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AI for Supply Chain Optimization: Navigating Challenges and Unleashing Opportunities
Author: Dr. Anya Sharma, PhD in Operations Research & Supply Chain Management, MIT; Senior Research Fellow, Institute for Data Science, University of California, Berkeley.
Keywords: AI for supply chain optimization, artificial intelligence, supply chain management, machine learning, predictive analytics, demand forecasting, inventory optimization, logistics optimization, supply chain resilience, digital transformation
Abstract: The global supply chain landscape is increasingly complex and volatile. This article explores the transformative potential of AI for supply chain optimization, examining both the significant opportunities presented by artificial intelligence and the inherent challenges in its implementation. We delve into specific applications of AI across various stages of the supply chain, analyze the benefits and limitations of different AI techniques, and discuss strategies for successful adoption.
1. Introduction: The Need for AI in Supply Chain Management
The modern supply chain faces unprecedented pressures. Globalization, fluctuating demand, geopolitical instability, and the increasing need for sustainability all contribute to a highly dynamic and unpredictable environment. Traditional supply chain management approaches, often reliant on historical data and rule-based systems, struggle to adapt effectively to this complexity. This is where AI for supply chain optimization steps in, offering the potential to revolutionize how businesses manage their supply chains and achieve significant improvements in efficiency, resilience, and profitability. AI for supply chain optimization leverages advanced algorithms and machine learning to analyze vast datasets, identify patterns, predict future trends, and automate decision-making processes, ultimately leading to more informed and agile supply chain operations.
2. Opportunities Presented by AI for Supply Chain Optimization
AI offers a wide range of opportunities for enhancing supply chain performance across various stages:
2.1 Demand Forecasting: AI algorithms, particularly deep learning models, can analyze historical sales data, economic indicators, social media trends, and even weather patterns to generate more accurate and timely demand forecasts. Improved forecasting reduces inventory holding costs, minimizes stockouts, and optimizes production planning. AI for supply chain optimization in this area directly translates to reduced waste and improved customer satisfaction.
2.2 Inventory Optimization: AI can optimize inventory levels by considering factors such as demand forecasts, lead times, storage costs, and risk of obsolescence. Machine learning models can learn from historical data to identify optimal inventory levels for each product, minimizing the risk of stockouts and overstocking. This is a crucial aspect of ai for supply chain optimization that impacts profitability significantly.
2.3 Logistics Optimization: AI can optimize transportation routes, warehouse layouts, and delivery schedules, reducing transportation costs and improving delivery times. AI-powered route optimization systems can consider real-time traffic conditions, weather patterns, and driver availability to determine the most efficient routes. AI for supply chain optimization in logistics can drastically reduce fuel consumption and delivery times.
2.4 Supply Chain Risk Management: AI can identify and assess potential supply chain disruptions, such as natural disasters, geopolitical events, and supplier failures. By analyzing various data sources, AI models can predict the likelihood and impact of potential disruptions, allowing companies to proactively mitigate risks. This proactive approach is a cornerstone of effective ai for supply chain optimization, reducing vulnerability and increasing resilience.
2.5 Predictive Maintenance: AI can predict equipment failures in manufacturing and transportation, allowing for proactive maintenance to prevent costly downtime. By analyzing sensor data and maintenance logs, AI models can identify patterns that indicate impending failures, enabling preventative maintenance to be scheduled before equipment malfunctions occur. This significantly improves efficiency within the supply chain.
3. Challenges in Implementing AI for Supply Chain Optimization
Despite the significant potential of AI, several challenges must be addressed for successful implementation:
3.1 Data Quality and Availability: AI algorithms require large amounts of high-quality data to function effectively. Many companies lack the necessary data infrastructure or the data itself to train and deploy AI models. Data silos, inconsistent data formats, and missing data are common challenges.
3.2 Integration with Existing Systems: Integrating AI solutions with existing enterprise resource planning (ERP) systems and other legacy systems can be complex and costly. Data compatibility issues and lack of interoperability can hinder seamless integration.
3.3 Talent Acquisition and Development: Implementing and managing AI solutions requires specialized skills and expertise. Finding and retaining skilled data scientists, AI engineers, and supply chain professionals with AI expertise can be challenging.
3.4 Explainability and Trust: Some AI algorithms, such as deep learning models, are often referred to as “black boxes,” making it difficult to understand how they arrive at their decisions. This lack of transparency can make it challenging for businesses to trust the AI’s recommendations, especially in critical supply chain decisions.
3.5 Cost of Implementation: Implementing AI solutions can be expensive, requiring significant investments in software, hardware, data infrastructure, and talent. The return on investment (ROI) may not be immediately apparent, which can deter some companies from adopting AI.
4. Strategies for Successful AI Adoption in Supply Chains
Successfully implementing AI for supply chain optimization requires a strategic approach:
Start with a clear business objective: Define specific, measurable, achievable, relevant, and time-bound (SMART) goals for AI implementation.
Identify the right use cases: Focus on specific areas where AI can deliver the greatest value.
Invest in data quality and infrastructure: Ensure data is accurate, consistent, and readily available.
Build a strong team: Assemble a team with the necessary skills and expertise.
Choose the right AI solutions: Select AI solutions that are well-suited to your specific needs and integrate seamlessly with existing systems.
Iterate and learn: Start with pilot projects and gradually expand AI adoption based on learnings.
Address ethical concerns: Ensure AI systems are fair, transparent, and accountable.
