CH-16-18 Global Sourcing and Procurement – Operations and Supply Chain Management : Book Guide

This Document Contains Chapters 16 to 18 CHAPTER 16 GLOBAL SOURCING AND PROCUREMENT Discussion Questions 1. What recent changes have caused supply chain management to gain importance? Changes include: a. Competitive pressures from foreign firms. b. Elevation of product quality to a very high level of importance. c. International marketing and international purchasing. d. Trends towards choosing sole-source suppliers and long term relationships. e. Product varieties and ranges are rapidly changing, and speed of delivery to market is essential. f. Product life cycles have shortened necessitating knowledge and control of inventories in the various pipelines. g. Adoption of JIT production has changed supplier relationships and has also increased the focus on reducing inventories. h. Trends in the legal system hold manufacturers liable for product failures, even though causes of failure may lie outside of the production system itself. i. Use of EDI in purchasing. j. The growth of supplier development. 2. Describe the differences between functional and innovative products. Functional products are staples that people buy in a wide range of retail outlets. Typically, they do not change much over time; and have low profit margins, stable predictable demand and long life cycles. Innovative products, on the other hand, give customers additional reasons to buy. Fashionable clothes and personal computers are examples of innovative products. Innovative products have short life cycles, high profit margins, and volatile demand. 3. What are characteristics of efficient, responsive, risk-hedging and agile supply chains? Can a supply chain be both efficient and responsive? Risk-hedging and agile? Why, or why not? Efficient supply chains are designed to minimize cost that requires high utilization, minimizing inventory, and selecting vendors based primarily on cost and quality, and designing products that are produced at minimum cost. Market-responsive supply chains are designed to minimize lead time to respond to unpredictable demand, thus minimizing stockout costs and obsolete inventory costs. Risk sharing supply chains are those that share resources so that risks in the supply chain can be shared. Agile are those supply chains that are flexible while still sharing risks of shortages across the supply chain. Generally, these supply chains carry excess capacity and higher buffer stocks. Vendor in responsive supply chains would be selected for speed, flexibility, and quality. It is possible to be both efficient and responsive, and both Risk-hedging and Agile, but Exhibit 16.4 helps illustrate why supply chains are generally not both. 4. With so much productive capacity and room for expansion in the United States, why would a company based in the United States choose to purchase items from foreign firm? Discuss the pros and cons. The use of foreign firms can provide a U.S. firm more alternatives in selecting a supplier. The pros are: more choices, potentially reduced costs in the areas of materials, transportation, production, and distribution, and potentially moving closer to a foreign market. The cons are: the distance is generally increased; communications problems are increased due to distance, culture, and technology; and there may be problems with customs, government regulations, political stability, etc. 5. As a supplier, which factors would you consider about a buyer (your potential customer) to be important in setting up a long-term relationship? The financial stability and credit worthiness of the company is of primary importance. The reputation of the company vis-à-vis their supplier is also very important. For example, is this a company that is fair with its suppliers and honors its payables in a timely fashion? Is the technological match between supplier and customer sufficient? Will delivery schedules and quantities be stable, facilitating smooth operations? 6. Describe how outsourcing works. Why would a firm want to outsource? Outsourcing is the act of moving some of a firm's internal activities and decision responsibilities to outside providers. The terms of the agreement are established in a contract. Outsourcing goes beyond the more common purchasing and consulting contracts because not only are the activities transferred, but also resources that make the activities occur are transferred. Reasons for outsourcing are listed in Exhibit 16.6. Some of the major categories from this exhibit include organizational, improvement, financial, revenue, cost, and employee driven reasons. 7. Have you ever purchased a product based on purchase price alone and were surprised by the eventual TCO, either in money or in time? Describe the situation. Answers will vary. A common example will likely be the purchase of a printer, especially an inkjet print. They are notorious for having extremely high operating costs due to the price of repeated ink purchases. Other examples might include buying an inexpensive car only to spend unexpected amounts of money on upkeep and repair. 8. Why might managers resist buying a more expensive piece of equipment that is known to have a lower TCO than a less expensive item? One reason may simply be ignorance on the part of the individual when it comes to TCO analysis, so the higher purchase price is difficult to justify. Also, for very large purchases there may be more concern about the immediate impact the purchase will have on quarterly financial result than there is about the long run cost of ownership. 9. Why is it desirable to increase a company’s inventory turnover ratio? One way to look at it is that it measures the average amount of inventory held as a percent of the total goods moved through the system in a year. It is a relative measure of the amount of inventory held in a company, and as such comparisons across different firms in an industry are meaningful, even if the raw numbers for the firms vary greatly in magnitude. As a general rule, lowering inventory carried reduces the supply chain costs. 10. Research and compare the inventory turnover ratios of three large retailers: Wal-Mart, Target, and Nordstrom’s. Use the same financial web site for all three, and compare numbers from the same time frame. What do these ratios tell you? Are you surprised by what you found? Using Forbes.com, and looking at 1st quarter 2012 figures, the ratios are Wal-Mart: 8.5, Target: 6.4, Nordstrom’s: 5.9. Students may not be surprised the Wal-Mart’s is highest but the magnitude of the difference may not be expected. Generally speaking, the low-price leader would need to have very efficient operations to compete, and Wal-Mart’s ratio indicates they are very good at managing supply chain inventory. The higher-priced, personal-attention approach by Nordstrom’s allows for a less efficient supply chain which is balanced by higher profit margins. Objective Questions 1. What term refers to the development and management of supplier relationships to acquire goods and services in a way that helps achieve the immediate needs of a business? Strategic sourcing 2. Sometimes a company may need to purchase goods or services that are unique, very complex, and/or extremely expensive. These would not be routine purchases, but there may be a number of vendors that could supply what is needed. What process would be used to transmit