Forecasting
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Forecasting

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Forecasting Forecasting Presentation Transcript

  • FORECASTING Presented By: Sagar Parmar Hitesh Aggarwal Madan Mavi Mahesh Gupta
  • MEANING:It is an art and science of predicting future events. Forecasting may involve taking historical data and projecting them into the future with some sort of mathematical model. It is a mathematical model adjusted by a manager’s good judgment. Effective planning in both the short and long run depends on a forecast of demand for the company’s products or services.
  • TYPES OF FORECASTS Organizations use three major types of forecasts in planning future operations. • Economic forecasts: It addresses the business cycle by predicting inflation rates, money suppliers, housing starts, and other planning indicators. • Technological forecasts: These are concerned with rates of technological progress, which can result in the birth of exciting new products, requiring new plants and equipments. •Demand forecasts: These are projections of demand for a company’s products or service. These are forecasts , also called sales forecasts, drive a company’s production, capacity, and scheduling systems and serve as inputs to financial, marketing, and personnel planning.
  • THE STRATEGIC IMPORTANCE OF FORECASTING The forecast is the only estimate of demand until actual demand becomes known. Forecasts of demand therefore drives decision in many areas. Let’s look at the impact of product forecast on three activities:1. 2. 3. Human Resources. Capacity Supply Chain Management
  • HUMAN RESOURCES Hiring, training, and laying off workers all depends on anticipated demand. If the human resource department must hire additional workers without warning, the amount of training declines and the quality of the workforce suffers. A large Louisiana chemical firm almost lost its biggest customer when a quick expansion to around-the-clock shifts led to a total breakdown in quality control on the second and third shifts. CAPACITY Capacity is inadequate, the resulting shortages can mean undependable delivery, loss of customers, and loss of market share. This is exactly what happened to Nabisco when it underestimated the huge demand for its new low-fat Snackwell Devil’s Food Cookies.
  • SUPPLY CHAIN MANAGEMENT Good supplier relations and the ensuring price advantages for materials and parts depends on accurate forecasts. e.g, auto manufacturers who want TRW Corp. to guarantee sufficient airbag capacity must provide accurate forecasts to justify TRW plant expansions.
  • SEVEN STEPS IN THE FORECASTING SYSTEM 1. 2. 3. 4. Forecasting follows seven basic steps. We use Disney World as an example of each step: Determine the use of the forecast: Disney uses park attendance forecasts to drive staffing, opening time, ride availability, and food suppliers. Select the item to be forecasted: For Disney World, there are six main parks. A forecast of daily attendance at each is the main number that determines labor, maintenance, and scheduling. Determine the time horizon of the forecast: is it short, medium, or long term? Disney develops daily, weekly, monthly, annual, and 5-year forecast. Select the forecasting model: Disney uses a variety of statistical models like, including moving averages, econometrics, and regression analysis. It also employs judgmental, or nonquantitative, models.
  • 5. 6. 7. Gather the data needed to make the forecast: Disney’s forecasting team employs 35 analysts and 70 field personnel to survey 1 million people/businesses every year. It also uses the firm called Global Insight for travel industry forecasts and gathers data on exchange rates, arrivals into the U.S., airline specials, Wall Street trends, and school vacation schedules. Make the forecast. Validate and implement the result: At Disney, forecasts are reviewed daily at the highest levels to make sure that the model, assumptions, and data are valid. Error measures are applied; then the forecasts are used to schedule personnel down to 15-minutes intervals.
  • QUANTITATIVE FORECASTING METHODS Time Series Models Such models predict on the assumption that the future is a function of the past. In other words , they look at what has happened over a period of time and use a series of past data to make a forecast. Types: 1) Moving Averages(Simple and Weighted) A moving average forecasts uses a number of historical actual data values to generate a forecast. Moving averages are useful if we can assume that market demands will stay fairly steady over time. Simple Moving Average= ∑ Demand in previous n periods n • When some past periods are to be given more weightage then Weighted Moving Average is used Weighted Moving Average = ∑(Weight for period n)(Demand in period n) ∑ Weights
  • Problems faced by this method: • Increasing the size of n smoothens out fluctuations better, but it makes the method less sensitive to real changes in the data. • Moving averages can’t pick up trends very well. Because they are averages , they will always stay within past levels and will not predict changes to either higher and lower levels. • Moving Averages require extensive records of past data. Exponential Smoothing It is a sophisticated weighted moving average forecasting method that is still fairly easy to use. It involves very little records keeping of past data .The basic exponential smoothing formula is: Ft=Ft-1 + α(At-1 – Ft-1) (0<=α>=) • Α is high when more weightage is given to recent data and α is low when low weightage is given to recent data. • This method is widely used in business and an important part of computerized inventory control systems.
  • Associative Models Such models usually consider several variables that are related to the quantity being predicted. Once these related variables have been found ,a statistical model is build and used to forecast the item of interest, e.g; the sales of Dell PCs may be related to the Dell’s advertising budget, the company’s prices, competitor’s prices and promotional strategies. In this case PCs sales would be called the dependent variable and the other variables would be called the independent variables. • Most common approach is Least Square Method or Linear-Regression Analyses.
  • FORECAST ERROR Forecasting error can be determined by comparing the forecasted values with the actual or observed values. If ft denotes the forecast in period t ,the forecast error (or deviation)is defined as ; Forecast error = Actual demand – Forecast value = At - F t Mean Absolute Deviation (MAD) – This value is computed by taking the sum of the absolute values of the individual forecast error and dividing by the no. of periods. MAD = ∑ |Actual – Forecast | n
  • Qualitative Forecasting Methods DELPHI METHOD - To overcome the limitation of above method, a committee is formed. A moderator creates a questionnaire & distributes to the participants. The identity of committee members is concealed. Their responses are summed up. A new set of questions is prepared. Steps involved-1. 2. 3. 4. 5. 6. 7. Choose experts to participate from different areas. Their questionnaire or email obtain forecasts. Summarize the results. Redistribute results with another new questionnaire. Summarizes again- refining forecasts. Carry on 3 to 6 rounds It results in forecasts that most participants have ultimately agreed to in spite of their initial disagreement.
  • EDUCATED GUESS •Judgment based on experience & intuition to estimate a sales forecast (by one person •Used for short term forecast when cost of forecast inaccuracy is low. •Such forecasts have to be made very frequently. SURVEY OF CUSTOMERS •Suitable when a company has few customers e.g. Automobile/defense contractors. Estimates are gathered from customers directly . EXECUTIVE COMMITTEE CONSENSUS •Forecast made by a committee of knowledge executive from different departments. Such forecast are compromise forecast not reflecting the extremes. •People from a lower level may not speak freely to refute the estimates of people saving above them.
  • SURVEY OF SALES FORCE Used for existing product when salespeople sell directly to customers & a good communication system exists in an organization. Estimates of future regional sales are obtained from sales people. These are refined by managers & total sales for all regions is estimated on its behalf. MARKET RESEARCH Suitable for new products or introduction of exiting product in new market segments. Then mail, questionnaires, surveys, telephone interviews- hypothesis is tested HISTORICAL ANALOGY For a new product a generic or existing product is used as a model. The analogies may be complementary product/substitutes. Knowledge of one product sales during various stages of its product life cycle is applied to the estimate of sales for a similar product.
  • QUESTIONS: Q1.) Define qualitative & quantitative forecasting methods. Q2.) Define forecast accuracy. Q3.) Name three underlying reasons why operations management must forecast. Q4.) Describe how forecasting is integral to business planning . Q5.) Name the four components or data patterns of longrange demand in forecasting. Please refer to the notes:
  • THANK YOU!!!!