1. Week 5
Fall 2

2. Forecasting

3. Forecasting Survey
How far into the future do you typically project when trying to forecast the health of your industry?
less than 4 months 3%
4-6 months 12%
7-12 months 28%
> 12 months 57%
Fortune Council survey, Nov 2005

4. Indices to forecast health of industry
Consumer price index 51%
Consumer Confidence index 44%
Durable goods orders 20%
Gross Domestic Product 35%
Manufacturing and trade inventories and sales 27%
Price of oil/barrel 34%
Strength of US $ 46%
Unemployment rate 53%
Interest rates/fed funds 59%
Fortune Council survey, Nov 2005

5. 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

6. 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

7. 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.

8. 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

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

10. 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

11. 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

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

13. Short-range to medium-range
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

14. 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

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

16. 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

17. 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

18. Sum of Demand In n Periods n
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

19. Simple Moving Average ORDERS MONTH PER MONTH Jan 120 Feb 90 Mar 100
Apr 75
May 110
June 50
July 75
Aug 130
Sept 110
Oct 90
ORDERS
MONTH PER MONTH

20. Simple Moving Average Daug+Dsep+Doct ORDERS MONTH PER MONTH Jan 120
Feb 90
Mar 100
Apr 75
May 110
June 50
July 75
Aug 130
Sept 110
Oct 90
ORDERS
MONTH PER MONTH 3
Daug+Dsep+Doct
MAnov = = 3
= 110 orders for Nov

21. Simple Moving Average ORDERS THREE-MONTH
Jan 120 –
Feb 90 –
Mar 100 –
Apr
May
June
July
Aug
Sept
Oct
Nov – 110.0
ORDERS THREE-MONTH
MONTH PER MONTH MOVING AVERAGE

22. Simple Moving Average Jan 120 – Feb 90 – Mar 100 – Apr 75 103.3
May
June
July
Aug
Sept
Oct
Nov – 110.0
ORDERS THREE-MONTH
MONTH PER MONTH MOVING AVERAGE = 5
= 91 orders for Nov
Example 8.1

23. Simple Moving Average ORDERS THREE-MONTH FIVE-MONTH
Jan 120 – –
Feb 90 – –
Mar 100 – –
Apr –
May –
June
July
Aug
Sept
Oct
Nov –
ORDERS THREE-MONTH FIVE-MONTH
MONTH PER MONTH MOVING AVERAGE MOVING AVERAGE

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

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

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

27. Weighted Moving Average Example
MONTH WEIGHT DATA
August 17% 130
September 33% 110
October 50% 90
November forecast
WMA3 = 3
i = 1 
Wi Di
= (0.50)(90) + (0.33)(110) + (0.17)(130)
= orders
3 Month = month = 91

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

29. Exponential Smoothing
Ft +1 = Dt + (1 - )Ft
where
Ft +1 = forecast for next period
Dt = actual demand for present period
Ft = 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

30. 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

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

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

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

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

35. reverse logistics is seen as being important.”
“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
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

38. 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

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
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
Tylenol Scare - Johnson and Johnson
German ordinance that put teeth in environmental reverse pipeline
Summer 1996 – UK Packaging and Packaging Waste Legislation
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. There is a 40 hectare (~100 acres) area in Kuwait with items waiting
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. Does this create a problem?
From GAO Audit Report

47. From GAO Audit Report

48. The Commercial Perspective
Reverse Logistics
The Commercial Perspective

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

51. 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.

52. 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 returns = 4 Days of Supply for all of Wal- Mart = 2000 Containers

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

54. Estimate of 2004 holiday returns: $13
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

55. 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:

56. 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

57. 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

58. 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.

59. 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 %. Typically, as many as 8-12 more steps per item in the reverse pipeline than items in the forward pipeline

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