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The reviews are coming in on RoadMap's guide to Demand Planning and Forecasting in the Age of AI. #Forecasting #LLM #MCP Scope: 17 Chapters, 8 Technical Appendices, and12+ Industry Case Studies (Life Sciences, FMCG, Consumer Electronics) Core Thesis & Overview Through the Looking Glass offers a sharp, empirically grounded examination of how demand planning has evolved from manual spreadsheets and 1950s exponential smoothing into the era of time-series foundation models and Large Language Model (LLM) agents. The book’s central premise is captured by its title: we have entered a "looking-glass world" where traditional dynamics are inverted. In the past, building a forecast model was the hard part, and management judgment was the final authority. Today, automated forecasting models—trained across hundreds of thousands of SKUs and callable instantly via APIs or Model Context Protocol (MCP) servers—are effectively a commodity. Consequently, the true scarce resource is executive judgment: knowing which model to trust, how to pressure-test underlying assumptions, when to override, and how to manage cross-functional consensus in Sales and Operations Planning (S&OP). Key Structural Themes The book is structured to bridge historical rigor with cutting-edge enterprise deployment across six major thematic movements: 1. The Historical Foundation: Traces the evolution from simple exponential smoothing and Box-Jenkins to Material Requirements Planning (MRP), enterprise resource planning (ERP) suites, and the birth of formal S&OP. 2. The Empirical Reality Check: A deep dive into the Makridakis Competitions (M1 through M6), automatic ARIMA/ETS industrialization, and expert systems like Geneva. 3. Handling Complexity & Hard Corners: Addresses intermittent, lumpy, and new-product demand ("the hard corners") before evaluating global machine learning tree models and probabilistic deep learning frameworks like DeepAR. 4. Foundation Models & Agentic AI Explores zero-shot/few-shot forecasting via time-series foundation models and the integration of LLM agents into enterprise planning workflows. 5. Industry Deep Dives & Case Studies Moves theory into practice across three distinct sectors: ○ Life Sciences: Navigating patient journeys, formulary changes, and survival curve modeling for specialty drugs. ○ Fast-Moving Consumer Goods (FMCG): Managing promotions, trade promotion optimization (TPO), cannibalization, and demand sensing. ○ Consumer Electronics: Handling short product life cycles, pre-orders, and supply allocation shocks. 6. Toolkits & Appendices (Appendices A–H): Provides practical technical architecture for practitioners, covering Geneva APIs, MCP servers, Monte Carlo simulations, and Marketing Mix Optimization. Contact us at https://lnkd.in/gsnk-cwQ to order your copy today!

Agentic Forecasting is now available! We are excited to announce that our forecasting API integrates directly with Claude through MCP, providing seamless functionality for users. Powered by Oracle OCI the API runs ~500 forecasts per second at 1/5 the compute dollar cost. This tool is now accessible to the public at the following link: https://lnkd.in/dh9PeD94 #MCP #AI #Agent #forecast #model #predict #Claude #LLM #agentic #API #pharma #sop #supplychain #planning #demandplanning #retail #CPG #biotech #news #breaking #atscale #OCI #Oracle

Check out our latest Case Study from the RoadMap Vault: https://lnkd.in/ej7pqJw6 #demandplanning #supplychain #forecasting #casestudy #replenishment #analytics #retail #CPG #planning

Tweaking your forecast inputs to make the numbers look better for management isn't forecasting. It's noise management. Calculate your forecast error cost, or try out TrailBlazer for free today.

If your planning team is still experiencing the same misses as when you started, the problem isn't effort. It's the process.

Tweaking inputs to make forecasts look better for management isn't forecasting. It's noise management. There's a better way.