The following are different types of forecasts:
- Demand Forecast
- Supply Forecast
- Price Forecast
Demand Forecast involves determining the firm’s demand for an item. This includes current and projected demand, inventory status and lead time.
Supply Forecast involves collection of data about current suppliers and producers, aggregate projected supply situation and technological and political trends which might affect supply.
Price Forecast is based on information gathered and analyzed about demand and supply. It provides a prediction of short term and long term prices and the underlying reasonsJor those trends.
Further, forecast can be classified as:
- Short Term Forecast
- Intermediate Term Forecast
- Long Term Forecast
Long Term Forecast usually covers more than three years are used for strategic or long term planning.
Intermediate Term Forecast usually range from one to three years and address budgeting issues and sales plans.
Short Term Forecast is more important for the operational logistics planning process. They project demand into next several mo4h 4pf one year or slightly more than one year.
Forecasting product demand is crucial to any supplier, manufacturer, or retailer. Forecasts of future demand will determine the quantities that should be purchased, produced, and shipped. Demand forecasts are necessary since the basic operations process, moving from the suppliers’ raw materials to finished goods in the customers’ hands, takes time. Most firms cannot simply wait for demand to emerge and then react to it. Instead, they must anticipate and plan for future demand so that they can react immediately to customer orders as they occur.
Firms that offer rapid delivery to their customers will tend to force all competitors in the market to keep finished good inventories in order to provide fast order cycle times. As a result, virtually every organization involved needs to manufacture or at least order parts based on a forecast of future demand. The ability to accurately forecast demand also affords the firm opportunities to control costs through leveling its production quantities, rationalizing its transportation, and generally planning for efficient logistics operations. In general practice, accurate demand forecasts lead to efficient operations and high levels of customer service, while inaccurate forecasts will inevitably lead to inefficient, high cost operations and/or poor levels of customer service. In many supply chains, the most important action we can take to improve the efficiency and effectiveness of the logistics process is to improve the quality of the demand forecasts.
Forecasting is a problem that arises in many economic and managerial contexts, and hundreds of forecasting procedures have been developed over the years, for many different purposes, both in and outside of business enterprises.
General Approaches to Forecasting
All firms forecast demand, but it would be difficult to find any two firms that forecast demand in exactly the same way. Over the last few decades, many different forecasting techniques have been developed. Many such procedures have been applied to the practical problem of forecasting demand in a logistics system, with varying degrees of success. Almost any forecasting procedure can be broadly classified into one of the following four basic categories based on the fundamental approach towards the forecasting problem that is employed by the technique.
Judgmental Approaches. The essence of the judgmental approaches to address the forecasting issue by assuming that someone else knows and can tell you the right answer. That is, in a judgment-based technique we gather the knowledge and opinions people who are in a position to know what demand will be. For example, we might conduct a survey of the customer base to estimate what our sales will be next month.
Experimental Approaches. Another approach to demand forecasting, which is appealing when an item is “new” and when there is no other information upon which to base a forecast, is to, conduct a demand experiment on a small group of customers and to extrapolate the results to a larger population. For example, firms will often test a new consumer product in a geographically isolated “test market” to establish its probable market share. This experience is then extrapolated to the-national market to plan product launch. Experimental approaches are very useful and necessary for new products but for existing products that have an accumulated historical demand record it seems intuitive that demand forecasts should somehow be based on this demand experience.
Relational/Causal Approaches. The assumption behind a causal or relational forecast is that, simply put, there is a reason why people buy our product. If we can understand what that reason (or set of reasons) is, we can use that understanding to develop a demand forecast. For example, if we sell umbrellas at a sidewalk stand, we would probably notice that daily demand is strongly correlated to the weather – we sell more umbrellas when it rains. Once we have established this relationship, a good weather forecast will help us order enough umbrellas to meet the expected demand.
“Time Series” Approaches. A time series procedure is fundamentally different than the first three approaches we have discussed. In a pure time series technique, no judgment or expertise or opinion is sought. We do not look for “causes” or relationships or factors which somehow “drive” demand. We do not test items or experiment with customers. By their nature, time series procedures are applied to demand data that are longitudinal rather than cross-sectional. That is, the demand data represent experience that is repeated over time rather than across items or locations. The essence of the approach is to recognize (or assume) that demand occurs over time in patterns that repeat themselves, at least approximately. If we can describe these general patterns or tendencies, without regard to their “causes”, we can use this description to form the basis of a forecast.
Methods of Forecasting
Time Series: Time series analysis comprises methods that attempt to understand such time series, often either to understand the underlying theory of the data points (where did they come from? what generated them?), or to make forecasts (predictions).
Moving Average: This simplest forecasting method is the moving average forecast. The method simply averages of the last m observations. It is useful for time series with a slowly changing mean.
Exponential Smoothing This method considers the entire past in its’ forecast, but weighs recent experience more heavily than less recent. The computations are simple because only the estimate of the previous period and the current data determine the new estimate. The method is useful for time series with a slowly changing mean.
Regression The moving average method does not respond well to a time series that increases or
decreases with time. Here we include a linear trend term in the model. The regression method approximates the model by constructing a linear equation that provides the least squares fit to the last m observations.
Seasonality We model seasonality with a multiplicative seasonal index. The data is adjusted by dividing by the index and the adjusted data is used to obtain forecasts using one of the methods above.