The application of Industry 4.0 technologies in sustainable logistics: a systematic literature review (2012-2020) to explore future research opportunities

Industry 4.0 real-world testing of dynamic organizational life cycle assessment (O-LCA) of a ceramic tile manufacturer

In manufacturing, Industry 4.0 operating models enable greener technologies. Thanks to digital technologies, environmental sustainability and organizational competitiveness are mutually reinforcing. The challenge for manufacturing organizations is to understand and quantify the magnitude of this synergistic action, and the holistic perspective of life cycle assessment tools may be a solution to the problem. Organizational Life Cycle Assessment (O-LCA) unlike Product Life Cycle Assessment (LCA) is still an under-researched methodology with few applications in manufacturing contexts. This paper aims to fill this gap by implementing and validating O-LCA in the case of an Italian ceramic tile manufacturer. Following the O-LCA guidelines and exploiting Industry 4.0 technologies to perform the inventory analysis, the environmental assessment was conducted in three different plants, comparing the sum of the partial impact results with the overall results scaled to the whole organization. The experimental results demonstrated the validity of the organizational approach as an appropriate methodological option to obtain relevant information on environmental performance that, being based on empirical evidence, better support decision-making processes. Furthermore, the study provides empirical evidence of how Industry 4.0 is an enabler not only for the adoption of greener technologies, but especially for facilitating the organizational environmental impact assessment that is the necessary condition in order to set up and maintain greener manufacturing contexts.

Enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data

With growing environmental concerns and the exploitation of ubiquitous big data, smart transportation is transforming logistics business and operations into a more sustainable approach. To answer questions in intelligent transportation planning, such as which data are feasible, which methods are applicable for intelligent prediction of such data, and what are the available operations for prediction, this paper offers a new deep learning approach called bi-directional isometric-gated recurrent unit (BDIGRU). It is merged to the deep learning framework of neural networks for predictive analysis of travel time and business adoption for route planning. The proposed new method directly learns high-level features from big traffic data and reconstructs them by its own attention mechanism drawn by temporal orders to complete the learning process recursively in an end-to-end manner. After deriving the computational algorithm with stochastic gradient descent, we use the proposed method to perform predictive analysis of stochastic travel time under various traffic conditions (especially for congestions) and then determine the optimal vehicle route with the shortest travel time under future uncertainty. Based on empirical results with big traffic data, we show that the proposed BDIGRU method can (1) significantly improve the predictive accuracy of one-step 30 min ahead travel time compared to several conventional (data-driven, model-driven, hybrid, and heuristics) methods measured with several performance criteria, and (2) efficiently determine the optimal vehicle route in relation to the predictive variability under uncertainty.

Measuring circularity of a manufacturing organization by using sustainable balanced scorecard

In recent years, circular economy has become a matter of great importance because of its ability to contribute toward economic, environmental, and social aspects of the sustainability. The circular economy approaches help in resource conservation by reducing, reusing, and recycling products/parts/components/materials. On the other hand, Industry 4.0 is coupled with emerging technologies, which support the firms in efficient resource utilization. These innovative technologies can transform the present manufacturing organizations by reducing resource extraction, CO2 emissions, environmental damage, and power consumption and improve it into a more sustainable manufacturing organization. Industry 4.0 along with circular economy concepts greatly improves the circularity performance. However, there is no framework found for measuring the circularity performance of the firm. Therefore, the current study aims to develop a framework for measuring performance in terms of circularity percentage. In this work, graph theory and matrix approach are employed for measuring the performance based on a sustainable balanced scorecard such as internal process, learning and growth, customer and financial with environmental and social perspectives. A case of an Indian barrel manufacturing organization is discussed for the illustration of proposed methodology. Based on "circularity index" of the organization and the maximum possible circularity index, the circularity was found to be 5.10%. It indicates that there is a huge potential for the improvement in the circularity of the organization. An in-depth sensitivity analysis and comparison are also performed to validate the findings. There are very few studies on measuring the circularity. The study developed the approach for measuring circularity, which may be utilized by industrialists and practitioners for improving the circularity.