5. Conclusion
AI for supply chain optimization offers immense potential to improve efficiency, resilience, and profitability. By addressing the challenges and adopting a strategic approach, businesses can unlock the transformative power of AI and create more agile, responsive, and sustainable supply chains. The future of supply chain management lies in leveraging the power of AI to navigate the complexities of the global marketplace and deliver exceptional results.
FAQs
1. What are the most common AI techniques used in supply chain optimization? Machine learning (including deep learning), natural language processing, and computer vision are frequently employed.
2. How can AI improve supply chain visibility? AI-powered dashboards and analytics platforms provide real-time insights into various aspects of the supply chain, enhancing visibility and enabling proactive decision-making.
3. What is the role of blockchain in AI-powered supply chains? Blockchain can enhance data security, transparency, and traceability, complementing the capabilities of AI systems.
4. How can AI help reduce supply chain disruptions? AI can predict potential disruptions by analyzing various data sources and help companies develop mitigation strategies.
5. What are the ethical considerations of using AI in supply chains? Issues like data privacy, algorithmic bias, and job displacement need careful consideration and mitigation.
6. What is the ROI of implementing AI in supply chain management? The ROI varies depending on the specific applications and implementation strategies; however, significant cost savings and efficiency gains are often reported.
7. What are some examples of companies successfully using AI in their supply chains? Many large companies across various sectors are leveraging AI; specific examples are often case studies protected by NDAs.
8. How can small and medium-sized enterprises (SMEs) benefit from AI in supply chain management? Cloud-based AI solutions and readily available tools make AI accessible even to smaller companies.
9. What are the future trends in AI for supply chain optimization? The integration of AI with other technologies like IoT and the rise of explainable AI (XAI) are key future trends.
Related Articles:
1. "Predictive Maintenance using AI in Logistics": Explores how AI-powered predictive maintenance reduces downtime and optimizes maintenance schedules for transportation assets.
2. "AI-Driven Demand Forecasting: A Case Study": Presents a detailed case study of a company successfully using AI for demand forecasting and its impact on inventory management.
3. "The Role of Machine Learning in Supply Chain Risk Management": Analyzes how machine learning algorithms can identify and assess potential risks across the supply chain.
4. "Optimizing Transportation Networks with AI": Focuses on the application of AI algorithms for route optimization, fleet management, and last-mile delivery optimization.
5. "Building a Data-Driven Supply Chain with AI": Discusses the importance of data quality and infrastructure for successful AI implementation in supply chains.
6. "The Ethical Implications of AI in Global Supply Chains": Explores the ethical considerations of AI use in supply chains, addressing concerns about fairness, bias, and transparency.
7. "AI and Sustainability in Supply Chain Management": Examines how AI can be leveraged to improve the environmental sustainability of supply chains.
8. "Cloud-Based AI Solutions for Supply Chain Optimization": Discusses the advantages of cloud-based AI solutions for businesses of all sizes.
9. "The Future of Work in AI-Powered Supply Chains": Analyzes the impact of AI on jobs within the supply chain sector and strategies for workforce adaptation.
Publisher: Supply Chain Digest – A leading online publication covering the latest news, trends, and best practices in supply chain management, known for its in-depth analysis and industry insights.
Editor: John Smith, Certified Supply Chain Professional (CSCP), with 20 years of experience in supply chain management and a strong background in data analytics.
| ai for supply chain optimization: Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods Minis, Ioannis, Zeimpekis, Vasileios, Dounias, Georgios, Ampazis, Nicholas, 2010-12-31 Computational Intelligence (CI) is a term corresponding to a new generation of algorithmic methodologies in artificial intelligence, which combines elements of learning, adaptation, evolution and approximate (fuzzy) reasoning to create programs that can be considered intelligent. Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods presents computational intelligence methods for addressing supply chain issues. Emphasis is given to techniques that provide effective solutions to complex supply chain problems and exhibit superior performance to other methods of operations research. |
| ai for supply chain optimization: Artificial Intelligent Techniques for Wireless Communication and Networking R. Kanthavel, K. Anathajothi, S. Balamurugan, R. Karthik Ganesh, 2022-02-24 ARTIFICIAL INTELLIGENT TECHNIQUES FOR WIRELESS COMMUNICATION AND NETWORKING The 20 chapters address AI principles and techniques used in wireless communication and networking and outline their benefit, function, and future role in the field. Wireless communication and networking based on AI concepts and techniques are explored in this book, specifically focusing on the current research in the field by highlighting empirical results along with theoretical concepts. The possibility of applying AI mechanisms towards security aspects in the communication domain is elaborated; also explored is the application side of integrated technologies that enhance AI-based innovations, insights, intelligent predictions, cost optimization, inventory management, identification processes, classification mechanisms, cooperative spectrum sensing techniques, ad-hoc network architecture, and protocol and simulation-based environments. Audience Researchers, industry IT engineers, and graduate students working on and implementing AI-based wireless sensor networks, 5G, IoT, deep learning, reinforcement learning, and robotics in WSN, and related technologies. |
| ai for supply chain optimization: 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 for supply chain optimization: Applications of Machine Learning Prashant Johri, Jitendra Kumar Verma, Sudip Paul, 2020-05-04 This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics. |
