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NEW CASES IN MANAGEMENT SCIENCE

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New Cases In Management Science

9B17E010

Mobile Blood Donor Clinic: A Discrete Event Simulation ModelRasha Kashef; Felipe Rodrigues;

While donating blood on campus, a management science graduate student noticed that the mobile blood donor clinic set up at his university’s community centre was a congested tandem queuing system. Finding one-and-one-half hours too long for donors to wait, the student considered how the process could be reduced by at least half an hour. He needed to devise a reasonably precise model to represent the donor flow in the clinic. Using either the mode or the average service times supplied by the nurses, the student could build a relatively straightforward discrete-event simulation model to identify bottlenecks and improve the donor flow.

Publication Date: August 17, 2017Discipline: Management Science;Length: 4 pagesIssues: queuing models, simulationIndustry: Health Care Services;Setting: Canada, Small organization, 2017Difficulty: Undergraduate/MBALearning Objective: This case is suitable for undergraduate classes in honours business administration or for MSc and MBA simulation courses. Previous knowledge of basic statistical distributions and formulas in Microsoft Excel is required for the case. This case provides an introduction to basic queuing theory, bottleneck analysis, and discrete-event simulation models. After completion of the case, students will be able toexplain basic queuing theory;create a simple discrete-event simulation model;analyze bottlenecks identified in the simulation model; andpropose changes to reduce bottlenecks in the simulation model.

9B17E008

JSW Steel Ltd.: A Logistics DilemmaAmol Dhaigude; Debmallya Chatterjee; Vishnu Kumar;

The customer relationship manager at JSW Steel Ltd., a large steel manufacturer in India, needed to analyze his available transportation and logistics options to meet an urgent order for a long-time and valued client. The manager needed to decide whether to send the shipment through the customary rail route or, instead, to use the new sea route that his company had recently developed. His dual objective was to meet the customer's requirements in time, while also delivering some financial benefit to boost his company’s quarterly results.

Publication Date: June 16, 2017Discipline: Management Science;Length: 7 pagesIssues: transportation, decision tree analysis, logisticsIndustry: Manufacturing;Setting: India, Large organization, 2015

Difficulty: MBA/PostgraduateLearning Objective: This case introduces decision tree analysis, specifically within a logistics set-up, to postgraduate students in management, industrial management, or industrial engineering courses. Students will utilize the data provided in the case to develop the decision tree and the expected payoffs. The case also provides insights on steel manufacturing and the challenges associated with that industry. After completion of the case, students will be able tounderstand decision-making under risk in logistics management;develop a decision tree from provided data and calculate the expected payoffs using the expected monetary value approach;analyze decision alternatives and their associated risks in decision-making; andevaluate the expected value of perfect information and associate the concept with decision-making.

9B17A033

Harley’s Corner: Positioning Dilemma in the Pet Food MarketMadhurima Deb;

In January 2016, the chief executive officer of Harley’s Corner, an India-based online retailer of gourmet dog food, faced a decision dilemma. He needed to decide on the most suitable positioning strategy for the business, which competed against several well-known brands, including other fresh, gourmet, home-cooked food for pets. Harley’s Corner needed to convey a proposition that ran deeper than simply representing an “all-natural, home-cooked” fresh pet food. How could he differentiate the offerings in a relevant way to retain his loyal customers, attract new customers, and thereby grow the business? What was the best way to position his brand, Harley’s Corner, which catered to a niche segment?

Publication Date: June 14, 2017Discipline: Marketing; Entrepreneurship; Management Science;Length: 14 pagesIssues: positioning strategy, multiple regression analysisIndustry: Other Services;Setting: India, Small organization, 2015-16Difficulty: MBA/PostgraduateLearning Objective: This case is appropriate for graduate, postgraduate, and executive education courses in marketing management, business research methods, marketing research, and management science. After completion of the case, students will be able to

describe positioning strategy and understand its relevance, andapply multiple regression analysis to design a positioning strategy that can provide a competitive positioning.

