Circular production and maintenance of automotive parts: An Internet of Things (IoT) data framework and practice review

Disruptive supply chain technology assessment for sustainability journey: A framework of probabilistic group decision making

The fourth industrial revolution, commonly recognized as Industry 4.0, has been ushered by modern and innovative intelligence and communication technologies. Concerns about disruptive technologies (DTs) are beginning to grow in developing countries, despite the fact that the trade-offs between implementation difficulties and realistic effects are still unknown. Hence, prioritization and promotion of such technologies should be considered when investing in them to ensure sustainability. The study aims to provide new critical insights into what DTs are and how to identify the significant DTs for sustainable supply chain (SSC). Understanding the DTs' potential for achieving holistic sustainability through effective technology adoption and diffusion is critical. To achieve the goal, an integrated approach combining the Bayesian method and the Best Worst Method (BWM) is utilized in this study to evaluate DTs in emerging economies' supply chain (SC). The systematic literature review yielded a total of 10 DTs for SSC, which were then evaluated using the Bayesian-BWM to explore the most critical DTs for a well-known example of the readymade garment (RMG) industry of Bangladesh. The results show that the three most essential DTs for SSC are "Internet of things (IoT)", "Cloud manufacturing", and "Artificial intelligence (AI)". The research insights will facilitate policymakers and practitioners in determining where to concentrate efforts during the technology adoption and diffusion stage in order to improve sustainable production through managing SC operations in an uncertain business environment.

Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study

Data availability is changing the way companies make decisions at various levels (e.g., strategical and operational). Researchers and practitioners are exploring how product–service system (PSS) providers can benefit from data availability and usage, especially when it comes to making decisions related to service delivery. One of the services that are expected to benefit most from data availability is maintenance. Through the analysis of the asset health status, service providers can make informed and timely decisions to prevent failures. Despite this, the offering of data-based maintenance service is not trivial, and requires providers to structure themselves to collect, analyze and use historical and real-time data properly (e.g., introducing suitable information flows, methods and competencies). The paper aims to investigate how a manufacturing company can re-engineer its maintenance service delivery process in a data-driven fashion. Thus, the paper presents a case study where, based on the Dual-perspective, Data-based, Decision-making process for Maintenance service delivery (D3M), an Italian manufacturing company reengineered its maintenance service delivery process in a data-driven fashion. The case study highlights the benefits and barriers coming with this transformation and aims at helping manufacturing companies in understanding how to address it.

A Novel Intelligent IoT System for Improving the Safety and Planning of Air Cargo Operations

Being the main pillar in the context of Industry 4.0, the Internet of Things (IoT) leads evolution towards a smarter and safer planet. Being human-centered, rather than machine-centered, as was the case of wireless sensor networks used in the industry for decades, the IoT may enhance human intelligence with situational awareness, early warning, and decision support tools. Focusing on air cargo transportation, the “INTELLICONT” project presented a novel solution capable of improving critical air cargo challenges such as the reduction of total aircraft weight, detection and suppression of smoke and/or fire in a container, elimination of permanent moving and locking hardware, loading and unloading logistics enhancement and maintenance. In the present work, the IoT-based monitoring and control system for intelligent aircraft cargo containers is presented from a hardware perspective. The system is based on low-cost, low-energy sensors that are integrated into the container, can track its status, and detect critical events, such as fire/smoke, impact, and accidental misuse. The focus has been given to the design and development of a system capable of providing better and safer control of the aircraft cargo during the loading/unloading operations and the flight. It is shown that the system could provide a breakthrough in the state of the art of current cargo container technology and aircraft cargo operations.