Supply chains are experiencing significant advances in digital technologies, particularly those associated with industry 4.0. An example of such technology is blockchain. Blockchain is a disruptive ...technology characterised by anonymity and identity, consensus mechanism, decentralisation, overall performance and expectancy, reliability of systems and data, and information transparency. Blockchain offers supply chain opportunities to strengthen end-to-end visibility and traceability, leading to enhanced levels of transparency. Supply chains are increasingly exploring blockchain technology and transparency, with many focusing on system development. This paper explores transparency in blockchain-based supply chains to understand the principles underlining its design. A systematic review of literature is used, accompanied by data-driven analysis. The results present the principles within a framework for transparency by design in blockchain-based supply chains. Limitations and areas for future work are also presented.
In developing nations, small and medium-sized enterprises (SMEs) in the manufacturing sector do not or are slowly adopting industry 4.0 (I4.0) concepts, making them less competitive. Yet, they serve ...as the backbone of emerging economies, thus creating a performance gap between them and their international competitors. As a result, more efforts will be required to provide adequate tools for SMEs to implement I4.0, especially in emerging economies and Africa. This study tackles the disconnect in sub–Saharan African manufacturing SMEs through an empirical research approach using the focus group discussion method, which is the first to collect opinions, reinforcing the current literature on I4.0 technologies, prospects, and challenges to implementation. Our findings underpin six critical drawbacks for implementing I4.0 by manufacturing SMEs in emerging economies and proffer mitigation strategies to establish Sub-Saharan Africa as a competitive, innovation-driven manufacturing powerhouse at the centre of Industry 4.0 development and adoption.
Over the past years, Malang (Indonesia) has encountered environmental problems due to municipal solid waste (MSW) overgeneration. Nevertheless, the increasing volume and complexity of MSW management ...in the city could be tackled using digital solutions. As a key location of ICT development, China is a key partner for Indonesia to improve its MSW management using digital technologies. As a miniature of China's sustainable waste management, Nanning represents a model of smart city that applies resource recovery paradigms using digitalization. Using a case-study approach, this work critically investigates how to strengthen waste recycling industry in Malang using digitalization-based circular economy (CE) according to Nanning's experiences in digitalizing waste recycling. It was found that lessons learnt from Nanning in MSW management were transferable and applicable for Malang to replicate its model, while paving the way forward for a transition towards a CE in waste sector. Traditionally, waste segregation at source was not properly conducted in Malang due to lack of environmental awareness and less public participation, while the waste management in Nanning is time-efficient and cost-effective. Nanning's experiences in digitalizing recyclable materials and/or recycled goods could facilitate Malang to shift towards a digitalization-based CE. Integrating automatic segregation and digitalization at waste banks is efficient in terms of time. With digitalization, over 90% of the solid waste in Malang is segregated properly, while diverting 60% of its non-biodegradable trash from landfills. Overall, the use of digital technologies has enabled waste banks to revolutionize waste industry, while promoting sustainable development at local level. To contribute their share to the UN SDGs, other cities in Indonesia are at their transition towards a digitalization-based CE. There is still a long way for them to attain full circularity and reap the benefits of digital transformation in the waste sector. Hence, it is necessary for stakeholders to promulgate effective policies and strengthen CE implementation on non-biodegradable waste through digital technologies.
Display omitted
•Recycling at waste banks contributed 15% of waste reduction in Malang in 2020.•With digitalization, over 95% of solid waste in Malang was segregated.•Digitalization reduced labor costs by 30% and shortened time to market by 5 days.•The operational cost of robots was 80% of labor for the same work on 5-year period.•With robotic arms, about 2000 picks hourly can be sorted out with 99% of accuracy.
