-------- Forwarded Message -------- Subject: [AISWorld] Special Issue: Industry experiences of Artificial Intelligence (AI): benefits and challenges in operations and supply chain management of the Production Planning & Control Date: Wed, 23 Oct 2019 23:31:51 +0000 From: FOSSO-WAMBA Samuel s.fosso-wamba@tbs-education.fr To: 'aisworld@lists.aisnet.org' aisworld@lists.aisnet.org

Call for papers Special Issue: Industry experiences of Artificial Intelligence (AI): benefits and challenges in operations and supply chain management Submission Deadline: November 30, 2019

Guest Editors Professor Samuel Fosso Wamba, Toulouse Business School, France Dr Maciel M. Queiroz, University of São Paulo, Brazil Professor Ashley Braganza, Brunel Business School, UK Dr Cameron Guthrie, Toulouse Business School, France

Recent cutting-edge technologies such as big data analytics, internet of things (IoT), smart factories and artificial intelligence (AI) are transforming the way people acquire and consume goods, firms manufacture and deliver produce, and logistics networks and society interact (Bibby & Dehe, 2018; Gölzer & Fritzsche, 2017). Together, these new concepts and technologies are said to usher in a Fourth Industrial Revolution (Schwab, 2017), or Industry 4.0 (Fatorachian & Kazemi, 2018). Business models and logistics production systems need to adapt to the new dynamics of production and consumption. The ubiquity of smartphones and apps is drastically changing the customer experience and expectations, allowing individuals to participate in various stages of the production process. For example, the combination of digital manufacturing, mobile and augmented reality technologies allow customers to provide feedback in a co-creation process (Mourtzis, Gargallis, & Zogopoulos, 2019), while IoT, sensors and data analytics enable the continuous collection of usage data throughout the entire product lifecycle. These new modes of relationship are already impacting the work of operations and supply chain managers. One of the most promising technologies for contemporary operations and supply chain management (OSCM) is artificial intelligence. AI emerged in the 1960s as "the science of making machines do things that would require intelligence if done by men" (Minsky, 1968). Today, a new generation of AI is being used to work on a vast array of issues including product recommendations and customisation, dynamic pricing, real-time production tracking, prevention of order shipment delays and inventory shortages, customer feedback collection for product development and supplier monitoring to minimise procurement costs (Syam & Sharma, 2018). In addition, a subset of AI known as machine learning is developing methods, (e.g. regression analysis, specific algorithms) and associated technologies (e.g. sensors, APIs) that allow computer systems to "learn" using historical data and act without human intervention. AI can also potentially be combined with Industry 4.0 cutting-edge technologies, such as big data analytics, blockchain, internet of things and cyber-physical systems. The use of AI "in supply chain ecosystems [...] in combination with human behaviour will create a new degree of intelligence, innovation, and collaboration" in organizations (Bienhaus & Haddud, 2018). Today's operations and supply chain managers need to gain a better understanding about how AI can be applied to OSCM problems. The use of AI applications within an OSCM context presents considerable managerial and organizational challenges. For example, in the adoption stage managers need to identify the requisite capabilities and potential obstacles to successful AI implementation. The potential impact of AI on operations management, production planning and control, productivity and performance also need to be investigated. Managers need to understand how AI initiatives affect the interplay of business, logistics and production systems at individual, organisational and supply chain levels. For example, AI can support individual worker's activities by performing repetitive tasks: AI commanded robots can audit manufacturing processes; robots can minimize the idleness of production systems when integrated with customers and suppliers; and AI can be used for predictive maintenance when combined with IoT and machine learning. Behind each opportunity lies a challenge for managers to successfully capture the benefits from AI. Little is known for instance about the contribution of AI driven robots to production systems. From an operations management perspective, a major challenge is how to use AI to gain insights for demand forecasting and production planning. More research is required into: strategies of AI use within organizations for existing OSCM problems (e.g. production planning and control, demand forecasting, operations management optimisation, distribution management); the impact of AI on production processes throughout the value chain; the drivers, enablers and obstacles to AI adoption and use; the development of new business models; and into the implications of AI for operations management practice. This special issue aims to explore the role of AI in OSCM, and especially how AI creates value in a digital age when combined with other Industry 4.0 cutting-edge technologies. Our objective is to stimulate research and debate both around how managers are using or could use AI to improve OSCM practice and performance, and create competitive advantage, as well as the enablers and inhibitors to adoption, integration and use. This special issue invites scholars, managers and practitioners to use case studies or other empirical methods to report in-depth on AI applications in operations and supply chain management. This special issue calls for contributions to:

