Workforce Optimization Algorithms That Save USD 6MM in Staffing Costs

Case Study

Workforce Optimization Algorithms
Boost Labor Productivity Worth USD 6MM

Business Objective

Our client is a leading is a leading retailer of technology products, services, and solutions.

Sales of their categories are highly influenced by the gadget-level expertise of associates in their stores and therefore they needed to generate a weekly labor schedule that optimizes costs as well as sales targets.


  • Complex business constraints with many ad-hoc adjustments done every month
  • Frequent changes to stores and department structures
  • No existing centralized system in the company to handle labor staffing.

Solution Methodology

  • Consolidated data and business rules into one centralized enterprise planning platform across the company
  • Studied various factors that affect sales and labor assignment such as store attributes, seasonality variations, customer demographics, fluctuations in store traffic, etc.
  • Predicted the baseline hours to be staffed given the store characteristics, historical patterns, and financial targets
  • Built and implemented an iterative algorithm on top of the baseline staffing model that accounts for the various planning constraints and arrives at an optimal staffing solution that adheres to all the business rules
  • Provided the ability to easily make policy-driven modifications to staffing at any desired level of granularity.

Business Impact

  • More labor hours (~50,000 more) across the stores for the same expense target.
  • The optimal staffing solution built by Tiger Analytics helped the client uncover savings potential worth USD 6 million.
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