1. Forecasting

2. Forecasting Survey How far into the future do you typically project when trying to forecast the health of your industry?  less than 4 months3%  4-6 months12%  7-12 months28%  > 12 months57% Fortune Council survey, Nov 2005

3. Indices to forecast health of industry Consumer price index 51% Consumer Confidence index44% Durable goods orders20% Gross Domestic Product35% Manufacturing and trade inventories and sales27% Price of oil/barrel34% Strength of US $46% Unemployment rate53% Interest rates/fed funds59% Fortune Council survey, Nov 2005

4. Forecasting Importance Improving customer demand forecasting and sharing the information downstream will allow more efficient scheduling and inventory management Boeing, 1987: $2.6 billion write down due to “raw material shortages, internal and supplier parts shortages” Wall Street Journal, Oct 23, 1987

5. Forecasting Importance “Second Quarter sales at US Surgical Corporation decline 25%, resulting in a $22 mil loss…attributed to larger than anticipated inventories on shelves of hospitals.” US Surgical Quarterly, Jul 1993 “IBM sells out new Aetna PC; shortage may cost millions in potential revenue.” Wall Street Journal, Oct 7, 1994

6. Principles of Forecasting Forecasts are usually wrong every forecast should include an estimate of error Forecasts are more accurate for families or groups Forecasts are more accurate for nearer periods.

7. Important Factors to Improve Forecasting Record Data in the same terms as needed in the forecast – production data for production forecasts; time periods Record circumstances related to the data Record the demand separately for different customer groups

8. Forecast Techniques Extrinsic Techniques – projections based on indicators that relate to products – examples Intrinsic – historical data used to forecast (most common)

9. Forecasting Forecasting errors can increase the total cost of ownership for a product - inventory carrying costs - obsolete inventory - lack of sufficient inventory - quality of products due to accepting marginal products to prevent stockout

10. Forecasting Essential for smooth operations of business organizations Estimates of the occurrence, timing, or magnitude of uncertain future events Costs of forecasting: excess labor; excess materials; expediting costs; lost revenues

11. Forecasting Predicting future events Predicting future events Usually demand behavior over a time frame Usually demand behavior over a time frame Qualitative methods Qualitative methods Based on subjective methods Based on subjective methods Quantitative methods Quantitative methods Based on mathematical formulas Based on mathematical formulas

12. Strategic Role of Forecasting Focus on supply chain management Focus on supply chain management Short term role of product demand Short term role of product demand Long term role of new products, processes, and technologies Long term role of new products, processes, and technologies Focus on Total Quality Management Focus on Total Quality Management Satisfy customer demand Satisfy customer demand Uninterrupted product flow with no defective items Uninterrupted product flow with no defective items Necessary for strategic planning Necessary for strategic planning

13. Strategic Role of Forecasting Focus on supply chain management Focus on supply chain management Short term role of product demand Short term role of product demand Long term role of new products, processes, and technologies Long term role of new products, processes, and technologies Focus on Total Quality Management Focus on Total Quality Management Satisfy customer demand Satisfy customer demand Uninterrupted product flow with no defective items Uninterrupted product flow with no defective items Necessary for strategic planning Necessary for strategic planning

14. Time Frame Time Frame Short-range, medium- range, long-range Short-range, medium- range, long-range Demand Behavior Demand Behavior Trends, cycles, seasonal patterns, random Trends, cycles, seasonal patterns, random Components of Forecasting Demand

15. Time Frame Short-range to medium-range Short-range to medium-range Daily, weekly monthly forecasts of sales data Daily, weekly monthly forecasts of sales data Up to 2 years into the future Up to 2 years into the future Long-range Long-range Strategic planning of goals, products, markets Strategic planning of goals, products, markets Planning beyond 2 years into the future Planning beyond 2 years into the future

16. Demand Behavior Trend Trend gradual, long-term up or down movement gradual, long-term up or down movement Cycle Cycle up & down movement repeating over long time frame up & down movement repeating over long time frame Seasonal pattern Seasonal pattern periodic oscillation in demand which repeats periodic oscillation in demand which repeats Random movements follow no pattern Random movements follow no pattern