| ai for supply chain optimization: AI and Machine Learning Impacts in Intelligent Supply Chain Pandey, Binay Kumar, Kanike, Uday Kumar, George, A. Shaji, Pandey, Digvijay, 2024-01-29 Businesses are facing an unprecedented challenge - the urgent need to adapt and thrive in a world where intelligent factories and supply chains are the new norm. The digital transformation of supply chains is essential for staying competitive, but it is a complex journey fraught with uncertainties. How can organizations harness the power of emerging technologies like Artificial Intelligence (AI) and Machine Learning (ML) to increase profitability, cut supply chain costs, elevate customer service, and optimize their networks? The answers to these questions are crucial for business survival and success. AI and Machine Learning Impacts in Intelligent Supply Chain is a groundbreaking book that offers a comprehensive solution to the challenges posed by the Industry 4.0 revolution. This book is your indispensable guide to navigating the intricate world of supply chain digital transformation using innovative technologies. It provides real-world examples and insights that illustrate how AI and ML are the keys to solving complex supply chain problems, from inventory management to route optimization and beyond. Whether you are an academic scholar seeking to delve into the impact of AI and ML on supply chain management or a business leader striving to gain a competitive edge, this book is tailored to meet your needs. |
| ai for supply chain optimization: The Quantitative Supply Chain Joannès Vermorel, 2018-01-26 The Quantitative Supply Chain represents a novel and disruptive perspective on the optimization of supply chains. It can be seen as a refoundation of many supply chain practices, in particular regarding inventory forecasting, and has been built to make the most of the latest statistical approaches and vast computing resources that are available nowadays. |
| ai for supply chain optimization: Artificial Intelligence in Industrial Applications Steven Lawrence Fernandes, Tarun K. Sharma, 2021-12-07 This book highlights the analytics and optimization issues in industry, to propose new approaches, and to present applications of innovative approaches in real facilities. In the past few decades there has been an exponential rise in the application of artificial intelligence for solving complex and intricate problems arising in industrial domain. The versatility of these techniques have made them a favorite among scientists and researchers working in diverse areas. The book is edited to serve a broad readership, including computer scientists, medical professionals, and mathematicians interested in studying computational intelligence and their applications. It will also be helpful for researchers, graduate and undergraduate students with an interest in the fields of Artificial Intelligence and Industrial problems. This book will be a useful resource for researchers, academicians as well as professionals interested in the highly interdisciplinary field of Artificial Intelligence. |
| ai for supply chain optimization: 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 for supply chain optimization: Utilization of AI Technology in Supply Chain Management Pandey, Digvijay, Pandey, Binay Kumar, Kanike, Uday Kumar, George, A. Shaji, Kaur, Prabjot, 2024-03-01 The surge in digital transformation and the integration of innovative technologies into manufacturing processes have given rise to a pressing issue in supply chain management. Businesses are in dire need of solutions to navigate this complexity and harness the true potential of intelligent supply chains. Utilization of AI Technology in Supply Chain Management is a comprehensive guide tailored for academic scholars seeking to unravel the mysteries of artificial intelligence (AI) and machine learning (ML) in the context of supply chain management. Amid the hype surrounding AI and ML, there exists a critical need to bridge the gap between human expertise and technological advancements. Utilization of AI Technology in Supply Chain Management addresses this necessity by delving into real-world instances where teams have successfully employed these innovative technologies to enhance supply chain performance, reduce inventory, and optimize routes. The adoption of AI and ML is not just a trend; it is the cornerstone of digital acceleration initiatives, making it imperative for scholars to understand and leverage these technologies effectively. |
| ai for supply chain optimization: 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 for supply chain optimization: ICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management Vinit Kumar Gunjan, Vicente Garcia Diaz, Manuel Cardona, Vijender Kumar Solanki, K. V. N. Sunitha, 2019-06-27 This book discusses reliability applications for power systems, renewable energy and smart grids and highlights trends in reliable communication, fault-tolerant systems, VLSI system design and embedded systems. Further, it includes chapters on software reliability and other computer engineering and software management-related disciplines, and also examines areas such as big data analytics and ubiquitous computing. Outlining novel, innovative concepts in applied areas of reliability in electrical, electronics and computer engineering disciplines, it is a valuable resource for researchers and practitioners of reliability theory in circuit-based engineering domains. |
| ai for supply chain optimization: The Humachine Nada R. Sanders, John D. Wood, 2019-09-09 There is a lot of hype, hand-waving, and ink being spilled about artificial intelligence (AI) in business. The amount of coverage of this topic in the trade press and on shareholder calls is evidence of a large change currently underway. It is awesome and terrifying. You might think of AI as a major environmental factor that is creating an evolutionary pressure that will force enterprise to evolve or perish. For those companies that do survive the silicon wave sweeping through the global economy, the issue becomes how to keep their humanity amidst the tumult. What started as an inquiry into how executives can adopt AI to harness the best of human and machine capabilities turned into a much more profound rumination on the future of humanity and enterprise. This is a wake-up call for business leaders across all sectors of the economy. Not only should you implement AI regardless of your industry, but once you do, you should fight to stay true to your purpose, your ethical convictions, indeed your humanity, even as our organizations continue to evolve. While not holding any punches about the dangers posed by overpowered AI, this book uniquely surveys where technology is limited, and gives reason for cautious optimism about the true opportunities that lie amidst all the disruptive change currently underway. As such, it is distinctively more optimistic than many of the competing titles on Big Technology. This compelling book weaves together business strategy and philosophy of mind, behavioral psychology and the limits of technology, leadership and law. The authors set out to identify where humans and machines can best complement one another to create an enterprise greater than the sum total of its parts: the Humachine. Combining the global business and forecasting acumen of Professor Nada R. Sanders, PhD, with the legal and philosophical insight of John D. Wood, Esq., the authors combine their strengths to bring us this profound yet accessible book. This is a must read for anyone interested in AI and the future of human enterprise. |
