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4-1 Operations Management Forecasting Chapter 4. 4-2 Examples  Predict the next number in the pattern: a) 3.7, 3.7, 3.7, 3.7, 3.7, ? b) 2.5, 4.5, 6.5,

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Presentation on theme: "4-1 Operations Management Forecasting Chapter 4. 4-2 Examples  Predict the next number in the pattern: a) 3.7, 3.7, 3.7, 3.7, 3.7, ? b) 2.5, 4.5, 6.5,"— Presentation transcript:

1 4-1 Operations Management Forecasting Chapter 4

2 4-2 Examples  Predict the next number in the pattern: a) 3.7, 3.7, 3.7, 3.7, 3.7, ? b) 2.5, 4.5, 6.5, 8.5, 10.5, ? c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, ?

3 4-3 Examples  Predict the next number in the pattern: a) 3.7, 3.7, 3.7, 3.7, 3.7, y = 3.7 b) 2.5, 4.5, 6.5, 8.5, 10.5, y = x c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, y = x + c i c 1 = 0; c 2 = 2; c 3 = 0; c 4 = -2; etc

4 4-4 Outline  What is Forecasting ?  Time horizons.  Life cycle.  Types of Forecasts.  Eight Steps in the Forecasting System.  Forecasting Approaches:  Overview of Qualitative Methods.  Overview of Quantitative Methods.

5 4-5 Outline - Continued  Time-Series Forecasting:  Moving Averages.  Exponential Smoothing.  Trend Projection.  Associative Forecasting Methods: Regression and Correlation Analysis.  Monitoring and Controlling Forecasts.  Forecasting in the Service Sector.

6 4-6 What is Forecasting?  Art and science of predicting future events.  Underlying basis of all business decisions.  Production & Inventory.  Personnel & Facilities.  Focus on forecasting demand. Sales will be $200 Million!

7 4-7  Short-range forecast: Usually < 3 months.  Job scheduling, worker assignments.  Medium-range forecast: 3 months to 3 years.  Sales & production planning, budgeting.  Long-range forecast: > 3 years.  New product planning, facility location. Types of Forecasts by Time Horizon

8 4-8 Short- vs. Long-term Forecasting  Medium & Long range forecasts:  Long range for design of system.  Deal with comprehensive issues.  Support management decisions regarding planning.  Short-term forecasts:  To plan detailed use of system.  Usually use quantitative techniques.  More accurate than longer-term forecasts.

9 4-9 Influence of Product Life Cycle  Stages of introduction and growth require longer forecasts than maturity and decline.  Forecasts useful in projecting:  staffing levels,  inventory levels, and  factory capacity (expansion and contraction), as product passes through life cycle stages.

10 4-10 Forecasting During the Life Cycle Hard to forecast. Need long-range forecasts. Often use qualitative models. IntroductionGrowthMaturityDecline Sales Forecasting critical, both for future magnitude and growth rate. Long-range forecasts still important. Easier to forecast. Use quantitative models. Hard to forecast, but forecasting is less important. Time

11 4-11 Eight Steps in Forecasting  Determine the use of the forecast.  Select the items to be forecast.  Determine the time horizon of the forecast.  Select the forecasting model(s).  Gather the data.  Make the forecast.  Validate and implement results.  Monitor forecasts and adjust when needed.

12 4-12 Realities of Forecasting  Assumes future will be like the past (causal factors will be the same).  Forecasts are imperfect.  Forecasts for groups of product are more accurate than forecasts for individual products.  Accuracy decreases with length of forecast.

13 4-13 Forecasting Approaches  Used when situation is ‘stable’ & historical data exist.  Existing products & current technology.  No significant changes expected.  Involves mathematical techniques.  Example: forecasting sales of color televisions. Quantitative Methods  Used when little data or time exist.  New products & technology.  Long time horizon.  Major changes expected.  Involves intuition, experience.  Example: forecasting for e-commerce sales. Qualitative Methods

14 4-14 Overview of Qualitative Methods  Jury of executive opinion.  Combine opinions from executives.  Sales force composite.  Aggregate estimates from salespersons.  Delphi method.  Query experts interatively.  Consumer market survey.  Survey current and potential customers.

15 4-15  Seek opinions/estimates from small group of high-level managers working together.  Combines managerial experience with statistical models. + Relatively quick. - ‘Group-think’. - Leader may dominate. Jury of Executive Opinion

16 4-16 Sales Force Composite  Each salesperson projects their sales.  Aggregate projections at district & national levels. + Sales rep’s know customers. - Must not reward inaccurate forecasts.  May over- or under-forecast to acquire more resources.

