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Chapter 7 Demand Forecasting in a Supply Chain

“Those who do not remember the past are condemned to repeat it” George Santayana (1863-1952) Spanish philosopher, essayist, poet and novelist. Chapter 7 Demand Forecasting in a Supply Chain. Forecasting -1 Moving Average Ardavan Asef-Vaziri Based on Operations management: Stevenson

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Chapter 7 Demand Forecasting in a Supply Chain

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  1. “Those who do not remember the past are condemned to repeat it” George Santayana (1863-1952) Spanish philosopher, essayist, poet and novelist Chapter 7Demand Forecastingin a Supply Chain

  2. Forecasting -1 Moving Average ArdavanAsef-Vaziri Based on Operations management: Stevenson Operations Management: Jacobs, Chase, and Aquilano Supply Chain Management: Chopra and Meindl USC Marshall School of Business Lecture Notes Chapter 7Demand Forecastingin a Supply Chain

  3. Forecasting Objectives • Introduce the basic concepts of forecasting and its importance within an organization. • Identify several of the more common forecasting methods • Measure and assess the errors that exist in all forecasts

  4. Uses of Forecasts Forecast: a prediction of the future value of a variable of interest, such as demand.

  5. Types of Forecasting • Qualitative Techniques • Time Series Analysis • Causal Relationship Forecasting

  6. Qualitative Methods • Non-quantitative forecasting techniques based on expert opinions and intuition. Typically used when there are no data available. • Subjective, judgmental • Based on intuition, estimates, and opinions • Expert Opinions • Market Research • Historical Analogies

  7. Time Series Methods • Analyzing data by time periods to determine if trends or patterns occur. • Moving average • Exponential smoothing • More sophisticated techniques available

  8. Causal Relationship Methods • Relating demand to an underlying factor other than time. (Regression) (Number of socks sold depends on number of running shoes sold.) • Multiple Regression Models

  9. FourBasic Characteristics of Forecasts • Forecasts are rarely perfect because of randomness. • Beside the average, we also need a measure of variation,which is called standard deviation • Forecasts are more accurate for groups of items than forindividuals. • Forecast accuracy decreases as the time horizon increases. I see that you willget an A this semester.

  10. Time Series Models • Models for short term decisions • Inventory decisions • Stock levels of Gameboys • Production planning decisions • Staffing decisions • Call center scheduling • Fast food chain

  11. Time Series Forecasts Find a relationship between demand and time. Demand Time

  12. Components of an Observation Observed variable (O) = Systematic component (S) + Random component (R) Level (current deseasonalized ) Trend (growth or decline) Seasonality (predictable seasonal fluctuation) • Systematic component: Expected value of the variable • Random component: The part of the forecast that deviates from the systematic component • Forecast error: difference between forecast and actual demand

  13. Time Series Techniques • Naive Forecast • Moving Average • Exponential Smoothing

  14. Naive Forecast We sold 250 wheels last week.... Now, next week we should sell.… 250 wheels F(t+1) = At At : Actual demand in period t F(t+1) : Forecast of demand for period t+1 The naive forecast can also serve as an accuracy standard for other techniques.

  15. Moving Average Three period moving average in period 7 is the average of: MA73 = (A7+ A6+ A5 )/3 Three period moving average in period t is the average of: MAt3 = (At+ At-1+ At-2 )/3 Ten period moving average in period t is the average of: MAt10 = (At+ At-1+ At-2 +At-3+ ….+ At-9 )/10

  16. Forecast Using Moving Average n period moving average in period t is the average of: MAtn = (At+ At-1+ At-2 +At-3+ ….+ At-n+1 )/n Forecast for period t+1 is equal to moving average for period t Ft+1 =MAtn Ft+1 =MAtn = (At+ At-1+ At-2 +At-3+ ….+ At-n+1 )/n

  17. An example for comparison of two Moving Averages Let’s develop 3-week and 6-week moving average forecasts for demand in week 13.

  18. 3-Period and 6-Period Moving Average (358+952+623)/3 (358+952+623+186+714+53)/6

  19. Graphical Comparison • 6-week MA is smoother than 3-week MA, which appears to result in better predictions. • How do we measure which one is doing better?

  20. How do we measure errors? Standard Deviation of Error = 1.25 MAD • Error is assumed to NORMALLY DISTRIBUTED with • A MEAN (AVERAGE) = 0 • STANDARD DEVIATION = 1.25* MAD

  21. MAD for One Method • But. Compare two or more forecasting techniques only over a period when data is available for all techniques.

  22. MAD to Compare Two or More Methods

  23. Moving Average Comparison • How many periods should we use for forecasting? • 6-week forecast is 518.8 and MAD is 295 • 3-week forecast is 420 and MAD is 371.4 • 6-week MAD is lower than 3-week MAD • Seems we prefer 6-week to 3-week. • So … should we use as many periods as possible?

  24. Check a Second Example

  25. MA comparison • Note that MAD is now lower for the 3-week MA than for the 6-week MA. • 3-week MAD is 93.6 • 6-week MAD is 171.9 • What is going on?

  26. Moving Average: Observations • A large number of periods will cause the moving average to respond slowly to changes. • When there is a obvious current trend in the data, using larger number of periods results in a forecast with larger error. • In general, there is a trade-off between using more periods to smooth out random variations and using less periods to more closely follow trends. • Try many different time window sizes, and choose the one with the lowest MAD.

  27. Tracking Signal

  28. Tracking Signal Are our observations within UCL and LCL? Is there any systematic error? Tracking Signal UCL Time LCL

  29. Tracking Signal Tracking Signal UCL Time LCL

  30. Tracking Signal Tracking Signal UCL Time LCL

  31. Predictions are usually difficult, especially about the future. • Yogi Berra • The former New York Yankees Catcher Chapter 7Demand Forecastingin a Supply Chain

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