Representatives of McKinsey & Company recently interviewed two German manufacturing executives. Thanks to outdated thinking and insufficient knowledge, they feel several companies have not connected their physical and information flows in their factories and supply chains. This is a key provision of Industry 4.0., and these companies are missing out on the opportunity to run their operations in the most optimal ways.
There are a few problems to consider. First off, most information systems are not connected to physical inventory and machines, even though the IoT has made this easy and inexpensive to do. Second, companies have taken steps to gather information from the IoT, but are not using it properly to streamline operations. Unfortunately, they do not yet employ IT staffers who understand machine-learning and other algorithms, and they do not have the IT architecture necessary to support big data.
A Straightforward Assessment of What is Wrong
Let’s examine what Siegfried Dais, Deputy Chairman of the Board of Management at German engineering company Bosch GmbH, and Heinz Derenbach, CEO of Bosch Software, said in their interview.
For the most part, they are quite clear about what needs to be done to tie physical and virtual worlds together to drive efficiencies in manufacturing under Industry 4.0. In their view, every component in the supply chain and factory is attached to the network. Each component transmits its status to the big data cloud, analytics algorithms, and operations systems. They call this "process2device," and it occurs when a “physical device becomes an active part of a business process: delivering data, sending events, and processing rules.”
Not only does each component report its location, “Everything is interlinked with everything else,” so every component in the subassembly knows which customer order it belongs to. Components report variations as they move along supply chains and across assembly lines in real time. This eliminates the lag between planning cycles and actual events present in the traditional approach.
The IoT, big data, and analytics is what makes this possible. The earliest adoptions in factories have been for preventive maintenance, the easiest factor to apply and understand. If a machine is overheating and vibrating because it needs a new filter, the IoT can create a work order in the PM system automatically, but integrating such information into the logistic systems is far more difficult.
Siegfried Dais states, “Take cyber-physical systems, which can tell us where every single unit is at any given time. Logistics players often use this tool, but with an old mind-set that fails to exploit the advancements the tool was designed to offer, so the first requirement is that logistics players truly use what's new.”
Heinz Derenbach points out what IT architects and programmers would call the need for abstraction… “In the connected world, we cannot separate the physical world from business processes… It is essential to translate the physical world into a format that can be handled by IT.”
A Heavy Lift for IT
All this requires more than just attaching a wireless card or sensors to machines and components. Companies need programmers, data scientists, and big data architects to work with factory planners and product managers to plug data into planning and operating models, and those models must change to accommodate real-time information.
This is going to be a problem for many (if not most) IT shops due to lagging skills. It often takes some understanding of mathematics and statistics to write and use analytics algorithms, but the average computer programmer does not understand this, so a company needs to hire additional data scientists. Algorithms coded into modern programming frameworks make this much easier in the long run. Furthermore, cloud platforms, such as IBM Watson and Databricks, can draw conclusions without programming in many cases.
What Industry 4.0 Means
The “4th industrial revolution” combines the IoT, big data, and analytics. McKinsey and others also add virtual reality and 3D printing. Proponents say these tools will overhaul manufacturing in the same way the steam engine, the assembly line, and early computing changed manufacturing.
This is a genuine game changer in the sense that advances in software and networking have made it inexpensive and easy to connect both virtual and physical worlds. This is known as abstracting the physical environment into the virtual one.
Here are the key pieces – the IoT lets companies connect components, machines, and even people to the cloud via low-cost sensors and wireless networks. Big IT companies like Yahoo, LinkedIn, Google, Twitter, and Facebook have written and given away big data software in the past like Spark and Hadoop, which has turned conventional data structures upside down. These free-form, distributed databases let companies process data streaming from machines and software applications with extreme ease.
Data science has also pushed the esoteric ideas of academics and operations researchers toward solving real-world business problems. Their difficult algorithms have been coded into easy-to-use APIs; distributed systems like Spark even let machine-learning run across architectures that scale without limit. Therefore, data coming from radically different sources and in differing formats can be understood and turned into actionable items.
The framework is in place; what we need now is education. We must study what other manufacturers have done, then apply them to daily operations and planning. The end result will be leaner manufacturing with higher profits, lower costs, and more satisfied customers.