Real-Time Value Chain Management

Value creation in the digital economy of the 21st century is characterized by the instantaneous processing and coordination of value chain activities across extended enterprises. Value chain management has evolved from automation and integration to the optimization of the entire value chain in real-time. This paper examines the real-time characteristics for each of the activities in the demand and supply value chains. A model is presented for real-time value chain management enabled by technologies in e-business, knowledge management and business intelligence

Innovative Logistics: Assessing AI’s Impact on Supply Chain Excellence

Many facets of corporate operations could be revolutionized by artificial intelligence (AI). Artificial Intelligence (AI) has the potential to improve supply chain inefficiencies, optimize logistics and transportation routes, and forecast demand based on data analysis. This may result in shorter lead times, lower costs, and better response to variations in demand. Using the Scopus database, this study examines and evaluates the uses of artificial intelligence (AI) in supply chain management (SCM). The goal is to close the existing research gap on the effects of AI on supply chain management (SCM) performance. This includes identifying AI techniques that can improve SCM performance, SCM subfields that have a high potential for AI enhancement, the effects of AI application on SCM performance, and how the performance can be explained from an agile-lean perspective. The Scopus database was used to list and categorize the current nations and areas involved in AI impact on SCM performance, document type, and subject area. In addition to addressing the existing research gap, this study delves into the challenges and ethical considerations surrounding the integration of artificial intelligence (AI) in supply chain management (SCM). The lack of standardized application methods poses a challenge for cross-enterprise comparisons, making it crucial to explore the variations in outcomes and impacts across diverse industries. Furthermore, the study highlights the absence of standardized metrics for assessing the return on investment (ROI) of AI in SCM, hindering businesses in evaluating the true value of their AI investments. An essential aspect examined in this research is the integration of AI systems with existing SCM frameworks, revealing potential limitations on data availability and accuracy. Ethical concerns, including issues of discrimination and the protection of sensitive data, emerge as critical considerations that demand greater attention in the context of AI-driven SCM solutions. This study aims to shed light on these ethical dimensions and emphasizes the necessity for a human-centric approach in developing AI solutions, with a focus on workforce development and training alongside process optimization and cost savings

IMPACT OF ARTIFICIAL INTELLIGENCE ON AGRICULTURAL, HEALTHCARE AND LOGISTICS INDUSTRIES

This qualitative research study was conducted to illustrate the relationships between Artificial Intelligence (AI) and non-tech businesses. AI is a broad branch of computer science. In information technology, the intelligent machine is a compliant and logical agent that recognizes its environment and takes full advantage of opportunities to achieve something. This paper provides detailed examples using AI outside of IT. The main method which is used for this research is literary analysis. The article consists of explanations about artificial intelligence in general, its impacts on logistics and transportation, agriculture and healthcare industries. Moreover, in this article, the methods used to leverage the developments of aforementioned industries are also mentioned and discussed.

Interrelationships between Circular Economy and Industry 4.0: A Research Agenda for Sustainable Supply Chains

The purpose of this article is to propose a novel classification of the interrelationships between I4.0 technologies and CE principles that highlights the most conclusive findings and extant gaps in the relevant research. A Systematic Literature Review has been developed to locate, select and evaluate relevant contributions made to CE interrelationships with I4.0 technologies. Studies have been analysed and classified according to the specific I4.0 technology and CE principle addressed (10Rs). The articles have been clustered into three main groups: (i) useful application of materials; (ii) extending the lifespan of products and their parts, and (iii) smarter product use and manufacture. A mind map of the investigated articles has been used to establish the interrelationships between individual technologies and each CE principle at the supply chain level. Based on this classification, a focus group interview (FGI) was held with experts to dig deeper into the interrelationships between I4.0 technologies and CE principles. The FGI results have identified how each as yet unexplored I4.0 technology could be linked to each CE principle. A Fuzzy Delphi (FD) study was also applied to identify the most relevant I4.0 technologies for improving CE principles and closing gaps in the literature regarding the 10R CE principles. In addition, guidelines have been established to assist with practical applications and generate a research agenda on the interrelationships between I4.0 technologies and CE principles at the supply chain level. Implications for theory include the extension of view from the research gaps between I4.0 technologies and the 10Rs identified in the literature; also, an FGI and FD were performed based on the detected research gaps to identify future lines of research for academics and offer useful guidance to directors and managers on I4.0 technology interrelationships for improving at least one of the 10R CE principles. The contribution to practice aims to enable managers to easily identify which technology from the I4.0 domain should be used to advance any given CE principle. Lastly, we provide useful guidance on the application of as yet-unused technologies to improve CE principles

Big Data and the Data Value Chain: Translating Insights from Business Analytics into Actionable Results - The Case of Unit Load Device (ULD) Management in the Air Cargo Industry

Business intelligence and analytics enjoy a great deal of attention today. However, there is a lack of studies considering the full data value chain from (raw) data through business analytics to valuable decisions, i.e. also scrutinizing the latter stages of the data value chain, namely timely deployment and operational usage of valuable insights as demanded by practice. Following a design science approach, we develop a concept for the fast and flexible integration of valuable insights into daily decision support. A key feature of our concept is to provide valuable insights from business intelligence in an understandable manner to decision makers using a rule-based expert systems approach. In order to demonstrate the feasibility of our concept, we implemented a prototype in a complex real-world scenario, i.e. unit load device (ULD) management in the air cargo industry. This research in progress presents our preliminary findings and outlines the potential of the proposed concept

