Lecture 05 forecasting models
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Lecture 05 forecasting models Lecture 05 forecasting models Document Transcript

  • Unit 2 Management of Conversion System Chapter 3: Forecasting Lesson 5: Forecasting ModelsLearning Objectives The role of Time in forecasting Types of forecasting Quantitative versus Qualitative Methods of forecastingHello students, today we will discuss a very interesting topic – forecasting.What comes to your mind when you think of the term forecasting? ……Everyday a shop owner thinks how many items he would be able to sell.The florist at the roadside keeps flower thinking in mind how much he would be able tosell by the end of the evening.Here they are applying forecasting- albeit on a miniscule scale.But let us probe further.What is Forecasting?Well, friends as we all know a very critical aspect of managing any organization is theplanning for the future. Hence, Forecasting is the art and science of predicting futureevents. Forecasts are required throughout an organization and at all levels of decisionmaking in order to plan for the future and make effective decisions. The principal use offorecasts in operations management is in predicting the demand for manufacturedproducts and services for time horizons ranging from several years down to 1 day.Depending on the planning horizon, forecasting can be classified in three ways: Short – range forecasting (up to 1 year) Medium – range forecasting (up to 3 years)
  • Long – range forecasting (more than 3 years)Ok. Then. So far, so good. Now let us explore further.Now, who’s going to tell me about the various types of forecasts?No, Come on.How about a forecast of today’s weather?You see light. Excellent.We march ahead then.Types of forecastsIn general, a contemporary business organization employs three distinct types offorecasts.These are given under: 1. Economic forecasts 2. Technological forecasts 3. Demand forecastsEconomic forecasts address the business cycle by predicting inflation rates, moneysupplies, housing starts, and other planning indicators.Technological forecasts are concerned with rates of technological progress, which canresult in the birth of exciting new products, requiring new plants and equipment.Demand forecasts are projections of demand for a company’s products or services.These forecasts, also called sales forecasts, drive a company’s production, capacity, andscheduling systems and serve as inputs to financial, marketing, and personnel planning.What is the strategic importance of forecasting?Forecasting plays a very important role in the following areas: Human resource management (- hiring, training and laying-off workers all depend on anticipated demand.)
  • Capacity planning (– when capacity is inadequate, the resulting shortages can mean undependable delivery, loss of customers, and loss of market share.) Supply – chain management (– good supplier relations and the ensuing price advantages for materials and parts depend on accurate forecasts.)Dear students, now that we have a clear idea of forecasting and its significance, let us tryto focus on the different facets of forecasting. Demand Forecast Facility Transportation and logistics capacity planning Production schedules Material Personnel planning hiring Personnel schedules
  • Forecasting ApproachesIt’s a bit like the story of three blind (sorry, visually impaired) men and an elephant.Perception. It seems, plays a very important role in this respect.There are numerous approaches to forecasting depending on the need of the decisionmaker. Broadly speaking, these can be categorized in two ways: Quantitative forecasting Qualitative forecasting Let’s go further and ask ourselves:-When to use qualitative methods?In general, we should consider using qualitative forecasting techniques when one or moreof the following conditions exist: 1. Little or no historical data on the phenomenon to be forecast exist. 2. The relevant environment is likely to be unstable during the forecast horizon. 3. The forecast has a long time horizon, such as more than three to five years.What are different Qualitative Methods of forecasting?The various Qualitative Methods in vogue are as follows: 1. Jury of executive opinion –
  • This method takes the opinions of a small group of high-level managers, often in combination with statistical models, and results in a group estimate of demand. 2. Sales force composite – In this approach, each salespeople estimates what sales will be in his or her region. These forecasts are then reviewed to ensure they are realistic, then combined at the district and national levels to reach an overall forecast. 3.Delphi method – This is an iterative group process. There are three different types of participants in the Delphi process: decision makers, staff personnel, and respondents. The decision makers usually consist of a group of five to ten experts who will be making the actual forecast. The staff personnel assist the decision makers by preparing, distributing, collecting, and summarizing a series of questionnaires and survey results. The respondents are a group of people whose judgments are valued and are being sought. This group provides inputs to the decision makers before the forecast is made. 4. Consumer market survey – This method take input from customers or potential customers regarding their future purchasing plans. It can help not only in preparing a forecast but also in improving product design and planning for new products. 5. Naïve approach – It assumes that demand in the next period is the same as demand in the most recent period. In other words, if sales of a product, say, Reliance WLL phones, were 100 units in January, we can forecast that February’s sales will also be 100 phones. Does this make any sense? It turns out that for some product lines, selecting this naïve approach is a cost-effective and efficient forecasting model.To illustrate, let us see how theses techniques are put into practice. In the followingpractical problem, we would examine the role of forecasting as applicable to POM inpractice
