Case Study 1: Andrew-Carter, Inc

Case Study 1: Andrew-Carter, Inc.I Andrew-Carter, Inc. (A-C) is a major Canadian producer and distributor of outdoor lighting fixtures. Its products are distributed throughout South and North America and have been in high demand for several years. The company operates three plants to manufacture fixtures and distribute them to five distribution centers (warehouses). During the present global slowdown, A-C has seen a major drop in demand for its products, largely because the housing market has declined. Based on the forecast of interest rates, the head of operations feels that demand for housing and thus for A-C’s products will remain depressed for the foreseeable future. A-C is considering closing one of its plants, as it is now operating with a forecast excess capacity of 34,000 units per week. The forecast weekly demands for the coming year are as follows: Warehouse 1 9,500 units Warehouse 2 13,500 Warehouse 3 11,500 Warehouse 4 15,500 Warehouse 5 8,500 Plant capacities, in units per week, are as follows: Plant 1, regular time 27,500 units Plant 1, on overtime 7,500 Plant 2, regular time 20,500 Plant 2, on overtime 5,500 Plant 3, regular time 25,500 Plant 3, on overtime 6,500 If A-C shuts down any plants, its weekly costs will change, because fixed costs will be lower for a non-operating plant. Table 1 shows production costs at each plant, both variable at regular time and overtime, and fixed when operating and shut down. Table 2 shows distribution costs from each plant to each distribution center. Table 1 Andrew-Carter, Inc., Variable Costs and Fixed Production Costs per Week Fixed Cost Per Week Plant # Variable Cost (Per Unit) Operating Not Operating 1, regular time $3.80 $14,500 $6,500 1, overtime 4.52 - - 2, regular time 3.78 12,500 5,500 2, overtime 4.48 - - 3, regular time 3.72 15,500 8,000 3, overtime 4.42 - - Table 2 Andrew-Carter, Inc., Distribution Costs per Unit Fixed Cost Per Week From Plants W1 W2 W3 W4 W5 1 $0.60 $S0.54 $0.59 $0.56 $0.66 2 0.50 0.62 0.60 0.66 0.67 3 0.66 0.63 0.61 0.64 0.45 Discussion Questions 1. Evaluate the various configurations of operating and closed plants that will meet weekly demand. Determine which configuration minimizes total costs. 2. Discuss the implications of closing a plant. Case Study 2: Andrew-Carter, Inc.II Andrew-Carter, Inc. (A-C) is a major Canadian producer and distributor of outdoor lighting fixtures. Its products are distributed throughout South and North America and have been in high demand for several years. The company operates three plants to manufacture fixtures and distribute them to five distribution centers (warehouses). During the present global slowdown, A-C has seen a major drop in demand for its products, largely because the housing market has declined. Based on the forecast of interest rates, the head of operations feels that demand for housing and thus for A-C’s products will remain depressed for the foreseeable future. A-C is considering closing one of its plants, as it is now operating with a forecast excess capacity of 34,000 units per week. The forecast weekly demands for the coming year are as follows: Warehouse 1 10,000 units Warehouse 2 14,000 Warehouse 3 12,000 Warehouse 4 16,000 Warehouse 5 9,000 Plant capacities, in units per week, are as follows: Plant 1, regular time 28,000 units Plant 1, on overtime 8,000 Plant 2, regular time 21,000 Plant 2, on overtime 6,000 Plant 3, regular time 26,000 Plant 3, on overtime 7,000 If A-C shuts down any plants, its weekly costs will change, because fixed costs will be lower for a non-operating plant. Table 1 shows production costs at each plant, both variable at regular time and overtime, and fixed when operating and shut down. Table 2 shows distribution costs from each plant to each distribution center. Table 1 Andrew-Carter, Inc., Variable Costs and Fixed Production Costs per Week Fixed Cost Per Week Plant # Variable Cost (Per Unit) Operating Not Operating 1, regular time $1.80 $13,500 $5,500 1, overtime 2.52 - - 2, regular time 1.78 11,500 4,500 2, overtime 2.48 - - 3, regular time 1.72 14,500 7,000 3, overtime 2.42 - - Table 2 Andrew-Carter, Inc., Distribution Costs per Unit Fixed Cost Per Week From Plants W1 W2 W3 W4 W5 1 $0.40 $S0.34 $0.39 $0.36 $0.66 2 0.30 0.42 0.40 0.46 0.67 3 0.46 0.43 0.41 0.44 0.25 Discussion Questions 1. Evaluate the various configurations of operating and closed plants that will meet weekly demand. Determine which configuration minimizes total costs. 2. Discuss the implications of closing a plant. Case Study 3: Using Revenue Management to Set Orlando Magic Ticket PricesI Revenue management was once the exclusive domain of the airline industry. But it has since spread its wings into the hotel business, auto rentals, and now even professional sports, with the San Francisco Giants, Boston Celtics, and Orlando Magic as leaders in introducing dynamic pricing into their ticketing systems. Dynamic pricing means looking at unsold tickets for every single game, every day, to see if the current ticket price for a particular seat needs to be lowered (because of slow demand) or raised (because of higher-than-expected demand). Pricing can be impacted by something as simple as bad weather or by whether the team coming to play in the arena is on a winning streak or has just traded for a new superstar player. For example, a few years ago, a basketball star was traded in midseason to the Denver Nuggets; this resulted in an immediate run-up in unsold ticket prices for the teams the Nuggets were facing on the road. Had the Nuggets been visiting the Orlando Magic 2 weeks after the trade and the Magic not raised prices, they would have been “leaving money on the table”. As the Magic became more proficient in revenue management, they evolved from (l) setting the price for each seat at the start of the season and never changing it; to (2) setting the