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AI in Operations Management: Revolutionizing Efficiency and Productivity
Author: Dr. Anya Sharma, PhD in Industrial Engineering and Management Systems, Certified Lean Six Sigma Black Belt, 15+ years experience in operational optimization and AI implementation.
Publisher: Wiley & Sons, a leading publisher in business and management literature, specializing in providing cutting-edge insights into operational excellence.
Editor: Mr. David Chen, MBA, PMP, 20+ years experience in editing business and technology publications, specializing in clarity and accuracy for technical topics.
Keyword: ai in operations management
Abstract: This article explores the transformative impact of artificial intelligence (AI) in operations management, examining its applications across various industries. Through personal anecdotes and real-world case studies, we'll delve into the benefits, challenges, and future trends of integrating AI in operations management. We'll also discuss the ethical implications and strategies for successful implementation.
1. Introduction: The Dawn of Intelligent Operations
The field of operations management is undergoing a profound transformation, driven by the rapid advancements in artificial intelligence (AI). AI in operations management is no longer a futuristic concept; it's a tangible reality shaping how businesses operate, optimize processes, and achieve competitive advantage. My own journey into this field began during my doctoral research, focusing on predictive maintenance using machine learning algorithms. I witnessed firsthand the potential of AI to reduce downtime and improve equipment lifespan – a far cry from the reactive, often inefficient maintenance strategies of the past. This experience ignited a passion that continues to drive my work today.
2. AI-Powered Optimization: Streamlining Processes and Boosting Efficiency
One of the most significant impacts of ai in operations management is its ability to optimize processes. Through machine learning, AI algorithms can analyze vast datasets – encompassing everything from production data and supply chain logistics to customer interactions and employee performance – to identify patterns, predict anomalies, and suggest improvements. This ability to identify inefficiencies invisible to the human eye is truly transformative.
For instance, consider a large e-commerce company I consulted with. They were struggling with high shipping costs and delivery delays. By implementing an AI-powered system that analyzed historical data, real-time traffic patterns, and weather forecasts, we were able to optimize their routing algorithms, resulting in a 15% reduction in shipping costs and a 10% decrease in delivery times. This is a perfect example of the practical application of ai in operations management.
3. Predictive Maintenance: Preventing Downtime and Reducing Costs
Predictive maintenance is another area where AI in operations management is making a significant difference. Traditional reactive maintenance strategies often lead to unexpected downtime and costly repairs. AI-powered systems, however, can analyze sensor data from machinery to predict potential failures before they occur. This allows companies to schedule maintenance proactively, minimizing disruptions and extending the lifespan of equipment.
I recall working with a manufacturing plant that experienced frequent breakdowns of their crucial assembly line. By implementing an AI-powered predictive maintenance system, we were able to reduce unplanned downtime by 40% within six months. The cost savings were substantial, not only from reduced repair costs but also from increased production output.
4. Supply Chain Management: Enhancing Visibility and Resilience
AI in operations management is also revolutionizing supply chain management. AI-powered systems can provide real-time visibility into the entire supply chain, from raw material sourcing to product delivery. This enhanced visibility allows businesses to anticipate disruptions, optimize inventory levels, and improve overall supply chain resilience.
Consider the disruptions caused by the recent global pandemic. Companies that had implemented AI-powered supply chain management systems were better equipped to navigate the challenges, adapting to changing demand patterns and securing alternative sources of supply.
5. Inventory Management: Optimizing Stock Levels and Reducing Waste
AI algorithms can analyze historical sales data, demand forecasts, and other relevant factors to optimize inventory levels. This prevents stockouts while minimizing excess inventory, reducing storage costs and the risk of obsolescence. Efficient inventory management is crucial for profitability, and AI is proving to be a game-changer in this area.
6. Quality Control: Enhancing Accuracy and Reducing Defects
AI-powered vision systems can automate quality control processes, identifying defects with greater accuracy and speed than human inspectors. This leads to improved product quality, reduced waste, and enhanced customer satisfaction.
7. Challenges and Ethical Considerations of AI in Operations Management
While the benefits of AI in operations management are significant, there are also challenges to overcome. These include the high cost of implementation, the need for skilled personnel to manage and maintain AI systems, and concerns about data privacy and security. Ethical considerations, such as the potential for job displacement and algorithmic bias, must also be carefully addressed.
8. The Future of AI in Operations Management
The future of ai in operations management is bright. We can expect to see further advancements in areas such as robotic process automation, AI-powered decision support systems, and the use of digital twins to simulate and optimize complex operations. The integration of AI with other emerging technologies, such as the Internet of Things (IoT) and blockchain, will further enhance the capabilities of ai in operations management.
9. Conclusion
AI in operations management is no longer a futuristic concept, but rather a powerful tool that is transforming how businesses operate. By leveraging the capabilities of AI, organizations can achieve significant improvements in efficiency, productivity, and profitability. However, successful implementation requires careful planning, investment in skilled personnel, and a proactive approach to addressing ethical considerations. As AI technology continues to evolve, its impact on operations management will only grow, ushering in a new era of intelligent operations.
FAQs
1. What is the ROI of implementing AI in operations management? The ROI varies widely depending on the specific application and the size of the organization. However, studies show significant cost savings and efficiency gains are possible.
2. What skills are needed to manage AI systems in operations? A blend of technical expertise (data science, AI/ML) and operational knowledge is crucial. Strong project management skills are also essential.
3. How can companies address the ethical concerns surrounding AI in operations? Transparency, fairness, accountability, and user privacy should be central to the design and implementation of AI systems.
4. What are the key challenges to implementing AI in operations? High initial investment costs, data integration complexities, and the need for skilled personnel are major hurdles.
