Leah Knight

Special Guest Blog Post written by Jeff Echel and Steve Lykken with e2open   Supply chain planners in high-tech don’t just manage shipments; they’re crisis managers, data detectives, and sometimes, referees in a high-stakes game of inventory tug-of-war. Why do these planners find themselves huddled in “war rooms,” surrounded by spreadsheets and urgent emails? It starts with relentless pressure: customers expect rapid, reliable service, but the reality is a maze of long lead times, outsourced manufacturing, and unpredictable global logistics. Securing critical components can take months, and a single misstep, like overstocking or missing a shipment, can ripple through the business, impacting revenue and margins. The chaos: War rooms and spreadsheet battles Add to that, the complexity of forecasting demand. Planners reconcile noisy, inconsistent data from retailers and distributors, often with little visibility, into . Forecasts are built, torn down, and rebuilt, sometimes manually, as teams try to align bottom-up channel data with top-down financial targets. Meanwhile, supply plans are constantly threatened by shortages, excess inventory, and last-minute changes. When demand surges or supply is disrupted, channels compete for limited stock, sometimes “stealing” from each other, and sometimes winning simply by being the loudest voice in the room. All of…

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…