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Operation Strategies

Operation Strategies. School of Engineering The University of the Thai Chamber of Commerce. Demand Forecasting. School of Engineering The University of the Thai Chamber of Commerce. Agenda. What is forecast? Elements of good forecasts The necessary steps in preparing a forecast

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Operation Strategies

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  1. Operation Strategies School of Engineering The University of the Thai Chamber of Commerce

  2. Demand Forecasting School of Engineering The University of the Thai Chamber of Commerce

  3. Agenda • What is forecast? • Elements of good forecasts • The necessary steps in preparing a forecast • Forecasting techniques • How to monitor a forecast • Forecast Accuracy

  4. How will demand grow ?Long time frames over which Boeing must plan. Boeing 737 Boeing 717 Mcdonell-Douglas11

  5. Boeing Long-term capacitydecisions

  6. Motto in OM class • It’s an old story, but an instructive note: Two shoe salesmen arrive on a primitive island where no one wears shoes. One cables his head office saying “No business. Shoes not worn”, the other sends a different message “Send more shoes. No competition.” John F. Kenedy

  7. 1. Introduction • Have you ever forecast?? • How much food and drink will I need for the party? • Will I get the job? • Which team will be a world champion in 2014? • Will the flooding in the next year? To make these forecasts, • One is current factors or conditions. • The other is past experience in a similar situation.

  8. 1. Introduction • Forecasting are the basis for budgeting and planning for capacity, sales, production and inventory, personnel, purchasing, and more. • Forecast play an important role in the planning process. • Forecasts affect decisions and activities throughout an organization, in accounting, finance, human resources, marketing, MIS, as well as operations, and other parts of an organization.

  9. 2. FORECAST: • There are two methods for forecasting. • Plan the system (involves long term plan about the types of products and service to offer). • Plan to use the system (involves short and intermediate term plan such as planning inventory , workforce levels, planning purchasing, budgeting and scheduling).

  10. Indo-China Logistics Center

  11. การออกแบบศูนย์กระจายสินค้าของจังหวัดพิษณุโลกการออกแบบศูนย์กระจายสินค้าของจังหวัดพิษณุโลก

  12. I see that you willget an A this semester. 2.1 Features Common to all Forecasts • Assumes causal systempast ==> future • Forecasts rarely perfect because of randomness • Forecasts more accurate forgroups vs. individuals • Forecast accuracy decreases as time horizon increases

  13. Forecasting time horizons - Short-range forecast (not more than one year; Planning purchasing, Job scheduling, Workforce levels and so on) - Medium-range forecast ( 3 months to 3 years; Production planning and budgeting, Cash budgeting) - long-range forecast (more than 3 years; planning for new products, Capital expenditures, Facility location and R&D

  14. The influence of product life cycle (PLC) 1 Introduction 2 Growth 3 Maturity 4 Decline

  15. Timely Accurate Reliable Easy to use Written Meaningful 3. Elements of a Good Forecast

  16. “The forecast” Step 6 Monitor the forecast Step 5 Gather and analyze data Step 4 Select a forecasting technique Step 3 Establish a time horizon Step 2 Select the items to be forecasted Step 1 Determine purpose of forecast 4. Steps in the Forecasting Process Step 7 Validate and Implement the results

  17. 5. Types of Forecasts • Judgmental - uses subjective inputs • Time series - uses historical data assuming the future will be like the past • Associative models or Casual Model – use equation that consists of one or more explanatory variables to predict the future. For example, demand for paint might be related to variables such as the price per gallon and the amount spent on advertising, as well as specific characteristics of the paint.

  18. 6. Judgmental Forecasts • Executive opinions • Sales force opinions • Consumer surveys • Outside opinion • Delphi method • Opinions of managers and staffs • Achieves a consensus forecast

  19. 7. Time Series Forecasts • is a time-ordered sequence of observations taken at regular intervals. • The data may be measurements of demand, earnings, profits, shipments, accidents, output and productivity. • Trend - long-term movement in data • Seasonality - short-term regular variations in data • Cycle – wavelike variations of more than one year’s duration • Irregular variations - caused by unusual circumstances • Random variations - caused by chance (Bird Flu)

  20. 7.1 Forecast Variations Irregularvariation Trend Cycles 90 89 88 Seasonal variations

  21. Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell.... 7.2 Naive Forecasts The forecast for any period equals the previous period’s actual value.

