CASE PROBLEM 12.1: Forecasting at State University During the past few years the legislature has severely reduced funding for State University. In reaction, the administration at State has significantly raised tuition each year for the past five years. A bargain five years ago, State is now considered an expensive state-supported university. Some parents and students now question the value of a State education, and applications for admission have declined. Since a portion of state educational funding is based on a formula tied to enrollments, State has maintained its enrollment levels by going deeper into its applicant pool and accepting less qualified students. On top of these problems, an increase in the college-age population is expected in this decade. Key members of the state legislature have told the university administration that State will be expected to absorb additional students during this decade. However, because of the economic outlook and the budget situation, State should not expect any funding increases for additional facilities, classrooms, dormitory rooms, or faculty. The university already has a classroom deficit in excess of 25%, and class sizes are above the average of their peer institutions. The president of the university, Tanisha Lindsey, established several task forces consisting of faculty and administrators to address these problems. These groups made a number of recommendations, including the implementation of total quality management (TQM) practices and more in-depth, focused planning. Discuss in general terms how forecasting might be used for planning to address these specific problems and the role of forecasting in initiating a TQM approach. Include in your discussion the types of forecasting methods that might be used. CASE PROBLEM 12.2: The University Bookstore Student Computer Purchase Program The University Bookstore is owned and operated by State University through an independent corporation with its own board of directors. The bookstore has three locations on or near the State University campus. It stocks a range of items, including textbooks, trade books, logo apparel, drawing and educational supplies, and computers and related products such as printers, modems, and software. The bookstore has a program to sell personal computers to incoming freshmen and other students at a substantial educational discount partly passed on from computer manufacturers. This means that the bookstore just covers computer costs with a very small profit margin remaining. Each summer all incoming freshmen and their parents come to the State campus for a three-day orientation program. The students come in groups of 100 throughout the summer. During their visit the students and their parents are given details about the bookstore’s computer purchase program. Some students place their computer orders for the fall semester at this time, while others wait until later in the summer. The bookstore also receives orders from returning students throughout the summer. This program presents a challenging supply chain management problem for the bookstore. Orders come in throughout the summer, many only a few weeks before school starts in the fall, and the computer suppliers require at least six weeks for delivery. Thus, the bookstore must forecast computer demand to build up inventory to meet student demand in the fall. The student computer program and the forecast of computer demand have repercussions all along the bookstore supply chain. The bookstore has a warehouse near campus where it must store all computers since it has no storage space at its retail locations. Ordering too many computers not only ties up the bookstore’s cash reserves but also takes up limited storage space and limits inventories for other bookstore products during the bookstore’s busiest sales period. Since the bookstore has such a low profit margin on computers, its bottom line depends on these other products. As competition for good students has increased, the university has become very quality-conscious and insists that all university facilities provide exemplary student service, which for the bookstore means meeting all student demands for computers when the fall semester starts. The number of computers ordered also affects the number of temporary warehouse and bookstore workers that must be hired for handling and assisting with PC installations. The number of truck trips from the warehouse to the bookstore each day of fall registration is also affected by computer sales. The bookstore student computer purchase program has been in place for 14 years. Although the student population has remained stable during this period, computer sales have been somewhat volatile. Following is the historical sales data for computers during the first month of fall registration: Year Computers Sold Year Computers Sold 1 518 8 792 2 651 9 877 3 708 10 693 4 921 11 841 5 775 12 1009 6 810 13 902 7 856 14 1103 Develop an appropriate forecast model for bookstore management to use to forecast computer demand for the next fall semester and indicate how accurate