Figure 8 shows a decision tree that will provide guidance to the forecaster with the selection of forecasting methodology that can be used. The decision tree contains questions about the data that is available to the forecaster as well as the state of knowledge about the situation that must be forecasted. The tree’s first question is whether statistical analyses can be done by having enough objective data available otherwise one must use the judgemental method.
When determining which judgemental procedures to use, one must first determine if the future will differ substantially from the past, whether there is a need for policy analysis and if the decision makers within the situation have conflicting interests. Considerations like whether forecasts are made for recurring and familiar problems and whether domain knowledge and information for problems of similar nature are available.
If quantitative methods can be used, due to the availability of objective data, it is the forecaster’s responsibility to determine if major changes are involved, the availability of time-series data and if any knowledge about causal relationships are available that can be used. In cases where little to no realistic knowledge exists about relationships, there should be determined if an expert’s expertise will be better than a policy analysis. If excellent information is available that the future will change and be considerably different than what it currently is and the absence or presence of interactions between the number of available variables and available number of observations the casual method will be selected. (Graefe, et al. 2010:9-11)
35 Figure 8: Selection Tree for Forecasting Methods
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In order to get the most accurate forecast results one need to start by analysing the purpose of the forecast. By answering the following questions together with the forecasting tree one will be able to find the best forecasting method in order to achieve the goal. Firstly, what is the purpose of the forecast? What is the time horizon for the forecast? Use the forecasting decision tree to find the appropriate method for the forecast. Gather and analyse the data. Make the forecast and lastly monitor the forecast.
By applying this method explained above to the two different practical examples one can find the right forecasting methodology. The brownfield project example that will be used in the case study is based on a project initiated by the Research and Technology (R&T) business unit. They were facing supply chain problems due to the increasing pressures to render a service to the ever expanding Sasol group of companies. Their logistical infrastructure was designed on the basis of rendering a service to the Sasol group of companies across two Sasol production sites. In recent years this has expanded to 8 production sites across a larger geographical region. This resulted in high storage cost, over procurement, slow customer, response times and decreased profit margins. The purpose of this forecast is to determine what capacity is required in terms of the logistical infrastructure in order for R&T to be able to render these services to a larger geographical area with more production sites. The forecast will help to establish cost, time and resources required in order to get the optimal results. The time range will be in the strategic time frame spanning over the long and medium term.
When analysing the data for the brownfield example, one see that some of the data consist of sufficient objective data, thus the path of using quantitative methods are followed for them. For the data that do not have enough objective data, one will have to use judgemental methods. This mean that the result will have multiple different forecasts that need to be combined using a decision making tool in order to get the best results.
For the greenfield project example, the need was identified for the fertiliser supply chain to be design due to plans to expanding the current product range with the addition of a new manufacturing facility as well as the debottlenecking of the current facilities that would increase the current output. The purpose of this study was to find the lowest possible cost with the highest rate of return on investment (ROI). This project time frame will be strategic and look at the next 2-5 years. When looking at the data and by using the decision tree, the
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findings shows that one will be required to use both judgemental as well as quantitative models. This is mainly due to the large number of data sets that needs to be incorporated in order to find the best solution. The data will be gathered from a range of sources; from SAP & BW for historical data on sales, production and storage; to data from SME’s on predicted sales; increased estimated capacity due to debottlenecking of the existing plan to new theoretical production rate of the new manufacturing facility. The end result is multiple forecasts that need to be incorporated into a decision making tool to tests different sensitivity analysis and scenarios in order for the decision makers to end up with the best possible result.