1. utdallas.edu/~metin 1 Planning Demand and Supply in a Supply Chain Forecasting and Aggregate Planning Chapters 8 and 9

2. utdallas.edu/~metin 2 Learning Objectives u Overview of forecasting u Forecast errors u Aggregate planning in the supply chain u Managing demand u Managing capacity

3. utdallas.edu/~metin 3 Phases of Supply Chain Decisions u Strategy or design:Forecast u Planning:Forecast u Operation/ExecutionActual demand u Since actual demands differ from the forecasts, … so does the execution from the plans. –E.g. Supply Chain degree plans for 40 students per year whereas the actual is ??

4. utdallas.edu/~metin 4 Characteristics of forecasts u Forecasts are always wrong. Include expected value and measure of error. u Long-term forecasts are less accurate than short-term forecasts. Too long term forecasts are useless: Forecast horizon –Forecasting to determine »Raw material purchases for the next week; Ericsson »Annual electricity generation capacity in TX for the next 30 years; Texas Utilities »Boat traffic intensity in the upper Mississippi until year 2100; Army Corps of Engineers u Aggregate forecasts are more accurate than disaggregate forecasts –Variance of aggregate is smaller because extremes cancel out »Two samples: {3,5} and {2,6}. Averages: 4 and 4. Totals : 8 and 8. »Variance of sample averages/totals=0 »Variance of {3,5,2,6}=5/2 –Several ways to aggregate »Products into product groups; Telecom switch boxes »Demand by location; Texas region »Demand by time; April demand

5. utdallas.edu/~metin 5 Forecasting Methods u Qualitative –Expert opinion »E.g. Why do you listen to Wall Street stock analysts? –What if we all listen to the same analyst? S/He becomes right! u Time Series –Static –Adaptive u Causal: Linear regression u Forecast Simulation for planning purposes

6. utdallas.edu/~metin 6 Components of an observation Observed demand (O) = Systematic component (S) + Random component (R) A touch of philosophy: Is the world random or everything is pre-determined? Pragmatic answer: Everything we cannot afford to study in detail is random! Level (current deseasonalized demand) Trend (growth or decline in demand) Seasonality (predictable seasonal fluctuation)

7. utdallas.edu/~metin 7 Time Series Forecasting Forecast demand for the next four quarters.

8. utdallas.edu/~metin 8 Time Series Forecasting

9. utdallas.edu/~metin 9 Master Production Schedule (MPS) u MPS is a schedule of future deliveries. A combination of forecasts and firm orders.

10. utdallas.edu/~metin 10 Aggregate Planning Chapter 8

11. utdallas.edu/~metin 11 Aggregate Planning (Ag-gregate: Past part. of Ad-gregare : Totaled) u If the actual is different than the plan, why bother sweating over detailed plans u Aggregate planning: General plan for our frequency decomposition –Combined products = aggregate product »Short and long sleeve shirts = shirt u Single product »AC and Heating unit pipes = pipes at Lennox Iowa plant –Pooled capacities = aggregated capacity »Dedicated machine and general machine = machine u Single capacity –E.g. SOM has 100 instructors –Time periods = time buckets »Consider all the demand and production of a given month together u When does the demand or production take place in a time bucket? u Increase the number of time buckets; decrease the bucket length.

12. utdallas.edu/~metin 12 Fundamental tradeoffs in Aggregate Planning Capacity: Regular time, Over time, Subcontract? Inventory: Backlog / lost sales, combination: Customer patience? Basic Strategies u Chase (the demand) strategy; produce at the instantaneous demand rate –fast food restaurants u Level strategy; produce at the rate of long run average demand –swim wear u Time flexibility; high levels of workforce or capacity –machining shops, army u Deliver late strategy –spare parts for your Jaguar

13. utdallas.edu/~metin 13 Matching the Demand Use inventory Use delivery time Use capacity Demand Adjust the capacity to match the demand Demand - Which is which? Level Deliver late Chase Time flexibility

14. utdallas.edu/~metin 14 Capacity Demand Matching Inventory/Capacity tradeoff u Level strategy: Leveling capacity forces inventory to build up in anticipation of seasonal variation in demand u Chase strategy: Carrying low levels of inventory requires capacity to vary with seasonal variation in demand or enough capacity to cover peak demand during season

15. utdallas.edu/~metin 15 Case Study: Aggregate planning at Red Tomato u Farm tools: u Shovels u Spades u Forks Aggregate by similar characteristics Generic tool, call it Shovel Same characteristics?