it appears to be. What other forecasts might be useful to the bookstore in managing its supply chain? CASE PROBLEM 12.3: Cascades Swim Club The Cascades Swim Club has 300 stockholders, each holding one share of stock in the club. A share of club stock allows the shareholder’s family to use the club’s heated outdoor pool during the summer upon payment of annual membership dues of $175. The club has not issued any stock in years, and only a few of the existing shares come up for sale each year. The board of directors administers the sale of all stock. When a shareholder wants to sell, he or she turns the stock into the board, which sells it to the person at the top of the waiting list. For the past few years, the length of the waiting list has remained relatively steady at approximately 20 names. However, during the past winter two events occurred that have suddenly increased the demand for shares in the club. The winter was especially severe, and subzero weather and heavy ice storms caused both the town and the county pools to buckle and crack. The problems were not discovered until maintenance crews began to prepare the pools for the summer, and repairs cannot be completed until the fall. Also during the winter, the manager of the local country club had an argument with her board of directors and one night burned down the clubhouse. Although the pool itself was not damaged, the dressing room facilities, showers, and snack bar were destroyed. As a result of these two events, the Cascades Swim Club was inundated with applications to purchase shares. The waiting list suddenly grew to 250 people as the summer approached. The board of directors of the swim club had refrained from issuing new shares in the past because there never was a very great demand, and the demand that did exist was usually absorbed within a year by stock turnover. In addition, the board has a real concern about overcrowding. It seemed that the present membership was about right, and there were very few complaints about overcrowding, except on holidays such as Memorial Day and the Fourth of July. However, at a recent board meeting a number of new applicants had attended and asked the board to issue new shares. In addition, a number of current shareholders suggested that this might be an opportunity for the club to raise some capital for needed repairs and to improve some of the existing facilities. This was tempting to the board. Although it had set the share price at $500 in the past, the board could set it at a much higher level now. In addition, an increase in attendance could create a need for more lifeguards. Before the board of directors could make a decision on whether to sell more shares and, if so, how many, the board members felt they needed more information. Specifically, they would like a forecast of the average number of people (family members, guests, etc.) who might attend the pool each day during the summer with the current number of shares. The board of directors has the following daily attendance records for June through August from the previous summer; it thinks the figures would provide accurate estimates for the upcoming summer. M-139 W-380 F-193 Su-399 T-177 Th-238 T-273 Th-367 Sa-378 M-197 W-161 F-224 W-172 F-359 Su-461 T-273 Th-308 Sa-368 Th-275 Sa-463 M-242 W-213 F-256 Su-541 F-337 Su-578 T-177 Th-303 Sa-391 M-235 Sa-402 M-287 W-245 F-262 Su-400 T-218 Su-487 T-247 Th-390 Sa-447 M-224 W-271 M-198 W-356 F-284 Su-399 T-239 Th-259 T-310 Th-322 Sa-417 M-275 W-274 F-232 W-347 F-419 Su-474 T-241 Th-205 Sa-317 Th-393 Sa-516 M-194 W-190 F-361 Su-369 F-421 Su-478 T-207 Th-243 Sa-411 M-361 Sa-595 M-303 W-215 F-277 Su-419 Su-497 T-223 Th-304 Sa-241 M-258 M-341 W-315 F-331 Su-384 T-130 T-291 Th-258 Sa-407 M-246 W-195 Develop a forecasting model to forecast daily demand during the summer. CASE PROBLEM 12.4: Forecasting Passenger Arrivals at the Gotham International Airport Since the terrorist attacks of 9/11 and the ensuing measures by the federal Transportation Security Administration (TSA) to increase airline security, airports have faced the problem of long waiting lines and waiting times at security gates. One of the key components of any effort by the TSA to operationally improve airport security procedures while reducing passenger waiting times and inconvenience is forecasting passenger arrivals at security checkpoints in order to determine how many security checkpoints and staff are needed. At the Gotham International Airport, TSA operations analysts would like to forecast passenger arrivals for next July, the airport’s busiest travel month of the year, for the purpose of determining how many security checkpoints they should staff during the month in order that waiting lines and times will not be excessively long. Demand for airline travel has generally been increasing during the past three years. There are two main concourses at Gotham International, East and West, each serving different airlines. The following table shows passenger arrivals at the West concourse for 10 days (selected randomly) in two-hour segments from 4:00 A.M. to 