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 0 Forecasts are usually wrong 0 every forecast should include an estimate of error 0 Forecasts are more accurate for families or groups 0 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 Usually demand behavior over a time frame Qualitative methods Based on subjective methods Quantitative methods Based on mathematical formulas

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

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

14. 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

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

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

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

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

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

20. 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 Simple Moving Average

21. 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 = 90 + 110 + 130 + 75 + 50 5 = 91 orders for Nov Simple Moving Average

22. 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

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

24. Weighted Moving Average WMA n = i = 1  W i D i 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

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

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

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

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

29. Forecast for Next Period 0 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

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

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

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

33. Reverse LogisticsReverse Logistics: Important or Irritant? The Reverse Logistics Association was founded in 2002 when research studies were completed which revealed that over $750 billion annually was being spent on reverse logistics processes in North America alone.

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

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

36. 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

37. Reverse Logistics - What is it? The Commercial Perspective 0 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. 0 Any activity that takes money from the company after the sale of the product

38. Typical Reverse Logistics Activities 0 Processing returned merchandise - damaged, seasonal, restock, salvage, recall, or excess inventory 0 Recycling packaging materials/containers 0 Reconditioning, refurbishing, remanufacturing 0 Disposition of obsolete stuff 0 Hazmat recovery

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

40. 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

41. 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

42. Reverse Logistics A US Army Perspective

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

44. 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.

45. 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.”

46. Reverse Logistics The Commercial Perspective

48. Mattel's expanded product recall of 19 million toys is pushing a lot of product back through the supply chain. Recall of 3912 items from Peanut Corporation of America Salmonella problems causing “constipation” of forward supply chains Dell recall of faulty laptop batteries - 2007 2010 – toys, pallets, Tylenol 2011 – 4 million Toyotas

49. Reverse Logistics 0 Rate of returns? 0 Cost to process a return? 0 Time to get the item back on the shelf if resaleable?

50. Costs - above the cost of the item 0 Merchandise credits to the customers. 0 The transportation costs of moving the items from the retail stores to the central returns distribution center. 0 The repackaging of the serviceable items for resale. 0 The cost of warehousing the items awaiting disposition. 0 The cost of disposing of items that are unserviceable, damaged, or obsolete.

51. 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

52. 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 -

53. 0 Estimate of 2004 holiday returns: $13.2 billion 0 % of estimated 2010/2011 holiday returns: 25% 0 If true, value of 2011 returns ~ $120 billion 0 Wal-Mart: $6 Billion in annual returns = 17,000 truck loads (>46 trucks a day) 0 Electronics: $10 Billion annually in returns 0 Personal Computers: approximately $95 per PC sold 0 79% of returned PCs have no defects 0 Home Depot ~ $10 million in returns in the stores alone 0 Local Wal-Mart ~ $1 million a month in returns

54. Is it a Problem? 0 European influence – spread to US - Green Laws 0 Estee Lauder - $60 million a year into land fills 0 FORTUNE 500 Company - $200 million over their $300 million budget for returns 0 Same Provider - 40,000 products returned per month; 55% no faults noted 0 K-Mart - $980 million in returns 1999 0 Warranty vice paid repairs 0 Recent survey of FORTUNE 500 Companies = 12% of companies:

55. More consequences 0 Increased Customer Wait Times 0 Loss of Confidence in the Supply System 0 Multiple orders for the same items 0 Excess supplies in the forward pipeline 0 Increase in “stuff” in the reverse pipeline 0 Constipated supply chain

56. 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

57. Results? 0 This potential for a stock out results in additional parts on the shelves at each location to prevent a stock out from occurring. 0 More stocks = “larger logistics footprint” = the need for larger distribution centers and returns centers.

58. Reverse Logistics 0 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%.” 0 Typically, as many as 8-12 more steps per item in the reverse pipeline than items in the forward pipeline

59. Electronics Reverse Logistics 0 $677 billion 0 $132 billion 0 60 million – 12 million 0 100 million 0 20-50 million metric tons 0 2-5% 0 70 % 0 4 billion pounds 0 4 million pounds 0 75 pounds/40,000 pounds

60. “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”

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

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