With the help of AI-based solutions, production processes in particular can be controlled more precisely. This can be achieved, for example, by precisely determining expected production quantities based on the data-supported evaluation of incoming orders. This provides logistics and purchasing managers with important recommendations on order quantities for parts and components that they need to order from their suppliers. The ERP system can take over such tasks automatically. Based on this information, it is also possible to determine the required capacities of employees and machines more precisely.

AI-supported software analyzes past sales data, for example, and calculates which sales volumes are most likely, taking into account certain parameters of current business development. From this, conclusions can again be drawn about upcoming production volumes and the resources required for this. Self-learning systems can also automatically refine their forecasts from year to year and consequently provide increasingly precise recommendations for action. This is because they can draw on constantly growing volumes of data and a constantly increasing wealth of experience to create their forecasts.

Increased delivery capability and planning reliability

AI and machine learning in the ERP environment help companies to be able to deliver at all times, but also to avoid overcapacity in the raw materials and finished goods warehouse. Companies can thus noticeably minimize the risk of unnecessary capital commitment due to unused stock. In addition, planning reliability increases with regard to all resources that have an influence on the production process - from the required components and machinery to individual workers.

With the help of AI and ML, holistic and cross-departmental analyses can be carried out in this regard: When calculating the production quantity of a specific product and the resources required for it, not only the existing stock levels are taken into account. The system also evaluates existing raw material quantities and available capacities in the machinery and workforce.

Sufficient data volume as a basic requirement

Another field of application for AI in the ERP environment is chatbots, which automate work processes by using voice-based user interfaces to carry out frequently recurring and identical processes that require a limited number of question and answer options in dialog form. For example, companies can use them to automate routine tasks such as recording income and expenses. For this to succeed, however, all the necessary data must be available in a consolidated database. This means that if AI and ML are to be integrated into one system, an integrated ERP system is a basic requirement. This is the only way to manage all relevant business areas of a company uniformly and reliably.

Most small and medium-sized enterprises (SMEs) have sufficient data to be able to carry out analyses based on AI and machine learning. However, this information is often not available in a standardized database, as so-called stand-alone solutions, i.e. independent systems with separate databases, are used for production control, warehouse management and purchasing. It is therefore advisable to first switch to an integrated system in order to create the necessary database for the implementation and use of corresponding automation technologies.

AI-supported ERP systems free up time for other tasks

And what does this kind of development mean for the company's most important asset, its workforce? Most employees are positive about increasing automation. In the PwC study "Upskilling Hopes and Fears", 70 percent of those surveyed stated that new technologies will improve their daily work. In this context, people will mainly be occupied in future with monitoring IT processes and evaluating results obtained from intelligent ERP systems.

Time-consuming routine tasks, on the other hand, will decrease significantly. This will free up employees' capacities, which they can use for activities directly related to value creation - such as the further development of strategic business areas. Overall, AI and ML will create new and more demanding fields of work and applications that will noticeably change the skillset of employees. Against this backdrop, according to the PwC study, many employees would like their employers to provide a greater number of training and education courses so that they can integrate the relevant applications into their day-to-day work. According to the study, 81 percent would take advantage of further training opportunities from their employer to better understand and use technologies such as artificial intelligence and machine learning. A clear appeal to companies.

Oliver Henrich, Vice President Product Engineering Central Europe, Sage