Thesis subject

Enhancing Efficiency and Decision-Making in Retail Operations through Data Analytics (BSc / MSc)

Efficiency and precision are the lifeblood of retail operations. In a global context, data analytics has emerged as the catalyst for transformative change. This thesis project embarks on a journey within SPAR’s complex supply chain.

Short description

This thesis will focus on optimizing retail operations at SPAR using data analytics. The goal is to improve operational efficiency and streamline decision-making. By employing advanced data analysis, this thesis seeks to insights that can significantly impact operational efficiency and decision making.

In this thesis, transactional data and supply chain data is made available by SPAR International. SPAR is the world’s largest voluntary food retail chain with over 13,623 stores in 48 countries worldwide with global sales of €41.2 BN. At SPAR, a wealth of data is meticulously collected and stored, spanning across a diverse array of products and stores.

Objectives and tasks

The following research objectives can be explored:

  1. Intelligent Reporting: Develop a reporting solution that provides insights and data-driven recommendations for strategic decision-making across various retail projects.
  2. Anomaly Detection: Identify anomalies and unusual patterns in retail operations data, enabling proactive management of operational issues.
  3. Network Analysis: Analyse the flow of products through the SPAR logistics network, identifying opportunities to reduce lead times and enhance end-to-end efficiency.
  4. Demand Sensing: Investigate methods to predict future consumer demand for products and services, shaping supply chain requirements and network development strategies.

Literature

  • Qi, M., Mak, H. Y., & Shen, Z. J. M. (2020). Data‐driven research in retail operations—A review. Naval Research Logistics (NRL), 67(8), 595-616. https://doi.org/10.1002/nav.21949

Requirements

  • Courses: Programming in Python (INF-22306), Statistics (MAT), Big Data (INF-33806) or Machine Learning (FTE-35306) or Business Information Analytics (INF-37306)
  • Required skills/knowledge: Experience in data analytics and willingness to learn new data-driven tools, general interest in the retail sector and operational excellence

    Key words: Data Science, Machine Learning, Retail, Food, KPI, Dashboard, Operation Anomalies, Supply Chain Networks, Value Chain, Demand Forecasts, Business Analytics.

    Contact person(s)

    Sander Breevaart (sander.breevaart@wur.nl)