Migration to Industry 4.0 – A Simplified Model
Advanced manufacturing or I4.0 entails huge opportunity and significant challenge for the manufacturing companies.
Manufacturing companies that have gone thru the I4.0 migration are reporting 30% and more increase in productivity. The challenge, though, is where to start, how to mitigate the risks of failure and how to make sure we are on track for successful migration.
From “Managing the Digital Transformation of Companies – ACATECH Study”:
“Many Industry 4.0 related projects within companies are currently launched in response to rising competitive pressures and growing need to tackle the digital transformation. However, a large proportion of these projects are failing. This is often because they don’t contribute significantly enough to the corporate objectives, are too strongly focused on individual technologies, or cannot be scaled up. Accordingly, to add value and support the company’s goals, individual pilot projects must be scalable and must deliver positive results within a short timeframe “.
Industry 4.0 transition is not a one step process and done, it is an on-going change in methods, management philosophies and behaviors and adaptation of new technologies, with measurements and on-going corrective actions.
To visualize the migration process I have arranged it in layers describing functionality and technology (see image below). The migration needs to be evolving. If you jump to layer 4 before you have assimilated the previous layers, you might succeed to bring value to a limited silo within your organization but you would miss on the overall benefit that Industry 4.0 migration can provide.
When looking at the table, it is easy to explain and set priorities and tasks to initiate the I4.0 migration.
Visibility: Almost all companies have ERP, some have MES and some have IIOT and workflow management system. In addition, many of the existing solutions (ERP/MES) are outdated and they cover just portion of the manufacturing processes. ERP/MES suffer also from lack of interoperability that results in unstructured data flow within the business that is not visible and not traceable.
Getting full visualization of your data is the entry point to improve your productivity. This is also the easiest step to perform with quick deployment, relatively low investment and high return.
Traceability: having full visualization of the data enables deployment of Business Intelligence. You can calculate already OEE – overall equipment effectiveness and trace back any issue to find root cause of problems.
Predictability and Adaptability: with deployment of IIOT, workflow management and MES you can collect big data like vibration and audio data from the machines. You can now apply AI models and analyze the data to predict problems before they occur. You can deploy predictive maintenance. You can also use the data and AI to support the autonomous machines that are in layer 4.
Autonomous: with the infrastructure in place and the people’s mindset to data driven factory, you can deploy advanced technologies effectively and benefit significant more agility, flexibility and efficiency.
Manufacturing companies should start by adopting the following steps to make a safe and successful I4.0 journey.
1. Expose management and employees across the plant to the importance and benefits of collecting and analyzing data for decision making. This can be done through in plant workshops, sending key people to data analysis training and adopting the “show me the evidence” approach at each decision point.
2. Evaluate your factory and identify “black holes”. Black holes are areas where data flows in unstructured way. Yes, data that is sent by email, chat or even spreadsheet, is not recorded in the organization memory, not analyzed, and is not traceable.
3. Once we have mapped our plant and identified the unstructured data, digitize the unstructured workflows by extending of the ERP and MES adopting MOM (manufacturing management system) and IIOT. This can be done gradually.
4. Now, with the digitalization of data you can bring in analytic and BI tools and move the organization to complete data-based decision making.
5. Specific critical areas, can be enhanced with AI/ML tools using big data to automate the decision process and add predictability to events.
6. Advanced technologies can elevate the factory productivity by adding automation, flexibility and innovation to the plant offering.
Briefery provides Mobile First, quick and easy to adapt and deploy, MOM solution to digitize and analyze the plant data.
Please contact us for webinars and further details. email@example.com