PRODUCTION PLANNING AND CONTROL CHAPTER 2: FORECASTING Forecasting the first step in planning. Estimating the future demand for products and services and the necessary resources to produce these outputs Sales forecasts - estimates of the future demand for products and services the starting point for all the other planning in operations management Medium range forecast Long range forecast Short range forecast To make strategic decisions about product, processes, and facilities. For a year or longer. Usually span for several months, usually deals with capacities and inventories. To assist in making decisions about operations issues that span only the next few days or weeks. Importance of forecasting New Facility Planning. Building new factory, or design, new production process will take years. Need to forecast long term demand for new and existing products Production Planning. Demands will vary monthly, the production capacity must be able to scale up or down which may take several months. Medium range forecast is necessary to this. Workforce scheduling Demands may vary week by week. The workforce need to be scale up or down. Need short range forecast to provide the lead time necessary. 1
Forecast Horizon Long Range Medium Range Short Range Time Span Years Months Weeks Examples of Things That Must be Forecasted New product line Old product line Factory capacities Capital funds Facility needs Product group Departmental capacities Workforce Purchase material Inventories Specific products Labour-skill classes Machine capacities Cash inventories Some Typical Units of Forecasts units, hours, etc Space, volume Units Hours, etc Workers, hours Units,etc. Units, Units Workers, hours Units, etc. units, Forecasting is an integral part of business planning. The inputs are processed through forcasting models or methods to develop demand estimates. These demand estimeates are not the sales forecasts; rather, they are the starting point for management teams to develop sales forecasts. The sales forecasts become inputs to both business strategy and production resources forecasts. SALES FORECAST Forecast of demand: - each product - each time period BUSINESS STRATEGY - marketing plan: advertising, sales, efforts, price, past sales - production plan: quality levels, customer service, capacity costs - finance plan: credit policies, billing policies Forecasting Method PRODUCTION RESOURCES FORECASTS Long range Medium range Short range 2
Qualitative Quantitative Quantitative Forecastings methods Usually based on judgements and opinions Educated guess Using intuition and experience Excutive committee consensus & Delphi Method Uses committee to generate sales forecasts for existing or new product or sevices Survey of sales force & customers Using survey primarily used for existing products and services Historical analogy & market surveys Primarily used for forecasting sales of new products and services Quantitative forecasting models Mathematical models based on historial data Models assume past data are relevant to the future Models are used with time series Time series a set of observed values measured over successive time periods, such as monthly sales for the last two years Forecasting models ~Long Range~ Linear regression Using least square method to identify the relationship between a dependent variable and one or more independent variables. Normally used in long range forecasting, although can also be used in short range if done carefully. 3
~Short Range~ Moving average A short range time series forecasting, forecast for the next period. The arithemetic average of the actual sales for a specific number of most recent past time periods is the forecast for the next time period. Weighted moving average Exponential smoothing Exponential smoothing with trend Forecast Accuracy How close forecasts come to actual data The accuracy can only be determine after certain time If forecasts are very close to the actual data they have high accuracy and forecast error is low. The accuracy of forecasting model is done by keeping a running tally on how far forecasts have missed the actual data points over time. If the accuracy of the model is low, the method is modified or changed to a new one. LONG RANGE FORECAST Estimate future conditions over time spans that are usually greater than one year. Necessary for strategic decisions Cycles, trends and seasonality o A cycle is a data pattern that may cover several years before it repeats itself o Long range trends are illustrated by an upward- or downward-sloping line o Seasonality is a data pattern that repeats itself after a period of time usually one year o Random fluctuation or noise, is a pattern resulting from random variation or unexplained causes Refer figure 3.2 for data patterns in long range forecasting. 4
Linear regression and correlation A forecasting model that establishes a relationship between a dependent variable and one or more independent variables Simple linear regression analysis one dependent variable Y = a + bx Y dependent variable to be forecast Formulas for simple linear regression analysis Table 3.5 Example 3.1 simple linear regression analysis: a time series Simple linear regression can also be used when the independent variable x represents a variable other than time. Example 3.2 Coefficient of correlation (r) explains the relative importance of the relationship between y and x; the sign of r shows the direction of the relationship, and the absolute value of r shows the strength of the relationship. Coefficient of determination (r 2 ) illustrates how much of the total variation in the dependent variable y is explained by x or the trend line. (r) and (r 2 ) are helpful measures of the strength of relationships. The stronger the relationship is, the more accurate the forecasts. Ranging forecast Linear regression analysis are estimates and subject to error Need to develop confidence intervals for forecast to deal with uncertainty Forecast ranging allows to deal with the uncertainty by developing best-estimate forecasts and the ranges within which the actual data are likely to fall Figure 3.4 shows graphically the errors in forecasting Syx, the standard error of forecast or standard deviation of the forecast, is a measure of how historical data points have been dispersed about the trend line. If Syx is small relative to the forecast, the past data points have been tightly grouped about the trend line. Example 3.3 : ranging time series forecasts. 5
Seasonality in time series forecasts Usually fluctuations that take place within one year and tend to be repeated annually can be caused by weather, holidays, etc. To develop forecast with linear regression analysis with seasonality: o Develop a seasonal index for each season o Use seasonal index to deseasonalise the data remove the seasonal pattern o Perform linear regression analysis o Use the seasonal indexes to reapply the seasonal pattern Example 3.4 SHORT RANGE FORECAST Usually estimates of future conditions over time spans that range from a few days to several weeks short period of time where cycles, seasonality, and trend patterns have little effect. Measure of forecast accuracy (1) standard error of the forecast (s yx ) (2) mean squared error (MSE) (s yx ) 2 (3) Mean absolute deviation (MAD) MAD = (Sum of absolute deviation for n periods)/n = Sum of (actual demand-forecast demand)/n Method Moving average method Averages the data from a few recent periods, and this average become the forecast for the next period. Exponential smoothing method Takes the forecast for the prior period and adds an adjustment to obtain the forecast for the next period using exponential smoothing formulas. Weighted moving average method Averages the data from a few recent periods, and this average become the forecast for the next period, and apply unequal weight to the historical data Exponential smoothing with trend Incorporate a trend component into exponentially smoothed forecasts. Useful for medium range forecast when seasonality & trend becoming important. SUCCESSFUL FORECASTING SYSTEM 6
Factors to select forecasting method Cost: Need trade off. Higher accuracy will cost more. Data available: Availability of relevent data. Time span: Short range or long range. Nature of product & services Impulse response and noise dampening Reasons for ineffective forecasting Failure to involve a broad cross section of people in forcasting. Individual is important but involvement of everybody with relevent information is equally important Failure to recognise that forecsting is itegral to business planning Failure to recognise that forecsting will always be wrong Failure to forecsting the right things. Forecast too many things will be too expensive and time consuming Failure to select an appropriate forecasting method Failure to track the performance of the forecasting models Sources of forecasting data Auto sales Consumer confidence index Consumer price index Durable goods Employment Gross domestic product Industrial production Personal income and consumption Retail sales Etc. 7