Demand-Driven Supply Chain
Background
GW Cosmetics faces the challenge of fulfilling sporadic and irregular customer demands within a relatively short timeframe. As a result, a conventional supply chain planning system, with its reliance on forecasts for production control, quickly reaches its limits. GW Cosmetics has previously attempted to use statistical forecast calculations to create a realistic assessment of future demand. However, the forecast error is so high that, on the one hand, forecasts are typically overwritten manually, and on the other hand, production and purchasing control is rarely based on forecasts. A fragmented IT environment further complicates the synchronization of customer demand with material supply and production.
Objectives
- To extend standard planning software with the innovative demand-driven MRP methodology (according to the Demand Driven Institute) in order to operate independently of forecast errors in daily business using simple replenishment rules and inventory principles, or to reduce forecasting effort to the necessary minimum. The challenge here arises because there are virtually no software providers that have implemented both a conventional planning methodology and a demand-driven planning methodology within a single software solution.
- Mapping of all standard processes and standard constraints within the software. ```
- Rule-based management by exception in daily operations to free up capacity for continuous development of the entire supply chain
- Integration of capacity planning, quantity planning, and strategic planning within a single software suite to reduce synchronization efforts and ensure decisions at all levels are based on consistent information
Milestones
Operational inventory management
Development of a customized MRP engine based on demand-driven rules
Mapping of control parameters according to defined material supply strategies across all bill of materials levels
Operational dashboard for planning order/production proposals
Operational dashboard for tracking orders and production
Results
Reduced planning effort
Reduced inventory
Increased delivery service level
Next steps:
Modular extension with a detailed capacity planning tool
Modular extension for strategic planning using machine learning algorithms and probabilistic forecasting