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Operations Management
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  1. Operations Management Chapter 4 – Forecasting PowerPoint presentation to accompany Heizer/Render Principles of Operations Management, 7e Operations Management, 9e

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

  3. 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

  4. Distinguishing Differences • 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

  5. Influence of Product Life Cycle • 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 Introduction – Growth – Maturity – Decline

  6. Introduction Growth Maturity Decline Best period to increase market share R&D engineering is critical Practical to change price or quality image Strengthen niche Poor time to change image, price, or quality Competitive costs become critical Defend market position Cost control critical Company Strategy/Issues CD-ROMs Internet search engines Analog TVs Drive-through restaurants LCD & plasma TVs Sales iPods 3 1/2” Floppy disks Xbox 360 Product Life Cycle Figure 2.5

  7. Introduction Growth Maturity Decline OM Strategy/Issues Product Life Cycle Product design and development critical Frequent product and process design changes Short production runs High production costs Limited models Attention to quality Forecasting critical Product and process reliability Competitive product improvements and options Increase capacity Shift toward product focus Enhance distribution Standardization Less rapid product changes – more minor changes Optimum capacity Increasing stability of process Long production runs Product improvement and cost cutting Little product differentiation Cost minimization Overcapacity in the industry Prune line to eliminate items not returning good margin Reduce capacity Figure 2.5

  8. Types of Forecasts • Economic forecasts • Address business cycle – inflation rate, money supply, housing starts, etc. • Technological forecasts • Predict rate of technological progress • Impacts development of new products • Demand forecasts • Predict sales of existing products and services

  9. Strategic Importance of Forecasting • Human Resources – Hiring, training, laying off workers • Capacity – Capacity shortages can result in undependable delivery, loss of customers, loss of market share • Supply Chain Management – Good supplier relations and price advantages

  10. Seven Steps in 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 • Make the forecast • Validate and implement results

  11. The Realities! • Forecasts are seldom perfect • Most techniques assume an underlying stability in the system • Product family and aggregated forecasts are more accurate than individual product forecasts

  12. 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

  13. 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

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

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

  16. 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 • ‘Group-think’disadvantage

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

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

  19. Consumer Market Survey • Ask customers about purchasing plans • What consumers say, and what they actually do are often different • Sometimes difficult to answer

  20. Time-Series Models Associative Model Overview of Quantitative Approaches • Naive approach • Moving averages • Exponential smoothing • Trend projection • Linear regression

  21. 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

  22. Trend Cyclical Seasonal Random Time Series Components

  23. Trend component Seasonal peaks Actual demand Demand for product or service Average demand over four years Random variation | | | | 1 2 3 4 Year Components of Demand Figure 4.1

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

  25. Number of Period Length Seasons Week Day 7 Month Week 4-4.5 Month Day 28-31 Year Quarter 4 Year Month 12 Year Week 52 Seasonal Component • Regular pattern of up and down fluctuations • Due to weather, customs, etc. • Occurs within a single year

  26. 0 5 10 15 20 Cyclical Component • Repeating up and down movements • Affected by business cycle, political, and economic factors • Multiple years duration • Often causal or associative relationships

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

  28. Naive Approach • 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

  29. ∑ demand in previous n periods n Moving average = Moving Average Method • MA is a series of arithmetic means • Used if little or no trend • Used often for smoothing • Provides overall impression of data over time

  30. Actual 3-Month Month Shed Sales Moving Average January 10 February 12 March 13 April 16 May 19 June 23 July 26 10 12 13 (10 + 12 + 13)/3 = 11 2/3 Moving Average Example (12 + 13 + 16)/3 = 13 2/3 (13 + 16 + 19)/3 = 16 (16 + 19 + 23)/3 = 19 1/3

  31. Moving Average Forecast 30 – 28 – 26 – 24 – 22 – 20 – 18 – 16 – 14 – 12 – 10 – Actual Sales Shed Sales | | | | | | | | | | | | J F M A M J J A S O N D Graph of Moving Average

  32. Weightedmoving average ∑(weight for period n) x (demand in period n) ∑ weights = Weighted Moving Average • Used when trend is present • Older data usually less important • Weights based on experience and intuition

  33. Weights Applied Period 3 Last month 2 Two months ago 1 Three months ago 6 Sum of weights Actual 3-Month Weighted Month Shed Sales Moving Average January 10 February 12 March 13 April 16 May 19 June 23 July 26 10 12 13 [(3 x 13) + (2 x 12) + (10)]/6 = 121/6 Weighted Moving Average [(3 x 16) + (2 x 13) + (12)]/6 = 141/3 [(3 x 19) + (2 x 16) + (13)]/6 = 17 [(3 x 23) + (2 x 19) + (16)]/6 = 201/2

  34. Potential Problems With Moving Average • Increasing n smooths the forecast but makes it less sensitive to changes • Do not forecast trends well • Require extensive historical data

  35. Weighted moving average 30 – 25 – 20 – 15 – 10 – 5 – Actual sales Sales demand Moving average | | | | | | | | | | | | J F M A M J J A S O N D Moving Average And Weighted Moving Average Figure 4.2

  36. Exponential Smoothing • 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

  37. 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 forecast a = smoothing (or weighting) constant (0 ≤a≤ 1)

  38. Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant a = .20

  39. New forecast = 142 + .2(153 – 142) Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant a = .20

  40. Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant a = .20 New forecast = 142 + .2(153 – 142) = 142 + 2.2 = 144.2 ≈ 144 cars

  41. Weight Assigned to Most 2nd Most 3rd Most 4th Most 5th Most Recent Recent Recent Recent Recent Smoothing Period Period Period Period Period Constant (a) a(1 - a) a(1 - a)2a(1 - a)3a(1 - a)4 a = .1 .1 .09 .081 .073 .066 a = .5 .5 .25 .125 .063 .031 Effect of Smoothing Constants

  42. 225 – 200 – 175 – 150 – Actual demand a = .5 Demand a = .1 | | | | | | | | | 1 2 3 4 5 6 7 8 9 Quarter Impact of Different 

  43. 225 – 200 – 175 – 150 – • Chose high values of  when underlying average is likely to change • Choose low values of  when underlying average is stable Actual demand a = .5 Demand a = .1 | | | | | | | | | 1 2 3 4 5 6 7 8 9 Quarter Impact of Different 

  44. Choosing  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 Forecast error = Actual demand - Forecast value = At - Ft

  45. Mean Absolute Deviation (MAD) Mean Squared Error (MSE) MAD = ∑ |Actual - Forecast| n ∑(Forecast Errors)2 n MSE = Common Measures of Error

  46. Mean Absolute Percent Error (MAPE) n i = 1 ∑100|Actuali - Forecasti|/Actuali n MAPE = Common Measures of Error

  47. Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloadeda = .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 Error

  48. ∑ |deviations| n MAD = For a = .10 = 82.45/8 = 10.31 For a = .50 = 98.62/8 = 12.33 Comparison of Forecast Error Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloadeda = .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

  49. For a = .10 = 1,526.54/8 = 190.82 For a = .50 = 1,561.91/8 = 195.24 ∑(forecast errors)2 n MSE = Comparison of Forecast Error Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloadeda = .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 MAD 10.31 12.33

  50. n i = 1 ∑100|deviationi|/actuali n MAPE = For a = .10 = 44.75/8 = 5.59% For a = .50 = 54.05/8 = 6.76% Comparison of Forecast Error Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloadeda = .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 MAD 10.31 12.33 MSE 190.82 195.24