| ai for supply chain optimization: Logistics Management and Optimization Through Hybrid Artificial Intelligence Systems Carlos Alberto Ochoa Ortiz Zezzatti, 2012-01-01 This book offers the latest research within the field of HAIS, surveying the broad topics and collecting case studies, future directions, and cutting edge analyses, investigating biologically inspired algorithms such as ant colony optimization and particle swarm optimization-- |
| ai for supply chain optimization: Least Squares Support Vector Machines Johan A. K. Suykens, Tony Van Gestel, Jos De Brabanter, 2002 This book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing spareness and employing robust statistics. The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nystrom sampling with active selection of support vectors. The methods are illustrated with several examples. |
| ai for supply chain optimization: Technology in Supply Chain Management and Logistics Anthony M. Pagano, Matthew Liotine, 2019-09-07 Technology in Supply Chain Management and Logistics: Current Practice and Future Applications analyzes the implications of these technologies in a variety of supply chain settings, including block chain, Internet of Things (IoT), inventory optimization, and medical supply chain. This book outlines how technologies are being utilized for product planning, materials management and inventory, transportation and distribution, workflow, maintenance, the environment, and in health and safety. Readers will gain a better understanding of the implications of these technologies with respect to value creation, operational effectiveness, investment level, technical migration and general industry acceptance. In addition, the book features case studies, providing a real-world look at supply chain technology implementations, their necessary training requirements, and how these new technologies integrate with existing business technologies. - Identifies emerging supply chain technologies and trends in technology acceptance and utilization levels across various industry sectors - Assists professionals with technology investment decisions, procurement, best values, and how they can be utilized for logistics operations - Features videos showing technology application, including optimization software, cloud computing, mobility, 3D printing, autonomous vehicles, drones and machine learning |
| ai for supply chain optimization: Artificial Intelligence for COVID-19 Diego Oliva, Said Ali Hassan, Ali Mohamed, 2021-07-19 This book presents a compilation of the most recent implementation of artificial intelligence methods for solving different problems generated by the COVID-19. The problems addressed came from different fields and not only from medicine. The information contained in the book explores different areas of machine and deep learning, advanced image processing, computational intelligence, IoT, robotics and automation, optimization, mathematical modeling, neural networks, information technology, big data, data processing, data mining, and likewise. Moreover, the chapters include the theory and methodologies used to provide an overview of applying these tools to the useful contribution to help to face the emerging disaster. The book is primarily intended for researchers, decision makers, practitioners, and readers interested in these subject matters. The book is useful also as rich case studies and project proposals for postgraduate courses in those specializations. |
| ai for supply chain optimization: Responsible AI and Analytics for an Ethical and Inclusive Digitized Society Denis Dennehy, Anastasia Griva, Nancy Pouloudi, Yogesh K. Dwivedi, Ilias Pappas, Matti Mäntymäki, 2021-08-25 This volume constitutes the proceedings of the 20th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2021, held in Galway, Ireland, in September 2021.* The total of 57 full and 8 short papers presented in these volumes were carefully reviewed and selected from 141 submissions. The papers are organized in the following topical sections: AI for Digital Transformation and Public Good; AI & Analytics Decision Making; AI Philosophy, Ethics & Governance; Privacy & Transparency in a Digitized Society; Digital Enabled Sustainable Organizations and Societies; Digital Technologies and Organizational Capabilities; Digitized Supply Chains; Customer Behavior and E-business; Blockchain; Information Systems Development; Social Media & Analytics; and Teaching & Learning. *The conference was held virtually due to the COVID-19 pandemic. |
| ai for supply chain optimization: AI IN ERP AND SUPPLY CHAIN MANAGEMENT Saurabh Suman Choudhuri, 2024-07-01 “AI in ERP and Supply Chain Management” is a comprehensive book that provides an in-depth discussion on the integration of artificial intelligence (AI) in the areas of enterprise resource planning (ERP) and supply chain management (SCM). This book explores the transformational impact of AI on these critical business areas, providing a practical guide to implementing and leveraging AI technologies. The book begins by explaining the basic concepts of AI and its various subfields, such as machine learning, natural language processing, and robotics. It further explains how these AI technologies can be applied to ERP and SCM to increase operational efficiency, optimize decision-making, and unlock new business opportunities. Readers are given valuable information about the potential applications of AI in ERP and SCM, ranging from demand forecasting and inventory management to logistics optimization and supply chain risk management. In addition, the authors discuss the challenges and considerations associated with implementing AI in ERP and SCM, such as data privacy, security, and ethical concerns. They provide guidance on selecting appropriate AI technologies, integrating them with existing systems, and ensuring successful deployment within an organization. The book also explores the future prospects of AI in ERP and SCM, highlighting emerging technologies such as the Internet of Things (IoT), big data analytics, and blockchain and how they can be combined with AI to create even more sophisticated and intelligent systems. “AI in ERP and Supply Chain Management” is a valuable resource for every profession interested in harnessing the power of AI to revolutionize ERP and SCM. With its comprehensive coverage, practical insights, and visionary outlook, this book provides a roadmap for organizations seeking to remain competitive in the era of AI-driven digital transformation. |