the company’s needs to the available vendors, asking for a detailed response to the needs? Request for proposal 3. Sony Electronics produces a wide variety of electronic products for the consumer marketplace, like laptop computers, PlayStation game consoles and tablet computers. What type of products would these be considered in Lee’s Uncertainty Framework? Innovative products 4. One product that Staples sells a lot of is copy paper. According to Lee’s Uncertainty Framework, what supply chain strategy is appropriate for this product? Efficient supply chain 5. What is the term used for the act of moving some of a company’s internal activities and decision-making processes to outside providers? Outsourcing 6. What is the term used for a company moving management of the complete cycle of material flow to an outside provider? Logistics outsourcing 7. Many bottled water manufacturers have recently worked with their suppliers to switch over to bottles using much less plastic than before, reducing the amount of plastic that needs to be transported, recycled, and/or disposed of. What sourcing practice is this an example of? Green sourcing 8. What term refers to the way some companies focus on what they do best and outsource other functions to key partners? Capability sourcing 9. What three main categories of costs are considered in figuring total cost of ownership? Acquisition costs, ownership costs, post-ownership costs 10. Which category of lifetime product costs is sometimes overemphasized, leading to a failure to fully recognize the total cost of ownership? Acquisition costs 11. Year: 0 1 2 3 Demand 200,000 300,000 500,000 Cost of Capital 0.15 Purchase Option Purchase Cost Per Unit 0.1 $20,000.00 $30,000.00 $50,000.00 Shipping/Unit 0.01 $2,000.00 $3,000.00 $5,000.00 Inventory charge/Unit 0.005 $1,000.00 $1,500.00 $2,500.00 Monthly charge 20 $240.00 $240.00 $240.00 Total Purchase Cost $23,240.00 $34,740.00 $57,740.00 Make Option Direct Material 0.05 $10,000.00 $15,000.00 $25,000.00 Direct Labor 0.03 $6,000.00 $9,000.00 $15,000.00 50% Surcharge 0.015 $3,000.00 $4,500.00 $7,500.00 Indirect Labor 0.011 $2,200.00 $3,300.00 $5,500.00 50% Surcharge 0.0055 $1,100.00 $1,650.00 $2,750.00 Overhead 100% DL 0.03 $6,000.00 $9,000.00 $15,000.00 Total Variable Manufacture Cost $28,300.00 $42,450.00 $70,750.00 Investment Engineer $30,000.00 Equipment $10,000.00 Cost Comparison Analysis Make Cost – Buy Cost $40,000.00 $5,060.00 $7,710.00 $13,010.00 Discount factor 1 0.86957 0.75614 0.65752 NPV (Make – Buy) $40,000.00 $4,400.00 $5,829.87 $8,554.29 Total NPV (Make - Buy) $58,784.15 Alternative: Option NPV Calculations Buy $143,226.27 $40,000.00 $24,608.70 $32,098.30 $46,519.27 Make $84,442.11 $20,208.70 $26,268.43 $37,964.99 Difference $58,784.15 Continuing to make in-house would cost us over $58,000 more in current dollars than buying from the supplier. We should accept the bid. 12. Requirement (annual forecast) 12,000 units Weight 22 pounds per engine Order processing cost $125.00 per order Inventory carry cost 20% of average inventory Lot Size (order quantity) 1,000 Units - given in the case Supplier 1 2 Unit Price $510 $505 Annual Purchase Cost $6,120,000 $6,060,000 One-Time Tooling Cost $22,000 $20,000 Orders per year 12 12 Order Processing Cost $1,500 $1,500 Inventory carry cost $51,000 $50,500 Distance 125 100 miles Weight per load 22,000 Transportation (Less-than-truckload) $1.20 per 2,000 lbs. per mile $19,800 $15,840 Total Cost $6,214,300 $6,147,840 $66,460 difference We would prefer supplier #2. Required lot size for truckload 1818 Units (40,000 lbs. max. load/22 lbs. per engine) Supplier 1 2 Unit Price $500 $505 Annual Purchase Cost $6,000,000 $6,060,000 One-Time Tooling Cost $22,000 $20,000 Orders per year 6.6 6.6 Annual Order Processing Cost $825 $825 Annual Inventory carry cost $90,900 $91,809 Distance 125 100 miles Weight per load 40,000 Transportation (truckload) $0.80 per 2,000 lbs. per mile $13,200 $10,560 Total Cost $6,126,925 $6,183,194 $56,269 difference Yes, it would make sense to order in truckload lots as we can reduce total costs. While carrying costs increase, purchase and transportation costs decrease by a greater amount. Note that if ordering in truckload lots, supplier #1 becomes the lowest choice option. In future years the cost would be reduced by the one-time tooling cost included here. 13. Which supply chain efficiency measure is more appropriate when the majority of inventory is held in distribution channels? Weeks of supply 14. What do you call the average total value of all items held in inventory for a firm, at cost? Average aggregate inventory value 15. a. 𝐼𝑇𝑅 = $322,000 = 3.5 $42,500+37,000+12,500 b. 𝑊𝑆 $42,500+37,000+12,500 𝑎𝑙𝑠𝑜 = 52 𝐼𝑇𝑅 16. Cost of Goods Sold 1.00*1000*52 Inventory Turnover = = =148.6 Average Aggregate Inventory Value 350 The problem tells us that we sell 4,000 QUARTER pound burgers a week, therefore we sell 1,000 pounds a week, and each pound of hamburger costs $1.00. The problem also tells us that on average, the store has 350 pounds of inventory on hand. By dividing the Cost of Goods Sold by Average Aggregate Inventory Value, We can figure the Inventory Turns. This means that their inventory turns 148.6 times a year. Average Aggregate Inventory Value 350 Weeks of Supply = *52 = *52 =.350 Cost of Goods Sold 1.00*1000*52 On average the restaurant has about a third of a week’s supply on hand. 17. a. Q1 Q2 Q3 Q4 Sales United States 300 350 405 375 Canada 75 60 75 70 Europe 30 33 20 15 COGS (Total) 280 295 340 350 Inventory Raw Materials 50 40 55 60 WIP and FG 100 105 120 150 DC Inventory United States 25 27 23 30 Canada 10 11 15 16 Europe 5 4 5 5 Total Inventory 190 187 218 261 Inventory Turnover 1.5 1.6 1.6 1.3 Using the end-of-quarter inventory numbers as a substitute for the average inventory level, we have the following quarterly and annual inventory turn values. Average inventory for the annual figure is based on the average of the 4 quarterly inventory numbers. Q1 Q2 Q3 Q4 Annual 280/190 = 1.474 295/187 = 1.578 340/218 = 1.560 350/261 = 1.341 1265/214 = 5.911 b. To increase the inventory turns, a firm needs to reduce the amount of inventory or increase sales or both. To increase turns, the item most readily within our control is the amount of inventory that the firm has on hand. The raw materials, WIP, and FG inventories are the most obvious targets for reduction. c. Average Aggregate Inventory Value 214 Weeks of Supply = *52 = *52 = 8.797 Cost of Goods Sold 1265 The 500M does not come into play in this problem. Analytics Exercise: Global Sourcing Decisions – Grainger Basic Data Total Current Volume (CBM) 190,000 Direct Ship Percentage 0.89 Direct Ship Volume (CBM) 169,100 Consolidation Center Volume 20,900 Shipping Cost Calculations Direct Ship by Container Type 20' 40' Volume (%) 21% 79% Volume (CBM) 35511 133589 Container Capacity Used 85% 85% Consolidation Center by Container Type Volume (%) 100% Volume (CBM) 20900 Container Capacity Used 96% Container Capacity (CBM) 34 67 Containers Shipped 1,229 2,671 Shipping Cost per Container $ 480.00 $ 600.00 Shipping Costs by Container Size $ 589,920 $ 1,602,600 Total Shipping Cost $ 2,192,520 Consolidation Center Operating Cost Calculations Number of Centers 4 Annual Fixed Cost per Center $ 75,000 Total Annual Fixed Cost $ 300,000 Variable Cost per CBM $ 4.90 Total Annual Variable Cost $ 102,410 Total Annual Consolidation Center Costs $ 402,410 Total China/Taiwan Logistics Cost $ 2,594,930 1. Evaluate the current China/Taiwan logistics costs. Assume a current total volume of 190,000 CBM and the 89% is shipped direct from the supplier plants in containers. Use the data from the case and assume that the supplier loaded containers are 85% full. Assume that consolidation centers are run at each of the four port locations. The consolidation centers only use 40’ containers and fill them to 96% capacity. Assume that it costs $480 to ship a 20’ container and $600 to ship a 40’ container. What is the total cost to get the containers to the United States? Do not include United States port costs in this part of the analysis. 