Intelligent ERP: More Security When Making Decisions

A lot has been said and written about artificial intelligence (AI). Fueled by digitalization, the process industry is becoming increasingly open to the use of AI. AI-supported decision-making processes automate processes in robotics as well as in speech and image processing. This makes it even more surprising that only a few ERP systems in process manufacturing already use this technology.

One of the most popular application areas of AI in the ERP environment is automatic inbound processing of invoices or expense documents. However, AI and intelligent assistants are a rare find in core processes. Here they could be used to help forecast sales, optimize stock, or define ideal production sequences. However, a prerequisite for this is that the relevant data is available at a reliable quality standard and reflects an end-to-end history.

Most new machinery and systems in production have established communication standards that allow them to integrate easily into adjacent systems in many different ways. ERP systems can therefore access data directly from field devices or an Open Platform Communications (OPC) server. However, the vast quantities of data and their complexity often overwhelm users when it comes to deriving data for scheduling or other material management decisions in a timely manner. This problem is exacerbated when several plants or the entire supply chain are involved in these decisions. Cloud-enabled services simplify this task. They not only make it easier to collect data, but also provide scalable processing power, therefore reducing the costs. This enables intelligent assistants to evaluate the data and provide the right information to facilitate management decisions.

The ability to track key performance indicators in real time helps people make decisions, or even leads to rule-based, autonomous control. For example, an intelligent assistant can use measured processing differences to determine the impact on stock, capacity, or quality. Moreover, it can even derive measures from them and trigger a service order in the ERP system, for example. Decisions like these go way beyond a simple if-then relationship. Intelligent assistants are much better equipped to assess recurrent patterns and can be trained to make better decisions in the future. This means tolerances are no longer static but able to adapt to the relevant situation.

When processing raw materials with fluctuating qualities, batch manufacturers pay close attention to those batches which optimally fulfill the requirements of the product to be produced. In the end, however, it is not just the selection of the batch that determines the outcome, but also a large number of other factors, such as the machines and tools used and whether employees were adequately qualified. This results in a complex optimization task where ERP systems deliver data, but no recommendations. Assistants are required to calculate recommendations that can be used to minimize conflicts, especially in the case of competing goals such as time, costs and quality.

Digitalization provides manufacturers with huge quantities of data. In addition, ERP systems already have a wealth of information and feature many processes that have been cultivated over the years. Intelligent assistants can combine these two aspects. The symbiosis of ERP systems, digitalization in production and intelligent assistants from the cloud leads to greater transparency and improved system support, which enables decisions to be made in the shortest possible time.

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