| ai for supply chain optimization: The Digital Supply Chain Bart L. MacCarthy, Dmitry Ivanov, 2022-06-09 The Digital Supply Chain is a thorough investigation of the underpinning technologies, systems, platforms and models that enable the design, management, and control of digitally connected supply chains. The book examines the origin, emergence and building blocks of the Digital Supply Chain, showing how and where the virtual and physical supply chain worlds interact. It reviews the enabling technologies that underpin digitally controlled supply chains and examines how the discipline of supply chain management is affected by enhanced digital connectivity, discussing purchasing and procurement, supply chain traceability, performance management, and supply chain cyber security. The book provides a rich set of cases on current digital practices and challenges across a range of industrial and business sectors including the retail, textiles and clothing, the automotive industry, food, shipping and international logistics, and SMEs. It concludes with research frontiers, discussing network science for supply chain analysis, challenges in Blockchain applications and in digital supply chain surveillance, as well as the need to re-conceptualize supply chain strategies for digitally transformed supply chains. |
| ai for supply chain optimization: Handbook of Research on Applied AI for International Business and Marketing Applications Christiansen, Bryan, Škrinjari?, Tihana, 2020-09-25 Artificial intelligence (AI) describes machines/computers that mimic cognitive functions that humans associate with other human minds, such as learning and problem solving. As businesses have evolved to include more automation of processes, it has become more vital to understand AI and its various applications. Additionally, it is important for workers in the marketing industry to understand how to coincide with and utilize these techniques to enhance and make their work more efficient. The Handbook of Research on Applied AI for International Business and Marketing Applications is a critical scholarly publication that provides comprehensive research on artificial intelligence applications within the context of international business. Highlighting a wide range of topics such as diversification, risk management, and artificial intelligence, this book is ideal for marketers, business professionals, academicians, practitioners, researchers, and students. |
| ai for supply chain optimization: The New (Ab)Normal Yossi Sheffi, 2020-10-01 Much has been written about Covid-19 victims, how scientists raced to understand and treat the disease, and how governments did (or did not) protect their citizens. Less has been written about the pandemic’s impact on the global economy and how companies coped as the competitive environment was upended. In his new book, The New (Ab)Normal, MIT Professor Yossi Sheffi maps how the Covid-19 pandemic impacted business, supply chains, and society. He exposes the critical role supply chains play in helping people, governments, and companies to manage the crisis. The book draws on executive interviews, pandemic media coverage, and historical analyses. Sheffi also builds on themes from his books The Resilient Enterprise (2005) and The Power of Resilience (2015) to enrich the narrative. The author paints a compelling picture of how the Covid-19 virus is changing many facets of human life and what our post-pandemic world might look like. This must-read book helps companies to redefine their business models and adjust to a fast-evolving economic landscape. The stage is set In Part 1 of the book, “What Happened,” the author looks at how companies fought to mend the global economic fabric even as the virus ripped more holes in it. Part 2, “Living with Uncertainty,” views the crisis through a supply chain risk management lens derived from Yossi Sheffi’s previous books. This perspective shows how companies create corporate immune systems to quickly recognize and manage large-scale disruptions. The ongoing pandemic is creating a new normal in life, work, and education—covered in Part 3, “Adjustment Required.” Consumer fears about the contagion as well as government mandates require businesses in industries such as retail, hospitality, entertainment, sports, and education to create “safe zones” for workers and customers. Many elements of the book – especially in Part 4, “Supply Chains for the Future” – show how the virus accelerated preexisting trends in technology adoption. China was the epicenter of the pandemic; it also was the first nation to be disrupted and recover. Part 5 of the book, “Of Politics and Pandemics,” explains why reports that companies are abandoning China in favor of other offshore manufacturing centers do not reflect reality. Fundamentally, The New (Ab)Normal is about businesses trying to create a better future in a time of extreme uncertainty – a point emphasized in Part 6, “The Next Opportunities.” The outlook is not necessarily gloomy. The advance of technology is accelerating, a trend that can level the playing field between small and large companies. Nimble small businesses are using a growing array of off-the-shelf cloud computing and mobile apps to deploy sophisticated technologies in their supply chains and customer interfaces. The New (Ab)Normal Another new normal is working from home. Remote working enables individuals to live anywhere and companies to recruit talent from anywhere. Education, especially higher education, faces a major disruption (and major opportunity) that is likely to shake the high-cost model of in-person education in favor of online or hybrid education. Regrettably, the book recognizes one trend accentuated by Covid-19--the growing inequality, and anticipates that the new normal will be more stratified. |
| ai for supply chain optimization: Supply Chain Optimization, Design, and Management Ioannis Minis, Vasileios Zeimpekis, Georgios Dounias, Nicholas Ampazis, 2011 Presents computational intelligence methods for addressing supply chain issues, emphasizing techniques that provide effective solutions to complex supply chain problems and exhibit superior performance to other methods of operations research. |
| ai for supply chain optimization: Logistics 4.0 Turan Paksoy, Cigdem Gonul Kochan, Sadia Samar Ali, 2020-12-17 Industrial revolutions have impacted both, manufacturing and service. From the steam engine to digital automated production, the industrial revolutions have conduced significant changes in operations and supply chain management (SCM) processes. Swift changes in manufacturing and service systems have led to phenomenal improvements in productivity. The fast-paced environment brings new challenges and opportunities for the companies that are associated with the adaptation to the new concepts such as Internet of Things (IoT) and Cyber Physical Systems, artificial intelligence (AI), robotics, cyber security, data analytics, block chain and cloud technology. These emerging technologies facilitated and expedited the birth of Logistics 4.0. Industrial Revolution 4.0 initiatives in SCM has attracted stakeholders’ attentions due to it is ability to empower using a set of technologies together that helps to execute more efficient production and distribution systems. This initiative has been called Logistics 4.0 of the fourth Industrial Revolution in SCM due to its high potential. Connecting entities, machines, physical items and enterprise resources to each other by using sensors, devices and the internet along the supply chains are the main attributes of Logistics 4.0. IoT enables customers to make more suitable and valuable decisions due to the data-driven structure of the Industry 4.0 paradigm. Besides that, the system’s ability of gathering and analyzing information about the environment at any given time and adapting itself to the rapid changes add significant value to the SCM processes. In this peer-reviewed book, experts from all over the world, in the field present a conceptual framework for Logistics 4.0 and provide examples for usage of Industry 4.0 tools in SCM. This book is a work that will be beneficial for both practitioners and students and academicians, as it covers the theoretical framework, on the one hand, and includes examples of practice and real world. |
