Part of our new "Quick Notes" series - this report answers your most pertinent questions of the topic. Do not be deceived by their short nature - these notes are only 22 pages or so. But these are 22 pages of potent dynamite that will supercharge your thinking in the right direction. Included are quick notes and some of the frequently asked questions (FAQs) on supply chain finance that we have encountered in our workshops, seminars, and other forums. Here are some of the topics and questions covered in these quick notes: What is meant by the term 'supply chain analytics'? Why is supply chain analytics so important to every company looking to achieve supply chain excellence, or better profitability? Is supply chain analytics same as data analytics? If not, then what is the difference between the two? How is supply chain analytics related to data visualisation? What is the history of supply chain analytics? What is meant by the term 'supply chain analytics'? Why is supply chain analytics so important to every company looking to achieve supply chain excellence, or better profitability? Is supply chain analytics same as data analytics? If not, then what is the difference between the two? How is supply chain analytics related to data visualisation? What is the history of supply chain analytics? What is the scope of supply chain analytics? What all does it cover? What are common methods of supply chain analytics? What is descriptive supply chain analytics? How does it work? What are some of the tools of descriptive supply chain analytics? What is diagnostics supply chain analytics? How does it work? What are some of the tools of diagnostics supply chain analytics? What is predictive supply chain analytics? How does it work? What are some of the tools of predictive supply chain analytics? What is prescriptive supply chain analytics? How does it work? What are some of the tools of prescriptive supply chain analytics? Could the supply chain analytics process be summarised in a generic methodology? What would such a generic methodology look like? What supply chain analytics tools usually come built into the ERP systems? What is their key deficiency? What other supply chain analytics tools are usually available? What are their key deficiencies? How could I test the efficacy of the supply chain analytics tools available off-the-shelf?

Part of our new "Quick Notes" series - this report answers your most pertinent questions of the topic. Do not be deceived by their short nature - these notes are only 36 pages or so. But these are 45 pages of potent dynamite that will supercharge your thinking in the right direction. Included are quick notes and some of the frequently asked questions (FAQs) on supply chain finance that we have encountered in our workshops, seminars, and other forums. Here are some of the topics and questions covered in these quick notes: It takes only two years to get a masters' degree in supply chain management today. After that it takes nearly twenty years of working under the tutelage of a supply chain master to get a modicum of mastery in the discipline itself. So, why strive towards supply chain mastery? What can masters do that other supply chain professionals cannot achieve? Are there any special powers that come with that title? How to achieve supply chain mastery? Are there levels of mastery? What are the five disciplines of supply chain in which you must achieve mastery? Who grants you the title of a supply chain master? Read these quick notes to get our take on the topic.

Part of our new "Quick Notes" series - this report answers your most pertinent questions of the topic. Do not be deceived by their short nature - these notes are only 28 pages or so. But these are 28 pages of potent dynamite that will supercharge your thinking in the right direction. Included are quick notes and some of the frequently asked questions (FAQs) on supply chain finance that we have encountered in our workshops, seminars, and other forums. Here are some of the topics and questions covered in these quick notes: What can you learn from the best examples of five flows of supply chain management to use in your company? What are the best examples of five flows of supply chain management? How did they get where they are now? Why are five flows important? How to integrate these into your business strategy?

