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OPSEARCH - Call for Papers: Special Issue on Intervention of emerging analytics and decisions for enhancing manufacturing and service operations resilience

Aim and Scope:

Numerous industries, spanning manufacturing and services sectors, are undergoing digital transformation to streamline their processes. However, compared to the services sector, digital transformation within manufacturing is progressing relatively slowly, largely due to the significant human involvement in decision-making processes. In recent times, though, the manufacturing sector has begun to explore the utilisation of analytics to enhance decision-making and overall performance (Lohmer et al., 2020; Dubey et al., 2020).

The advent of new technologies and the abundance of big data necessitate innovative solutions to automate large-scale processes and expedite execution times. Big data analytics, coupled with emerging techniques, effectively unearth, interpret, and communicate meaningful patterns in data, simplifying decision-making processes (Dubey et al., 2019). Multinational manufacturing organisations are increasingly leveraging analytics to understand business data, thereby describing, predicting, and improving their business performance.

Diagnostic analytics, predictive analytics, prescriptive analytics, and cognitive analytics are gaining momentum in various areas such as predictive maintenance, supply chain optimization, quality control, enhanced demand forecasting, and data-driven innovation. Moreover, the integration of emerging innovative technologies of the 21st century, such as the Internet of Things (IoT) and blockchain technology, demands novel applications of emerging techniques like large-scale language models and artificial intelligence to collect and analyse real-time data, guiding practitioners to make innovative decisions for gaining a competitive edge (Ivanov et al., 2019; Ali et al., 2021).

For instance, IoT-connected devices collect and transmit data on supply and demand, inventory levels, production planning, and operational hiccups (Birkel & Hartmann, 2020; Kadadevaramth et al., 2020). In the face of technological disruptions, it becomes essential to identify frugal techniques tailored to the context, continuously pinpoint areas for improvement, and revolutionise operations (Hoberg & Alicke, 2016). Cases such as the increasing use of electric vehicles, which are imposing extraordinary loads on electric power system infrastructure, underscore the need for effective analytics to devise suitable decisions to enhance the efficiency and resilience of the power grid (Liu et al., 2024).

Furthermore, the challenge of maintaining stable and robust production against various disruptions in multistate manufacturing systems (MMSs) emphasises the expectation from the operations research community to contribute to operation and maintenance (O&M) methods that optimise MMS resilience (Cai et al., 2024). Additionally, climate change is exerting significant pressure on manufacturing and service sector operations, necessitating the revision of processes and the effective utilization of various techniques to overcome disruptions and enhance both resilience and sustainability.

There is also a strong need within the research community to derive valuable insights by analysing data to compare the magnitude of current disruptive events with past occurrences and devise strategies for quicker recovery. Technological innovation and data analytics have thus become burgeoning topics for decision science researchers to contribute to the sustenance of manufacturing and service operations resilience.

As the current supply chain crisis unfolds and organizations prepare strategies to manage future disruptions, achieving resilience has become a top priority in both manufacturing and service sectors. The growing interconnectivity between organisations, individuals, and physical systems, supported by recent developments in information and communication technologies, underscores the crucial role that collaborative networks can play in digital transformation processes (Camarinha-Matos et al., 2024). Consequently, there has been significant attention directed towards deploying innovative analytics to enhance manufacturing and service operations resilience. There is a strong demand for data-driven analytical tools to support the evaluation and definition of strategies aimed at achieving resiliency (Cohen, 2022).

We are pleased to announce an open call for new articles for a special issue focusing on the Intervention of Emerging Analytics and Decisions for Enhancing Manufacturing and Service Operations Resilience.

Topics Covered:

We invite researchers, practitioners, and industry experts to submit their work on a wide range of subjects related to the Intervention of Emerging Analytics and Decisions for Enhancing Manufacturing and Service Operations Resilience. Contributions are encouraged to explore various aspects of this theme.