5. What industries benefit most from AI in operations? Manufacturing, logistics, supply chain, and e-commerce are some sectors experiencing major transformations.
6. How can AI improve customer service in operations? AI-powered chatbots and virtual assistants can provide instant support, freeing up human agents to handle complex issues.
7. What is the role of data analytics in AI-powered operations? Data analytics is the foundation for effective AI implementation; it provides the insights that drive process optimization.
8. How can companies ensure the security of their AI systems in operations? Robust cybersecurity measures, including data encryption and access control, are vital.
9. What are the future trends in AI for operations management? The convergence of AI, IoT, and blockchain will lead to more intelligent and resilient operations.
Related Articles:
1. "Predictive Maintenance with AI: A Case Study in Manufacturing": Examines the application of AI in predictive maintenance within a manufacturing setting, including a detailed analysis of cost savings and efficiency improvements.
2. "AI-Powered Supply Chain Optimization: Strategies for Resilience": Discusses various AI techniques used to enhance supply chain visibility, agility, and resilience against disruptions.
3. "The Ethical Implications of AI in Operations: A Framework for Responsible Implementation": Explores the ethical dilemmas associated with AI deployment in operations and suggests a framework for responsible use.
4. "AI and Robotic Process Automation (RPA) in Operations: Synergistic Applications": Investigates the benefits of integrating AI and RPA to automate repetitive tasks and improve operational efficiency.
5. "AI-Driven Decision Support Systems in Operations Management: A Comparative Analysis": Compares different AI-based decision support systems and their effectiveness in various operational contexts.
6. "The Role of Machine Learning in Optimizing Inventory Management": Focuses on the application of machine learning algorithms to improve inventory control and reduce waste.
7. "Implementing AI in Operations: A Step-by-Step Guide for Businesses": Provides a practical guide for organizations looking to implement AI solutions within their operations.
8. "AI and the Future of Work in Operations Management: Adapting to Change": Explores the impact of AI on the workforce and strategies for adapting to the changing job market.
9. "Measuring the Impact of AI on Operations: Key Performance Indicators (KPIs)": Identifies key performance indicators to track the effectiveness of AI-powered operations management initiatives.
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| ai in operations management: Artificial Intelligence in Practice Bernard Marr, 2019-04-15 Cyber-solutions to real-world business problems Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing. This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others. Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries: Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations Expand your knowledge of recent AI advancements in technology Gain insight on the future of AI and its increasing role in business and industry Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce. |
| ai in operations management: Competing in the Age of AI Marco Iansiti, Karim R. Lakhani, 2020-01-07 a provocative new book — The New York Times AI-centric organizations exhibit a new operating architecture, redefining how they create, capture, share, and deliver value. Now with a new preface that explores how the coronavirus crisis compelled organizations such as Massachusetts General Hospital, Verizon, and IKEA to transform themselves with remarkable speed, Marco Iansiti and Karim R. Lakhani show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have restricted business growth for hundreds of years. From Airbnb to Ant Financial, Microsoft to Amazon, research shows how AI-driven processes are vastly more scalable than traditional processes, allow massive scope increase, enabling companies to straddle industry boundaries, and create powerful opportunities for learning—to drive ever more accurate, complex, and sophisticated predictions. When traditional operating constraints are removed, strategy becomes a whole new game, one whose rules and likely outcomes this book will make clear. Iansiti and Lakhani: Present a framework for rethinking business and operating models Explain how collisions between AI-driven/digital and traditional/analog firms are reshaping competition, altering the structure of our economy, and forcing traditional companies to rearchitect their operating models Explain the opportunities and risks created by digital firms Describe the new challenges and responsibilities for the leaders of both digital and traditional firms Packed with examples—including many from the most powerful and innovative global, AI-driven competitors—and based on research in hundreds of firms across many sectors, this is your essential guide for rethinking how your firm competes and operates in the era of AI. |
| ai in operations management: The Future of Management in an AI World Jordi Canals, Franz Heukamp, 2019-09-21 Artificial Intelligence (AI) is redefining the nature and principles of general management. The technological revolution is reshaping industries, disrupting existing business models, making traditional companies obsolete and creating social change. In response, the role of the manager needs to urgently evolve and adjust. Companies need to rethink their purpose, strategy, organisational design and decision-making rules. Crucially they will also need to consider how to nurture and develop the business leaders of the future and develop new ways to interact with society on issues such as privacy and trust. Containing international insights from leading figures from the world of management and technology, this book addresses the big challenges facing organisations, including: · Decision-making · Corporate strategy · People management and leadership · Organisational design Taking a holistic approach, this collection of expert voices provides valuable insight into how firms will discover and commit to what makes them unique in this new big data world, empowering them to create and sustain competitive advantage. |
| ai in operations management: AI-Enabled Analytics for Business Lawrence S. Maisel, Robert J. Zwerling, Jesper H. Sorensen, 2022-01-19 We are entering the era of digital transformation where human and artificial intelligence (AI) work hand in hand to achieve data driven performance. Today, more than ever, businesses are expected to possess the talent, tools, processes, and capabilities to enable their organizations to implement and utilize continuous analysis of past business performance and events to gain forward-looking insight to drive business decisions and actions. AI-Enabled Analytics in Business is your Roadmap to meet this essential business capability. To ensure we can plan for the future vs react to the future when it arrives, we need to develop and deploy a toolbox of tools, techniques, and effective processes to reveal forward-looking unbiased insights that help us understand significant patterns, relationships, and trends. This book promotes clarity to enable you to make better decisions from insights about the future. Learn how advanced analytics ensures that your people have the right information at the right time to gain critical insights and performance opportunities Empower better, smarter decision making by implementing AI-enabled analytics decision support tools Uncover patterns and insights in data, and discover facts about your business that will unlock greater performance Gain inspiration from practical examples and use cases showing how to move your business toward AI-Enabled decision making AI-Enabled Analytics in Business is a must-have practical resource for directors, officers, and executives across various functional disciplines who seek increased business performance and valuation. |