2. Evaluate an alternative that involves consolidating all 20’ volume and using only a single consolidation center in Shanghai/Ningbo. Assume that all the existing 20’ volume and the existing consolidation center volume were sent to this single consolidation center by suppliers. This new consolidation center volume would be packed into 40’ containers filled to 96% and shipped to the United States. The existing 40’ volume would still be shipped direct from the suppliers at 85% capacity utilization. Basic Data Total Current Volume (CBM) 190000 Direct Ship Percentage 0.7031 Direct Ship Volume (CBM) 133589 Consolidation Center Volume 56411 Shipping Cost Calculations Direct Ship by Container Type 20' 40' Volume (%) 0% 100% Volume (CBM) 0 133589 Container Capacity Used 85% 85% Consolidation Center by Container Type Volume (%) 100% Volume (CBM) 56411 Container Capacity Used 96% Container Capacity (CBM) 34 67 Containers Shipped 0 3223 Shipping Cost per Container $ 480.00 $ 600.00 Shipping Costs by Container Size $ - $ 1,933,800 Total Shipping Cost $ 1,933,800 Consolidation Center Operating Cost Calculations Number of Centers 1 Annual Fixed Cost per Center $ 75,000 Total Annual Fixed Cost $ 75,000 Variable Cost per CBM $ 1.40 Total Annual Variable Cost $ 78,975 Total Annual Consolidation Center Costs $ 153,975 Total China/Taiwan Logistics Cost $ 2,087,775 Assuming the new consolidation center has the same fixed cost as before (questionable given the increase in volume), the new approach saves $507,155 per year. 3. What should be done based on your analytics analysis? What have you not considered that may make your analysis invalid or that may strategically limit success? What do you think Grainger management should do? Consolidating the 20’ volume and using only a single Consolidation Center looks very attractive from this analysis. However, there are other issues to be considered. - For one, we have not considered the increased cost to the suppliers that currently pack their own 20’ containers. These suppliers will need to bear the cost of shipping their goods to the Shanghai/Ningbo consolidation center. This cost will probably be pushed back to Grainger in the long run. - There will also be some added cost for the suppliers that currently ship to consolidation centers directly. These will all need to use the Shanghai/Ningbo now, which might not be as close as their current consolidation center. - The cost calculations also assume that the Shanghai/Ningbo center can handle the increased workload and the fixed cost will remain the same. Neither of these assumptions is guaranteed (or even likely). We may want to seriously consider using two consolidation centers with the other being in Yantian/Hong Kong. It may be attractive to have consolidation centers in both Shanghai/Ningbo and Yantian/Hong Kong since these are the most heavily used ports. Assumptions regarding the consolidation center fixed costs would need to be tested as well. CHAPTER 17 ENTERPRISE RESOURCE PLANNING SYSTEMS Discussion Questions 1. Describe the benefits of using an ERP system. Primary benefits come from the integration of planning, execution, and reporting across all functional units in a firm. Since a single database is used, all units are accessing real-time data, allowing them to make decisions immediately based on what is happening right now. Better coordination of all functional units leads to leaner, more efficient supply chains. 2. Are the ERP systems discussed in the chapter appropriate for use in all firms? Explain. ERP systems provide many benefits, but those benefits can only outweigh the high cost of implementation and operation in larger firms. Simpler management software systems may be more appropriate for smaller companies. 3. Describe how an ERP system fits into the overall information system structure in a firm. ERP systems provide a single, centralized backbone to support planning, control and reporting needs through the enterprise. All functional areas will interact with the ERP system, and all transactional data will be stored there, but other add-on systems may also be used to support specific needs of a functional area. 4. ERP systems provide a wealth of information that can be used and analyzed in a wide variety of ways. What is an inherent risk with so much information? Companies can suffer information overload if the systems are not designed to provide the right information in the right manner for the firm’s needs. Companies need to configure the systems to provide the functions they need, and they need to train all their personnel on using the systems properly and to their full potential. 5. Visit the vendors’ websites to read up on both SAP and Microsoft Dynamics ERP systems. Provide a list of four ways in which the two differ in their approach to implementing ERP. Answers will vary based on students’ research. You could also change the question to include other major ERP vendors. 6. Briefly describe how supply chain planning and control is managed in an ERP system. The key is coordination between all on the functions within the supply chain. Demand forecasting feeds production planning which then coordinates with vendors for delivery of supplies and materials. Inventory systems are used in support of production planning, and production execution will in turn provide input to the inventory systems when production is complete. Distribution will use inventory and storage information to support the movement of goods from storage to support customer orders. Financial and managerial accounting interacts with all functional areas as transactions impact standard accounts. 7. Explain how an ERP system can improve the evaluation and analysis of performance metrics. By integrating corporate data and coordinating between functional units in real time, ERP systems allow performance analysis in the present time frame – right now. Traditional systems also allow for analysis of performance metrics, but they always look at past data, so any analysis would lag what is happening in the present. In a dynamic environment that is an important difference. 