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Mms Renta a Car

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M02_TURB7293_09_SE_WC02.1.QXD 12/22/09 2:38 PM Page 1 W2 APPLICATION CASE W2.

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1. 1 Part 1: The Intelligence Phase ONLINE FILE W2. 1 THE MMS RUNNING CASE MMS Rent-a-Car, based in Atlanta, Georgia, has outlets at major airports and cities throughout North America. Founded by CEO Elena Markum several years ago, it has seen fast growth over the past few years, mainly because it offers quality service, fast, at convenient locations. MMS is highly competitive, able to offer cars at slightly lower rates than its competitors because most of its airport facilities are located near but not at the airport.

A keen user of information systems, MMS tracks competitors’ prices, stored in a large data warehouse, through its Web-based enterprise information system portal, CLAUDIA (Come Learn About statUs for Deals and Information on Autos). CLAUDIA also tracks sales, fleet status, other internal status information, and external information about the economy and its relevant components. CLAUDIA has been a great success in keeping MMS competitive. The First Meeting Elena calls the meeting to order: ELENA: Thank you all for coming on such short notice. I’m glad that we could schedule this meeting through our new scheduling module of CLAUDIA.

I know you have all read my e-mail about our latest problem—sales are off by 10 percent. Basically, this will put us in the red for the year if it continues for another 4 months. CLAUDIA’s forecasting system that links to our RMS (revenue management system) indicates that sales will continue to decrease for the next 4 months, even after we adjust prices. Folks, what’s going on? I want to know what has caused this problem, how we can fix it, and how we can prevent it from happening again. Aside from solving the problem, I want to develop some knowledge about it and use it as an opportunity to improve our business.

MARLA: Frankly, Elena, I don’t understand it! I noticed a slight dip in sales 2 months ago but was so busy with our new fleet acquisitions that I planned to go back and look into what happened when I finished replacing the fleet later this week. I should have passed word on to our analysts to have a look back then. Sorry. ELENA: No problem, Marla. I should have noticed it myself. I’m glad you were at least aware and ready to move on it. So, we have evidence of a problem. What else do we have? S HARON : My up-to-date reports from the travel industry indicate that over the past 6 months there has been a slight increase in business

Problems Elena has called a meeting of her vice presidents to discuss a problem that she noticed yesterday while tapping into CLAUDIA. Rentals are off about 10 percent nationally from the MMS projections for last month. Furthermore, CLAUDIA’s forecasts indicate that they will continue to decrease. Elena wants to know why. This morning, the following VPs are present: Sharon Goldman, Marketing (CMO) Michael Lee, Operations (COO) Marla Dana, Fleet Acquisitions (CFAO) Tonia van de Stam, Information Systems (CIO) Mark Lams, Knowledge Systems (CKO) Jelene Thompson, Accounting (CAO) Rose Franklin, Finance (CFO) 2-1

M02_TURB7293_09_SE_WC02. 1. QXD 12/22/09 2:38 PM Page 2 2-2 Part II • Computerized Decision Support overall. More people are flying for business meetings, conventions, trade shows, and pleasure. And the same proportion of them is renting cars in North America. This is true for all of our primary markets—major cities and airports—but not for our secondary markets in the smaller cities, where most rentals are for business. Overall, business should be up. Vacation business is up quite a bit from the central Florida theme parks advertising specials and major conventions. Both political party conventions were held in major cities.

Data indicate that our rentals did not increase while the total market did. Our earlier forecasts indicated that business should have increased; our rental rates reflect this, as does our increased fleet size, by 15 percent. The cars should be moving— but they’re not! ELENA: How about the advertising impacts? ROSE: Our financials indicate that we have been spending more on advertising in our primary markets. Yet those are where our sales are dropping fastest. JELENE: I agree. Though our records were about 3 weeks behind, now they are up-to-date, and they’ll stay up-to-date, thanks to our upgrade to CLAUDIA.

I’m looking at the current data right now on our secure wireless network, and we’re definitely down. ELENA: Okay. Our advertising expenditures are up. That’s because we made that deal with GMC (Gold Motors Corporation). We just finished replacing our entire fleet with GMC cars and vans, right, Marla? MARLA: Absolutely! The cars are much more reliable and cheaper to maintain than the ones that had the transmissions burning out every 45,000 miles (72,000 km). These cars and vans are the national best-sellers, have great reputations, and are of high quality. They have the highest safety records in most categories.

