How to Improve Product Demand Forecasting With Machine Learning

As businesses try to thrive in a competitive modern economy, they increasingly turn to their massive data stores as a resource. After all, the growth of eCommerce generates significant amounts of transactional data containing information on customers and the products they purchase.

Making actionable sense out of all this data used to be a process that required data analysts to search through databases for actionable insights. The advent of AI and machine learning in business revolutionized this manual task, allowing businesses to train ML models to perform predictive analysis on customer data. This approach lets businesses better forecast the demand for their existing products, while also informing the process of designing new products. 

The use of machine learning to forecast product demand becomes even more critical considering the current supply chain issues hampering businesses across the globe. Retailers in particular have felt the effects of supply chain issues and those unable to predict demand have seen significant losses.

In contrast, Walmart’s Smart Forecasting team allows the largest brick and mortar retailer in the world to adjust their strategies based on demand to help mitigate the effects of supply chain issues. Walmart’s scalable forecasting platform uses machine learning to generate weekly forecasts for more than 100 million item-store combinations every week.

Over time, a model learns from earlier forecasts, improving its accuracy as it consumes more data. So let’s look more closely at how machine learning makes product demand forecasting more accurate and valuable to the modern business.

Product Demand Forecasting Improves These Interrelated Processes 

When optimizing product demand forecasting, a few critical processes need to be analyzed. Simply stated, making these processes more efficient also boosts the overall efficacy of any forecasts. In fact, an interrelationship exists between these processes and the demand forecasts themselves. 

For example, supplier relationship management becomes easier when having an accurate reading of current and future customer demand. Businesses understand how many products to order, helping them determine what suppliers to engage, as well as the supply chains to be used. Not surprisingly, a detailed analysis of an organization’s customer relationship process also informs its interactions with suppliers. 

Of course, supply chain operations also matter. More accurate demand forecasts optimizes order fulfillment processing, ensuring the desired products are in stock and available for transport. It also prevents unsold products from hogging store shelves. 

Ultimately, improved demand forecasting helps marketing campaigns as well. Data as an output from those campaigns also feeds into the demand forecasts themselves. Needless to say, these complex interrelationships are a perfect scenario for machine learning.

Using Machine Learning to Benefit Product Demand Forecasting 

Traditional demand forecasting methodology leverages both quantitative and qualitative analyses. In recent years, these efforts began to use machine learning models gaining a variety of benefits. Most important among these are more accurate forecasts delivered with higher velocity. 

Without machine learning, Walmart could not train their forecasting models at scale every week. Their In Week Adjustments (IWA) algorithm also leverages historical sales patterns and linear models to predict demand. Additional data also gets fed into the ML models used in forecasting, helping them improve their accuracy over time.

A best-case scenario results in a robust system with the ability to improve its forecasting as scenarios and their underlying data change. It helps retailers and other businesses find those critical patterns hidden in data resulting in more effective product designs and a significant increase in sales. 

Building a Product Demand Forecasting Tool With Machine Learning 

Crafting a machine learning-based tool to forecast product demand requires a focused effort, like any other modern software development project. A rigorous data analysis effort is needed at the beginning of the initiative. The project team must gather and analyze all available data for the various processes detailed earlier. Ensuring the accuracy and consistency of the data becomes critical at this juncture.

Walmart started training their demand forecasting tool with 52 weeks of store-item-week sales. They also pre-processed the data to ensure the data used to train their model met their specific goals.

Documenting the business goals of this initiative also needs to happen before any code gets written. A deep understanding of the project goals helps in generating the metrics to track the accuracy of the models’ forecasting and the ultimate success of the project. Also identify the different product types and time spans to be used in the forecasting. Note that the data analysis and the business goal development processes can happen simultaneously. 

Of course, data cleaning remains a critical task in any machine learning initiative. The forecast tool uses a variety of data elements in both structured and unstructured formats from external and internal sources. These obviously include sales transactions and customer demographics, but don’t forget about supply chain information, even including weather, which also influences customer demand. 

When grasping this wide array of data, it begins to make sense why machine learning is such a gamechanger for product demand forecasting. Still, the project team’s data scientists need to ensure all data is consistent, accurate, and relevant to forecasting product demand. The overall understanding of the data by the entire project team is also critical. 

At this juncture, the project team begins developing and training the machine learning model. Choosing the right algorithms to perform product demand forecasting remains a critical piece of this effort. A time series approach leveraging either the ARIMA or SARIMA models works for many demand forecasting use-cases. Still, deep analysis from the data scientists on the project might discover another approach. 

Provide Training Before Deploying the Tool

After the demand forecasting tool is fully tested, it becomes time to deploy it into production. However, before this happens ensure all future users of the tool receive training in its operation. This training must include operational details of the machine learning models used.

In fact, identifying a group of power users earlier in the project lets you use them as a QA resource. Their opinions just might provide the keen insights the team needs to validate and make adjustments to the ML models. It’s another case where business domain knowledge helps the model development process. 

Ultimately, this overview provides useful insights into how machine learning benefits the practice of forecasting product demand. Walmart’s use of machine learning in demand forecasting improved forecasts for 70% of produce and grocery categories, some by as much as 300 BPS.

Due to a large data science team, Walmart has been ahead in the product demand forecasting game for a long time. However, with the democratization of machine learning, and more access to freelance data science talent, other retailers can finally gain essential insights into their product demand.

If your company wants to leverage machine learning to solve a business problem, partnering with experts in the technology remains the wisest approach. At Gigster, we are experienced in the latest software development technology, including AI and machine learning. Connect with us to transform your company’s great ideas into a state of the art software tool.  

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