Case Study: Optimal Expansion Strategy for 'Superstore' Retail Chain

Background: Superstore is a fast$-$growing urban department store$-$style retailer. They currently have stores spread across a number of states and looking to expand. With expansion come questions related to optimal locations, stock assortment, and predicted demand.

Data Provided:

$1.$ Superstore.xlsx: This file contains sales data for all stores for the past $10$ years. Order numbers, locations, dates, and even profit margins are included in this worksheet.

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Assignment Requirements:

$1.$ Data$-$driven decision making: Use the data provided to determine two cities should consider for its next set of stores.

$2.$ Sampling and Probability: From the sales data, pick random samples to estimate sales for the new stores. Use probability techniques to predict the range of sales figures.

$3.$ Predictive Modeling:

$$ Use single and multiple regressions to predict sales for new products.

$$ Build a predictive model to estimate the monthly sales for the new stores for the first year.

$4.$ Simulation: Use Monte Carlo simulation or Event Logic and Entity Life$-$Cycle Logic Models to forecast sales and stock demands for different seasons.

$5.$ Linear Programming:

$$ Use Solver to determine the optimal product assortment for the new stores, given constraints like shelf space, supplier agreements, and predicted demand.

$$ Factor in simultaneous decision problems, ensuring every decision complements the other.

$6.$ Visualization and Presentation: In Excel, create dashboards and charts that showcase your findings in a clear, visually appealing manner. Your final presentation should tell a compelling story about how Superstore should approach its expansion.