All of the standard models came in first: subcompacts, compacts, mid-size, full-size, and minivans. About 6 weeks ago, we started getting in the hot new GMC Spider 1600 convertible. We have an exclusive deal on this hot little number. It looks like the sporty 1971 Fiat Spider but is built to new quality standards. It’s fun to drive—they let me have one for a year before we got the fleet in! They are expensive, and GMC owns the domestic market. We should be able to rent these out all the time. We have 5 at each agency across the country, and by year’s end we should have 10.

SHARON: We got an exclusive with them for the next 3 years. They only give the fleet discount to us, we feature their cars in our advertising, and they feature us in theirs. And the Spider came to us right off the new assembly line in Pittsburgh. ELENA: I have one of the Spiders, too. So I suspect that they’re constantly rented out, aren’t they? MICHAEL: Well, no. Only about half of them are rented. The rental rates were supposed to be set pretty high, but our RMS recommends setting it at the same price as a compact. We hedged a little and set the price to about 10 percent higher.

Some local agency offices are overriding the system and setting the prices 15 percent less, and they still can’t move them. ELENA : How about the other classes of cars? MICHAEL: Rentals are down about 8 percent nationally on all the other ones. E LENA : So sales are down 8 percent for everything but the Spider, and the Spider, which should be a hot seller, is off by 50 percent. I know from CLAUDIA that our inventory is okay. All the new cars came in on schedule, and we were able to sell the used cars through electronic auction sites and carmax. com. Folks, we definitely have a big problem.

M ICHAEL : As COO, I see that this is primarily my problem, though all of you here are involved. We’ve never had this happen before, so I really don’t know how to classify the problem. But I think we can get at most of the information we need. This situation is only a symptom of the problem. We need to identify the cause so we can correct the problem. I want some time to get my analysts and Tonia’s moving on it. I will need some major help from Sharon’s people, M02_TURB7293_09_SE_WC02. 1. QXD 12/22/09 2:38 PM Page 3 Chapter 2 • Decision Making, Systems, Modeling, and Support 2-3 and probably a bit from everyone else.

Sharon and I talked before the meeting. We both have a feeling that there is something wrong with how we are marketing the new cars, but we don’t have enough information just yet to identify it. I hope that once we solve this problem, we’ll have a nice piece of strategic knowledge for Mark to put into the knowledge management system (KMS). I’ll tentatively schedule a meeting through CLAUDIA next week, as close to this time as possible, depending on people’s previous commitments. I’ll e-mail the major results as we go. I’m sure we’ll know something before the next meeting. ELENA: Thanks, Michael.

Okay, folks! We know we have a serious problem. We’ve seen its effects. Michael will assume ownership and move ahead. I also want our IS analysts looking at data even before anyone requests them. That includes any weird economic trends or events—and look at the underlying structure and parameters of our forecasting models, okay, Tonia? Sharon, you look into the advertising. See if there are any external events or trends or reports on the cars that could affect our rentals. The RMS has been accurate until now. It’s been able to balance price, supply, and demand, but something happened.

Thank you all and have a great day. APPLICATION CASE W2. 1. 2 Part 2: The Design Phase Later on the day of the first meeting, Michael Lee has his top analyst, Stephanie Elberson, look into what might have happened. Michael recognized that it was too early to start looking into criteria, solutions, and more (he had studied decision making in a DSS course in his MBA program). He was still trying to understand the problem and separate the problem that could be analyzed from the symptoms. He wanted to make the connection between the two, but he felt that something was fundamentally wrong, and CLAUDIA could ot identify it. A good decision maker relies on judgment and has a good feel for what makes sense and what does not. Michael is one of the best. Stephanie put together a team of analysts and started formulating areas to investigate. One member of the team, Dot Frank, worked closely with Sharon’s analyst, Phil Abrams, to establish the accuracy of the forecasting model. Amy Lazbin, on Stephanie’s team, looked into databases of operational data available internally and economic data available through subscription services. The latter data focused on the auto rental, automobile, and general economic areas.

The analysis team initially set the data mining tools on automatic to establish relationships in the data. For the most part, Amy was able to verify most of the relationships and assumptions that were already in the forecasting models and the RMS. Nothing new popped up from the artificial neural networks, clustering analysis algorithms, and statistical regression models. The pricing model and the forecasting models were all right, though there were some new fluctuations, and the errors were higher when the team looked into how well they had performed over the past 2 months as this new problem arose.

