Strategies for Automating Your Forecasting Process

Posted by: Justin Croft

Feb 17, 2022 2:27:53 PM

Forecast AutomationForecasting is a discipline that reduces companies' exposure to risk. The process of forecasting alerts us to upcoming shortages in supply or demand, allowing us to take precautionary measures before we find ourselves in a negative cash-flow scenario. Forecasting is truly a critical business function within any organization. Automating your forecasting process can help you maximize efficiency while reducing costs and improving accuracy. This article will outline how you can use a variety of tools and approaches to automate various aspects of your forecasting process.

The Benefits of Automatic Forecasting

In the past, companies have relied on spreadsheets to perform their forecasting. While this method may still be preferable for some companies, many organizations are discovering the benefits of fully automating their processes--especially when it comes to generating a large number of forecasts or lower-volume demand signals that can be automated by a system. For example, an organization not only can automate its entire forecast process from start to finish but also can move beyond the traditional rule-based forecasting approach and deliver more accurate and actionable insights using statistical algorithms.

Today's business environment is dynamic, and users must adapt their tools accordingly in order to respond quickly to market demands. The increased use of automation within a variety of business processes is largely due to its ability to improve accuracy and efficiency.

How Automation Can Improve Your Forecasting

Forecasters have traditionally been spreadsheet jockeys, spending hours upon hours producing what is probably the most valuable piece of information your organization can provide management: future demand! In order to deliver this insight, however, they must "guess" based on whatever available data you possess. This process can be time-consuming and laborious when it involves hundreds or thousands of products with varying degrees of impact on profitability across multiple geographies around the globe.

There are two primary ways automation will help improve your forecasting: machine learning and optimization. Machine learning allows systems to formulate basic "rules" for new data and adjust as necessary over time, whereas optimization takes a more scientific approach to modeling outputs. Both can help increase accuracy while minimizing volatility.

Machine Learning for Forecasting

In today's context, machine learning is the process of automatically recognizing patterns in large amounts of data using algorithms. In forecasting, this technique allows systems to recognize statistical patterns in historical data by analyzing key drivers such as seasonality, promotional activity, and similar products that have historically influenced demand signals. By incorporating these insights, a manufacturer or distributor can fine-tune its forecasting algorithm to produce more accurate results.

For example, a pharmaceutical company may combine machine-learning techniques with a promotion model containing a series of rules whereby regional market conditions are accounted for through weighted factors involving promotion history. As a result, the model will adjust forecasts to reflect not only the actual promotion activity but also the anticipated probability of success associated with that activity. Even though promotions are historically effective in driving demand for certain products, there is typically less certainty surrounding their impact. This type of model holds great promise as an accurate forecasting tool because it can be adjusted over time and leverages both the statistical data (the "known") and qualitative data (such as product attributes) involved in promoting products.

Optimization for Forecasting

With optimization, systems can "learn" from historical data through multiple cycles what factors influence demand the most, then apply those findings to generate more-accurate future forecasts using complex mathematical models based on complex rules.

Typically, optimization incorporates a large number of variables and a multitude of rules in order to incorporate every possible influence on demand. Optimization is more complex than machine learning because it can involve hundreds or thousands of coefficients for each product, whereas with machine learning the system only needs to use a handful of rules to produce results. Although this technique tends to be highly accurate, there is little room for adjusting forecasts over time. In other words, once optimized, your forecast model works well until you need to optimize it again by adding new data—and that process may require months.

In contrast, sometimes you want quick insight into what will happen with current conditions remaining unchanged from last year. For example, if you know that your company's fiscal year ends in January and you want to determine its annual revenue by quarter, an optimization model may not be the best choice. Machine-learning techniques may help you estimate this demand more quickly than waiting for the year-end data or doing a complete optimization of historical data each time.

It is important to understand that while both machine learning and optimization can improve forecasting accuracy, they are not mutually exclusive. They work well together because their respective strengths make up for each other's weaknesses: Optimization tends to generate accurate results with a high degree of certainty, but it takes a long time to achieve those results. Meanwhile, machine learning allows systems to "learn" from new data more readily—but there is always some element of uncertainty with any forecasts, given that the future is unknown.

Forecasting Accuracy Matters

The degree to which you need accurate forecasts—and the time frame within which you need them—will influence the approach that you choose to use. All three techniques have their place in forecasting, but sometimes one method will give more useful results than another, depending on how much historical information is available and your timeframe for analysis. For example, if you are forecasting quarterly product demand within a specific channel or geography, optimization may be the best choice because it often produces highly accurate forecasts quickly. However, if you need longer-term projections (six months or more) and don't want to make multiple submissions over extended timeframes as part of the modeling process machine learning might be a better solution. Meanwhile, if you want to predict demand in a specific channel or geography but don't have significant historical data available, machine learning might be your best bet.

Forecasting can provide actionable insights into future demand for products and services without waiting until the last minute. It is an iterative process that enables companies to adjust strategies as they develop through each stage of planning—whether it's assessing how much product should be ordered from suppliers based on expected sales volume or determining how many staff members should be hired to support a busy season. This can go a long way toward improving a company's operational efficiency and mitigating risk associated with disequilibrium between supply and demand.

If you are interested in learning more, please join QueBIT for its upcoming webinar on “Strategies for Automating Your Forecasting Process” on March 10th, 2022.

Topics: Machine Learning for forecasting, Automate Forecasting


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