Our client sought a production planning optimization solution that would help it:
- Balance plant cost and capacity with shipping costs
- Make effective decisions between running overtime and allocating production to another plant based on both cost and service levels
A prescriptive analytics platform and MVP implementation approach
The team from West Monroe reviewed and assessed the maturity of supply chain planning, order management, and production planning processes to understand challenges and issues in production planning. Through this analysis, the team identified certain activities that were eroding value and increasing customer service costs.
West Monroe then created a proof of concept using River Logic prescriptive analytics and decision support technology. The team used the proof of concept to analyze one week of historical operational and financial data and model the impact of alternative decisions. This model helped the client see how certain production decisions could reduce inefficiency and improve operational effectiveness – demonstrating the value of a prescriptive analytics platform for improving production planning.
Because the client wanted to begin realizing value from the River Logic engine as soon as possible, West Monroe employed a “minimum viable product” (MVP) approach that prioritized initial and rapid deployment of specific capabilities that support daily production planning decisions. Once those capabilities are in place, the project team will continue building out a robust solution that enables the company to benefit further from the engine; for example, moving from product family to SKU level costing, real-time staffing levels, and adding make-to-stock functionality.
Rapid cost savings
By using an MVP approach to implement its new production planning engine, the client will begin realizing benefits from prescriptive analytics within three months – with even greater value to come as it adopts the solution’s full set of capabilities over the following six months. The client estimates that, when fully implemented, the solution will drive weekly savings in the $80,000 to $200,000 range.
The original article can be found here.