10:00 P.M. for the month of July for the past three years. Year 1 Day 4–6 A.M. 6–8 A.M. 8–10 A.M. 10–noon noon–2 P.M. 2–4 P.M. 4–6 P.M. 6–8 P.M. 8–10 P.M. 1 2400 2700 3200 1400 1700 1800 1600 800 200 2 1900 2500 3100 1600 1800 2000 1800 900 300 3 2300 3100 2500 1500 1500 1800 1900 1100 200 4 2200 3200 3100 2200 1900 2400 2100 1200 400 5 2400 3300 3400 1700 2200 2100 2000 1000 600 6 2600 2800 3500 1500 1700 1900 1500 1100 300 7 1900 2800 3100 1200 1500 2000 1400 900 400 Day 4–6 A.M. 6–8 A.M. 8–10 A.M. 10–noon noon–2 P.M. 2–4 P.M. 4–6 P.M. 6–8 P.M. 8–10 P.M. 8 2000 2700 2500 1500 2000 2300 1900 1000 200 9 2400 3200 3600 1600 2100 2500 1800 1400 200 10 2600 3300 3100 200 2500 2600 2400 1100 400 11 3100 3900 4100 2200 2600 2300 2500 1100 300 12 2800 3400 3900 1900 2100 2500 2000 1200 300 13 2700 3800 4300 2100 2400 2400 2400 1200 400 14 2400 3500 4100 2400 3000 3200 2600 1200 700 Year 2 15 3300 3700 4000 2600 2600 2700 2900 1000 300 16 3500 4000 3800 2300 2700 3100 3000 900 200 17 2900 4100 3900 2400 3000 3200 2500 1100 500 18 3400 3800 4200 2000 2500 3000 2200 1000 300 19 3600 3600 4000 2300 2600 2800 2600 1200 200 20 3700 3700 4000 2200 2600 2700 2400 1200 200 21 4400 4400 4500 2600 3300 3400 3000 1200 400 Day 4–6 A.M. 6–8 A.M. 8–10 A.M. 10–noon noon–2 P.M. 2–4 P.M. 4–6 P.M. 6–8 P.M. 8–10 P.M. 22 4200 4500 4300 2500 3400 3600 3100 1400 300 23 4500 4500 4700 2700 3400 3500 2900 1200 300 24 4600 4600 4600 2500 3200 3500 2800 1300 300 Year 3 25 4500 4300 4400 2900 3300 3300 3300 1500 400 26 4200 4300 4500 3000 4000 3400 3000 1500 600 27 4500 4500 5100 3300 4000 3700 3100 1200 300 28 4300 4200 4300 2800 3500 4000 3300 1100 400 29 4900 4100 4200 3100 3600 3900 3400 1400 500 30 4700 4500 4100 3000 4000 3700 3400 1200 500 Develop a forecast for daily passenger arrivals at the West concourse at Gotham for each time period for July of year 4. Discuss the various forecast model variations that might be used to develop this forecast. CASE PROBLEM 13.1: The Instant Paper Clip Office Supply Company Christie Levine is the manager of the Instant Paper Clip Office Supply Company in Louisville. The company attempts to gain an advantage over its competitors by providing quality customer service, which includes prompt delivery of orders by truck or van and always being able to meet customer demand from its stock. In order to achieve this degree of customer service, it must stock a large volume of items on a daily basis at a central warehouse and at three retail stores in the city and suburbs. Christie maintains these inventory levels by borrowing cash on a daily basis from the First American Bank. She estimates that for the coming fiscal year the company’s demand for cash to pay for inventory will be $17,000 per day for 305 working days. Any money she borrows during the year must be repaid with interest by the end of the year. The annual interest rate currently charged by the bank is 9%. Any time Christie takes out a loan to purchase inventory, the bank charges the company a loan origination fee of $1200 plus 2¼ points (2.25% of the amount borrowed). Christie often uses EOQ analysis to determine optimal amounts of inventory to order for different office supplies. Now she is wondering if she can use the same type of analysis to determine an optimal borrowing policy. Determine the amount of the loan Christie should borrow from the bank, the total annual cost of the company’s borrowing policy, and the number of loans the company should obtain during the year. Also determine the level of cash on hand at which the company should apply for a new loan given that it takes 15 days for a loan to be processed by the bank. Suppose the bank offers Christie a discount as follows. On any loan amount equal to or greater than $500,000, the bank will lower the number of points charged on the loan origination fee from 2.25% to 2.00%. What would be the company’s optimal amount borrowed? CASE PROBLEM 13.2: The Texas Gladiators Apparel Store The Texas Gladiators won the Super Bowl last year. As a result, sportswear such as hats, sweatshirts, sweatpants, and jackets with the Gladiators’ logo are popular. The Gladiators operate an apparel store outside the football stadium. It is near a busy highway, so the store has heavy customer traffic throughout the year, not just on game days. In addition, the stadium has high school or college football and soccer games almost every week in the fall, and baseball games in the spring and summer. The most popular single item the stadium store sells is a red and silver baseball-style cap with the Gladiators’ logo on it. The cap has an elastic headband inside it, which conforms to different head sizes. However, the store has had a difficult time keeping the cap in stock, especially during the time between the placement and receipt of an order. Often customers come to the store just for the hat; when it is not in stock, customers are upset, and the store management believes they tend to go to other competing stores to purchase their Gladiators’ clothing. To rectify this problem, the store manager, Jessica James, would like to develop