Topics of interest include, but are not limited to:

  • Data analytics and optimization techniques for sustainable operations.
  • Decision support systems integrating sustainability criteria into decision-making processes.
  • Modelling resilience in the face of digital transformation and environmental concerns.
  • Supply chain management and digitalization for sustainable manufacturing and service operations.
  • Case studies and best practices highlighting successful implementation of digital analytics for enhancing manufacturing and service operations resilience.
  • Digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0.
  • Artificial intelligence in building production resilience.
  • Manufacturing and service supply chain resilience.
  • Adoption of emergent technologies for risk management in the era of digital manufacturing.
  • Role of digital technologies in supply chain resilience.
  • Quantitative methods for supply chain resilience.
  • Big data for manufacturing cyber physical systems.
  • Energy resilience via shared autonomous electric vehicles: Optimization approaches.
  • Production and maintenance scheduling optimization.

Important Dates: 

Submission deadline: 30 July 2024
First round of review date: 30 November 2024
Final round of review date: 30 January 2025
Notification to authors: 30 April 2025
Final versions due by: 30 June 2025


Manuscript Submission Information:

Please follow the instructions of the journal while preparing your manuscript. A guide for authors can be found at https://www.springer.com/journal/12597/submission-guidelines (this opens in a new tab)

Please submit your manuscript through the journal’s homepage at https://www.springer.com/journal/12597 (this opens in a new tab)

To ensure a paper is considered for the Special Issue, reply “yes” when asked during submission whether it is intended for a special issue and select the Special Issue on "Intervention of emerging analytics and decisions for enhancing manufacturing and service operations resilience" from the drop-down menu.

For questions, please contact:

Guest Editors:
S. G. Ponnambalam, Professor (HAG), School of Mechanical Engineering, Vellore Institute of Technology, Vellore - 632014, India
E-mail: ponnambalam.g@vit.ac.in (this opens in a new tab)
ORCID Id: 0000-0003-4973-733X (this opens in a new tab)

Nachiappan Subramanian, Professor of Operations & Logistics Management and Supply Chains, University of Sussex Business School, University of Sussex, Falmer Brighton, BN1 9SL, United Kingdom
E-mail: N.Subramanian@sussex.ac.uk (this opens in a new tab)
ORCID ID: 0000-0003-4076-6433 (this opens in a new tab)


References:
Ali, I., Golgeci, I., & Arslan, A. (2023). Achieving resilience through knowledge management practices and risk management culture in agri-food supply chains. Supply Chain Management: An International Journal, 28(2), 284-299.

Birkel, H. S., & Hartmann, E. (2020). “Internet of Things – the future of managing supply chain risks”. Supply Chain Management : An International Journal, 25 No(5), 535–548.

Cai, Y., He, Y., Shi, R., Feng, T., & Li, J. (2024). Resilience-oriented approach of dynamic production and maintenance scheduling optimisation considering operational uncertainty. International Journal of Production Research, 1–24. https://doi.org/10.1080/00207543.2024.2329324 (this opens in a new tab).

Camarinha-Matos, L.M., Rocha, A.D. & Graça, P. Collaborative approaches in sustainable and resilient manufacturing. J Intell Manuf 35, 499–519 (2024). https://doi.org/10.1007/s10845-022-02060-6 (this opens in a new tab).

Cohen, M. A. (2022). Application of Analytics to Achieve Supply Chain Resilience. IFAC-PapersOnLine, 55(10), 2852-2856.

Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., Roubaud, D., & Foropon, C. (2021). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research, 59(1), 110-128.

Dubey, R., Gunasekaran, A., Bryde, D. J., Dwivedi, Y. K., & Papadopoulos, T. (2020). Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting. International Journal of Production Research, 58(11), 3381–3398.

Hoberg, K., & Alicke, K. (2016). The customer experience. Supply Chain Management Review, 9(10), 28–37.

Kadadevaramth, R. S., Sharath, D., Ravishankar, B., & Mohan Kumar, P. (2020). A Review and development of research framework on Technological Adoption of Blockchain and IoT in Supply Chain Network Optimization. 2020 International Conference on Mainstreaming Block Chain Implementation (ICOMBI) (pp. 1–8). IEEE.

Liu, J., Abdin, A., & Puchinger, J. (2024). Improving critical buildings energy resilience via shared autonomous electric vehicles—A sequential optimization framework. Computers & Operations Research, 163, 106513.

Lohmer, J., Bugert, N., & Lasch, R. (2020). Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study. International journal of production economics, 228, 107882.

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