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As the global focus on supply chain management has shifted toward the importance of digitalization, resilience and sustainability to ensure viability, this paradigm merits special consideration in the industrial supplier selection process in a VUCA (Volatile, Uncertain, Complex and Ambiguous) world. Additionally, the increasing geopolitical challenges further complicate the industrial supplier selection process, necessitating robust decision-making frameworks. Thus, this paper aims to present a decision-making system using a fuzzy inference system (FIS) for industrial supplier evaluation and selection, considering a new criterion: viability.

Fuzzy set theory, particularly a FIS, is used to address the subjectivity of decision-makers’ preferences. The suggested method’s validity is evaluated using a real automotive case study for industrial supplier selection situations.

Seventeen key criteria for viable industrial supplier selection were identified and used to evaluate and select the case study firm’s industrial supplier. The chosen supplier (B) demonstrated superior resilience, sustainability and digitalization capabilities, making it preferable to others. Specifically, supplier (B) exhibited exceptional adaptability to disruptions, a strong commitment to sustainable practices and advanced digital integration that enhances operational efficiency.

This study provides valuable insights for researchers and professionals by proposing a comprehensive industrial supplier selection system. Integrating diverse criteria is essential for viable performance in supply chains that enhances robustness and adaptability, supporting more strategic decision-making in supplier evaluation amid global and network-related challenges.

This novel paper introduces a new criterion, i.e. viability, in the industrial supplier selection process in the VUCA environment. In addition, it proposes a decision-making system for viable supplier performance evaluation. Furthermore, it validates the proposed FIS in an automotive case study.

The health crisis has highlighted the shortcomings of the industry sector which has revealed its vulnerability. To date, there is no guarantee of a return to the “world before”. The ability of companies to cope with these changes is a key competitive advantage requiring the adoption/mastery of industry 4.0 technologies. Therefore, companies must adapt their business processes to fit into similar situations.

The proposed methodology comprises three steps. First, a comparative analysis of the existing CPSs is elaborated. Second, following this analysis, a deep learning driven CPS framework is proposed highlighting its components and tiers. Third, a real industrial case is presented to demonstrate the application of the envisioned framework. Deep learning network-based methods of object detection are used to train the model and evaluation is assessed accordingly.

The analysis revealed that most of the existing CPS frameworks address manufacturing related subjects. This illustrates the need for a resilient industrial CPS targeting other areas and considering CPSs as loopback systems preserving human–machine interaction, endowed with data tiering approach for easy and fast data access and embedded with deep learning-based computer vision processing methods.

This study provides insights about what needs to be addressed in terms of challenges faced due to unforeseen situations or adapting to new ones. In this paper, the CPS framework was used as a monitoring system in compliance with the precautionary measures (social distancing) and for self-protection with wearing the necessary equipments. Nevertheless, the proposed framework can be used and adapted to any industrial or non-industrial environments by adjusting object detection purpose.

This article presents a review of the existing state-of-the-art literature concerning Supply Chain Management 4.0 (SCM 4.0) and identifies and evaluates the relationship between digital technologies and Supply Chain Management.

A literature review of state-of-the-art publications in the subject field and a bibliometric analysis were conducted.

The paper identifies the impact of novel technologies on the different supply chain processes. Furthermore, the paper develops a roadmap framework for future research and practice.

The proposed work is useful for both academics and practitioners as it outlines the pillar components for every supply chain transformation. It also proposes a range of research questions that can be used as a base to guide the future research direction of the field.

This paper presents a novel and original literature review-based study on SCM4.0 as no comprehensive review is available where bibliometric analysis, motivations, barriers and technologies' impact on different SC processes have been considered.

In the era of industry 4.0, managing the design is a challenging mission. Within a dynamic environment, several disciplines have adopted the complex adaptive system (CAS) perspective. Therefore, this paper aims to explore how we may deepen our understanding of the design process as a CAS. In this respect, the key complexity drivers of the design process are discussed and an organizational decomposition for the simulation of the design process as CAS is conducted.

The proposed methodology comprises three steps. First, the complexity drivers of the design process are presented and are matched with those of CAS. Second, an analysis of over 111 selected papers is presented to choose the appropriate model for the design process from the CAS theory. Third, the paper provides methodological guidelines to develop an organizational decision support system that supports the complexity of the design process.

An analysis of the key drivers of design process complexity shows the need to adopt the CAS theory. In addition to that, a comparative analysis between all the organizational methodologies developed in the literature leads the authors to conclude that agent-oriented Software Process for engineering complex System is the appropriate methodology for simulating the design process. In this respect, a system requirements phase of the decision support system is conducted.

The originality of this paper lies in the fact of analysing the complexity of the design process as a CAS. In doing so, all the richness of the CAS theory can be used to meet the challenges of those already existing in the theory of the design.