9B17E005

RBC: Social Network AnalysisPeter C. Bell; Ramasastry Chandrasekhar;

In October 2013, the Royal Bank of Canada (RBC), Canada’s largest bank, hired a new head of Enterprise Fraud Strategy, a department tasked with protecting RBC’s global customers from fraud. The department head’s immediate priority was to prevent fraudulent transactions by RBC’s own customers—a phenomenon called first-party fraud—by implementing a bourgeoning technology called social network analysis (SNA). The technology used predictive analytics and big data to forecast the occurrence of first-party fraud. The head of Enterprise Fraud Strategy had three primary questions: First, how should SNA be used to bring down the ratio of fraud alerts to actual fraud at RBC? Second, how should the cost of maintaining SNA protocols be reduced? Finally, how should the issues around systemic performance of SNA be resolved?

Publication Date: June 13, 2017Discipline: Management Science;Length: 8 pagesIssues: big data, data analytics, fraudIndustry: Finance and Insurance;Setting: Canada, Large organization, 2013Difficulty: Undergraduate/MBALearning Objective: Students should take on the perspective of the case’s central figure to find out which rule (or combination of rules) provides the best fraud detection for RBC and how effective these rules would be if implemented for all RBC customers. After using the case, students will be able to do the following:Highlight the features of first-party fraud, particularly its “connected explosion.”Discuss the unique attributes of social network analysis.Demonstrate the importance of data analytics in detecting first-party fraud.Identify the important trade-offs in fraud detection.Use advanced analytical methods to test out various fraud detection rules and construct a “best” model for fraud detection.

9B17E004

LUX* Resorts & Hotels: Optimal Room Mix Marketing DecisionsSingfat Chu; Nitesh Pandey;

In early 2016, LUX* Resorts & Hotels, a hotel group headquartered in Mauritius, needed to determine the room mix to sell to foreign guests to achieve a specific earnings target. The hotel group also intended to sell some of the remaining rooms to local residents at affordable (discounted) prices. The objective was to maximize the revenue stream to finance local corporate social responsibility projects. The case highlights the practical use of Microsoft Excel Solver, an optimization tool, to resolve routine planning challenges faced by a hotel group.

Publication Date: May 17, 2017Discipline: Management Science; International;Length: 4 pagesIssues: hospitality, hotel, optimization, EBITA, discounting, CSRIndustry: Accommodation & Food Services;Setting: Mauritius, Large organization, 2016Difficulty: Undergraduate/MBALearning Objective: This case is suitable for undergraduate, graduate, and specialized courses in analytics, finance, and hospitality management. It demonstrates how an optimization

tool, Microsoft Excel Solver, can be effectively applied to routine hospitality management problems, which are often tackled through time-consuming, trial-and-error attempts. After completing the case, students should be able to

use Microsoft Excel to determine the correct room mix to achieve a hotel’s earnings target;understand why and how to use an optimization template to resolve hospitality management issues;consider how to make an earnings target easier to achieve; anddiscuss alternative ways a hotel can increase its corporate social responsibility project financing.

9B17E001

Asian Grill: Finding the Optimal Table MixKyle Maclean; Srinivas Krishnamoorthy;

In early December 2016, the owner of the Asian Grill restaurant in Henderson, Nevada, noticed that the restaurant had busy nights where customers left due to long wait times, yet there were empty seats due to small groups occupying large tables. She had a proposed new table layout before her and had to evaluate whether this would allow the restaurant to seat more customers on her busiest nights (Thursday to Saturday), or whether a different table layout would be even better.

Publication Date: January 30, 2017Discipline: Management Science;Length: 3 pagesIssues: queuing, simulation, restaurant industry, optimization, Excel, @RISKIndustry: Accommodation & Food Services;Setting: United States, Small organization, 2016Difficulty: Undergraduate/MBALearning Objective: This case provides students with an opportunity to model a complicated yet common service-industry problem. From a technical side, it presents a common, easy-to-understand situation that can be used for creating and developing a queuing simulation. The case is intended for use in either an advanced undergraduate business course or an MBA environment. The content requires familiarity with modelling and analytics content (queuing simulations). More advanced students can use this case to practice simulation optimization through the use of third-party software such as @RISK. After working through the case and assignment questions, students will be able tounderstand how modelling techniques can be used in operations;identify a variety of situations where modelling can be useful; andexplain the benefits of variance-reduction techniques.

