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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&page=instructions
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.fr<mailto:s.fosso-wamba@tbs-education.fr>
Dr Maciel M. Queiroz, University of São Paulo, Brazil,
maciel.queiroz@usp.br<mailto:maciel.queiroz@usp.br>
Professor Ashley Braganza, Brunel Business School, UK,
ashley.braganza@brunel.ac.uk<mailto:ashley.braganza@brunel.ac.uk>
Dr Cameron Guthrie, Toulouse Business School, France,
c.guthrie@tbs-education.fr<mailto:c.guthrie@tbs-education.fr>
For more info:
https://think.taylorandfrancis.com/industry-experiences-of-artificial-intelligence-benefits-and-challenges-in-operations-and-supply-chain-management/
......................................................................................
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.fr<mailto:s.fosso-wamba@tbs-education.fr>
| +33 5 61 29 50 54 |
www.fossowambasamuel.com<http://www.fossowambasamuel.com/>
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