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Case Study: Optimizing Batch Production, Sourcing, and Inventory Management


Services and Operations Management: Optimizing Batch Production, Sourcing, and Inventory Management

An interview with Michael Ketzenberg

The Company

A manufacturer of beverage products.

The Challenge

Manufacturing in a high-volume batch-production facility creates operational challenges in sourcing, production, and inventory management. Production planning and sourcing decisions were time consuming and error prone, often due to long production lead times and the need to forecast well in advance of actual customer demand. Distribution was not coordinated. Customers were often supplied with products in multiple less-than-truckload quantities from separate warehouses. In addition, the company suffered from excess inventory of some products and out-of-stocks on others.

The Solution

The company wanted to solve for these operational problems via predictive and systematic decision models. Key decisions to explore included:

  • Determining the timing, sequencing of batch production, and batch production size for each product SKU
  • Identifying appropriate inventory and safety stock levels to hedge against demand uncertainty
  • Determining which customers to supply with which products from which plants
  • Identifying suppliers to source raw materials, the associated supply quantities, and their timing

Extensive data was gathered concerning process capacity at the various plants, production requirements and constraints, and transactional data, including customer orders.

A linear program, developed in C and Simplex, was created to optimize production, inventory, and distribution decisions on a continuous basis. An objective function was defined to minimize the sum of production costs, setup costs, and inventory-related costs. Constraints were modeled regarding capacity, labor, and materials. Optimal batch sizes were determined to better rationalize setup costs with market mediation costs. The program also identified and optimized trans-shipment quantities between plants, warehouses, and customer locations.

The Results

The software package was designed to make it easy for the client to input updates to the model and obtain easy-to-understand, accurate, and updated data as a basis for ongoing decision making. The client runs the model on a continuous basis, updating its inputs to make better operational decisions.