9B17E002

Linear Regression: A High-Level OverviewGregory S. Zaric;

Linear regression is a common tool used in statistics and is considered the foundation for most predictive analytics. It creates a line of best fit in a data set and uses that line to explain the relationship between two quantities, which helps forecast future values. This note provides a high-level, non-technical overview of linear regression.

Publication Date: January 27, 2017Discipline: Management Science;Length: 5 pagesIssues: statistics, linear regression, analytics, forecasting, regression analysis

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Difficulty: MBA/PostgraduateLearning Objective: This note is intended as preparatory reading for a senior management-level workshop on big data or analytics. The note contains examples of graphs and formulae and explains how these can be applied in various business situations.

9B16E037

Risks and Rewards in Professional TennisFredrik Odegaard;

Founded in 1972, the Association of Tennis Professionals (ATP) was the governing body of three professional men’s tennis circuits: the ATP World Tour, the ATP Challenger Tour, and the ATP Champions Tour. In addition to the ATP World Tour tournaments, there were four highly coveted and extremely competitive Grand Slam tournaments, one of which was Wimbledon. In 2014, two professional tennis players sought to determine their respective odds of placing first at Wimbledon, by using an understanding of probability, stochastic modelling, and Markov chain application. Both players were ranked outside the top 100 and faced a momentous task.

Publication Date: December 22, 2016Discipline: International; Management Science;Length: 7 pagesIssues: probability, stochastic process, Markov chain, sports analytics, tennisIndustry: Arts, Entertainment, Sports and Recreation;Setting: United Kingdom, 2014Difficulty: Undergraduate/MBALearning Objective: This case is suitable for undergraduate and MBA level courses on analytics. Using the case, students will learn about the following concepts:Probabilities and expected value.Stochastic modelling.Decision/event tree.Markov chain application with finite time horizon.Matrix multiplication in Microsoft Excel.

9B16E033

Food Truck ForecasterMehmet A. Begen; Jen Littleton;Samantha Wong;Rob Yellin;

The owner of a food truck based in Hamilton, Ontario, was looking over the first year of her operations. In addition to working in Hamilton, she had tried to maximize her revenues by driving to several other cities and charging various prices for each burger, depending partly on the fresh ingredients available in each city. Besides location, the owner had collected data on a few other factors—the weather, the day of the week, the city’s population, and whether a festival was going on—that had had an impact on the demand for her product. She wondered whether analytics could help her decide where to sell and how much to charge on a daily basis. The owner also wondered whether this decision-making and data-collection process could be automated since she would be using it every day.

Publication Date: November 14, 2016Discipline: Management Science;Length: 4 pagesIssues: regression, simulation, Excel VBA, predictionIndustry: Accommodation & Food Services;Setting: Canada, Small organization, 2014

Difficulty: Undergraduate/MBALearning Objective: To use a mix of analytics techniques such as regression and simulation to build a spreadsheet model that can help the decision-makerTo use Visual Basic for Applications (VBA) concepts to make the spreadsheet user-friendly and automate some of the tasks in the model

9B16E030

Transfer Value of Soccer PlayersHubert Pun; Philip Kim;

In 2016, a soccer fan closely followed the scores, standings, and transfers of players in the European soccer leagues. Having witnessed countless exchanges of players from one team to another, she wondered about the factors that team managers considered when determining the players’ market values. She gathered data on 77 notable soccer players in an effort to better understand the essential factors that made the players valuable. Could she use the variables she had collected to also build a functioning model that would predict a player’s market value?

Publication Date: October 12, 2016Discipline: Management Science; International;Length: 2 pagesIssues: linear regression, statistics, sport, analyticsIndustry: Arts, Entertainment, Sports and Recreation;Setting: 2015Difficulty: Undergraduate/MBALearning Objective: This case is suitable for a management science elective course focusing on linear regression at the MBA or undergraduate level. After completion of the case, students will be familiar with the following concepts and techniques:correlation and multicollinearity;continuous, binary, and categorical variables;the criteria for using linear regression;statistical significance and p-values; andusing and interpreting the linear regression model

9B16E029

HomeZilla: Attracting Homebuyers through Better PhotosGregory S. Zaric; Hongmei Sun;

In November 2014, the founder and chief executive officer of HomeZilla, in Toronto, Canada, was considering how to provide value-added services to his business. One of the company’s main services was to work with real estate agents to provide web listings to attract home shoppers. Many Internet companies were analyzing web-browsing data in an effort to better understand user behaviour and thus improve their business. Inspired by this trend, the founder considered how his company could use web-browsing data to better attract home shoppers on the company’s website and thereby add value to his business.

