Operations Planning and Control: Forecasting Methods Overview

Forecasting is a crucial process in operations management, involving the estimation of future events based on past and present information. This chapter covers the significance of forecasts, characteristics of forecasting, role in decision-making, various forecasting methods (qualitative and quantitative), types of forecasts by time horizon, and quantitative approaches like the Naïve Method. Understanding forecasting is essential for effective decision-making and planning in operations management.

• Operations Management
• Forecasting Methods
• Decision-Making
• Qualitative Methods
• Quantitative Approaches

cyprian Follow

Uploaded on Jul 22, 2024 | 3 Views

Download Presentation

Please find below an Image/Link to download the presentation.

The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. Download presentation by click this link. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.

Presentation Transcript

1. Chapter 4 Operations Planning and Control

2. 1. Forecasting

3. Topics Overview of forecasting Forecasting methods Forecasting Errors 3

4. What is forecasting? Forecasting is the process of estimating events whose actual outcomes have not yet been observed. The process of predicting future events based on past and present information. Predicting [estimating] future [unknown] events. 4

5. Characteristics of forecasts Forecasts are always wrong. Should include expected value and measure of error. Long-term forecasts are less accurate than short-term forecasts. Too long term forecasts are useless: Forecast horizon Aggregate forecasts are more accurate than disaggregate forecasts 5

6. Forecasting Role in Decision-Making and its Relationship with Operations Management Objectives And Constraints External and Internal Data Planned Performance Forecasts Managers Operations Updated Forecasts Actual Resources Performance

7. 7

8. Forecasting Methods 1. Qualitative methods Forecasting methods are based on judgments, opinions, intuition, emotions, or personal experiences. Executive Judgment Sales Force Composite Market Research/Survey Delphi Method Quantitative methods Forecasting methods are based on mathematical (quantitative) models, and are objective in nature. Na ve forecasting Simple moving average Weighted moving average Exponential smoothing Regression ARIMA and Two stage EWMA 2. 8

9. Types of Forecasts by Time Horizon Quantitative methods Short-range forecast Usually < 3 months Job scheduling, worker assignments Medium-range forecast 3 months to 2 years Sales/production planning Long-range forecast > 2 years New product planning Detailed use of system Design of system Qualitative Methods

10. Quantitative Approaches Na ve Method Demand in next period is the same as demand in most recent period Easy but usually not good 10

11. Quantitative Approaches Simple Moving Average Assumes an average is a good estimator of future behavior Useful when there is little or no trend Used for smoothing A + A + A + ... + A + t 1 - t - t 2 - t n 1 F = + t 1 n Ft+1 n A t = Forecast for the upcoming period, t+1 = Number of periods to be averaged = Actual occurrence in period t 11

12. Quantitative Approaches Weighted Moving Average Gives more emphasis to recent data A w = F + + w A + w A + ... + w A + t 1 1 t 2 1 - t 3 - t 2 n - t n 1 Weights decrease for older data sum to 1.0 Simple moving average models weight all previous periods equally 12

13. Example Period 1 2 3 4 5 6 7 8 Actual 300 315 290 345 320 360 375 Determine forecast for periods 7 & 8 2-period moving average 4-period moving average 2-period weighted moving average with t-1 weighted 0.6 and t-2 weighted 0.4 Exponential smoothing with alpha=0.2 and the period 6 forecast being 375

14. Problem Solution Period Actual 2-Period 4-Period 2-Per.Wgted. Expon. Smooth. 1 300 2 315 3 290 4 345 5 320 6 360 7 375 340.0 328.8 344.0 372.0 8 367.5 350.0 369.0 372.6

15. Quantitative Approaches Exponential Smoothing Assumes the most recent observations have the highest predictive value gives more weight to recent time period Ft+1 = Ft + (At - Ft) Ft+1= Forecast value for time t+1 At = Actual value at time t = Smoothing constant Need initial forecast Ft to start. 15

16. Example See the previous example

17. Quantitative Approaches: Linear Regression A time series technique that computes a forecast with trend by drawing a straight line through a set of data using this formula: Y = a + bx where Y = forecast for period X X = the number of time periods from X = 0 a = value of y at X = 0 (Y intercept) b = slope of the line 17

18. Quantitative Approaches: Linear Regression Identifydependent (y) and independent (x) variables Solve for the slope of the line Solve for the y intercept Develop your equation for the trend line Y=a + bX 18