8. Why is the cash-to-cash cycle time such an important supply chain performance measure? It is a good measure of cash flow related to supply chain operations. Reducing the cycle time makes more cash available for the firm to use throughout the supply chain. Objective Questions 1. What company is the largest ERP vendor? SAP 2. What term relates to the posting and tracking of activities that document a business? Transaction processing 3. Is it true that for an ERP system to be effective, it must be completely purchased from a single vendor? No 4. ERP systems from different vendors vary quite a bit, but typically they will focus on at least what four major areas? Finance, manufacturing and logistics, sales and marketing, human resources 5. Which common ERP module category is typically the largest and most complex? Manufacturing and logistics 6. What is the term for a computer program often used to facilitate database queries that are not part of a standard ERP system? Data warehouse 7. What are the four main functions within SAP’s supply chain software? Supply chain planning, supply chain execution, supply chain collaboration, supply chain coordination 8. What term refers to the sharing of information with partners to coordinate production, enabling everyone to work together to increase visibility and responsiveness? Collaborative manufacturing 9. What part of SAP gives users personalized access to a range of information, applications, and services supported by the system? Enterprise portal 10. What supply chain metric measures how many complete orders were filled and shipped on time? Perfect order fulfillment 11. 𝐴𝑅𝑑 = 6.75 (given) 𝐼 $300,000 𝐼𝑑 = = = 8.57 𝐶𝑑 $35,000 𝐴𝑃 200,000 𝐴𝑃𝑑 = = = 5.71 𝐶𝑑 35,000 𝐶𝑎𝑠ℎ − 𝑡𝑜 − 𝑐𝑎𝑠ℎ 𝑐𝑦𝑐𝑙𝑒 𝑡𝑖𝑚𝑒 = 𝐴𝑅𝑑 + 𝐼𝑑 − 𝐴𝑃𝑑 = 6.75 + 8.57 − 5.71 = 𝟗.𝟔𝟏 𝐝𝐚𝐲𝐬 CHAPTER 18 FORECASTING Discussion Questions 1. Why is forecasting necessary in OSCM? Companies need to plan for the future to ensure they are able to meet the needs of the market efficiently and effectively. Most decisions take some time to implement. Facility decisions may take more than a year to implement, while distribution decisions may only take a week or so. Because of this lead time, firms cannot simply wait until a need arises to make a decision on how to deal with that need. Instead, they need to plan ahead, and forecasts allow them to do this based on the best information available. 2. It is a common saying that the only thing certain about a forecast is that it will be wrong. What is meant by this? Because forecasting involves making predictions about the future, there is no way for a forecast to reliably be perfect. There are too many unknowns about the future, which makes the entire planning (for the future) process in supply chain management a very challenging process. 3. From the choice of simple moving average, weighted moving average, exponential smoothing, and linear regression analysis, which forecasting technique would you consider the most accurate? Why? The answer depends on the nature of the demand you are trying to forecast. If there is any trend to the demand, a simple moving average will always lag behind the actual demand. On the other hand, if demand is relatively flat over the long run with only random variations from one period to the next, the latter three methods will always be adjusting the forecast in response to random variation. In this case a simple moving average will be the best choice. If there are seasonal or cyclical components to demand, then none of these methods will be particularly good as a forecasting tool. 4. All forecasting methods using exponential smoothing, adaptive smoothing, and exponential smoothing including trend require starting values to get the equations going. How would you select the starting value for, say Ft-1? Starting values can be simply an average of the early periods, or a guess. If the starting value is taken some period back (as opposed to starting to use the equations on very recent data) the equation will have a chance to adjust as it is carried forward to today. 5. How is a seasonal index computed from a regression line analysis? Seasonal index is equal to the actual value (data point) divided by the value computed from the regression line. To lessen the effects of random errors, the indices may be averaged over several years for that same period. 6. Discuss the basic differences between the mean absolute deviation and mean absolute percent error. The mean absolute percent error can be used to compare forecasting accuracy when the average demand for each item is different. The mean absolute percent error (MAPE) is the expected error measured as a fraction of demand, whereas the MAD is just the average error of the forecast. The MAPE is usually a better measure. 7. What implications do forecast errors have for the search for ultra-sophisticated statistical forecasting models? The existence of unavoidable forecast errors seems to suggest that no matter what kind of model one uses—simple or sophisticated—a perfect forecast is unattainable. Since forecasts are predictions of the future based on present and past data, there is ample opportunity for very serious forecast errors to be caused by changes in the conditions that generated the data. This could lead to an invalid forecast or at least one that contains added error. Therefore, one could be easily persuaded to stop searching for ways to make more accurate forecasts and look instead for ways to quickly respond and adapt to demand changes. 8. Causal relationships are potentially useful for which component of a time series? A time series creates an equation, such as y = a + bx, where a is the y-intercept. Therefore, if there is a relationship between y and x, it would show up as the slope b. If there is no relationship, b would be zero. There is some question as to the relationship being truly “causal,” since many relationships may depend on other factors outside of the analysis. 9. Let’s say you work for a company that makes prepared breakfast cereals like corn flakes. Your company is planning to introduce a new hot breakfast product made from whole grains that would require some minimal preparation by the consumer. This would be a completely new product for the company. How would you propose forecasting initial demand for this product? Student answers will vary, but they should generally be centered on the qualitative techniques discussed in the chapter. 10. How has the development of the Internet affected the way companies forecast in support of their supply chain planning process? Answers will vary, but basically this comes down to the availability of immediate and large scale collaboration in the forecasting process thanks to the Internet. Students might draw an analogy between this and the benefits of ERP in global supply chains. 11. What sorts of risks do you see in reliance on the Internet in the use of Collaborative Planning, Forecasting and Replenishment (CPFR)? Students will likely come up will several ideas, but information security risks should be a common part of their responses. Ensuring that you systems and the information they share are secure from prying eyes and malicious hackers should be a primary concern of any I.T. department. Objective Questions 1. What is the term for forecasts used for making day-to-day decisions about meeting demand? Tactical forecasts 2. What category of forecasting techniques uses managerial judgment in lieu of numerical data? Qualitative techniques 3. Three quarters ago (Oct, Nov, Dec last year) = 200 + 225 + 250 = 675 Two quarters ago (Jan, Feb, Mar this year) = 125 + 135 + 135 = 395 One quarter ago (Apr, May, Jun this year) = 190 + 200 + 190 = 580 For Jul Aug Sep, using a three-quarter average the forecast would be = (675 + 395 + 580)/3 = 550 4. Third most recent quarter 275 + 375 +350 = 1000 Second most recent quarter 425 +400 + 350 = 1175 Most recent quarter 350 + 275 + 350 = 975 WMA = (.25*1000) + (.25 * 1175) + (.50 * 975) = 1031.25 5. Use the actual demand from February through April to develop May’s forecast: (45 + 81 + 90)/3 = 72 6. a. Ft+1 = Ft + (At – Ft),  = .20 Month Demand Forecast Absolute Deviation January 100 80 20 February 94 84 10 March 106 86 20 April 80 90 10 May 68 88 20 June 94 84 10 Total 90 b. MAD = 90/6 = 15 7. a - c. Exponential Month Demand smoothing Absolute deviation Tt Ft FITt Absolute deviation 1 31 31.00 1.00 30.00 31.00 2 34 31.00 3.00 1.00 31.00 32.00 2.00 3 33 31.90 1.10 1.18 32.60 33.78 0.78 4 35 32.23 2.77 1.11 33.55 34.66 0.34 5 37 33.06 3.94 1.14 34.76 35.90 1.10 6 36 34.24 1.76 1.24 36.23 37.47 1.47 7 38 34.77 3.23 1.11 37.03 38.14 0.14 8 40 35.74 4.26 1.10 38.10 39.19 0.81 9 40 37.02 2.98 1.17 39.43 40.60 0.60 10 41 37.91 3.09 1.11 40.42 41.54 0.54 MAD 2.90 0.86 Based upon the MAD of each forecast, the exponential smoothing with trend is the better forecasting model. 