Machine and plant manufacturers are familiar with this problem: The very complex planning of purchased parts and in-house production of a multi-variant production is already a difficult challenge without inaccurate planning values. Also, the data often does not match reality; production planning is then calculated with 10 instead of 20 days, for example. The result of this is very inaccurate delivery dates and frequent deadline postponements. The manufacturing industry has the advantage that a lot of data is already available in several systems. The disadvantage is that the data collected is usually far removed from realistic values. The consequence is very imprecise planning, which makes it difficult to give customers precise delivery dates.
The use of machine learning helps to achieve strong improvements, for example, to determine replenishment times more reliably and precisely. Inaccurate data can be eliminated through data cleansing using machine learning and master data quality can be increased in the long term. Historical SAP data such as supplier, materials or purchase orders help to improve replenishment lead times by up to 42 percent.
The precise planning values can then be integrated, among other things, into mobile, dynamic store floor management, in which key productivity figures can be viewed at every machine via a dashboard directly on the shop floor. In doing this, production can also be made digital and paperless.
Machine learning improves planning values in mechanical and plant engineering.