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Outsourcing, Near-sourcing, and Supply Chain Flexibility in the Apparel Industry (A)
Ovchinnikov, Anton S.; Pyshkov, Alexander Case QA-0854 / Published October 14, 2016 / 5 pages.
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Product Overview

Timm Veizenburg is an entrepreneur who is reviving his family's tradition of dress-shirt making. He realizes that an issue that initially looked like a markdown management problem may, in fact, be rooted in procurement. Operating in the modern data-rich environment, Timm asks an associate to prepare data about one product line, and together they examine the company's current procurement practice, where the production is outsourced to supply chain partners in western Ukraine, as well as two alternatives, with local sourcing and supply chain flexibility. The case presents a realistic dataset students can work with to master their data-analysis and modeling skills, with an application to operations and supply chain analytics. The case works well in business analytics electives within an MBA program, in specialized business analytics programs, as well as in Executive Education, and is effective in demonstrating approaches to data-driven decision making in managing operations and supply chains.

Learning Objectives

Obtain data-driven understanding of operations and supply chain management in fashion/apparel retail industry; Understand and be able to analyze the key tradeoffs in outsourcing versus near-sourcing and in supply chain flexibility; Improve the ability to make data-driven business decisions; Improve specific analytical skills needed to perform regression analysis, Monte Carlo simulation analysis and interpretation; Combine data analysis and simulation; Optimize business decisions based on data analysis and simulation; Build spreadsheet models, or depending on the instructor's approach code/build models in other software, such as R; Visualize data in Excel or by using specialized software, such as Tableau.

  • Overview

    Timm Veizenburg is an entrepreneur who is reviving his family's tradition of dress-shirt making. He realizes that an issue that initially looked like a markdown management problem may, in fact, be rooted in procurement. Operating in the modern data-rich environment, Timm asks an associate to prepare data about one product line, and together they examine the company's current procurement practice, where the production is outsourced to supply chain partners in western Ukraine, as well as two alternatives, with local sourcing and supply chain flexibility. The case presents a realistic dataset students can work with to master their data-analysis and modeling skills, with an application to operations and supply chain analytics. The case works well in business analytics electives within an MBA program, in specialized business analytics programs, as well as in Executive Education, and is effective in demonstrating approaches to data-driven decision making in managing operations and supply chains.

  • Learning Objectives

    Learning Objectives

    Obtain data-driven understanding of operations and supply chain management in fashion/apparel retail industry; Understand and be able to analyze the key tradeoffs in outsourcing versus near-sourcing and in supply chain flexibility; Improve the ability to make data-driven business decisions; Improve specific analytical skills needed to perform regression analysis, Monte Carlo simulation analysis and interpretation; Combine data analysis and simulation; Optimize business decisions based on data analysis and simulation; Build spreadsheet models, or depending on the instructor's approach code/build models in other software, such as R; Visualize data in Excel or by using specialized software, such as Tableau.