Forecasting needs methods

The model method of the forecast is acceptable only at sufficient knowledge of cause and effect associations between initial and final data forecast. This method is based on the application of models, which imitate correlation of groxmd water properties and composition vs. external factors. A clearly formulated problem enables identification of most important factors and processes, maximum simplification of the model and choose the most optimal solution needed mathematical equations or their systems and available programs. 

It is important to remember that every forecast will be wrong. Forecasts are always a prediction of the future. It is random luck if demand actually equals the forecast. As shown in Figure 8.1, there is a distribution of probable demand around the forecast point. So, a good forecast includes not only an expected amount, but also the probable range as shown by the distribution. The reason we make the forecast is to coordinate between all the functions and firms in the supply chain. To help coordinate the supply chain the forecast needs to be reasonably accurate. In the next sections we will consider some simple forecasting methods and some simple methods to evaluate the accuracy of the forecast. 

For many, this view will remain a remote hope. For these companies, their supply chains are too long most of their sales are at Christmas there are too many players to make coordination feasible and, besides, their crystal balls won t produce the accurate forecasts needed to be successful. ITowever, those who are successful in managing their supply chains will make this transition. In the following sections, we ll trace the evolution of this transition from batch to flow and some of the enabling methodologies that work in many supply chains. We believe that companies with "uncontrollable" external barriers like seasonality can also tailor these methods to make life easier. 

The need for Improved prediction capabilities Is addressed by (1) Investigating the relationship between present properties of a material, the history of these properties, and the future performance of the material by (2) developing short-term measurement and test methods to forecast the long-term performance of materials, and by (3) constructing basic models for predicting lifetime performance of materials. 

The book presents a well-defined procedure for adding or subtracting independent variables to the model variable and covers how to apply statistical forecasting methods to the serially correlated data characteristically found in clinical and pharmaceutical settings. The standalone chapters allow you to pick and choose which chapter to read first and hone in on the information that fits your immediate needs. Each example is presented in computer software format. The author uses MiniTab in the book but supplies instructions that are easily adapted for SAS and SPSSX, making the book applicable to individual situations. 

The Free-Wilson method has the advantage that one does not need to estimate physical property descriptors for the compounds. This property also makes it difficult, if not impossible, to forecast the bioactivity of untested substituents. Moreover, the assumption of a constant effect on the potency of a particular substituent at a particular position breaks down in the case of a nonlinear relationship of potency with log P. 

When Croxton et al. (2002) detailed the sub-process of determine forecasting procedures, they explain that the first step is to xmderstand what type of forecast is needed, then what data is available, and finally, select a forecasting method which will depend on the environment that the forecasting is taking place. They presented a two-by-two matrix to show which forecast approach is appropriate based on demand variability and demand volume, as shown in Fig. 4.3. 

For the APS to be effective for collaborative forecasting and planning throughout the supply chain, it is necessary that a reliable method for passing information between the different APS systems be in use. When a supplier has only one or two major customers, it is possible for them to have the same type of software as their customer. When a supplier has many customers, they cannot have software that matches each of their customer s needs. 

Continuing to use the example above, where demand for the last 4 weeks was 100,120, 130, and 120 for weeks 1 to 4, respectively, we will forecast demand in week 5. To do this we must have the forecast for period 4 to get the forecast for period 4, we need the forecast for period 3. This continues until period 1, when we recognize that we need to initiate the method by creating a forecast for period 1 using a different method. One common technique to obtain the first week s forecast is to say the forecast for period 1 was equal to actual demand for period 1 (i.e., we set the forecast for week 1 equal to 100 since actual demand for week 1 was 100). The formula for the exponential moving average is ... 

Each of these types of inventory behaves differently and has to be managed differently. The major distinction between them is how their demand is managed. The demand for the independent inventory must be forecast as explained in Chapter 8. The demand for the dependent inventory can be calculated, because the amount of the type of inventory that is needed is directly related to the demand for the final product. For example, the demand for some components, which are in a finished item, is always dependent on the number of a finished item that we need. First, we will examine how to manage independent inventory. Then we will investigate the methods to manage dependent inventory. 

Qualitative forecasting methods are usually based on subjective judgments and past historical data are not needed. They are most useful for new products or services. Section 2.3 discusses in detail the various qualitative forecasting methods. 

These methods are based on subjective opinions obtained from company executives and consumers. These methods are ideally suited for forecasts where there are no past data or past data is not reliable due to the changes in environment (e.g., peacetime data on spare part needs for military aircrafts are not useful for forecasts during war time). Qualitative methods are commonly used for strategic decisions where long-term forecasts are necessary. The qualitative approaches vary in sophistication from scientifically conducted consumer surveys to intuitive judgments from top executives. Quite frequently qualitative methods are also used as supplements to quantitative forecasts. In a 1994 survey of forecasting in practice, 78% of top 500 companies responded that they always or frequently used qualitative methods for forecasting (Sanders and Manrodt, 1994). 

This approach uses scientifically designed customer surveys to determine their needs for products and services. The survey results are tabulated at the corporate level and forecasts are prepared. This method is referred to as the "grassroots" approach since it directly involves end users. This approach is frequently employed for estimating demands for brand new product lines or services. 

Consider again the 6-month time series data on demand given in Table 2.4. We will apply Holt s method to determine the forecast for month 7. In order to get started with Holt s method, we need the... 

Taco Bell (Hueter and Swart, 1998) Lunch time sales were forecasted using the moving average method and were used for estimating labor needs at Taco Bell restaurants. 

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