17. Forms of Forecast Movement Time (a) Trend Time (d) Trend with seasonal pattern Time (c) Seasonal pattern Time (b) Cycle Demand Demand Demand Demand Random movement Figure 8.1

18. Forecasting Methods Time series Time series Regression or causal modeling Regression or causal modeling Qualitative methods Qualitative methods Management judgment, expertise, opinion Management judgment, expertise, opinion Use management, marketing, purchasing, engineering Use management, marketing, purchasing, engineering Delphi method Delphi method Solicit forecasts from experts Solicit forecasts from experts

19. Time Series Methods Statistical methods using historical data Statistical methods using historical data Moving average Moving average Exponential smoothing Exponential smoothing Linear trend line Linear trend line Assume patterns will repeat Assume patterns will repeat Naive forecasts Naive forecasts Forecast = data from last period Forecast = data from last period

20. Moving Average Average several periods of data Average several periods of data Dampen, smooth out changes Dampen, smooth out changes Use when demand is stable with no trend or seasonal pattern Use when demand is stable with no trend or seasonal pattern stock market analysis - trend analysis stock market analysis - trend analysis

21. Moving Average Average several periods of data Average several periods of data Dampen, smooth out changes Dampen, smooth out changes Use when demand is stable with no trend or seasonal pattern Use when demand is stable with no trend or seasonal pattern Sum of Demand In n Periods n

22. Simple Moving Average Jan120 Feb90 Mar100 Apr75 May110 June50 July75 Aug130 Sept110 Oct90 ORDERS MONTHPER MONTH

23. Jan120 Feb90 Mar100 Apr75 May110 June50 July75 Aug130 Sept110 Oct90 ORDERS MONTHPER MONTH Example 8.1 MA nov = 3 = 90 + 110 + 130 3 = 110 orders for Nov Simple Moving Average D aug +D sep +D oct

24. Jan120– Feb90 – Mar100 – Apr75103.3 May11088.3 June5095.0 July7578.3 Aug13078.3 Sept11085.0 Oct90105.0 Nov –110.0 ORDERSTHREE-MONTH MONTHPER MONTHMOVING AVERAGE Example 8.1 Simple Moving Average

25. Jan120– Feb90 – Mar100 – Apr75103.3 May11088.3 June5095.0 July7578.3 Aug13078.3 Sept11085.0 Oct90105.0 Nov –110.0 ORDERSTHREE-MONTH MONTHPER MONTHMOVING AVERAGE Example 8.1 = 90 + 110 + 130 + 75 + 50 5 = 91 orders for Nov Simple Moving Average

26. Example 8.1 Simple Moving Average Jan120– – Feb90 – – Mar100 – – Apr75103.3 – May11088.3 – June5095.099.0 July7578.385.0 Aug13078.382.0 Sept11085.088.0 Oct90105.095.0 Nov –110.091.0 ORDERSTHREE-MONTHFIVE-MONTH MONTHPER MONTHMOVING AVERAGEMOVING AVERAGE

27. Weighted Moving Average Adjusts moving average method to more closely reflect data fluctuations Adjusts moving average method to more closely reflect data fluctuations

28. Weighted Moving Average WMA n = i = 1  Wi DiWi DiWi DiWi Di where W i = the weight for period i, between 0 and 100 percent  W i = 1.00 Adjusts moving average method to more closely reflect data fluctuations Adjusts moving average method to more closely reflect data fluctuations

29. Weighted Moving Average Example MONTH WEIGHT DATA August 17%130 September 33%110 October 50%90

30. Weighted Moving Average Example MONTH WEIGHT DATA August 17%130 September 33%110 October 50%90 November forecast WMA 3 = 3 i = 1  Wi DiWi DiWi DiWi Di = (0.50)(90) + (0.33)(110) + (0.17)(130) = 103.4 orders 3 Month = 110 5 month = 91

31. Averaging method Averaging method Weights most recent data more strongly Weights most recent data more strongly Reacts more to recent changes Reacts more to recent changes Widely used, accurate method Widely used, accurate method Exponential Smoothing

32. F t +1 =  D t + (1 -  )F t where F t +1 =forecast for next period D t =actual demand for present period F t =previously determined forecast for present period  =weighting factor, smoothing constant Averaging method Averaging method Weights most recent data more strongly Weights most recent data more strongly Reacts more to recent changes Reacts more to recent changes Widely used, accurate method Widely used, accurate method Exponential Smoothing

