Production Management Forecasting and Operations Management

1. Production ManagementForecasting and Operations Management Mohammad Najjar, PhD Management Science

2. Learning Objectives Define Forecasting Explore Qualitative Techniques of Forecasting Explore Quantitative Techniques of Forecasting ApplyQuantitative Techniques of Forecasting

3. What is Forecasting • Process of predicting a future event • Underlying basis of all business decisions • Production • Inventory • Personnel • Facilities

4. Forecasting Time Horizons • Short-range forecast • Up to 1 year, generally less than 3 months • Purchasing, job scheduling, workforce levels, job assignments, production levels • Medium-range forecast • 3 months to 3 years • Sales and production planning, budgeting • Long-range forecast • 3+ years • New product planning, facility location, research and development

5. Forecasting Time Horizons • Medium/long range forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants and processes • Short-term forecasting usually employs different methodologies than longer-term forecasting • Short-term forecasts tend to be more accurate than longer-term forecasts

6. Forecasting & Product Life Cycle Introduction – Growth – Maturity – Decline • Introduction and growth require longer forecasts than maturity and decline • As product passes through life cycle, forecasts are useful in projecting • Staffing levels • Inventory levels • Factory capacity

7. Types of Forecasting • Economic forecasts • Address business cycle – inflation rate, money supply. • Technological forecasts • Predict rate of technological progress • Impact development of new products • Demand forecasts • Predict sales of existing products and services

8. Strategic Importance of Forecasting • Forecasting drives decisions in many areas, • Examples • Human Resources – Hiring, training, laying off workers • Capacity/Production – Capacity shortages can result in undependable delivery, loss of customers, loss of market share

9. Seven Steps for Forecasting • Determine the use of the forecast • Select the items to be forecasted • Determine the time horizon of the forecast • Select the forecasting model(s) • Gather the data needed to make the forecast • Make the forecast • Validate and implement results

10. Forecasting Realities • Forecasts are seldom perfect, unpredictable outside factors may impact the forecast • Most techniques assume an underlying stability in the system • Product family and aggregated forecasts are more accurate than individual product forecasts

11. Forecasting Approaches Qualitative Methods • Used when situation is vague and little data exist • New products • New technology • Involves intuition, experience • e.g., forecasting sales on Internet

12. Forecasting Approaches Quantitative Methods • Used when situation is ‘stable’ and historical data exist • Existing products • Current technology • Involves mathematical techniques • e.g., forecasting sales of color televisions

13. Overview of Qualitative Approach • Jury of executive opinion • Pool opinions of high-level experts, sometimes augment by statistical models • Delphi method • Panel of experts, queried iteratively

14. Overview of Qualitative Approach • Sales force composite • Estimates from individual salespersons are reviewed for reasonableness, then aggregated • Market Survey • Ask the customer

15. Qualitative Approach: Jury of Executive Opinion • Involves small group of high-level experts and managers • Group estimates demand by working together • Combines managerial experience with statistical models • Relatively quick

16. Qualitative Approach: Delphi Method Decision Makers (Evaluate responses and make decisions) Staff (Administering survey) Respondents (People who can make valuable judgments) • Iterative group process, continues until consensus is reached • 3 types of participants • Decision makers • Staff • Respondents

17. Qualitative Approach: Sales Force Composite • Each salesperson projects his or her sales • Combined at district and national levels • Sales reps know customers’ wants • May be overly optimistic

18. Qualitative Approach: Market Survey • Ask customers about purchasing plans • Useful for demand and product design and planning • What consumers say, and what they actually do may be different • May be overly optimistic

19. Overview of Quantitative Approach Time-series models Associative model • Naive approach • Moving averages • Exponential smoothing • Trend projection • Linear regression

20. Time-series Forecasting • Set of evenly spaced numerical data • Obtained by observing response variable at regular time periods • Forecast based only on past values, no other variables important • Assumes that factors influencing past and present will continue influence in future

21. Time-series Components • Seasonal • Cyclical • Trend • Random

22. Time-series Components Seasonal peaks Actual demand line Demand for product or service | | | | 1 2 3 4 Time (years) Trend component Average demand over 4 years Random variation

23. Trend Component • Persistent, overall upward or downward pattern • Changes due to population, technology, age, culture, etc. • Typically several years duration

24. Seasonal Component • Regular pattern of up and down fluctuations • Due to weather, customs, etc. • Occurs within a single year

25. Cyclical Component 0 5 10 15 20 • Repeating up and down movements • Affected by business cycle, political, and economic factors • Multiple years duration

