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Forecasting

3. Forecasting. Learning Objectives. List the elements of a good forecast. Outline the steps in the forecasting process. Describe at least three qualitative forecasting techniques and the advantages and disadvantages of each.

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Forecasting

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  1. 3 Forecasting

  2. Learning Objectives • List the elements of a good forecast. • Outline the steps in the forecasting process. • Describe at least three qualitative forecasting techniques and the advantages and disadvantages of each. • Compare and contrast qualitative and quantitative approaches to forecasting.

  3. Learning Objectives • Briefly describe averaging techniques, trend and seasonal techniques, and regression analysis, and solve typical problems. • Describe two measures of forecast accuracy. • Describe two ways of evaluating and controlling forecasts. • Identify the major factors to consider when choosing a forecasting technique.

  4. FORECAST: • A statement about the future value of a variable of interest such as demand. • “An estimate of sales in physical units for a specified future period under proposed marketing plan or program and under the assumed set of economic and other forces outside the org for which the forecast is made”-American Marketing Association • Forecasting is used to make informed decisions. • Based on the past data • Important component of strategic and operational planning • Long-range • Short-range

  5. Forecasts • Forecasts affect decisions and activities throughout an organization • Accounting, finance • Human resources • Marketing • MIS • Operations • Product/service design

  6. Uses of Forecasts

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

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

  9. Forecast should be : • Accurate – with small errors • Unbiased – so they do not always under-or-over estimate demand • Responsive – To changes in demand • Not affected – By the odd unusual figure • In time – For its purpose • Cost effective • Easy to understand

  10. Method on forecasting depends on many factors such as: • Time covered in the future • Availability of historical data • Relevance of historical data to the future • Type of product • Variability of demand • Accuracy needed and cost of errors • Benefits expected from the forecasts • Amount of money and time available for the forecast

  11. Methods available: • Long-term forecasts: Look ahead several years-the time needed to build a new factory or organize new facilities.(Plan budgets and etc) • Medium-term forecasts: Look ahead 3 months and a year-The time needed to replace and old product by a new one. • Short-term forecasts: Describing the continuing demand for a product or scheduling operations.

  12. Alternative approaches: • Projective methods: Look at the pattern of past demand and extent this into the future • Causal methods: Look at the factors that affect demand and use these to forecast.

  13. Steps in the Forecasting Process “The forecast” Step 6 Monitor the forecast Step 5 Make the forecast Step 4 Obtain, clean and analyze data Step 3 Select a forecasting technique Step 2 Establish a time horizon Step 1 Determine purpose of forecast

  14. Types of Forecasts • Judgmental: uses subjective inputs, commonly used techniques in business and industries. • Time series:uses historical data, assuming the future will be like the past • Associative (bersekutu) models:uses explanatory (penjelasan) variables to predict the future

  15. Judgmental Forecasts • Subjective assessments. • Executive opinions : Biased and subjective, the accuracy of predicted demand depends upon the skill, expertise and experience of the person making the forecast. Used for demand forecasting of established products. • Personal insight, panel consensus (sebulat suara), market survey, historical analogy and Delphi method

  16. Judgmental Forecasts • Sales force opinions • Consumer surveys (dari pengguna) • Outside opinion (pendapat luar) • Delphi method (The most formal) • Opinions of managers and staff • Achieves a consensus forecast • For minimising bias, this method is used

  17. Time Series Forecasts • Refers to the past data arranged in a chronological order as a dependent variable and time as an independent variable. • Does not study the factors that influence the demand Year 1993-94 1994-95 1995-96 1996-97 No. of TV sets 20 30 40 58

  18. Time Series Forecasts • Trend (T): long-term movement in data • Seasonality: short-term regular variations in data. • Example : The sales of paint is highest during Diwali festival, sale of umbrella and rain coat during monsoon and warm clothes during winter.

  19. 3. Cycles: wavelike variations of more than one year’s duration 4. Irregular (tidaktetap) variations: caused by unusual circumstances 5. Random variations (perbezaan): caused by chance

  20. Forecast Variations Figure 3.1 Irregularvariation Trend Cycles 90 89 88 Seasonal variations

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

  22. Naive (Naif) 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. Uses of Naive Forecasts • Stable time series data • F(t) = A(t-1) • The last data point becomes the forecast for the next period. If the demand for product last week was 20 cases, the forecast for this week is 20 cases.

  24. 2. Seasonal variations • F(t) = A(t-n) • The forecast for this season is equal to the value of the series last season. • The forecast for highway traffic volume this Friday is equal to the highway traffic volume last Friday.

  25. 3. Data with trends • F(t) = A(t-1) + (A(t-1) – A(t-2))

  26. Techniques for Averaging • Moving average • Weighted moving average • Exponential (Kadar pertumbuhan) smoothing

  27. Moving Averages At-n+ … At-2 + At-1 Ft = MAn= n wnAt-n+ … wn-1At-2 + w1At-1 Ft = WMAn= n • Moving average: A technique that averages a number of recent actual values, updated as new values become available. • Weighted moving average: More recent values in a series are given more weight in computing the forecast.

  28. Exponential Smoothing Forecast for the period t(Ft)= Forecast demand for the last period +  (actual demand for the last period – forecast demand for the last period) Ft = Ft-1 + (At-1 - Ft-1)  = smoothing constant

  29. Moving average F6= 43+40+41/3 = 41.33 If actual demand in period 6 turns out to be 38, the moving average forecast for Period 7 would be F7 = 40+41+38/3 = 39.67

  30. Weighted Moving Average • The past data on the load on the weaving machines as follow: a) Compute the load using 5moving average for the month of Dec. b) Compute a weighted three months MA for Dec where the weights are 0.5 the latest, 0.3 and 0.2 for the other months.

  31. a) b) Dec=0.5x970 + 0.3x860 + 0.2x750 =947.8 machine hours

  32. Lets see the exercise!

  33. Quiz 2 • Find the forecast for week 11 by using: • Five-weeks moving average • Using the smoothing factor of 0.5 is used • Calculate the sales.

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