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BA 360 quiz 3 - Google Docs
Introduction to Operations and Supply Chain Management (BA 360)
San Diego State University
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Forecasting : the art and science of predicting future events by using past data in a systematic way; may involve taking historical data (such as past sales) + projecting them into the future w a math model; may be a subjective or an intuitive prediction (e., “this is a great new product and will sell 20% more than the old one”). It may be based on demand-driven data, such as customer plans to purchase, and projecting them into the future. Or the forecast may involve a
combination of these, that is, a mathematical model adjusted by a manager’s good judgment. Errors as planning horizon so reduce lead ti↑ ↑ me and build in flex. A forecast is usually classified by the future time horizon that it covers. Long term forecasts are less accurate than short term ones 3 categories of time horizons: 1) short range forecast- a time span of up to 1 year but is generally less than 3 months. It is used for planning purchasing, job scheduling, workforce levels, job assignments, and production levels, 2) medium range forecast - spans from 3 months to 3 years. It is useful in sales planning, production planning and budgeting, cash budgeting, and analysis of various operating plans and 3) long range forecast - Generally 3 years or more in time span, long-range forecasts are used in planning for new products, capital expenditures, facility location or expansion, and research and development. intermediate and long-range forecasts deal with more comprehensive issues supporting management decisions regarding planning and products, plants, and processes. Implementing some facility decisions, such as GM’s decision to open a new Brazilian manufacturing plant, can take 5 to 8 years from inception to completion. Second, short-term forecasting usually employs different methodologies than longer-term forecasting. Mathematical techniques, such as moving averages, exponential smoothing, and trend extrapolation (all of which we shall examine shortly), are common to short-run projections. Broader, less quantitative methods are useful in predicting such issues as whether a new product, like the optical disk recorder, should be introduced into a company’s product line. Finally, as you would expect, short-range forecasts tend to be more accurate than longer-range forecasts. Factors that influence demand change every day. Thus, as the time horizon lengthens, it is likely that forecast accuracy will diminish. It almost goes without saying, then, that sales forecasts must be updated regularly to maintain their value and integrity. After each sales period, forecasts should be reviewed and revised. Economic forecasts: address the business cycle by predicting inflation rates, money supplies, housing starts, and other planning indicators Technological forecasts: concerned with rates of technological progress, which can result in the birth of exciting new products, requiring new plants and equipment. Demand forecasts: projections of demand for a company’s products or services. The forecast is the only estimate of demand until actual demand becomes known. impact of product demand forecast on three activities: (1) supply chain management (collaborative planning, forecasting, and replenishment CPFR that combines the intelligence of multiple supply-chain partners, goal: to create significantly more accurate info that can power the supply chain to greater sales and profits), (2) human resources, (hiring, training, and laying off workers that depend on the demand) and (3) capacity (When capacity is inadequate, the resulting shortages can lead to loss of customers and market share) Forecasting steps- Determine the use of the forecast -> select the items to be forecasted -> determine the time horizon -> select the forecasting models -> gather the data needed to make the forecast -> make the forecast -> Validate and implement the results Both product family and aggregated forecasts are more accurate than individual product forecasts Quantitative vs qualitative forecasts (factors as the decision maker’s intuition, emotions, personal experiences, and value system in reaching a forecast) Jury of executive opinion : the opinions of a group of high-level experts or managers, often in combination with statistical models, are pooled to arrive at a group estimate of demand. Delphi Method : decision makers, staff personnel, and respondents that provide input to the decision makers before a forecast is made. Sales force composite: each salesperson estimates what sales will be in his or her region. Market survey: input from customers or potential customers regarding future purchasing plans Time Series model (naive approach, moving avgs., exponential smoothing and trend projection. predict on the assumption that the future is a function of the past) vs associative model (linear regression. incorporate the variables or factors that might influence the quantity being forecast) 4 components of Time series: trend (the gradual upward or downward movement of the data over time such as Changes in income, population, age distribution, or cultural views), seasonality (a data pattern that repeats itself after a period of days, weeks, months, or quarters), cycles (patterns in the data that occur every several years. They are usually tied into the business cycle and are of major importance in short-term business analysis and planning) and random variations/noise (“blips” in the data caused by chance and unusual situations. They follow no discernible pattern, so they cannot be predicted) Irregular Variation : one-time variation that is explainable Naive approach: the most cost-effective and efficient objective forecasting model bc it provides a starting point against which more sophisticated models that follow can be compared Moving average forecast : uses a number of historical actual data values to generate a forecast. Useful if we can assume that market demands will stay fairly
steady over time. Formula: n Exponential smoothing : involves very little record keeping of past data and is fairly easy to use. New forecast
∑ demand in previous n periods
=Last period’s forecast + (last period’s actualα demand - last period’s forecast). Forecast error = actual demand - forecast value
Mean absolute deviation (MAD)= n how much the forecast missed the target
∑actual f orecast | |
Mean squared error (MSE) = n tends to accentuate large deviations (the square of MAD)
∑(f orecast errors) 2
Mean absolute percent error (MAPE)= n easiest to interpret (the average percent error)
∑ 00 actual f orecast actual
n i=1 1 | |/
Forecast including trend (FIT)= exponentially smoothed forecast avg + exponentially smoothed trend. Trend adjusted exp. Smoothing = (actual demand α last
period) + (1- )(forecast last period + trend estimateα last period) Trend projection : fits a trend line to a series of historical data points and then projects the slope of the line into the future for medium- to long-range forecasts If we decide to develop a linear trend line by a precise statistical method, we can apply the least-squares method. This approach results in a straight line that minimizes the sum of the squares of the vertical differences or deviation s from the line to each of the actual observations Seasonal variations : regular movements in a time series that relate to recurring events such as weather or holidays Multiplicative seasonal model: seasonal factors are multiplied by an estimate of average demand to produce a seasonal forecast Find the avg. historical demand each season -> compute the avg demands over all months -> compute a seasonal index -> estimate next year’s total annual demand -> divide the estimate of total annual demand by the number of seasons and multiply it by the seasonal index for each month (seasonal forecast)
BA 360 quiz 3 - Google Docs
Course: Introduction to Operations and Supply Chain Management (BA 360)
University: San Diego State University
