Week 5 Fall 2

Forecasting

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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 = = 90 + 110 + 130 3 = 110 orders for Nov

Simple Moving Average ORDERS THREE-MONTH Jan 120 – Feb 90 – Mar 100 – Apr 75 103.3 May 110 88.3 June 50 95.0 July 75 78.3 Aug 130 78.3 Sept 110 85.0 Oct 90 105.0 Nov – 110.0 ORDERS THREE-MONTH MONTH PER MONTH MOVING AVERAGE

Simple Moving Average Jan 120 – Feb 90 – Mar 100 – Apr 75 103.3 May 110 88.3 June 50 95.0 July 75 78.3 Aug 130 78.3 Sept 110 85.0 Oct 90 105.0 Nov – 110.0 ORDERS THREE-MONTH MONTH PER MONTH MOVING AVERAGE = 90 + 110 + 130 + 75 + 50 5 = 91 orders for Nov Example 8.1

Simple Moving Average ORDERS THREE-MONTH FIVE-MONTH Jan 120 – – Feb 90 – – Mar 100 – – Apr 75 103.3 – May 110 88.3 – June 50 95.0 99.0 July 75 78.3 85.0 Aug 130 78.3 82.0 Sept 110 85.0 88.0 Oct 90 105.0 95.0 Nov – 110.0 91.0 ORDERS THREE-MONTH FIVE-MONTH MONTH PER MONTH MOVING AVERAGE MOVING AVERAGE

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

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

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

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) = 103.4 orders 3 Month = 110 5 month = 91

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reverse Logistics A US Army Perspective

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

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.

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.”

Does this create a problem? From GAO Audit Report

From GAO Audit Report

The Commercial Perspective Reverse Logistics The Commercial Perspective

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

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.

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

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 -

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

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:

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

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

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.

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

“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”

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

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