26. Random Component M T W T F • Erratic, unsystematic, ‘residual’ fluctuations • Due to random variation or unforeseen events • Short duration and nonrepeating

27. Quantitative Approach: • Naïve Method • Assumes demand in next period is the same as demand in most recent period • e.g., If January sales were 68, then February sales will be 68 • Sometimes cost effective and efficient • Can be good starting point

28. Quantitative Approach: • Moving Average Method • Moving Average is a series of arithmetic means • Used if little or no trend • Used often for smoothing

29. Quantitative Approach: • Moving Average Method Example 10 12 13 (10 + 12 + 13)/3 = 11 2/3 (12 + 13 + 16)/3 = 13 2/3 (13 + 16 + 19)/3 = 16

30. Quantitative Approach: • Weighted Moving Average Method • Used when some trend might be present • Older data usually less important • Weights based on experience and intuition Weighted moving average

31. Quantitative Approach: • Weighted Moving Average Method 10 12 13 [(3 x 13) + (2 x 12) + (10)]/6 = 12 1/6

32. Quantitative Approach: • Weighted Moving Average Method 10 12 13 [(3 x 13) + (2 x 12) + (10)]/6 = 12 1/6

33. Potential Problems with Moving Average Methods • Increasing n smooths the forecast but makes it less sensitive to changes • Does not forecast trends well • Requires extensive historical data

34. Graph of Moving Average Methods Moving average Weighted moving average Actual sales 30 – 25 – 20 – 15 – 10 – 5 – Sales demand | | | | | | | | | | | | J F M A M J J A S O N D Month

35. Quantitative Approach: • Exponential Smoothing • Form of weighted moving average • Weights decline exponentially • Requires smoothing constant () • Ranges from 0 to 1 • Subjectively chosen • Involves little record keeping of past data

36. Quantitative Approach: • Exponential Smoothing New forecast = Last period’s forecast + a (Last period’s actual demand – Last period’s forecast) Ft = Ft – 1 + a(At – 1 - Ft – 1) where Ft = new forecast Ft – 1 = previous period’s forecast a = smoothing (or weighting) constant (0 ≤ a ≤ 1) At – 1 = previous period’s actual demand

37. Quantitative Approach: • Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant a = .20

38. Quantitative Approach: • Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant a = .20 New forecast = 142 + .2(153 – 142)

39. Quantitative Approach: • Exponential Smoothing Predicted demand (previous period) = 142 cars Actual demand (previous period) = 153 Smoothing constant a = .20 New forecast = 142 + .2(153 – 142) = 142 + 2.2 = 144.2 ≈ 144 cars

40. Quantitative Approach: • Exponential Smoothing with Different a 225 – 200 – 175 – 150 – Actual demand a = .5 Demand a = .1 | | | | | | | | | 1 2 3 4 5 6 7 8 9 Quarter

41. Quantitative Approach: • Exponential Smoothing with Different a 225 – 200 – 175 – 150 – Actual demand a = .5 • Chose high values of  when underlying average is likely to change • Choose low values of  when underlying average is stable Demand a = .1 | | | | | | | | | 1 2 3 4 5 6 7 8 9 Quarter

42. Quantitative Approach: • Exponential Smoothing and Choosing a The objective is to obtain the most accurate forecast no matter the technique We generally do this by selecting the model that gives us the lowest forecast error

43. Quantitative Approach: • Exponential Smoothing and Choosing a Common Measures of Error Mean Absolute Deviation (MAD)

44. Quantitative Approach: • Exponential Smoothing and Choosing a Determining the MAD

45. Quantitative Approach: • Exponential Smoothing and Choosing a Determining the MAD

46. Quantitative Approach: • Exponential Smoothing and Choosing a Common Measures of Error Mean Squared Error (MSE)

47. Quantitative Approach: • Exponential Smoothing and Choosing a Determining the MSE

48. Quantitative Approach: • Exponential Smoothing and Choosing a Common Measures of Error Mean Absolute Percent Error (MAPE)

49. Quantitative Approach: • Exponential Smoothing and Choosing a Determining the MAPE

50. Quantitative Approach: • Exponential Smoothing and Choosing a Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded a = .10 a = .10 a = .50 a = .50 1 180 175 5.00 175 5.00 2 168 175.5 7.50 177.50 9.50 3 159 174.75 15.75 172.75 13.75 4 175 173.18 1.82 165.88 9.12 5 190 173.36 16.64 170.44 19.56 6 205 175.02 29.98 180.22 24.78 7 180 178.02 1.98 192.61 12.61 8 182 178.22 3.78 186.30 4.30 82.45 98.62 Comparison of Forecast Errors