Publication Date: September 27, 2016Discipline: Management Science;Length: 6 pagesIssues: statistics, data science, regression, big dataIndustry: Other Services;Setting: Canada, Small organization, 2014Difficulty: Undergraduate/MBALearning Objective: After completion of the case, students will have a familiarity with several Microsoft Excel tools and concepts, including data cleaning, descriptive data analysis, exploratory

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analysis, pivot tables, hypothesis tests, logistic regression, principal component analysis, statistical analysis using R, and survival models, including the proportional hazards model. The case is accompanied by a Microsoft Excel file of a large data set with more than 29,000 rows of page views.

9B16E023

Lucas Wang: Stop-Loss StrategyHubert Pun; Hongmei Sun;

On February 29, 2016, an investor, who was a business school graduate, purchased one share of Tesla Motors, Inc. stock for $191.93. In doing so, the investor wondered what trading strategy should be followed over the next six months to maximize returns while minimizing risk. In choosing a strategy, he wanted to make good use of the knowledge gained from his financial analytics classes. To that end, he did not know if he should choose a buy andhold, or a stop-loss strategy with an optimal stop-loss threshold. The investor wondered how he would make this decision. Would a six month comparison of the stop-loss strategy with thresholds from 1 per cent to 99 per cent versus the buy and hold strategy resolve his dilemma?

Publication Date: July 07, 2016Discipline: Management Science;Length: 4 pagesIssues: efficient frontier; simulation; @RISK

Setting: Canada, 2016Difficulty: Undergraduate/MBALearning Objective: This case is suitable for use in MBA and other graduate-level programs in courses on financial analytics and stock trading strategies. After completion of the case, students will be able to:evaluate stop-loss and buy and hold strategies;calculate the lognormal model for a stock price;determine an efficient frontier for stop-loss strategies;use simulation to evaluate trading strategies and risk preference; anduse @RISK software to calculate returns.

9B16E022

Estimating Cisco's Future Cash FlowsHubert Pun; Salar Ghamat;

In March 2016, an investor wanted to evaluate the strategy and stability of Cisco Systems, Inc. by using historical income statements to predict future cash flows. He knew that he might be able to use time series analysis to evaluate the company’s performance in the near future, but he was unsure how to proceed if the historical data would not be predictive of the future performance. In particular, the investor wondered, how would he determine the likelihood that the company’s next quarter would yield similar net income to the previous quarter?

Publication Date: July 06, 2016Discipline: Management Science;Length: 5 pagesIssues: regression analysis, time series, simulation, normally distributed randIndustry: Information, Media & Telecommunications;Setting: 2016Difficulty: Undergraduate/MBALearning Objective: This case is suitable for an undergraduate or MBA management science or finance course. After completion of

this case, students should be able to:be familiar with the concept of time series;understand regression analysis;be familiar with normally distributed random variables;understand non-negative random numbers; andbe familiar with the concept of simulation.

9B16E011

Absolute and Relative References In Microsoft ExcelKyle Maclean; Lauren E. Cipriano; Gregory S. Zaric;

The purpose of this note is to illustrate how to use cell references in Microsoft Excel. References are necessary when using functions and building models. This teaching note provides students with practice exercises and a supporting Excel workbook.

Publication Date: June 30, 2016Discipline: Management Science;Length: 11 pagesIssues: Excel, spreadsheet, modelling , analytics

Difficulty: Undergraduate/MBALearning Objective: Upon completion of this note and its associated activities, students will be able to:

Understand the difference between absolute and relative referencing.Know when it is appropriate to use each type of referencing.Know the keyboard shortcuts to quickly implement each type of referencing.

9B16E016

Advanced Logic Functions in Microsoft ExcelKyle Maclean; Lauren E. Cipriano; Gregory S. Zaric; John Lyons;

The purpose of this technical note is to introduce and illustrate the use of Microsoft Excel’s built-in advanced logic functions. Excel’s advanced logic functions add logical capabilities to normal descriptive statistics functions. The note includes practice exercises and their solutions, and a supporting Excel workbook for students.

