Adam Robinson

Special Guest Blog Post written by Chris Cunnane with InterSystems   Demand sensing and demand forecasting are both crucial aspects of optimizing supply chains, but they do have slightly different functions in their approach and focus. Demand sensing uses real-time data and analytics to identify and respond to immediate demand fluctuations, while demand forecasting uses historical data to predict future demand over a longer period (months or years). Different methods, such as statistical modeling and machine learning, are used to enhance the accuracy and adaptability of these processes. Both areas are crucial for companies when it comes to projecting sales, managing inventory, and coordinating replenishment. In the end, the goal is to accurately predict customer demand by using predictive models to forecast future demand. InterSystems surveyed 450 senior supply chain practitioners and stakeholders to examine key supply chain technology challenges, trends, and decision-making strategies across five key use cases: fulfillment optimization; demand sensing and forecasting; supply chain orchestration; production planning optimization; and environmental, social, and governance (ESG). This blog focuses on demand sensing and forecasting.   Current State of Demand Sensing and Forecasting According to the survey results, when asked how they currently forecast demand, 36% of respondents indicated that…

In today’s increasingly complex global supply chain landscape, Tom Moore keeps his message refreshingly straightforward: ProvisionAI helps large companies discover hidden costs and eliminate them. Organizations such as Procter & Gamble, Nestlé, and Unilever have leveraged the company’s technology to uncover and eliminate inefficiencies—particularly in transportation and warehousing—that traditional systems fail to detect. The outcome is significant and often delivers immediate savings. But Moore believes many of these problems stem from misunderstandings about the very technologies companies rely on.   Misnamed Systems & Misaligned Expectations Before the interview officially began, Moore reflected on the surprisingly inaccurate names assigned to modern supply chain technologies. ERP systems rarely plan resources across the enterprise, despite what their name suggests. Warehouse Management Systems, while certainly used in warehouses, don’t actually “manage” much at all. People behind keyboards still make most of the critical decisions. This disconnect in terminology shapes faulty expectations. Many organizations believe their planning systems will truly plan the supply chain, yet most tools merely react to demand signals. If ABC Company orders ten cases, the system automatically replenishes—without considering warehouse capacity, transportation availability, downstream implications, or cost-to-serve. Moore characterizes this as both an old problem and a new one, and it…