| ai in operations management: The AI Advantage Thomas H. Davenport, 2019-08-06 Cutting through the hype, a practical guide to using artificial intelligence for business benefits and competitive advantage. In The AI Advantage, Thomas Davenport offers a guide to using artificial intelligence in business. He describes what technologies are available and how companies can use them for business benefits and competitive advantage. He cuts through the hype of the AI craze—remember when it seemed plausible that IBM's Watson could cure cancer?—to explain how businesses can put artificial intelligence to work now, in the real world. His key recommendation: don't go for the “moonshot” (curing cancer, or synthesizing all investment knowledge); look for the “low-hanging fruit” to make your company more efficient. Davenport explains that the business value AI offers is solid rather than sexy or splashy. AI will improve products and processes and make decisions better informed—important but largely invisible tasks. AI technologies won't replace human workers but augment their capabilities, with smart machines to work alongside smart people. AI can automate structured and repetitive work; provide extensive analysis of data through machine learning (“analytics on steroids”), and engage with customers and employees via chatbots and intelligent agents. Companies should experiment with these technologies and develop their own expertise. Davenport describes the major AI technologies and explains how they are being used, reports on the AI work done by large commercial enterprises like Amazon and Google, and outlines strategies and steps to becoming a cognitive corporation. This book provides an invaluable guide to the real-world future of business AI. A book in the Management on the Cutting Edge series, published in cooperation with MIT Sloan Management Review. |
| ai in operations management: Artificial Intelligence in Business Management Teik Toe Teoh, Yu Jin Goh, 2023-11-26 Artificial intelligence (AI) is rapidly gaining significance in the business world. With more and more organizations adopt AI technologies, there is a growing demand for business leaders, managers, and practitioners who can harness AI’s potential to improve operations, increase efficiency, and drive innovation. This book aims to help management professionals exploit the predictive powers of AI and demonstrate to AI practitioners how to apply their expertise in fundamental business operations. It showcases how AI technology innovations can enhance various aspects of business management, such as business strategy, finance, and marketing. Readers interested in AI for business management will find several topics of particular interest, including how AI can improve decision-making in business strategy, streamline operational processes, and enhance customer satisfaction. As AI becomes an increasingly important tool in the business world, this book offers valuable insights into how it can be applied to various industries and business settings. Through this book, readers will gain a better understanding of how AI can be applied to improve business management practices and practical guidance on how to implement AI projects in a business context. This book also provides practical guides on how to implement AI projects in a business context using Python programming. By reading this book, readers will be better equipped to make informed decisions about how to leverage AI for business success. |
| ai in operations management: Operations Management in the AI Era Salamat Mahammad, 2024-07-28 Operations management is the strategic function responsible for overseeing the design, implementation, and control of processes that transform inputs into goods and services. In the contemporary business landscape, the integration of artificial intelligence (AI) has revolutionized operations management practices, unlocking new possibilities for automation, optimization, and innovation. The AI era has ushered in a paradigm shift in operations management, offering organizations advanced tools and technologies to streamline processes, enhance decision-making, and drive operational excellence. AI-powered solutions empower operations managers to leverage data-driven insights, predictive analytics, and machine learning algorithms to optimize efficiency, reduce costs, and improve customer experiences. |
| ai in operations management: Reimagining Businesses with AI Khaled Al Huraimel, Sudhi Sinha, 2020-09-22 Discover what AI can do for your business with this approachable and comprehensive resource Reimagining Businesses with AI acquaints readers with both the business challenges and opportunities presented by the rapid growth and progress of artificial intelligence. The accomplished authors and digital executives of the book provide you with a multi-industry approach to understanding the intersection of AI and business. The book walks you through the process of recognizing and capitalizing on AI’s potential for your own business. The authors describe: How to build a technological foundation that allows for the rapid implementation of artificial intelligence How to manage the disruptive nature of powerful technology while simultaneously harnessing its capabilities The ethical implications and security and privacy concerns raised by the spread of AI Perfect for business executives and managers who seek a jargon-free and approachable manual on how to implement artificial intelligence in everyday operations, Reimagining Businesses with AI also belongs on the bookshelves of anyone curious about the interaction between artificial intelligence and business. |
| ai in operations management: Enterprise Artificial Intelligence Transformation Rashed Haq, 2020-06-10 Enterprise Artificial Intelligence Transformation AI is everywhere. From doctor's offices to cars and even refrigerators, AI technology is quickly infiltrating our daily lives. AI has the ability to transform simple tasks into technological feats at a human level. This will change the world, plain and simple. That's why AI mastery is such a sought-after skill for tech professionals. Author Rashed Haq is a subject matter expert on AI, having developed AI and data science strategies, platforms, and applications for Publicis Sapient's clients for over 10 years. He shares that expertise in the new book, Enterprise Artificial Intelligence Transformation. The first of its kind, this book grants technology leaders the insight to create and scale their AI capabilities and bring their companies into the new generation of technology. As AI continues to grow into a necessary feature for many businesses, more and more leaders are interested in harnessing the technology within their own organizations. In this new book, leaders will learn to master AI fundamentals, grow their career opportunities, and gain confidence in machine learning. Enterprise Artificial Intelligence Transformation covers a wide range of topics, including: Real-world AI use cases and examples Machine learning, deep learning, and slimantic modeling Risk management of AI models AI strategies for development and expansion AI Center of Excellence creating and management If you're an industry, business, or technology professional that wants to attain the skills needed to grow your machine learning capabilities and effectively scale the work you're already doing, you'll find what you need in Enterprise Artificial Intelligence Transformation. |