Publication Date: June 30, 2016Discipline: Management Science;Length: 12 pagesIssues: spreadsheet, modelling, analytics, Excel

Difficulty: Undergraduate/MBALearning Objective: After completing this technical note, students will be able to do the following:Use advanced logic functions in Microsoft Excel (SUMIF(S),COUNTIF(s),AVERAGEIF(S))Recognize situations when these functions may be useful

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9B16E010

Basic Mathematical Operations in Microsoft ExcelKyle Maclean; Lauren E. Cipriano; Gregory S. Zaric;

This teaching note provides step-by-step illustrations and practice problems to assist students in learning how to use basic mathematical functions in Microsoft Excel. These functions are the necessary building blocks for many spreadsheet models. This teaching note includes practice exercises, answers, and a supporting Excel workbook for students.

Publication Date: June 30, 2016Discipline: Management Science;Length: 14 pagesIssues: Excel , spreadsheet, modelling , analytics

Difficulty: Undergraduate/MBALearning Objective: This case is suitable for use in MBA and other business related courses that require students to develop spreadsheets using Excel. After completion, students will be able to: Perform basic mathematical operations in a spreadsheet using +, –, *, /, and ^.Understand the structure of Excel’s built-in functions.Use built-in functions to perform the mathematical operations SUM, PRODUCT, SQRT, EXP, LN, and SUMPRODUCT.

9B16E017

Data Tables in Microsoft ExcelKyle Maclean; Lauren E. Cipriano; Gregory S. Zaric;

The purpose of this note is to illustrate the use of Data Tables in Microsoft Excel. Data Tables are commonly used for performing sensitivity analyses, sometimes called “What If?” analyses, and for preparing data for graphs. Data Tables can also be used to enable a Monte Carlo simulation in a spreadsheet in more advanced models. The technical note includes practice exercises and their solutions, and a supporting Excel workbook for students.

Publication Date: June 30, 2016Discipline: Management Science;Length: 22 pagesIssues: Excel, spreadsheet, modelling, analytics

Difficulty: Undergraduate/MBALearning Objective: After completion of this technical note and its associated activities, students will be able to use the Data Tables feature in Microsoft Excel to do the following:Build one-way Data Tables with one or more outputs.Build two-way Data Tables.Control when the spreadsheet updates the calculations.

9B16E014

Descriptive Statistics in Microsoft ExcelKyle Maclean; Lauren E. Cipriano; Gregory S. Zaric;

The purpose of this note is to illustrate the use of Microsoft Excel functions for generating descriptive statistics for continuous data. These functions are common when analyzing data to provide insights for decision making. The note includes practice exercises and their solutions, and a supporting Excel workbook for students.


Difficulty: Undergraduate/MBALearning Objective: After completion of this note and its associated activities, students should be able to do the following:

Use the Microsoft Excel functions that identify the minimum, maximum, rank, average, median, variance, and standard deviation of a single continuous variable data set.Use the Microsoft Excel functions for computing the covariance and the correlation between two continuous variables in a data set.

9B16E018

Goal Seek in Microsoft ExcelKyle Maclean; Lauren E. Cipriano; Gregory S. Zaric;

The purpose of this note is to illustrate the use of Microsoft Excel’s Goal Seek feature. The technical note includes practice exercises and their solutions, and a supporting Excel workbook for students.


Difficulty: Undergraduate/MBALearning Objective: After completion of this note and its related activities, students will be able to do the following: Perform an analysis using Excel’s Goal Seek feature.Recognize situations when Goal Seek would be useful.

9B16E012

Good Modelling Practices in Microsoft ExcelKyle Maclean; Lauren E. Cipriano; Gregory S. Zaric;

The purpose of this note is to illustrate the use of good modelling practices in Microsoft Excel. An effective spreadsheet model can be a valuable decision-making tool for businesses wanting to improve or expand their operations. To do so, a model must provide information relevant to the decision maker.


Difficulty: Undergraduate/MBALearning Objective: Upon completion of this note, students will be

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able to: Clearly state inputs and assumptions,Separate model inputs from analysis,Reliably perform analyses, andCreate a spreadsheet designed to enable understanding and use of the model by other users.