prices for each seat at season onset, based on the popularity of the opponent, the day of the week, and the time of season—but keeping the prices frozen once the season began (see Table 1); to (3) pricing tickets based on projected demand, but adjusting them frequently to match market demand as the season progressed. Table 1 An Example of Variable Pricing for a $68 Terrace V seat in Zone 103 Opponent Popularity Rating Number of Games in The Category Price Tier I 4 $192 Tier II 4 $175 Tier III 5 $90 Tier IV 7 $80 Tier V 15 $65 Tier VI 10 $49 Tier VII 7 $45 Average $73 To track market demand, the Magic use listed prices on Stub Hub and other online ticket exchange services. The key is to sell out all 18,500 seats every home game, keeping the pressure on Anthony Perez, the director of business strategy, and Chris Dorso, the Magic’s vice president of sales. Perez and Dorso use every tool available to collect information on demand, including counting unique page views at the Ticketmaster Web site. If, for example, there are 7,000 page views for the Miami Heat game near Thanksgiving, it indicates enough demand that prices of unsold seats can be notched up. If there are only 200 Ticketmaster views for the Utah Jazz game 3 days later, there may not be sufficient information to make any changes yet. With a database of 650,000, the Magic can use e-mail blasts to react quickly right up to game day. The team may discount seat prices, offer other perks, or just point out that prime seats are still available for a game against an exciting opponent. Discussion Questions 1. After researching revenue (yield) management in airlines, describe how the Magic system differs from that of American or other air carriers. 2. The Magic used its original pricing systems of several years ago and set the price for a Terrace V, Zone 103 seat at $68 per game. There were 230 such seats not purchased as part of season ticket packages and thus available to the public. If the team switched to the 7- price dynamic system (illustrated in Table 1), how would the profit-contribution for the 45-game season change? (Note that the 45-game season includes 4 preseason games.) 3. What are some concerns the team needs to consider when using dynamic pricing with frequent changes in price? Case Study 4: Using Revenue Management to Set Orlando Magic Ticket PricesII Revenue management was once the exclusive domain of the airline industry. But it has since spread its wings into the hotel business, auto rentals, and now even professional sports, with the San Francisco Giants, Boston Celtics, and Orlando Magic as leaders in introducing dynamic pricing into their ticketing systems. Dynamic pricing means looking at unsold tickets for every single game, every day, to see if the current ticket price for a particular seat needs to be lowered (because of slow demand) or raised (because of higher-than-expected demand). Pricing can be impacted by something as simple as bad weather or by whether the team coming to play in the arena is on a winning streak or has just traded for a new superstar player. For example, a few years ago, a basketball star was traded in midseason to the Denver Nuggets; this resulted in an immediate run-up in unsold ticket prices for the teams the Nuggets were facing on the road. Had the Nuggets been visiting the Orlando Magic 2 weeks after the trade and the Magic not raised prices, they would have been “leaving money on the table”. As the Magic became more proficient in revenue management, they evolved from (l) setting the price for each seat at the start of the season and never changing it; to (2) setting the prices for each seat at season onset, based on the popularity of the opponent, the day of the week, and the time of season—but keeping the prices frozen once the season began (see Table 1); to (3) pricing tickets based on projected demand, but adjusting them frequently to match market demand as the season progressed. Table 1 An Example of Variable Pricing for a $68 Terrace V seat in Zone 103 Opponent Popularity Rating Number of Games in The Category Price Tier I 2 $182 Tier II 2 $165 Tier III 3 $80 Tier IV 5 $70 Tier V 13 $55 Tier VI 8 $39 Tier VII 5 $35 Average $63 To track market demand, the Magic use listed prices on Stub Hub and other online ticket exchange services. The key is to sell out all 18,500 seats every home game, keeping the pressure on Anthony Perez, the director of business strategy, and Chris Dorso, the Magic’s vice president of sales. Perez and Dorso use every tool available to collect information on demand, including counting unique page views at the Ticketmaster Web site. If, for example, there are 7,000 page views for the Miami Heat game near Thanksgiving, it indicates enough demand that prices of unsold seats can be notched up. If there are only 200 Ticketmaster views for the Utah Jazz game 3 days later, there may not be sufficient information to make any changes yet. With a database of 650,000, the Magic can use e-mail blasts to react quickly right up to game day. The team may discount seat prices, offer other perks, or just point out that prime seats are still available for a game against an exciting opponent. Discussion Questions 1. After researching revenue (yield) management in airlines, describe how the Magic system differs from that of American or other air carriers. 2. The Magic used its original pricing systems of several years ago and set the price for a Terrace V, Zone 103 seat at $68 per game. There were 230 such seats not purchased as part of season ticket packages and thus available to the public. If the team switched to the 7- price dynamic system (illustrated in Table 1), how would the profit-contribution for the 45-game season change? (Note that the 45-game season includes 4 preseason games.) 3. What are some concerns the team needs to consider when using dynamic pricing with frequent changes in price?