Matching supply with demand, this book is suitable for operations management MBAs. It demands rigorous analysis on the part of students without requiring consistent use of sophisticated mathematical modeling to perform it.

Cachon Matching Supply with Demand 4e is a clear, concise and more rigorous approach to an introductory Operations management course. Written by Wharton authors who use their guiding principles “real operations, real solutions” to bring the text and concepts to life, writing the majority of chapters from the perspective of specific companies. The “real solutions” refers to providing students with tools and strategies they can implement in practice and apply the authors models in a realistic operational setting. The authors strive for “real simple” by using as little mathematical notation as possible, focusing on many real world examples and consistent terminology and phrasing throughout.

Effective development and management of a supply chain network is an invaluable source of sustainable advantage in today’s turbulent global marketplace, where demand is difficult to predict and supply chains need to be more flexible as a result. This updated 4th edition of the bestselling Logistics and Supply Chain Management is a clear-headed guide to all the key topics in an integrated approach to supply chains, including: • The link between logistics and customer value. • Logistics and the bottom line measuring costs and performance. • Creating a responsive supply chain. • Managing the global pipeline. • Managing supply chain relationships. • Managing risk in the supply chain. • Matching supply and demand. • Creating a sustainable supply chain. • Product design in the supply chain.

This edited book examines the challenges and opportunities arising from today’s sharing economy from an operations management perspective. Individual chapter authors present state-of-the-art research that examines the general impact of sharing economy on production and consumption; the intermediary role of a sharing platform; crowdsourcing management; and context-based operational problems. Sharing economy refers to a market model that enables and facilitates the sharing of access to goods and services. For example, Uber allows riders to share a car. Airbnb allows homeowners to share their extra rooms with renters. Groupon crowdsources demands, enabling customers to share the benefit of discounted goods and services, whereas Kickstarter crowdsources funds, enabling backers to fund a project jointly. Unlike the classic supply chain settings in which a firm makes inventory and supply decisions, in sharing economy, supply is crowdsourced and can be modulated by a platform. The matching-supply-with-demand process in a sharing economy requires novel perspectives and tools to address challenges and identify opportunities. The book is comprised of 20 chapters that are divided into four parts. The first part explores the general impact of sharing economy on the production, consumption, and society. The second part explores the intermediary role of a sharing platform that matches crowdsourced supply with demand. The third part investigates the crowdsourcing management on a sharing platform, and the fourth part is dedicated to context-based operational problems of popular sharing economy applications. “While sharing economy is becoming omnipresence, the operations management (OM) research community has begun to explore and examine different business models in the transportation, healthcare, financial, accommodation, and sourcing sectors. This book presents a collection of the state-of-the-art research work conducted by a group of world-leading OM researchers in this area. Not only does this book cover a wide range of business models arising from the sharing economy, but it also showcases different modeling frameworks and research methods that cannot be missed. Ultimately, this book is a tour de force – informative and insightful!” Christopher S. Tang Distinguished Professor and Edward Carter Chair in Business Administration UCLA Anderson School of Management

800x600 This case study examines how Toyota China is using demand forecasting, production planning, and customer management techniques to align supply and demand in China's fast-growing automotive market. The authors illuminate the China market's unique channels and challenges, including requirements to project sales for new models with no historical data. They focus on realistic issues and challenges throughout, offering exceptional value to both students and practitioners. Authors: Xiaoying Liang, City University of Hong Kong; Lijun Ma, Shenzhen University; Houmin Yan, City University of Hong Kong.

Originally taught mainly in business schools, supply chain management has become a common elective and graduate course in engineering colleges. The increasing demand for engineers with supply chain knowledge has fed this shift. However, supply chain management textbooks that have a reasonable coverage of quantitative analysis techniques are few and

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.