Case Study

Machine Learning and Inventory Optimization Accelerate Success for Uni-Select

Machine Learning and Inventory Optimization Accelerate Success for Uni-Select

Uni-Select is a leader in the distribution of automotive refinish and industrial paint and related products in North America, as well as a leader in the automotive aftermarket parts business in Canada and in the United Kingdom.

Demand fluctuation, particularly around seasonality, is a major challenge. Despite its efforts to improve processes, inventory management performance did not reach target levels.

In a calendar year since implementing Automatic Policy Tuning (APT), a capability within Manhattan Demand Forecasting and Replenishment, Uni-Select has achieved striking results, including:

  • Reduced inventory
  • Better fill rates
  • Significantly fewer demand for exceptions
  • Reduced manual changes
  • Increased planner productivity
  • Improved receiving dock performance

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