Case Study

Using Supply Chain Guru For S&OP

Model Intricacies of Production and Multiple BOMs
Account for Seasonal and Regional Changes in Demand
Work from the same database as network design projects

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Using Supply Chain Guru For S&OP

Challenge

A multinational food manufacturer had performed network optimization on their Latin American supply chain using LLamasoft® Supply Chain Guru® but needed a solution to perform both production and distribution planning for their regional operations. Given the high complexity of the production planning and the need to consistently meet inventory demand coupled with the distribution intricacies of their Latin America network, they required a tool flexible enough to determine optimal production, inventory and distribution plans to reduce overall cost and robust enough to deliver consistent, timely analyses.

Solution

Although they had purchased Supply Chain Guru initially to perform network optimization work, the food manufacturing company found that Supply Chain Guru’s ability to model BOMs, production details and times, transportation policies, supplier lead times, purchasing rules and changeover times enabled them to use it for production and inventory planning as well. The same flexibility enabled the company to model the intricacies of the multiple production lines, SKUs, packaging conditions and characteristics, new SKU introductions, and a highly seasonal and regional demand to build detailed production plans at the monthly and weekly level.

Results

Using Supply Chain Guru as both a network design and optimization program and an S&OP tool has helped the food manufacturer significantly reduce the time and effort needed to generate accurate planning forecasts. In addition, they have gained flexibility and the ability to quickly execute analysis on alternate scenarios proposed by top management in the organization. Almost immediately the company saw a significant reduction in inventory holding costs from improved inventory levels, as well as a drop in production costs due to more efficient capacity utilization.

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