Forecasting is used in almost every area of business today. It is an essential and basic tool for managing an organization of any size whether it is small or large. Any kind of business generates the huge volume of data, which needs to be analyzed for the growth of business and to understand their customers. Data analysts spend a considerable amount of time to make a forecast based on the historical data using tools and statistical models. For every crucial part of the business forecasting or prediction plays a remarkable role, for the e.g. forecast of sales volume, the recommendation of the product or stock prices, demand in next quarter or year, and trend of your business. These are the key factors and the backbone of decision-making in most of the organizations. It is a business necessity which pays off if analyzed and optimized effectively.

Sources of data generation and requirement of data collection are being increased over the time and it gets complex in nature. So, it’s necessary to understand your own components of data like seasonality, irregularity, and cyclicality. And to do that, there are so many tools and statistical models are available in the market.
As a result, it is important to select the right forecasting method to handle the increasing variety and complexity of data to forecast correctly. However, before selecting the forecasting model, a data analyst or forecaster needs to have answers to the following questions.

•  What’s need to forecast and purpose of that?
• Must have the idea about the variable relationships.
• Is the volume of data being sufficient in terms of population and sample?
• Are the sources of data being trustable or correct?

These questions or points will help the data analyst to direct themselves to the right forecasting model and build an accurate set of growth projections for their businesses.

Types of Forecasting Models?

In statistics, there are two types of basic methods by which a business forecast can be made. These are categorized broadly into qualitative and quantitative models.

Qualitative Models

This method is less frequently used and involves forecasting demand based on less measurable factors or variables such as market forces, economic demand, and potential demand. Qualitative forecasting approaches could be measured an art learned by inventory planners over years of practice. Inventory forecasting practices are inseparable from current stock review and reorder methods.
Usually, qualitative models are used to make short-term forecasts. The qualitative model is used when the availability of data is low. These models are frequently used in predicting numbers based on:

Market Research: It incorporates procedures for testing hypothesis from the available numbers for real markets.

Delphi Method: This method involves taking opinions from experts through questionnaires and then using it into a forecast as an input. It gives the better idea and direction about the data and forecast.

The objective of qualitative models is to forecast numbers based on logical and unbiased opinions. Many organizations use a combination of both the methods to forecast sales and revenues.
However, there are a few limitations to this method. The first one is that it depends solely on opinions which may be wrong. Secondly, the accuracy of this method is not high and mostly depends on human judgments.

Quantitative Models

The qualitative forecasting approach is a statistical model based on historical data. It involves using historical sales data to forecast future demand for goods procurement or sales. It can be used for small and large datasets, however, the more data available, the more accurate picture of historical demand will be attained. While it may provide a basis for forecasting, demand can be unpredictable based on variable market conditions or product seasonality. Unpredicted peaks in demand can result in product outages and quiet periods may result in the costly additional product, which can build up carrying costs resulting in diminishing returns.

Quantitative models are used when the data is available for several years and we can build relationship among variables. Further, quantitative models can be categorized as:

Regression Model: The model uses the least square technique to form an equation based on dependent and one or more independent variables.

Econometric Model: The econometric model tests the relationships between variables such as sales, promotional campaign, and customers over time. The model forms interdependent regression equations.

Time-Series Model: The objective of a time-series model is to discover patterns in historical data and extrapolate it into forecasts. It uses exponential smoothing, ARIMA, and trend analysis to forecast data for the next time-periods.

Leading Indicator: This model uses the relationship between different macro economic activities to identify leading indicators and estimate the performance of the lagging indicators.

Both qualitative and quantitative models provide decision-makers with numbers that are useful in production planning, financing, and business optimization. A successful forecaster removes irregularity and non-stationary components in data. However, there are a few factors which might lead to wrong forecasts. This happens when:

1. The data is inaccurate.
2. The data is produced with a lag and requires revision.
3. The data is a proxy for the decision-making criteria.

So, it is crucial to address them before jumping on any business decision.
Review your forecast
Avoid forecasts that are irrationally exuberant or overly conservative by targeting the sweet spot in between. To perform the quality test of your forecast, we should consider the following checks while reviewing the forecast accuracy: –

1 Accuracy: – We often refer to the accuracy in terms of how closely our forecast follows the highs and lows of the real-time data and the situations.

2 Comprehensive: – A comprehensive forecast, is one that provides visibility into every part of the data and considers each marginal factor. And decide whether or not the uncertainty lies in the data

3 Defendable: – As a final check, the forecaster should be able to successfully accurately explain, using evidence. The check would he/she be able to defend their position with conviction, balancing data and experience?
Accurately forecasting is essential for maintaining profitability and understand the business.

End Note

Forecasting plays a major role in long-term business planning. An accurate analysis of data is vital in managing the growth of the organization, and ultimately in ensuring its success. However, necessary steps should be taken to review the forecasts before making the blueprint of any business decision. A right forecast will make your business more profitable and pave the way to a successful organization. In a nutshell, forecasting is like a magical crystal ball that can see the future when asked the rights questions and used the right techniques for all your business problems.

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