8. a. F (this month) = (325 + 350 + 400)/3 = 358 b. F (next month) = (300 + 325 + 350)/3 = 325 c. F (two months ago) = 450 + .20(400 – 450) = 440 F (one month ago) = 440 + .20(350 – 440) = 422 F (this month) = 422 + .20(325 – 422) = 403 9. Ft = 300 = .30 At = 288 Tt = 8 = .40 FITt = Ft +Tt = 300+8 = 308 Ft+1 = FITt +(At −FITt ) = 308+.3(288−308) = 308−6 = 302 Tt+1 =Tt +(Ft+1 −FITt ) = 8+.4(302−308) = 8−2.4 = 5.6 FITt+1 = Ft+1 +Tt+1 = 302+5.6 = 307.6 10. Year Demand F(t)* 2005 270 2006 356 2007 398 2008 456 341 2009 358 364 2010 500 363 2011 410 390 2012 376 394 * Forecasts have been rounded to integer values, which may result in minor rounding differences. F(2008) is calculated as average demand from years 2005-07. Later forecasts are based on a simple exponential smoothing model with alpha = .20. 11. a. F5 = (700 + 600 + 400)/3 = 567 b. F4 = F3 + (A3 – F3) = 350 + .20(600 – 350) = 400 F5 = F4 + (A4 – F4) = 400 + .20(700 – 400) = 460 12. a. FSeptember = (170 + 180 + 140)/3 = 163.3 b. FSeptember = .50(170) + .30(180) + .20(140) = 167.0 c. FJuly = FJune + (AJune – FJune) = 130 + .3(140 - 130) = 133.00 FAugust = FJuly + (AJuly – FJuly) = 133.00 + .3(180 – 133.00) = 147.10 FSeptember = FAugust + (AAugust – FAugust) = 147.10 + .3(170 – 147.10) = 153.97 13. a. FOctober = (75 + 80 + 60 + 75)/4 = 72.5 b. FOctober = FSeptember + (ASeptember – FSeptember) = 65 + .2(75 – 65) = 67.0 c. y = 405/6 = 67.5 t = 21/6 = 3.5 b = ty2−nntty = 1485−6(3.5)672 .5 = 3.86 t − 2 91−6(3.5) a = y −bt = 67.5 – 3.86(3.5) = 54.00 Y = a + bt = 54.0 + 3.86t Using Excel, the intercept a can be found using the INTERCEPT() function. The slope b can be found with the SLOPE() function. d. FOctober = 54.00 + 3.86(7) = 81.01 14. t Y ty t2 y2 Y (y-Y)2 1 4200 4200 1 17640000 3958.97 58093.360 2 4300 8600 4 18490000 4151.28 22117.028 3 4000 12000 9 16000000 4343.59 118053.912 4 4400 17600 16 19360000 4535.90 18468.113 5 5000 25000 25 25000000 4728.21 73872.452 6 4700 28200 36 22090000 4920.51 48625.904 7 5300 37100 49 28090000 5112.82 35036.160 8 4900 39200 64 24010000 5305.13 164128.863 9 5400 48600 81 29160000 5497.44 9493.754 60200 6.5 Using Excel, the intercept a can be found using the INTERCEPT() function. The slope b can be found with the SLOPE() function. You can also use the Regression tool from the Data Analysis menu. Either way, Excel’s results are identical. Month Forecast 13 6266.67 14 6458.97 15 6651.28 16 6843.59 17 7035.90 18 7228.21 19 7420.51 20 7612.82 21 7805.13 22 7997.43 23 8189.74 24 8382.05 b. Sty = = 269.85 Therefore, 3 standard errors of the estimate would be 3(269.85) = 809.55 or 810 15. a. FJuly = .60(15) + .30(16) + .10(12) = 15.0 b. FJuly = (15 + 16 + 12) / 3 = 14.3 c. FJuly = FJune + (AJune – FJune) = 13 + .2(15-13) = 13.4 d. t y ty t2 1 12 12 1 2 11 22 4 3 15 45 9 4 12 48 16 5 16 80 25 Total 21 81 91 Average 3.5 13.5 6 15 36 Y = a + bt = 10.8 + .77t e. FJuly, where July is the 7th month. Y = a + bt = 10.8 + .77(7) = 16.2 16. TS 1 TS 1: Since there has been a rapid rise in the trend, the forecast will shortly be outside of the limits. Therefore, the forecasting model is poor. TS 2 TS 2: This is within the limits. Therefore, the forecast is acceptable. TS 3 TS 3: This series is rising rapidly, and is outside of the limits. Consequently, the model is poor. 17. a-c. For the exponential smoothing forecast we need a beginning forecast – this solution uses the average of the first three months demand for the April forecast and the exponential smoothing model for the remaining forecasts. Other choices will produce different answers. Month Demand 3-Mo. Absolute Exponential Absolute MA Deviation Smoothing Deviation January 110 February 130 March 150 April 170 130 40 130 40 May 160 150 10 142 18 June 180 160 20 147.4 32.6 July 140 170 30 157.18 17.18 August 130 160 30 152.03 22.03 September 140 150 10 145.42 5.42 MAD 23.3 22.5 Based upon MAD, the exponential smoothing model appears to be slightly better. 18. Month Forecast Actual Deviation RSFE Absolute deviation Sum of absolute deviations MAD TS April 250 200 -50 -50 50 50 50.0 -1 May 325 250 -75 -125 75 125 62.5 -2 June 400 325 -75 -200 75 200 66.7 -3 July 350 300 -50 -250 50 250 62.5 -4 August 375 325 -50 -300 50 300 60.0 -5 September 450 400 -50 -350 50 350 58.3 -6 For September, the MAD is 58.3 and the TS is –6. The model is performing poorly since the tracking signal is –6 and moving in a downward direction. The model is consistently over-forecasting demand. Absolute Sum of Absolute Month Forecast Actual Deviation RSFE deviation deviations MAD TS 1 140 137 -3 -3 3 3 3.00 -1.00 2 140 133 -7 -10 7 10 5.00 -2.00 3 140 150 10 0 10 20 6.67 0.00 4 140 160 20 20 20 40 10.00 2.00 5 140 180 40 60 40 80 16.00 3.75 6 150 170 20 80 20 100 16.67 4.80 a. For month 8, the MAD is 23.75 b. The tracking signal for month 8 is 7.16 c. The tracking signal is too large, so the forecast should be considered poor. It is not effectively dealing with an apparent upward trend in demand. Sum of Absolute Absolute Week Forecast Actual Deviation RSFE deviation deviations MAD TS 1 800 900 100 100 100 100 100 1.0 2 850 1000 150 250 150 250 125 2.0 3 950 1050 100 350 100 350 117 3.0 4 950 900 -50 300 50 400 100 3.0 5 1000 900 -100 200 100 500 100 2.0 6 975 1100 125 325 125 625 104 3.1 For week 6, the MAD is 104, and the tracking signal is 3.1. This is a fairly high value, which indicates the model is unacceptable. Period Forecast Actual Deviation RSFE Absolute Sum of Absolute deviation deviations MAD TS 1 1500 1550 50 50 50 50 50.0 1.00 2 1400 1500 100 150 100 150 75.0 2.00 3 1700 1600 -100 50 100 250 83.3 0.60 4 1750 1650 -100 -50 100 350 87.5 -0.57 5 1800 1700 -100 -150 100 450 90.0 -1.67 a. For period 5, the MAD = 90.00, and the TS = -1.67 b. Looking solely at the value of the TS, the model seems acceptable since the tracking signal is only 1.67 off the mean. However, the MAD has been increasing since the first period, and the downward trend over the last several periods in the graph is cause for concern that there may be some bias in the model. Month (t) Demand 3-mo. MA Absolute deviation 3-mo WMA Absolute deviation Ft Absolute deviation Tt Ft FITt Absolute deviation 1 62 61.00 1.80 60.00 61.80 2 65 61.30 1.82 61.86 63.68 3 67 62.41 1.94 64.07 66.01 4 68 64.67 3.33 65.40 2.60 63.79 4.21 2.03 66.31 68.33 0.33 5 71 66.67 4.33 67.10 3.90 65.05 5.95 2.00 68.23 70.23 0.77 6 73 68.67 4.33 69.30 3.70 66.84 6.16 2.07 70.46 72.53 0.47 7 76 70.67 5.33 71.40 4.60 68.68 7.32 2.11 72.67 74.78 1.22 8 78 73.33 4.67 74.10 3.90 70.88 7.12 2.22 75.14 77.36 0.64 9 78 75.67 2.33 76.40 1.60 73.02 4.98 2.28 77.55 79.83 1.83 10 80 77.33 2.67 77.60 2.40 74.51 5.49 2.11 79.28 81.39 1.39 11 84 78.67 5.33 79.00 5.00 76.16 7.84 1.99 80.98 82.96 1.04 12 85 80.67 4.33 81.60 3.40 78.51 6.49 2.08 83.27 85.35 0.35 MAD 4.07 3.46 6.17 0.89 Based upon MAD, the exponential smoothing with trend component appears to be the best method. This should not be a surprise given the apparent upward trend. 