| ai for supply chain optimization: Data Analytics and Artificial Intelligence for Inventory and Supply Chain Management Dinesh K. Sharma, Madhu Jain, 2022-11-08 This book considers new analytics and AI approaches in the areas of inventory control, logistics, and supply chain management. It provides valuable insights for the retailers and managers to improve business operations and make more realistic and better decisions. It also offers a number of smartly designed strategies related to inventory control and supply chain management for the optimal ordering and delivery policies. The book further uses detailed models and AI computing approaches for demand forecasting to planning optimization and digital execution tracking. One of its key features is use of real-life examples, case studies, practical models to ensure adoption of new solutions, data analytics, and AI-lead automation methodologies are included.The book can be utilized by retailers and managers to improve business operations and make more accurate and realistic decisions. The AI-based solution, agnostic assessment, and strategy will support the companies for better alignment and inventory control and capabilities to create a strategic road map for supply chain and logistics. The book is also useful for postgraduate students, researchers, and corporate executives. It addresses novel solutions for inventory to real-world supply chain and logistics that retailers, practitioners, educators, and scholars will find useful. It provides the theoretical and applicable subject matters for the senior undergraduate and graduate students, researchers, practitioners, and professionals in the area of artificial intelligent computing and its applications in inventory and supply chain management, inventory control, and logistics. |
| ai for supply chain optimization: The AI-Powered Enterprise Seth Earley, 2020-04-28 Learn how to develop and employ an ontology, the secret weapon for successfully using artificial intelligence to create a powerful competitive advantage in your business. The AI-Powered Enterprise examines two fundamental questions: First, how will the future be different as a result of artificial intelligence? And second, what must companies do to stake their claim on that future? When the Web came along in the mid-90s, it transformed the behavior of customers and remade whole industries. Now, as part of its promise to bring revolutionary change in untold ways to human activity, artificial intelligence--AI--is about to create another complete transformation in how companies create and deliver value to customers. But despite the billions spent so far on bots and other tools, AI continues to stumble. Why can't it magically use all the data organizations generate to make them run faster and better? Because something is missing. AI works only when it understands the soul of the business. An ontology is a holistic digital model of every piece of information that matters to the business, from processes to products to people, and it's what makes the difference between the promise of AI and delivering on that promise. Business leaders who want to catch the AI wave--rather than be crushed by it--need to read The AI-Powered Enterprise. The book is the first to combine a sophisticated explanation of how AI works with a practical approach to applying AI to the problems of business, from customer experience to business operations to product development. |
| ai for supply chain optimization: Intelligent Decision Making: An AI-Based Approach Gloria Phillips-Wren, Nikhil Ichalkaranje, 2008-03-04 Intelligent Decision Support Systems have the potential to transform human decision making by combining research in artificial intelligence, information technology, and systems engineering. The field of intelligent decision making is expanding rapidly due, in part, to advances in artificial intelligence and network-centric environments that can deliver the technology. Communication and coordination between dispersed systems can deliver just-in-time information, real-time processing, collaborative environments, and globally up-to-date information to a human decision maker. At the same time, artificial intelligence techniques have demonstrated that they have matured sufficiently to provide computational assistance to humans in practical applications. This book includes contributions from leading researchers in the field beginning with the foundations of human decision making and the complexity of the human cognitive system. Researchers contrast human and artificial intelligence, survey computational intelligence, present pragmatic systems, and discuss future trends. This book will be an invaluable resource to anyone interested in the current state of knowledge and key research gaps in the rapidly developing field of intelligent decision support. |
| ai for supply chain optimization: Artificial Intelligence Harvard Business Review, 2019 Companies that don't use AI to their advantage will soon be left behind. Artificial intelligence and machine learning will drive a massive reshaping of the economy and society. What should you and your company be doing right now to ensure that your business is poised for success? These articles by AI experts and consultants will help you understand today's essential thinking on what AI is capable of now, how to adopt it in your organization, and how the technology is likely to evolve in the near future. Artificial Intelligence: The Insights You Need from Harvard Business Review will help you spearhead important conversations, get going on the right AI initiatives for your company, and capitalize on the opportunity of the machine intelligence revolution. Catch up on current topics and deepen your understanding of them with the Insights You Need series from Harvard Business Review. Featuring some of HBR's best and most recent thinking, Insights You Need titles are both a primer on today's most pressing issues and an extension of the conversation, with interesting research, interviews, case studies, and practical ideas to help you explore how a particular issue will impact your company and what it will mean for you and your business. |
