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Recent developments of control charts, identification of big data sources and future trends of current research

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  • Aykroyd, Robert G.
  • Leiva, Víctor
  • Ruggeri, Fabrizio

Abstract

Control charts are one of the principal tools to monitor dynamic processes with the aim of rapid identification of changes in the behaviour of these processes. Such changes are usually associated with a move from an in-control condition to an out-of-control condition. The paper briefly reviews the historical origins and includes examples of recent developments, focussing on their use in fields different from the industrial applications in which they were initially derived and often employed. It also focusses on cases which depart from the commonly used Gaussian assumption and then considers potential effects of the big data revolution on future uses. A bibliometric analysis is also presented to identify distinct groups of research themes, including emerging and underdeveloped areas, which are hence potential topics for future research.

Suggested Citation

  • Aykroyd, Robert G. & Leiva, Víctor & Ruggeri, Fabrizio, 2019. "Recent developments of control charts, identification of big data sources and future trends of current research," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 221-232.
    • Handle: RePEc:eee:tefoso:v:144:y:2019:i:c:p:221-232
    DOI: 10.1016/j.techfore.2019.01.005
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    Cited by:

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    3. Hansu Hwang & SeJin An & Eunchang Lee & Suhyeon Han & Cheon-hwan Lee, 2021. "Cross-Societal Analysis of Climate Change Awareness and Its Relation to SDG 13: A Knowledge Synthesis from Text Mining," Sustainability, MDPI, vol. 13(10), pages 1-21, May.
    4. Fernando Garrigós-Simón & Silvia Sanz-Blas & Yeamduan Narangajavana & Daniela Buzova, 2021. "The Nexus between Big Data and Sustainability: An Analysis of Current Trends and Developments," Sustainability, MDPI, vol. 13(12), pages 1-24, June.
    5. Kalaiarasi Kalaichelvan & Soundaria Ramalingam & Prasantha Bharathi Dhandapani & Víctor Leiva & Cecilia Castro, 2024. "Optimizing the Economic Order Quantity Using Fuzzy Theory and Machine Learning Applied to a Pharmaceutical Framework," Mathematics, MDPI, vol. 12(6), pages 1-22, March.
    6. Che-Wei Chang, 2020. "Constructing a Novel Early Warning Algorithm for Global Budget Payments," Mathematics, MDPI, vol. 8(11), pages 1-11, November.
    7. Bag, Surajit & Pretorius, Jan Ham Christiaan & Gupta, Shivam & Dwivedi, Yogesh K., 2021. "Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    8. Hanen Chaouch & Samia Charfeddine & Sondess Ben Aoun & Houssem Jerbi & Víctor Leiva, 2022. "Multiscale Monitoring Using Machine Learning Methods: New Methodology and an Industrial Application to a Photovoltaic System," Mathematics, MDPI, vol. 10(6), pages 1-16, March.
    9. Francesco Badia & Fabio Donato, 2022. "Opportunities and risks in using big data to support management control systems: A multiple case study," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(3), pages 39-63.
    10. Mourad Kchaou & Cecilia Castro & Rabeh Abbassi & Víctor Leiva & Houssem Jerbi, 2024. "Security Control for a Fuzzy System under Dynamic Protocols and Cyber-Attacks with Engineering Applications," Mathematics, MDPI, vol. 12(13), pages 1-34, July.
    11. Ahmad Kamal Mohd Nor & Srinivasa Rao Pedapati & Masdi Muhammad & Víctor Leiva, 2022. "Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data," Mathematics, MDPI, vol. 10(4), pages 1-37, February.

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