5 Most Commonly Used Quantitative Techniques in Business Forecasting

Quantitative Techniques In Business Forecasting July 26, 2020

Jin Hyun

Writer

Predicting the future performance of a business can be a fine art. Forecasting, therefore, is crucial to ensuring owners and managers are making the right decisions in terms of inventory control and employee onboarding.

By accurately forecasting a company's future financial situation and expected demand in the months to come, businesses can adjust their inventory control accordingly while ensuring only the optimal number of employees are working at a given time.

Quantitative forecasting is a technique that relies on physical data and sales precedents. There are many different quantitative models to choose from and selecting the most beneficial strategy will vary depending on the company's situation and goals.

Why Use Quantitative Models?

The main advantage of utilizing quantitative techniques is based on available hard data. This ensures forecasting results are as objective as possible, as the importance is placed on numerical information and quantifiable past performance rather than the opinions of industry experts or customers.

The more historical sales data a company has to work with, the more reliable the forecasting results will be, as they'll have a better chance of producing accurate seasonal averages throughout the years.

For example, a business could accurately predict how much a particular product is likely to sell within a specific period of time, purely based on past performance and previous sales information.

5 Common Techniques

This method of forecasting requires more than just a few quick glances at charts and other past data; some approaches may require more complex calculations than others.

Here are a few examples of quantitative techniques companies may wish to put into practice.

1. Regression analysis method

By examining the relationship between two different variables (independent and dependent), businesses can determine how one factor may affect another.

For example, they can compare the volume of sales to the changing seasons. If results show that sales tend to increase or decrease depending on the time of year, the company's sales would be the dependent variable as it relies on the season. From here, they can examine how closely these two variables are related to gain a more accurate picture of how different seasons will affect future sales demand.

2. Econometrics model

This method involves using mathematics to develop equations that help to explain the relationship between different economic agents. Information from this forecast can show the connection between variables such as inflation, exchange rates, GNP, and how changes in these factors affect a business's performance.

3. Index number method

Otherwise known as the barometric method, this technique is more useful for short-term quantitative forecasting. In this method, businesses measure the state of the economy during several different periods in time using index numbers.

The resulting forecast will predict where the economy is headed in the near future and businesses can prepare for falls or increases in demand depending on the health of the local or global economic conditions.

4. Input-output analysis

Also referred to as the end-use technique or I-O, this is another form of quantitative economic research. Information from this forecast allows businesses to predict how a specific input might create a certain output.

For example, an I-O system will take into account the price of materials, worker input to meet demand, and how much money they may invest.

5. Trend or time series analysis

This method of forecasting is based on extensive historical data as it assumes past trends will continue and repeat in the future. This method is recommended for short-term projections, as the only information utilized is previous sales data.

The analysis examines trends, cyclical and seasonal changes, as well as irregular variations in sales to identify a trend in the data.

4 Steps for Implementing Quantitative Methods

There are four general steps that all quantitative methods must follow in order to be effective in practice-

1. The business chooses a question or problem to ask of their data. Such as, "will this product continue to sell through a specific holiday or season?"

2. The business should then look for related past data that is likely to help solve the current issue. For example, it would be helpful to pull previous sales information regarding that particular item during the same holiday season from the years prior.

3. It's important to then choose a route that reflects the business's needs the most. For example, if the company is looking to produce general, short-term forecasts, they should select the time series analysis technique. However, if they are looking to observe the effects of certain variables such as seasonal fluctuations, the regression analysis method would provide more accurate results.

4. The analysis will then take place to observe any consistent patterns in the data before a forecast is made. Users will analyze this forecast in comparison to actual events and determine whether this methodology can be reliable in the future. Changes should be made accordingly to adjust to current situations and increase the accuracy of the forecasts.

Automating the Process

Unfortunately, quantitative forecasting in any kind of business is at risk of human error. This is why many companies choose to automate the task.

Modern forecasting software can simplify the complete analysis and historical data selection processes by seamlessly integrating with the company's POS system. This will eliminate the need for manual data collection and spreadsheet manipulation, therefore significantly reducing the risk of errors in data.

Automating quantitative techniques in business forecasting will ensure maximum flexibility, accessibility, and accuracy as each complex calculation is automatically applied with the click of a button. By implementing this forecasting technology, businesses can minimize risks and errors while also producing more reliable projections quicker than ever before.