33. Forecast for Next Period Forecast = (weighting factor)x(actual demand for period)+(1-weighting factor)x(previously determined forecast for present period) 0 >  <= 1 Lesser reaction to recent demand Greater reaction to recent demand

34. Seasonal Adjustments Repetitive increase/ decrease in demand Repetitive increase/ decrease in demand Use seasonal factor to adjust forecast Use seasonal factor to adjust forecast

35. Seasonal Adjustments Repetitive increase/ decrease in demand Repetitive increase/ decrease in demand Use seasonal factor to adjust forecast Use seasonal factor to adjust forecast Seasonal factor = S i = DiDiDDDiDiDD = demand for period/sum of demand

36. Seasonal Adjustment 2008 12.68.66.317.545.0 2009 14.110.37.518.250.1 2010 15.310.68.119.653.6 Total 42.029.521.955.3148.7 DEMAND (1000’S PER QUARTER) YEAR1234Total

37. Seasonal Adjustment 2008 12.68.66.317.545.0 2009 14.110.37.518.250.1 2010 15.310.68.119.653.6 Total 42.029.521.955.3148.7 DEMAND (1000’S PER QUARTER) YEAR1234Total S 1 = = = 0.28 D1D1DDD1D1DD42.0148.7 S 2 = = = 0.20 D2D2DDD2D2DD 29.5148.7 S 4 = = = 0.37 D4D4DDD4D4DD 55.3148.7 S 3 = = = 0.15 D3D3DDD3D3DD21.9148.7

38. Seasonal Adjustment 2008 12.68.66.317.545.0 2009 14.110.37.518.250.1 2010 15.310.68.119.653.6 Total 42.029.521.955.3148.7 DEMAND (1000’S PER QUARTER) YEAR1234Total S i 0.280.200.150.37

39. Seasonal Adjustment 2008 12.68.66.317.545.0 2009 14.110.37.518.250.1 2010 15.310.68.119.653.6 Total 42.029.521.955.3148.7 DEMAND (1000’S PER QUARTER) YEAR1234Total S i 0.280.200.150.37 Forecast for 2011 using simple 3 year moving ave Forecast for 1st qtr 2011

40. Forecast Accuracy Find a method which minimizes error Find a method which minimizes error Error = Actual - Forecast Error = Actual - Forecast

41. Forecast Control Reasons for out-of-control forecasts Reasons for out-of-control forecasts Change in trend Change in trend Appearance of cycle Appearance of cycle Weather changes Weather changes Promotions Promotions Competition Competition Politics Politics

42. Reverse Logistics: Important or Irritant? Estimated $100 billion industry in 2006 Survey shows considerable spending on Returns

43. “In an ideal world, reverse logistics would not exist.” Jim Whalen, “In Through the Out Door,” Warehousing Management, March 2001

44. “Now, more than ever, reverse logistics is seen as being important.” Dale Rogers, Going Backwards, 1999

45. Reverse Logistics - What is it? The Army’s Definition The return of serviceable supplies that are surplus to the needs of the unit or are unserviceable and in need of rebuild or remanufacturing to return the item to a serviceable status

46. Reverse Logistics - What is it? The Commercial Perspective Reverse Logistics is the process of moving products from their typical final destination to another point, for the purpose of capturing value otherwise unavailable, or for the proper disposal of the products. Any activity that takes money from the company after the sale of the product

47. Typical Reverse Logistics Activities Processing returned merchandise - damaged, seasonal, restock, salvage, recall, or excess inventory Recycling packaging materials/containers Reconditioning, refurbishing, remanufacturing Disposition of obsolete stuff Hazmat recovery

48. Why Reverse Logistics? Competitive advantage Customer service - Very Important: 57% - Important: 18% - Somewhat/unimportant:23% Bottom line profits

49. Reverse Logistics - New Problem? Sherman Montgomery Ward’s - 1894 Recycling/remanufacturing in 1940s World War II - 77,000,000 square feet of storage across Europe with over $6.3 billion in excess stuff Salvage and reuse of clothing and shoes in the Pacific Theater World War II