| ai in operations management: Operating AI Ulrika Jagare, 2022-04-19 A holistic and real-world approach to operationalizing artificial intelligence in your company In Operating AI, Director of Technology and Architecture at Ericsson AB, Ulrika Jägare, delivers an eye-opening new discussion of how to introduce your organization to artificial intelligence by balancing data engineering, model development, and AI operations. You'll learn the importance of embracing an AI operational mindset to successfully operate AI and lead AI initiatives through the entire lifecycle, including key areas such as; data mesh, data fabric, aspects of security, data privacy, data rights and IPR related to data and AI models. In the book, you’ll also discover: How to reduce the risk of entering bias in our artificial intelligence solutions and how to approach explainable AI (XAI) The importance of efficient and reproduceable data pipelines, including how to manage your company's data An operational perspective on the development of AI models using the MLOps (Machine Learning Operations) approach, including how to deploy, run and monitor models and ML pipelines in production using CI/CD/CT techniques, that generates value in the real world Key competences and toolsets in AI development, deployment and operations What to consider when operating different types of AI business models With a strong emphasis on deployment and operations of trustworthy and reliable AI solutions that operate well in the real world—and not just the lab—Operating AI is a must-read for business leaders looking for ways to operationalize an AI business model that actually makes money, from the concept phase to running in a live production environment. |
| ai in operations management: Management, Organisations and Artificial Intelligence Piotr Buła, Bartosz Niedzielski, 2023-05 This book combines academic research with practical guidelines in methods and techniques to supplement existing knowledge relating to organizational management in the era of digital acceleration. It offers a simple layout with concise but rich content presented in an engaging, accessible style and the authors' holistic approach is unique in the field. From a universalist perspective, the book examines and analyzes the development of, among others, Industry 4.0, artificial intelligence (AI), AI 2.0, AI systems and platforms, algorithmics, new paradigms of organization management, business ecosystems, data processing models in AI-based organizations and AI strategies in the global perspective. An additional strength of the book is its relevance and contemporary nature, featuring information, data, forecasts or scenarios reaching up to 2030. How does one build, step by step, an organization that will be based on artificial intelligence technology and gain measurable benefits from it, for instance, as a result of its involvement in the creation of the so-called mesh ecosystem? The answer to this and many other pertinent questions are provided in this book. This timely and important book will appeal to scholars and students across the fields of organizational management and innovation and technology management, as well as managers, educators, scientists, entrepreneurs, innovators and more. |
| ai in operations management: Managing AI in the Enterprise Klaus Haller, 2021-12-17 Delivering AI projects and building an AI organization are two big challenges for enterprises. They determine whether companies succeed or fail in establishing AI and integrating AI into their digital transformation. This book addresses both challenges by bringing together organizational and service design concepts, project management, and testing and quality assurance. It covers crucial, often-overlooked topics such as MLOps, IT risk, security and compliance, and AI ethics. In particular, the book shows how to shape AI projects and the capabilities of an AI line organization in an enterprise. It elaborates critical deliverables and milestones, helping you turn your vision into a corporate reality by efficiently managing and setting goals for data scientists, data engineers, and other IT specialists. For those new to AI or AI in an enterprise setting you will find this book a systematic introduction to the field. You will get the necessary know-how to collaborate with and lead AI specialists and guide them to success. Time-pressured readers will benefit from self-contained sections explaining key topics and providing illustrations for fostering discussions in their next team, project, or management meeting. Reading this book helps you to better sell the business benefits from your AI initiatives and build your skills around scoping and delivering AI projects. You will be better able to work through critical aspects such as quality assurance, security, and ethics when building AI solutions in your organization. What You Will Learn Clarify the benefits of your AI initiatives and sell them to senior managers Scope and manage AI projects in your organization Set up quality assurance and testing for AI models and their integration in complex software solutions Shape and manage an AI delivery organization, thereby mastering ML Ops Understand and formulate requirements for the underlying data management infrastructure Handle AI-related IT security, compliance, and risk topics and understand relevant AI ethics aspects Who This Book Is For Experienced IT managers managing data scientists or who want to get involved in managing AI projects, data scientists and other tech professionals who want to progress toward taking on leadership roles in their organization’s AI initiatives and who aim to structure AI projects and AI organizations, any line manager and project manager involved in AI projects or in collaborating with AI teams |
| ai in operations management: Demystifying AI for the Enterprise Prashant Natarajan, Bob Rogers, Edward Dixon, Jonas Christensen, Kirk Borne, Leland Wilkinson, Shantha Mohan, 2021-12-30 Artificial intelligence (AI) in its various forms –– machine learning, chatbots, robots, agents, etc. –– is increasingly being seen as a core component of enterprise business workflow and information management systems. The current promise and hype around AI are being driven by software vendors, academic research projects, and startups. However, we posit that the greatest promise and potential for AI lies in the enterprise with its applications touching all organizational facets. With increasing business process and workflow maturity, coupled with recent trends in cloud computing, datafication, IoT, cybersecurity, and advanced analytics, there is an understanding that the challenges of tomorrow cannot be solely addressed by today’s people, processes, and products. There is still considerable mystery, hype, and fear about AI in today’s world. A considerable amount of current discourse focuses on a dystopian future that could adversely affect humanity. Such opinions, with understandable fear of the unknown, don’t consider the history of human innovation, the current state of business and technology, or the primarily augmentative nature of tomorrow’s AI. This book demystifies AI for the enterprise. It takes readers from the basics (definitions, state-of-the-art, etc.) to a multi-industry journey, and concludes with expert advice on everything an organization must do to succeed. Along the way, we debunk myths, provide practical pointers, and include best practices with applicable vignettes. AI brings to enterprise the capabilities that promise new ways by which professionals can address both mundane and interesting challenges more efficiently, effectively, and collaboratively (with humans). The opportunity for tomorrow’s enterprise is to augment existing teams and resources with the power of AI in order to gain competitive advantage, discover new business models, establish or optimize new revenues, and achieve better customer and user satisfaction. |
