demand planning

Project Management Toolkit

QKS Group's Supply Chain Inventory Optimization (SCIO) market research provides a comprehensive anal...

Provide chain leaders know the ache of misaligned stock. A high-demand product sells out simply as a promotion goes dwell. In the meantime, pallets of out of da

QKS Group's Supply Chain Planning (SCP) market research provides a comprehensive analysis of the glo...

It was a pleasure to attend the 5th IMA and OR Society Conference at Aston University, Birmingham, and to present my research with Anna Sroginis on model-based demand classification. A great crowd of ...

📘 FORECASTING METRICS THAT DON'T LIEChoosing, Testing, and Monitoring Forecast Metrics for Demand, Inventory, and Risk DecisionsValery Manokhin, PhD, MBA, CQF · 310 pages · 12 chapters · Python throughoutA model that wins on MAPE can lose money on the shelf. A 15% accuracy gain can hide a 12% bias. An interval that looks tight can be wrong a third of the time.Forecast metrics are decision instruments, not neutral truth detectors — and most teams are still choosing them by habit. This is the complete reference for forecast evaluation: what each metric actually elicits, what it rewards, where it breaks, and what to use instead.Why this book-------------Most forecasting books spend one chapter on evaluation. This one spends twelve. Every metric arrives with its assumptions stated, every criticism carries a remedy, and every chapter opens with a failure that really happens in production — the forecast that won on paper and lost in production, the spare parts disaster, the reconciliation illusion, the model that rotted silently.A retrospective Walmart M5 case study runs through the whole book, so the same data is re-examined as the metrics get more demanding. Its cohort-selection limitations are stated explicitly rather than glossed over.What's inside — all twelve chapters, complete---------------------------------------------------Part I — Foundations of Honest Evaluation✓ Why metrics mislead, the three types of drift, and a decision-centred taxonomy that replaces the usual alphabet soup✓ Temporal validation, naive baselines, and statistical tests for whether a difference is real at all✓ MAE, MSE and RMSE through elicitation — the median versus the mean, objective mismatch, and ranking reversals across metricsPart II — Scale, Bias, and Intermittent Demand✓ MAPE's fatal asymmetry, why sMAPE made it worse, and where WAPE and MAAPE actually help✓ MASE, RMSSE and WRMSSE — including the denominator trap that quietly invalidates published comparisons✓ RMSSE-B: an author proposal for scaling against the business benchmark rather than a statistical one✓ Bias metrics, tracking signals, the bias–accuracy decomposition, and Forecast Value Added for judging whether human overrides earn their keep✓ Intermittent demand in full: ADI and Syntetos–Boylan classification, Periods in Stock, lead-time demand, SPEC, and Croston/SBA/TSBPart III — Distributions, Dependence, and Decisions✓ Calibration and sharpness, PIT histograms, reliability diagrams, pinball loss, CRPS, interval and Winkler scores, log score✓ Formal calibration tests (Kupiec, Christoffersen) and conformal prediction's coverage guarantee under exchangeability✓ Energy and variogram scores, coherence metrics, temporal hierarchies, copula-based evaluation✓ The accuracy–utility gap: ranked probability score, information ratio, newsvendor cost and regret, service levels and fill ratePart IV — Production Evaluation Systems✓ Drift and failure modes, monitoring with PSI and CUSUM, forecast stability and revision volatility✓ Alerting thresholds, triage from alarm to diagnosis, retraining triggers✓ Metric bundles, scorecards, domain bundles for supply chain, energy and finance, evaluation governance, and a maturity modelWhat you get------------✓ The book — 310-page PDF, 7×10, fully indexed and cross-referenced✓ Companion source — Python code, data-acquisition instructions, and reproducibility checks for selected numerical and semantic claims✓ Overleaf source — the full LaTeX project✓ A metric specification sheet — the book's single sign and denominator contract, so a disagreement about a sign has one place to be settledYou obtain the M5 source data separately under its own terms.Who this is for---------------✓ Data scientists and ML engineers who need evaluation that survives review✓ Demand planners and analytics leads in retail, manufacturing, finance and energy who make operational decisions from forecasts✓ Researchers, analysts and students who want one authoritative reference on forecast accuracyFormulas are given in full, but the book is written for people who have to justify a metric to a business, not only to a journal. No heavy maths background required.------------------------------------------All twelve chapters are written and shipping today. This is an actively maintained book — corrections, new case studies and new material arrive automatically at no extra cost, for life.------------------------------------------------------------🔥 Measure what matters. Stop being fooled by metrics.https://valeman.gumroad.com/l/forecasting_metrics