23. Month Forecast Actual Deviation RSFE Absolute deviation Sum of absolute deviations MAD TS May 450 500 50 50 50 50 50.00 1.00 June 500 550 50 100 50 100 50.00 2.00 July 550 400 -150 -50 150 250 83.33 -0.60 August 600 500 -100 -150 100 350 87.50 -1.71 September 650 675 25 -125 25 375 75.00 -1.67 October 700 600 -100 -225 100 475 79.17 -2.84 The TS itself is acceptable. However, you would like to see the TS going back and forth between positive and negative. It has been headed down since June. If this trend continues, the forecasts will be unacceptable. This forecast should be closely monitored to see if the downward trend continues, or if this occurred by random chance. 24. Average from Seasonal Deseasonalized t*deseasonalized t y same quarter factor demand t2 demand 1 4800 3833.33 1.23 3902.61 1 3902.61 2 3500 2766.67 0.89 3942.77 4 7885.54 3 4300 3500.00 1.12 3829.05 9 11487.14 4 3000 2366.67 0.76 3950.70 16 15802.82 5 3500 1.23 2845.65 25 14228.26 6 2700 0.89 3041.57 36 18249.40 7 3500 1.12 3116.67 49 21816.67 8 2400 0.76 3160.56 64 25284.51 9 3200 1.23 2601.74 81 23415.65 10 2100 0.89 2365.66 100 23656.63 11 2700 1.12 2404.29 121 26447.14 Sum 78 37400 37400.00 219041.20 12 1700 0.76 2236.84 26842.08 y = 3116.67 t = 6.5 b = tyd − ntyd = -168.24 2 2 t − nt a = yd −bt = 4210.25 Forecast Seasonal (Yd*seasonal Period (t) Yd factor factor) 13 2023.08 1.23 2488.28 14 1854.84 0.89 1646.54 15 1686.60 1.12 1894.04 16 1518.35 0.76 1152.97 Calculations were done in Excel. Hand calculations may result in some rounding differences. 25. b - d. Average from same bi-monthly Seasonal Deseasonalized t*deseasonalized t y period factor demand t2 t*y demand 1 109 112.0 0.865 125.95 1 109 125.95 2 104 108.0 0.835 124.62 4 208 249.25 3 150 154.5 1.194 125.65 9 450 376.94 4 170 176.0 1.360 125.00 16 680 500.02 5 120 123.0 0.950 126.26 25 600 631.30 6 100 103.0 0.796 125.65 36 600 753.88 7 115 0.865 132.88 49 805 930.18 8 112 0.835 134.21 64 896 1073.68 9 159 1.194 133.19 81 1431 1198.68 1553 1553.00 10234.70 Simple Seasonal Seasonal Period (t) Forecast Yd factor Forecast 13 136.80 135.79 0.865 117.5 14 137.94 136.77 0.835 114.1 15 139.08 137.75 1.194 164.4 16 140.21 138.73 1.360 188.7 17 141.35 139.71 0.950 132.8 18 142.48 140.69 0.796 112.0 26. Average from same quarterly Seasonal Deseasonalized t*deseasonalized t y period factor demand t2 demand 1 160 187.5 1.003 159.47 1 159.47 2 195 217.5 1.164 167.54 4 335.09 3 150 177.5 0.950 157.92 9 473.77 4 140 165.0 0.883 158.56 16 634.24 5 215 1.003 214.28 25 1071.42 6 240 1.164 206.21 36 1237.24 7 205 0.950 215.83 49 1510.79 Total 36 1495 1495.00 7143.53 8 190 0.883 215.18 1721.52 = 186.875 t = 4.5 b = = 9.91 a = yd −bt = 142.30 Period Yd Seasonal factor Forecast 9 231.45 1.003 232 10 241.35 1.164 281 11 251.26 0.950 239 12 261.17 0.883 231 27. Within Excel, two methods can be used to compute the intercept and slope from a regression model. We can use the Regression tool within Data Analysis in the Data menu to perform a full-blown regression analysis, or we can use the INTERCEPT() and SLOPE() functions to get a and b in a traditional regression model. Using those functions we get: a = 15.143 b = 1.024 We can now use decomposition to quantify the seasonal factors and forecast for the next four quarters. First procedure: t y Trend Actual/ Trend Seasonal factor 1 12 0.742 0.766 2 18 17.19 1.047 1.087 3 26 18.21 1.427 1.341 4 16 19.24 0.832 0.802 5 16 20.26 0.790 6 24 21.29 1.128 7 28 22.31 1.255 8 18 23.33 0.771 Period Y Seasonal factor Forecast 9 24.36 0.766 18.66 10 25.38 1.087 27.60 11 26.40 1.341 35.42 12 27.43 0.802 21.99 Second procedure: We could also deseasonalize the data first, and perform a regression on the deseasonalized data. Full calculations for this alternate method are shown below: Average from same Seasonal Deseasonalized t*deseasonalized t y quarterly period factor demand t2 demand 1 12 14 0.71 16.93 1 16.93 2 18 21 1.06 16.93 4 33.86 3 26 27 1.37 19.02 9 57.06 4 16 17 0.86 18.59 16 74.35 5 16 0.71 22.57 25 112.86 6 24 1.06 22.57 36 135.43 7 28 1.37 20.48 49 143.37 Total = 36 158 158.00 741.14 8 18 0.86 20.91 167.29 a = yd −bt = 16.520 Period Yd Seasonal factor Forecast 9 22.98 0.71 16.29 10 23.70 1.06 25.20 11 24.42 1.37 33.38 12 25.13 0.86 21.63 = 19.75 = 4.5 b = = 0.718 28. Average from same Seasonal Deseasonalized t*deseasonalized t y quarterly period factor demand t2 demand 1 205 340.0 0.736 278.48 1 278.48 2 140 207.5 0.449 311.63 4 623.25 3 375 530.0 1.147 326.80 9 980.40 4 575 770.0 1.667 344.91 16 1379.63 5 475 0.736 645.27 25 3226.33 6 275 0.449 612.12 36 3672.74 7 685 1.147 596.95 49 4178.66 Total 36 3695 3695.00 18970.24 8 965 1.667 578.84 4630.75 a = yd −bt = 210.87 Period (t) Yd Seasonal factor Forecast (Yd*seasonal factor) 9 712.88 0.736 525 10 768.66 0.449 345 11 824.44 1.147 946 12 880.22 1.667 1467 29. a. Company A Period EPS Forecast Absolute  = 0.10 deviation Forecast  = 0.30 Absolute deviation 2009-I 1.67 1.67 1.67 II 2.35 1.67 0.68 1.67 0.68 III 1.11 1.74 0.63 1.87 0.76 IV 1.15 1.68 0.53 1.64 0.49 2010-I 1.56 1.62 0.06 1.50 0.06 II 2.04 1.62 0.42 1.52 0.52 III 1.14 1.66 0.52 1.67 0.53 IV 0.38 1.61 1.23 1.51 1.13 2011-I 0.29 1.48 1.19 1.17 0.88 II -0.18 1.36 1.54 0.91 1.09 III -0.97 1.21 2.18 0.58 1.55 IV 0.20 0.99 0.79 0.12 0.08 2012-I -1.54 0.91 2.45 0.14 1.68 II 0.38 0.67 0.29 -0.36 0.74 III 0.64 -0.14 MAD 0.96 0.79 Company B Forecast Absolute Forecast Absolute Period EPS  = 0.10 deviation  = 0.30 deviation 2009-I 0.17 0.17 0.17 II 0.24 0.17 0.07 0.17 0.07 III 0.26 0.18 0.08 0.19 0.07 IV 0.34 0.19 0.15 0.21 0.13 2010-I 0.25 0.20 0.05 0.25 0.00 II 0.37 0.21 0.16 0.25 0.12 III 0.36 0.22 0.14 0.29 0.07 IV 0.44 0.24 0.20 0.31 0.13 2011-I 0.33 0.26 0.07 0.35 0.02 II 0.40 0.26 0.14 0.34 0.06 III 0.41 0.28 0.13 0.36 0.05 IV 0.47 0.29 0.18 0.37 0.10 2012-I 0.30 0.31 0.01 0.40 0.10 II 0.47 0.31 0.16 0.37 0.10 III 0.32 0.40 MAD 0.12 0.08 b. MAD Company A Company B  = 0.10 .96 .12  = 0.30 .79 .08 Based upon MAD, an  of .30 performs better than .10. c. Company A t y Average from same quarter Seasonal factor Deseasonalized demand t2 t*deseasonalized demand 1 1.67 0.495 0.723 2.309 1 2.309 2 2.35 1.148 1.677 1.401 4 2.803 3 1.11 0.427 0.624 1.780 9 5.341 4 1.15 0.577 0.843 1.365 16 5.458 5 1.56 0.723 2.157 25 10.783 6 2.04 1.677 1.217 36 7.299 7 1.14 0.624 1.828 49 12.798 8 0.38 0.843 0.451 64 3.607 9 0.29 0.723 0.401 81 3.608 10 -0.18 1.677 -0.107 100 -1.073 11 -0.97 0.624 -1.556 121 -17.113 12 0.20 0.843 0.237 144 2.848 13 -1.54 0.723 -2.129 169 -27.676 14 0.38 1.677 0.227 196 3.172 Total 105 9.58 9.580 1015 14.165 y = 0.684 t = 7.5 Forecast Seasonal (Yd*seasonal Period (t) Yd factor factor) 15 -1.217 0.624 -0.76 16 -1.470 0.843 -1.24 17 -1.725 0.723 -1.25 18 -1.978 1.677 -3.32 19 -2.232 0.624 -1.39 20 -2.485 0.843 -2.09 Company B t y Average from same quarter Seasonal Deseasonalized factor demand t2 t*deseasonalized demand 1 0.17 0.263 0.764 0.223 1 0.223 2 0.24 0.370 1.077 0.223 4 0.446 3 0.26 0.343 0.999 0.260 9 0.781 4 0.34 0.417 1.213 0.280 16 1.121 5 0.25 0.764 0.327 25 1.636 6 0.37 1.077 0.344 36 2.061 7 0.36 0.999 0.360 49 2.522 8 0.44 1.213 0.363 64 2.902 9 0.33 0.764 0.432 81 3.887 10 0.40 1.077 0.371 100 3.714 11 0.41 0.999 0.410 121 4.513 12 0.47 1.213 0.388 144 4.651 13 0.30 0.764 0.393 169 5.104 14 0.47 1.077 0.436 196 6.110 Total 105 4.81 4.810 1015 39.672 Forecast Seasonal (Yd*seasonal Period (t) Yd factor factor) 15 0.462 0.999 0.46 16 0.478 1.213 0.58 17 0.494 0.764 0.38 18 0.510 1.077 0.55 19 0.525 0.999 0.53 20 0.541 1.213 0.66 The results indicate that Company A’s EPS is on a downward trend, while Company B’s EPS is growing. 