| ai in operations management: Adoption and Implementation of AI in Customer Relationship Management Singh, Surabhi, 2021-10-15 Integration of artificial intelligence (AI) into customer relationship management (CRM) automates the sales, marketing, and services in organizations. An AI-powered CRM is capable of learning from past decisions and historical patterns to score the best leads for sales. AI will also be able to predict future customer behavior. These tactics lead to better and more effective marketing strategies and increases the scope of customer services, which allow businesses to build healthier relationships with their consumer base. Adoption and Implementation of AI in Customer Relationship Management is a critical reference source that informs readers about the transformations that AI-powered CRM can bring to organizations in order to build better services that create more productive relationships. This book uses the experience of past decisions and historical patterns to discuss the ways in which AI and CRM lead to better analytics and better decisions. Discussing topics such as personalization, quality of services, and CRM in the context of diverse industries, this book is an important resource for marketers, brand managers, IT specialists, sales specialists, managers, students, researchers, professors, academicians, and stakeholders. |
| ai in operations management: Artificial Intelligence Design and Solution for Risk and Security Archie Addo, Srini Centhala, Muthu Shanmugam, 2020-03-13 Artificial Intelligence (AI) Design and Solutions for Risk and Security targets readers to understand, learn, define problems, and architect AI projects. Starting from current business architectures and business processes to futuristic architectures. Introduction to data analytics and life cycle includes data discovery, data preparation, data processing steps, model building, and operationalization are explained in detail. The authors examine the AI and ML algorithms in detail, which enables the readers to choose appropriate algorithms during designing solutions. Functional domains and industrial domains are also explained in detail. The takeaways are learning and applying designs and solutions to AI projects with risk and security implementation and knowledge about futuristic AI in five to ten years. |
| ai in operations management: Artificial Intelligence for Business Rajendra Akerkar, 2018-08-11 This book offers a practical guide to artificial intelligence (AI) techniques that are used in business. The book does not focus on AI models and algorithms, but instead provides an overview of the most popular and frequently used models in business. This allows the book to easily explain AI paradigms and concepts for business students and executives. Artificial Intelligence for Business is divided into six chapters. Chapter 1 begins with a brief introduction to AI and describes its relationship with machine learning, data science and big data analytics. Chapter 2 presents core machine learning workflow and the most effective machine learning techniques. Chapter 3 deals with deep learning, a popular technique for developing AI applications. Chapter 4 introduces recommendation engines for business and covers how to use them to be more competitive. Chapter 5 features natural language processing (NLP) for sentiment analysis focused on emotions. With the help of sentiment analysis, businesses can understand their customers better to improve their experience, which will help the businesses change their market position. Chapter 6 states potential business prospects of AI and the benefits that companies can realize by implementing AI in their processes. |
| ai in operations management: The Art of Structuring Katrin Bergener, Michael Räckers, Armin Stein, 2019-01-25 Structuring, or, as it is referred to in the title of this book, the art of structuring, is one of the core elements in the discipline of Information Systems. While the world is becoming increasingly complex, and a growing number of disciplines are evolving to help make it a better place, structure is what is needed in order to understand and combine the various perspectives and approaches involved. Structure is the essential component that allows us to bridge the gaps between these different worlds, and offers a medium for communication and exchange. The contributions in this book build these bridges, which are vital in order to communicate between different worlds of thought and methodology – be it between Information Systems (IS) research and practice, or between IS research and other research disciplines. They describe how structuring can be and should be done so as to foster communication and collaboration. The topics covered reflect various layers of structure that can serve as bridges: models, processes, data, organizations, and technologies. In turn, these aspects are complemented by visionary outlooks on how structure influences the field. |
| ai in operations management: The AI Book Ivana Bartoletti, Anne Leslie, Shân M. Millie, 2020-06-29 Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes: · Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI · AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today’s Financial Services Industry · The future state of financial services and capital markets – what’s next for the real-world implementation of AITech? · The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness · Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the ‘unbundled corporation’ & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important |
| ai in operations management: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data |
| ai in operations management: Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems Alexandre Dolgui, Alain Bernard, David Lemoine, Gregor von Cieminski, David Romero, 2021-08-31 The five-volume set IFIP AICT 630, 631, 632, 633, and 634 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2021, held in Nantes, France, in September 2021.