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Supply Chain Management

Supply Chain Management. Lecture 11. Outline. Today Homework 2 Chapter 7 Thursday Chapter 7 Friday Homework 3 Due Friday February 26 before 5:00pm. Announcements. FEI Student Financial Awards Program

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Supply Chain Management

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  1. Supply Chain Management Lecture 11

  2. Outline • Today • Homework 2 • Chapter 7 • Thursday • Chapter 7 • Friday • Homework 3 • Due Friday February 26 before 5:00pm

  3. Announcements • FEI Student Financial Awards Program • Awards are presented to Finance or Accounting majorsfrom schools in Colorado. Each award can either go to an undergraduate or graduate student. This year there are five awards for $1,200 each. • The criteria includes the following three factors: • Students who have performed well academically, • Students who have potential leadership skills in the business field, and • Students who have financial need. • All applications are due to the committee no later than March 25, 2010. Applications and information are available in the office of Bonnie Beverly (KOBL S315A) or Consuelo Delval (KOBL S328) (paper applications only)

  4. Announcements • What? • Tour the Staples Fulfillment Center in Brighton, CO • Informal Lunch-and-Learn • Up to 20 students with a Operations Management major • When? • Weeks of March 15 or March 29 • There is a fair amount of time involved in the activity • Transit is close to an hour in each direction • Probably 2 hours onsite 

  5. Forecasting Examples • Walt Disney World • Daily forecast of attendance (weather forecasts, previous day’s crowds, conventions, seasonal variations) • Add more cast members and add street activities to manage high demand • Amazon Kindle • Kindle sold out in 5.5 hours • Kindle was not in stock for another 5 months • FedEx customer service center • Goal is to answer 90% of all calls within 20 seconds • Makes extensive use of forecasting for staffing decisions and to ensure that customer satisfaction stays high

  6. Characteristics of Forecasts • Forecasts are always wrong! • Long-term forecasts are less accurate than short-term forecasts • Aggregate forecasts are more accurate than disaggregate forecasts • Information gets distorted when moving away from the customer

  7. Types of Forecasts • Qualitative • Primarily subjective, rely on judgment and opinion • Time series • Use historical demand only • Causal • Use the relationship between demand and some other factor to develop forecast • Simulation • Imitate consumer choices that give rise to demand

  8. Role of Forecasting Supplier Manufacturer Distributor Retailer Customer Push Push Push Pull Push Push Pull Push Pull Is demand forecasting more important for a push or pull system?

  9. L Level (current deseasonalized demand) T Trend (growth or decline in demand) S Seasonality (predictable seasonal fluctuation) Time Series Forecasting Observed demand = Systematic component + Random component The goal of any forecasting method is to predict the systematic component of demand and estimate the random component

  10. Components of an Observation Level (L) Forecast(F) Ft+n = Lt The moving-average method is used when demand has no observable trend or seasonality

  11. Example: Moving Average Method • A supermarket has experienced the following weekly demand of coffee over the last four weeks • 120, 127, 114, and 122 Determine LevelLt = (Dt+Dt-1+…+Dt-N+1)/N ForecastFt+n = Lt

  12. Example: Tahoe Salt

  13. Example: Tahoe Salt • Demand forecasting using Moving Average

  14. Components of an Observation Level (L) Forecast(F) Ft+n = Lt The simple exponential smoothing is used when demand has no observable trend or seasonality

  15. Example: Simple Exponential Smoothing Method • A supermarket has experienced the following weekly demand of coffee over the last four weeks • 120, 127, 114, and 122 Determine initial levelL0 = (∑i Di)/ n Determine levelsLt+1 = Dt+1 + (1 – )*Lt ForecastFt+n = Lt  = 0.1

  16. Example: Tahoe Salt

  17. Example: Tahoe Salt • Demand forecasting using simple exponential smoothing

  18. Components of an Observation Trend (T) Forecast(F) Ft+n = Lt + nTt Holt’s method is appropriate when demand is assumed to have a level and a trend

  19. Example: Holt’s Method • An electronics manufacturer has seen demand for its latest MP3 player increase over the last six months • 8415, 8732, 9014, 9808, 10413, 11961 Determine initial levelL0 = INTERCEPT(y’s, x’s)T0 = LINEST(y’s, x’s)

