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In recent years, sustainable supply chain management practices have been adopted by companies that desire to reduce the negative environmental and social impacts within their supply chains. Within this perspective, a circular approach has been developed in the supply chain literature. Circular economy models and solutions assisted by industry 4.0 technologies have been developed to transform products in the end of their life cycle into new products with different use. In this paper an industry 4.0 waste-to-energy solution is developed and applied in a pilot case study comprised by a real-world supply chain to evaluate the sustainability performance of circular supply chain management (CSCM). The findings show that redesigning supply chains for circular economy with the use of Industry 4.0 technologies, can enable circular supply chain management. Clear benefits are provided linking the proposed solution to the six circular economy dimensions of the ReSOLVE model i.e. regenerate, share, optimise, loop, virtualise, and exchange. Improved availability of personnel (5% and 15%) and fleet resources (15%) are identified as some of the key quantitative benefits, while supply chain traceability through the full visibility and automation offered by the proposed solution, are some of the key non-quantifiable outcomes. The present work seeks to contribute to the existing literature by providing empirical evidence of how industry 4.0 and circular economy are applied in practice. Implications for managers and policy makers, along with the study limitations and further research paths are also presented.

Today, the use of IoT devices, the inter-connectivity of machines and systems, and the development of novel analytic algorithms which are main characteristics of Industry 4.0 are also transferred to other domains connected to the industrial one. The advances on industrial domain have impact on waste management one as these domains are strictly connected. The connection with sensors on industrial partners premises, the big data availability and the significant advances on data analytics, enable the waste management companies to smarten their domain and automate many of their solutions and processes. In this work, we present a data analytics platform for the optimization of waste management procedures. By using this platform, a waste management company is able to monitor and analyze sensors data from industrial partners bins and optimize its planning based on different analytic tools and historical data. Besides the platform description, special focus is given on data analytics algorithms and methods.

The fourth industrial revolution and the digitisation of supply chains have led companies to realize that the adoption of Industry 4.0/Internet of Things (IoT) solutions creates opportunities for more sustainable management. The sustainable management of scrap metal is a challenging task for all the organisations that participate in the supply chain and especially for scrap metal producers and waste management companies. Although metals are considered to be infinitely recycled, scrap metal management is often inefficient due to several factors including collection processes, communication and marketplace limitations, which have significant environmental and economic impacts. The purpose of this paper is to provide evidence of the impact of an IoT solution on the sustainable supply chain management (SSCM) performance. A case study from a scrap metal producer that operates in the lift industry and a waste management company is presented, in order to illustrate how the deployment of a state-of-the-art industry 4.0 solution has the potential to improve sustainability both in the firm level and in the supply chain level. Direct benefits of the introduced solution are the automation of monitoring and negotiation procedures for the produced scrap metal. Indirectly, the proposed solution is beneficial in terms of CO2 emissions’ reduction, resources availability and response time optimization. The results validate the framework for assessing SSCM for Industry 4.0 developed by Manavalan and Jayakrishna (2019) and demonstrate that Ιndustry 4.0 solutions have the potential to improve, among others, the economic, environmental and social sustainability in supply chain management. The present study contributes to the literature by bridging the gap between theoretical developments and real-world cases in the fields of industry 4.0 and SSCM. Managerial implications, limitations and future research opportunities are also provided.

Collaborative manufacturing ecosystems provide a high volume of data, collected from factories and commonly related to machine data, sensor measurements and production processes. Wide variations are noted in these data alongside with the data related to the supply-chain and transactions over the aforementioned ecosystems. Semantics and ontologies are commonly used in order to bridge variances in datasets. Furthermore, deep learning techniques perform different kind of analyses over such high volumes of data. This paper introduces a collaboration scheme between a Semantic Framework and a Deep Learning Toolkit. More precisely, this work describes how the ecosystem’s data were modeled and stored using ontologies, became available and analyzed by the continuous learning algorithms of the Deep Learning Toolkit and finally how they are sent back to the Semantic Framework, enhancing a semantic matchmaker’s efficiency in order to support the automated decision making inside the collaborative ecosystem.

Manufacturing eco-systems aim to connect data and services between factories, suppliers and customers. Most of them are built as agent-based eco-systems which act as web-based operating systems for business connections between enterprises. The connection of supply and demand entities participating in an eco-system by exploiting knowledge and data from the business entities has become imperative for them, in order to adapt to the dynamically changing market requirements. This paper introduces a web-based semantic ontological framework designed for collaborative agent-based manufacturing eco-systems. The proposed framework and its core components enable the information modeling of the manufacturing services and the supply chain concepts. A Collaborative Manufacturing Services Ontology able to describe both manufacturing domain and e-commerce domain is offered alongside with an Application Programming Interface for the effortless manipulation of the ontological resources. Furthermore, a Rule-based Matchmaking engine able to match requesters with possible suppliers, and to evaluate offers from suppliers based on different requesters’ criteria and preferences is provided.

The exploitation of big volumes of data in Industry 4.0 and the increasing development of cognitive systems strongly facilitate the realm of predictive maintenance for real-time decisions and early fault detection in manufacturing and production. Cognitive factories of Industry 4.0 aim to be flexible, adaptive, and reliable, in order to derive an efficient production scheme, handle unforeseen conditions, predict failures, and aid the decision makers. The nature of the data streams available in industrial sites and the lack of annotated reference data or expert labels create the challenge to design augmented and combined data analytics solutions. This paper introduces a cognitive analytics, self- and autonomous-learned system bearing predictive maintenance solutions for Industry 4.0. A complete methodology for real-time anomaly detection on industrial data and its application on injection molding machines are presented in this study. Ensemble prediction models are implemented on the top of supervised and unsupervised learners and build a compound prediction model of historical data utilizing different algorithms’ outputs to a common consensus. The generated models are deployed on a real-time monitoring system, detecting faults in real-time incoming data streams. The key strength of the proposed system is the cognitive mechanism which encompasses a real-time self-retraining functionality based on a novel double-oriented evaluation objective, a data-driven and a model-based one. The presented application aims to support maintenance activities from injection molding machines’ operators and demonstrate the advances that can be offered by exploiting artificial intelligence capabilities in Industry 4.0.