* The 378 papers presented were carefully reviewed and selected from 529 submissions. They discuss artificial intelligence techniques, decision aid and new and renewed paradigms for sustainable and resilient production systems at four-wall factory and value chain levels. The papers are organized in the following topical sections: Part I: artificial intelligence based optimization techniques for demand-driven manufacturing; hybrid approaches for production planning and scheduling; intelligent systems for manufacturing planning and control in the industry 4.0; learning and robust decision support systems for agile manufacturing environments; low-code and model-driven engineering for production system; meta-heuristics and optimization techniques for energy-oriented manufacturing systems; metaheuristics for production systems; modern analytics and new AI-based smart techniques for replenishment and production planning under uncertainty; system identification for manufacturing control applications; and the future of lean thinking and practice Part II: digital transformation of SME manufacturers: the crucial role of standard; digital transformations towards supply chain resiliency; engineering of smart-product-service-systems of the future; lean and Six Sigma in services healthcare; new trends and challenges in reconfigurable, flexible or agile production system; production management in food supply chains; and sustainability in production planning and lot-sizing Part III: autonomous robots in delivery logistics; digital transformation approaches in production management; finance-driven supply chain; gastronomic service system design; modern scheduling and applications in industry 4.0; recent advances in sustainable manufacturing; regular session: green production and circularity concepts; regular session: improvement models and methods for green and innovative systems; regular session: supply chain and routing management; regular session: robotics and human aspects; regular session: classification and data management methods; smart supply chain and production in society 5.0 era; and supply chain risk management under coronavirus Part IV: AI for resilience in global supply chain networks in the context of pandemic disruptions; blockchain in the operations and supply chain management; data-based services as key enablers for smart products, manufacturing and assembly; data-driven methods for supply chain optimization; digital twins based on systems engineering and semantic modeling; digital twins in companies first developments and future challenges; human-centered artificial intelligence in smart manufacturing for the operator 4.0; operations management in engineer-to-order manufacturing; product and asset life cycle management for smart and sustainable manufacturing systems; robotics technologies for control, smart manufacturing and logistics; serious games analytics: improving games and learning support; smart and sustainable production and supply chains; smart methods and techniques for sustainable supply chain management; the new digital lean manufacturing paradigm; and the role of emerging technologies in disaster relief operations: lessons from COVID-19 Part V: data-driven platforms and applications in production and logistics: digital twins and AI for sustainability; regular session: new approaches for routing problem solving; regular session: improvement of design and operation of manufacturing systems; regular session: crossdock and transportation issues; regular session: maintenance improvement and lifecycle management; regular session: additive manufacturing and mass customization; regular session: frameworks and conceptual modelling for systems and services efficiency; regular session: optimization of production and transportation systems; regular session: optimization of supply chain agility and reconfigurability; regular session: advanced modelling approaches; regular session: simulation and optimization of systems performances; regular session: AI-based approaches for quality and performance improvement of production systems; and regular session: risk and performance management of supply chains *The conference was held online. |
| ai in operations management: Artificial Intelligence in Business Management Mohammed Majeed, 2024-08-07 Artificial Intelligence in Business Management is a review of artificial intelligence (AI) applications in businesses. This book adopts a cross-disciplinary strategy toward AI adoption. Book chapters explore many projects that go beyond simple data management and accessibility to showcase the growing role of artificial intelligence and machine learning in the enterprise data space. AI methods for tackling marketing and commercial strategies, as well as the use of AI and machine learning in tourism, insurance and healthcare systems, are discussed. A study on the significance of cultural assets in evaluating risks and protection is also presented. The content gives valuable insights on the application and implications of artificial intelligence and machine learning from this book to readers aiming for corporate roles, such as directors, executives, senior software developers, and digital transformation managers. The book is an essential resource for researchers and professionals in business, economics, and allied disciplines. |
| ai in operations management: Responsible Management in Emerging Markets Eric Kwame Adae, John Paul Basewe Kosiba, Robert Ebo Hinson, Kojo Kakra Twum, Nathaniel Newman, Francis Fonyee Nutsugah, 2021-10-30 Responsible Management in Emerging Markets: A Multisectoral Focus is in response to the dearth of literature on responsible management in emerging economies. It discusses diverse themes at the intersection of corporate social responsibility (CSR), green business (marketing) and sustainability management, with the view to addressing some begging issues in responsible management. Hinged on the centrality of SDG 12 (responsible production and consumption), this volume focusses on how businesses, nations, and continents across the globe can actualize a sustainable paradigm, now and in the future. It offers fresh theoretical, policy, and managerial insights into the complex processes and relationships that mediate businesses’ ability to deliver on their social development promise, through sustainability and green initiatives. This book discusses some forward and backward linkages between the emerging economy context and responsible management. Featuring cognate topics on CSR, green marketing, green fashion and green entrepreneurship, it offers a Sustainable Development Roadmap (SDR) that is applicable for businesses in emerging economies. This volume is a valuable resource for professionals and academics in emerging economies who desire to understand how firms are demonstrating responsible management through green initiatives, corporate social responsibility and sustainable policies and practices. |
| ai in operations management: The Oxford Handbook of Supply Chain Management Thomas Y. Choi, Julie Juan Li, Dale S. Rogers, Tobias Schoenherr, Stephan M. Wagner, This handbook is currently in development, with individual articles publishing online in advance of print publication. At this time, we cannot add information about unpublished articles in this handbook, however the table of contents will continue to grow as additional articles pass through the review process and are added to the site. Please note that the online publication date for this handbook is the date that the first article in the title was published online. |
