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.
Tracking signal
A measure of whether the forecast is keeping pace with any genuine upward or downward changes in demand. This is used to detect forecast bias.
Causal Relationship Forecasting
Causal relationship forecasting involves using independent variables other than time to predict future demand. To be of value for the purpose of forecasting, any independent variable must be a leading indicator
For example, we can expect that an extended period of rain will increase sales of umbrellas and raincoats. The rain causes the sale of rain gear. This is a causal relationship, where one occurrence causes another. If the causing element is known far enough in advance, it can be used as a basis for forecasting.
Often, leading indicators are not causal relationships, but in some indirect way they may suggest that some other things might happen. Other noncausal relationships just seem to exist as a coincidence
Linear relationship is a type of casual relationship.
Multiple Regression Analysis
Another forecasting method is multiple regression analysis, in which a number of variables are considered, together with the effects of each on the item of interest. For example, in the home furnishings field, the effects of the number of marriages, housing starts, disposable income, and the trend can be expressed in a multiple regression equation as
S = A + Bm(M) + Bh(H) + Bi(I) + Bt(T)
QUALITATIVE TECHNIQUES IN FORECASTING
Qualitative forecasting techniques generally take advantage of the knowledge of experts and require much judgment. These techniques typically involve processes that are well defined to those participating in the forecasting exercise. For example, in the case of forecasting the demand for new fashion merchandise in a retail store, the firm can include a combination of input from typical customers expressing preferences and from store managers who understand product mix and store volumes, where they view the merchandise and run through a series of exercises designed to bring the group to a consensus estimate. The point is that these are no wild guesses as to the expect
The following are samples of qualitative forecasting techniques.
- Market Research - You may have been involved in market surveys through a marketing class. Market research is used mostly for product research in the sense of looking for new product ideas, likes and dislikes about existing products, which competitive products within a particular class are preferred, and so on. Again, the data collection methods are primarily surveys and interviews.
- Panel Consensus - In a panel consensus, the idea that two heads are better than one is extrapolated to the idea that a panel of people from a variety of positions can develop a more reliable forecast than a narrower group. Panel forecasts are developed through open meetings with a free exchange of ideas from all levels of management and individuals. the term executive judgment is usually used for panel consensus at a higher level of the organization, considering eg. strategy.
- Historical Analogy - In trying to forecast demand for a new product, an ideal situation would be one where an existing product or generic product could be used as a model. There are many ways to classify such analogies—for example, complementary products, substitutable or competitive products, and products as a function of income
Delphi Method
As we mentioned under panel consensus, a statement or opinion of a higher-level person will likely be weighted more than that of a lower-level person.
Delphi method conceals the identity of the individuals participating in the study. Everyone has the same weight. Procedurally, a moderator creates a questionnaire and distributes it to participants. Their responses are summed and given back to the entire group along with a new set of questions.
WEB-BASED FORECASTING: COLLABORATIVE PLANNING, FORECASTING, AND REPLENISHMENT (CPFR)
Collaborative planning, forecasting, and replenishment (CPFR) is a Web-based tool used to coordinate demand forecasting, production and purchase planning, and inventory replenishment between supply chain trading partners. CPFR is being used as a means of integrating all members of an n-tier supply chain, including manufacturers, distributors, and retailers. As depicted in Exhibit 18.17, the ideal point of collaboration utilizing CPFR is the retail-level demand forecast, which is successively used to synchronize forecasts, production, and replenishment plans upstream through the supply chain.
As with most new corporate initiatives, there is skepticism and resistance to change. One of the largest hurdles hindering collaboration is the lack of trust over complete information sharing between supply chain partners.
CPFR’s objective is to exchange selected internal information on a shared Web server in order to provide for reliable, longer-term future views of demand in the supply chain. CPFR uses a cyclic and iterative approach to derive consensus supply chain forecasts. It consists of the following five steps:
Step 1. Creation of a front-end partnership agreement.
Step 2. Joint business planning.
Step 3. Development of demand forecasts.
Step 4. Sharing forecasts.
Step 5. Inventory replenishment.