16. utdallas.edu/~metin 16 Aggregate Planning at Red Tomato Tools

17. utdallas.edu/~metin 17 Aggregate Planning What is the cost of production per tool? That is materials plus labor. Overtime production is more expensive than subcontracting. What is the saving achieved by producing a tool in house rather than subcontracting?

18. utdallas.edu/~metin 18 1. Aggregate Planning (Decision Variables) W t = Number of employees in month t, t = 1,..., 6 H t = Number of employees hired at the beginning of month t, t = 1,..., 6 L t = Number of employees laid off at the beginning of month t, t = 1,..., 6 P t = Production in units of shovels in month t, t = 1,..., 6 I t = Inventory at the end of month t, t = 1,..., 6 S t = Number of units backordered at the end of month t, t = 1,..., 6 C t = Number of units subcontracted for month t, t = 1,..., 6 O t = Number of overtime hours worked in month t, t = 1,..., 6 Did we aggregate production capacity?

19. utdallas.edu/~metin 19 2. Objective Function: 3. Constraints u Workforce size for each month is based on hiring and layoffs u Production (in hours) for each month cannot exceed capacity (in hours)

20. utdallas.edu/~metin 20 3. Constraints u Inventory balance for each month Period t Period t+1 Period t-1

21. utdallas.edu/~metin 21 3. Constraints u Overtime for each month

22. utdallas.edu/~metin 22 Execution u Solve the formulation, see Table 8.3 –Total cost=$422.275K, total revenue=$640K u Apply the first month of the plan u Delay applying the remaining part of the plan until the next month u Rerun the model with new data next month u This is called rolling horizon execution

23. utdallas.edu/~metin 23 Aggregate Planning at Red Tomato Tools This solution was for the following demand numbers: What if demand fluctuates more?

24. utdallas.edu/~metin 24 Increased Demand Fluctuation Total costs=$432.858K. 16000 units of total production as before why extra cost? With respect to $422.275K of before.

25. utdallas.edu/~metin 25 Manipulating the Demand Chapter 9

26. utdallas.edu/~metin 26 Matching Demand and Supply u Supply = Demand u Supply Lost revenue opportunity u Supply > Demand => Inventory u Manage Supply – Productions Management u Manage Demand – Marketing

27. utdallas.edu/~metin 27 Managing Predictable Variability with Supply Manage capacity »Time flexibility from workforce (OT and otherwise) »Seasonal workforce, agriculture workers »Subcontracting »Counter cyclical products: complementary products u Similar products with negatively correlated demands –Snow blowers and Lawn Mowers –AC pumps and Heater pumps »Flexible capacities/processes: Dedicated vs. flexible a,b, c,d Similar capabilitiesOne super facility a b c d a b c d

28. utdallas.edu/~metin 28 Managing Predictable Variability with Inventory u Component commonality –Remember fast food restaurant menus –Component commonality increase the benefit of postponement. »More on this later u Build seasonal inventory of predictable products in preseason –Nothing can be learnt by procrastinating u Keep inventory of predictable products in the downstream supply chain

29. utdallas.edu/~metin 29 Managing Predictable Variability with Pricing Revisit Red Tomato Tools u Manage demand with pricing –Original pricing: »Cost = $422,275, Revenue = $640,000, Profit=$217,725 u Demand increases from discounting –Market growth –Stealing market share from competitors –Forward buying »stealing your own market share from the future Discount of $1 in a period increases that period’s demand by 10% (market and market share growth) and moves 20% of next two months demand forward Can you gather this information –price sensitivity of the demand- easily? Does your company have this information?

30. utdallas.edu/~metin 30 Off-Peak (January) Discount from $40 to $39 Cost = $421,915, Revenue = $643,400, Profit = $221,485

31. utdallas.edu/~metin 31 Peak (April) Discount from $40 to $39 Cost = $438,857, Revenue = $650,140, Profit = $211,283 Discounting during peak increases the revenue but decreases the profit!

32. utdallas.edu/~metin 32 Demand Management u Pricing and Aggregate Planning must be done jointly u Factors affecting discount timing and their new values –Consumption: 100% increase in consumption instead of 10% increase –Forward buy, still 20% of the next two months –Product Margin: Impact of higher margin. What if discount from $31 to $30 instead of from $40 to $39.)

