Supporting Tool for The Transition of Existing Small and Medium Enterprises Towards Industry 4.0

• URL: http://arxiv.org/abs/2010.12038v1
• Date: Thu, 15 Oct 2020 15:57:23 GMT
• Title: Supporting Tool for The Transition of Existing Small and Medium Enterprises Towards Industry 4.0
• Authors: Miguel Baritto, Md Mashum Billal, S. M. Muntasir Nasim, Rumana Afroz Sultana, Mohammad Arani, Ahmed Jawad Qureshi
• Abstract summary: The main purpose of this work is to propose a methodology to support SMEs managers in better understanding the specific requirements for the implementation of Industry 4.0 solutions. A proposed methodology will be helpful for SMEs manager to take a decision regarding when and how to migrate to Industry 4.0.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid growth of Industry 4.0 technologies such as big data, cloud computing, smart sensors, machine learning (ML), radio-frequency identification (RFID), robotics, 3D-printing, and Internet of Things (IoT) offers Small and Medium Enterprises (SMEs) the chance to improve productivity and efficiency, reduce cost and provide better customer experience, among other benefits. The main purpose of this work is to propose a methodology to support SMEs managers in better understanding the specific requirements for the implementation of Industry 4.0 solutions and the derived benefits within their firms. A proposed methodology will be helpful for SMEs manager to take a decision regarding when and how to migrate to Industry 4.0.

Related papers

• MLOps: A Multiple Case Study in Industry 4.0 [0.8287206589886879]
  MLOps refers to the processes, tools, and organizational structures used to develop, test, deploy, and manage ML models reliably and efficiently. This study describes four of the companies' Industry 4.0 scenarios and provides relevant insights into their implementation and the challenges they faced in numerous projects.
  arXiv  Detail & Related papers  (2024-07-12T09:17:26Z)
• Enabling Efficient and Flexible Interpretability of Data-driven Anomaly Detection in Industrial Processes with AcME-AD [5.315104943095396]
  We present the first industrial application of AcME-AD, showcasing its effectiveness through experiments. AcME-AD is model-agnostic, offering flexibility, and prioritizes real-time efficiency. These tests demonstrate AcME-AD's potential as a valuable tool for explainable AD and feature-based root cause analysis within industrial environments.
  arXiv  Detail & Related papers  (2024-04-29T09:11:41Z)
• Integrating MLSecOps in the Biotechnology Industry 5.0 [49.97673761305336]
  This chapter provides a perspective of how Machine Learning Security Operations (MLSecOps) can help secure the biotechnology Industry 5.0. The chapter provides an analysis of the threats in the biotechnology Industry 5.0 and how ML algorithms can help secure with industry best practices.
  arXiv  Detail & Related papers  (2024-02-12T17:21:12Z)
• Towards the implementation of Industry 4.0: A methodology-based approach oriented to the customer life cycle [0.05524804393257919]
  We present two contributions that can help the software engineers of those SMEs to incorporate Industry 4.0 technologies in the context of the customer life cycle. The first contribution is a methodology that can help those software engineers in the task of creating new software services, aligned with Industry 4.0. The second contribution is a system developed for a real manufacturing scenario, using the proposed methodology.
  arXiv  Detail & Related papers  (2024-01-31T08:31:08Z)
• Machine Learning Meets Advanced Robotic Manipulation [48.6221343014126]
  The paper reviews cutting edge technologies and recent trends on machine learning methods applied to real-world manipulation tasks. The rest of the paper is devoted to ML applications in different domains such as industry, healthcare, agriculture, space, military, and search and rescue.
  arXiv  Detail & Related papers  (2023-09-22T01:06:32Z)
• Embedded Software Development with Digital Twins: Specific Requirements for Small and Medium-Sized Enterprises [55.57032418885258]
  Digital twins have the potential for cost-effective software development and maintenance strategies. We interviewed SMEs about their current development processes. First results show that real-time requirements prevent, to date, a Software-in-the-Loop development approach.
  arXiv  Detail & Related papers  (2023-09-17T08:56:36Z)
• Trends and Challenges Towards an Effective Data-Driven Decision Making in UK SMEs: Case Studies and Lessons Learnt from the Analysis of 85 SMEs [0.0]
  Data Science can support SMEs to optimise production processes, anticipate customers' needs, predict machinery failures and deliver efficient smart services. integrating data science decisions into an SME requires both skills and IT investments. This paper presents trends and challenges towards an effective data-driven decision making for organisations based on a case study of 85 SMEs.
  arXiv  Detail & Related papers  (2023-05-24T17:23:32Z)
• Benchmarking Automated Machine Learning Methods for Price Forecasting Applications [58.720142291102135]
  We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions. Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part. We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
  arXiv  Detail & Related papers  (2023-04-28T10:27:38Z)
• Online Service Migration in Edge Computing with Incomplete Information: A Deep Recurrent Actor-Critic Method [18.891775769665102]
  Multi-access Edge Computing (MEC) is an emerging computing paradigm that extends cloud computing to the network edge. Service migration needs to decide where to migrate user services for maintaining high Quality-of-Service (QoS) We propose a new learning-driven method, namely Deep Recurrent ActorCritic based service Migration (DRACM), which is usercentric and can make effective online migration decisions.
  arXiv  Detail & Related papers  (2020-12-16T00:16:24Z)
• AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes [56.65379135797867]
  We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes. We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm.
  arXiv  Detail & Related papers  (2020-08-29T14:57:53Z)
• Technology Readiness Levels for AI & ML [79.22051549519989]
  Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results. We propose a proven systems engineering approach for machine learning development and deployment.
  arXiv  Detail & Related papers  (2020-06-21T17:14:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.