  20. Example: Holt’s Method • An electronics manufacturer has seen demand for its latest MP3 player increase over the last six months • 8415, 8732, 9014, 9808, 10413, 11961 Determine initial levelL0 = INTERCEPT(y’s, x’s)T0 = LINEST(y’s, x’s) Determine levelsLt+1 = Dt+1 + (1 – )*(Lt + Tt)Tt+1 = (Lt+1 – Lt) + (1 – )*Tt ForecastFt+n = Lt + nTt  = 0.1,  = 0.2

  21. Example: Tahoe Salt

  22. Example: Tahoe Salt • Demand forecasting using Holt’s method

  23. Components of an Observation Seasonality (S) Forecast(F) Ft+n = (Lt + Tt)St+n

  24. Time Series Forecasting Observed demand = Systematic component + Random component L Level (current deseasonalized demand) T Trend (growth or decline in demand) S Seasonality (predictable seasonal fluctuation)

  25. Static Dt: Actual demand L: Level T: Trend S: Seasonal factor Ft: Forecast Adaptive Dt: Actual demand Lt: Level Tt: Trend St: Seasonal factor Ft: Forecast Static Versus Adaptive Forecasting Methods

  26. Determine deason. demandDt = L + Tt Determine seasonal factorsSt = Dt / Dt Determine seasonal factorsSi =AVG(Si) Example: Static Method • A theme park has seen the following attendance over the last eight quarters (in thousands) • 54, 87, 192, 130, 80, 124, 265, 171 Determine initial levelL = INTERCEPT(y’s, x’s)T = LINEST(y’s, x’s) ForecastFt = (L + Tt)Si

  27. Example: Tahoe Salt

  28. Static Forecasting Method

  29. Static Forecasting Method • Deseasonalize demand • Demand that would have been observed in the absence of seasonal fluctuations • Periodicity p • The number of periods after which the seasonal cycle repeats itself • 12 months in a year • 7 days in a week • 4 quarters in a year • 3 months in a quarter

  30. Deseasonalize demand

  31. Periodicity p is odd Periodicity p is even Deseasonalize demand

  32. Deseasonalize demand Deseasonalizing demand around t= (2,4), that is, year 2 and 4th quarter, when p is odd

  33. Deseasonalize demand Assume p = 3, hence a seasonal cycle consists of three periods

  34. Deseasonalize demand Deseasonalized demand for t=(2,4) = 18,000 + 23,000 + 38,000 = 26,333

  35. Deseasonalize demand Deseasonalizing demand around t= (2,4), that is, year 2 and 4th quarter, when p is even

  36. Deseasonalize demand Assume p = 4, hence a seasonal cycle consists of four periods

  37. Deseasonalize demand What happens if you take the average demand?

  38. Deseasonalize demand

  39. Deseasonalize demand

  40. Periodicity p is odd Periodicity p is even Deseasonalize demand

  41. Example: Tahoe Salt

  42. Static Forecasting Method

  43. Determine deason. demandDt = L + Tt Determine seasonal factorsSt = Dt / Dt Determine seasonal factorsSi =AVG(Si) Static Forecasting Method Deasonalize demandDepends on number periods in a seasonal cycle Determine initial levelL = INTERCEPT(y’s, x’s)T = LINEST(y’s, x’s) ForecastFt = (L + Tt)Si

  44. Example: Tahoe Salt • Demand forecast using Static forecasting method

  45. Example: Winter’s Model • A theme park has seen the following attendance over the last eight quarters (in thousands) • 54, 87, 192, 130, 80, 124, 265, 171 Determine initial levelsL0 = From static forecastT0 = From static forecastSi,0 = From static forecast Determine levelsLt+1 = (Dt+1/St+1)+ (1 – )*(Lt + Tt)Tt+1= (Lt+1 – Lt) + (1 – )*Tt St+p+1 = (Dt+1/Lt+1) + (1 – )*St+1 ForecastFt+1 = (Lt + Tt)St+1

  46. Example: Tahoe Salt

  47. Example: Tahoe Salt • Demand forecast using Winter’s method

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