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Industrial Intelligence in Supply Chain Network Design

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.

Your benefit

The potential of AI-based methods in PSIglobal

  • Analysis of process data streams through qualitative labeling for anomaly detection and improvement of planning quality through artificial intelligence
  • Data harmonization in M&A projects to accelerate integration
  • Analysis of customer ordering behavior to reduce logistics costs
  • Evaluation of shipment data to generate multi-criteria transport tariffs
  • Forecast of seasonal/volatile order and shipment data to increase planning reliability

    New and improved evaluation options through Deep Qualicision

    AI analyzes data streams

    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).

    Detecting anomalies in master 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.

    Analysis of customer ordering behavior

    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.

    Forecast of seasonal/volatile order and shipment data

    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.

    Generating multi-criteria transport tariffs

    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).

    Data harmonization in M&A projects

    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.

    Photo above: © Siarhei/