50. Key Dates in Reverse Logistics World War II – the advent of refurbished automobile parts due to shortages 1984 - Tylenol Scare - Johnson and Johnson 1991 - German ordinance that put teeth in environmental reverse pipeline Summer 1996 – UK Packaging and Packaging Waste Legislation 1998 - first real study of reverse logistics in the US - University of Nevada, Reno 2001 – EU goal of 50-65% recovering or recycling of packaging waste

51. Reverse Logistics A US Army Perspective

52. Operation Iraqi Freedom The US Army moved the equivalent of 150 Wal-Mart Supercenters to Kuwait in a matter of a few months

53. Military Operations and Excess “In battle, troops get temperamental and ask for things which they really do not need. However, where humanly possible, their requests, no matter how unreasonable, should be answered.” George S. Patton, Jr.

54. Jane’s Defence Weekly “Recent report (Aug 2003): There is a 40 hectare (~100 acres) area in Kuwait with items waiting to be retrograded back to the US.”

55. From GAO Audit Report Does this create a problem?

56. From GAO Audit Report

57. Reverse Logistics The Commercial Perspective

59. Reverse Logistics Reverse Logistics Rate of returns? Cost to process a return? Time to get the item back on the shelf if resaleable?

60. Costs - above the cost of the item Costs - above the cost of the item –Merchandise credits to the customers. –The transportation costs of moving the items from the retail stores to the central returns distribution center. –The repackaging of the serviceable items for resale. –The cost of warehousing the items awaiting disposition. –The cost of disposing of items that are unserviceable, damaged, or obsolete.

61. Costs Process inbound shipment at a major distribution center = 1.1 days Process inbound return shipment = 8.5 days Cost of lost sales Wal-Mart: Christmas 2003 - returns = 4 Days of Supply for all of Wal-Mart = 2000 Containers PalmOne - 25% return rate on PDAs

62. More Costs Hoover - $40 Million per year Cost of processing $85 per item Unnamed Distribution Company - $700K items on reverse auction 2001 - over $60 billion in returns; $52 billion excess to systems; $40 billion to process 2010 – majority of cell phones -

63. Is it a problem? Estimate of 2004 holiday returns: $13.2 billion % of estimated 2004/2005 holiday returns: 25% Wal-Mart: $6 Billion in annual returns = 17,000 truck loads (>46 trucks a day) Electronics: $10 Billion annually in returns Personal Computers: $1.5 Billion annually = approximately $95 per PC sold 79% of returned PCs have no defects Home Depot ~ $10 million in returns in the stores alone Local Wal-Mart ~ $1 million a month in returns

64. Is it a Problem? European influence – spread to US - Green Laws Estee Lauder - $60 million a year into land fills FORTUNE 500 Company - $200 million over their $300 million budget for returns Same Provider - 40,000 products returned per month; 55% no faults noted K-Mart - $980 million in returns 1999 Warranty vice paid repairs Recent survey of FORTUNE 500 Companies = 12% of companies:

65. More consequences Increased Customer Wait Times Loss of Confidence in the Supply System Multiple orders for the same items Excess supplies in the forward pipeline Increase in “stuff” in the reverse pipeline Constipated supply chain

66. Impact? Every resaleable item that is in the reverse supply chain results in a potential stock out or “zero balance” at the next level of supply. Creates a “stockout” do-loop

67. Results? This potential for a stock out results in additional parts on the shelves at each location to prevent a stock out from occurring. More stocks = “larger logistics footprint” = the need for larger distribution centers and returns centers.

68. Reverse Logistics According to the Reverse Logistics Executive Council, the percent increase in costs for processing a return, as compared to a forward sale, is an astounding 200-300%. Typically, as many as 8-12 more steps per item in the reverse pipeline than items in the forward pipeline

69. “The truth is, for one reason or another, materials do come back and it is up to those involved in the warehouse to effectively recover as much of the cost for these items as possible.” - Whalen, “In Through the Out Door”

70. RFID and Returns Visibility Tracking Component tracking Data Warehouse on what, why, when Altered products Not for every product

71. Impacts of Reverse Logistics Forecasting Carrying costs Processing costs Warehousing Distribution Transportation Personnel Marketing

72. Next Week Next Week: Resource Planning, Supply Chain Security, The Beer Game