| ai for supply chain optimization: AI and Machine Learning Applications in Supply Chains and Marketing Masengu, Reason, Tsikada, Charles, Garwi, Jabulani, 2024-10-18 While artificial intelligence (AI) simulates human intelligence in machines, machine learning (ML) enables systems to learn from data without explicit programming. In marketing and supply chain management, AI and ML empower businesses to analyze consumer behavior, personalize experiences, optimize advertising strategies, forecast consumer demands, manage inventory, plan routes, and mitigate risks. Businesses can enhance efficiency, accuracy, decision-making, customer engagement, and cost-effectiveness when integrating AI and ML in marketing and supply chain operations. Further research is necessary to drive success in the dynamic marketplace. AI and Machine Learning Applications in Supply Chains and Marketing bridges the gap between theoretical knowledge and practical application of AI and ML in marketing and supply chain management. It examines emerging technologies that can revolutionize industries by transforming business operations. This book covers topics such as data analysis, sustainable development, and blockchain, and is a useful resource for business owners, economists, marketing professionals, engineers, computer scientists, academicians, and researchers. |
| ai for supply chain optimization: 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 for supply chain optimization: Inventory Optimization Nicolas Vandeput, 2020-08-24 In this book . . . Nicolas Vandeput hacks his way through the maze of quantitative supply chain optimizations. This book illustrates how the quantitative optimization of 21st century supply chains should be crafted and executed. . . . Vandeput is at the forefront of a new and better way of doing supply chains, and thanks to a richly illustrated book, where every single situation gets its own illustrating code snippet, so could you. --Joannes Vermorel, CEO, Lokad Inventory Optimization argues that mathematical inventory models can only take us so far with supply chain management. In order to optimize inventory policies, we have to use probabilistic simulations. The book explains how to implement these models and simulations step-by-step, starting from simple deterministic ones to complex multi-echelon optimization. The first two parts of the book discuss classical mathematical models, their limitations and assumptions, and a quick but effective introduction to Python is provided. Part 3 contains more advanced models that will allow you to optimize your profits, estimate your lost sales and use advanced demand distributions. It also provides an explanation of how you can optimize a multi-echelon supply chain based on a simple—yet powerful—framework. Part 4 discusses inventory optimization thanks to simulations under custom discrete demand probability functions. Inventory managers, demand planners and academics interested in gaining cost-effective solutions will benefit from the do-it-yourself examples and Python programs included in each chapter. Events around the book Link to a De Gruyter Online Event in which the author Nicolas Vandeput together with Stefan de Kok, supply chain innovator and CEO of Wahupa; Koen Cobbaert, Director in the S&O Industry practice of PwC Belgium; Bram Desmet, professor of operations & supply chain at the Vlerick Business School in Ghent; and Karl-Eric Devaux, Planning Consultant, Hatmill, discuss about models for inventory optimization. The event will be moderated by Eric Wilson, Director of Thought Leadership for Institute of Business Forecasting (IBF): https://youtu.be/565fDQMJEEg |
| ai for supply chain optimization: Digital Supply Networks: Transform Your Supply Chain and Gain Competitive Advantage with Disruptive Technology and Reimagined Processes Amit Sinha, Ednilson Bernardes, Rafael Calderon, Thorsten Wuest, 2020-07-21 Deliver unprecedented customer value and seize your competitive edge with a transformative digital supply network Digital tech has disrupted life and business as we know it, and supply chain management is no exception. But how exactly does digital transformation affect your business? What are the breakthrough technologies and their capabilities you need to know about? How will digital transformation impact skills requirements and work in general? Do you need to completely revamp your understanding of supply chain management? And most importantly: How do you get started? Digital Supply Networks provides clear answers to these and many other questions. Written by an experienced team comprised of Deloitte consultants and leading problem-driven scholars from a premier research university, this expert guide leads you through the process of improving operations building supply networks, increasing revenue, reimagining business models, and providing added value to customers, stakeholders, and society. You’ll learn everything you need to know about: Stages of development, roles, capabilities, and the benefits of DSN Big data analytics including its attributes, security, and authority Machine learning, Artificial Intelligence, Blockchain, robotics, and the Internet of Things Synchronized planning, intelligent supply, and digital product development Vision, attributes, technology, and benefits of smart manufacturing, dynamic logistics, and fulfillment A playbook to guide the digital transformation journey Drawing from real world-experience and problem-driven academic research, the authors provide an in-depth account of the transformation to digitally connected supply networks. They discuss the limitations of traditional supply chains and the underlying capabilities and potential of digitally-enabled supply flows. The chapters burst with expert insights and real-life use cases grounded in tomorrow’s industry needs. Success in today’s hyper-competitive, fast-paced business landscape, characterized by the risk of black swan events, such as the 2020 COVID-19 global pandemic, requires the reimagination and the digitalization of complex demand-supply systems, more collaborative and connected processes, and smarter, more dynamic data-driven decision making―which can only be achieved through a fully integrated Digital Supply Network. |
| ai for supply chain optimization: Introduction to Algorithmic Marketing Ilya Katsov, 2017-12 A comprehensive guide to advanced marketing automation for marketing strategists, data scientists, product managers, and software engineers. The book covers the main areas of marketing that require programmatic micro-decisioning - targeted promotions and advertisements, eCommerce search, recommendations, pricing, and assortment optimization. |
| ai for supply chain optimization: Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems Alexandre Dolgui, Alain Bernard, David Lemoine, Gregor von Cieminski, David Romero, 2021-09-01 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 for supply chain optimization: Reinforcement Learning Phil Winder Ph.D., 2020-11-06 Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a range of value and policy gradient methods Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying website |
