What criteria would you advice to be applied when choosing a forecasting technique or techniques?

Forecasting is a useful tool that helps businesses to predict and evaluate future sales patterns, giving them the data, they need to make informed decisions. Forecasting allows organizations to understand what lies ahead and adjust their operations accordingly.

Forecasts are useful tools for making predictions and analyzing future results. Companies may use the information to analyze the long-term impact of changes, prepare responses to such changes, forecast economic swings, and manage competitive pricing. To produce highly accurate forecast projections, business executives must first select the best forecasting strategy for their specific needs.

We'll look at some of the approaches that are employed throughout the world and how to pick the right one for a particular business situation.

Forecasting is classified into two types: qualitative and quantitative forecasting methods.

Qualitative Techniques

Qualitative approaches are those that use knowledge about the company, market, product, and customer to make a forecasting decision. Forecasting employs a variety of qualitative methodologies. The Delphi Method, Market Research, Expert Opinion, and other methodologies are essentially dependent on opinion.

In forecasting, the Delphi technique is widely applied. A panel of specialists is questioned about a topic, and analysis is performed based on their written judgments to provide a forecast.

The market research technique is a more structured and systematic approach for estimating market sentiments and forecasting based on multiple assumptions. Customer surveys and questionnaires are used in the market research demand forecasting techniques to forecast future demand.

Also called panel consensus approaches, implies that bringing together a panel of experts will result in more accurate forecasts. There is no moderating here, and the panelists arrive at their own conclusions on the forecast.

In the forecasting of new product sales, qualitative methodologies are commonly used. Because the new items have no previous data, these methodologies serve as the foundation for forecasting. It may also be used to predict sales in a new market. The majority of the approaches rely on a lengthy questionnaire that is distributed to experts or survey participants. The analysis is carried out based on the comments and views in order to get the best forecast possible. For a short-term projection, qualitative forecasting techniques perform well. When it comes to long-term forecasting, the market research approach may outperform the other methods. Most businesses do numerous forecasting techniques to gain a more accurate picture. 

When compared to quantitative approaches, the cost of qualitative forecasting is generally quite high. The time it takes to create such projections is likewise considerable, ranging from two to three months or more.

Quantitative Techniques

Quantitative forecasting is the process of analyzing a large amount of data in order to find important connections and patterns that may be used to predict future outcomes. Quantitative methods of forecasting include historical data and statistical tools to create a forecast. Quantitative techniques are divided into two categories: Casual and Time Series forecasting methods.

Time series forecasting methods also known as the statistical forecasting method, create predictions about future outcomes based on historical data. This information is obtained and documented over a period of time, such as a company's revenues for a certain quarter over the previous five years. Because business patterns and trends tend to repeat themselves, forecasters can utilize clear and steady past data to guide and plan for future actions.

Frequent application of time series forecasting is in sales, inventory, and margin forecasting. For a short- to medium-term forecast of up to a year, time series forecasting techniques perform well. To forecast effectively using time series forecasting, a minimum of two years of data is necessary where seasonality is present. In comparison to qualitative procedures, time-series techniques are relatively cheaper. Depending on the intricacy of the data, forecasting might take anywhere from a day to a month.

Causal forecasting is a strategy that assumes a cause-and-effect relationship exists between the forecasted variable and one or more other independent variables. Influences on the dependent variable are taken into account in this technique. As a consequence, forecasting data might range from internal sales data to external data such as surveys, macroeconomic indicators, product features, and so on. Typically, causal models are updated on a regular basis to ensure that the most up-to-date information is included in the model.

The majority of causal forecasting models are most effective for medium-term forecasting (up to a year). Causal forecasting may be used to make detailed predictions. It may also be used for any forecast in which the dependent variable is influenced by many forces.

The factors listed above provide a quick overview of the subtleties to consider when selecting any forecasting approach. Analysts must, however, consider other criteria such as business knowledge, stage of business (new, growing, or stable), and market knowledge when determining the best approach. For example, assessing the stage of business is crucial since different forecasting methodologies are used at different phases. For a new firm with no previous data, it's critical to conduct surveys or panel discussions to make an estimate, whereas developing and steady-state businesses can utilize a combination of time series and causal forecasting methodologies to generate an accurate prognosis. Many more current forecasting techniques, as well as variants on classic ones, have emerged to address various issues. However, in this article, the emphases are on those that are most typically used in predicting exercises. Businesses must choose the appropriate technique with care, and a deep grasp of the technique is just as vital as a thorough understanding of the business or the issue at hand. With the increased need for data-driven forecasting, firms should think about making forecasting a top priority. This will guarantee that organizations use forecasting correctly and stay up to date on the most recent forecasting methodologies.

Selecting the Appropriate Technique

As you will see, there are many techniques available for forecasting purposes, which makes it difficult for people to select the most appropriate technique. In fact, there is rarely one best technique for any given forecasting situation and manytimes it is advisable to use a combination of quantitative and judgmental techniques (see the section on Combining Forecasting Techniques).

• In general, selection of an appropriate technique can be guided by the focal product's stage in its life cycle.

For example, forecasting sales of emerging products which have little or no sales history must rely on more judgmental techniques. As the product becomes more mature and more data is available, simple time series models become more useful. Causal models can ultimately be used with a rich data history. To see the relationship between choice of technique and product life-cycle stage, see Table A1 and A2.

• In general, selection of an appropriate technique can be guided by considering the following key factors about the forecasting situation.

1. Forecast Horizon: Basically, you want to make sure that the technique allows you to pick up changes that might occur during the forecast time interval. For example,

• Short-term: < 3 months

• In the short-term, seasonal fluctuations and randomness are the major influences on sales. Because forecasts at many firms are typically for periods greater than 3 months, short-term forecasting methods are not emphasized in this guide.

• Medium-term: 3 months to 2 years

• These medium-term forecasts require that fluctuations of a medium-term nature (e.g., economic and competitive conditions) are accounted for by the technique. Since cyclical change and trend are important factors in this time frame, techniques such as regression analysis and time-series methods are useful.

• Long-term: > 2 years

• Here, the major consideration is with expected trends, as well as economic, competitive, and technological conditions which can only be estimated subjectively. Judgmental methods are usually best employed here.

2. Data requirements. Techniques differ by virtue of how much data is required to successfully employ the technique. For example, Box-Jenkins models require many data points while judgmental techniques require little or none.

3. Pattern of past data. The pattern of a product's previous sale history is an important factor to consider. While the major pattern is the trend, there are also cyclic and seasonal patterns to consider. Certain techniques are best suited for capturing the different patterns in the data. In Table A1 and A2, it is shown how well each technique captures various pattern elements in the data.

4. Explanatory requirements. Whereas some techniques are based purely on the pattern of past data and may do quite well at forecasting, manytimes these are not useful by themselves since it is difficult to explain the forecast to others who wish to understand the causal factors underlying the forecast. Certain techniques (e.g. regression, leading indicators, judgemental methods) are particularly well suited to incorporating causal relationships.

Table A1 and A2 also cross-lists the various techniques described in this guide with the above factors. In addition to the stage in the product's life cycle, use these factors to fine-tune your selection of an appropriate set of techniques.

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