New IT driven rapid manufacturing for emergency response

Hybrid combinatorial remanufacturing strategy for medical equipment in the pandemic

The COVID-19 pandemic hit the medical supply chain, creating a serious shortage of medical equipment. To meet the urgent demand, one realistic way is to collect abandoned medical equipment and then remanufacture, where the disassembled modules are shared with all stock-keeping units (SKUs) to improve utilization. However, in an emergency, the equipment should be processed sequentially and immediately, which means the decision is short-sighted with limited information. We propose a hybrid combinatorial remanufacturing (HCR) strategy and develop two reinforcement learning frameworks based on Q-learning and double deep Q network to find the optimal recovery option. In the frameworks, we transform HCR problem into a maze exploration game and propose a rule of descending epsilon-greedy selection on reweighted valid actions (DeSoRVA) and Espertate knowledge dictionary to combine the cost-minimizing objective with human judgment and the global state of the problem. A real-time environment is further implemented where the quality status of the in-transit equipment is unknown. Numerical studies show that our algorithms can learn to save cost, and the larger scale of the problem is, the more cost-down can be achieved. Moreover, the sophisticated knowledge refined by Espertate is effective and robust, which can handle remanufacturing problems at different scales corresponding to the volatility of the pandemic.

Integrating operational excellence strategies with Industry 4.0 technologies through reconfigurable manufacturing system practices

Purpose

Because of the COVID-19 pandemic and changing market demands, competition for manufacturing industries is increasing and they face numerous challenges. In such a case, it is necessary to use multiple strategies, technologies and practices to improve organizational performance and, as a result, to integrate them for ease of adoption. The purpose of this research is to identify advanced Industry 4.0 technologies, operational excellence (OPEX) strategies and reconfigurable manufacturing system (RMS) practices. The study also computes their weights, as well as identifies and prioritizes the performance metrics for the same.

Design/methodology/approach

A thorough review of relevant articles was conducted to identify 28 OPEX strategies, RMS practices and advanced technologies, as well as the 17-performance metrics. The stepwise weight assessment ratio analysis approach was used to compute the weights of the selected practices, while the WASPAS approach was used to prioritize the performance metrics. While developing the framework, the industry expert’s expertise was incorporated in the form of their opinions for pairwise comparison.

Findings

According to the study findings, advanced Industry 4.0 technologies were the most prominent for improving organizational performance. As a result, integrating Industry 4.0 technologies with OPEX strategies can assist in improving the performance of manufacturing organizations. The prioritized performance metrics resulted in the production lead time ranking first and the use of advanced technologies ranking second. This emphasizes the significance of meeting dynamic customer needs on time while also improving quality with the help of advanced technologies.

Practical implications

The developed framework can help practitioners integrate OPEX strategies and advanced technologies into their organizations by adopting them in order of importance. Furthermore, the ranked performance metrics can assist managers and practitioners in evaluating the manufacturing system and, as a result, strategic planning for improvement.

Originality/value

According to the authors, this is a novel approach for integrating OPEX strategies with advanced Industry 4.0 technologies, and no comparable study has been found in the current literature.

Repurposing production operations during COVID-19 pandemic by integrating Industry 4.0 and reconfigurable manufacturing practices: an emerging economy perspective

The Covid-19 pandemic has had a significant impact on manufacturing industries and supply chains. Manufacturing industries are struggling to repurpose their production activities and ramp up the supply chain to bridge the demand–supply gap. A framework that can cover Industry 4.0 technologies and reconfigurable manufacturing system (RMS) practices is desperately needed. The current study focuses primarily on the framework that could assist managers in decision-making and the stepwise adoption of RMS during repurposing. The extensive literature review was conducted to identify the prominent Industry 4.0 technologies and RMS practices. To compute the weights of selected practices, the novel Pythagorean fuzzy analytical hierarchy process (AHP) was used; while the Pythagorean fuzzy combined compromise solution (CoCoSo) method was used to prioritize the selected performance metrics. To test the robustness of the developed framework, a sensitivity analysis was carried out. According to the findings, smart factory adoption (SFA) practices were the most significant among the major criteria, followed by reconfiguration practices (RCP), soft computing practices (SCP), sustainable & circular economy practices (SCE), and quality practices (QPS). SFA's advanced technologies and SCP's computer algorithms certainly assist in the repurposing of production activities (RPO). The results of the sensitivity analysis demonstrated the robustness of the developed framework. The developed framework will be useful during RPO, and the identified practices can make a significant contribution. Advanced technologies and sustainable practices can help to improve the organization's work culture. Managers will be able to evaluate the organization's performance with the help of identified performance metrics. The work presented here may be the first attempt to develop a framework for RPO in a pandemic situation.

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.

How Digital Twin Concept Supports Internal Transport Systems?—Literature Review

In the Industry 4.0 era, the Digital Twin has become one of the most promising enabling technologies supporting material flow. Although the literature on the Digital Twin is becoming relatively well explored, including a certain number of review papers, the context of the Digital Twins application in internal transport systems has not been investigated so far. This paper thoroughly reviews the research on the Digital Twins applied in internal transport systems concerning major research trends within this research area and identification of future research directions. It provides clarification of various definitions related to the Digital Twin concept, including misconceptions such as a digital shadow, a digital model, and a digital mirror. Additionally, the relationships between terms such as material handling, material flow, and intralogistics in the context of internal transport systems coupled with the Digital Twin are explained. This paper’s contribution to the current state of the art of the Digital Twins is three-fold: (1) recognition of the most influential and high-impact journals, papers, and researchers; (2) identification of the major research trends related to the Digital Twins applications in internal transport systems, and (3) presentation of future research agendas in investigating Digital Twins applied for internal transport systems.

Applications of additive manufacturing (AM) in sustainable energy generation and battle against COVID-19 pandemic: The knowledge evolution of 3D printing

Sustainable and cleaner manufacturing systems have found broad applications in industrial processes, especially aerospace, automotive and power generation. Conventional manufacturing methods are highly unsustainable regarding carbon emissions, energy consumption, material wastage, costly shipment and complex supply management. Besides, during global COVID-19 pandemic, advanced fabrication and management strategies were extremely required to fulfill the shortfall of basic and medical emergency supplies. Three-dimensional printing (3DP) reduces global energy consumption and CO₂ emissions related to industrial manufacturing. Various renewable energy harvesting mechanisms utilizing solar, wind, tidal and human potential have been fabricated through additive manufacturing. 3D printing aided the manufacturing companies in combating the deficiencies of medical healthcare devices for patients and professionals globally. In this regard, 3D printed medical face shields, respiratory masks, personal protective equipment, PLA-based recyclable air filtration masks, additively manufactured ideal tissue models and new information technology (IT) based rapid manufacturing are some significant contributions of 3DP. Furthermore, a bibliometric study of 3D printing research was conducted in CiteSpace. The most influential keywords and latest research frontiers were found and the 3DP knowledge was categorized into 10 diverse research themes. The potential challenges incurred by AM industry during the pandemic were categorized in terms of design, safety, manufacturing, certification and legal issues. Significantly, this study highlights the versatile role of 3DP in battle against COVID-19 pandemic and provides up-to-date research frontiers, leading the readers to focus on the current hurdles encountered by AM industry, henceforth conduct further investigations to enhance 3DP technology.