33. utdallas.edu/~metin 33 January Discount: 100% increase in consumption, sale price = $40 ($39) Off peak discount: Cost = $456,750, Revenue = $699,560 Profit=$242,810

34. utdallas.edu/~metin 34 Peak (April) Discount: 100% increase in consumption, sale price = $40 ($39) Peak discount: Cost = $536,200, Revenue = $783,520 Profit=$247,320

35. utdallas.edu/~metin 35 Performance Under Different Scenarios Use rows in bold to explain Xmas discounts. The product, with less (forward buying/market growth) ratio, is discounted more. What gift should you buy on the special days (peak demand) when retailers supposedly give discounts? E.g. Think of flowers on valentine’s day. How about diamonds? For flowers, what is (forward buying/market growth) due to discounting? How about for diamonds? Need empirical data. What is available?

36. utdallas.edu/~metin 36 Empirical Data: Who spends / How much on Valentine’s day u The average consumer spends $122.98 on 2008 Valentine’s Day, similar to $119.67 of 2007. Total US spending on Valentine’s Day is $17.02 B by 18+. u Spending –by gender »Men again dishes out the most in 2008, spending an average of $163.37 on gifts and cards, compared to an average of $84.72 spent by women. –by age »Adults: 25-34 spend $160.37. »Young adults: 18-24 spend $145.59. »Upper Middle age: 45-54 spend $117.91. »Lower Middle age: 35-44 spend $116.35. »Elderly: 55-64 spend $110.97. u Gifts »56.8% of all consumers give a greeting card. »48.2% plan a special night out. »48.0% buy candy. »35.9% buy flowers. »12.3% give a gift card. »11.8% buy clothing. »??.?% buy diamonds –Source: National Retail Federation www.nrf.com Where is forward buy or market growth due to discounting?

37. utdallas.edu/~metin 37 Factors Affecting Promotion Timing

38. utdallas.edu/~metin 38 Aside: Continuous Compounding u If my $1investment earns an interest of r per year, what is my interest+investment at the end of the year? Answer: (1+r) u If I earn an interest of r/2 per six months, what is my interest+ investment at the end of the year? Answer: (1+r/2) 2 u If I earn an interest of (r/m) per (12/m) months, what is my interest+investment? Answer: (1+r/m) m u Think of continuous compounding as the special case of discrete-time compounding when m approaches infinity. u What if I earn an interest of (r/infinity) per (12/infinity) months? See the appendix of scaggregate.pdf for more on continuous compounding.

39. utdallas.edu/~metin 39 Deterministic Capacity Expansion Issues u Single vs. Multiple Facilities –Dallas and Atlanta plants of Lockheed Martin u Single vs. Multiple Resources –Machines and workforce; or aggregated capacity u Single vs. Multiple Product Demands –Have you aggregated your demand when studying the capacity? u Expansion only or with Contraction –Is there a second-hand machine market? u Discrete vs. Continuous Expansion Times –Can you expand SOM building capacity during the spring term? u Discrete vs. Continuous Capacity Increments –Can you buy capacity in units of 2.313832? u Resource costs, economies of scale u Penalty for demand-capacity mismatch –Recallable capacity: Electricity block outs vs Electricity buy outs »Happens in Wisconsin Electricity market »What if American Airlines recalls my ticket u Single vs. Multiple decision makers

40. utdallas.edu/~metin 40 A Simple Model No stock outs. x is the size of the capacity increments. δ is the increase rate of the demand.

41. utdallas.edu/~metin 41 Infinite Horizon Total Discounted Cost u f(x) is expansion cost of capacity increment of size x u C(x) is the long run (infinite horizon) total discounted expansion cost

42. utdallas.edu/~metin 42 Solution of the Simple Model Solution can be: Each time expand capacity by an amount that is equal to 30-week demand.

43. utdallas.edu/~metin 43 Shortages, Inventory Holding, Subcontracting u Use of Inventory and subcontracting to delay capacity expansions

44. utdallas.edu/~metin 44 Stochastic Capacity Planning: The case of flexible capacity u Plant 1 and 2 are tooled to produce product A u Plant 3 is tooled to produce product B u A and B are substitute products –with random demands D A + D B = Constant 1 2 3 A B Plants Products y 1A =1, y 2A =1, y 3A =0 y 1B =0, y 2B =0, y 3B =1

45. utdallas.edu/~metin 45 Capacity allocation u Say capacities are r 1 =r 2 = r 3 =100 u Suppose that D A + D B = 300 and D A >100 and D B >100 Scenario DADA DBDB X 1A X 2A X 3A X 1B X 2B X 3B Shortage 1200100 0 2150 1005010050 B 31002001000 100 B With plant flexibility y 1A =1, y 2A =1, y 3A =0, y 1B =0, y 2B =0, y 3B =1. If the scenarios are equally likely, expected shortage is 50.