30. a. We can use the Regression tool or the LINEST() function within Excel. Answers using LINEST function in Microsoft Excel follow. Sales Price Advertising Fitted Values 400 280 600 451.72 700 215 835 977.21 900 211 1100 1090.98 1300 210 1400 1195.40 1150 215 1200 1095.85 1200 200 1300 1231.99 900 225 900 929.25 1100 207 1100 1118.62 980 220 700 898.79 1234 211 900 1025.98 925 227 700 850.42 800 245 690 722.80 Constant 2191.3374 Price -6.9094 Advertising 0.3250 y=a+b1x1 +b2x2 = 2191.3374−6.9094x1 +.3250x2 Where a = y intercept x1 = price b1 = slope of price x2 = advertising b2 = slope of advertising b. Price has a larger effect on sales because it slope value is much higher (-6.9094 versus .3250). Price actually has a negative effect since raising price decreases sales. c. Sales = 2191.3374 - 6.9094 (300) + .3250 (900) Sales = 411.04 31. Quarter I II III IV Last year 23,000 27,000 18,000 9,000 This year 19,000 24,000 15,000 Being left up to the student to develop a method, and given the limited amount of historical data, there could be several good answers to this problem. An inspection of the data makes it seem obvious that there is seasonality in demand. Also the year-to-year figures in each quarter support an assumption of a negative long-term trend. One simplistic solution would be to manually follow the patterns in the existing demand data and forecast demand that is somewhat lower than fourth quarter last year, say 7,000. Same Qtr Seasonal Deasonalized Seasonal Quarter t Demand Average Factor Demand Yd Forecast Last Year I 1 23000 21000 1.167 19714 II 2 27000 25500 1.417 19059 III 3 18000 16500 0.917 19636 IV 4 9000 9000 0.500 18000 This Year I 5 19000 1.167 16286 II 6 24000 1.417 16941 III 7 15000 0.917 16364 IV 8 9000 0.500 15480.3 7740 Another more analytical approach would be to apply decomposition to the existing data, but we do not have two full years’ worth of data, so computing a seasonal factor for quarter IV is a concern. We could develop seasonal factors based solely on last year’s data, or we could use a substitute data point to complete this year’s data. The following solution uses last year’s quarter IV data as this year’s quarter IV demand to build the seasonal factors. Regression is run on the deseasonalized demand for the first seven quarters. Based on the results, this year’s quarter IV forecast is developed. Average 18000 Intercept = 20519.7 Found using INTERCEPT() function and deseasonalized data Slope = -629.925 Found using SLOPE() function 32. Since we our interested in forecasting the next four years, many of the procedures presented in the text will not work. For example, moving average and exponential smoothing will only project one period into the future. Therefore, of the methods presented in the text, regression appears to be the logical approach. Examination of the graph of revenue over time suggests that there maybe a slight upward trend. Additionally, there may be a cyclical component, possibly 6 or 7 years. With the limited data, it is very difficult to determine the cycle. Consequently, simple regression appears to be the available choice for the forecast. t y 1 4865.9 2 5067.4 3 5515.6 4 5728.8 5 5497.7 6 5197.7 7 5094.4 8 5108.8 9 5550.6 10 5738.9 11 5860.0 Total 66 59225.8 Using LINEST(): b = 55.62 a = 5050.444 Period Forecast 12 5718 13 5774 33. What forecasting technique makes use of written surveys or telephone interviews? Marketing research 34. Which qualitative forecasting technique was developed to ensure the input from every participant in the process is weighted equally? Delphi method 35. When forecasting demand for new products, sometimes firms will use demand data from similar existing products to help forecast demand for the new product. What technique is this an example of? Historical analogy 36. Often times, firms will work with their partners across the supply chain to develop forecasts and execute production and distribution between the partners. What technique does this describe? Collaborative planning, forecasting, and replenishment (CPFR) 37. How many steps are there in collaborative planning, forecasting, and replenishment (CPFR)? Five 38. What is the first step in CPFR? Creation of the front-end partnership agreement ANALYTICS EXERCISE: Forecasting Supply Chain Demand – Starbucks Corporation A good first step in developing a forecasting model is to create a plot of the historical demand data. There does not appear to be an obvious trend in demand at any of the DCs, though there is quite a bit of variation. With no evident trend, both moving average and exponential smoothing models would be appropriate. 1, 2. Both the MA and ES models are shown on the following pages. A summary of their performance measures is shown in the table on the next page (best measures in each column are bolded). Performance of the two MA models is similar – neither one seems to be consistently better than the other. The same can be said for the two ES models, though they do appear to perform slightly better than the MA models. The ES model with an alpha of .2 appears to be the best. 3. Aggregating demand at a single DC will result in better overall forecasting performance than forecasting for five DCs separately. The variations in demand for the separate DCs will tend to cancel each other out somewhat when aggregating demand and forecasting for a single DC. In each performance measure, the aggregated forecast error was about half that of the sum or average measure for the separate DCs. Other factors to consider would be the transportation costs which might be higher serving from a central DC rather than a regional one nearer the customers. We should also consider risk of natural disasters or other causes that might disrupt transportation modes from the single DC and interrupt the supply chain for this item. ATL BOS CHI DAL LA Avg of DCs MAD 11.44 10.08 19.85 10.64 9.87 12.37 MAPE 0.288 0.243 0.420 0.223 0.214 0.278 TS -0.117 1.224 -0.638 -1.222 1.992 N/A MAD 11.17 10.63 18.45 12.37 9.58 12.44 MAPE 0.281 0.256 0.391 0.260 0.207 0.279 TS -0.322 1.750 -0.011 -0.808 1.982 N/A MAD 10.76 9.85 17.77 10.51 8.57 11.49 MAPE 0.271 0.237 0.376 0.221 0.185 0.258 TS -0.512 1.225 -1.63 -0.65 1.52 N/A MAD 11.87 10.55 18.32 9.84 9.57 12.03 MAPE 0.299 0.254 0.388 0.207 0.207 0.271 TS -0.046 1.032 -0.241 0.024 1.510 N/A Performance Measures of the Four Forecasting Models Historical Demand 3-week MA BOS CHI CHI DAL 41.7 41.0 54.7 49.0 45.0 231.3 42.7 33.7 45.3 44.7 37.7 204.0 38.3 32.0 42.0 37.3 36.7 186.3 38.3 34.0 33.3 34.7 43.0 183.3 40.0 38.7 37.0 39.0 45.7 200.3 43.3 42.3 41.7 42.0 46.7 216.0 41.0 44.7 58.3 51.7 53.3 249.0 34.3 49.7 60.7 61.7 53.3 259.7 35.3 40.3 54.0 66.3 49.7 245.7 41.0 45.0 39.0 63.3 40.0 228.3 47.3 41.3 50.0 54.3 39.3 232.3 35.7 45.3 52.3 45.3 43.7 222.3 38.3 39.7 