Planning Demand and Supply in a Supply Chain

1. Planning Demand and Supply in a Supply Chain Forecasting and Aggregate Planning Chapters 8 and 9

2. Learning Objectives • Overview of forecasting • Forecast errors • Aggregate planning in the supply chain • Managing demand • Managing capacity

3. Phases of Supply Chain Decisions • Strategy or design: Forecast • Planning: Forecast • Operation/ExecutionActual demand • 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. Characteristics of forecasts • Forecasts are always wrong. Include expected value and measure of error. • 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 • 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. Forecasting Methods • 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! • Time Series • Static • Adaptive • Causal: Linear regression • Forecast Simulation for planning purposes

6. Master Production Schedule (MPS) • MPS is a schedule of future deliveries. A combination of forecasts and firm orders.

7. Aggregate Planning Chapter 8

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

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

10. - Which is which? • Level • Deliver late • Chase • Time flexibility Matching the Demand Adjust thecapacity to match the demand Demand Use capacity Demand Demand Use inventory Use delivery time Demand

11. Capacity Demand Matching Inventory/Capacity tradeoff • Level strategy: Leveling capacity forces inventory to build up in anticipation of seasonal variation in demand • 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

12. Case Study: Aggregate planning at Red Tomato • Farm tools: • Shovels • Spades • Forks Aggregate by similar characteristics Same characteristics? Generic tool, call it Shovel

13. Aggregate Planning at Red Tomato Tools

14. 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?

15. 1. Aggregate Planning (Decision Variables) Wt= Number of employees in month t, t = 1, ..., 6 Ht = Number of employees hired at the beginning of month t, t = 1, ..., 6 Lt = Number of employees laid off at the beginning of month t, t = 1, ..., 6 Pt= Production in units of shovels in month t, t = 1, ..., 6 It = Inventory at the end of month t, t = 1, ..., 6 St= Number of units backordered at the end of month t, t = 1, ..., 6 Ct= Number of units subcontracted for month t, t = 1, ..., 6 Ot= Number of overtime hours worked in month t, t = 1, ..., 6 Did we aggregate production capacity?

16. 2. Objective Function: 3. Constraints • Workforce size for each month is based on hiring and layoffs • Production (in hours) for each month cannot exceed capacity (in hours)

17. 3. Constraints • Inventory balance for each month Period t+1 Period t-1 Period t

18. 3. Constraints • Overtime for each month

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

20. Aggregate Planning at Red Tomato Tools This solution was for the following demand numbers: What if demand fluctuates more?

21. Increased Demand Fluctuation Total costs=$432.858K. 16000 units of total production as before why extra cost? With respect to $422.275K of before.

22. Manipulating the Demand Chapter 9

23. Matching Demand and Supply • Supply = Demand • Supply < Demand => Lost revenue opportunity • Supply > Demand => Inventory • Manage Supply – Productions Management • Manage Demand – Marketing

24. Managing Predictable Variability with Supply Manage capacity • Time flexibility from workforce (OT and otherwise) • Seasonal workforce, agriculture workers • Subcontracting • Counter cyclical products: complementary products • Similar products with negatively correlated demands • Snow blowers and Lawn Mowers • AC pumps and Heater pumps • Flexible capacities/processes: Dedicated vs. flexible a d d a a,b, c,d c c b b Similar capabilities One super facility

25. Managing Predictable Variability with Inventory • Component commonality • Remember fast food restaurant menus • Component commonality increase the benefit of postponement. • More on this later • Build seasonal inventory of predictable products in preseason • Nothing can be learnt by procrastinating • Keep inventory of predictable products in the downstream supply chain

26. Managing Predictable Variability with PricingRevisit Red Tomato Tools • Manage demand with pricing • Original pricing: • Cost = $422,275, Revenue = $640,000, Profit=$217,725 • 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?

27. Off-Peak (January) Discount from $40 to $39 Cost = $421,915, Revenue = $643,400, Profit = $221,485

28. 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!

29. Demand Management • Pricing and Aggregate Planning must be done jointly • 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.)

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

31. Peak (April) Discount: 100% increase in consumption, sale price = $40 ($39) • Peak discount: Cost = $536,200, Revenue = $783,520 • Profit=$247,320

32. 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? Needempirical data. What is available?

