligence (AI) rapidly analyze those large datasets, uncover new relationships and surface new insights. Insights provide decision-makers with timely and relevant decision-supporting information, as well as optimized, connected operations.
Will jobs be destroyed?
The fear is understandable that robotic process automation will be a net destroyer of good manufacturing jobs. Leading experts suggest otherwise. According to the WEC Forum and McKinsey whitepaper, Industry 4.0 should be an “injector of human capital… transforming work to make it less repetitive, more interesting, diversified and productive.”
Industry 4.0 is an opportunity to shift low-value tasks to systems and machines. It’s an opportunity to democratize decision-making based on the availability of timely analysis.
The following real-world scenario shows a company using self-optimizing systems to improve its competitive positioning.
The company’s primary product is a grain-based fuel. Through its bulk production process, the company produces various coproducts and byproducts. Coproduct and by-product yield vary relative to primary product yield. And, all yield varies based on production parameters relating to speed, humidity, temperature, vibration, weather conditions, system pressures and raw material grades, among others.
To truly optimize for profit, production systems would need continuous adjustment. Without information technology and automation, continuous optimization wasn’t cost effective. The incremental cost of staff would exceed the incremental margin benefits.
We then modelled another scenario, one where software systems crunch the data and make microchanges to production processing controls. The business case was supportive. While initial technology acquisition and implementation costs were high, subsequent costs to maintain and optimize the system would be low, and certainly lower than the annually recurring salary costs in the first scenario.
The model demonstrated a payback within a few years, along with a significant return-on-investment over a 10-year forecast period.
Pillars of Industry 4.0
The framers of Industry 4.0 had this type of scenario in mind when the concept was introduced in 2011. Industry 4.0 was conceived as part of a German government initiative to counter threats to its manufacturing industries by China and other low-cost producing nations. By computerizing manufacturing, Germany protects its position as a manufacturing powerhouse notwithstanding its high-wage labor environment.
The authors proposed a framework based on smart, cyber-physical systems that connect equipment, software and people. The framework is based on the following four pillars:
1. Interconnection. The systems connect people, machines, sensors, devices and software through IIoT and allow communication among them.
2. Information transparency. Data collected through interconnection must be available to operators for decision-making.
3. Technical assistance. The intent is twofold: a) to shift low-value tasks from people to cyber-physical systems, and b) for systems to arm personnel with analyses and information for timely, effective decisions.
4. Decentralized decisions. Systems make decisions and take actions autonomously.
In one common approach to Industry 4.0 adoption, many consulting and advisory firms advocate a proof-of-concept approach where a quick win demonstrating value incentivizes teams to expand Industry 4.0 to other functional areas.
This approach assumes that an implementing company is either testing with a non-strategic initiative, such as energy management, or that it otherwise has a well-built foundation. If the underlying data, process and technology architecture is strong, it makes sense to test a closed cyberphysical loop. In contrast, if a company foundation is shaky, a proof-of-concept is probably premature.
In our case example, the fuel company didn’t have a strong foundation. Its data was inaccurate, its processes manual and inefficient. Its systems didn’t meet its needs. As a result, the company’s cyber environment is incapable of mirroring its physical environment, let alone optimizing it.
The company needs to first build a strong foundation by:
- Architecting an environment that spans enterprise resources planning, manufacturing execution system and distributed control system environments with the IIoT, business intelligence and Big Data warehousing;
- Properly implementing those solutions; and
- Assuring that the cyber-world mirrors the physical world through system adoption and disciplined business processing.
Once it builds this foundation, the company can wade into a strategy-driving Industry 4.0 proof-of-concept project.
We’ve discussed new revenue streams, elevated employee responsibilities and system architectures. It’s about how your company’s people do their work. How can Big Data sets turn into new revenue streams? How can front-line workers turn into front-line decision-makers? Who will manage and maintain the data and systems? Who will assure system integrity and security?
Organization models that worked well in the past won’t work in future. They weren’t meant to support Industry 4.0 concepts. The benefits Industry 4.0 offers can’t be achieved without rethinking an organization’s structure.
Making changes to organizational structures and embedded cultures can be exceedingly difficult. Changes must be well-timed. Don’t create new roles for data science or anticipate new revenue streams before the underlying processes and systems are in place.
Take a holistic approach that links organizational changes to business processes and technology systems. That way, the right systems will be doing the right work. More important, the right people will be doing the right work with the right systems.
Original content can be found at Control Engineering.