Randy Carr

Special Guest Blog Post written by Parabola Founder & CEO, Alex Yaseen   Operators are under pressure. They’re expected to move faster, do more with less, and somehow keep everything running smoothly while their systems, tools, and data are a mess. Now, AI is adding a whole new layer. On one hand, it’s exciting. The potential is real. On the other, a lot of teams feel stuck. They know they should be using AI, but they’re not sure where it fits into their day-to-day work. These are the five questions I get asked most often—whether someone’s trying to get started with automation, or trying to figure out how AI actually helps. 1. Can we automate this, or is it too messy? This question usually comes from someone deep in a spreadsheet that was never meant to scale. The short answer is: yes, you can probably automate it. But the longer answer is that you’ll need to rethink the process first. AI can help summarize, transform, and clean data—but it won’t fix a broken workflow. That’s on you. The best teams pair structured automation with lightweight AI to get leverage. Think: using rules and logic to standardize a workflow, and AI…

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…