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The landscape of demand forecasting, data science and machine learning is rapidly evolving, as companies seek innovative approaches to handle the intricate intersection between technology and consumer demand.
As businesses adapt to increasingly complex market conditions, the role of demand forecasting has become central to success. Demand forecasting isn’t just about predicting what products will be sold but understanding the deeper drivers of consumer behavior.
According to Nicholas Wegman, PhD., senior director and artificial intelligence scientist at Zebra, “Price is still the number-one thing that drives consumer behavior. Holidays, social media, weather — all these things have an impact, but price is still the most prominent.” He emphasizes the critical need to integrate various factors, including social media trends, economic conditions and product availability, into forecasting models. As consumer behavior becomes more dynamic, companies must not only forecast demand but also account for shifts in pricing and external factors like inflation and tariffs. By considering these diverse elements, businesses can develop more accurate and responsive demand forecasting strategies that align with the ever-changing market landscape. This holistic approach ensures that companies can adapt swiftly to market changes, leveraging machine learning and AI to stay ahead of trends and disruptions.
A Game-Changer for Speed and Accuracy
The traditional methods of demand forecasting, often based on simple statistical models, are being replaced by machine learning, which can process a far greater number of variables. Wegman stresses the necessity of rapid iteration to stay ahead of trends. “With machine learning,” he says, “you need platforms and techniques that allow you to rapidly iterate on new factors that are affecting demand.”
However, the pace of change in the data science field presents its own challenges. As new models and open-source libraries emerge constantly, staying up to date with the latest technologies is an ongoing process. “Not only do you have to keep up on the business side of how to bring in all this external data,” Wegman says, “but you’ve also got to keep up with the latest models.” Quick testing and deployment of various models ensures that companies can adapt swiftly to market changes. This capability is crucial in today’s fast-moving consumer packaged goods and retail environments, where businesses can’t afford long development cycles to test models.
The Importance of Real-Time Adaptation
In an era where disruptions can occur rapidly — whether due to geopolitical events, natural disasters or shifts in consumer sentiment — the ability to adapt to real-time data is a significant advantage, which includes reacting to unexpected, “black swan“ events. “You need something that is as unbiased as possible,” Wegman says. “How do you model those spikes?”
Successful forecasting goes beyond simply predicting demand. By integrating external data sources — such as social media trends and macroeconomic indicators — into the forecasting process, companies can quickly adjust to sudden changes in the market. A holistic view is crucial for businesses that need to respond to dynamic consumer preferences and market conditions.
The Omnichannel Shift
One of the most profound shifts in recent years has been the rise of omnichannel shopping. The traditional boundaries between in-store and online shopping have blurred, creating new forecasting challenges. Consumers today are more likely to shop across multiple channels — whether buying online and picking up in-store, shopping in-store and ordering online, or using a mix of both.
Wegman says this shift is fundamentally changing the way businesses forecast demand. “The omnichannel world has really exposed that people are a lot less concerned about the channel they’re buying from,” he says. “Before, maybe they were always buying in-store, but now people move between channels, and that’s changing what they buy.”
For instance, a product’s popularity might vary between in-store and online settings, with different items being favored depending on the shopping medium. This can be addressed by providing businesses with the tools needed to create more granular demand forecasts, going beyond the store level to include insights at the ZIP code level.
A more detailed approach to demand forecasting is possible through the integration of various data sources, including social media, weather patterns and local economic trends. As consumer behavior becomes more fragmented across channels, understanding these micro-level patterns is key for retailers and CPG companies to accurately meet demand without overstocking or understocking products.
One of the biggest challenges in demand forecasting is ensuring that models developed by data scientists align with the needs of the business. Collaboration between data scientists and business stakeholders is crucial for maintaining the relevance of forecasting models. “The conversation between the data scientists and the business is so important to understand what the business thinks about when they’re planning,” Wegman says. Collaboration ensures that the models are aligned with business strategies and can be adjusted to accommodate unique factors, such as regional consumer preferences or the impact of specific marketing campaigns.
Leveraging AI and Machine Learning
Integrating AI into demand forecasting is about balancing short- and long-term forecasting needs. It means improving short-term accuracy while simultaneously enabling businesses to make better decisions over the long term.
Companies also benefit from machine learning models that are designed to scale and can be deployed across multiple departments and business functions. Scalability is vital for ensuring that forecasting solutions can keep up with the demands of large, complex organizations. “Once you’ve validated on a small department,” Wegman says, “you need confidence that your forecasting pipeline will scale immediately.”
As organizations continue their journey toward AI and machine learning adoption, embracing the technology in stages works well for many businesses. They’re integrating AI into their existing workflows, with the ultimate goal of transitioning to AI-first operations.
Getting valuable insights into demand forecasting today requires the ability to manage the complexities and opportunities within demand forecasting with agility and intelligence. Machine learning and external data sources ensure that businesses can stay ahead of the curve in an ever-evolving landscape. What’s more, fostering collaboration between data scientists and business stakeholders enables organizations to make more informed, agile decisions, positioning them for success in an increasingly unpredictable market.
Wegman’s advice for organizations is to start now. Embracing AI and machine learning for demand forecasting has become a necessity for companies looking to thrive in a competitive world.
Resource Link: https://www.zebra.com/us/en/software/workcloud-solutions/workcloud-demand-intelligence-suite.html
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