Demand Management and FORECASTING Operations Management Dr. Ron Lembke

Demand Management Coordinate sources of demand for supply chain to run efficiently, deliver on time Independent Demand Things demanded by end users Dependent Demand Demand known, once demand for end items is known

Affecting Demand Increasing demand Changing Timing of demand Marketing campaigns Sales force efforts, cut prices Changing Timing of demand Incentives for earlier or later delivery At capacity, don’t actively pursue more

Predicting the Future We know the forecast will be wrong. Try to make the best forecast we can, Given the time we want to invest Given the available data The “Rules” of Forecasting: The forecast will always be wrong The farther out you are, the worse your forecast is likely to be. Aggregate forecasts are more likely to accurate than individual item ones

Time Horizons Different decisions require projections about different time periods: Short-range: who works when, what to make each day (weeks to months) Medium-range: when to hire, lay off (months to years) Long-range: where to build plants, enter new markets, products (years to decades)

Forecast Impact Finance & Accounting: budget planning Human Resources: hiring, training, laying off employees Capacity: not enough, customers go away angry, too much, costs are too high Supply-Chain Management: bringing in new vendors takes time, and rushing it can lead to quality problems later

Qualitative Methods Sales force composite / Grass Roots Market Research / Consumer market surveys & interviews Jury of Executive Opinion / Panel Consensus Delphi Method Historical Analogy - DVDs like VCRs Naïve approach

Quantitative Methods Time Series Methods 0. All-Time Average 1. Simple Moving Average 2. Weighted Moving Average 3. Exponential Smoothing 4. Exponential smoothing with trend 5. Linear regression Causal Methods Linear Regression

Time Series Forecasting Assume patterns in data will continue, including: Trend (T) Seasonality (S) Cycles (C) Random Variations

All-Time Average To forecast next period, take the average of all previous periods Advantages: Simple to use Disadvantages: Ends up with a lot of data Gives equal importance to very old data

4/7/2009 2009 Farm Angels: Ty: 1.000, Jacob 0.833, Noah 0.667 (6 at bats)

End of 2008 season

Moving Average Compute forecast using n most recent periods Jan Feb Mar Apr May Jun Jul 3 month Moving Avg: June forecast: FJun = (AMar + AApr + AMay)/3 If no seasonality, freedom to choose n If seasonality is N periods, must use N, 2N, 3N etc. number of periods

Moving Average Advantages: Disadvantages: Ignores data that is “too” old Requires less data than simple average More responsive than simple average Disadvantages: Still lacks behind trend like simple average, (though not as badly) The larger n is, more smoothing, but the more it will lag The smaller n is, the more over-reaction

Simple and Moving Averages

Centered MA CMA smoothes out variability Plot the average of 5 periods: 2 previous, the current, and the next two Obviously, this is only in hindsight FRB Dalls graphs

Centered Moving Average Take average of n periods, Plot the average in the middle period Not useful for forecasting More stable than actuals If seasonality, n = season length (4wks, 12 mo, etc.)

CMA - # Periods to Average What if data has 12-month cycle? Ja F M Ap My Jn Jl Au S O N D Ja F M Avg of Jan-Dec gives average of month 6.5: (1+2+3+4+5+6+7+8+9+10+11+12)/12=6.5 Avg of Feb-Jan gives average of month 6.5: (2+3+4+5+6+7+8+9+10+11+12+13)/12=7.5 How get a July average? Average of other two averages

Stability vs. Responsiveness Real-time accuracy Market conditions Stable Forecasts being used throughout the company Long-term decisions based on forecasts Don’t whipsaw those folks

Centered Moving Average To center even-number of periods 12: take half each of 1 and 13, plus sum of 2-12. F14 = 0.5 A1 + A2 + A3 + A4 + A5 + A6 + A7 + A8 + A9 + A10 + A11 + A12 + 0.5 A13 This is exactly the same as what you get by taking the average of the averages from previous slide

Old Data Comparison of simple, moving averages clearly shows that getting rid of old data makes forecast respond to trends faster Moving average still lags the trend, but it suggests to us we give newer data more weight, older data less weight.

