To support Supply Chain practitioners along the above mentioned three steps leading to the Supply Chain optimisation, Sopra Steria has developed specific AI models and assets specifically for high added value business cases:
#1 - Design to conceive and configure
The product throughput has to be secured to ensure a good service level. One key exercise is Supply Chain processes and network engineering. Whether it be for logistics site placement, products, and component routing configuration, or capacity management, the industrial network design is often a very combinatorial challenge. Sopra Steria has developed optimisation algorithms drastically accelerating the time required to solve these types of problems, either to get the network well-configured, or to define the strategic sourcing organisation maximising the value of the procurement process.
More specifically at the local level, dedicated algorithms have also been implemented to optimise the design of the sites themselves. For environmental purposes, the energy mix consumption can be monitored by optimising production scheduling taking into account the local renewable energy's availability. For internal logistics efficiency, the warehouse or plant operations are a source of massive gains in terms of time savings and resource optimisation.
#2 - Stabilise to ensure resilience with decision consistency
In the rolling horizon context of Supply Chain operations, the dynamics of the system can be critical to the resilience of the network. The robustness of decisions in Supply Chain Management, in particular procurement, production, or logistic plans, is one of the main levers to improve the resilience of the Supply Chain. It ensures stability and consistency through time by ensuring that transactions are in line with the trajectory of the overall system. Sopra Steria has invested in specific AI and optimisation modules that can be called upon to improve the robustness of plans. In logistics for instance, Sopra Steria has developed transportation plan optimisation algorithms, taking into account the overall vision of the flows from upstream activity to the distribution centers. In end-to-end activity workload planning, Sopra Steria’s AI modules are capable of optimising the plans under operational constraints while smoothing out the activities over time. In Supply Chain Engineering, the focus has been made on inventory management parameter optimisation and product classification to improve relevance for management policies.
#3 - Refine to monitor and manage
Once designed and secured, the Supply Chain processes, and network have to be controlled efficiently on a regular and high-frequency basis. Responsiveness and adaptability of decisions are key for precise monitoring and management of operations. That is why Supply Chain practitioners need decision support for decision optimisation, guaranteeing quick answers, and performing results. To that end, Sopra Steria has developed AI modules for logistics re-routing optimisation and specific allocation problems.
Whether in production, procurement, or resource balance scheduling, allocations have to be decided with respect to the industrial context and business objectives to drive Supply Chain operations. By taking into consideration the constraints and key performance indicators to follow upon, Sopra Steria’s AI assets enable decision-makers to understand and optimise the key drivers impacting the Supply Chain performance.
Benefits