Supply Chain Intelligence

This book deals with complex problems in the fields of logistics and supply chain management and discusses advanced methods, especially from the field of computational intelligence (CI), for solving them. The first two chapters provide general introductions to logistics and supply chain management on the one hand, and to computational intelligence on the other hand. The subsequent chapters cover specific fields in logistics and supply chain management, work out the most relevant problems found in those fields, and discuss approaches for solving them. Chapter 3 discusses problems in the field of production and inventory management. Chapter 4 considers planning activities on a finer level of granularity which is usually denoted as scheduling. In chapter 5 problems in transportation planning such as different types of vehicle routing problems are considered. While chapters 3 to 5 rather discuss planning problems which appear on an operative level, chapter 6 discusses the strategic problem of designing a supply chain or network. The final chapter provides an overview of academic and commercial software and information systems for the discussed applications. There appears to be a gap between general textbooks on logistics and supply chain management and more specialized literature dealing with methods for computational intelligence, operations research, etc., for solving the complex operational problems in these fields. For readers, it is often difficult to proceed from introductory texts on logistics and supply chain management to the sophisticated literature which deals with the usage of advanced methods. This book fills this gap by providing state-of-the-art descriptions of the corresponding problems and suitable methods for solving them.

The book provides an introduction to logistics and supply chain management and the application of evolutionary computation, focusing on specific fields related to supply chain issues, from strategic sourcing decisions, and production planning and control to inventory to logistics and its application using evolutionary / heuristics techniques. Bridging the gap between management research, decision-making and computer analysis, this interdisciplinary book features state-of-the-art descriptions of the corresponding problems and advanced methods for solving them.

Supply chain management contends with structures and processes for delivering goods and services to customers. It addresses the core functions of connected businesses to meet downstream demand. This innovative volume provides an authoritative and timely guide to the overarching issues that are ubiquitous throughout the supply chain. In particular, it addresses emerging issues that are applicable across supply chains--such as data science, financial flows, human capital, internet technologies, risk management, cyber security, and supply networks. With chapters from an international roster of leading scholars in the field, the Oxford Handbook of Supply Chain Management is a necessary resource for all students and researchers of the field as well as for forward-thinking practitioners.

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 Market Intelligence: A Managerial Handbook for Building Sourcing Strategies charts the course to success for executives who are seeking to lead their organizations to supply-chain maturity. This volume is intended for supply-chain executives who struggle with the challenges of an uncertain supply market environment and whose supply chain function is constantly buffeted by unexpected changes in customer requirements and commodity prices. The author offers a more effective path, describing an integrated approach to supply chain management based upon solid market intelligence. This path is derived from the authors' multiple engagements with organizations such as Suncor Energy, Bank of America, Shell Oil, Honda of America, General Motors, Boston Scientific, Visteon, Federal Express, Sonoco, Duke Energy, Caterpillar, John Deere, GlaxoSmithKline, and many others. He emphasizes that the elements of a successful plan do not arise from a simple vision, instead they require much hard work and a focused approach backed by solid leadership. This book begins by defining supply market intelligence and discussing opportunities, the establishment of a project team, and conducting an internal business intelligence assessment. The book then examines the development of business and market intelligence, supplier evaluations, and sourcing strategies. It also explores how to execute a sourcing strategy, manage a strategic supplier relationship, and redesign an organization for effective supply-chain intelligence and strategic sourcing. This volume offers a benchmarking maturity model tool that covers all facets of end-to-end supply-chain management.

Practitioners in apparel manufacturing and retailing enterprises in the fashion industry, ranging from senior to front line management, constantly face complex and critical decisions. There has been growing interest in the use of artificial intelligence (AI) techniques to enhance this process, and a number of AI techniques have already been successfully applied to apparel production and retailing. Optimizing decision making in the apparel supply chain using artificial intelligence (AI): From production to retail provides detailed coverage of these techniques, outlining how they are used to assist decision makers in tackling key supply chain problems. Key decision points in the apparel supply chain and the fundamentals of artificial intelligence techniques are the focus of the opening chapters, before the book proceeds to discuss the use of neural networks, genetic algorithms, fuzzy set theory and extreme learning machines for intelligent sales forecasting and intelligent product cross-selling systems. Helps the reader gain an understanding of the key decision points in the apparel supply chain Discusses the fundamentals of artificial intelligence techniques for apparel management techniques Considers the use of neural networks in selecting the location of apparel manufacturing plants

Discover how your supply chain and operations work impacts financial performance, and how to align your efforts to help your company succeed — the fastest, best way to supercharge your own career! As a supply chain or operations professional, you may clearly understand your operational performance goals. But if you want to get promoted, you need to know how your day-to-day work powerfully impacts the financial metrics your top executives care about most.

This book discusses and conceptualizes practices on real-time strategy, focusing on the interplay between strategy and business intelligence. Combining strategic practices and business intelligence systems, the authors demonstrate how managerial practices can be developed in the age of digitization. Also developing the concept of strategic agility, the book provides perspectives from a range of disciplines including strategic practices and decision making, customer relationship management, human resource management, competitive intelligence, supplier network management and business intelligence systems. Presenting managerial frameworks and guidelines, Real-time Strategy and Business Intelligence explores how to improve utilization of business intelligence systems in real-time decision making. Providing practical and future-oriented insights backed by examples and best practices, the authors present a clearly conceptualized theoretical framework.