The team noticed that the neural networks outperformed the regression-based systems a bit, so they set up an information system and marketing group to look into how they could improve the regression-based models with neural networks. (This was a new opportunity, which led them to return to the intelligence phase with a new set of issues. ) Stephanie was puzzled. She met with Michael 2 days later to discuss what she was going to do next. She also invited the marketing team and the IS team to each send someone to the meeting. Phil Abrams and Marina Laksey (from IS) joined the team at this point.

The meeting was held in the electronic meeting center (EMC), where they would be able to analyze data and use the group support system (GSS). Here’s how the meeting went: STEPHANIE: Thank you all for coming today. As you know, we are working hard on the problem— or rather the symptoms—to try to get to the heart of the problem. Data mining tools helped M02_TURB7293_09_SE_WC02. 1. QXD 12/22/09 2:38 PM Page 4 2-4 Part II • Computerized Decision Support a bit, but there is something fundamentally wrong, and we have yet to find it. Any ideas?

MARINA: Stephanie, we used the data mining tools and looked at most, if not all, of the data we normally look at. And we usually look at standard views through our spreadsheet-type interface. I know we have to look “outside the box. ” First off, the four of us need to fire up our new, powerful OLAP (online analytical processing) software, DOT (Data on Time). It taps into our data warehouse and other data, but it goes beyond data mining by allowing us to poke about in the data. We just got the software in 2 weeks ago, and I have already gone through the training course.

It has many of the features that CLAUDIA has, but it allows us to look into multidimensional data from any of our data sources in any “slice” we choose. It also lets us link into other databases and data marts like the one that marketing has. Let me start it up! PHIL: I agree. I learned how to use the OLAP software on my own, and I’ve developed some interesting views of our marketing data that show relationships we did not believe possible. The graphics are almost automatic. Let’s try it! The team saw the bumps in the data but had no idea what had caused them. At least they could see them.

When they tapped into the advertising plans, they noticed a slight inverse relationship with sales and advertising. When they asked Phil about it, he said: PHIL: Sales dropped 2 weeks after our new jointmarketing campaign began. We heavily advertised the new cars. Every national and local TV commercial prominently displayed the Spider. We have data on that in our marketing databases. I know you don’t normally look at that. Here, let me bring them up. Hmmm! We show how much air time each commercial played where, and what was in them. Let me do a little slicing and aggregating here. Aha! I see. We are mostly advertising the cars nationally.

Sales are very weak in primary markets, but they’re also a bit weak in secondary markets. Ah! Ah! Ah! One problem we have is distribution. We have over half the cars in the wrong places. We need to move all the Spiders from the secondary markets to the primary markets. But I think we have another problem: The pricing, supply, and demand data that we are using to predict rentals don’t make sense. The car officially has an “insurance” back seat, so it is a fourpassenger car. But you’d be lucky to get a carryon suitcase back there. Since we didn’t have data on it, someone in our group entered it as a four-seat compact with two doors.

The system thinks it is a car ideal for a small family or a single businessperson on a budget. These rent well in the Midwest in the secondary markets but badly in the convention areas, where there are men who are going through their midlife crises and single women who like to rent sporty cars. We have a lot of analyses to do here on where we are advertising what. I’m not sure who rents what where, but I suspect that we can target our ads better once we determine our market clusters—like males in Nebraska, 45 years old, traveling to San Diego for trade conferences.

We have the data; we just need to apply them better. MICHAEL: Hold on. Before I start moving cars around, we need to analyze this a bit more. We’ve never had a car like the Spider, so we need to investigate its properties and which categories of customers would ideally want it. Part of the solution jumped at us. But what are we trying to do? If I remember correctly, a few years ago we ran a “try before you buy” promotion in conjunction with our previous car supplier. People could rent our excess stock on our off-days for half the rental rate for up to 3 days.

If they bought the car from a dealer in the area, they got the rental price back. If not, they had fun with the car. It worked well. We noticed that people who liked the car they rented had a tendency to rent them again, especially in our primary markets. We have a lot to look into. I want to recap what we have. We know that our goal is to maximize net profit. This is clearly our principle of choice. We need to come up with criteria that describe the impact of alternatives and determine how they affect our bottom line. Our RMS sets prices so that we can ideally do that.

