Forecasting Models

All business managers must look to the future to make important decisions about their operations. They make projections concerning various aspects of the business and the markets where it operates to inform planning and decision-making. Various tools help them make these projections.

A forecasting model is one such tool employed by businesses. It utilizes statistical and data analysis methods to calculate projections. Forecasting models predict various types of behavior, including sales, revenue and profits, product demand and prices, inventory, labor conditions, and other industry trends that can impact the business.

Analysts input various types of information, such as sales and financial data, market trends, demographic trends, and other data, to calculate their projections. The results will reflect the data input type, the model's objective, and the methods used.

Forecasting models make projections about many different variables impacting the business. Investors use forecasting to project earnings estimates and future stock prices of a potential acquisition.

Lenders employ credit forecasting, which examines past credit history to determine the creditworthiness of individual customers or projections of future cash flow to evaluate an applicant for a business loan.

Business managers use forecasting to help them make informed projections about various business aspects. Projections of consumer demand and other market trends help managers make marketing decisions.

Companies that manufacture products use forecasting to set production goals and make decisions about purchasing and inventory. Similarly, they use forecasting to project and assess supply chain trends and the availability of critical resources.

Finally, all businesses rely on a healthy workforce to sustain their operations. Forecasting calculates trends in the labor supply, which can impact hiring conditions and salaries.

Forecasting models can be divided into one of two basic categories according to their approach to calculating their projections.

Quantitative forecasting relies on the analysis of data and numbers to make projections. Models analyze numerical data such as sales figures, prices, or the gross domestic product (GDP) to make calculations.

In contrast, qualitative forecasting relies on information based on others' opinions. Models rely on subjective information from sources such as focus groups, surveys, polls, or consulting information as the basis for their projections.

Both categories of forecasting have pros and cons; ideally, a business will employ some combination of the two to arrive at the most informed and insightful forecasts.

Within the two basic categories of forecasting, there are different methodologies that forecasters employ:

Time Series Model - A time series model analyzes historical data statistically to make future projections. Data that measure patterns or trends over time are inputted into the model to generate projections about how those trends or patterns will behave going forward.

This type of model itself can employ one of several different methods:

1. Autoregressive Integrated Moving Average (ARIMA) utilizes statistical methods, including moving averages and regression analysis, to calculate future projections based on historical patterns.

2. Exponential Smoothing (ES) is a statistical method similar to ARIMA, which smooths out moving averages by assigning greater weight to more recent figures and less weight to more distant observations. This method is often used to make short-term projections.

3. Seasonal Decomposition of Time Series (STL) models account for seasonal fluctuations in historical information by "decomposing" the data into separate seasonal, trend, and irregular or residual patterns, then combining them into an aggregate analysis. This method is useful for analyzing historical data that fluctuates significantly during certain seasonal periods, such as gas prices that peak during the summer or retail sales that increase over the Christmas holidays.

4. The vector auto-regressive (VAR) model is a time series model that analyzes the historical patterns of multiple variables (multivariate) and how they interact with and impact each other.

5. Econometric Models analyze the statistical relationship between various economic activities and the behavior of one specific economic pattern or phenomenon.

6. Judgmental Forecasting Models are a form of qualitative forecasting that relies on opinions, judgments, and experience to make projections. They can employ any one of a number of methods, such as expert opinion from professionals, market research, including surveys, polls, and focus groups, and the analysis of scenarios or historical analogies, to arrive at projections.

7. The Delphi Method is another qualitative method that employs the gathering of opinions rendered by a panel of experts through multiple rounds of surveys. Panelists are queried, and their opinions are shared anonymously with the group after each round. Individuals are allowed to adjust their responses based on the group's collective response. This method seeks to combine the benefits of expert opinion with consensus building in the rendering of forecasts.

Forecasting models are an important tool in modern accounting. They provide valuable information that supports many essential functions:

The projections produced by forecasting models support strategic planning by providing insight into how the business and the environment will perform in the future. This allows management to set goals, allocate resources, and devise competitive strategies.

Forecasting models anticipate future trends and risks, helping businesses perform essential risk management. The data helps leaders plan for uncertainties and devise contingency plans in adverse conditions.

Forecasting models also support resource optimization. Supply chain and inventory projections help managers maximize the allocation and usage of resources, increase operational efficiency, and minimize waste.

Businesses can also use forecasting models to maximize their market competitiveness. Insight derived from projections about market trends, such as consumer demand and pricing, gives businesses a competitive edge and allows them to proactively plan and respond to these trends.

Forecasting models also aid financial management. Having reliable data on important metrics such as revenue projections, cost estimates, financial performance, and growth supports more informed planning, budgeting, and investment decisions.

Forecasting models face many challenges. Like all statistical analyses, the quality of their output is only as good or as reliable as the quality of the data input. Especially in the case of quantitative methods, which are entirely driven by data analysis, accountants must take care to use reliable, timely, and relevant data.

They must account for data fluctuations due to seasons and cycles, as well as changes in markets, products, and other variables examined.

Complexity can also be a downfall in forecasting models. Accountants should keep things as simple as possible and avoid the temptation to rely on overly complex models, which may introduce a higher risk of error and may not be necessary.

When examining patterns, analysts must always account for external factors. Focusing too narrowly on a data set to the exclusion of other variables that could equally impact the forecast will lead to less reliable projections. Economic, market and industry trends that impact the business must be considered.

Several basic steps are typically followed in the process of constructing and utilizing forecasting models to ensure accuracy and increase the value of the projections:

1. Data Collection - The first step entails data collection. Gathering the relevant historical information for modeling ensures the accuracy and completeness of the analysis.

2. Model Selection - Once the data is gathered, model selection involves choosing an appropriate forecasting model that fits the data and will allow analysts to fulfill their forecast objectives.

3. Model Training - Analysts improve the reliability of their forecasts by conducting model training. They conduct trial runs using test historical data and compare the results with a separate validation data set. The comparisons allow them to evaluate and adjust the model’s parameters where necessary to improve its accuracy.

4. Integrate the Forecasting Model - Finally, business managers integrate the forecasting model and the resulting projections into the larger decision-making process for the business.

Data and insights can be leveraged to drive strategic initiatives, like planning, budgeting, and marketing strategies.