9B16E015

Histograms and the Normal Distribution in Microsoft ExcelKyle Maclean; Lauren E. Cipriano; Gregory S. Zaric;

Histograms are graphs that provide a large amount of useful information for all types of data and display the frequency of observations in a data set. The purpose of this note is to introduce histograms for several types of data, provide examples of insights that can be gained and calculations that can be performed using histograms, and demonstrate how to build histograms in Microsoft Excel. In addition, this note reviews the Normal distribution, which can be used to approximate many empirical distributions. The note includes practice exercises and their solutions, and a supporting Excel workbook for students.


Difficulty: Undergraduate/MBALearning Objective: After completion of this note and its associated activities, students should be able to: Understand the difference between categorical, ordinal, discrete, and continuous data,Read and interpret a histogram,Build a histogram using the Microsoft Excel functions COUNTIF and FREQUENCY, andEstimate probabilities from a histogram and from a Normal distribution.

9B16E013

Introduction to Logic Functions in Microsoft ExcelKyle Maclean; Lauren E. Cipriano; Gregory S. Zaric; John Lyons;

The purpose of this note is to introduce the concept of logical statements in Microsoft Excel and to illustrate the use of Excel’s built-in logic functions. The note includes practice exercises and their solutions, and a supporting Excel workbook for students.


Difficulty: Undergraduate/MBALearning Objective: After completion of this note and its associated activities, students should be able to do the following:

Compare values using =, >, and <</li>Use the built-in functions in Microsoft Excel to evaluate compound logical statements (such as AND and OR).Use the IF function to produce a specific output in response to a true or false outcome resulting from a logical assessment.

9B16E009

Introduction to Microsoft ExcelLauren E. Cipriano; Gregory S. Zaric;

The purpose of this note is to introduce students to the basic use and vocabulary of Microsoft Excel. Excel is commonly used to do quantitative analysis in business. Students will encounter Excel applications in just about every area of business, including finance, accounting, operations management, marketing, and analytics.


Difficulty: Undergraduate/MBALearning Objective: Upon completion of this note, students will be able to: Understand some Excel specific vocabulary.Enter and format data in a spreadsheet.Use keyboard navigation in Excel.Copy and paste data.Sort data.

9B16E007

Ratnagiri Alphonso Orchard: Bayesian Decision AnalysisDebdatta Pal;

In late 2012, the owner of the Ratnagiri Alphonso Orchard considered whether or not to purchase information from a climatology firm regarding the probability of unseasonable rains that could damage some or all of his family’s mango harvest. The owner needed to decide whether or not he should lease the orchard to a fruit merchant or keep the orchard for his family to harvest, despite the possibility of rain. Would the climatology firm provide helpful information, or should he make an independent decision? Regardless of whether he purchased information from the climatology firm, what was the best informed decision he could make?

Publication Date: April 12, 2016Discipline: Entrepreneurship; International; Management Science;Length: 2 pagesIssues: Decision analysis, statistics, probability, agribusiness, agriculture, lease, empirical analysis, risk tolerance, bayesian, a priori, Bayes decision rule, conditional probability, joint probability, unconditional probability, posterior probability, uncertainty, prior probabilityIndustry: Agriculture, Forestry, Fishing and Hunting;Setting: India, Small organization, 2012Difficulty: Undergraduate/MBALearning Objective: This case is suitable for graduate or undergraduate business courses in statistics and quantitative techniques, management science, or risk management and agri-business. Students are provided with the data needed to calculate the value of information and develop a decision tree. As a result of discussing this case, students will be able to:Understand the concepts of probability theory and apply those concepts to calculate probabilities;Formulate a payoff table from a problem description;Apply Bayes’ decision rule for decision analysis;Formulate and solve a decision tree;

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Evaluate the worth of decision-making information; andDetermine an agri-entrepreneur’s tolerance for risk.