46. utdallas.edu/~metin 46 Capacity allocation with more flexibility u Say capacities are r 1 =r 2 = r 3 =100 u Suppose that D A + D B = 300 and D A >100 and D B >100 Scenario DADA DBDB X 1A X 2A X 3A X 1B X 2B X 3B Shortage 1200100 0 0 2150 10050 1000 3 2001000 0 With plant flexibility y 1A =1, y 2A =1, y 3A =0, y 1B =0, y 2B =1, y 3B =1. Flexibility can decrease shortages. In this case, from 50 to 0.

47. utdallas.edu/~metin 47 A Formulation with Known Demands: D j =d j u i denotes plants u j denotes products, not necessarily substitutes u c ij tooling cost to configure plant i to produce j u m j contribution to margin of producing/selling a unit of j u r i capacity at plant i u D j =d j product j demand u y ij =1 if plant i can produce product j, 0 o.w. u x ij =units of j produced at plant i - If D A =200 and D B =100, then y 1A =y 2A =y 3B =1. - If D A =100 and D B =200, then y 1A =y 2B =y 3B =1. Solutions depend on scenarios:

48. utdallas.edu/~metin 48 Unknown Demands: D j =d j k with probability p k u D j =d j k product j demand under scenario k u x ij k = units of j produced at plant i if scenario k happens u y ij =1 if plant i can produce product j, 0 o.w. u Does y ij differ under different scenarios? Should my variable depend on scenarios? (Yes / No) Anticipatory variable and Nonanticapatory variable

49. utdallas.edu/~metin 49 Reality Check: How do car manufacturers assign products to plants? u With the last formulation, we treated the problem of assigning products to plants. u This type of assignment called for tooling/preparation of each plant appropriately so that it can produce the car type it is assigned to. u These tooling (nonanticipatory) decisions are made at most once a year and manufacturers work with the current assignments to meet the demand. u When market conditions change, the product-to-plant assignment is revisited. –Almost all car manufacturers in North America are retooling their previously truck manufacturing plants to manufacture compact cars as consumer demand basically disappeared for trucks with high gas prices. –Also note that the profit margin made from a truck sale is 2-5 times more than the margin made from a car sale. No wonder why manufacturers prefer to sell trucks! u In the following pages, you will find the product to plant assignment of major car manufacturers in the North America. These assignments were updated in the summer of 2008 just about the time when manufacturers started talking about retooling plants to produce compact cars.

50. utdallas.edu/~metin 50 All of Toyota Plants in the North America Toyota. Tijuana, Mexico Tacoma Toyota. Long Beach Hino Nummi: Toyota-GM. Freemont. Corolla, Tacoma, Pontiac Vibe Toyota. San Antonio Tundra Toyota. Blue Springs Highlander Toyota. Georgetown Avalon, Camry, Solara Toyota. Princeton Tundra, Suquoia, Sienna Toyota-Subaru. LaFayette Camry Toyota. Cambridge Corolla, Matrix, Lexus, Rav4

51. utdallas.edu/~metin 51 All of Honda Plants in the North America Honda. El Salto, Me Accord Honda. Lincoln Odyssey, Pilot Honda. Marysville Accord, Acura Honda. Decatur TBO in 2008 Honda. Alliston, Ca. Civic, Acura, Odyssey, Pilot, Ridgeline

52. utdallas.edu/~metin 52 All of Nissan Plants in the North America Nissan. Canton Quest, Armada, Titan, Infiniti, Altima Nissan. Smyrna Frontier, Xterra, Altima, Maxima, Pathfinder

53. utdallas.edu/~metin 53 All of Hyundai-Kia Plants in the North America Hyundai. Montgomery Sonata, Santa Fe Kia. LaGrange TBO in 2009

54. utdallas.edu/~metin 54 All of Mercedes and BMW Plants in the North America Mercedes. Tuscaloosa M, R classes BMW. Spartanburg Z4, X5, X6 M roadster, coupes

55. utdallas.edu/~metin 55 All of Ford Plants in the North America Ford. Hermosillo, Mex. Ford Fusion, Lincoln MKZ, Mercury Milan Ford. Kansas City Escape, Escape Hybrid, Mazda Tribute, Mercury Mariner, F-150 Ford. Cuatitlan, Mex. F-150, 250, 350, 450, 550,Ikon Ford. Saint Paul Ranger, Mazda B series Ford. Louisville F-250, F-550, Explorer, Mercury Mountaineer Ford. Chicago Taurus, Mercury Sable Ford. Avon Lake E Series Ford. Saint Thomas, Ca. Crown Victoria, Grand Marquis Ford. Oakville, Ca. Edge, Lincoln MKX Ford. Wayne Focus, Expedition, Lincoln Navigator Ford. Flat Rock Mustang, Mazda 6 Ford. Dearborn F-150, Lincoln Mark LT Ontario, Michigan, Illinois, Indiana, Ohio in Focus