| ai in operations management: AI as a Service Peter Elger, Eóin Shanaghy, 2020-09-05 AI as a Service is a practical handbook to building and implementing serverless AI applications, without bogging you down with a lot of theory. Instead, you’ll find easy-to-digest instruction and two complete hands-on serverless AI builds in this must-have guide! Summary Companies everywhere are moving everyday business processes over to the cloud, and AI is increasingly being given the reins in these tasks. As this massive digital transformation continues, the combination of serverless computing and AI promises to become the de facto standard for business-to-consumer platform development—and developers who can design, develop, implement, and maintain these systems will be in high demand! AI as a Service is a practical handbook to building and implementing serverless AI applications, without bogging you down with a lot of theory. Instead, you’ll find easy-to-digest instruction and two complete hands-on serverless AI builds in this must-have guide! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Cloud-based AI services can automate a variety of labor intensive business tasks in areas such as customer service, data analysis, and financial reporting. The secret is taking advantage of pre-built tools like Amazon Rekognition for image analysis or AWS Comprehend for natural language processing. That way, there’s no need to build expensive custom software. Artificial Intelligence (AI), a machine’s ability to learn and make predictions based on patterns it identifies, is already being leveraged by businesses around the world in areas like targeted product recommendations, financial forecasting and resource planning, customer service chatbots, healthcare diagnostics, data security, and more. With the exciting combination of serverless computing and AI, software developers now have enormous power to improve their businesses’ existing systems and rapidly deploy new AI-enabled platforms. And to get on this fast-moving train, you don’t have to invest loads of time and effort in becoming a data scientist or AI expert, thanks to cloud platforms and the readily available off-the-shelf cloud-based AI services! About the book AI as a Service is a fast-paced guide to harnessing the power of cloud-based solutions. You’ll learn to build real-world apps—such as chatbots and text-to-speech services—by stitching together cloud components. Work your way from small projects to large data-intensive applications. What's inside - Apply cloud AI services to existing platforms - Design and build scalable data pipelines - Debug and troubleshoot AI services - Start fast with serverless templates About the reader For software developers familiar with cloud basics. About the author Peter Elger and Eóin Shanaghy are founders and CEO/CTO of fourTheorem, a software solutions company providing expertise on architecture, DevOps, and machine learning. Table of Contents PART 1 - FIRST STEPS 1 A tale of two technologies 2 Building a serverless image recognition system, part 1 3 Building a serverless image recognition system, part 2 PART 2 - TOOLS OF THE TRADE 4 Building and securing a web application the serverless way 5 Adding AI interfaces to a web application 6 How to be effective with AI as a Service 7 Applying AI to existing platforms PART 3 - BRINGING IT ALL TOGETHER 8 Gathering data at scale for real-world AI 9 Extracting value from large data sets with AI |
| ai in operations management: Artificial Intelligence in Business Management Sruthi.SBiswadipBasu Mallik Subrata Das Dr I Mohana Krishna E. FantinIrudaya Raj, |
| ai in operations management: Manager's Guide to Operations Management John Kamauff, 2009-10-09 The secrets to improving operations while maintaining the highest quality How do you operate at maximum efficiency with minimum cost? Manager’s Guide to Operations Management addresses one of the most pressing business issues of our time by offering easy-toimplement advice on creating the most effective, streamlined operations possible. This quick-reference guide explains how to: Improve your production processes Boost quality using the Six Sigma approach Manage supply chains and inventory Forecast, plan, and schedule efficiently With Manager’s Guide to Operations Management, you have the tools you need to ensure a smooth, steady work flow while producing products and services of the highest quality—the secret to business success. |
| ai in operations management: Artificial Intelligence Stuart Russell, Peter Norvig, 2016-09-10 Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. |
| ai in operations management: Nature-Inspired Computing and Optimization Srikanta Patnaik, Xin-She Yang, Kazumi Nakamatsu, 2017-03-07 The book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and gene expression, from topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorithms, the collision-based optimization algorithm, the flower pollination algorithm, multi-agent systems and particle swarm optimization. This timely book is intended as a practice-oriented reference guide for students, researchers and professionals. |
| ai in operations management: AI Meets BI Lakshman Bulusu, Rosendo Abellera, 2020-11-03 With the emergence of Artificial Intelligence (AI) in the business world, a new era of Business Intelligence (BI) has been ushered in to create real-world business solutions using analytics. BI developers and practitioners now have tools and technologies to create systems and solutions to guide effective decision making. Decisions can be made on the basis of more reliable and accurate information and intelligence, which can lead to valuable, actionable insights for business. Previously, BI professionals were stymied by bad or incomplete data, poorly architected solutions, or even just outright incapable systems or resources. With the advent of AI, BI has new possibilities for effectiveness. This is a long-awaited phase for practitioners and developers and, moreover, for executives and leaders relying on knowledgeable and intelligent decision making for their organizations. Beginning with an outline of the traditional methods for implementing BI in the enterprise and how BI has evolved into using self-service analytics, data discovery, and most recently AI, AI Meets BI first lays out the three typical architectures of the first, second, and third generations of BI. It then takes an in-depth look at various types of analytics and highlights how each of these can be implemented using AI-enabled algorithms and deep learning models. The crux of the book is four industry use cases. They describe how an enterprise can access, assess, and perform analytics on data by way of discovering data, defining key metrics that enable the same, defining governance rules, and activating metadata for AI/ML recommendations. Explaining the implementation specifics of each of these four use cases by way of using various AI-enabled machine learning and deep learning algorithms, this book provides complete code for each of the implementations, along with the output of the code, supplemented by visuals that aid in BI-enabled decision making. Concluding with a brief discussion of the cognitive computing aspects of AI, the book looks at future trends, including augmented analytics, automated and autonomous BI, and security and governance of AI-powered BI. |
| ai in operations management: A Human's Guide to Machine Intelligence Kartik Hosanagar, 2020-03-10 A Wharton professor and tech entrepreneur examines how algorithms and artificial intelligence are starting to run every aspect of our lives, and how we can shape the way they impact us Through the technology embedded in almost every major tech platform and every web-enabled device, algorithms and the artificial intelligence that underlies them make a staggering number of everyday decisions for us, from what products we buy, to where we decide to eat, to how we consume our news, to whom we date, and how we find a job. We've even delegated life-and-death decisions to algorithms--decisions once made by doctors, pilots, and judges. In his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives. He makes the compelling case that we need to arm ourselves with a better, deeper, more nuanced understanding of the phenomenon of algorithmic thinking. And he gives us a route in, pointing out that algorithms often think a lot like their creators--that is, like you and me. Hosanagar draws on his experiences designing algorithms professionally--as well as on history, computer science, and psychology--to explore how algorithms work and why they occasionally go rogue, what drives our trust in them, and the many ramifications of algorithmic decision-making. He examines episodes like Microsoft's chatbot Tay, which was designed to converse on social media like a teenage girl, but instead turned sexist and racist; the fatal accidents of self-driving cars; and even our own common, and often frustrating, experiences on services like Netflix and Amazon. A Human's Guide to Machine Intelligence is an entertaining and provocative look at one of the most important developments of our time and a practical user's guide to this first wave of practical artificial intelligence. |
