Ch 18 - Forecasting Flashcards
Forecasting in business
A lot about planning in functional areas;
Forecasting is the basis of corporate planning and control.
In the functional areas of finance and accounting, forecasts provide the basis for budgetary planning and cost control.
Marketing relies on sales forecasting to plan new products, compensate sales personnel, and make other key decisions. Production and operations personnel use forecasts to make periodic decisions involving supplier selection, process selection, capacity planning, and facility layout, as well as for continual decisions about purchasing, production planning, scheduling, and inventory.
What is strategic forecasts?
Medium and long-term forecasts that are used for decisions related to strategy and aggregate demand.
What do we expect the demand to be for a group of products over the next year, for example? Some forecasts are used to help set the strategy of how, in an aggregate sense, we will meet demand.
strategic forecasts are most appropriate when making decisions related to overall strategy, capacity, manufacotring process design, service process design, location and distribution design, sourcing, sales and operations planning. These all involve medium and long-term decisions that relate to how demand will be met strategically.
Tactical forecasts
Forecasts are also needed to determine how a firm operates processes on a day-to-day basis. For example, when should the inventory for an item be replenished, or how much production should we schedule for an item next week? These are tactical forecasts where the goal is to estimate demand in the relatively short term, a few weeks or months.
Difference between strategy and tactics?
A strategy is concerned with long term actions and tactics is a part of the strategy, focusing on short term actions (operative level)?
Truth about forecasts
Bear in mind that a perfect forecast is virtually impossible. Too many factors in the business environment cannot be predicted with certainty. Therefore, rather than search for the perfect forecast, it is far more important to establish the practice of continual review of forecasts and to learn to live with inaccurate forecasts. h the practice of continual review of forecasts and to learn to live with inaccurate forecasts. This is not to say that we should not try to improve the forecasting model or methodology or even to try to influence demand in a way that reduces demand uncertainty.
Qualitative VS Quantitative models
qualitative techniques that use managerial judgment and also atquantitative techniques that rely on mathematical models
QUANTITATIVE FORECASTING MODELS
Forecasting can be classified into four basic types: qualitative, time series analysis, causal relationships, and simulation.
Time series analysis, the primary focus of this chapter, is based on the idea that data relating to past demand can be used to predict future demand. Past data may include several components, such as trend, seasonal, or cyclical influences, and are described in the following section. Causal forecasting, which we discuss using the linear regression technique, assumes that demand is related to some underlying factor or factors in the environment. Simulation models allow the forecaster to run through a range of assumptions about the condition of the forecast.
Components of Demand
In most cases, demand for products or services can be broken down into six components: average demand for the period, a trend, seasonal element, cyclical elements, random variation, and autocorrelation.
Remember that we are illustrating long-term patterns, so these should be considered when making long-term forecasts. When making short-term forecasts, these patterns are often not so strong.
Random demand
When demand is random, it may vary widely from one week to another. Where high autocorrelation exists, the rate of change in demand is not expected to change very much from one week to the next.
Autocorrelation
Autocorrelation denotes the persistence of occurrence. More specifically, the value expected at any point is highly correlated with its own past values. In waiting line theory, the length of a waiting line is highly autocorrelated. That is, if a line is relatively long at one time, then shortly after that time we would expect the line still to be long.
Cyclical influence
Cyclical influence on demand may come from such occurrences as political elections, war, economic conditions, or sociological pressures. Random variations are caused by chance events. Statistically, when all the known causes for demand (average, trend, seasonal, cyclical, and autocorrelative) are subtracted from total demand, what remains is the unexplained portion of demand.
A widely used forecasting method?
plots data and then searches for the curve pattern (such as linear, S-curve, asymptotic, or exponential) that fits best
Which forecasting model a firm should choose depends on:
- Time horizon to forecast (>2 –> Use time series)
- Data availability
- Accuracy required
- Size of forecasting budget
- Availability of qualified personnel
Moving average
A forecast based on average past demand.
Weighted moving average
A forecast made with past data where more recent data is given more significance than older data.
Exponential smoothing
A time series forecasting technique using weights that decrease exponentially (1 – α) for each past period.
Exponential smoothing is the most used of all forecasting techniques. It is an integral part of virtually all computerized forecasting programs, and it is widely used in ordering inventory in retail firms, wholesale companies, and service agencies.
In the exponential smoothing method, only three pieces of data are needed to forecast the future: the most recent forecast, the actual demand that occurred for that forecast period, and a smoothing constant alpha (α). This smoothing constant determines the level of smoothing and the speed of reaction to differences between forecasts and actual occurrences.
Exponentially smoothed forecasts can be corrected somewhat by adding in a trend adjustment. To correct the trend, we need two smoothing constants. Besides the smoothing constant α, the trend equation also uses a smoothing constant delta (δ). Both alpha and delta reduce the impact of the error that occurs between the actual and the forecast. If both alpha and delta are not included, the trend overreacts to errors.
Decomposition of a Time Series
A time series can be defined as chronologically ordered data that may contain one or more components of demand: trend, seasonal, cyclical, autocorrelation, and random. Decomposition of a time series means identifying and separating the time series data into these components. In practice, it is relatively easy to identify the trend (even without mathematical analysis, it is usually easy to plot and see the direction of movement) and the seasonal component (by comparing the same period year to year). It is considerably more difficult to identify the cycles (these may be many months or years long), the autocorrelation, and the random components. (The forecaster usually calls random anything left over that cannot be identified as another component.)
Forecast Errors
The difference between actual demand and what was forecast.
Sources of Error
Errors can come from a variety of sources.
statistical errors in regression analysis, we are referring to the deviations of observations from our regression line.
adding confidence band to reduce the unexplained error
Can be biased or random
What is biased and random errors?
Errors can be classified as bias or random. Bias errors occur when a consistent mistake is made. Sources of bias include the failure to include the right variables; the use of the wrong relationships among variables; employing the wrong trend line; a mistaken shift in the seasonal demand from where it normally occurs; and the existence of some undetected secular trend. Random errors can be defined as those that cannot be explained by the forecast model being used.
Measurement of Error
Several common terms used to describe the degree of error are standard error (“SE”), mean squared error (or variance), and mean absolute deviation.
What is standard error?
The standard error (SE) of a statistic is the approximate standard deviation of a statistical sample population. The standard error is a statistical term that measures the accuracy with which a sample distribution represents a population by using standard deviation. In statistics, a sample mean deviates from the actual mean of a population—this deviation is the standard error of the mean.
Mean absolute deviation (MAD)
The mean absolute deviation (MAD) was in vogue in the past but subsequently was ignored in favor of standard deviation and standard error measures. In recent years, MAD has made a comeback because of its simplicity and usefulness in obtaining tracking signals. MAD is the average error in the forecasts, using absolute values. It is valuable because MAD, like the standard deviation, measures the dispersion of some observed value from some expected value.
Mean absolute percent error (MAPE)
The average error measured as a percentage of average demand.