56. utdallas.edu/~metin 56 All of Chrysler Plants in the North America Chrysler. Toluca, Mex. Chrysler PT Cruise, Dodge Journey Ontario, Michigan, Illinois, Indiana, Ohio in Focus Chrysler. Saltillo, Mex. Dodge Ram Chrysler. Newark Dodge Durango, Chrysler Aspen Will close in 2009 Chrysler. Fenton-South Grand Voyager, Grand Caravan, Cargo Van Chrysler. Fenton-North Dodge Ram Chrysler. Belvidere Dodge Caliber, Jeep Compass, Jeep Patriot Chrysler. Toledo Jeep Liberty, Dodge Nitro Chrysler. Brampton, Ca Chrysler 300, Dodge Challenger, Dodge Charger Chrysler. Windsor, Ca Dodge Grand Caravan, Chrysler Town Chrysler. Detroit-Jefferson North Jeep Grand Cherokee and Commander Chrysler. Detroit-Conner Ave. Dodge Viper, SRT 10 Roadster Chrysler. Warren Dodge Ram, Dakota, Mitsubishi Raider Chrysler. Sterling Heights Dodge Avenger, Chrysler Sebring

57. utdallas.edu/~metin 57 All of GM Plants in the North America GM. Ramos Arizpe, Mex. Pontiac Aztek, Chevy Cavalier, Chevrolet Checy, Pontiac Sunfire, Buick Rendezvous Ontario, Michigan, Illinois, Indiana, Ohio in Focus GM. Silao, Mex. Chevrolet Suburban, Chevrolet Avalanche, GMC Yukon, Cadillac Escalade GM. Toluca, Mex. Chevrolet Kodiak Truck Stopping in 2008 GM. Arlington Chevy Tahoe, Suburban, GMC Yukon, Cadillac Escalade GM. Shreveport Chevy Colorado, GMC Canyon, Isuzu brands, Hummer H3 GM. Fairfax Chevy Malibu, Malibu Maxx, Saturn Aura GM. Wentzville Chevy Express, GMC Savana GM. Doraville Chevy Uplander, Pontiac Montana GM. Spring Hill Saturn Ion and Vue Currently down GM. Bowling Green Cadillac XLR, Chevy Corvette GM. Wilmington Saturn L series, Pontiac Solstice GM. Lordstown Chevy Cobalt, Pontiac Pursuit, G4, G5 GM. Moraine Chevy Trailblazer, GMC Envoy, Oldsmobile Bravada, Isuzu Ascender, Saab 9-7X Will stop in 2010 GM. Fort Wayne Chevy Silverado, GMC Sierra GM. Janisville Chevy Tahoe, Suburban, GMC Yukon Will stop in 2010 GM. Oshawa, Ca Chevy Impala, Buick Allure, Chevy Silverado, GMC Sierra. Trucks will stop in 2009. GM. Lansing-Grand River Cadillac E-SRX GM. Lansing-Delta Township Buick Enclave, Saturn Outlook, GMC Acadia GM. Flint GMC Sierra, Chevy Silverado, Chevy - GMC medium trucks. GM. Pontiac Chevy Silverado, GMC Sierra GM. Detroit Buick Lucerne, Cadillac DTS GM. Orion Pontiac G6, Chevrolet Malibu

58. utdallas.edu/~metin 58 Summary of Learning Objectives u Forecasting u Aggregate planning u Supply and demand management during aggregate planning with predictable demand variation –Supply management levers –Demand management levers u Capacity Planning

59. utdallas.edu/~metin 59 Material Requirements Planning u Master Production Schedule (MPS) u Bill of Materials (BOM) u MRP explosion u Advantages –Disciplined database –Component commonality u Shortcomings –Rigid lead times –No capacity consideration

60. utdallas.edu/~metin 60 Optimized Production Technology u Focus on bottleneck resources to simplify planning u Product mix defines the bottleneck(s) ? u Provide plenty of non-bottleneck resources. u Shifting bottlenecks

61. utdallas.edu/~metin 61 Just in Time production u Focus on timing u Advocates pull system, use Kanban u Design improvements encouraged u Lower inventories / set up time / cycle time u Quality improvements u Supplier relations, fewer closer suppliers, Toyota city u JIT philosophically different than OPT or MRP, it is not only a planning tool but a continuous improvement scheme