Case Study: Retail Workforce Optimization

Context

• Chain of retail stores
• Approximately 100 locations in North America

Problem

• Optimize in-store workforce scheduling in order to maximize profitability
• Sales are highly related to the quality of service provided
• High sales variability depending on store location and seasonalities

Solution

• ApSTAT developed a forecasting model capable of accounting for:
  • The expected traffic in each store, in 15-minute intervals, varying according to daily, weekly and monthly seasonalities
  • The conversion rate (what fraction of consumers entering the store make the decision to buy an item); strongly depends on the quality of service, namely how many salespeople are in the store
  • The average basket (what is the average amount purchased, given a conversion)
• The model can optimize the required number of salespeople in the store at each time of the day
• The model can adapt to new locations for which historical data has not yet been collected

Benefits

• The average store saves $88/day or more, 50% of the days
• Maximization of technological investments: increase profitability by exploiting accumulated historical sales and traffic data
• Can schedule the right number of salespeople, at the right time, at the right place

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