We have some errors in our marketing database; we must rethink how we advertise and how we distribute our stock. Okay. I’ll meet with the VP team in a couple of days. I’m going to e-mail them information about what we’ve uncovered and where to M02_TURB7293_09_SE_WC02. 1. QXD 12/22/09 2:38 PM Page 5 Chapter 2 • Decision Making, Systems, Modeling, and Support 2-5 find the data. First I’ll talk to Sharon so she can get busy with some ideas on marketing. At a Meeting 2 Days Later: Same Place, Same People S TEPHANIE : Good morning. Those of us in the trenches think we’ve got it! Here’s what’s going on.

We have several problems, each of which we have developed some alternatives for. We’re going to discuss what we think are the best ones for each situation. Some we can implement right away; others will take some time. Let’s start with our objective: to maximize profit. Our principle of choice is profit maximization. This part of the problem was easy. Our RMS recognizes this and adjusts prices automatically to maximize profit on an annual basis. There are some errors in the price elasticity curve for the Spider, but, in general, the real question now is how to manage demand.

Our advertising influences demand, as does our inventory. We need for the right product to appeal to the right customers. There are many criteria that we need to measure, from quality to color to size, and customer service, car availability, and so on, in terms of how they affect rentals. We are doing this, but need to do a better job of it in order to track our rentals. We have a team analyzing this right now. In a few weeks, they will have some concrete recommendations for system upgrades to the RMS. Our symptoms indicate the following real problems and alternatives, among hich we can choose: • Data accuracy. We need to change the profile of the Spider from a compact to a sports car. We need to develop the RMS profile from what little data we’ve got. Fortunately, we can tap into market data that our faculty consultants at the University of Georgia (UGA) have gathered for us in their research. One of the faculty members drives a Spider as well. • Inventory imbalance. We have done some analysis to determine what the real demand for the Spider is, how it affects the demand for other cars, and vice versa. We built an optimization model and solved it. Based on our urrent advertising, we have determined that by moving about 15 percent of our fleet around (and not too far), we can take care of most of the demand imbalance. We recommend moving all the Spiders from secondary to primary markets right away. We also want to move some of our minivans and full-size cars around. Later, we can adjust advertising to push some secondary market demand. • Advertising imbalance. We advertise where our customers are, but they rent elsewhere, and for different reasons. We need to do a better job of identifying customer homes to determine what to advertise where.

Our analysis shows rentals are off partly because we indicate that we have the Spider. Young to middle-aged men and single women want to rent it, but we stock out where they are going. For example, we discovered that middle-aged men and women from the Midwest rent compacts in the secondary Midwest markets, but those in the primary markets on the coasts want to rent the Spider. We are still analyzing effects like this and should be able to complete the work in about a week to determine how to realign our advertising efforts. • “Try before you buy. ” This actually is an opportunity, not a problem.

When we saturate Spider demand in primary markets, we should get some additional Spiders in the secondary markets and reestablish the “try before you buy” campaign. This car will be a real boon in this effort. Sharon’s group has already established a cooperative agreement with GMC. They’re interested, and it should boost our profitability on these cars by 18 percent. • Discount substitutes. We discovered that many customers called or got on our Web site to rent the Spider. When they found out that we didn’t have one for them, rather than rent a different car, many were so annoyed that they rented a car from one f our competitors, usually a Toyota MR-2. This happened in almost all of our primary markets. In our secondary markets, people really didn’t want the Spider but instead wanted full-size cars. Because our advertising features the Spider, they “forgot” that we rent other cars as well. Actually, we forgot to remind them. Our advertising is backfiring on us. We should immediately M02_TURB7293_09_SE_WC02. 1. QXD 12/22/09 2:38 PM Page 6 2-6 Part II • Computerized Decision Support discount substitutes for the Spider until we get the Spiders in place next week. • Florida theme park demand.

We have a unique opportunity here. Florida theme parks have been advertising heavily in Europe because the euro is strong relative to the dollar. We must increase advertising in Europe either with the theme parks or separately. Phil is confident that we can run a joint campaign. Marketing will look into this and how we might be able to get customers to pay in advance in euros. To do this, we may need to move minivans to Florida from as far away as Tennessee. What it boils down to is that we want to be more aggressive in balancing our stock to meet demand and tie this into the RMS and advertising.

