Volatility fueled by social media, geopolitical change, and innovation requires more than traditional forecasting methods. How can machine learning help in this case?
In a fast-paced business environment, the evolution of consumer choices poses one challenge for businesses: demand volatility. blaming geopolitical changes; social media influence, fierce competition between companies, and sometimes a global pandemic. Traditional prediction mechanisms do not always provide accurate results based solely on historical data. And what about the different datasets and multiple considerations that directly impact consumer demand dynamics? Thankfully, AI and machine learning (ML) are coming to our rescue and revolutionizing demand forecasting. Masu.
In this article, we step into the realm of machine learning demand forecasting and assess how it goes beyond traditional forecasting techniques to provide deep insights into predicting future purchases for a thriving consumer base.
What exactly is demand forecasting? And what are traditional forecasting techniques?
Demand forecasting is the process of predicting customer needs for future products or services. It helps make inventory adjustments, or inventory decisions, and informed supply to meet consumer needs.
Traditional or statistical forecasting includes methods such as linear regression, simple exponential smoothing, ARIMA, and ARIMAX. Although these methods offer a high level of transparency, they are based only on historical data and are applied in a perfect scheme of situations that are not necessarily prone to disruption. Will we completely abandon traditional methods? After revealing the capabilities of machine learning in prediction, we will analyze this later in this article.
How will machine learning revolutionize demand forecasting?
Machine learning, on the other hand, operates on multiple data sources containing many variables that influence consumer demand. This not only relies on historical purchase behavior data collected over the past two years, for example, but also takes into account current factors to drive advanced predictive analytics.
Machine learning models are built on data-driven predictions that take into account internal and external factors that influence the demand for products and services. Data sources used by machine learning include marketing polls, macroeconomic indicators, weather forecasts, local events, social media influence, competitor activity, historical data, and more. These data sources can safely be categorized as structured data such as past purchase orders, customer's POS information, inventory, sales transactions, and unstructured data such as social media. marketing campaignreviews and more.
ML predictive models use complex mathematical algorithms to understand complex relationships within datasets while adapting to unstable conditions. Common ML predictive models include artificial neural networks, classification and regression trees (CART), generalized regression neural networks, and Gaussian processes.
Traditional predictive models primarily use linear regression techniques, while machine learning models use a combination of linear and nonlinear techniques to arrive at predictions. The result is a high level of prediction accuracy and a minimal loss function. Error metrics such as mean absolute percent error, root mean square error, and weighted root mean square error are observed to be significantly smaller for ML models than for statistical models.
That said, ML is ideal for predictive analytics and short-to-medium-term forecasting with volatile demand patterns when launching new products and services and dynamic business environments.For example, major dairy brands Granarolo achieved prediction accuracy of 85-95% by integrating machine learning with existing systems.
How to maximize the benefits of ML in demand forecasting?
To take full advantage of ML's capabilities, companies should choose one that is compatible with their existing ERP or inventory management system to ensure smooth operations. Solutions can only provide accurate results by leveraging large, high-quality datasets, so businesses need to know the data sources from which their solutions obtain information. Organizations should implement extensive training programs to ensure that their staff can use ML solutions seamlessly. Companies can also choose to purchase his ERP or WMS with built-in models or build custom models that require sufficient investment. Finally, the ML solution should be thoroughly tested to ensure that the level of accuracy of the predictions is acceptable. Otherwise, incorrect predictions from ML models will prevent brands from having the right inventory to meet consumer needs.
How are traditional predictive models still valid?
Although ML models provide a holistic approach to prediction, traditional predictive models cannot be completely abandoned. Statistical predictive models provide high transparency and are ideal for medium- to long-term planning. They are well-suited for products and services that can weather the storm of demand fluctuations and never run out of options.
conclusion
Statistical forecasting methods have been around for a long time, but the growing demand for models that predict masked market trends and avoid volatility has led to ML-driven demand forecasting. As with any complex AI-based model with high computational power, machine learning has requirements to run optimally, including smooth integration with existing systems, investment, and training resources. When integrated and leveraged successfully, it enables companies to accurately forecast and drive operational efficiencies and cost savings across the supply chain. Combining the services of ML with human intervention can support strategic decision-making for increased growth and profitability.