Inventory Analysis, Analytics and Optimization

About This Book

Inventory Analytics: From Data to Forecasting, Optimization & Decisions is a practitioner-focused reference for supply-chain analysts, demand and supply planners, inventory managers, consultants, and supply-chain leaders who want to move from spreadsheet-based inventory management to data-driven decision making. The book takes the reader through the complete journey from raw supply-chain data to an actionable inventory decision. It combines inventory theory with practical implementation using Excel, SQL, Python, Power BI, Pyomo, HiGHS and AI. Rather than treating forecasting, inventory policy, optimization and reporting as separate subjects, the book connects them into one decision system: Business Problem → Data → Analytics → Forecast → Uncertainty → Inventory Policy → Optimization → Simulation → Dashboard → Decision → Production → Governance The book begins with the fundamentals of inventory economics, demand, lead time, service levels, safety stock, reorder points, inventory policies and inventory classification. It then progresses into practical analytics using Excel and SQL before moving into statistical forecasting, machine learning, Python-based analytics and inventory optimization. Advanced topics include AR, MA, ARMA, ARIMA, SARIMA, SARIMAX, TBATS, Prophet, intermittent-demand forecasting, forecast evaluation, forecast-to-inventory translation, multi-item optimization, constrained replenishment, multi-echelon inventory optimization, stochastic optimization and simulation-optimization. The forecasting ladder and model-selection matrix are designed to help practitioners select model complexity based on demand characteristics rather than using sophisticated models indiscriminately. The book then moves beyond analytical models into Power BI decision dashboards, data pipelines, MLOps, model monitoring and governed AI agents. The production framework covers model versioning, testing, monitoring, release gates, ownership, rollback and retirement. A common synthetic business environment, NorthStar Supply Co., runs throughout the book, allowing readers to apply concepts consistently across inventory analytics, forecasting, optimization, SQL, Python and Power BI. The accompanying project repository is designed to provide datasets, notebooks, SQL scripts, optimization models, dashboards, tests and deployment artefacts. The ultimate objective is not simply to calculate inventory metrics or build predictive models. It is to develop the ability to convert supply-chain data and uncertainty into defensible business decisions.