Towards the smart and sustainable transformation of Reverse Logistics 4.0: a conceptualization and research agenda

The recent advancement of digitalization and information and communication technology (ICT) has not only shifted the manufacturing paradigm towards the Fourth Industrial Revolution, namely Industry 4.0, but also provided opportunities for a smart logistics transformation. Despite studies have focused on improving the smartness, connectivity, and autonomy of isolated logistics operations with a primary focus on the forward channels, there is still a lack of a systematic conceptualization to guide the coming paradigm shift of reverse logistics, for instance, how "individualization" and "service innovation" should be interpreted in a smart reverse logistics context? To fill this gap, Reverse logistics 4.0 is defined, from a holistic perspective, in this paper to offer a systematic analysis of the technological impact of Industry 4.0 on reverse logistics. Based on the reported research and case studies from the literature, the conceptual framework of smart reverse logistics transformation is proposed to link Industry 4.0 enablers, smart service and operation transformation, and targeted sustainability goals. A smart reverse logistics architecture is also given to allow a high level of system integration enabled by intelligent devices and smart portals, autonomous robots, and advanced analytical tools, where the value of technological innovations can be exploited to solve various reverse logistics problems. Thus, the contribution of this research lies, through conceptual development, in presenting a clear roadmap and research agenda for the reverse logistics transformation in Industry 4.0.

Intelligent monitoring method of tridimensional storage system based on deep learning

Growing international trade requires more flexible warehouse management to match it. In order to achieve more effective warehouse management efficiency, a shelf status-detection method based on deep learning is proposed. Firstly, the image acquisition of a multi-level shelf containing multiple bays is performed under different time and lighting conditions. Due to the difference in image characteristics between the bottom shelf on the ground and the upper shelf on the non-ground level, the collected images were divided into two groups: floor images and shelf images; and the warehouse status recognition was performed on the two groups separately. The two sets of images are cropped and center projection transformed separately to obtain the region of interest. On this basis, the improved residual network model is used to construct different depot detection models for the two sets of images, respectively, and the above algorithm is verified by actual measurements. In this paper, 102,614 images of 3246 depots with different states of non-ground layer, and 27,903 images of ground layer are collected. They are divided into training set and test set according to the ratio of 4:1, and the accuracy of training set is 99.6%, and the accuracy of test set is 99.3%. The experimental outcomes provide a theoretical method and technical support for the intelligent warehouse system management.

A systematic scientometric review of sustainable rail freight transportation

The negative externalities of freight transport have caught the attention of scholars and practitioners to study sustainable freight transportation. Past studies have reviewed sustainable logistics from varying perspectives, but the rail mode-specific sustainable logistics has not been thoroughly reviewed. This sets the motivation to review existing research on sustainable rail freight transportation (SRFT). A science mapping approach was used to develop and visualize bibliographic networks of 378 articles published between 2001 and 2022 and indexed in the Scopus database. Four scientometric analysis techniques, namely journal co-citations; countries/organizations/authors co-authorship; document co-citations; and keywords co-occurrence, were employed in the VOSviewer software to reveal conceptual structure, social structure, and influential themes of the SRFT domain. Based on the results, the SRFT knowledge was categorized into six thematic branches (31 sub-branches), namely intermodal transportation for decarbonization; green policies, risk, and energy assessment research; savings in externalities for a sustainable future; decision-making with environmental and economic considerations; case studies and applications in SRFT research; and technological advancements towards sustainability. Finally, future research directions were proposed in the form of research questions. This systematic literature review will facilitate the researchers, practitioners, and policymakers to understand the status quo, existing research gaps, and emerging research topics in the SRFT research domain. This study is restricted to research articles and review articles published in English and indexed in the Scopus database.

A two-stage decision-support approach for improving sustainable last-mile cold chain logistics operations of COVID-19 vaccines

The COVID-19 pandemic has become a global health and humanitarian crisis that catastrophically affects many industries. To control the disease spread and restore normal lives, mass vaccination is considered the most effective way. However, the sustainable last-mile cold chain logistics operations of COVID-19 vaccines is a complex short-term planning problem that faces many practical challenges, e.g., low-temperature storage and transportation, supply uncertainty at the early stage, etc. To tackle these challenges, a two-stage decision-support approach is proposed in this paper, which integrates both route optimization and advanced simulation to improve the sustainable performance of last-mile vaccine cold chain logistics operations. Through a real-world case study in Norway during December 2020 and March 2021, the analytical results revealed that the logistics network structure, fleet size, and the composition of heterogeneous vehicles might yield significant impacts on the service level, transportation cost, and CO2 emissions of last-mile vaccine cold chain logistics operations.

