Case Study

Modeling Warehouse Capacity to Support Monthly S&OP

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Modeling Warehouse Capacity to Support Monthly S&OP

Challenge

A large global retailer of athletic shoes and apparel wanted to have a more detailed view into capacity within its four primary European DCs in order to improve resource utilization, support monthly S&OP planning and reduce penalty costs. The company had monthly volume/productivity data but no real connection to corresponding capacity. High intra-month variations in processing capacity as well as machine bottlenecks were constraining DC productivity.

Product is sourced globally, stored and processed in four European DCs and then shipped to customers via 3PLs. Each of the four DCs is dedicated to a specific product category.
Processing at DCs includes:

  • Full case
  • Repack and picking
  • Value-added services (VAS) including customer labeling/tagging and hanging
  • E-commerce

Solution

The company worked with the LLamasoft solutions team to build a detailed short-term model of processing activity within the four DCs, using LLamasoft® Supply Chain Guru®. The model included a weekly view of volume requirements and a detailed view of product flow in each DC as well as 3PL capacity in order to:

  • Predict capacity requirements for expected customer deliveries
  • Identify the best split to improve resource utilization and level processing
  • Support the monthly S&OP process—supporting decisions on the amount of product to be pulled forward (i.e. sold earlier), or hold off inbound (i.e. process it later)

Model inputs included staff productivity, staff to infrastructure ratio, infrastructure efficiency and number of “effective” available work hours per period.

High-level model flow
high-level-flow-chart

Results

By separating capacity data by DC, the model allowed the company to balance inbound and outbound volumes, thus creating a positive or negative net effect on inventories. This model is used on an ongoing basis to support monthly decision making. Benefits include:

  • Improved accuracy in predicting capacity bottlenecks
  • Faster analysis turnaround
  • Better leveling of processing requirements throughout the month and throughout DCs
  • Reduced frequency and volume of penalty costs for overtime, offsite storage and trailer rentals
  • Modeling framework for making long-term warehouse infrastructure improvements

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