19. Quantitative Approaches: Linear Regression Linear Regression Problem: A maker of golf shirts has been tracking the relationship between sales and advertising dollars. Use linear regression to find out what sales might be if the company invested $53,000 in advertising next year. Sales $ (Y) (X) 1 130 32 4160 2304 2 151 52 7852 2704 3 150 50 7500 2500 4 158 55 8690 3025 5 153.85 53 Tot 589 189 28202 9253 Avg 147.25 47.25 Adv.$ XY X^2 Y^2 XY n X Y = b 2 2 X n X 16,900 22,801 22,500 24964 ( )( ) 28202 4 47.25 147.25 2 = = b 1.15 ( ) 1.15 9253 4 47.25 ( ) = = a Y b X 147.25 47.25 = a 92.9 = + = + Y a bX + 92.9 1.15X = 87165 ( ) = Y 92.9 1.15 53 153.85 19

20. Quantitative Approaches ARIMA Method Auto Regressive Integrated Moving Average is part of the linear models that is capable of representing both stationary and non- stationary time series. Note that stationary processes vary about a fixed level, and non- stationary processes have no natural constant mean level. 20

21. ARIMA continued Autoregressive Models AR(p) = + + 2 ,..., + + + It takes the form of Y Y Y Y 0 1 1 2 t t t p t p t = response variable at time observation (predictor variable) at time regression coefficients to be estimated error term at time t Y Y t t = t k t k = = i t Autoregressive models are appropriate for stationary time series, and the coefficient 0is related to the constant level of the series. An AR(p) model is a regression model with lagged values of the dependent variable in the independent variable positions, hence the name autoregressive model. 21

22. ARIMA continued Moving Average MA(q) = + It takes a form An MA(q) model is a regression model with the dependent variable, Yt, depending on previous values of the errors rather than on the variable itself. ... Y t q 1 1 2 2 t t t t q = = = response variable at time constant mean of the process regression coefficients to be estimated error in time period - = Y t t i t k t k MA models are appropriate for stationary time series. The weights i do not necessarily sum to 1 and may be positive or negative. 22

23. ARIMA continued ARMA(p,q) Models A model with autoregressive terms can be combined with a model having moving average terms to get an ARMA(p,q) model: Y Y Y = + + + + + ... ... Y t q 0 1 1 2 2 1 1 2 2 t t t p t p t t t q ARMA(p,q) models can describe a wide variety of behaviors for stationary time series. Note that: ARMA(p,0) = AR(p) ARMA(0,q) = MA(q) 23

24. ARIMA continued ARIMA(p,d,q) Models Models for non-stationary series are called Autoregressive Integrated Moving Average models, or ARIMA(p,d,q), where d indicates the amount of differencing. The first step in model identification is to determine whether the series is stationary. If the series is not stationary, it can often be converted to a stationary series by differencing: the original series is replaced by a series of differences and an ARMA model is then specified for the differenced series (in effect, the analyst is modeling changes rather than levels). 24

25. Quantitative Approaches Two stage EWMA Stage one = + (1 ) Y Y Y 1 t t t = + (1 ) Y Y Y 1 t Stage 2 t t = + Y Y Y t t t = dY Y Y 1 t t t 25

26. Measures of Forecast Error n t=1 A - F t t A. A. MAD = Mean Absolute Deviation MAD = Mean Absolute Deviation MAD = n 2 n t=1 ( ) A -F t t B. MSE = Mean Squared Error B. MSE = Mean Squared Error MSE= n C. RMSE = Root Mean Squared Error C. RMSE = Root Mean Squared Error RMSE = MSE n t=1 ( ) A -F D. D. MPE = Mean Percentage Error MPE = Mean Percentage Error t t 1 n MPE= F t Ideal value =0 (i.e no forecasting error)

27. 2. Aggregate Production Planning

28. The Role of the Aggregate Plan

29. Aggregate Planning Based on composite (representative) products: Simplifies calculations Forecasts for grouped items are more accurate Considers trade-offs between holding inventory & short-term capacity based on workforce

30. Aggregate Production Planning Purpose: specify the combination of production rate, workforce level, and inventory on-hand that satisfies the forecasted demand at the lowest cost. Production rate: quantity of product produced per unit of time (autos/day). Workforce level: number of workers required to meet a specific level of output. Inventory on hand: unsold units carried over from one period to the next.

