This edited volume presents state-of-the-art research that can leverage large-scale sensory data collected in grocery/retail stores where a single customer visit may generate nearly 10,000 data points. For decades, retail shelf space optimization has been confined to the analysis of product allocation decisions over a limited number of shelves, often taken in isolation. Such models incorporated interesting concepts relating to space and cross-space elasticity in the design of planograms. Although useful, these models have not addressed the bigger picture of planning store shelf space in a more holistic manner. It is important to note that the space planning analytics in the book are particularly important in an era where e-commerce is on the rise and brick-and-mortar retailing is declining and experiencing severe crises (the retail apocalypse).This is the first research-oriented book that examines novel problems in store space analytics, triggered by modern-day sensory technologies, customer trackers, and transactional tools (point-of-sales, etc.). In fact, such transformative technologies have prompted the development of new and exciting business practices, accompanied by the need for powerful data-driven models and analyses in retail shelf space and layout planning. The book will facilitate developing algorithms and decision tools that allow a better leverage of the data collected from these mediums.
Author(s): Ahmed Ghoniem, Bacel Maddah
Series: International Series in Operations Research & Management Science, 339
Publisher: Springer
Year: 2023
Language: English
Pages: 191
City: Cham
Preface
References
Contents
Effect of Customer Travel Behavior on Grid Layout and Shelf Space Allocation in Retail Facilities
1 Introduction
2 Prior Literature on Retail Facility Layout Design
3 Modeling Framework for Design and Evaluation of Grid Layouts
3.1 Layout Generation and Evaluation
3.2 Solution Methodology
4 Customer's Route Selection Strategy
4.1 Computational Results and Discussion
5 Conclusion
References
A Solver-Free Heuristic for Store-Wide Shelf Space Allocation
1 Introduction
2 Literature Review
3 Problem Statement and Optimization Model
3.1 Notation
3.2 Optimization Model for Shelf Space Allocation
4 Solver-Free Heuristic
5 Application to a Supermarket in Beirut
6 Conclusions and Directions for Future Research
References
In-Store Traffic Density Estimation
1 Introduction and Motivation
2 Literature Review
3 Shopping Basket Data and Store Description
4 Methodology
4.1 Traffic Density: Predictor Variables
4.2 Shelf-to-Shelf Distance Matrix
4.3 Regression Model
4.3.1 Support Vector Regression
4.3.2 Regression Tree
4.3.3 Kernel Regression
4.3.4 Gaussian Processes
4.3.5 Beta Regression
5 Results
6 Conclusions
References
A Simulation Based Tool to Guide Periodic Changes in a Supermarket Layout
1 Introduction
2 Relevant Literature
2.1 The Facility Design Problem
2.2 Store Layout
2.3 Impulse Purchases
2.4 Shelf Space Allocation
2.5 Travel Patterns
3 Optimization Framework
4 Block Layout Optimization
4.1 K-Medoids
4.2 Customer Profiles
4.3 Simulation
4.3.1 Generating Customer Shopping Lists
4.3.2 Customer Paths Generation
5 Detailed Layout
6 Evaluating Different Block Layouts
7 Case Study of a Grocery Store in Western New York
7.1 Data Collection and Analysis
7.1.1 Supermarket Layout
7.1.2 Customer Path Data
7.1.3 Customer Transaction Data: Creation of Customer Clusters and Must-Have and Impulse Item Designation
7.2 Computational Study
7.3 Managerial Insights
7.3.1 Changes in Customer Lifestyles
7.3.2 Impact of Item Prices
7.3.3 Changes in List of Products
8 Conclusions and Future Research
References
Data-Driven Analytical Grocery Store Design
1 Introduction
2 Literature Review
3 The Case Study
4 Methodology
4.1 The Apriori Algorithm
4.2 The High Utility Data Mining Algorithm
4.3 Characterizing Adjacencies
4.4 Shelf Space Allocation and the Revenue Function
5 Discrete-Event Simulation Modeling of the Migros Store
6 Tabu Search Optimization for Store Layout Design
7 Computational Experience
8 Conclusions and Discussion
References
Optimizing Stock-Keeping Unit Selection for Promotional Display Space at Grocery Retailers
1 Introduction
2 Literature Review
3 Methodology
3.1 Direct-Static Methodology
3.1.1 Sales Response Function
3.1.2 Incremental Display Profit
3.1.3 Static Optimization of SKU Choice
3.2 Hierarchical-Static Methodology
3.2.1 Sales Response Function
3.2.2 Incremental Display Profit
3.2.3 Static Optimization of the SKU Choice
4 Data Description
4.1 Estimation Data
4.2 Optimization Data (Single Store)
5 Application and Assessment of Methodology
5.1 Direct Estimation/Optimization
5.2 Hierarchical Estimation/Optimization
5.3 Static Benchmark Comparison
6 Dynamic Optimization
6.1 Application of the Dynamic Optimization
6.2 Benchmark Comparison for Dynamic Case
7 Conclusion
References
Merchandise Placement Optimization
1 The Value of Placement
1.1 Plan Modification Optimization (``PMO'')
1.2 Empirical Results
2 Forecasting the Value of Placement
2.1 Biclustering of Locational Impact on Demand
Demand Normalization and the Fractional Response Matrix
The Standard Response Matrix
The Biclustering Algorithm
2.2 Empirical Results
Example 1: 5 Items/3 Fixtures/Max 3 Clusters
Example 2: Disguised Store Data with 45 Items/7 Fixtures/Max 20 Clusters
2.3 A Note on Data Scarcity
The Fixture-Oriented Regression Model
Tackling the Sparsity of Fixture-IC Coefficient Matrices with Biclustering
3 Conclusions
Appendix
References
Problems and Opportunities of Applied Optimization Models in Retail Space Planning
1 Introduction
2 Shelf Space Allocation in Practice
3 State-of-the-Art Shelf Space Optimization Approaches
4 Blending Research and Practice
5 Improvements of Shelf Space Optimization in Practice
5.1 Additional Model Features
5.1.1 Integrating Shelf Space, Shelf Types and Store Layout Planning
5.1.2 Accounting for Product Grouping
5.1.3 Determine the Efficient Product Allocation Type
5.1.4 Extending Approaches to Multi-Store Concepts
5.1.5 Consider Omnichannel Opportunities
5.2 Enhanced Demand Effects
5.2.1 Explore the Effectiveness of Various Demand Effects
5.2.2 Consider Complementary Effects and Cross-Selling
5.2.3 Integrating Assortment Decisions if Decision-Relevant
5.3 Planning Approaches and Scope
5.3.1 Replanning Requires Rebuilding
5.3.2 Consider the Objectives of Different Stakeholders
6 Conclusion
References