We also want to refine our advertising model to handle new types of cars, like sports cars, and update demand data more frequently. Michael, this is what we want to present to the VPs on Monday. Is that okay? MICHAEL: Perfect! We have identified the real problems and have good alternatives. I really appreciate the completed staff work. If this all works out, the end-of-the-year bonuses for this team should be excellent. Let’s go have lunch! I’m buying! APPLICATION CASE W2. 1. 3 Part 3: The Choice Phase Monday’s Meeting: With All Vice Presidents, Stephanie, and Her Team ELENA : Thank you again for coming.

Stephanie, Michael tells me you’re on to something. Let’s hear what you have to say. STEPHANIE: Well, we think we’ve discovered what to do. But first let me outline what the real problems are and some suggested solutions and why these are appropriate solutions. Next Stephanie essentially outlines the details from the meeting described in Part 2 of the Running Case. There is a little discussion to clarify a few points: ELENA: Amazing. I’m glad Mark recommended acquiring DOT 3 months ago. Though expensive, it’s already paid off. Can you get me specifics on the bottom line for each alternative? STEPHANIE: Not accurate ones for each just yet.

Some will take up to a couple of weeks. We do have estimates on all of them. Here are the results in my PowerPoint presentation. ELENA: Hmmm. Okay. I want those data on the Spider updated immediately—and some of them moved to where they’ll rent. MARLA: It’s already done. I took steps right away once Michael told me what happened. After all, it’s my responsibility. I already gave some updated data to IS. They’ve adjusted the RMS. Preliminary data indicate that they have improved our profitability already. In a couple of markets where it was relatively inexpensive, I have moved some cars around based on the DSS model’s recommendation.

It worked! I think we should make the major changes recommended by the solution to the model. My estimates, just from these few markets, are that it will work just as the model predicts. SHARON: We’re looking into how to modify our marketing and tie it into the RMS. We’re also running models on how European marketing should work. We’ll know in a week what to do. ELENA: Excellent! Here’s where we stand. We’re going to adjust the profile data of the Spider and all models frequently, move cars around, and discount substitutes until we can get the imbalance fixed.

We’ll decide on what to do about the other issues after the rest of the analysis is completed. M02_TURB7293_09_SE_WC02. 1. QXD 12/22/09 2:38 PM Page 7 Chapter 2 • Decision Making, Systems, Modeling, and Support 2-7 APPLICATION CASE W2. 1. 4 Part 4: The Implementation Phase The implementation of the first couple decisions was relatively easy. Transport vehicles were rented, and cars were moved. Discounts were easy to establish for substitute cars because this could be done as routinely as when there was a normal stockout.

A customer would first be offered the opportunity to upgrade. If the customer turned it down, the upgrade would be offered free. This worked 95 percent of the time, even in the case of the Spider. Sales were up, and the company was projected to be profitable with these small changes. Elena got the results of the additional analyses. They all made sense. She decided, with the advice of her VPs and the analysts, to go ahead with all the recommendations, but she held back on European marketing until a presence in Europe could be established in major markets.

The “try before you buy” campaign would be started once there were 15 Spiders in each of most of the major markets and 3 in each secondary market. She also approved adding new data and features to CLAUDIA. When the advertising effort was refined and tied to the RMS, profits soared. Every member of Stephanie’s team and all the VPs involved got a generous end-of-year bonus, an extra week’s vacation, and a gift of a free GMC Spider. SUMMARY AND CONCLUSION MMS ran into new problems when it changed its fleet.

CLAUDIA was not equipped to handle new cars that were unlike others from past experience, and it did not track events as well as trends. Simon’s (1977) four phases of decision making, along with feedback, were followed, even though the problems were not really identified in the first phase. Successful problem solving was ultimately accomplished using Web-based DSS. Case Questions 1. What is meant by a symptom versus a problem? Relate these ideas to the case. 2. Why is problem ownership so important? 3. Even though the problem was not identified at the end of the intelligence phase, what was? . How was the design phase performed in this case? 5. The choice phase seemed like a combination of design, choice, and implementation. Is this a problem? 6. The implementation phase seemed to involve elements of all the phases. Is this a problem? 7. How were new problems or opportunities handled as they arose? 8. Why do you suppose some alternatives were either modified or postponed? Source: This fictional decision-making case is loosely based on several real situations. Thanks to Professor Elena Karahanna at the University of Georgia for inspiring it.

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