📘 Mastering Modern Time Series Forecasting (early access)The book trusted by data science leaders in 100+ countries. Unlock the toolkit behind today’s most powerful forecasting systems. 💸 Pricing 🎉 Standard Edition Price: $40 | Minimum: $35Will increase to $80+ as content grows. A tremendous amount of work and expertise has gone into this book, which is designed to deliver exponential improvement to your forecasting skills, your company's bottom line and ROI, and your career. Forecasting is one of the most in-demand skills across nearly every industry today. As the content continues to grow, if you find value in it—or simply want to support the project—you're welcome to contribute whatever it’s worth to you ❤️. 🧠 Why This Book Stands Out🔑 Forecasting models are only 5% of the equation.The other 95%? It’s the hard-earned knowledge of metrics, validation, deployment, failure modes, and real-world constraints — insights that are often missing or buried in internet noise and social media fluff.🔍 It starts with what actually matters: solid foundations.Learn how to properly evaluate forecasts, recognize when they're failing, and build with confidence — not on shaky assumptions, but on methods that stand up to real-world pressure.💎 You’ll also learn how to assess the forecastability of a time series — a critical step for managing your time, setting stakeholder expectations, and realistically estimating how far forecasting accuracy can be pushed before diminishing returns kick in.🧠 Built for understanding — not just coding.Go beyond black-box code. Grasp model mechanics and decision-making logic to truly understand how and why things work.💻 Clear, transparent, production-ready code.No obfuscation, no throwaway scripts. Every example is fully documented, reusable, and ready for real-world use.🔄 Continuously improved through real feedback.This is a living resource shaped by an active community of readers. Many improvements and additions come directly from their thoughtful feedback — and all readers get lifetime updates, including new chapters and bonus tools. Thank you to all contributors — your insights are recognized and appreciated in the book.📚 Comprehensive, real-world coverage.From classical time series models to deep learning and forecasting-specific transformers (FTSMs), the book covers a wide range — but always with a practical lens. Every method has been tested in production or validated against strong academic benchmarks. No fluff, just tools that work.📈 Real ROI — for your company and your career.Readers often see immediate improvements in model accuracy, interpretability, and stakeholder trust. No more silent failures or fragile production systems. This book helps you build forecasting solutions that earn trust, drive business results, and accelerate your career.✍️ About the AuthorWritten by Valeriy Manokhin, PhD, MBA, CQF — a seasoned forecasting expert, data scientist, and machine learning researcher with publications in top academic journals.Valeriy has advised both startups and large enterprises, helping them build and rebuild forecasting systems at scale. He has led successful forecasting initiatives for global organizations — including winning competitive tenders from multinational companies, outperforming major consulting firms like BCG and specialized AI startups focused on forecasting. He has delivered production-grade solutions for industry leaders such as Stanley Black & Decker and GfK.His methods have driven multimillion-dollar business impact, and his training programs have reached professionals in over 40 countries. This book is now used in more than 100+ countries and has become a #1-ranked title in Machine Learning, Forecasting, and Time Series across major platforms.🌍 Trusted By and Taught ToValeriy’s expertise is trusted by leaders at:Amazon, Apple, Google, Meta, Nike, BlackRock, Morgan Stanley, Target, NTT Data, Mars Inc., Lidl, Publicis Sapient, and more.His frameworks are followed by professionals from:University of Chicago, KTH (Sweden), UBC (Canada), DTU (Denmark), and other world-class institutions.👤 Students include:VPs of Engineering, AI Leads, Principal & Lead Data Scientists, ML Engineers, Consultants, Professors, Founders, Researchers, and PhD students.🎓 Want a Live, Interactive Learning Experience?Pair this book with the Modern Forecasting Mastery course on Maven.Join live cohort sessions with Valeriy, get direct feedback, and build models with peers.Next cohort → maven.com/valeriy-manokhin/modern-forecasting-mastery📦 What You Get📥 Instant access to the book — start reading immediately.🔄 Free updates — including new chapters, bug fixes, and bonus content.💬 Exclusive access to the private Discord community — connect with fellow readers, get additional materials, early bonuses, special discounts, and join live events with the author.🔓 Pro Edition Bonus Pack (Early Access – $65) 🔥🔥🔥 Includes everything above, plus:✅ Premium Forecasting Templates — plug-and-play workflows✅ Extended Case Studies — deep analyses across major industries✅ Cheat Sheets & Flashcards — quick-reference model guides and best practices✅ Behind-the-Scenes Notebooks — annotated walkthroughs and exploratory pipelines✅ Forecast Model Selection Toolkit — Python notebooks to benchmark, optimize, and compare📈 Ideal for professionals and teams who want to build and deploy faster—and sidestep the guesswork.https://valeman.gumroad.com/l/MasteringModernTimeSeriesForecastingPro💸 New Pricing effective 16th June - grab your copy before price increase 🎉 Standard Edition Price: $45 | Minimum: $39Will increase to $80+ as content grows.If you find value or simply want to support the project, feel free to pay what it’s worth to you ❤️Ready to take your forecasting skills from stats to neural nets, and from theory to real-world deployment?👉 Hit “Buy Now” and start mastering forecasting like never before.

QKS Group is an enterprise advisory services delivering analyst-led technology strategy, market intelligence, and digital transformation insights.

Explore 102 demand planning software statistics, AI trends, market growth, ROI, cloud adoption and forecasts shaping 2026.

QKS Group's Supply Chain Planning (SCP) market research provides a comprehensive analysis of the glo...