17 4-17 Delphi Method  Iterative group process.  3 types of people:  Decision makers.  Staff.  Respondents. + Reduces ‘group-think’. - Takes time. Respondents Staff (Make forecast) (Provide input to decision makers) Decision Makers (Administer)

18 4-18 Consumer Market Survey  Ask customers about purchasing plans. + Relatively simple. - What consumers say, and what they actually do are often different. How many hours will you use the Internet next week?

19 4-19 Quantitative Forecasting Methods Quantitative Forecasting Linear Regression Associative Models Exponential Smoothing Moving Average Time Series Models Trend Projection

20 4-20  Set of evenly spaced numerical data.  From observing response variable at regular time periods.  Forecast based only on past values.  Assumes that factors influencing past will continue influence in future.  Example: Year: Sales: What is a Time Series?

21 4-21 Trend Seasonal Cyclical Random Time Series Components

22 4-22 Product Demand over 4 Years Year 1 Year 2 Year 3 Year 4 Demand for product or service

23 4-23 Product Demand over 4 Years Actual demand line Year 1 Year 2 Year 3 Year 4 Seasonal peaks Trend component Demand for product or service Random variation Cyclic component

24 4-24  Persistent, overall upward or downward pattern.  Due to population, technology etc.  Several years duration. Time Trend Component

25 4-25  Regular pattern of up & down fluctuations.  Due to weather, customs etc.  Occurs within 1 year.  Quarterly, monthly, weekly, etc. Time Demand Summer Seasonal Component

26 4-26  Repeating up & down movements.  Due to interactions of factors influencing economy.  Usually 2-10 years duration. Year Demand Cycle Cyclical Component

27 4-27  Erratic, unsystematic, ‘residual’ fluctuations.  Due to random variation or unforeseen events.  Union strike  Tornado  Short duration & non-repeating. Random Component

28 4-28  Any value in a time series is a combination of the trend, seasonal, cyclic, and random components.  Multiplicative model: Y i = T i · S i · C i · R i  Additive model: Y i = T i + S i + C i + R i General Time Series Models

29 4-29 Naive Approach  Demand in next period is the same as demand in most recent period.  e.g., If May sales were 48, then June sales will be 48.  Sometimes cost effective & efficient.  Usually not good.

30 4-30  MA is a series of arithmetic means.  Used if little or no trend.  Used often for smoothing. MA n n  Demand in previous periods periods Moving Average Method

31 4-31 You’re manager of a museum store that sells historical replicas. You want to forecast sales (in thousands) for months 4 and 5 using a 3-period moving average. Month 14 Month 2 6 Month 35 Month 4? Month 5? Moving Average Example

32 4-32 Moving Average Forecast MonthResponse Y i Moving Total (n=3) Moving Average (n=3) 14NA ? 5? 4+6+5=15 15/3=5 6 ?

33 4-33 Month Moving Average Graph Sales Actual Forecast

34 4-34 Actual Demand for Month 4 = 3

35 4-35 Month Moving Average Graph Sales Actual Forecast

36 4-36 Moving Average Forecast MonthResponse Y i Moving Total (n=3) Moving Average (n=3) 14NA =1414/3= ?

37 4-37 Month Moving Average Graph Sales Actual Forecast

38 4-38 Actual Demand for Month 5 = 7 MonthResponse Y i Moving Total (n=3) Moving Average (n=3) 14NA =1414/3= ?

39 4-39 Month Moving Average Graph Sales Actual Forecast

40 4-40 Moving Average Forecasts MonthResponse Y i Moving Total (n=3) Moving Average (n=3) 14NA =1515/3= =1414/3= ? 5+3+7=1515/3=5.0

41 4-41 Month Moving Average Graph Sales Actual Forecast

42 4-42  Gives more emphasis to recent data.  Weights decrease for older data.  Weights sum to 1.0.  May be based on intuition.  Sum of digits weights: numerators are consecutive.  3/6, 2/6, 1/6  4/10, 3/10, 2/10, 1/10 WMA = Σ [(Weight for period n)  (Demand in period n)] ΣWeights Weighted Moving Average Method

43 4-43 Weighted Moving Average: 3/6, 2/6, 1/6 MonthResponse Y i Weighted Moving Average 14 NA /6 = ? ? ?