31. Managing Demand Pricing Advertising and Promotion Backlogs and Reservations Develop Alternative Products 13-6

32. Managing Supply (Capacity) Overtime/Undertime Hiring/Firing of Personnel Temporary/Part-time Personnel Subcontracting Adjusting Inventories Adjusting Lead Times 13-7

33. Aggregate Planning: Objectives and Approaches Objectives: Match Supply and Demand (Effectiveness) Minimize Costs (Efficiency) Approaches Reactive approach: Allow volume forecasts based on Marketing plan to drive production planning Proactive approach: Coordinate Marketing & Production plans to level demand using advertising & price incentives 13-5

34. Aggregate Plan Strategies Chase strategy: Match the production rate to meet the demand rate by adjusting the workforce level (hiring/firing) as the demand rate changes. Minimize finished good inventories by matching demand fluctuations. Level strategy: Use a stable workforce working at a constant production rate. Use inventories and backorders to absorb demand peaks and valleys.

35. Pure Aggregate Strategies

36. Chase Plan Example Chase hires and fires staff to exactly meet each periods demand Period 1 = (500 units x .64 std.)/160 = 2 people, need to fire 16 people

37. Level Plan Example Level production rate= 28,000 units/7 periods= 4000 units Level workforce= (4000 units x .64 std.)/160 = 16 people

38. Hybrid Strategies Combine elements of the chase/level strategies with other options: Stable workforce but variable work rate (overtime/undertime). Subcontract production or hire part-time or temporary workers to cover short-term peaks.

39. Preliminary Considerations Identify the point of departure: How much capacity is currently in use? Identify the magnitude of change needed Identify the anticipated duration the modified capacity is necessary

40. Developing the Aggregate Plan Step 1- Choose strategy: level, chase, or Hybrid Step 2- Determine the aggregate production rate Step 3- Calculate the size of the workforce Step 4- Test the plan as follows: Calculate Inventory, expected hiring/firing, overtime needs Calculate total cost of plan Step 5- Evaluate performance: cost, service, human resources, and operations

41. Aggregate Planning Costs Basic production costs (fixed and variable): material costs, labor costs, overtime pay. Production rate-change costs: hiring, training, layoff/firing, adding/cutting shifts. Inventory holding costs: cost of capital, storage, insurance, taxes, spoilage, shrinkage, obsolescence. Backlog costs: expediting, loss of customer goodwill, loss of sales revenue from cancelled orders (due to product unavailability).

42. Aggregate Planning Techniques Trial-and error (usually employing spreadsheets): costing out various production planning scenarios to determine which has the lowest cost. Mathematical approaches: Linear programming. Linear decision rule (LDR). Heuristic approaches.

43. Plan for Companies with Tangible Products Plans A, B, C, D Plan A: Level aggregate plan using inventories and back orders Plan B: Level plan using inventories but no back orders Plan C: Chase aggregate plan using hiring and firing Plan D: Hybrid plan using initial workforce and overtime as needed

44. Problem Data for Plans A, B, C, D A B 4 5 6 7 8 9 Cost Data Regular time labor cost per hour Overtime labor cost per hour Subcontracting cost per unit (labor only) Back order cost per unit per period Inventory holding cost per unit per period Hiring cost per employee Firing cost per employee $12.50 $18.75 $125.00 $25.00 $10.00 $800.00 $500.00 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Capacity Data Beginning workforce (employees) Beginning inventory (units) Production standard per unit (hours) Regular time available per period (hours) Overtime available per period (hours) 90 0 8 160 40 Demand Data (units) Period 1 Period 2 Period 3 Period 4 Period 5 Period 6 Period 7 Period 8 1920 2160 1440 1200 2040 2400 1740 1500 Total Number of Periods 8