| ai for supply chain optimization: The AI Economy Roger Bootle, 2019-11-26 Gold winner in Business Technology category, 2020 Axiom Business Book Awards Extraordinary innovations in technology promise to transform the world, but how realistic is the claim that AI will change our lives? In this much needed book the acclaimed economist Roger Bootle responds to the fascinating economic questions posed by the age of the robot, steering a path away from tech jargon and alarmism towards a rational explanation of the ways in which the AI revolution will affect us all. Tackling the implications of Artificial Intelligence on growth, productivity, inflation and the distribution of wealth and power, THE AI ECONOMY also examines coming changes to the the way we educate, work and spend our leisure time. A fundamentally optimistic view which will help you plan for changing times, this book explains AI and leads you towards a more certain future. Extraordinary innovations in technology promise to transform the world, but how realistic is the claim that AI will change our lives? In this much needed book the acclaimed economist Roger Bootle responds to the fascinating economic questions posed by the age of the robot, steering a path away from tech jargon and alarmism towards a rational explanation of the ways in which the AI revolution will affect us all. Tackling the implications of Artificial Intelligence on growth, productivity, inflation and the distribution of wealth and power, THE AI ECONOMY also examines coming changes to the the way we educate, work and spend our leisure time. A fundamentally optimistic view which will help you plan for changing times, this book explains AI and leads you towards a more certain future. |
| ai for supply chain optimization: Quantum Computing and Supply Chain Management: A New Era of Optimization Hassan, Ahdi, Bhattacharya, Pronaya, Dutta, Pushan Kumar, Verma, Jai Prakash, Kundu, Neel Kanth, 2024-07-23 Today's supply chains are becoming more complex and interconnected. As a result, traditional optimization engines struggle to cope with the increasing demands for real-time order fulfillment and inventory management. With the expansion and diversification of supply chain networks, these engines require additional support to handle the growing complexity effectively. This poses a significant challenge for supply chain professionals who must find efficient and cost-effective solutions to streamline their operations and promptly meet customer demands. Quantum Computing and Supply Chain Management: A New Era of Optimization offers a transformative solution to these challenges. By harnessing the power of quantum computing, this book explores how supply chain planners can overcome the limitations of traditional optimization engines. Quantum computing's ability to process vast amounts of data from IoT sensors in real time can revolutionize inventory management, resource allocation, and logistics within the supply chain. It provides a theoretical framework and practical examples to illustrate how quantum algorithms can enhance transparency, optimize dynamic inventory allocation, and improve supply chain resilience. |
| ai for supply chain optimization: Automated Planning Malik Ghallab, Dana Nau, Paolo Traverso, 2004-05-03 Publisher Description |
| ai for supply chain optimization: Supply Chain Management For Dummies Daniel Stanton, 2017-11-29 Everyone can impact the supply chain Supply Chain Management For Dummies helps you connect the dots between things like purchasing, logistics, and operations to see how the big picture is affected by seemingly isolated inefficiencies. Your business is a system, made of many moving parts that must synchronize to most efficiently meet the needs of your customers—and your shareholders. Interruptions in one area ripple throughout the entire operation, disrupting the careful coordination that makes businesses successful; that's where supply chain management (SCM) comes in. SCM means different things to different people, and many different models exist to meet the needs of different industries. This book focuses on the broadly-applicable Supply Chain Operations Reference (SCOR) Model: Plan, Source, Make, Deliver, Return, and Enable, to describe the basic techniques and key concepts that keep businesses running smoothly. Whether you're in sales, HR, or product development, the decisions you make every day can impact the supply chain. This book shows you how to factor broader impact into your decision making process based on your place in the system. Improve processes by determining your metrics Choose the right software and implement appropriate automation Evaluate and mitigate risks at all steps in the supply chain Help your business function as a system to more effectively meet customer needs We tend to think of the supply chain as suppliers, logistics, and warehousing—but it's so much more than that. Every single person in your organization, from the mailroom to the C-suite, can work to enhance or hinder the flow. Supply Chain Management For Dummies shows you what you need to know to make sure your impact leads to positive outcomes. |
| ai for supply chain optimization: Sustainable Logistics and Supply Chains Meng Lu, Joost De Bock, 2015-09-04 This book addresses the main challenges affecting modern logistics and supply chains and is organized according to five main themes: supply chain strategy and management, information and communication technology (ICT) for logistics and related business models, vertical and horizontal collaboration, intelligent hubs (e.g. ports and cities) and policy for sustainable logistics. The key findings presented are based on both extensive research and on business cases. The book examines logistics from a comprehensive viewpoint embracing the entire supply chain. The overarching advanced logistics and supply chain concept at the heart of this book endeavors to contribute to a sustainable intelligent transport system by making it more efficient, cost-effective, safe, reliable and competitive. Specifically, the book focuses on the need for a variety of supply chain, logistics and transport options, on the potential offered by technological developments, infrastructural and organizational aspects, information flows, the financial and legal domain, harmonization and the complexity of implementation. In closing, the book presents new approaches to the coordination of sound business and governance models. |
| ai for supply chain optimization: Data Science for Supply Chain Forecasting Nicolas Vandeput, 2021-03-22 Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting. This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical traditional models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves. This hands-on book, covering the entire range of forecasting—from the basics all the way to leading-edge models—will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting. |
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