44 4-44 Weighted Moving Average: 3/6, 2/6, 1/6 MonthResponse Y i Weighted Moving Average 14 NA /6 = ? 25/6 = /6 = 5.333

45 4-45  Increasing n makes forecast:  Less sensitive to changes.  Less sensitive to recent data.  Weights control emphasis on recent data.  Do not forecast trend well.  Require historical data. Moving Average Methods Moving Average Methods

46 4-46 Moving Average Graph Time Demand Actual

47 4-47 Moving Average Graph Time Demand Actual Small n Large n

48 4-48 Weighted Moving Average Graph Time Demand Actual Large weight on recent data Small weight on recent data

49 4-49  Form of weighted moving average.  Weights decline exponentially.  Most recent data weighted most.  Requires smoothing constant (  ).  Usually ranges from 0.05 to 0.5  Should be chosen to give good forecast.  Involves little record keeping of past data. Exponential Smoothing Method

50 4-50  F t = F t -1 +  ( A t -1 - F t -1 )  F t = Forecast value for time t  A t-1 = Actual value at time t-1   = Smoothing constant  Need initial forecast F t-1 to start.  Could be given or use moving average. Exponential Smoothing Equation

51 4-51 You want to forecast product demand using exponential smoothing with  =.10. Suppose in the most recent month (month 6) the forecast was 175 and the actual demand was 180. Month 6180 Month 7 ? Month 8? Month 9? Month 10? Exponential Smoothing Example

52 4-52 α F t = F t -1 + α ( A t -1 - F t -1 ) Month Actual Forecast,F t ( αααα =.10) (Given) 7? ( ) = ? 9? 10? 11 ? Exponential Smoothing - Month 7

53 4-53 Exponential Smoothing - Month 8 α F t = F t -1 + α ( A t -1 - F t -1 ) Actual Forecast, F t ( α =.10) (Given) ( ) = ? ( ) = ? ? ? Month

54 4-54 Exponential Smoothing Solution α F t = F t -1 + α ( A t -1 - F t -1 ) Actual Forecast, F t ( α =.10) (Given) ( ) = ( ) = ? ( ) = ? ? Month

55 ( ) = Exponential Smoothing Solution α F t = F t -1 + α ( A t -1 - F t -1 ) Actual Forecast, F t ( α =.10) (Given) ( ) = ( ) = ( ) = ? ( ) = Month

56 4-56 Month Sales Actual Forecast Exponential Smoothing Graph

57 4-57 α  Increasing α makes forecast:  More sensitive to changes.  More sensitive to recent data.  α  α controls emphasis on recent data.  Do not forecast trend well.  Trend adjusted exponential smoothing - p Exponential Smoothing Methods Exponential Smoothing Methods

58 4-58 Exponential Smoothing Graph Time Demand Actual

59 4-59 Exponential Smoothing Graph Time Demand Actual Large α Small α

60 4-60 F t =  A t  (1-  ) A t  (1-  ) 2 A t Forecast Effects of Smoothing Constant  Weights Prior Period  2 periods ago  (1 -  ) 3 periods ago  (1 -  ) 2 ==  = 0.10  = % 9% 8.1% 90%9%0.9%

61 4-61 Choosing  - Comparing Forecasts  A good method has a small error.  Choose  to produce a small error.  Error = Demand - Forecast Error > 0 if forecast is too low Error < 0 if forecast is too high MAD = Mean Absolute Deviation : Average of absolute values of errors. MSE = Mean Squared Error : Average of squared errors. MAPE = Mean Absolute Percentage Error : Average of absolute value of percentage errors.

62 4-62  Mean Absolute Deviation (MAD)  Mean Squared Error (MSE) Forecast Error Equations

63 4-63  Mean Absolute Percentage Error (MAPE) Forecast Error Equations

64 4-64 MAD F1 = 9/4 = 2.25 F2 = 10/4 = 2.5 MSE F1 = 31/4 = 7.75 F2 = 26/4 = 6.5 MAPE F1 = = 17.1% F2 = = 15.6% Forecast Error Example Actual F1 F1 error F2 F2 error

65 4-65 MAD F1 = 9/4 = 2.25 F2 = 10/4 = 2.5 MSE F1 = 31/4 = 7.75 F2 = 26/4 = 6.5 MAPE F1 = = 17.1% F2 = = 15.6% Which Forecast is Best?


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