Insight into the Expected Impact of Sustainable Development in the Context of Industry 4.0: A Documentary Analysis Approach Based on Multiple Case Studies across the World

Although industry 4.0 has gained increased attention in the industry, academic, and governmental fields, there is a lack of information about the relationship between this digital transformation and sustainable development. This work explores the concept of sustainability applied in industry 4.0 and the main advantages that this revolution incorporates into society. To this end, a conscientiously documented investigation was conducted by reviewing actual case studies or scenarios where sustainability was applied in different manufacturing industries, enterprises, or research fields worldwide. A critical and descriptive analysis of the information was performed to identify the main tools and procedures that can be implemented in the industry to address the triple bottom line perspective of industry 4.0, and the results are presented in this document. From the analysis, it was observed that currently, I4.0 has been mainly adopted to improve efficiency and cost reduction in manufacturing companies. However, since only a few enterprises embrace the social paradigm of I4.0, a significant gap in understanding and unbalance is visualized. Therefore, we conclude that there is a lack of information on social benefits and the barriers that must be overcome from the social perspective. On the other hand, this work highlights the importance of adopting industry 4.0 as a positive way to improve the performance of emerging technologies, such as fuel cells, solar cells, and wind turbines, while producing products or services with high efficiency and profitability incomes. For practitioners, this work can provide insightful information about the real implications of I4.0 from a sustainability perspective in our daily life and the possible strategies to improve sustainable development.

Towards the Smart Circular Economy Paradigm: A Definition, Conceptualization, and Research Agenda

The digital age we live in offers companies many opportunities to jointly advance sustainability and competitiveness. New digital technologies can, in fact, support the incorporation of circular economy principles into businesses, enabling new business models and facilitating the redesign of products and value chains. Despite this considerable potential, the convergence between the circular economy and these technologies is still underinvestigated. By reviewing the literature, this paper aims to provide a definition and a conceptual framework, which systematize the smart circular economy paradigm as an industrial system that uses digital technologies during the product life-cycle phases to implement circular strategies and practices aimed at value creation. Following this conceptualization, the classical, underlying circular economy principle, ‘waste equals food’, is reshaped into an equation more fitting for the digital age—that is to say, ‘waste + data = resource’. Lastly, this paper provides promising research directions to further develop this field. To advance knowledge on the smart circular economy paradigm, researchers and practitioners are advised to: (i) develop research from exploratory and descriptive to confirmatory and prescriptive purposes, relying on a wide spectrum of research methodologies; (ii) move the focus from single organizations to the entire ecosystem and value chain of stakeholders; (iii) combine different enabling digital technologies to leverage their synergistic potential; and (iv) assess the environmental impact of digital technologies to prevent potential rebound effects.

Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design

The world has witnessed the digital transformation and Industry 4.0 technologies in the past decade. Nevertheless, there is still a lack of automation and digitalization in certain areas of the manufacturing industry; in particular, warehouse automation often has challenges in design and successful deployment. The effective management of the warehouse and inventory plays a pivotal role in the supply chain and production. In the literature, different architectures of Warehouse Management Systems (WMSs) and automation techniques have been proposed, but most of those have focused only on particular sections of warehouses and have lacked successful deployment. To achieve the goal of process automation, we propose an Internet-of-Things (IoT)-based architecture for real-time warehouse management by dividing the warehouse into multiple domains. Architecture viewpoints were used to present models based on the context diagram, functional view, and operational view specifically catering to the needs of the stakeholders. In addition, we present a generic IoT-based prototype system that enables efficient data collection and transmission in the proposed architecture. Finally, the developed IoT-based solution was deployed in the warehouse of a textile factory for validation testing, and the results are discussed. A comparison of the key performance parameters such as system resilience, efficiency, and latency rate showed the effectiveness of our proposed IoT-based WMS architecture.

A Review on Remanufacturing Reverse Logistics Network Design and Model Optimization

Remanufacturing has gained great recognition in recent years due to its economic and environmental benefits and effectiveness in the value retention of waste products. Many studies on reverse logistics have considered remanufacturing as a key node for network optimization, but few literature reviews have explicitly mentioned remanufacturing as a main feature in their analysis. The aim of this review is to bridge this gap. In total, 125 papers on remanufacturing reverse logistics network design have been reviewed and conclusions have been drawn from four aspects: (1) in terms of network structure, the functional nodes of new hybrid facilities and the network structure combined with the remanufacturing technologies of products are the key points in the research. (2) In the mathematical model, the multi-objective function considered from different aspects, the uncertainty of recovery time and recovery channel in addition to quantity and quality, and the selection of appropriate algorithms are worth studying. (3) While considering product types, the research of a reverse logistics network of some products is urgently needed but inadequate, such as medical and furniture products. (4) As for cutting-edge technologies, the application of new technologies, such as intelligent remanufacturing technology and big data, will have a huge impact on the remanufacturing of a reverse logistics network and needs to be considered in our research.