Weighted Moving Average FJun = (AMar + AApr + AMay)/3 = (3AMar + 3AApr + 3AMay)/9 Why not consider: FJun = (2AMar + 3AApr + 4AMay)/9 FJun = 2/9 AMar + 3/9 AApr + 4/9 AMay Ft = w1At-3 + w2At-2 + w3At-1 Complicated: Have to decide number of periods, and weights for each Weights have to add up to 1.0 Most recent probably most relevant, gets most weight Carry around n periods of data to make new forecast

Weighted Moving Average Wts = 0.5, 0.3, 0.2

Setting Parameters Weighted Moving Average Trial and Error Number of Periods Individual weights Trial and Error Evaluate performance of forecast based on some metric

Exponential Smoothing F10 = F9 + 0.2 (A9 - F9) F10 = 0.8 F9 + 0.2 (A9 - F9) At-1 Actual demand in period t-1 Ft-1 Forecast for period t-1  Smoothing constant >0, <1 Forecast is old forecast plus a portion of the error of the last forecast. Formulas are equivalent, give same answer

Exponential Smoothing Smoothing Constant between 0.1-0.3 Easier to compute than moving average Most widely used forecasting method, because of its easy use F1 = 1,050,  = 0.05, A1 = 1,000 F2 = F1 + (A1 - F1) = 1,050 + 0.05(1,000 – 1,050) = 1,050 + 0.05(-50) = 1,047.5 units BTW, we have to make a starting forecast to get started. Often, use actual A1

Exponential Smoothing Alpha = 0.3

Exponential Smoothing Alpha = 0.5

Exponential Smoothing We take: And substitute in to get: and if we continue doing this, we get: Older demands get exponentially less weight

Choosing  Low : if demand is stable, we don’t want to get thrown into a wild-goose chase, over-reacting to “trends” that are really just short-term variation High : If demand really is changing rapidly, we want to react as quickly as possible

Averaging Methods Simple Average Moving Average Weighted Moving Average Exponentially Weighted Moving Average (Exponential Smoothing) They ALL take an average of the past With a trend, all do badly Average must be in-between 30 20 10

Trend-Adjusted Ex. Smoothing

Trend-Adjusted Ex. Smoothing Forecast including trend for period 1 is Suppose actual demand is 115, A1=115

Trend-Adjusted Ex. Smoothing Forecast including trend for period 2 is Suppose actual demand is 120, A2=120

FIT5=F5+T5 F6 A5 F5

Selecting  and  You could: Try an initial value for each parameter. Try lots of combinations and see what looks best. But how do we decide “what looks best?” Let’s measure the amount of forecast error. Then, try lots of combinations of parameters in a methodical way. Let  = 0 to 1, increasing by 0.1 For each  value, try  = 0 to 1, increasing by 0.1

Evaluating Forecasts How far off is the forecast? What do we do with this information? Forecasts Demands

Measuring the Errors Method 1 forecasts are low, high, etc. Period A-F Method 1 Method 2 1 100 10 2 -100 3 4 5 6 7 8 9 RSFE Method 1 forecasts are low, high, etc. Method 2 forecasts always too low. Running Sum of Forecast Errors, RSFE Sum of all periods Also known as the Bias

Evaluating Forecasts Mean Absolute Deviation Mean Squared Error Percent Error

MAD of examples MAD shows that method 1 is off by a larger amount Period |A-F| Method 1 Method 2 1 100 10 2 3 4 5 6 7 8 9 MAD MAD shows that method 1 is off by a larger amount Method 2 was biased However, overall, Method 2 seems preferable

Tracking Signal To monitor, compute tracking signal If >4 or <-4 something is wrong Top should sum to 0 over time. If not, forecast is biased.

Monitoring Forecast Accuracy Monitor forecast error each period, to see if it becomes too great 4 Upper Limit Forecast Error -4 Lower Limit Forecast Period

Updating MAD Simplified calculation avoids keeping running total of all errors and demands: Standard Deviation can be estimated from MAD:

Techniques for Trend Determine how demand increases as a function of time t = periods since beginning of data b = Slope of the line a = Value of yt at t = 0

Computing Values

Linear Regression Four methods Fits a trend and intercept to the data. Type in formulas for trend, intercept Tools | Data Analysis | Regression Graph, and R click on data, add a trendline, and display the equation. Use intercept(Y,X), slope(Y,X) and RSQ(Y,X) commands Fits a trend and intercept to the data. R2 measures the percentage of change in y that can be explained by changes in x. Gives all data equal weight. Exp. smoothing with a trend gives more weight to recent, less to old.

