Demand forecasting is a method to predict future consumer demand for products based on historical data. There are numerous benefits to having such insight into the future. Demand forecasts can dramatically improve supply chain management, profits, cash flows, and risk assessments. Demand forecasting is ultimately about optimizing systems, by reducing costs and avoiding prospective losses.
The age-old problem for companies has been satisfying demand without losing money by overstocking or having two few items when demand is high, resulting in lost sales. Companies often end up either wasting money on buying and storing excess stock, or they are unable to process orders because they are out of stock. Because of this problem, people have used intuition and easily visible trends to forecast demand; however, with big data and AI, we are able to achieve new levels of accuracy in our predictions.
Businesses have accumulated vast amounts of data in the past 50 years. In order to reckon with this data, analysts have developed demand forecasting methods that rely on simple software and many assumptions. In a high-stakes process like demand forecasting, companies should leverage the best AI solutions available today. In order for businesses to embrace the real power of their data, they need AI.
AI-based demand forecasting can take real-time data as input, and effectively detect a change in trends and patterns on the data, information that would be highly valuable for decision-makers. Additionally, the models can be retrained as new data is fed in and potentially improve predictive accuracy. AI-based demand forecasting equips demand planners with abilities to draw out trends from a large amount of data with complex time-related patterns.
One of the findings of the most recent M4 time series competition is the advantage of ensembling machine learning algorithms with traditional statistical models to increase predictive accuracy, allowing to fit more intricate relations that some pure traditional statistical models or pure machine learning models cannot capture (Makridakis, Assimakopoulos, & Spiliotis, 2019).
Cognistx has extensive experience creating and applying state of the art methods and models for demand forecasting. We have implemented a routine to automate demand prediction and find the best-fitting models, based on an ensemble of machine learning and statistical models. Our company has developed a food short-term demand prediction algorithm for a major retail company to improve inventory management and to be able to get the food ready to eat at the right time. We implemented a predictive model for any coming three hours, as well as a predictive model for any coming two days. These models capture the effect of a specific hour and date, weather, and local events on demand, assuming no major changes in the time-series patterns. With the use of these predictive capabilities, a business can plan accurately and increase inventory management efficiency and client satisfaction.
AI applied to demand forecasting can also be leveraged to predict short-term shipping demand for logistics and supply chain companies. Particularly, when the company has a myriad of routes of operation and every single route has a specific data pattern over time, such as intermittent demand, complex seasonal patterns, and different trends, a thoughtful and research-based selection of the right machine learning and statistical models for each route is required to successfully improve logistics operations efficiency. The predictive models for shipping demand usually incorporate past data, external factors, and spatial relations into consideration. Those models should be automatically and periodically retrained as more information is available to increase the model performance. At Cognistx we have worked tackling this problem. We have predicted daily demand for every single route and every specific customer, helping our clients improve their operational efficiency.
Leveraging AI tools, data scientists can make use of big data and tremendous amounts of computing power, to automate the creation of predictive models for demand forecasting, automatically finding the best-fitting model for hold-out data. This process can be applied in real circumstances to mass datasets from internal ERP, CRM, IoT and systems, and external data from market analysts, social media data, and weather data. The more relevant data the AI has, the more robust forecasts it can provide.
The most iconic feature of AI is real-time responses to change. AI’s real-time processing feature allows us to identify anomalies on demand and to predict, in a timely manner allowing decision-makers to act accordingly. Demand predictions would enable businesses to allocate equipment and personal resources optimally resulting in higher profits.