Threatcasting in a Military Setting

The intersection of digital and physical security is critical to the future security of our military and national defense. Coming technological advances widen the attack plain over the next decade including cyber, physical and kinetic vulnerabilities. Visualizing what the future will hold and what new threat vectors will emerge is a task that traditional military planning mechanisms struggle to accomplish given the wicked problem space. Helping to understand and plan for the future operating environment is the basis of a research effort known as Threatcasting. Arizona State University’s School for the Future of Innovation in Society in collaboration with the Army Cyber Institute at West Point use the threatcasting process to give researchers a structured way to envision and plan for risks ten years in the future. For many organization the scope of this problem can seem overwhelming. Threatcasting, as an analytic technique, focuses on the intersection between cyber and physical domains and how it can revolutionize or paralyze the future. Threatcasting uses inputs from social science, technical research, cultural history, economics, trends, expert interviews, and even a little science fiction. These inputs allow the creation of potential futures. By placing the threats into an effects based model (e.g. a person in a place with a problem), it allows organizations to understand what needs to be done immediately and also in the future to disrupt possible threats. The Threatcasting framework also exposes what events could happen that indicate the progression towards an increasingly possible threat landscape. Threatcasting draws strength from futures studies, a field that provides theoretical and applied tools designed to shed light on deep uncertainties and complexities that futures hold. Foresight tools are rooted in exploratory, rather than predictive, methods of futures thinking, learning, and strategy as a means to prepare and plan for long-term outcomes that are difficult to imagine and impossible to predict. Such methods often stand in contrast to causal, linear, ‘plan and predict’ thinking that characterizes many contemporary practices of making and knowing futures. As national security and technological possibilities change rapidly, new threats and opportunities become ever present. Threatcasting is a means to make-sense and anticipate military futures so that relevant institutions are able to anticipate, manage, navigate uncertainty and complexity ahead. This chapter will use the weaponization of artificial intelligence as a case study to walk readers through the research technique and results. Specifically, we will outline two case studies where the technique was applied with specific results. One case study focuses on the digital and physical supply chain in private industry (Cisco Systems) and the second investigates similar threats to the military’s supply chain (Military Logistics Officers). The weaponization of any organization\u27s supply chain and logistics systems poses a significant threat to national and global economic security. The very systems that are the engine of economies and the lifeline of goods and services to the world’s population could and most probably will be turned against the very people and organizations that they serve. This new threat landscape and associated challenges will affect industry, militaries and governments through loss of revenue, productivity and even loss of life. This weaponization will allow adversities whether they are criminal, state sponsored, terrorists or hacktivists to transform these systems from engines of productivity to enemies on the inside. Upon reading this chapter, the student/practitioner will: - Have an understanding of the threatcasting methodology so to be able to apply it against other problems of interest - Appreciate the close ties between the advancement of technology and the effect to society, economies, and national security - Apply the Threatcasting methodology to the specific problem of supply chains and the weaponization of Artificial Intelligence - Create powerful narratives and fact-based illustrations to provide decision makers on the resultshttps://digitalcommons.usmalibrary.org/aci_books/1022/thumbnail.jp

Safety of the Introduction of Self-driving Vehicles in a Logistics Environment

Thanks to the increasing speed of technological development brought by the 21st century, many of the processes in industrial production have been automated. The next big step in this progress of artificial intelligence could potentially be in corporate logistics, road haulage, and even private transport. Naturally, automation within corporations and in critical infrastructure raises ethical and trust-related issues, reflected in the attitude of emerging markets toward this form of development. This research aims to discover the current state of development of self-driving vehicles through comprehensive literary research and to present those ethical problems, the solutions to which are paramount for society to accept and to incorporate the concept of self-driving vehicles. As a result of the analysis of a knowledge-based system with the inductive inference method in primary research, the current strengths and weaknesses of automation in the field of logistics are revealed. The paper summarizes the results of the primary research and projects them for the future of automation

Artificial intelligence in operations management and supply chain management : an exploratory case study

With the development and evolution of information technology, competition has become more and more intensive on a global scale. Many companies have forecast that the future of operation and supply chain management (SCM) may change dramatically, from planning, scheduling, optimisation, to transportation, with the presence of artificial intelligence (AI). People will be more and more interested in machine learning, AI, and other intelligent technologies, in terms of SCM. Within this context, this particular research study provides an overview of the concept of AI and SCM. It then focuses on timely and critical analysis of AI-driven supply chain research and applications. In this exploratory research, the emerging AI-based business models of different case companies are analysed. Their relevant AI solutions and related values to companies are also evaluated. As a result, this research identifies several areas of value creation for the application of AI in the supply chain. It also proposes an approach to designing business models for AI supply chain applications.© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

AI-enabled Integration in the Supply Chain - A Solution in the Digitalization Era

Artificial Intelligence (AI) is getting increased attention from various manufacturing industries, including fashion and textiles, due to its ability to work effectively, similar to human intelligence. This Systematic Literature Review (SLR) paper proposes potential future research directions that emphasize the impacts of AI on supply chain integration (SCI) efforts through information sharing (IS). A structured literature review of articles in the 2010-2021 period, addressing geographic location, journals, publishers, authors, research designs, and applied theories, has been used to prepare this paper. The additional discussion of AI incorporates information from the structured review to conclude the findings and suggest future research directions. The authors have used the Scopus database and prestigious peer-reviewed journals to search for relevant papers using suitable keywords. This paper concluded that the Asian region has the highest concentration of publications and that AI adoption positively affects the IS-SCI relationship. Empirical quantitative research design and resource-based view theory are prominent among the reviewed publications. This SLR paper is limited by not having the impacts of AI discussed at the subset level