9B16D006

Jilltronics Security Systems: Vendor Selection Using Multi-Factor AnalysisOwen P. Hall; Kenneth Ko;

At a Jilltronics Systems board meeting in 2015, the chief executive officer (CEO) indicated an interest in reviewing the firm’s supply chain strategy in light of increasing product and service demand. Jilltronics, a regional player in the U.S. home security market with sales approaching $100 million annually, had experienced significant growth in both the new housing and retrofit markets. The CEO expressed his desire to take a more analytical approach in developing the supply chain strategy that might include expanding the number of vendors. He reported to the board that this initiative could pose some risks due to the challenges associated with managing multiple suppliers. The board meeting concluded with the CEO tasking the chief analytics officer with developing a vendor selection assessment plan and reporting her findings at the next board meeting.

Publication Date: March 21, 2016Discipline: Management Science; Operations Management;Length: 3 pagesIssues: Vendor selection, multi-attribute analysis, sensitivity analysis, benefit-cost analysis, electronic securityIndustry: Manufacturing;Setting: United States, Medium organization, 2015Difficulty: Undergraduate/MBALearning Objective: This case can be used in a graduate-level core analytics or operations management course. The case focuses on the use of multi-factor analysis in selecting qualified vendors. It can be used as an in-class activity (70 to 90 minutes) or as an out-of-class team exercise. After completion of this case, students should be able to:Appreciate the basic concepts behind multi-criteria decision analysis.Recognize the trade-offs associated with vendor selection.Comprehend the fundamentals of benefit-cost analysis.Understand how to conduct sensitivity analysis on key model factors.

9B16E002

TRIAS: Decision on Cable Ladder ProductionSingfat Chu; Leo Hermanto;

In July 2015, PT Trias Indra Saputra (TRIAS), Indonesia’s leading welded cable ladder producer, had just won its largest-ever tender bid. The news could get even better if it adopted alternative materials that were potentially more profitable. The production director was tasked with weighing whether TRIAS should fulfill the tender using its traditional supplier or the new materials. While more expensive, the new materials would cut out several production processes and associated costs. However, using the new suppliers presented significant risks. Only two mills, in Korea and Japan, supplied the appropriate material, and both companies presented different prices and import costs. Since TRIAS could be penalized if it did not supply the goods on time, the production manager also had to calculate the likelihood that each company could be delayed in supplying the order and how much that would reduce profits if it happened.

Publication Date: February 22, 2016

Discipline: Management Science; International;Length: 4 pagesIssues: Decision tree, analytics, sensitivity analysis, procurement, delay penaltyIndustry: Manufacturing;Setting: Indonesia, Large organization, 2015Difficulty: Undergraduate/MBALearning Objective: This case is suitable for quantitative courses in analytics and decision making for MBA or EMBA students. After completion of the case, students should be able to: Discover the stages, risks, assumptions, and costs involved in analyzing the choice of suppliers;Evaluate the methods used to make a decision with numerous parameters; andUse a decision tree to analyze and calculate the merits of various options.

9B16E001

APLO: Optimal Supply of Street LightsSingfat Chu; Nicole Lo;Clark Hsu;Masahiro Okumura;R.J. Guzman;Lisa Santoso;

APLO was a reputable supplier of LED lighting systems for diverse countries from Taiwan to the United Kingdom. In 2015, APLO signed a contract to supply and install 30,000 pieces of 60-watt ecological street lights in several towns within one of Indonesia’s provinces. With over 250 million inhabitants, Indonesia was in dire need of modernizing its infrastructure as it expanded economically. Having already been contracted for street lighting in several parts of Jakarta, Indonesia’s capital city, APLO was eager for more projects across Indonesia. Would APLO be able to make this its showcase project and win many more contracts throughout Indonesia’s 34 provinces?

Publication Date: February 10, 2016Discipline: Management Science; International;Length: 4 pagesIssues: Work balance, Project Management, Optimization, CostingIndustry: Manufacturing;Setting: Indonesia;Taiwan, Medium organization, 2014Difficulty: Undergraduate/MBALearning Objective: This case is suitable for quantitative courses in optimization, analytics, project management, and possibly costing or accounting. After completion of the case, students should be able to:Create a metric that helps a project become the company’s showcase to help attain more projects in the future.Consider budget cap and other constraints pertaining to certification, compatibility, and incompatibility for a project.Apply accounting, costing, and optimization concepts to balance quality versus cost trade-off.

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For more information or to order any of these or other materials, contact:Ivey Publishing

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t. 519.661.3208 | tf. 800.649.6355 (in Canada and the United States)f. 519.661.3882 | e. [email protected]

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