an inventory control policy that would ensure that customers would be able to purchase the cap 99% of the time they asked for it. Jessica has accumulated the following demand data for the cap for a 30-week period. (Demand includes actual sales plus a record of the times a cap has been requested but not available and an estimate of the number of times a customer wanted a cap when it was not available but did not ask for it.) The store purchases the hats from a small manufacturing company in Jamaica. The shipments from Jamaica are erratic, with a lead time of 20 days. In the past, Ms. James has placed an order whenever the stock got down to 150 caps. What level of service does this reorder point correspond to? What would the reorder point and safety stock need to be to achieve the desired service level? Discuss how Jessica James might determine the order size of caps and what additional, if any, information would be needed to determine the order size. Week Demand Week Demand Week Demand 1 38 11 28 21 52 2 51 12 41 22 38 3 25 13 37 23 49 4 60 14 44 24 46 5 35 15 45 25 47 6 42 16 56 26 41 7 29 17 62 27 39 8 46 18 53 28 50 9 55 19 46 29 28 10 19 20 41 30 34 CASE PROBLEM 13.3: Pharr Foods Company Pharr Foods Company produces a variety of food products including a line of candies. One of its most popular candy items is “Far Stars,” a bag of a dozen individually wrapped star-shaped candies made primarily from a blend of dark and milk chocolates, macadamia nuts, and a blend of heavy cream fillings. The item is relatively expensive, so Pharr Foods only produces it for its eastern market encompassing urban areas such as New York, Atlanta, Philadelphia, and Boston. The item is not sold in grocery or discount stores but mainly in specialty shops and specialty groceries, candy stores, and department stores. Pharr Foods supplies the candy to a single food distributor, which has several warehouses on the East Coast. The candy is shipped in cases with 60 bags of the candy per case. Far Stars sell well despite the fact that they are expensive at $9.85 per bag (wholesale). Pharr uses high-quality, fresh ingredients and does not store large stocks of the candy in inventory for very long periods of time. Pharr’s distributor believes that demand for the candy follows a seasonal pattern. It has collected demand data (i.e., cases sold) for Far Stars from its warehouses and the stores it supplies for the past three years, as follows. Demand (cases) Month Year 1 Year 2 Year 3 January 192 212 228 February 210 223 231 March 205 216 226 April 260 252 293 May 228 235 246 June 172 220 229 Demand (cases) Month Year 1 Year 2 Year 3 July 160 209 217 August 147 231 226 September 256 263 302 October 342 370 410 November 261 260 279 December 273 277 293 The distributor must hold the candy inventory in climate-controlled warehouses and be careful in handling it. The annual carrying cost is $116 per case. The item must be shipped a long distance from the manufacturer to the distributor. In order to keep the candy as fresh as possible, trucks must be air-conditioned and shipments must be direct, and are often less-than-truckload. As a result, ordering cost is $4700. Pharr Foods makes Far Stars from three primary ingredients it orders from different suppliers: dark and milk chocolate, macadamia nuts, and, a special heavy cream filling. Except for its unique star shape, a Far Star is almost like a chocolate truffle. Each Far Star weighs 1.2 ounces and requires 0.70 ounce of blended chocolates, 0.50 ounce of macadamia nuts, and 0.40 ounce of filling to produce (including spillage and waste). Pharr Foods orders chocolate, nuts, and filling from its suppliers by the pound. The annual ordering cost is $5700 for chocolate, and the carrying cost is $0.45 per pound. The ordering cost for macadamia nuts is $6300, and the annual carrying cost is $0.63 per pound. The ordering cost for filling is $4500, and the annual average carrying cost is $0.55 per pound. Each of the suppliers offers the candy manufacturer a quantity-discount price schedule for the ingredients as follows: Chocolate Macadamia Nuts Filling Price Quantity (lb) Price Quantity (lb) Price Quantity (lb) $3.05 0–50,000 $6.50 0–30,000 $1.50 0–40,000 2.90 50,001–100,000 6.25 30,001– 70,000 1.35 40,001– 80,000 2.75 100,001– 150,000 5.95 70,000+ 1.25 80,000+ 2.60 150,000 + Determine the inventory order quantity for Pharr’s distributor. Compare the optimal order quantity with a seasonally adjusted forecast for demand. Does the order quantity seem adequate to meet the seasonal demand pattern for Far Stars? That is, is it likely that shortages or excessive inventories will occur? Can you identify the causes of the seasonal demand pattern for Far Stars? Determine the inventory order quantity for each of the three primary ingredients that Pharr Foods orders from its suppliers. Discuss the possible impact of the order policies of the food distributor and Pharr Foods on quality management and supply chain management. CASE PROBLEM 14.1: Seats for Sale Blokie State’s football program has risen to the ranks of the elite