Causal Forecasting Linear regression seeks a linear relationship between the input variable and the output quantity. For example, furniture sales correlates to housing sales Not easy, multiple sources of error: Understand and quantify relationship Someone else has to forecast the x values for you

Video sales of Shrek 2? Shrek did $500m at the box office, and sold almost 50 million DVDs & videos Shrek2 did $920m at the box office

Video sales of Shrek 2? Assume 1-1 ratio: 920/500 = 1.84 1.84 * 50 million = 92 million videos? Fortunately, not that dumb. January 3, 2005: 37 million sold! March analyst call: 40m by end Q1 March SEC filing: 33.7 million sold. Oops. May 10 Announcement: In 2nd public Q, missed earnings targets by 25%. May 9, word started leaking Stock dropped 16.7%

Lessons Learned Flooded market with DVDs Guaranteed Sales 5 years ago Promised the retailer they would sell them, or else the retailer could return them Didn’t know how many would come back 5 years ago Typical movie 30% of sales in first week Animated movies even lower than that 2004/5 50-70% in first week Shrek 2: 12.1m in first 3 days American Idol ending, had to vote in first week

The Human Element Colbert says you have more nerve endings in your gut than in your brain Limited ability to include factors Can’t include everything If it feels really wrong to your gut, maybe your gut is right

Washoe Gaming Win, 1993-96 What did they mean when they said it was down three quarters in a row? 1993 1994 1995 1996

Seasonality Seasonality is regular up or down movements in the data Can be hourly, daily, weekly, yearly Naïve method N1: Assume January sales will be same as December N2: Assume this Friday’s ticket sales will be same as last

Seasonal Factors Seasonal factor for May is 1.20, means May sales are typically 20% above the average Factor for July is 0.90, meaning July sales are typically 10% below the average

Seasonality & No Trend Sales Factor Spring 200 200/250 = 0.8 Summer 350 350/250 = 1.4 Fall 300 300/250 = 1.2 Winter 150 150/250 = 0.6 Total 1,000 Avg 1,000/4=250

Seasonality & No Trend If we expected total demand for the next year to be 1,100, the average per quarter would be 1,100/4=275 Forecast Spring 275 * 0.8 = 220 Summer 275 * 1.4 = 385 Fall 275 * 1.2 = 330 Winter 275 * 0.6 = 165 Total 1,100

Trend & Seasonality Deseasonalize to find the trend Calculate seasonal factors Deseasonalize the demand Find trend of deseasonalized line Project trend into the future Project trend line into future Multiply trend line by seasonal component.

Washoe Gaming Win, 1993-96 Looks like a downhill slide Silver Legacy opened 95Q3 Otherwise, upward trend 1993 1994 1995 1996 Source: Comstock Bank, Survey of Nevada Business & Economics

Washoe Win 1989-1996 Definitely a general upward trend, slowed 93-94

1989-2007

1989-2007

1998-2007 Cache Creek Thunder Valley 9/11 CC Expands

Centered Moving Average

2003-2012 - Deseasonalized

2011 Forecast using 2003-10 SR Data for LR Seasonal Indexes calculated using 2003-10 data

1. Compute Seasonal Indexes

2. Divide actual by Seasonal Indexes

3.LR on Deseasonalized Data

4.Project Straight Line into Future

5.Multiply Linear line times Indexes

How Did We Do? – 2012 Actual Win

1.Compute Seasonal Relatives Q1 Q2 Q3 Q4 2003 240,114,703 259,349,602 279,784,440 246,068,018 2004 231,607,546 259,849,383 297,401,507 259,617,607 2005 245,793,646 269,238,341 294,810,396 257,014,585 2006 245,775,176 269,670,481 294,839,349 257,155,338 2007 244,648,019 273,460,685 284,733,890 246,352,794 2008 227,915,101 237,045,466 258,990,669 206,203,166 2009 190,098,500 211,913,667 217,227,445 185,971,111 2010 187,016,132 198,330,968 209,608,491 175,601,589 2011 174,138,905 192,122,889 203,912,214 175,510,911 avg 220,789,748 241,220,165 260,145,378 223,277,235 236,358,131 SR 0.934 1.021 1.101 0.945

2.Deseasonalize Year Quarter Gaming Win Seasonal Index Deseasonalized 2008 1 190,098,500 0.934 203,502,775 2 211,913,667 1.021 207,642,335 3 217,227,445 1.101 197,364,541 4 185,971,111 0.945 196,866,394 2009 187,016,132 200,203,062 198,330,968 194,333,409 209,608,491 190,442,251 175,601,589 185,889,365

3.LR on Deseasonalized data 2009Q1-2011Q4 Period Deseasonalized 1 203,502,775 2 207,642,335 3 197,364,541 4 196,866,394 5 200,203,062 6 194,333,409 7 190,442,251 8 185,889,365 9 186,417,833 10 188,250,460 11 185,266,831 12 185,793,374 Intercept = 206,203,550 Slope = -1,954,743 R-squared = 0.848

4.Project trend line into future Intercept = 206,203,550 Slope = -1,954,743

5.Multiply by Seasonal Relatives

How Did We Do? – 2012 Actual Win

Summary Calculate indexes Deseasonalize Do a LR Divide actual demands by seasonal indexes Do a LR Project the LR into the future Seasonalize Multiply straight-line forecast by indexes