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Business Analytics

Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann

Chapter 1

Introduction to Business Analytics - all with Video Answers

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Chapter Questions

Problem 1

Quality Versus Low Cost. Car manufacturer Lexus competes in the luxury car category and is known for its quality. The decision to compete on quality rather than low cost is an example of what type of decision: strategic, tactical, or operational? Explain. LO 1

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Problem 2

Package Delivery. Every day, logistics companies such as United Parcel Service (UPS) must decide how to route their trucks, that is, the order in which to deliver the packages that have been loaded on a truck. UPS developed a system called ORION (On-Road Integrated Optimization and Navigation) which dynamically routes its trucks based on packages on the truck and traffic conditions. It is estimated that ORION saves UPS about 10 million gallons of fuel per year. Is the decision of how to route a truck a strategic, tactical, or operational decision? Explain. LO 1

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Problem 3

Airline Decisions. JetBlue is a major U.S. airline that ranked seventh in the U.S. in 2021 based on number of passengers carried. JetBlue has 270 aircraft and services 104 destinations. Consider three decisions below that an airline such as JetBlue must make. For each decision, tell if it is strategic, tactical, or operational and explain. LO 1
a. Which crew should be assigned to a given flight?
b. Should we compete on low cost or quality?
c. What type of aircraft should we purchase?

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Problem 4

Choosing a College. Millions of high school graduates decide annually to enroll in a college or university. Many factors can influence a student's choice of where to enroll. List the five steps of the decision-making process and discuss each step of the process, where the decision to be made is choosing which college to attend. LO 2

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02:50

Problem 5

Package Delivery (Revisited). Consider again the ORION system discussed in problem 2. Is ORION an example of descriptive, predictive, or prescriptive analytics? Explain. LO 3

Patina Herring
Patina Herring
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Problem 6

Quality Control for Boxes of Cereal. A control chart is a graphical tool to help determine if a process may be exhibiting non-random variation that needs to be investigated. If non-random variation is present, the process is said to be "out of control" otherwise it is said to be "in control." The following figure shows a control chart for a production line that fills boxes of cereal. Based on past data, we can calculate the mean weight of a box of cereal when the process is in control. The mean weight is 16.05 ounces. We can also calculate control limits, an upper control limit (UCL) and a lower control limit (LCL). New samples are collected over time and the data indicates that the process is in control so long as the new sample weights are between UCL and LCL. As shown in the chart, only sample 5 is outside of the control limits. LO 3
a. Is the control chart an example of descriptive, predictive, or prescriptive analytics?
b. Suppose the control cart is part of a data dashboard and the chart is combined with a rule that does the following. If four consecutive sample mean weights are outside of the control limits, the production line is automatically stopped, and a message appears on the dashboard. The message says "The production line is stopped. The process may be out of control. Please inspect the fill machine." Is this new enhanced control chart combined with a rule an example of descriptive, predictive, or prescriptive analytics?

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Problem 7

Amazon Books. An example of a response from Amazon when this textbook you are reading was chosen online follows. It indicates that some people who purchased this text also tended to purchase The World Is Flat, Fundamentals of Corporate Finance, and Strategic Marketing Management. In this application of analytics, is Amazon using descriptive, predictive, or prescriptive analytics? Explain. LO 3

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Problem 8

Employee Retention. Human resource (HR) analytics or people analytics are terms used for the use of analytics to manage a company's workforce. Google, Microsoft, and Walmart, for example, use people analytics to help retain their best people, ensure a diverse workforce, and better understand the strengths and areas needing improvement. LO 3,4
a. One application of people analytics is to build a model that estimates the probability of an employee departing the company within the next six months. Inputs to the model could be market demand for the skills the person possesses, how long the person has been with the company, and a major life event recently occurring for the person (e.g., divorce). Is this type of model descriptive, predictive, or prescriptive? Explain. b. How could you use the model described in part (a) to help improve the workforce?

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Problem 9

Supermarket Checkout Lanes. A supermarket has been experiencing long lines during peak periods of the day. The problem is noticeably worse on certain days of the week, and the peak periods are sometimes different according to the day of the week. There are usually enough workers on the job to open all checkout lanes. The difficulty for store managers is knowing when to call some of the workers stocking shelves up to the front of the store to work the checkout lanes. LO 4
a. How could analytics be used to help the supermarket?
b. What data would be needed?

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01:17

Problem 10

Pricing Subcompact Rental Cars. Setting the right price for a product is an important business decision. If the product is priced too high, the demand could be very low. Set the price too low, demand may be high, but we are potentially leaving money on the table because the revenue per unit is low. Pricing analytics involves finding the right tradeoff between price charged and demand so as to maximize revenue. Suppose we need to set the price for renting a subcompact automobile for one day. Let us outline the decision process:
Step 1. Identify and define the problem. We need to set a price per day for a midsize rental car.
Step 2. Determine the criteria that will be used to evaluate alternative solutions. Our goal in setting the price is to maximize revenue per day.
Step 3. Determine the set of alternative solutions. Based on historical data and the competition, we will consider a broad price range from $\$ 10$ per day to $\$ 60$ per day per car.
Step 4. Evaluate the alternatives. We will evaluate proposed prices based on the expected revenue per day.
Step 5. Choose an alternative. We will choose the price that maximizes expected revenue.
We can use data and analytics to complete steps 4 and 5 of the decision process. LO 3, 4
a. Based on historical or test-market data, we can estimate a model that gives expected revenue as a function of price, as shown below. The dots represent the data (price and demand combinations) and the estimated model is the line in the chart: Demand $=-1.4028$ (Price) +102.65 . For example, for price of $\$ 35$, Demand $=$ $-1.4028(35)+102.65=53.552$ vehicles. So, we estimate that at a price of $\$ 35$ per day, the demand will be about 54 vehicles. Is this estimated equation, a descriptive, predictive, or prescriptive model? Explain.
figure cant copy
Our goal (step 5) is to find the price that maximizes expected revenue. Revenue $=$ demand $\times$ price which is $(-1.4028$ (Price $)+102.65) \times($ Price $)=$ -1.40228 (Price) ${ }^2+102.65$ (Price). The revenue as a function of price is shown below for $\$ 10$ increments of price.
b. What is the price that maximizes revenue?
c. Is step 5 , visually inspecting the revenue function to find a revenue-maximizing price, descriptive, predictive, or prescriptive analytics? Explain.
figure cant copy

Dominador Tan
Dominador Tan
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