Kusha Ahmad is tasked with facilitating headcount reduction following the acquisition of the Societe Francaise de Biotechnologie (SFB) by Big American Pharmaceuticals (BAP), and the subsequent closure of the SFB office in Lyon, France. In accordance with regulations introduced in 2017, staff are entitled to a "rupture conventionnelle collective". With the aim of reaching unbiased and rational decisions, Kusha uses data analysis (exclusively) to identify which people to whom the offer will be made. The case follows Kusha as she develops a roadmap to take her from data to decision making. https://publishing.insead.edu/case/integration-planning-sfb-a
Isabelle, vice-president of customer loyalty and insight at a big bank, has led the development of a package of new products/services for clients, and a five-minute presentation to explain the offering. In a pilot test, where client managers randomly select walk-in customers and offer to go through the presentation, some agree to listen but others don't have the time. Several months later, when data about client profitability is available, she notices that average profit from clients who listened to the presentation is lower than those who did not. Disappointed by the outcome and at a loss to understand why, she pulls the customer-profile data hoping that data analysis will explain the decrease in profitability.
A bank manager is thinking about how to manage the new private banking advisers (PBA) practice in her region. For that she needs to determine how many PBAs she needs, where they should be located, and to which branches each of them should be assigned. This three-case series exposes students to the underlying sequence of analytical tasks, which culminate in solving an integer-programming optimization model-a main tool of prescriptive analytics. The assignment part of the problem (e.g., given the home-base locations of advisers, how should branches be assigned) is solvable in Excel, but the higher-level problem of where the PBAs should be located is beyond the scale of the built-in Excel solver. The case's teaching note recommends the Gurobi solver (which is available to students for free), that is controlled through a Python interface (which is on track to becoming world's most popular programming language). The (A) case presents the problem and data ""as they are."" The (B) case focuses on the descriptive analytics task of visualizing the branch locations to gain intuition. Tableau software is used with default and advanced mapping functionality. The (C) case focuses on the predictive analytics task of estimating travel times between branches. Python code for that is provided, which repeatedly calls Google Maps to obtain the travel times-a task known as the application programming interface (API). By following the A-B-C sequence, the students will have all they need to build the optimization model. With A-B, they have a lot of intuition but still need to estimate travel times. With just the A case, the situation is mostly open ended: it is up to the students to decide what to do, in addition to how to do it. The case is suitable for advanced undergraduate or MBA electives on analytics, or for fast-growing Masters in Analytics programs.
A bank manager is thinking about how to manage the new private banking advisers (PBA) practice in her region. For that she needs to determine how many PBAs she needs, where they should be located, and to which branches each of them should be assigned. This three-case series exposes students to the underlying sequence of analytical tasks, which culminate in solving an integer-programming optimization model-a main tool of prescriptive analytics. The assignment part of the problem (e.g., given the home-base locations of advisers, how should branches be assigned) is solvable in Excel, but the higher-level problem of where the PBAs should be located is beyond the scale of the built-in Excel solver. The case's teaching note recommends the Gurobi solver (which is available to students for free), that is controlled through a Python interface (which is on track to becoming world's most popular programming language). The (A) case presents the problem and data ""as they are."" The (B) case focuses on the descriptive analytics task of visualizing the branch locations to gain intuition. Tableau software is used with default and advanced mapping functionality. The (C) case focuses on the predictive analytics task of estimating travel times between branches. Python code for that is provided, which repeatedly calls Google Maps to obtain the travel times-a task known as the application programming interface (API). By following the A-B-C sequence, the students will have all they need to build the optimization model. With A-B, they have a lot of intuition but still need to estimate travel times. With just the A case, the situation is mostly open ended: it is up to the students to decide what to do, in addition to how to do it. The case is suitable for advanced undergraduate or MBA electives on analytics, or for fast-growing Masters in Analytics programs.
A bank manager is thinking about how to manage the new private banking advisers (PBA) practice in her region. For that she needs to determine how many PBAs she needs, where they should be located, and to which branches each of them should be assigned. This three-case series exposes students to the underlying sequence of analytical tasks, which culminate in solving an integer-programming optimization model-a main tool of prescriptive analytics. The assignment part of the problem (e.g., given the home-base locations of advisers, how should branches be assigned) is solvable in Excel, but the higher-level problem of where the PBAs should be located is beyond the scale of the built-in Excel solver. The case's teaching note recommends the Gurobi solver (which is available to students for free), that is controlled through a Python interface (which is on track to becoming world's most popular programming language). The (A) case presents the problem and data ""as they are."" The (B) case focuses on the descriptive analytics task of visualizing the branch locations to gain intuition. Tableau software is used with default and advanced mapping functionality. The (C) case focuses on the predictive analytics task of estimating travel times between branches. Python code for that is provided, which repeatedly calls Google Maps to obtain the travel times-a task known as the application programming interface (API). By following the A-B-C sequence, the students will have all they need to build the optimization model. With A-B, they have a lot of intuition but still need to estimate travel times. With just the A case, the situation is mostly open ended: it is up to the students to decide what to do, in addition to how to do it. The case is suitable for advanced undergraduate or MBA electives on analytics, or for fast-growing Masters in Analytics programs.
This case, along with its B case (UVA-QA-0865), is an effective vehicle for introducing students to the use of machine-learning techniques for classification. The specific context is predicting customer retention based on a wide range of customer attributes/features. The specific techniques could include (but are not limited to): regressions (linear and logistic), variable selection (forward/backward and stepwise), regularizations (e.g., LASSO), classification and regression trees (CART), random forests, graduate boosted trees (xgboost), neural networks, and support vector machines (SVM). The case is suitable for an advanced data analysis (data science, machine learning, and artificial intelligence) class at all levels: upper-level business undergraduate, MBA, EMBA, as well as specialized graduate or undergraduate programs in analytics (e.g., masters of science in business analytics [MSBA] and masters of management analytics [MMA]) and/or in management (e.g., masters of science in management [MScM] and masters in management [MiM, MM]). The teaching note for the case contains the pedagogy and the analyses, alongside the detailed explanations of the various techniques and their implementations in R (code provided in Exhibits and supplementary files). Python code, as well as the spreadsheet implementation in XLMiner, are available upon request.
This case, along with its A case (UVA-QA-0864), is an effective vehicle for introducing students to the use of machine learning techniques for classification. The specific context is predicting customer retention based on a wide range of customer attributes/features. The specific techniques could include (but are not limited to): regressions (linear and logistic), variable selection (forward/backward and stepwise), regularizations (e.g., LASSO), classification and regression trees (CART), random forests, graduate boosted trees (xgboost), neural networks, and support vector machines (SVM). The case is suitable for an advanced data analysis (data science, machine learning, and artificial intelligence) class at all levels: upper-level business undergraduate, MBA, EMBA, as well as specialized graduate or undergraduate programs in analytics (e.g., masters of science in business analytics [MSBA] and masters of management analytics [MMA]) and/or in management (e.g., masters of science in management [MScM] and masters in management [MiM, MM]). The teaching note for the case contains the pedagogy and the analyses, alongside the detailed explanations of the various techniques and their implementations in R (code provided in Exhibits and supplementary files). Python code, as well as the spreadsheet implementation in XLMiner, are available upon request.
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.
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.