Advanced manufacturing or Industry 4.0 (I4.0) entails huge opportunity and anticipation. We are all fascinated by the new technologies that are showing more and more in the shopfloor as well as in our lives in general.
We are excited by, for example; (i) Artificial Intelligence with IOT that provides predictive maintenance or visual inspection, (ii) Robots that can, solder, polish, paint cars and other products, (iii) Autonomous Vehicles that can go from one place to another independently, (iv) Use of Additive Manufacturing to instantly produce products that previously took months and huge initial cost, (v) Drones to inspect and monitor large area facilities or deliver products autonomously, and (vi) Augmented Reality/Virtual Reality that enables instance 3D guidance of the workforce or even virtual operation of real machines.
In the vision of many, those disruptive technologies will lead to a whole digital factory where the entire process is automated and fully autonomous. If we want to look at a sector that is already close to doing complete digital factory, it is the Semiconductor sector, building Fabrication Plants (FABs). Estimates put the cost of building a new fab over one billion U.S. dollars with values as high as $3–4 billion and more not being uncommon.
It is clear that the investments and risks associated with building a whole digital factory are unbearable for most manufacturing companies. Aside from the cost associated with the building a whole digital factory, studies show that even with limited I4.0 related projects, a large amount of them fail and do not yield the anticipated results.
Why do many I4.0 projects fail?
1. They don’t contribute significantly enough to the corporate objectives – many of the disruptive technologies are great ideas but they are not mature enough and not scalable to contribute significantly for a large manufacturing company.
2. Many of the AI initiatives are based on assumption and then trial and error or very long learning cycles. The cost is high and so is the risk. There is no certainty that the AI development will result in higher efficiency or higher return.
3. Regulation limits – the regulation is lagging behind disruptive technologies. As a result, technologies like autonomous cars or drones and many other life changing technologies, are shown more in lab environments than in real life situations.
4. The fear of change – Digitalization of the plant and adaptation of any I4.0 technologies requires change management.
To succeed in any I4.0 project, management needs to be committed, to provide cross-factory reasoning for the purpose on hand. But this should not turn off the common manufacturer. On the contrary, there are many ways to advance and digitize your operations, add agility, be more competitive and start your Industry 4.0 journey at reasonable cost and minimal risk.
To explain this better, I like to use a simplified layers’ design of I4.0 architecture. I will focus on layers 1-3, which in many references to I4.0 are somewhat neglected, yet, there is huge benefits in advancing at layers 1-3 with minimal risk. The table shows I4.0 layers and related technology. The second column shows the layers and to its right, the technology or elements related to it. While layers 4,5 relate more to the disruptive technologies (high cost, high risk), layers 1-3 are focusing on digitalization, full transparency and analytics to provide visibility and traceability of the entire operations.
Interconnection, Visibility and Traceability – Every manufacturing plant most likely has several software systems. Many of the machines already have built-in sensors and control panels to alert and visualize the machine status and show error codes. Many of the manufacturing plants also have MES (manufacturing execution system) that collect data from the machines, analyze the data and show OEE (overall equipment effectiveness).
The problem is that the plant usually has different kinds and different generations of machines with different data outputs. Additonally, some of the machines do not even connect at all. Hence, there is a problem by design – ERP and MES are not enough to automate all the tasks in the plant and in between there are people. In most plants, and for the visible future, people will still monitor the machines (or robots), do the maintenance and fault maintenance, and finally, setup and solve ongoing events and conflicts.
Workforce data and communication today is hidden information for the most part. Think of all the unstructured communication flow within the plant, emails, chat, phone calls and even spreadsheets – they are all unstructured data. As such, there is no accumulation of history, no learning system, no analysis of anomalies and root cause of problem, and no dashboards that can show holistic view of the shopfloor.
Digitalization of workforce’ workflows with connectors to the ERP and MES, enables full transparency of the operations and the ability to trace any event to its root cause. More on the benefit of digitalization of the operations: Routing and escalation – in the unstructured plant there is no automation of routing and escalation. Routing and escalation are done based on internal procedures and on best efforts of the employee.
In digitized plants, routing and escalation is done automatically which can save significantly on time from event to resolution. The automated routing also helps to avoid bottlenecks of the operations; an event does not have to wait for the shopfloor employee to find the relevant expert to fix the problem. The system will route the problem to the relevant expert automatically and if need be, escalate to the next level of support.
Learning system – digitalization of events and their resolution enables not just instant historical view of the same events but also instant learning of possible resolution and listing them by best practice on top. Additional saving to the event – resolution cycle. Lack of visibility – “no core elements” lack of visibility is another problem. It is intensified at complex manufacturing plants that have various silos that are not directly connected to the production line.
The MES will monitor mainly the machines producing the product but it will overlook, for example, the forklift feeding the production line or in-house metal shop employees that are doing internal repairs. Forklift that fails will halt the production line in a similar way to the production machine. Maintenance Repair work that is not monitored, can last more than the standard set time because of lack of spare parts for example.
Labor Intensive Production Lines – while in machine production factories, we have plenty of data from the machines and the MES that we can analyze and draw insights from, there are many factories that are still labor intensive. Factories that are doing electronic assemblies or food packaging, for example, or any factory with custom work.
Digitalization of the production line is possible and it will provide the insights on production progress VS planned, OLE – Overall Labor Effectiveness (Availability of the labor VS set standard * Performance – Production yield VS set standard * Quality – number of failed product quality check VS set standard). OLE on its own is not enough, we also want to realize why has production stopped? Is it because of missing part or raw material or unclear assembly instructions, etc., or if the problem is quality, what type of quality issues are repeating?
Digitizing Labor– Intensive Production line, will add the visibility and traceability. Analysis of the data is possible to identify abnormalities and root causes. Reduce tedious repeating tasks: Many of the tasks the workforce is doing, can be automated and by automation, reduce the tedious tasks from the workforce.
Analytics and expert system enable traceability, analysis of history of events, identification of anomalies and root causes, and present a dashboard with the KPIs (key performance indicators) for agile and transparent system. The dashboard is built in different resolution to suit the relevant manager. Threshold setup, assures that the manager is not overwhelmed with too much information but just what he needs to respond or act upon.
Conclusion
The landscape is changing rapidly, faster than ever before. New, disruptive technologies are introduced. Industry 4.0 principles provide guidance to the new evolving structure of advanced manufacturing. We should embrace the change and view it as an opportunity while at the same time be aware of the required investments and risk associated with each adaptation of new technology. While some of the I4.0 technologies are still premature, there are many that are spreading in use day by day and we must consider adaptation of such proactively. At the same time, there are proven technologies at a reasonable cost and minimal risk that enable immediate, complete digitalization of the factory operations. To stay competitive, it is compulsory to adopt those, the sooner the better.
David Ackermann
CEO,
Briefery Ltd.
