Supply chain optimization is a very complex topic. In order to be able to identify value-added potential in your logistics chain, a large number of different data (sources) usually have to be compared with each other. These are exactly the strengths of artificial intelligence.
Using applied algorithms and specific programming, computers are made to deliver comprehensive evaluations and prognoses for concrete application problems in the shortest possible time. For this purpose, we use various AI methods at PSI Group, such as Deep Qualicision and machine learning.
New and improved evaluation options through Deep Qualicision
Qualitatively evaluate business processes into directly measurable data based on machine learning with KPIs (e.g. negative correlations such as delays compared to planned dates on this data).
The use of artificial intelligence offers the possibility of fully automated comparison of large volumes of data and, for example, to detect anomalies in master data. This way, errors in the basic data can be detected and corrected at an early stage as well as corrected in the source systems.
Which articles are often ordered or delivered together? AI will provide answers. After all, it is precisely these items that should possibly be produced or stored at the same location. Complex logistics processes can thus be optimized and costly consolidation transports avoided – the basis for scenario management.
By combining historical data with article- and customer-specific, regional sales forecasts as well as demographic or weather or seasonal data, exact forecasts can be generated about the expected development of order and shipment data.
Shipment data can be evaluated in detail so that the determination of transport costs can be calculated in a very differentiated manner, taking into account various criteria (in addition to weight and distance classes, criteria such as source/destination regions, article properties, surcharges or discounts).
In the context of M&A projects, data harmonization is usually inevitable. In this case, AI supports the merging of two databases, the matching of customer/article master data and the identification of identical articles.