Forecasting and AI
Experiment as much as you can
In the realm of demand forecasting, where precision and adaptability are paramount, Artificial Intelligence (AI) emerges as a transformative force capable of reshaping traditional approaches. AI not only enhances the accuracy of predictions but also accommodates a comprehensive understanding of diverse factors that wield influence over demand. Beyond the conventional scope, factors such as weather patterns, economic conditions, and even sentiment analysis are seamlessly integrated into the forecasting process, providing a holistic perspective. This synergy between AI and demand forecasting opens new avenues for businesses to stay ahead of market fluctuations and consumer behavior, fostering a more agile and responsive supply chain.
Fail often and fast
Given the profound impact that AI can have on demand forecasting, I strongly recommend organizations to embark on experimental initiatives incorporating this cutting-edge technology into their forecasting frameworks. Conducting pilot projects and proof-of-concept studies can illuminate the potential benefits and challenges specific to each business context. By embracing AI-powered forecasting tools, businesses can gain a competitive edge, unlocking insights from an expansive array of factors that were previously challenging to incorporate. This experimentation not only positions organizations as early adopters of innovative technologies but also equips them to proactively navigate the complexities of a rapidly evolving market landscape.
Crash and burn. Create an enviroment that is used to make mistakes. Especially adopting this new technology.
The primary goal of this project was to develop a demand forecasting capability by leveraging advanced AI algorithms. We aimed to develop a solution that not only improved forecasting accuracy but also considered an extensive range of factors influencing demand, including weather patterns, economic conditions, and sentiment analysis.
We integrated AI technologies into the client’s existing demand forecasting infrastructure by deploying machine learning models capable of learning from historical data, identifying patterns, and adapting to evolving market conditions.
Our joint team developed adaptive forecasting models that dynamically adjusted predictions based on real-time inputs. This approach allowed for a more agile response to sudden shifts in demand patterns. The incorporation of AI significantly improved the accuracy of demand forecasts, especially due to fact we could take in a lot of qualitative factors.
Therefore the adaptive nature of the models allowed us to respond swiftly to changes in external factors. This enabled our client to proactively adjust inventory levels and promotional strategies, optimizing resources and maximizing sales opportunities.
In this project we used a set of forecasting models to forecast demand and implement AI into the existing platforms. Parallel we used Log-Hub to test and experiment with the new setup.
Project tools:
Interested in AI and supply chain modeling?
We specialize in guiding organizations with the transition to digital supply chain management and the use of quantative optimization and simulation models. Our expertise lies in leveraging these advanced analytics tools to streamline operations, enhance visibility, and optimize supply chain performance.
