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On June 25, 2021, Professor Pratik Modi, chairperson of the postgraduate program at the Institute of Rural Management Anand (IRMA), attended a meeting with the director and chairpersons of different academic areas and programs, where it was decided that the upper limit on the number of elective courses offered by each area should be done away with, considering the requirements of students and faculty members. Although the decision to remove the upper limit seemed to be beneficial to all parties, it would pose a challenge in terms of scheduling classes, given the number of constraints involved. Modi was determined that a solution could be found using the tools and techniques of operations research, which was taught as one of the core subjects at IRMA.

This exercise aims to teach students about 1) Targeting Policies; and 2) Algorithmic decision making, and 3) Retention management.

Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a "comp sheet" of similar properties that have recently sold, and has done a walkthrough. The iBuyer has access to an automated valuation model powered by real, large-scale market transaction data, but has not seen the property in person. The simulation provides students with an opportunity for experiential learning around the use of data and algorithms to inform market decisions. This role is the updated confidential information for iBuyers, to be distributed for a second round of play. It is one of eight role documents to be assigned to students in the simulation.

Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a "comp sheet" of similar properties that have recently sold, and has done a walkthrough. The iBuyer has access to an automated valuation model powered by real, large-scale market transaction data, but has not seen the property in person. The simulation provides students with an opportunity for experiential learning around the use of data and algorithms to inform market decisions. This role is the updated confidential information for homebuyers, to be distributed for a second round of play. It is one of eight role documents to be assigned to students in the simulation.

Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a "comp sheet" of similar properties that have recently sold, and has done a walkthrough. The iBuyer has access to an automated valuation model powered by real, large-scale market transaction data, but has not seen the property in person. The simulation provides students with an opportunity for experiential learning around the use of data and algorithms to inform market decisions. This role is the confidential information for iBuyer 3, and is one of eight role documents to be assigned to students in the simulation.

Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a "comp sheet" of similar properties that have recently sold, and has done a walkthrough. The iBuyer has access to an automated valuation model powered by real, large-scale market transaction data, but has not seen the property in person. The simulation provides students with an opportunity for experiential learning around the use of data and algorithms to inform market decisions. This role is the confidential information for iBuyer 2, and is one of eight role documents to be assigned to students in the simulation.

Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a "comp sheet" of similar properties that have recently sold, and has done a walkthrough. The iBuyer has access to an automated valuation model powered by real, large-scale market transaction data, but has not seen the property in person. The simulation provides students with an opportunity for experiential learning around the use of data and algorithms to inform market decisions. This role is the confidential information for iBuyer 1, and is one of eight role documents to be assigned to students in the simulation.

Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a "comp sheet" of similar properties that have recently sold, and has done a walkthrough. The iBuyer has access to an automated valuation model powered by real, large-scale market transaction data, but has not seen the property in person. The simulation provides students with an opportunity for experiential learning around the use of data and algorithms to inform market decisions. This role is the confidential information for Homebuyer 3, and is one of eight role documents to be assigned to students in the simulation.

Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a "comp sheet" of similar properties that have recently sold, and has done a walkthrough. The iBuyer has access to an automated valuation model powered by real, large-scale market transaction data, but has not seen the property in person. The simulation provides students with an opportunity for experiential learning around the use of data and algorithms to inform market decisions. This role is the confidential information for Homebuyer 2, and is one of eight role documents to be assigned to students in the simulation.

Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a "comp sheet" of similar properties that have recently sold, and has done a walkthrough. The iBuyer has access to an automated valuation model powered by real, large-scale market transaction data, but has not seen the property in person. The simulation provides students with an opportunity for experiential learning around the use of data and algorithms to inform market decisions. This role is the confidential information for Homebuyer 1, and is one of eight role documents to be assigned to students in the simulation.

This exercise and its accompanying handouts, designed to be used in courses for non-MBA audiences that explore aspects of entrepreneurship, guide students through the development of their very own business plan. From researching and deciding on a business concept, to developing prospective customer profiles and understanding target markets, to creating financial and operations projections, this set of materials helps students step by step as they build a deliverable, functional business plan over the course of multiple class sessions.

This role-playing exercise aims to tackle issues related to inter-organizational collaboration to scale up innovations. The role-playing experience helps learners discover, appreciate, and learn to govern inherent tensions in inter-organizational collaboration. The exercise is set in the Canadian auto parts industry, which could benefit tremendously from more sophisticated robotic technology to assist the manufacturing process. The role play asks learners to engage in an inter-organizational collaboration agreement. Each participant will represent a company whose in-house knowledge, resources or capabilities can contribute to developing a robot.

In the summer of 2022, it became clear that Netflix would introduce an ad-supported tier alongside its existing subscription plans in the near future. Speculation abounded as to the details of the new tier: How many minutes of advertising would it include? What picture quality would it offer? How many screens could be viewed at the same time? And, critically, how much would it cost the consumer per month? In this exercise, students are tasked with trying to assess whether Netflix should indeed go ahead with introducing a new ad-supported tier - and if so, how? - or whether the company would be better off sticking with its existing tiers and repricing them - and if so, by how much? Students need to evaluate which of these options would maximize the number of subscribers, and importantly, profits. Students have at their disposal results from market research (specifically a conjoint analysis study), financial reports, and industry data. The exercise also allows for a conceptual discussion of the merits of alternative revenue models: ad-driven vs. pure subscription-driven vs. a hybrid of the two.

Joy4Home Brands, the maker of novel houseware items, was gearing up for its launch. The company would be introducing two lines: kitchenware products and storage containers. The initial go-to-market plan called for a direct to consumer (DTC) channel strategy. While Joy4Home had a handle on its customer acquisition efforts, it had yet to determine the DTC pricing for each line. Moreover, two additional opportunities had recently emerged. The first was a B2B opportunity involving a modified kitchenware line, and the second was a brick-and mortar wholesale proposal for larger storage containers. The Chief Marketing Officer (CMO) had market research data and other information to help her determine the optimal pricing scheme for these various sales avenues. Recommendations were needed soon.

This exercise can be used in conjunction with the main case "Zalora: Data-Driven Pricing" to facilitate class discussion without requiring data analysis from the students. Instead, the exercise presesnts reports that were created by the data science team to answer the pricing question presented in the case. The students analyze those reports to facilitate learnings about data genertaing process, descriptive analysis, statistical analysis, and other aspects important for data driven decision making.

Vroom Inc. (Vroom) was a video conferencing technology company located in London, Ontario, Canada. The founder of Vroom was reviewing the company’s financial performance for its second fiscal year. He had already reviewed the company’s operating decisions over the past fiscal year and was now eager to review the company’s financing and investing transactions.

Vroom Inc. (Vroom) was a video conferencing technology company located in London, Ontario, Canada. The founder of Vroom was reviewing the company's financial performance for its second fiscal year. He had already reviewed the company's operating decisions over the past fiscal year and was now eager to review the company's financing and investing transactions.

This exercise helps students practice bringing vulnerability, compassion, and curiosity to a discussion. Over two rounds, groups of students are asked to both answer questions about themselves and listen as others answer, sharing in an environment of confidentiality and care.

Maya Fonseca, the marketing analyst for video streaming service FilmCast, and her colleague Rupert Cruz had to interpret the results of a conjoint analysis conducted by their company. FilmCast was a large company, competing against Videosource and Webflickstream. The marketing analysts were debating whether it made sense to lower the price of their services and if the conjoint analysis results supported this idea.