45. Plan A - Level Using Inventory & Backorders (Table 13-5) First calculate the level production rate (14400/8=1800) D Plan A: Level Aggregate Plan, Using Inventories and Backorders E F G H I J K L M 3 4 5 6 7 8 9 Compute Level Production Rate Total Demand 14400 0 14400 1800 Less: Beginning Inventory Total Net Demand Average Demand Per Period <-- Production Rate for Level Plan 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Compute Workforce Needed Units per Employee per Period 20 90 0 0 Employees Required Number to Hire Number to Fire Period Detailed Plan Computations 1 2 3 4 5 6 7 8 Total 14400 Demand (units) (net of beg. Inventory) Cumulative demand (units) Period production (units) Cumulative production (units) Cum.Dem. Minus Cum.Prod. Ending Inventory (units) 1920 1920 1800 1800 120 0 120 2160 4080 1800 3600 480 0 480 1440 5520 1800 5400 120 0 120 1200 6720 1800 7200 -480 480 0 2040 8760 1800 9000 -240 240 0 2400 11160 1800 10800 360 0 360 1740 12900 1800 12600 300 0 300 1500 14400 1800 14400 0 0 0 14400 720 1380 Backorders (units) Cost Calculations for Plan A Regular time labor cost Overtime labor cost Inventory holding cost Back order cost $1,440,000 $0 $7,200 $34,500 Hiring cost Firing cost Total Cost $0 $0 $1,481,700

46. Plan A Evaluation Back orders were 13.9% of demand (1380) Worst performance was period 2 at 21% of demand Marketing will not be satisfied at these levels Workable plan for operations No employees hired or fired, no overtime or undertime needed, and output is constant No human resource problems are anticipated

47. Plan B Level, Inventory but No Backorders (Table 13-7) Set the level rate equal to the peak cumulative demand/period D Plan B: Level Aggregate Plan, Using Inventories but No Backorders E F G H I J K L M N 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 Period Detailed Plan Computations 1 2 3 4 5 6 7 8 Total 14400 Demand (units) (net of beg. Inventory) Cumulative demand (units) Cumulative demand/periods Period production (units) Cumulative production (units) Cum.Dem. Minus Cum.Prod. Ending Inventory (units) 1920 1920 1920 2040 2040 -120 120 0 2160 4080 2040 2040 4080 0 0 0 1440 5520 1840 2040 6120 -600 600 0 1200 6720 1680 2040 8160 -1440 1440 0 2040 8760 1752 2040 10200 -1440 1440 0 2400 11160 1860 2040 12240 -1080 1080 0 1740 12900 1842.857 2040 14280 -1380 1380 0 1500 14400 1800 2040 16320 -1920 1920 0 16320 7980 0 Backorders (units) Compute Level Production Rate and Workforce Needed Production Rate (units) Units per Employee per Period Employees Needed Number to Hire Number to Fire 2040 20 102 12 0 Cost Calculations for Plan B Regular time labor cost Overtime labor cost Inventory holding cost Back order cost $1,632,000 $0 $79,800 $0 Hiring cost Firing cost Total Cost $9,600 $0 $1,721,400

48. Plan B Evaluation Plan B costs $240K (16%) more than plan A and has ending inventory of 7980 units To be fair, Plan B built 1920 additional units ($192K) which will be sold later Plan B costs $2.58 more per unit (2.5%) Marketing satisfied by 100% service level Workable Operations and HR plan- hire 12, no OT or UT, and level production

49. Plan C Chase Using Hires and Fires (Table 13- 9) The production rate equals the demand each period D Plan C: Chase Aggregate Plan, Using Hiring and Firing (no overtime) E F G H I J K L M N 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 Beginning Number of Employees Units per Worker per Period 90 20 (used to compute workforce size requirement each period) Period Detailed Plan Computations 1 2 3 4 5 6 7 8 Total Demand (units) (net of beg. Inventory) Production per period (units) Employees needed in period 1920 1920 96 6 0 2160 2160 108 12 0 1440 1440 72 0 36 1200 1200 60 0 12 2040 2040 102 42 0 2400 2400 120 18 0 1740 1740 87 0 33 1500 1500 75 0 12 14400 720 78 93 Number to hire Number to fire Cost Calculations for Plan C Regular time labor cost Overtime labor cost Inventory holding cost Back order cost $1,440,000 $0 $0 $0 Hiring cost Firing cost Total Cost $62,400 $46,500 $1,548,900

50. Plan C Evaluation Costs an additional $2 per unit more than Plan B Marketing is satisfied again by 100% service level From Operations and HR standpoint, not easy to implement: Need space, tools, equipment for up to 120 people in period 6 and only have 60 people in period 4 High training costs and potential quality problems Low morale likely due to poor job security

Load More ...