Express Bike Works: Washing in Style

Arun Sasi; Amol S. Dhaigude

Product #:9B20E009

Supplier:Ivey

Discipline:Entrepreneurship, Management Science, Sustainability

Setting:India, 2018

Details:Case (PDF) • 4 pages • 2020-04-28

Subjects:

Geography:

Asia India

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$9.71

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In April 2018, the owner of Express Bike Works (EBW), an Indian start-up that provided automated motorcycle washing, among other services, was planning to expand his business to various locations in South India, such as Udupi, Chennai, and Bangalore. He needed to decide whether to expand through self-owned stores or franchise stores and wondered how to select appropriate store locations. His objective was to come up with an expansion strategy that would maximize his payoff.

Learning Objectives

This case is intended to teach decision theory in undergraduate- and graduate-level courses in management, industrial management, and industrial engineering. It introduces the concepts of decision theory analysis (probabilistic decision models) and deals with decision tree construction using the manual method and the TreePlan tool. Students will calculate decision tree development and payoff calculations. The case also involves analysis of the expected value of perfect information, Bayes’ theorem, analysis of the expected value of sample information, and sensitivity analysis of probability estimates. Finally, this case provides insights into the challenges associated with the management of start-ups. After working through the case and assignment questions students will be able to

make decisions in contexts that involve risk;
develop a decision tree from given data and calculate expected payoffs with the expected monetary value approach;
understand the Excel TreePlan software and draw a decision tree;
evaluate the expected value of perfect information and associate it with decision making;
explain the concepts of prior probability, conditional probability, joint probability, unconditional probability, and posterior probability (Bayes’ theorem);
evaluate the expected value of sample information and associate it with decision making; and
describe the sensitivity of decision tree output using a simulation.