C&F

Factory data rarely comes from one system, one technology stack, or one generation of equipment. So how do you build an architecture that can connect it at scale? Unified Namespace (UNS) offers a viable approach. Przemysław Warecki, Head of IoT & Realtime Integrations at C&F, brought hands-on experience from a Big Pharma manufacturing project into research exploring how this architecture can support scalable factory data integration. The resulting paper has been published in the Journal of Pharmaceutical Innovation by Springer Nature.

AGI hasn't arrived yet. The reasons why say a lot about where AI will pay off in your organization. In the new edition of our newsletter: 🔵 why a robot still struggles to make coffee in an unfamiliar kitchen, while AI solves Olympiad-level math 🔵 why verification is the best predictor of where AI performs well 🔵 how much of your own work can be checked automatically, and why that decides how much autonomy an AI agent can get Based on Marcin Ludzia, PhD's latest article on AGI and what it means for CIOs. Read it below ⬇

Resilience isn't just protecting the physical network. It's protecting the decision system behind it: the one that senses risk, escalates it, acts, and learns. Miss that, and the network alone won't save you.

One of the first things I would look at in a CPG Decision Intelligence initiative is a simple decision inventory: what decisions are made repeatedly, who owns them, what data supports them, and what is the business impact of getting them wrong. In CPG, many of these decisions sit around pricing, promotions and short-term supply. But before getting excited about AI or recommendations, the basics need to work: governed data, consistent KPI definitions across functions, and enough traceability to understand how a recommendation was produced and why. For me, this is where the real value of Decision Intelligence starts. Not with another dashboard, but with reducing the time between a signal, a decision and an action. In our new white paper, we look at how to approach this step by step, what to assess first, and where Decision Intelligence can start creating value in CPG: https://lnkd.in/dkYNNRFr

McKinsey surveyed more than 1,200 managers about how their organizations decide. Fewer than half said decisions get made on time. That matches what I see in CPG delivery. The models are fine, the pipeline is fine, and the number on the dashboard is correct. What takes the time is working out what sits behind it, and then the recommendation waits for a planning meeting, a sign-off, or someone to reconcile it against a spreadsheet nobody else can read. We spend a large share of the first phase of Decision Intelligence projects on that stretch specifically. Who owns the decision, what constraints have to travel with the recommendation, and how far in advance it has to arrive to be useful. Clients rarely ask about it upfront. It tends to be where the weeks go. Our C&F paper on Decision Intelligence in CPG covers it in detail: https://lnkd.in/dCfEfTKh

Prices and demand in CPG move week to week. Approvals often still take a month. That gap is the whole commercial argument for Decision Intelligence, and unlike most AI business cases, you can actually put a number on it. The white paper breaks down what the delay costs across pricing and promotions, and what a realistic first year of fixing it looks like. If you own any of those decisions, our Decision Intelligence paper for CPG is worth the read: https://lnkd.in/eEd4wjbJ