BearingPoint’s digital supply chain model introduces a real-time, end-to-end supply chain concept. Material flow and master data on suppliers, logistics service providers, production, products and customers are merged in one supply chain control tower. By combining external and internal data and using IoT, big data and in-memory technologies, business can be controlled in real time. Our idea is to move from reaction to prediction. We address risks proactively, e.g., by analyzing news, traffic and weather data, enabling you to react before critical issues arise. A complete view of your supply chain allows you to significantly reduce material shortages and transport costs, and to reduce inventory levels to an absolute minimum.
Digital supply chain at a glance: end-to-end transparency, multi-modal for transportation and materials.
Currently, your supplier, transport, inventory and production data are in separate systems and of different granularities. As a consequence, an end-to-end view of your supply chain at a material/product/SKU level is not available.
Our connected supply chain approach solves this by combining external and internal data and using IoT, big data and in-memory technologies. It covers the entire informational and physical life cycle to meet demand and to assure business continuity.
Call-off order, call-off confirmation, transport advice note and confirmation, pick-up, advance-shipping notice, track&trace, condition monitoring, yard management and goods receipt are planned and tracked at the material number level. All information is visualized in one supply chain control tower that interacts directly with the connected ERP systems. In our concept, there is no need to jump from one system to another – the supply chain control tower is your single point of control and information.
All data is monitored in real time and the status at each step is compared with planned versus actual. To improve the efficiency of your daily operations, we focus only on events that might have an impact on your supply continuity.
Furthermore, our idea is to move from reaction to prediction. To do so we address risks proactively by analyzing news, traffic and weather data. Enabled by IoT and smart devices, we provide condition monitoring to identify quality issues (temperature, moisture, g-forces) that occur during transport so that countermeasures can be initiated proactively.
We have experience in realizing this concept with different software applications and landscapes. An exemplary application can be found here: www.log360.net