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1 Adeng Pustikaningsih, M.Si. Dosen Jurusan Pendidikan Akuntansi Fakultas Ekonomi Universitas Negeri Yogyakarta CP: adengpustikaningsih@uny.ac.id

2 Operations Management Forecasting Chapter 4 4-2

3 What is Forecasting? Process of predicting a future event Underlying basis of all business decisions Production Inventory Personnel Facilities Sales will be $200 Million! 4-3

4 Types of Forecasts Economic forecasts Address business cycle, e.g., inflation rate, money supply etc. Technological forecasts Predict rate of technological progress Predict acceptance of new product Demand forecasts Predict sales of existing product 4-4

5 Types of Forecasts by Time Horizon Short-range forecast Up to 1 year; usually less than 3 months Job scheduling, worker assignments Medium-range forecast 3 months to 3 years Sales & production planning, purchasing, budgeting Long-range forecast 3 + years New product planning, facility location 4-5

6 What Do We Forecast - Aggregation Clustering goods or services that have similar demand requirements and common processing, labor, and materials requirements: Red shirts White shirts Blue shirts Big Mac Quarter Pounder Regular Hamburger Shirts Pounds of Beef $ $ What about Why units do of we measurement aggregate?? 4-6

7 Realities of Forecasting Forecasts are seldom perfect Most forecasting methods assume that there is some underlying stability in the system Both product family and aggregated product forecasts are more accurate than individual product forecasts 4-7

8 Response Trend Component Persistent, overall upward or downward pattern Due to population, technology etc. Several years duration Time 4-8

9 Response Seasonal Component Regular pattern of up & down fluctuations Due to weather, customs etc. Summer Time 4-9

10 Response Cyclical Component Repeating up & down movements Due to interactions of factors influencing economy Usually 2-10 years duration Cycle Time 4-10

11 Demand for product or service Product Demand Seasonal peaks Trend component Actual demand line Random variation Average demand over four years Year 1 Year 2 Year 3 Year

12 Forecasting Approaches Qualitative Methods Used when situation is vague & little data exist New products New technology Involves intuition, experience e.g., forecasting sales on Internet Quantitative Methods Used when situation is stable & historical data exist Existing products Current technology Involves mathematical techniques e.g., forecasting sales of color televisions 4-12

13 Overview of Qualitative Methods Jury of executive opinion Pool opinions of high-level executives, sometimes augmented by statistical models Delphi method Panel of experts, queried iteratively Sales force composite Estimates from individual salespersons are reviewed for reasonableness, then aggregated Consumer Market Survey Ask the customer 4-13

14 Overview of Quantitative Approaches Naïve approach Moving averages Exponential smoothing Trend projection Seasonal variation Linear regression Time-series Models Associative Models 4-14

15 What is a Time Series? Set of evenly spaced numerical data Observing the response variable at regular time intervals Forecast based only on past values Assumes that factors influencing the past and present will continue to influence the future Example Year: Sales:

16 Naive Approach Assumes 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 1995 Corel Corp. 4-16

17 Simple Moving Average Forecast Demand in Previous n n Periods F = A + A + A + A t t 1 t 2 t 3 t 4 F = A + A + A t t 1 t 2 t

18 Weighted Moving Average Forecast = Σ (Weight for period n) (Demand in period n) Σ Weights F =.4A +.3A +.2A +.1A t t 1 t 2 t 3 t 4 F =.7A +.2A +.1A t t 1 t 2 t

19 Exponential Smoothing Method Form of weighted moving average Weights decline exponentially Most recent data weighted most Requires smoothing constant ( ) Ranges from 0 to 1 Subjectively chosen Involves little record keeping of past data 4-19

20 Exponential Smoothing Forecast = (Demand last period) + (1 ) ( Last forecast) F = A + (1 ) (F ) t t 1 t 1 = A + F F t 1 t 1 t 1 = F + (A F ) t 1 t 1 t 1 Forecast = Last forecast + (Last demand Last forecast) 4-20

21 Exponential Smoothing with Trend Adjustment Forecast = Exponentially smoothed forecast (F t ) + Exponentially smoothed trend (T ) t T t = (Forecast this period Forecast last period) + (1- ) (Trend estimate last period) = (F t - F t-1 ) + (1- )T t

22 Seasonal Variation Quarter Year 1 Year 2 Year 3 Year Total Average Seasonal Index = Actual Demand Average Demand 45 = = Forecast for Year 5 =

23 Seasonal Variation Quarter Year 1 Year 2 Year 3 Year /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = 0.39 Quarter Average Seasonal Index 1 ( )/4 = ( )/4 = ( )/4 = ( )/4 = 0.50 Forecast 650(0.20) = (1.30) = (2.00) = (0.50) =

24 Overview of Quantitative Approaches Naïve approach Moving averages Exponential smoothing Trend projection Seasonal variation Linear regression Time-series Models Associative Models 4-24

25 Linear Regression Factors Associated with Our Sales Advertising Pricing Competitors Economy Weather Sales Independent Variables Dependent Variable 4-25

26 Sales (in $ hundreds of thousands) 4 Scatter Diagram Sales vs. Payroll Now What? Area Payroll (in $ hundreds of millions) 4-26

27 Types of Forecasts by Time Horizon Short-range forecast Up to 1 year; usually less than 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 Time Series Associative Qualitative 4-27

28 Forecast Error 4-28

29 Forecast Error E = A F t t t

30 Forecast Error - CFE CFE Cumulative sum of Forecast Errors CFE = E t Positive errors offset negative errors Useful in assessing bias in a forecast 4-30

31 Forecast Error - MSE MSE Mean Squared Error MSE = E 2 t n Accentuates large deviations 4-31

32 Forecast Error - MAD MAD Mean Absolute Deviation MAD = E t n Widely used, well understood measurement of forecast error 4-32

33 Forecast Error - MAPE MAPE Mean Absolute Percent Error MAPE = 100 E t / A t n Relates forecast error to the level of demand 4-33

34 Forecast Error E t = A t F t CFE = E t MSE = E t n 2 MAD = MAPE = E t n 100 E t / A t n 4-34

35 Monitoring & Controlling Forecasts We need a TRACKING SIGNAL to measure how well the forecast is predicting actual values TS = Running sum of forecast errors (CFE) Mean Absolute Deviation (MAD) = E E / n t t 4-35

36 Plot of a Tracking Signal Upper control limit Lower control limit Signal exceeded limit Tracking signal CFE / MAD Acceptable range Time 4-36

37 Forecasting in the Service Sector Presents unusual challenges special need for short term records needs differ greatly as function of industry and product issues of holidays and calendar unusual events 4-37

38 20 Forecast of Sales by Hour for Fast Food Restaurant

39 Summary Demand forecasts drive a firm s plans - Production - Capacity - Scheduling Need to find the forecasting method(s) that best fit our pattern of demand no one right tool - Qualitative methods e.g. customer surveys - Time series methods (quantitative) rely on historical demand to predict future demand - Associative models (quantitative) use historical data on independent variables to predict demand e.g. promotional campaign Track forecast error to determine if forecasting model requires change 4-39

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