Part of our new "Quick Notes" series - this report answers your most pertinent questions of the topic. Do not be deceived by their short nature - these notes are only 22 pages or so. But these are 22 pages of potent dynamite that will supercharge your thinking in the right direction. Included are quick notes and some of the frequently asked questions (FAQs) on supply chain finance that we have encountered in our workshops, seminars, and other forums. Here are some of the topics and questions covered in these quick notes: What is Supply chain AI and why everyone is moving towards it? What is the advantage of integrating AI in a supply chain environment? What are the current applications of AI in SCM? What are the current trends in the field of AI which aid improvements in SCM? Which are the experimental models in AI meant for supply chain management? What is Supply chain AI and why everyone is moving towards it? What is the advantage of integrating AI in a supply chain environment? What are the current applications of AI in SCM? What are the current trends in the field of AI which aid improvements in SCM? Which are the experimental models in AI meant for supply chain management? What are the fully developed AI models that are currently used in the business environment? Out of the four stages of supply chain analytics - descriptive, diagnostic, predictive and prescriptive - which one has the most potential application of supply chain AI? How does the predictive nature of AI algorithms help in forecasting? What is the current cost reduction that AI brings into SCM? In this current ongoing COVID -19 crisis can we use AI to reduce the decision making time and to process huge amount of data. If yes how? What are the requirements for a company to incorporate the use of AI in its supply chain? What are the future trends that we need to look in AI? Which are the industries that can gain more from an AI-powered SCM? What is the role of the current ERP and DSS tools when deploying AI?

Master a complete, five-step roadmap for leveraging Big Data and analytics to gain unprecedented competitive advantage from your supply chain. Using Big Data, pioneers such as Amazon, UPS, and Wal-Mart are gaining unprecedented mastery over their supply chains. They are achieving greater visibility into inventory levels, order fulfillment rates, material and product delivery… using predictive data analytics to match supply with demand; leveraging new planning strengths to optimize their sales channel strategies; optimizing supply chain strategy and competitive priorities; even launching powerful new ventures. Despite these opportunities, many supply chain operations are gaining limited or no value from Big Data. In Big Data Driven Supply Chain Management, Nada Sanders presents a systematic five-step framework for using Big Data in supply chains. You'll learn best practices for segmenting and analyzing customers, defining competitive priorities for each segment, aligning functions behind strategy, dissolving organizational boundaries to sense demand and make better decisions, and choose the right metrics to support all of this. Using these techniques, you can overcome the widespread obstacles to making the most of Big Data in your supply chain — and earn big profits from the data you're already generating. For all executives, managers, and analysts interested in using Big Data technologies to improve supply chain performance.

Supply Chain Analytics introduces the reader to data analytics and demonstrates the value of their effective use in supply chain management. By describing the key supply chain processes through worked examples, and the descriptive, predictive and prescriptive analytic methods that can be applied to bring about improvements to those processes, the book presents a more comprehensive learning experience for the reader than has been offered previously. Key topics are addressed, including optimisation, big data, data mining and cloud computing. The author identifies four core supply chain processes – strategy, design, execution and people – to which the analytic techniques explained can be applied to ensure continuous improvement. Pedagogy to aid learning is incorporated throughout, including an opening section for each chapter explaining the learnings designed for the chapter; worked examples illustrating how each analytic technique works, how it is applied and what to be careful of; tables, diagrams and equations to help ‘visualise’ the concepts and methods covered; chapter case studies; and end-of-chapter review questions and assignment tasks. Providing both management expertise and technical skills, which are essential to decision-makers in the supply chain, this textbook should be essential reading for advanced undergraduate and postgraduate students of supply chain analytics, supply chain leadership, and supply chain and operations management. Its practice-based and applied approach also makes it valuable for operating supply chain practitioners and those studying for professional qualifications. Online resources include chapter-by-chapter PowerPoint slides, tutorial exercises, written assignments and a test bank of exam questions.

An incredible volume of data is generated at a very high speed within the supply chain and it is necessary to understand, use and effectively apply the knowledge learned from analyzing data using intelligent business models. However, practitioners and students in the field of supply chain management face a number of challenges when dealing with business models and mathematical modelling. Supply Chain Analytics and Modelling presents a range of business analytics models used within the supply chain to help readers develop knowledge on a variety of topics to overcome common issues. Supply Chain Analytics and Modelling covers areas including supply chain planning, single and multi-objective optimization, demand forecasting, product allocations, end-to-end supply chain simulation, vehicle routing and scheduling models. Learning is supported by case studies of specialist software packages for each example. Readers will also be provided with a critical view on how supply chain management performance measurement systems have been developed and supported by reliable and accurate data available in the supply chain. Online resources including lecturer slides are available.