with postseason bowl games in each of the past 10 years, including a national championship game. The Blokes (as the fans are called) fill the stadium each game. Season tickets are increasingly difficult to find. In response to the outstanding fan support, Blokie State has decided to use its bowl revenues to expand the stadium to 75,000 seats. The administration is confident that all 75,000 seats can be sold at the normal price of $40 per game ticket; however, Frank Pinto’s job, as athletic director, is to get as much revenue out of the stadium expansion as possible. In addition to stadium boxes for the truly endowed, Frank would like to take this opportunity to repurpose existing seats. A certain number of seats (yet to be determined) would be set aside for premium ticket holders who would pay $200 per ticket for the privilege of 50-yard line seats with chair backs and access to indoor concessions. The question is, how many fans would be willing to pay such a premium? If too many seats are designated in the premium sections, they could remain vacant. Too few premium seats would lose potential revenue for the program. Frank has decided that if the plan has any chance of success, unsold premium seats should not be sold at reduced rates. It would be better to donate them to local charities instead. Gathering data from his cohorts at peer institutions, Frank has put together the following probability distribution of premium ticket holders. The data begin with 1000 tickets since Frank already has requests for 999 tickets from alumni donors. He is asking for your help in performing the analysis. No. of Premium Tickets Probability 1,000 0.10 5,000 0.30 10,000 0.24 15,000 0.15 20,000 0.10 25,000 0.06 30,000 0.05 • a. Using revenue management, determine how many seats should be reserved for premium ticket holders. • b. Considering your answer to part (a) and the possible outcomes listed above, how much total revenue (i.e., regular and premium) can be expected from ticket sales? • c. The administration is unsure about Frank’s plan. The VP of Finance thinks an expected value of the number of premium seats would produce better results. How would the number of premium seats change using expected value? Considering the possible outcomes, which approach yields the most potential revenue? (See Chapter 1 Supplement for an explanation of expected value.) CASE PROBLEM 14.2: Erin’s Energy Plan The discussion was getting heated. A brightly colored chart at the front of the room told the story all too well. At Waylan Industries sales were up, profits down. “Larry, there’s no way we can hit our profit objective with your high cost of production. You’ve got to cut back.” “Why pick on me? We’re running bare bones as it is. Last winter’s energy prices really killed us. How can you cut costs with a 250% increase in energy prices?” Everyone stared at Erin. Erin swallowed hard and spoke up. “You know, we’ve never had to stockpile fuel before and I’m not sure how far we can go in managing demand, but I’ve been collecting data on purchase options….” “Well, let’s see it then.” “Although it’s not how we purchase energy, I’ve converted the prices to millions of BTUs for comparison. Coal costs $8 per million BTUs, but we can only burn 500 million per quarter to stay within our environmental air standards. Natural gas costs $32 per million BTUs, and petroleum $46. We can burn 1000 million BTUs of each per quarter. Coal and natural gas can be stored for later use at a holding cost of 30 percent of purchase price per quarter. The holding cost for petroleum is 20 percent. Electricity cannot be stored, but it can be reserved in advance. The cost of electricity also varies considerably by season of the year, as do our energy needs. The cost of electricity from the local utility is $20 per million BTUs in the spring, $40 in the summer, $24 in the fall, and $70 in the winter. These are averages from last year. A nearby utility quotes slightly higher prices at $22, $44, $26, and $75 for spring, summer, fall, and winter. As far as availability is concerned, 4000 million BTUs of electricity are available in the spring and fall, 5000 in the summer and winter. We can save some money by contracting with the utilities in advance of the season. I’ve summarized the options in the table here, along with our energy needs. What I need now is some help analyzing the data. Larry, could you …” Before Erin could finish, Tom spoke up. “Yes, Larry—you and Erin work up an energy plan—like those aggregate production plans you’re always bringing in. And have it ready by Thursday.” Source: This case was developed with the help of Richard Hirsh, Professor of Science and Technology at Virginia Tech. USED IN Spring Summer Fall Winter PURCHASED IN Utility A Utility B Utility A Utility B Utility A Utility B Utility A Utility B Spring $20 $22 $40 $44 $20 $22 $45 $50 $40 $44 $22 $24 $50 $60 $24 $26 $60 $65 $70 $75 Summer Fall Winter Demand 1500 5000 Use Erin’s data to develop an aggregate energy plan for Waylan Industries. 5000 10,000
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