The digital transformation of the production sector is setting the scene for a major industrial change. The need for supporting companies in this transformation is currently covered by several ...maturity models, generally operationalized through standardized questionnaires, which provide, as an outcome, an assessment of the current maturity stage and a set of general improvement recommendations according to it. However, to provide companies with a more tangible support, there is a need for more individual approach. In order to deal with this need, this paper proposes, following a design science research framework, a novel approach based on Problem-Based Learning for structuring the assessment procedure as a dialectic process. This approach aims at facilitating the contextualization of the assessed company and, consequently, the identification of context-specific improvement recommendations. The proposed approach, supported by a maturity model used for framing information collected during the assessment process, is tested in three industrial cases. Although these have been assessed at the same maturity stage, different improvement recommendations have been proposed according to contextual factors such as strategic goals, core processes and key performance indicators.
Industry 4.0 (I4.0), also known as the fourth industrial revolution, describes the digitalization of manufacturing industries. The transition to I4.0 is crucial for manufacturing firms to sustain ...competitive advantage and seize new opportunities. Most research has focused on the technological aspects of I4.0 in the form of product and process innovations. Despite I4.0's rising attention from both researchers and practitioners, little research exists about I4.0 business model (BM) innovation, even though BM innovations can be more successful than product or process innovations. To address this research gap, we analyze 32 case studies of I4.0 BM innovators. We develop a taxonomy to characterize I4.0 BMs and derive 13 patterns of I4.0 BMs by applying the taxonomy to the case studies. Three super-patterns are identified: integration, servitization, and expertization. Integration innovates a BM with new processes and integrates parts of the supply chain. New combined products and services are the basis for servitization. Expertization is a hybrid of product- and process-focused BMs, which includes consulting services and multi-sided platforms. This study contributes to research with a framework for describing, analyzing, and classifying BMs for I4.0. The findings deepen the understanding of how I4.0 impacts ecosystem roles, BMs, and service systems. Archetypal patterns show how firms can leverage I4.0 concepts and build a conceptual basis for future research. The taxonomy supports practitioners in evaluating the I4.0-readiness of their existing BM. The patterns additionally illustrate opportunities for becoming an I4.0 firm.
•We extend the technically-driven Industry 4.0 research with business models.•We develop a taxonomy for describing, analyzing, and classifying business models.•13 archetypal patterns show how Industry 4.0 impacts business models.•Three areas of Industry 4.0 business models emerge:•Integrating parts of the value chain, servitization, and consulting or platforms.
In recent years, there have been great advances in industrial Internet of Things (IIoT) and its related domains, such as industrial wireless networks (IWNs), big data, and cloud computing. These ...emerging technologies will bring great opportunities for promoting industrial upgrades and even allow the introduction of the fourth industrial revolution, namely, Industry 4.0. In the context of Industry 4.0, all kinds of intelligent equipment (e.g., industrial robots) supported by wired or wireless networks are widely adopted, and both real-time and delayed signals coexist. Therefore, based on the advancement of software-defined networks technology, we propose a new concept for industrial environments by introducing software-defined IIoT in order to make the network more flexible. In this paper, we analyze the IIoT architecture, including physical layer, IWNs, industrial cloud, and smart terminals, and describe the information interaction among different devices. Then, we propose a software-defined IIoT architecture to manage physical devices and provide an interface for information exchange. Subsequently, we discuss the prominent problems and possible solutions for software-defined IIoT. Finally, we select an intelligent manufacturing environment as an assessment test bed, and implement the basic experimental analysis. This paper will open a new research direction of IIoT and accelerate the implementation of Industry 4.0.
Digital assets are highly vulnerable and always prone to malicious intervention. Identification of causes of such intervention for timely support and assistance remains a key challenge for businesses ...to remain functional and thrive with the competition. A framework is proposed in this paper for identifying cyber risk, threat, and countermeasure, based on breach databases and textual information processing. Alongside, a multi-objective optimisation of a mixed-integer non-linear problem (MINLP) is made post linearisation to find out a suitable trade-off between cyber risk and investment. The model helps in effective decision-making by finding the proneness of suppliers (as nodes) in the sequence of reducing vulnerability and pairing of categorised factors. The web scrapping and historical databases are processed to extract relationships among categorised factors using natural language processing (NLP). Pareto optimal pairs are obtained to explain the application of the current contribution in terms of risk-cost trade-off. It helps in forming preventive strategies with a suitable amount of investment and the required order of precedence or susceptibility.
