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Artificial Intelligence – also called AI – is a groundbreaking technology and a central topic today and for sure will be one tomorrow too. Machine learning, voice or face recognition, virtual assistance… Artificial Intelligence utilisation is wide and adapted to every type of business. Do you know why you should use this new technology? Sales forecasting first!
In terms of forecasting, AI will transform how a company interacts with its own data for and from sales. … But it does need you to learn faster; it needs your guidance and input to amplify what data it has been given. You will need some patience for this type of tool as it learns about your business.
The rise of cloud computing, smart devices and IoT have all resulted in a massive data explosion. By 2025, the amount of data will double every 12 hours. AI now has the ability to consume data far more efficiently than humans, delivering on the possibility of insights and predictions never before imagined. But McKinsey reports that only 33% of organizations are effectively using internal and external data to take advantage of AI capabilities.
Predicting future demand with traditional methods in the face of a shifting market can become a serious barrier to visibility into what lies ahead. Many sources reinforce that the number one obstacle to achieving supply chain goals and objectives continues to be forecast accuracy and demand variability. This trend continues despite the investment many have made in technology to better predict customer demand.
AI can also play a strong role in discerning patterns and delivering insights for short term forecasts. Predicting actual orders using data like page-views and current inventory positions from vendor portals, companies can set up recommendations on inventory positioning by weighing the tradeoff of stockouts vs excess inventory, also significantly improving service levels.
Organizations are seeing that they can reinvent how they forecast with the latest AI technologies. Cloud computing is bringing scale forecasting . AutoML (automated machine learning) helps build more effective models. Hierarchical forecasting allows for better reconciled top-down and bottom up forecasting. And, algorithms that take advantage of deep learning are delivering improvements through cross-learning, using internal and external data inputs to detect patterns. The resulting insights provide organizations with the ability to adapt to shifting external dynamics and accelerate enterprise-wide decisions about supply chain scalability, sourcing strategies, product portfolio roadmaps and capacity planning can mean the difference between competitive dominance and obsolescence.