Smarter Sales Forecasting: Artificial neural networks (ANNs)

An artificial neural network (ANN) is a mathematical system modeled after the human brain. Artificial intelligence has made major advances in recent years, and ANNs are no exception. 

With advancements in machine-learning software and faster computers, ANNs have grown ever more powerful and accurate. The result: better forecasts for sales and operations planning, improved customer service, increased return on investment (ROI), and reduced marketing expenses.

The first step to smarter sales forecasting is understanding that an ANN doesn’t just spit out a forecast — it actually learns from input data such as historical demand data or even current demand trends to create its forecasts. 

And as this article Mover Edge will show, ANNs can perform a multitude of forecasting tasks — from inventory to sales and operations planning, from sales and customer support to marketing.

How Does ANNs Help ?

ANNs can save time, improve accuracy, and lower costs. The software can perform many forecasting tasks and quickly generate a forecast that is as accurate as or even more accurate than those generated by humans. And the best part is: ANNs learn from their mistakes — and they never get old. As new data becomes available, they continue to learn and improve their forecasts.

ANNs can offer the following benefits:

1. Improved accuracy. 

ANNs are highly accurate and efficient because they have the ability to learn from their mistakes. They can build on past successes and quickly recognize trends that might have been missed by a human analyst.

2. Lower costs. 

Just as ANNs are faster and more accurate than a human analyst, they are also less costly. As ANNs do not require salaries, health benefits or vacations, they can offer the same level of accuracy for less cost than manual methods.

3. Faster forecasting process.

 Today, most traditional forecasting approaches involve re-keying historical demand into a computer program and then feeding that new data back into the forecast model over and over again until the forecast is finalized. This approach is error-prone and can take days or even weeks to complete.

With ANN’s, most of the work is done ahead of time. The analyst generates the model and data, then the software trains the model to better recognize patterns and trends in these new data. The training process can take place in a few minutes — far less than humanity can do manually. When the results are ready, the analyst simply inputs new forecast data and runs those numbers through his or her model.

4. Better forecasting.

ANNs can dramatically improve forecasting accuracy and speed, but this is not the only reason to use ANNs for forecasting. Forecasting with ANNs is also a much better experience than forecasting by hand. 

Human analysts must sit with their spreadsheets and figures, generating their forecasts one number at a time. ANNs, on the other hand, process hundreds or even thousands of numbers in mere seconds — the full forecast can be completed in minutes. This allows the analyst to work on many more tasks at once; it’s not uncommon for an analyst to generate forecasts for all product lines and regions in less than 10 minutes.

5. Increased forecasting flexibility.

ANNs can produce accurate forecasts by incorporating historical demand data or current demand trends into the model. An analyst can easily define how he or she wants to incorporate these new data points into a forecast, instead of having to implement change management on a different timetable. 

They can also use these models for strategic planning — in fact, some experts say that an ANN model should be used for strategic planning as well as for day-to-day forecasting.

6. Improved communication and decision support .

Forecasts provide much better communication and decision-making capabilities than what is possible with traditional techniques. Forecasting is a valuable tool for communicating organizational objectives and planning new product development, product discontinuation, MROs (maintenance, repair, and operations), R&D programs and marketing campaigns.

What Makes an ANN?

An ANN is a computing system modeled after the functioning of the human brain. The key to every ANN is the neuron, which is composed of inputs and outputs. The inputs include all information that affects the processing of a particular output, such as sales volume, weather, product lines and so on. The outputs are determined by neural networks (NN) — products or services that can lead to an increase in sales volume through improved customer service and upgraded promotions during holiday periods.

The NN is the chain reaction created by a network of interconnected neurons. The process begins with input data (e.g., customer demand), followed by processing (running numbers through an algorithm), followed by output data (e.g., the forecast is completed). The NN can be used for a multitude of operations, including sales forecasting, inventories, product design or business decisions.

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