frank davis 7/25/2002demand forecasting in a supply chain1

7/25/2002

Demand Forecasting in a Supply Chain 1Frank Davis

Demand Forecasting in a Supply Chain

7/25/2002


Why do you forecast?

• Who is involved in forecasting?– Marketing – Why? Do they influence

forecast?– Production – Why? How do they influence?– Distribution – Why? How do they influence?– Channel Members – Why? How do they

influence?– Suppliers – Why? How do they influence?

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Characteristics of Forecasts

• Why are forecasts always wrong? Does this mean we should not forecast? What does it mean?

• Why are long-term forecasts less accurate than short-term forecasts?

• Why are aggregate forecasts typically more accurate than disaggregate forecasts? Are there cases when this would not be the case?

• Who needs to make forecasts? Should everyone in the supply chain use the same forecast?

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When do you use?

• Qualitative – subjective judgment call• Time series – crank the numbers• Causal – correlate with known variables• Simulation – combine various methods• How do you determine which method to use?• Would you use the same method to forecast

– the outcome of the Miami football game – the amount of Coke to produce and – staffing for a hospital emergency room?

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Basis for a forecast

• What do you use for a forecast?• How do you forecast score of upcoming football

game?– Qualitative - Poll sportscasters (experts) – Time series - Look at scores of last 10 games

• Level of scoring• Trends• Does schedule make difference

– Causal – look at player and coaching data– Combination – time series plus modification by player

and coaching data

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Basic Questions

• What is the objective of forecasting?– Why is the forecast horizon important?

• Should all groups use the same forecast?• Should demand forecasts be based on sales?• Why is it important to identify the factors that

influence demand?• When would you have different forecasts for

different customer segments? • Which forecasting method is best?• How do you determine how good a specific

method is?

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Forecasting method

• Static – once level, trend and seasonal factors determined keep using the same formula

• Adaptive – new data may reveal something about level, trend and seasonal changes – recalculate new formula each time

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Time Series - Static

• Information needed– Demand level– Demand trend– Cyclical effect– Error

• Modify forecast by causal factors

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Static time series forecasting• Four steps

– Deseasonalize know demands to prime regression model • Must cover full cycle (each season)• Average for each season if odd number of seasons• See eq. 4.2 for even number of seasons

– Use deseasonalized demand to calculate level and trend• use regression to calculate intercept and trend

– Use intercept and trend to forecast deseasonalized demand– Calculate Seasonal Factor (actual demand/forecast)– Calculate average seasonal factor– Calculate seasonal forecast

• Use average seasonal factor to adjust trend [(level + trends * period) * average seasonal factor]

– See if forecast is good• How big is your error?• Is the forecast bias? (positive or negative)• How much confidence can you have in forecast?

– Spreadsheet to illustrate class problem (Save this to you hard drive so you can work on it.)

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Relationship between Beginning, End and Average of Period

0

5

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25

1-Jan Ave 31-Jan

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Average for three Months

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1-Jan 1-Feb 1-Mar 1-Apr

Sales

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Average for 4 months

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35

Jan-03 Feb-03 Mar-03 Apr-03 May-03

Sales

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Seasonal Adjustments

• If one season how would you determine average demand for season

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1000

2000

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Summer

Sales

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Seasonal Adjustment

• If you have an odd number of seasons in the cycle how would you determine average sales rate in the middle of the cycle?

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15000

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25000

Sales

Summer

Fall

Winter

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Seasonal Adjustments

• If you have a 4 season cycle how would you calculate average sales for the middle of the fall quarter?

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Spring Fall Spring

Sales

3-D Column 2

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How do you do Seasonal Adjustment?

• Odd number of seasons in cycle– See equations 4,2 bottom

• Even number of seasons in cycle– Equation 4.2 top– Why can’t you calculate first 2 seasons– Why can’t you calculate last 2 seasons

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Data hard to interpret

Quarter Demand for Natural Gas.com

Year Quarter Period t Demand1998 2 1 80001998 3 2 130001998 4 3 230001999 1 4 340001999 2 5 100001999 3 6 180001999 4 7 230002000 1 8 380002000 2 9 120002000 3 10 130002000 4 11 320002001 1 12 41000

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Plot helps see periods

Quarterly Demand

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50000

1 2 3 4 5 6 7 8 9 10 11 12

Quarter

Dem

and

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Adaptive Model Steps (adjust as you go)

• Initialize just like static– Level– Trend– Cyclical

• Forecast– Prior forecast adjusted by actual demand for

period

• Estimate error• Modify forecast based on prior error

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Adaptive Model Steps

• Moving average– Average of n preceding periods (Eq. 4.9)– Forecast equal to average of last n periods– When is the moving average appropriate?

• Exponential Smoothing– Forecast = α(prior forecast) + (1- α) last demand

• Concept (adjust last forecast by current experience)

– Use same approach on • Level• Trend – Holt’s model• Trend and Season – Winter’s model

• Which method is best?• Can you modify forecast to reflect other casual factors?

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How do you determine best forecasting method?

• What is purpose of forecast? What are you trying to do?

• What method do you use to evaluate value of forecast?

• What do you look for?– Mean absolute error– Bias

• What do you do if error is too high?

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Expectations of this class

• When to forecast

• Different forecasting methods

• Forecasting horizon

• What do you forecast

• Availability of tools to assist you but you need to know how to evaluate each method

• How do you cope with forecast error?

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• Do you need to know how to calculate each model?– Firms will have software packages– You need to understand them conceptually– Advanced classes will go into more detail

• You do need to know that the model is not as important as knowing how to check for accuracy of method – error testing

frank davis 7/25/2002demand forecasting in a supply chain1

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