Retail Labor Planning Model – Alix Partners Carolyn Taricco Erin Gripp Victoria Cohen

Alix Partners “AlixPartners is a global firm of senior business and consulting professionals that specializes in improving corporate financial and operational performance, executing corporate turnarounds and providing litigation consulting and forensic accounting services when it really matters – in urgent, high-impact situations.”

Project Description Develop a model, using real sales and labor data for a U.S. retail chain, that can predict, by store, what the future labor needs are given specific sales targets.

Project Goals The model should predict future labor needs given specific sales targets Separate stores into productivity groups given labor, sales, and attributes Create additional analysis that would be interesting to the end user

Project Objective Expected approach: run regression analyses to come up with a formula for calculating labor amount given sales amount. The model will need to take in sales, labor, and store attribute data to run an analysis and come up with: 1. Best fit equation or each store allowing someone to input a sales target and get an approximate number of labor needed to meet target 2. For each store, a list of similar performing stores 3. For each store, denote whether any of the attributes are contributors to their labor productivity

Success & Completion Criteria Develop an applicable model using a different approach then previously used. If time allows, compare to original approach to determine benefit provided.

Project Assumptions Data provided (attributes, sales data, etc.) Model needs to be applicable to other chains

Our Solution Developed a systematic approach that calculates a suggested amount of labor hours for a given store to be considered efficient, relative to the most efficient stores based on the historical labor data we received.

Analysis of Situation: Approaches Data Envelopment Analysis (DEA) – Approach for evaluating efficiency of individual units (DMUs, in our case Stores) Efficiency is estimated relative to other units in the sample Benefits over Regression – Uses a series of optimizations (1 for each DMU) rather than a single optimization for all observations For each inefficient store, it indicates the efficient reference set » those on the efficient frontier against which the DMU is directly compared

Analysis of Situation: Considerations Produces a measure of how efficiently inputs are utilized to obtain outputs The input being labor hours and output being sales dollars We also took into account 2 other inputs that influence sales: – General Manager Tenure – Turnover

Technical Description Sorted given data on 519 stores using AWK – Labor hours, sales dollars, attributes Efficiency Frontiers Generated using DEA – By quarters – 3 Tiers Constraints – Limit the weight on the 2 attributes to no more than 15% of the inputs

Conclusion & Critique Identified highly efficient individual stores Labor Hours and Sales Dollars of these stores Found that the stores with tier 1 efficiency levels in quarter : *$ sales dollars generated per labor hour

Q Total Number of Stores496 Level 1 Level 2 Level 3 Total Efficient4 5 2 Level 1 Score Level 2 Score Store Number Q Example

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