•Digitalisation & intelligent robotics are tools in value chain of waste management.•Municipal waste management development is defined by Circular Economy Package.•4 segments: Collection & Logistics, ...Machines & WTP, Business Models and Data Tools.•Extensive survey of (inter)national data (period 2001–2019): 114 sources utilized.•Smart Waste Factory: Machines are digitally connected and communicate with waste.
The general aim of circular economy is the most efficient and comprehensive use of resources. In order to achieve this goal, new approaches of Industry 4.0 are being developed and implemented in the field of waste management. The innovative K-project: Recycling and Recovery of Waste 4.0 - “ReWaste4.0” deals with topics such as digitalisation and the use of robotic technologies in waste management. Here, a summary of the already published results in these areas, which were divided into the four focused topics, is given: Collection and Logistics, Machines and waste treatment plants, Business models and Data Tools. Presented are systems and methods already used in waste management, as well as technologies that have already been successfully applied in other industrial sectors and will also be relevant in the waste management sector for the future. The focus is set on systems that could be used in waste treatment plants or machines in the future in order to make treatment of waste more efficient. In particular, systems which carry out the sorting of (mixed) waste via robotic technologies are of interest. Furthermore “smart bins” with sensors for material detection or level measurement, methods for digital image analysis and new business models have already been developed. The technologies are often based on large amounts of data that can contribute to increase the efficiency within plants. In addition, the results of an online market survey of companies from the waste management industry on the subject of waste management 4.0 or “digital readiness” are summarized.
One MES model in Digital Manufacturing Vukadinovic, Vojin; Majstorovic, Vidosav; Zivkovic, Jovan ...
Procedia CIRP,
2022, 2022-00-00, Letnik:
112
Journal Article
Recenzirano
Odprti dostop
Digital manufacturing is base for Industry 4.0, based on advanced digital-oriented technologies, smart products (advanced production mode and new characteristics), and smart supply - chain ...(procurement of raw materials and delivery of finished products). Bidirectional exchange of information in collaborative manufacturing, using it exchange also for digital platforms of design of the innovative products. In this paper we are show developed model of Serbian digital factory with selected examples for the MES area.
Purpose
The purpose of this paper is to conduct a state-of-the-art review of the ongoing research on the Industry 4.0 phenomenon, highlight its key design principles and technology trends, identify ...its architectural design and offer a strategic roadmap that can serve manufacturers as a simple guide for the process of Industry 4.0 transition.
Design/methodology/approach
The study performs a systematic and content-centric review of literature based on a six-stage approach to identify key design principles and technology trends of Industry 4.0. The study further benefits from a comprehensive content analysis of the 178 documents identified, both manually and via IBM Watson’s natural language processing for advanced text analysis.
Findings
Industry 4.0 is an integrative system of value creation that is comprised of 12 design principles and 14 technology trends. Industry 4.0 is no longer a hype and manufacturers need to get on board sooner rather than later.
Research limitations/implications
The strategic roadmap presented in this study can serve academicians and practitioners as a stepping stone for development of a detailed strategic roadmap for successful transition from traditional manufacturing into the Industry 4.0. However, there is no one-size-fits-all strategy that suits all businesses or industries, meaning that the Industry 4.0 roadmap for each company is idiosyncratic, and should be devised based on company’s core competencies, motivations, capabilities, intent, goals, priorities and budgets.
Practical implications
The first step for transitioning into the Industry 4.0 is the development of a comprehensive strategic roadmap that carefully identifies and plans every single step a manufacturing company needs to take, as well as the timeline, and the costs and benefits associated with each step. The strategic roadmap presented in this study can offer as a holistic view of common steps that manufacturers need to undertake in their transition toward the Industry 4.0.
Originality/value
The study is among the first to identify, cluster and describe design principles and technology trends that are building blocks of the Industry 4.0. The strategic roadmap for Industry 4.0 transition presented in this study is expected to assist contemporary manufacturers to understand what implementing the Industry 4.0 really requires of them and what challenges they might face during the transition process.
PDF
