The old approach to production was fragmented. Each department had its own software, its own data, and its own way of handling it, resulting in a well-known fragmentation problem. In some cases , the transformation required by Industry 4.0 has mitigated this problem, but it has not solved it entirely. With each part of the company on a separate path, having a cohesive view of company performance has always been complex. Communication gaps, bottlenecks, and in general problems due to compartmentalization are still the main brake on the transformation of manufacturing companies into Smart Factories, and the progressive loss of competitiveness.
Over the years, technology has come a long way in increasing enterprise connectivity. Cloud computing and universal accessibility enable manufacturers to have insight at all times. ERPs have become the backbone of manufacturing, aggregating data and making it readily available, but more importantly enabling advanced levels of analysis impossible in a non-integrated system.
The benefits of Industry 4.0 have been known for some time: according to a 2017 McKinsey report, innovation has brought a 30-50% reduction in downtime and a 40-50% increase in labor productivity. ERP adoption in Industry 4.0 is the natural evolution of this process to achieve Smart Factory status, which to date is the high point of technological competitiveness.
ERP and Industry 4.0: the main areas for improvement
The first point that needs to be made is that in many ways the transition from Industry 4.0 to smart factory is not as disruptive as other steps of digital transformation. On the contrary, in many cases it is its natural evolution. Let us now see in what contexts the greater integration of an ERP into business processes can bring
substantial benefits, harmonizing communications and enabling more timely and efficient data analysis.
Efficiency of production cycles
Unnecessary bottlenecks such as products that do not meet the quality expected by customers, production delays due to machine breakdowns or inadequate raw materials, can arise due to lack of adequate information and data, or any non-ideality in the production process. Inefficiency in production cycles greatly affects profits.
An ERP such as SAP S/4HANA allows monitoring of, for example, time per cycle, production time per individual product, and coordination of necessary tasks to identify inefficiencies and potential critical issues.
Reducing production downtime
Products can be delivered to customers on the agreed timelines if and only if every piece of machinery is working perfectly. Sudden breakdowns, supply problems, and other inefficiencies can undermine the smoothness of the production process.
An ERP with, for example, a predictive maintenance system and monitoring of the entire production chain can effectively identify any critical issues and help prevent them.
Balancing workflows
Bottlenecks and efficiency issues can occur in a production line when a location is overused or underused. If workload balancing requires manual intervention in task assignment and timing, this can introduce inefficiencies.
An ERP solution offers the advantage of monitoring the average time required to perform a job on the production line. As a result, supervisors can have more accurate time estimates to properly balance the production line. In more advanced solutions, it is possible for this balancing
to occur automatically.
Access to real-time information
In some cases, companies do not have adequate infrastructure or tools to monitor production lines centrally. This can result in a delay in the transfer of information to the respective authorities.
An ERP is can create a context-sensitive factory environment that can track and monitor critical activities in production in real time using a decentralized communication system for optimal management of production processes.
Practical steps for global ERP adoption in Industry 4.0
The transition to the smart factory in manufacturing encounters all the critical issues typical of systems that make production efficiency a key asset. Even if the benefits are considerable in fact, it is not always possible to make all the necessary upgrades in the short or medium term, precisely because this could force prolonged production stoppages.
The best thing to do in such cases is to create, together with specialists, a medium- (or long-term) plan that allows for as transparent a transition as possible and, where necessary, an adjustment or change in machinery.
First of all, it is necessary to accurately survey the assets so as to identify the most obsolete resources that could be blocking the process.
In the first phase, it is necessary to identify the individual building blocks of the architecture. For example, whether the machinery has monitoring systems and what kind, and most importantly, identify strategies for moving from a context of isolated systems to one of integrated systems.
In fact, the first fundamental step is that data and information from all departments, from orders to production, via purchasing and logistics, for example, are universally usable within the structure. Often for administrative transitions a reorganization, or simply the final adoption of an ERP solution, is sufficient, while for production aspects it may be necessary to introduce new monitoring systems, for example using IIoT solutions for machinery, or with the transition to new generation machinery that natively has the necessary tools. This transition will also include data harmonization, for example through the adoption of standards.
The second step is related to the use of data for process automation. Once you have verified that the data is flowing correctly, you can leverage our ERP solution for real-time monitoring, identification of critical issues, and in general for all the improvements offered by being able to manage, analyze, and verify data in real time, for example, to activate a second supply of materials, to properly plan shipments without overloading logistics, and so on. Real-time data analysis at this point will open the door to the next step.
Finally, consistently collected and verified data will allow the company to implement truly smart solutions, for example predictive maintenance and predictive quality analytics but not only. Thanks to advanced, machine learning-based systems, it will be possible to enable advanced predictive systems, such as the ability to use the digital twin to test a new production chain before actual implementation, or the centralized, real-time coordination of multiple plants, even those located in far-flung territories.
