Optimizing Supply Chain Analytics by Publishing Noah (6 results)

Taschenbuch. Condition: Neu. Neuware.

Paperback. Condition: new. Paperback. Supply chains generate enormous amounts of data, but data alone does not create better decisions. The real advantage comes from knowing how to turn operational data into accurate forecasts, smarter inventory decisions, stronger supplier strategies, and faster responses to disruption.Optimizing Supply Chain Analytics with Machine Learning provides a practical guide to applying machine learning and predictive analytics across modern supply chain operations. It explains how organizations can use data-driven methods to improve demand forecasting, inventory management, logistics, procurement, supplier performance, risk management, and strategic planning.Inside, you will learn how to: Prepare and evaluate supply chain data for machine learningIdentify operational problems that predictive analytics can addressBuild more accurate demand forecasting modelsPredict inventory requirements and reduce stockouts and excess stockImprove warehouse, transportation, and distribution performanceAnalyze supplier performance and predict supplier riskDetect potential disruptions before they become costly problemsSelect meaningful supply chain KPIs and build actionable performance reportsTurn predictive insights into practical business decisionsIntegrate machine learning models into existing supply chain workflowsScale AI-driven solutions across supply chain operationsMaintain data quality, monitor model performance, and support continuous improvementThe book focuses on practical application rather than theory alone. Each topic connects analytics and machine learning concepts to real supply chain challenges, helping readers understand not only what predictive models can do, but also how their results can support better operational decisions.Whether you work in supply chain management, logistics, procurement, inventory planning, operations, analytics, or business intelligence, this guide can help you develop a more data-driven approach to supply chain performance.From demand forecasting and inventory optimization to supplier intelligence, logistics efficiency, and disruption management, Optimizing Supply Chain Analytics with Machine Learning shows how organizations can use modern analytical techniques to reduce inefficiencies, improve visibility, strengthen resilience, and make more informed decisions.If you want to move beyond traditional supply chain reporting and learn how machine learning can support smarter, more predictive operations, this book provides a practical starting point.Why readers will find this book valuable: Practical explanations without unnecessary technical complexitySupply chain applications of machine learningDemand, inventory, logistics, procurement, and risk analyticsGuidance for implementing predictive solutionsStrategies for turning analytical results into measurable business improvementsBuild a stronger analytical foundation, make better use of supply chain data, and discover how machine learning can help create more efficient, responsive, and resilient operations. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condition: New. Print on Demand.

Paperback. Condition: new. Paperback. Supply chains generate enormous amounts of data, but data alone does not create better decisions. The real advantage comes from knowing how to turn operational data into accurate forecasts, smarter inventory decisions, stronger supplier strategies, and faster responses to disruption.Optimizing Supply Chain Analytics with Machine Learning provides a practical guide to applying machine learning and predictive analytics across modern supply chain operations. It explains how organizations can use data-driven methods to improve demand forecasting, inventory management, logistics, procurement, supplier performance, risk management, and strategic planning.Inside, you will learn how to: Prepare and evaluate supply chain data for machine learningIdentify operational problems that predictive analytics can addressBuild more accurate demand forecasting modelsPredict inventory requirements and reduce stockouts and excess stockImprove warehouse, transportation, and distribution performanceAnalyze supplier performance and predict supplier riskDetect potential disruptions before they become costly problemsSelect meaningful supply chain KPIs and build actionable performance reportsTurn predictive insights into practical business decisionsIntegrate machine learning models into existing supply chain workflowsScale AI-driven solutions across supply chain operationsMaintain data quality, monitor model performance, and support continuous improvementThe book focuses on practical application rather than theory alone. Each topic connects analytics and machine learning concepts to real supply chain challenges, helping readers understand not only what predictive models can do, but also how their results can support better operational decisions.Whether you work in supply chain management, logistics, procurement, inventory planning, operations, analytics, or business intelligence, this guide can help you develop a more data-driven approach to supply chain performance.From demand forecasting and inventory optimization to supplier intelligence, logistics efficiency, and disruption management, Optimizing Supply Chain Analytics with Machine Learning shows how organizations can use modern analytical techniques to reduce inefficiencies, improve visibility, strengthen resilience, and make more informed decisions.If you want to move beyond traditional supply chain reporting and learn how machine learning can support smarter, more predictive operations, this book provides a practical starting point.Why readers will find this book valuable: Practical explanations without unnecessary technical complexitySupply chain applications of machine learningDemand, inventory, logistics, procurement, and risk analyticsGuidance for implementing predictive solutionsStrategies for turning analytical results into measurable business improvementsBuild a stronger analytical foundation, make better use of supply chain data, and discover how machine learning can help create more efficient, responsive, and resilient operations. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…