  22. 7.2 Naïve Forecasts • Simple to use • Virtually no cost • Quick and easy to prepare • Data analysis is nonexistent • Easily understandable • Cannot provide high accuracy • Can be a standard for accuracy

  23. 7.2 Uses for Naïve Forecasts • Stable time series data • F(t) = A(t-1) • Seasonal variations • F(t) = A(t-n) • Data with trends • F(t) = A(t-1) + (A(t-1) – A(t-2))

  24. 7.2 Naïve Methods • Uses a single previous value of a time series as the basis of a forecast

  25. 7.3 Techniques for Averaging • Generate forecasts that reflect recent values of a time series. • Work best when a series tends to vary around an average • Moving average • Weighted moving average • Exponential smoothing

  26. 7.3.1 Moving average • Uses a number of the most recent actual data values in generating a forecast. i = an indexthat corresponds to periods n = number of periods in the moving average Ai = actual value in period i MA = Moving Average Ft = Forecast for period t

  27. Example 1 • Compute a three period moving average forecast given demand for shopping carts for the last five periods. If actual demand in period 6 turns out to be 39. What is F7?

  28. 7.3.2 Weighted Moving Average • A weighted average is similar to a moving average, except that it assigns more weight to the most recent values in a time series. • For instance, the most recent value might be assigned a weight of .40, the next most recent value a weight of .30, the next after that a weight of .20, and the next after that a weight of .10. • That weights sum to 1.00, and that the heaviest weights are assigned to the most recent values.

  29. 7.3.2 Weighted Moving Average • Compute weighted average forecast using a weight .4 for the most recent period, .3 for the next most recent, .2 for the next, and .1 for the next. • If the actual demand for period 6 is 39, forecast demand for period 7 using the same weights as in part a.

  30. 7.3.2 Weighted Moving Average Note that if four weights are used, only the four most recent demands are used to prepare the forecast.

  31. 7.3.2 Weighted Moving Average • The weighted average is more reflective of the most recent occurrences. • The choice of weights is somewhat arbitrary and generally involves the use of trial and error to find a suitable weighting scheme.

  32. 7.3.3 exponential smoothing • Exponential smoothing is a sophisticated weighted averaging method that is still relatively easy to use and understand. Each new forecast is based on the previous forecast plus a percentage of the difference between that forecast and the actual value of the series at that point.

  33. 7.3.3 Exponential Smoothing α represents a percentage of the forecast error. Therefore, each new forecast is equal to the previous forecast plus a percentage of the previous error. Suppose the previous forecast was 42 units, actual demand was 40 units, and α = .10. the new forecasts F = 42 + .10(40-42) = 41.8 Then if the actual demand turns out to be 43, the next forecast would be?? Ans. 41.92

  34. 7.3.3 Exponential Smoothing • An alternate form of formula reveals the weighting of the previous forecast and the latest actual demand: • For example: F = 42 + .10(40-42) = (0.9)(42) + (.10)(40) = 41.8

  35. Example 2 • The following table illustrates two series of forecasts for a data set and the resulting error for each period. One forecast uses α = .10 and one uses α = .40. The following figure plots the actual data and both sets of forecasts.

  36. Example 2 - Exponential Smoothing

  37. Actual .4  .1 Picking a Smoothing Constant

  38. 7.3.3 Exponential Smoothing • The closer α is to zero, the slower the forecast will be to adjust to forecast errors. (the greater the smoothing, emphasis the previous data) • The closer the value of α is to 1.00, the greater the responsiveness and the less the smoothing. (emphasis the present data )

  39. 7.4 Techniques for trend • Develop an equation that will suitably describe trend • The trend component may be linear, or it may not. • Two important techniques that can be used to develop forecasts • Trend equation • Extension of exponential smoothing

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