Adaptive Forecasting Systems

If you're in manufacturing, you know that accurate demand forecasting is critical. It's the difference between smooth operations, happy customers, and a healthy bottom line – versus scrambling to meet unexpected demand, dealing with excess inventory and having liquidity issues, or losing out on potential sales and not meeting your Sales / EBITDA targets. But with constantly shifting customer preferences, disruptive market trends, and global events throwing curveballs, it's also one of the toughest nuts to crack. While often reliable in stable environments (especially in settings with lots of high-frequency transactions and no data sparsity), traditional stats-based forecasting methods aren't built for the complexity and volatility of today's market. They rely on historical data and often miss those subtle signals, indicating a major shift is on the horizon. Traditional stats-based approaches are also not that effective for businesses with high data sparsity (e.g., larger tickets, choppier transaction volume) That's where AI/ML-enabled forecasting comes in. Unlike foundational stats forecasting, it can include various structured and unstructured data, such as social media sentiment, competitor activity, and various economic indicators. One of the most significant advancements in recent years is the rise of powerful open-source AI/ML packages for forecasting. These tools, once the domain of large enterprises with extensive resources or turnkey solution providers (with hefty price tags), are now readily accessible to companies of all sizes, offering a significant opportunity to level the playing field and drive smarter decision-making. The power of AI and ML in demand forecasting is more than just theoretical. Companies across various industries are already reaping the benefits: • Marshalls: This UK manufacturer used AI to optimize inventory management during the pandemic. It made thousands of model-driven decisions daily and managed orders worth hundreds of thousands of pounds. • P&G: Their PredictIQ platform, powered by AI and ML, significantly reduced forecast errors, improving inventory management and cost savings. • Other Industries: Retailers, e-commerce companies, and even the energy sector are using AI to predict everything from consumer behavior to energy demand, with impressive results. If you're in manufacturing or distribution and haven't explored upgrading your demand forecasting (and S&OP) capabilities, I highly encourage you to invest. These capabilities are table stakes nowadays, and forecasting on random spreadsheets and basic methods (year-over-year performance, moving average, etc.) is not cutting it anymore.

The forecast was solid. The assumptions were fine. We still lost $6.7M. 1 year ago, I sat across from a telecom CFO who looked like he hadn’t slept in a week. “We’re already $4.2M over budget. And that’s before the fiber supplier pushed their rates. If this goes south, I’m the one holding the bag.” I knew that feeling. When your forecast is outdated, vendors are behind, and everyone’s chasing you for answers, it’s not just stressful, it’s dangerous. Here’s the truth: Forecasts don’t fail because they’re wrong. They fail because they’re TOO slow. In telecom, delays pile up fast - fiber, permitting, contractors, energy access. By the time your spreadsheet catches the signal, it’s already too late. That month almost broke us. We were rolling out a $68M fiber expansion across two metro markets. The model was solid. Phased CapEx. Built-in buffers. Then came the hits: Week 4: vendor delay. Week 6: trenching paused. Week 9: OT surges. Week 11: $6.7M projected overrun. Too late. Commitments were locked. Our dynamic model wasn’t dynamic at all it took 5 days and 2 analysts to update. And margin leaked out while we refreshed Excel tabs. So we ran a test. We piloted an AI forecasting tool on one segment. No system overhaul. It pulled from our existing data - vendor logs, field reports, real-time updates and learned fast. After 60 days: – AI forecast deviation: 2.9% – Traditional model: 11.6% Same inputs. 4x tighter accuracy. 6 months later: – Forecast cycle time cut 85% – Overrun risk down 28% – Payment delays down 41% – 1.8 FTEs freed from manual work More importantly => we stopped reacting. We started steering. I wasn’t explaining misses. I was leading with insight. Here’s what I learned: AI doesn’t replace finance. It gives it foresight. 90% of Infrastructure CFOs sit on fragmented data. PM tools, vendor reports, accounting systems - but can’t act fast enough. That’s not a data problem. It’s a visibility problem. And it’s costing you. Start small. One wedge: CapEx, vendor risk, tower maintenance. Run a 6-week sprint. Compare the results. Then scale. You don’t need a massive AI team. You need faster insight. If I could go back, I’d start earlier. In infrastructure, delay is the real killer - not bad forecasts, but slow ones. Found this helpful? → Repost it. Someone in your network needs it. → DM me “Clarity” and I’ll send the sprint blueprint we use with telecom, energy, and logistics CFOs. → Follow me for weekly playbooks on AI, finance, and infrastructure. Let’s stop playing defense - and make your margin predictable.

AI is Turning Agricultural Price Forecasting into a Foresight Engine!! The last two years have seen a quantum leap in how AI tackles agricultural price forecasting. We’ve moved beyond static predictions to always-on, adaptive systems powered by: 🔹 Large Language Models (LLMs) that scan market reports, trade bulletins, and farmer discussions to extract real-time economic signals. 🔹 Multimodal AI merging time-series data, satellite imagery, text, and even audio for richer context. 🔹 Attention mechanisms that focus models on the most relevant features for precise forecasting. 🔹 Explainable AI (XAI), ensuring transparency, trust, and regulatory compliance. The result? Price forecasting is evolving from a black-box tool into a transparent decision-support system. Soon, we’ll see: Self-learning models that adapt continuously. Climate-aware forecasts that integrate seasonal and local agronomic data. Blockchain-verified datasets to prevent market manipulation. Predictive supply chain simulations to optimize logistics before disruptions hit. Imagine: delayed sowing detected by satellite, dry-spell warnings from weather models, reduced exports flagged in trade data — all synthesized by AI into a risk alert weeks in advance, advising farmers, traders, and policymakers on proactive interventions. This shift isn’t just about “What will prices be?” — it’s about shaping markets, stabilizing incomes, and securing food systems. Those who embed AI into their decision-making DNA today will be the ones shaping tomorrow’s agricultural landscape. 📖 Read the full article titled "Artificial Intelligence for Agricultural Price Prediction: The Next Frontier in Market Intelligence" to explore how this transformation is unfolding — and how you can be part of it.

🎉 📄 Delighted to share our new article titled "Dynamic model selection in enterprise forecasting systems using sequence modeling," co-authored with Jinhang Jiang and Karthik Srinivasan, published in Decision Support Systems (DSS)! In this work, we studied how to identify the best model(s) from legacy forecasting systems for individual time series over the long term—a key challenge in large-scale forecasting. We proposed a dynamic model selection framework that adapts a NLP-style sequence modeling to learn and generate switching patterns directly from historical model performance, offering a proactive, scalable, and adaptive solution for enterprise environments. #AI #forecasting #dynamicmodelselection #timeseries #decisionsupportsystems https://lnkd.in/gKqucj_p