* In-depth cases reporting on AI technology implementation challenges and benchmarks in operations and SCM; * Case studies reporting AI adoption in logistics and production systems. What are the facilitators and barriers? * The impact and benefits provided by AI technologies in operations and SCM; * Case studies reporting on the organisational capabilities (management, technological) required to support successful AI project implementation; * Productivity and performance improvements in production planning and control through AI; * How can managers use AI applications to capture benefits, efficiency, productivity and value using customer product feedback? * How can robots in manufacturing and logistics activities be employed to improve productivity and performance? What is the role of robots in a production system? * Novel conceptual models and ways of theorising about AI, operations and SCM; * Are extant theories sufficient to explain the adoption and spread of AI or what new theories are required for AI in OSCM? * The link between AI and the innovation improvement capacity of an organisation's logistics and production systems; * The contribution of AI to knowledge and learning in logistics and production systems; * Barriers and benefits related to the integration of AI technologies across the supply chain; * How is AI impacting the decision-making process in logistics and production systems? What are the consequences for the management learning and knowledge? * Frameworks to explain AI implementation in operations and SCM contexts; * Frameworks and case studies to explain the adoption and use of AI combined with other cutting-edge technologies in operations and SCM contexts;

· The effects of AI on business models. How are business models changing with the adoption of AI and related technologies? What changes do new business models bring to operations management?

· The critical success factors in AI diffusion stages in operations and SCM.

Papers concerning these and other related critical issues in operations and supply chain challenges are encouraged. The special issue aims to sharpen the focus on, and raise the awareness of these critical issues, especially those facing developing economies as well as advanced industrial economies, and to promote research, both theoretical and empirical, on specific digitalisation related problems and innovative practices to address these problems.

References

Bibby, L., & Dehe, B. (2018). Defining and assessing industry 4.0 maturity levels-case of the defence sector. Production Planning & Control, 29(12), 1030-1043.

Bienhaus, F., & Haddud, A. (2018). Procurement 4.0: factors influencing the digitisation of procurement and supply chains. Business Process Management Journal, 24(4), 965-984.

Fatorachian, H., & Kazemi, H. (2018). A critical investigation of Industry 4.0 in manufacturing: theoretical operationalisation framework. Production Planning & Control, 29(8), 633-644.

Gölzer, P., & Fritzsche, A. (2017). Data-driven operations management: organisational implications of the digital transformation in industrial practice. Production Planning & Control, 28(16), 1332-1343.

Minsky, M. L. (1968). Semantic information processing. Cambridge, Mass.: MIT Press.

Mourtzis, D., Gargallis, A., & Zogopoulos, V. (2019). Modelling of Customer Oriented Applications in Product Lifecycle using RAMI 4.0. Procedia Manufacturing, 28, 31-36.

Schwab, K. (2017). The fourth industrial revolution. New York: Crown Business.

Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135-146.

All papers will be peer reviewed and should conform to Production Planning & Control publication standards available at: http://www.tandfonline.com/action/authorSubmission?journalCode=tppc20&pa... All submissions should be made online at the Production Planning & Control Scholar One Manuscripts website (https://mc.manuscriptcentral.com/tppc). New users should first create an account. Once logged on to the site, submissions should be made via the Author Centre. Online user guides and access to a helpdesk are available on this website.

Contact Professor Samuel Fosso Wamba, Toulouse Business School, France, s.fosso-wamba@tbs-education.frmailto:s.fosso-wamba@tbs-education.fr Dr Maciel M. Queiroz, University of São Paulo, Brazil, maciel.queiroz@usp.brmailto:maciel.queiroz@usp.br Professor Ashley Braganza, Brunel Business School, UK, ashley.braganza@brunel.ac.ukmailto:ashley.braganza@brunel.ac.uk Dr Cameron Guthrie, Toulouse Business School, France, c.guthrie@tbs-education.frmailto:c.guthrie@tbs-education.fr

For more info: https://think.taylorandfrancis.com/industry-experiences-of-artificial-intell...

...................................................................................... Dr Samuel FOSSO WAMBA, Ph.D., HDR Professor in Information Systems and Data Science| TBS Service | Toulouse Business School Head of The Artificial Intelligence and Business Analytics Cluster s.fosso-wamba@tbs-education.frmailto:s.fosso-wamba@tbs-education.fr | +33 5 61 29 50 54 | www.fossowambasamuel.comhttp://www.fossowambasamuel.com/ www.tbs-education.comhttp://www.tbs-education.fr/en?utm_source=signature-en&utm_medium=email-en&utm_campaign=tbs [Toulouse Business School]http://www.tbs-education.fr/en?utm_source=signature-en&utm_medium=email-en&utm_campaign=tbs [TBS news]http://www.tbs-education.fr/en/go/signature-blockcom-en/?utm_source=signature-en&utm_medium=email-en&utm_campaign=campagne

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