58.3 RS 42.7 FE 47.3 226.3 ATL BOS CHI DAL LA Total -8.7 -15.0 -10.7 -22.0 -13.0 -69.3 -6.3 -13.7 -22.0 -24.7 -7.7 -74.3 -7.7 -4.7 -42.0 -27.0 9.7 -71.7 -8.0 1.3 -20.3 -21.7 6.7 -42.0 7.0 8.7 -9.3 -9.7 7.0 3.7 -6.3 14.3 21.0 12.3 34.3 75.7 -29.3 24.7 24.7 30.7 21.0 71.7 -5.7 -7.0 -8.0 34.0 2.7 16.0 6.0 14.7 -35.0 22.7 -2.0 6.3 2.0 13.7 21.0 2.3 -4.0 35.0 -22.3 2.3 6.0 -14.0 4.7 -23.3 -3.0 2.0 -1.3 -12.3 17.0 2.3 -1.3 12.3 -12.7 -13.0 19.7 5.0 -5 45 62 62 42 43 254 -4 38 18 22 35 40 153 -3 30 48 72 40 54 244 -2 58 40 44 64 46 252 -1 37 35 48 43 35 198 1 33 26 44 27 32 162 2 45 35 34 42 43 199 3 37 41 22 35 54 189 4 38 40 55 40 40 213 5 55 46 48 51 46 246 6 30 48 72 64 74 288 7 18 55 62 70 40 245 8 58 18 28 65 35 204 9 47 62 27 55 45 236 10 37 44 95 43 38 257 11 23 30 35 38 48 174 12 55 45 45 47 56 248 13 40 50 47 42 50 229 13-week Average: 39.69 41.54 47.23 47.62 Absolute Deviation 46.23 222.31 Week ATL BOS CHI DAL LA Total 1 8.67 15.00 10.67 22.00 13.00 69.33 2 2.33 1.33 11.33 2.67 5.33 5.00 3 1.33 9.00 20.00 2.33 17.33 2.67 4 0.33 6.00 21.67 5.33 3.00 29.67 5 15.00 7.33 11.00 12.00 0.33 45.67 6 13.33 5.67 30.33 22.00 27.33 72.00 7 23.00 10.33 3.67 18.33 13.33 4.00 8 23.67 31.67 32.67 3.33 18.33 55.67 9 11.67 21.67 27.00 11.33 4.67 9.67 10 4.00 1.00 56.00 20.33 2.00 28.67 11 24.33 11.33 15.00 16.33 8.67 58.33 12 19.33 0.33 7.33 1.67 12.33 25.67 13 2.67 9.87 Sum of DC MAD: 61.87 0.288 0.243 0.420 0.223 0.214 0.142 Average DC MAPE: 0.278 -0.117 1.224 -0.638 -1.222 1.992 0.159 Historical Demand 5-week MA BOS CHI DAL 41.6 40.6 49.6 44.8 43.6 220.2 39.2 33.4 46.0 41.8 41.4 201.8 40.6 36.8 48.4 43.2 42.0 211.0 42.0 35.4 38.4 42.2 42.0 200.0 38.0 35.4 40.6 37.4 40.8 192.2 41.6 37.6 40.6 39.0 43.0 201.8 41.0 42.0 46.2 46.4 51.4 227.0 35.6 46.0 51.8 52.0 50.8 236.2 39.8 41.4 53.0 58.0 47.0 239.2 41.6 45.8 47.4 61.0 48.0 243.8 38.0 45.4 56.8 59.4 46.4 246.0 36.6 41.8 49.4 54.2 41.2 223.2 44.0 39.8 46.0 49.6 44.4 223.8 RS FE ATL BOS CHI DAL LA Total -8.6 -14.6 -5.6 -17.8 -11.6 -58.2 -2.8 -13.0 -17.6 -17.6 -10.0 -61.0 -6.4 -8.8 -44.0 -25.8 2.0 -83.0 -10.4 -4.2 -27.4 -28.0 0.0 -70.0 6.6 6.4 -20.0 -14.4 5.2 -16.2 -5.0 16.8 11.4 10.6 36.2 70.0 -28.0 29.8 27.2 34.2 24.8 88.0 -5.6 1.8 3.4 47.2 9.0 55.8 1.6 22.4 -22.6 44.2 7.0 52.6 -3.0 20.6 25.0 26.2 -3.0 65.8 -18.0 5.2 3.2 4.8 -1.4 -6.2 0.4 8.4 -1.2 -2.4 13.4 18.6 -3.6 18.6 -0.2 -10.0 19.0 23.8 -5 45 62 62 42 43 254 -4 38 18 22 35 40 153 -3 30 48 72 40 54 244 -2 58 40 44 64 46 252 -1 37 35 48 43 35 198 1 33 26 44 27 32 162 2 45 35 34 42 43 199 3 37 41 22 35 54 189 4 38 40 55 40 40 213 5 55 46 48 51 46 246 6 30 48 72 64 74 288 7 18 55 62 70 40 245 8 58 18 28 65 35 204 9 47 62 27 55 45 236 10 37 44 95 43 38 257 11 23 30 35 38 48 174 12 55 45 45 47 56 248 13 40 50 47 42 50 229 13-week Average: 39.69 41.54 47.23 47.62 Absolute Deviation 46.23 222.31 Week ATL BOS CHI DAL LA Total 1 8.60 14.60 5.60 17.80 11.60 58.20 2 5.80 1.60 12.00 0.20 1.60 2.80 3 3.60 4.20 26.40 8.20 12.00 22.00 4 4.00 4.60 16.60 2.20 2.00 13.00 5 17.00 10.60 7.40 13.60 5.20 53.80 6 11.60 10.40 31.40 25.00 31.00 86.20 7 23.00 13.00 15.80 23.60 11.40 18.00 8 22.40 28.00 23.80 13.00 15.80 32.20 9 7.20 20.60 26.00 3.00 2.00 3.20 10 4.60 1.80 47.60 18.00 10.00 13.20 11 15.00 15.40 21.80 21.40 1.60 72.00 12 18.40 3.20 4.40 7.20 14.80 24.80 13 5.60 9.58 Sum of DC MAD: 62.20 0.281 0.256 0.391 0.260 0.207 0.140 Average DC MAPE: 0.278 -0.322 1.750 -0.011 -0.808 1.982 0.765 Historical Demand ES,  = 2 BOS CHI DAL 41.7 41.0 54.7 49.0 45.0 231.3 39.9 38.0 52.5 44.6 42.4 217.5 40.9 37.4 48.8 44.1 42.5 213.8 40.2 38.1 43.5 42.3 44.8 208.8 39.7 38.5 45.8 41.8 43.9 209.7 42.8 40.0 46.2 43.6 44.3 216.9 40.2 41.6 51.4 47.7 50.2 231.1 35.8 44.3 53.5 52.2 48.2 233.9 40.2 39.0 48.4 54.7 45.5 227.9 41.6 43.6 44.1 54.8 45.4 229.5 40.7 43.7 54.3 52.4 43.9 235.0 37.1 41.0 50.4 49.5 44.8 222.8 40.7 41.8 49.3 RS 49.0 FE 47.0 227.9 ATL BOS CHI DAL LA Total -8.7 -15.0 -10.7 -22.0 -13.0 -69.3 -3.6 -18.0 -29.2 -24.6 -12.4 -87.8 -7.5 -14.4 -56.0 -33.7 -0.9 -112.6 -9.7 -12.5 -44.5 -35.9 -5.7 -108.4 5.6 -5.0 -42.3 -26.8 -3.6 -72.0 -7.2 3.0 -16.5 -6.4 26.1 -1.0 -29.4 16.4 -5.8 15.9 15.9 12.9 -7.2 -9.9 -31.3 28.7 2.7 -17.0 -0.4 13.1 -52.7 29.0 2.2 -9.0 -5.0 13.5 -1.9 17.2 -5.3 18.5 -22.7 -0.2 -21.2 2.7 -1.2 -42.5 -4.8 3.8 -26.6 0.2 10.0 -17.4 -5.5 12.1 -28.9 -6.8 13.0 -16.2 -5 45 62 62 42 43 254 -4 38 18 22 35 40 153 -3 30 48 72 40 54 244 -2 58 40 44 64 46 252 -1 37 35 48 43 35 198 1 33 26 44 27 32 162 2 45 35 34 42 43 199 3 37 41 22 35 54 189 4 38 40 55 40 40 213 5 55 46 48 51 46 246 6 30 48 72 64 74 288 7 18 55 62 70 40 245 8 58 18 28 65 35 204 9 47 62 27 55 45 236 10 37 44 95 43 38 257 11 23 30 35 38 48 174 12 55 45 45 47 56 248 13 40 50 47 42 50 229 13-week Average: 39.69 41.54 47.23 47.62 Absolute Deviation 46.23 222.31 Week ATL BOS CHI DAL LA Total 1 8.67 15.00 10.67 22.00 13.00 69.33 2 5.07 3.00 18.53 2.60 0.60 18.47 3 3.95 3.60 26.83 9.08 11.48 24.77 4 2.16 1.88 11.54 2.26 4.82 4.18 5 15.27 7.50 2.23 9.19 2.15 36.35 6 12.78 8.00 25.78 20.35 29.72 71.08 7 22.22 13.40 10.63 22.28 10.23 13.86 8 22.22 26.28 25.50 12.82 13.18 29.91 9 6.78 22.98 21.40 0.26 0.54 8.07 10 4.58 0.38 50.88 11.79 7.44 27.46 11 17.66 13.69 19.29 14.43 4.05 61.03 12 17.87 4.04 5.44 2.55 11.24 25.17 13 2.99 8.57 Sum of DC MAD: 57.47 0.271 0.237 0.376 0.221 0.185 0.135 Average DC MAPE: 0.258 -0.512 1.225 -1.628 -0.652 1.520 -0.539 Historical Demand ES,  = 4 BOS CHI DAL 41.6 40.6 49.6 44.8 43.6 220.2 38.2 34.8 47.4 37.7 39.0 196.9 40.9 34.9 42.0 39.4 40.6 197.8 39.3 37.3 34.0 37.6 45.9 194.3 38.8 38.4 42.4 38.6 43.6 201.8 45.3 41.4 44.6 43.6 44.5 219.5 39.2 44.1 55.6 51.7 56.3 246.9 30.7 48.4 58.2 59.0 49.8 246.1 41.6 36.3 46.1 61.4 43.9 229.3 43.8 46.6 38.5 58.9 44.3 232.0 41.1 45.5 61.1 52.5 41.8 242.0 33.8 39.3 50.6 46.7 44.3 214.8 42.3 41.6 48.4 RS 46.8 FE 49.0 228.1 ATL BOS CHI DAL LA Total -8.6 -14.6 -5.6 -17.8 -11.6 -58.2 -1.8 -14.4 -19.0 -13.5 -7.6 -56.1 -5.7 -8.2 -39.0 -17.9 5.9 -64.9 -7.0 -5.5 -18.0 -15.5 -0.1 -46.1 9.2 2.1 -12.4 -3.1 2.4 -1.9 -6.1 8.6 15.0 17.3 31.8 66.7 -27.2 19.6 21.4 35.6 15.5 64.8 0.1 -10.8 -8.8 41.6 0.7 22.7 5.4 14.9 -27.9 35.1 1.8 29.4 -1.3 12.3 28.7 19.3 -4.5 54.4 -19.4 -3.2 2.6 4.8 1.7 -13.5 1.8 2.5 -3.0 5.1 13.4 19.7 -0.5 10.9 -4.4 0.2 14.4 20.6 -5 45 62 62 42 43 254 -4 38 18 22 35 40 153 -3 30 48 72 40 54 244 -2 58 40 44 64 46 252 -1 37 35 48 43 35 198 1 33 26 44 27 32 162 2 45 35 34 42 43 199 3 37 41 22 35 54 189 4 38 40 55 40 40 213 5 55 46 48 51 46 246 6 30 48 72 64 74 288 7 18 55 62 70 40 245 8 58 18 28 65 35 204 9 47 62 27 55 45 236 10 37 44 95 43 38 257 11 23 30 35 38 48 174 12 55 45 45 47 56 248 13 40 50 47 42 50 229 13-week Average: 39.69 41.54 47.23 47.62 Absolute Deviation 46.23 222.31 Week ATL BOS CHI DAL LA Total 1 8.60 14.60 5.60 17.80 11.60 58.20 2 6.84 0.24 13.36 4.32 4.04 2.08 3 3.90 6.14 20.02 4.41 13.42 8.75 4 1.34 2.69 20.99 2.36 5.95 18.75 5 16.20 7.61 5.59 12.41 2.43 44.25 6 15.28 6.57 27.36 20.45 29.46 68.55 7 21.17 10.94 6.41 18.27 16.32 1.87 8 27.30 30.44 30.15 5.96 14.79 42.12 9 5.38 25.74 19.09 6.42 1.12 6.73 10 6.77 2.56 56.55 15.85 6.33 25.04 11 18.06 15.53 26.07 14.51 6.20 67.98 12 21.16 5.68 5.64 0.29 11.72 33.21 13 1.03 9.57 Sum of DC MAD: 60.15 0.299 0.254 0.388 0.207 0.207 0.131 Average DC MAPE: 0.271 -0.046 1.032 -0.241 0.024 1.510 0.708 Solution Manual for Operations and Supply Chain Management F. Robert Jacobs, Richard B. Chase 9780078024023, 9780077824921, 9781260238907, 9780077228934, 9781259666100