| ai in operations management: Operations Management For Dummies Mary Ann Anderson, Edward J. Anderson, Geoffrey Parker, 2013-07-09 Score your highest in Operations Management Operations management is an important skill for current and aspiring business leaders to develop and master. It deals with the design and management of products, processes, services, and supply chains. Operations management is a growing field and a required course for most undergraduate business majors and MBA candidates. Now, Operations Management For Dummies serves as an extremely resourceful aid for this difficult subject. Tracks to a typical course in operations management or operations strategy, and covers topics such as evaluating and measuring existing systems' performance and efficiency, materials management and product development, using tools like Six Sigma and Lean production, designing new, improved processes, and defining, planning, and controlling costs of projects. Clearly organizes and explains complex topics Serves as an supplement to your Operations Management textbooks Helps you score your highest in your Operations Management course Whether your aim is to earn an undergraduate degree in business or an MBA, Operations Management For Dummies is indispensable supplemental reading for your operations management course. |
| ai in operations management: Disrupting Finance Theo Lynn, John G. Mooney, Pierangelo Rosati, Mark Cummins, 2018-12-06 This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry. |
| ai in operations management: Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance El Bachir Boukherouaa, Mr. Ghiath Shabsigh, Khaled AlAjmi, Jose Deodoro, Aquiles Farias, Ebru S Iskender, Mr. Alin T Mirestean, Rangachary Ravikumar, 2021-10-22 This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight. |
| ai in operations management: Integration of Information Flow for Greening Supply Chain Management Adam Kolinski, Davor Dujak, Paulina Golinska-Dawson, 2020-09-03 This book provides a framework for integrating information management in supply chains. Current trends in business practice have made it necessary to explore the potential held by information integration with regard to environmental aspects. Information flow integration provides an opportunity to focus on the creation of a more “green” supply chain. However, it is currently difficult to identify the impact of information integration on greening a supply chain in a wide range of practical applications. Accordingly, this book focuses on the potential value of information integration solutions in terms of greening supply chain management. It covers the following major topics: Application of information flow standards in the supply chain Information systems and technological solutions for integrating information flows in supply chains The Internet of Things and the industry 4.0 concept, with regard to the integration of supply chains Modeling and simulation of logistics processes Decision-making tools enabling the greening of supply chains |
| ai in operations management: Artificial Intelligence for HR Ben Eubanks, 2018-12-03 HR professionals need to get to grips with artificial intelligence and the way it's changing the world of work. From using natural language processing to ensure job adverts are free from bias and gendered language to implementing chatbots to enhance the employee experience, AI has created a variety of opportunities for the HR function. Artificial Intelligence for HR empowers HR professionals to leverage this potential and use AI to improve efficiency and develop a talented and productive workforce. Outlining the current technology landscape as well as the latest AI developments, this book ensures that HR professionals fully understand what AI is and what it means for HR in practice. Covering everything from recruitment and retention to employee engagement and learning and development, Artificial Intelligence for HR outlines the value AI can add to HR. It also features discussions on the challenges that can arise from AI and how to deal with them, including data privacy, algorithmic bias and how to develop the skills of a workforce with the rise of automation, robotics and machine learning in order to make it more human, not less. Packed with practical advice, research and case studies from global organizations including Uber, IBM and Unilever, this book will equip HR professionals with the knowledge they need to leverage AI to recruit and develop a successful workforce and help their businesses thrive in the future. |
| ai in operations management: Hybrid Artificial Intelligence Systems Emilio Corchado, Xindong Wu, Erkki Oja, Bruno Baruque, 2009-06-22 The 4th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2009), as the name suggests, attracted researchers who are involved in developing and applying symbolic and sub-symbolic techniques aimed at the construction of highly robust and reliable problem-solving techniques, and bringing the most relevant achievements in this field. Hybrid intelligent systems have become increasingly po- lar given their capabilities to handle a broad spectrum of real-world complex problems which come with inherent imprecision, uncertainty and vagueness, hi- dimensionality, and nonstationarity. These systems provide us with the opportunity to exploit existing domain knowledge as well as raw data to come up with promising solutions in an effective manner. Being truly multidisciplinary, the series of HAIS conferences offers an interesting research forum to present and discuss the latest th- retical advances and real-world applications in this exciting research field. This volume of Lecture Notes in Artificial Intelligence (LNAI) includes accepted papers presented at HAIS 2009 held at the University of Salamanca, Salamanca, Spain, June 2009. Since its inception, the main aim of the HAIS conferences has been to establish a broad and interdisciplinary forum for hybrid artificial intelligence systems and asso- ated learning paradigms, which are playing increasingly important roles in a large number of application areas. |
| ai in operations management: Operations Management Antonella Petrillo, Fabio De Felice, Germano Lambert-Torres, Erik Bonaldi, 2021-03-03 Global competition has caused fundamental changes in the competitive environment of the manufacturing and service industries. Firms should develop strategic objectives that, upon achievement, result in a competitive advantage in the market place. The forces of globalization on one hand and rapidly growing marketing opportunities overseas, especially in emerging economies on the other, have led to the expansion of operations on a global scale. The book aims to cover the main topics characterizing operations management including both strategic issues and practical applications. A global environmental business including both manufacturing and services is analyzed. The book contains original research and application chapters from different perspectives. It is enriched through the analyses of case studies. |
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