33. Empirical Data: Who spends / How much on Valentine’s day • 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+. • 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. • 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?

34. Factors Affecting Promotion Timing

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

36. Deterministic Capacity Expansion Issues • Single vs. Multiple Facilities • Dallas and Atlanta plants of Lockheed Martin • Single vs. Multiple Resources • Machines and workforce; or aggregated capacity • Single vs. Multiple Product Demands • Have you aggregated your demand when studying the capacity? • Expansion only or with Contraction • Is there a second-hand machine market? • Discrete vs. Continuous Expansion Times • Can you expand SOM building capacity during the spring term? • Discrete vs. Continuous Capacity Increments • Can you buy capacity in units of 2.313832? • Resource costs, economies of scale • 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 • Single vs. Multiple decision makers

37. A Simple Model No stock outs. x is the size of the capacity increments. δ is the increase rate of the demand.

38. Infinite Horizon Total Discounted Cost • f(x) is expansion cost of capacity increment of size x • C(x) is the long run (infinite horizon) total discounted expansion cost

39. Solution of the Simple Model Solution can be: Each time expand capacity by an amount that is equal to 30-week demand.

40. Shortages, Inventory Holding, Subcontracting • Use of Inventory and subcontracting to delay capacity expansions

41. 1 A 2 B 3 Plants Stochastic Capacity Planning: The case of flexible capacity • Plant 1 and 2 are tooled to produce product A • Plant 3 is tooled to produce product B • A and B are substitute products • with random demands DA + DB = Constant y1A=1, y2A=1, y3A=0 y1B=0, y2B=0, y3B=1 Products

42. Capacity allocation • Say capacities are r1=r2= r3=100 • Suppose that DA + DB = 300 and DA >100 and DB >100 With plant flexibility y1A=1, y2A=1, y3A=0, y1B=0, y2B=0, y3B=1. If the scenarios are equally likely, expected shortage is 50.

43. Capacity allocation with more flexibility • Say capacities are r1=r2= r3=100 • Suppose that DA + DB = 300 and DA >100 and DB >100 With plant flexibility y1A=1, y2A=1, y3A=0, y1B=0, y2B=1, y3B=1. Flexibility can decrease shortages. In this case, from 50 to 0.

44. A Formulation with Known Demands: Dj=dj • i denotes plants • j denotes products, not necessarily substitutes • cij tooling cost to configure plant i to produce j • mj contribution to margin of producing/selling a unit of j • ri capacity at plant i • Dj=dj product j demand • yij=1 if plant i can produce product j, 0 o.w. • xij=units of j produced at plant i Solutions depend on scenarios: - If DA=200 and DB=100, then y1A=y2A=y3B=1. - If DA=100 and DB=200, then y1A=y2B=y3B=1.

45. Unknown Demands: Dj=djk with probability pk • Dj=djkproduct j demand under scenario k • xijk= units of j produced at plant i if scenario k happens • yij=1 if plant i can produce product j, 0 o.w. • Does yijdiffer under different scenarios? Should my variable depend on scenarios? (Yes / No) Anticipatory variable and Nonanticapatory variable

46. Reality Check: How do car manufacturers assign products to plants? • With the last formulation, we treated the problem of assigning products to plants. • This type of assignment called for tooling/preparation of each plant appropriately so that it can produce the car type it is assigned to. • These tooling (nonanticipatory) decisions are made at most once a year and manufacturers work with the current assignments to meet the demand. • 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! • 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.

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

48. All of Honda Plants in the North America Honda. Alliston, Ca. Civic, Acura, Odyssey, Pilot, Ridgeline Honda. Decatur TBO in 2008 Honda. Marysville Accord, Acura Honda. Lincoln Odyssey, Pilot Honda. El Salto, Me Accord

49. All of Nissan Plants in the North America Nissan. Smyrna Frontier, Xterra, Altima, Maxima, Pathfinder Nissan. Canton Quest, Armada, Titan, Infiniti, Altima

50. All of Hyundai-Kia Plants in the North America Kia. LaGrange TBO in 2009 Hyundai. Montgomery Sonata, Santa Fe