An industry 4.0 framework for the quality inspection in gearboxes production

Nowadays, the development of Internet of Things (IoT) technologies have been enhancing the factory digitalization with several advantages in terms of production efficiency, product quality, and cost reduction. This opportunity encourages the implementation of digital twins related to physical systems for controlling the production workflow in real time. Firstly, the paper studies the enabling technologies for supporting the defect analysis in the context of Industry 4.0 for mechanical workpieces. Secondly, the approach aims to study the integration between the CAD geometry and the quality check process for the inspection planning. A Knowledge-Based tool has been proposed to support the configurations of the quality control chain for each CAD geometry. The test case is focused on the fragmented production of customized gearbox parts

A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture

Modern market scenarios are imposing a radical change in the production concept, driving companies’ attention to customer satisfaction through increased product customization and quick response strategies to maintain competitiveness. At the same time, the growing development of Industry 4.0 technologies made possible the creation of new manufacturing paradigms in which an increased level of autonomy is one of the key concepts to consider. Taking the advantage from the recent development around the semi-heterarchical architecture, this work proposes a first model for the throughput control of a production system managed by such an architecture. A cascade control algorithm is proposed considering work-in-progress (WIP) as the primary control lever for achieving a specific throughput target. It is composed of an optimal control law based on an analytical model of the considered production system, and of a secondary proportional-integral-derivative controller capable of performing an additional control action that addresses the error raised by the theoretical model’s. The proposed throughput control algorithm has been tested in different simulated scenarios, and the results showed that the combination of the control actions made it possible to have continuous adjustment of the WIP of the controlled production system, maintaining it at the minimum value required to achieve the requested throughput with nearly zero errors

Anarchic manufacturing: implementing fully distributed control and planning in assembly

This paper demonstrates that a distributed control and planning system can fulfil an idealised mixed-model assembly problem and compete with traditional systems. The anarchic manufacturing system is a distributed planning and control system, based on a free market structure, where system elements have decision-making authority and autonomy. Mixed-model assembly is typically managed centrally for production planning and control, using simplification and hierarchical structures to manage complexity. In developing anarchy, inter-job cooperation is implemented to synergise jobs together and fulfil global objectives efficiently. The anarchic system maximises available flexibility, through embracing complexity, and reduces myopic decision making by maximising an agent’s lifetime profitability. Through agent-based simulation experiments, the anarchic system is compared to fixed and flexible centralised systems. The proposed system outperforms traditional systems when the scenario’s structural flexibility allows agile and delayed dynamic decision making. Additionally, the anarchic system managed dynamic bottleneck disruptions as effectively as flexible centralised systems

A conceptual framework for smart production planning and control in Industry 4.0

[EN] This article aims to introduce the challenge (i.e., integration of new collaborative models and tools) posed by the automation and collaboration of industrial processes in Industry 4.0 (I4.0) smart factories. Small- and medium-sized enterprises (SMEs) are particularly confronted with new technological and organisational changes, but a conceptual framework for production planning and control (PPC) systems in the I4.0 context is lacking. The main contributions of this article are to: (i) identify the functions making up traditional PPC and smart production planning and control in I4.0 (SPPC 4.0); (ii) analyse the impact of I4.0 technologies on PPC systems; (iii) propose a conceptual framework that provides the systematic structuring of how a PPC system operates in the I4.0 context, dubbed SPPC 4.0. Thus SPPC 4.0 is proposed by adopting the axes of the RAMI 4.0 reference architecture model, which compiles and contains the main concepts of PPC systems and I4.0. It also provides the technical description, organisation and understanding of each aspect, which can provide a guide for academic research and industrial practitioners to transform PPC systems towards I4.0 implementations. Finally, theoretical implications and research gaps are provided.The research leading to these results received funding from the European Union H2020 Program with grant agreements No. 958205 "Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)" and No. 825631 "Zero-Defect Manufacturing Platform (ZDMP)"; the "Industrial Production and Logistics Optimization in Industry 4.0" (i4OPT) (Ref. PROMETEO/2021/065) project granted by the Valencian Regional Government; and the PAI-12-21 open-access support from the Universitat Politecnica de Valencia.Cañas, H.; Mula, J.; Campuzano-Bolarín, F.; Poler, R. (2022). A conceptual framework for smart production planning and control in Industry 4.0. Computers & Industrial Engineering. 173:1-12. https://doi.org/10.1016/j.cie.2022.10865911217

Approaches of production planning and control under Industry 4.0: A literature review

Purpose: Industry 4.0 technologies significantly impact how production is planned, scheduled, and controlled. Literature provides different classifications of the tasks and functions of production planning and control (PPC) like the German Aachen PPC model. This research aims to identify and classify current Industry 4.0 approaches for planning and controlling production processes and to reveal researched and unexplored areas of the model. It extends a reduced version that has been published previously in Procedia Computer Science (Herrmann, Tackenberg, Padoano & Gamber, 2021) by presenting and discussing its results in more detail. Design/methodology/approach: In an exploratory literature review, we review and classify 48 publications on a full-text basis with the Aachen PPC model’s tasks and functions. Two cluster analyses reveal researched and unexplored tasks and functions of the Aachen PPC model. Findings: We propose a cyber-physical PPC architecture, which incorporates current Industry 4.0 technologies, current optimization methods, optimization objectives, and disturbances relevant for realizing a PPC system in a smart factory. Current approaches mainly focus on production control using real-time information from the shop floor, part of in-house PPC. We discuss the different layers of the cyber-physical PPC architecture and propose future research directions for the unexplored tasks and functions of the Aachen PPC model. Research limitations/implications: Limitations are the strong dependence of results on search terms used and the subjective eligibility assessment and assignment of publications to the Aachen PPC model. The selection of search terms and the texts’ interpretation is based on an individual’s assessment. The revelation of unexplored tasks and functions of the Aachen PPC model might have a different outcome if the search term combination is parameterized differently. Originality/value: Using the Aachen PPC model, which holistically models PPC, the findings give comprehensive insights into the current advances of tools, methods, and challenges relevant to planning and controlling production processes under Industry 4.0Peer Reviewe

Evaluating Blockchain Success: Integrating Organizational Decentralization with the DeLone and McLean IS Success Model

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementBlockchain technology is a distributed ledger without an intermediate where delivers decentralized consensus. The tremendous potential of this technology including anonymity, persistency, auditability, and traceability along with decentralization caused blockchain to receive attention globally. This study aims to identify the role of decentralization in blockchain success at firms by proposing a theoretical model based on the theory of success in information systems. The research model was empirically tested using 193 responses over an online survey questionnaire. The result reveals that service quality, system quality, and information quality were explained by decentralization. Likewise, decentralization and user’s satisfaction are an important criterion for the Net impact of blockchain success. Furthermore, this study explores the positive influence of decentralization as a moderator between the relationship of the user’s satisfaction and net impact. The findings have theoretical and practical implications for academics and managers

Towards System State Dispatching in High-Variety Manufacturing

This study proposes a shift towards system state dispatching in the production control literature on high-variety manufacturing. System state dispatching lets the decision on what order to produce next be driven by system-wide implications while trading of an array of control objectives. This contrasts the current literature that uses hierarchical order review and release methods that control the system at release, whilst myopic priority rules control order dispatching based on local queue information. We develop such a system state dispatching method, called FOCUS, and test it using simulation. The results show that FOCUS enables a big leap forward in production control performance. Specifically, FOCUS reduces the number of orders delivered late by a factor of one to eight and mean tardiness by a factor of two to ten compared to state-of-the-art production control methods. These results are consistent over a wide variety of conditions related to routing direction, routing length, process time variability and due date tightness