  • We shall see how Delphi method of forecasting is applied.POM in practice- Forecasting with the Delphi method*American Hoist and Derrick is a manufacturer of construction equipment, with annualsales of several million dollars. Their sales forecast is an actual planning figure and isused to develop the master production schedule, cash flow projections, and work-forceplans. On of the important components of their forecasting process is the use of theDelphi method of judgmental forecasting. In 1975, top management wanted an accurate 5-year forecast of their sales inorder to plan for expansion of production capacity. The Delphi method was used inconjunction with regression models and exponential smoothing in order to generate aforecast. A panel of 23 key personnel was established, consisting of those who had beenmaking subjective forecasts, those who had been using them or were affected by theforecasts, and those who had a strong knowledge of the market and corporate sales. Threerounds of the Delphi method were performed, each requesting estimates of: Gross national product; Construction equipment industry shipments; American Hoist and Derrick construction equipment group shipments; and American Hoist and Derrick corporate value of shipments.As the Delphi technique progressed, responses for each round were collected, analyzed,and summarized, and reported back to the panel. In the third-round questionnaire, notonly were the responses of the first two rounds included, but – in addition – related facts,figures, and views of external experts wee sent. As a result of the Delphi experiment, the 1995sales forecast error was less than0.33 percent; in 1996 the error was under 4 percent. This was considerable improvementover previous forecast errors of plus or minus 20 percent. In fact, the Delphi forecastswere more accurate than regression models or exponential smoothing which had forecasterrors of 10 to 15 percent. An additional result of the exercise was educational in nature.Managers developed a uniform outlook on business conditions and corporate salesvolume and thus had a common base for decision making.
  • *Adapted from Applied Production and Operations Management (James R. Evans et al), West publishing CompanyLet us now discuss about quantitative approach of forecasting.Quantitative MethodsThe chief Quantitative methods are: 1. Moving averages 2. Exponential smoothing Time series models 3. Trend projection 4. Linear regression  Causal modelThe time series models of forecasting predict on the basis of the assumption that thefuture is a function of the past. In other words, they look at what has happened over aperiod of time and use a series of past data to make a forecast. If we are predictingweekly sales of washing machine, we use the past weekly sales for washing machine inmaking the forecast.A causal model incorporates into the model the variables or relationships that mightinfluence the quantity being forecast. A causal model for washing machine sales mightinclude relationships such as new housing, advertising budget, and competitors’ prices.Moving over to a structured approach to forecasting, let me introduce the basic stepsinvolved in this process:-Steps in ForecastingThere are eight steps to a forecasting system.These are: 1. Determine the use of the forecast – (What objectives are we trying to achieve?) 2. Select the items that are to be forecasted 3. Determine the time horizon of the forecast –
  • (Is it short, medium, or long – range? 4. Select the forecasting model 5. Gather the data needed to make the forecast 6. Validate the forecasting model 7. Make the forecast 8. Implement the resultsWe now focus our attention to one of the most widely used and effective method offorecasting.Time Series ForecastingA time series is based on a sequence of evenly spaced (weekly, monthly, quarterly, andso on) data points. Forecasting time series data implies that future values are predictedonly from past values and that other variables, no matter how potentially valuable,are ignored.Decomposition of a Time SeriesThere are four main ways of decomposing the time series: Trend Seasonality Cycles Random variationsTwo general forms of time series models are used in statistics. The most widely used is amultiplicative model, which assumes that demand is the product of the four components: Demand = T × S × C × R , where T denotes Trend S denotes Season C denotes Cycles R denotes random variables An additive model provides an estimate by adding the components together. It is stated as:
  • Demand = T + S + C + RMoving AveragesMoving averages are useful if we can assume that market demands will stay fairly steadyover time. Moving average can be defined as the summation of demands of total periodsdivided by the total number of periods.Mathematically,Moving average = ∑ Demand in previous n periods / nwhere n is the number of periods in the moving average – for example, four, five, or sixmonths, respectively, for a four -, five -, or six – period moving average.To make the calculation of moving average more clear, we take the sales of Washingmachine at Arvee Electronics. Month Actual Washing Three-month moving machine sales, units average January 10 February 12 March 13 April 16 (10 + 12 + 13) / 3 = 11.67 May 19 (12 + 13 + 16) / 3 = 13.67 June 23 (13 + 16 + 19) / 3 = 16 July 26 (16 + 19 + 23) / 3 = 19.33 August 30 (19 + 23 + 26) / 3 = 22.67 September 28 (23 + 26 + 30) / 3 = 26.33 October 18 (26 + 30 + 28) / 3 = 28 November 16 (30 + 28 + 18) / 3 = 25.33 December 14 (28 + 18 +16) / 3 = 20.67
  • Points to ponder