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• W3000441679 abstract "Because of their cross-functional nature in the company, enhancing Production Planning and Control (PPC) functions can lead to a global improvement of manufacturing systems. With the advent of the Industry 4.0 (I4.0), copious availability of data, high-computing power and large storage capacity have made of Machine Learning (ML) approaches an appealing solution to tackle manufacturing challenges. As such, this paper presents a state-of-the-art of ML-aided PPC (ML-PPC) done through a systematic literature review analyzing 93 recent research application articles. This study has two main objectives: contribute to the definition of a methodology to implement ML-PPC and propose a mapping to classify the scientific literature to identify further research perspectives. To achieve the first objective, ML techniques, tools, activities, and data sources which are required to implement a ML-PPC are reviewed. The second objective is developed through the analysis of the use cases and the addressed characteristics of the I4.0. Results suggest that 75% of the possible research domains in ML-PPC are barely explored or not addressed at all. This lack of research originates from two possible causes: firstly, scientific literature rarely considers customer, environmental, and human-in-the-loop aspects when linking ML to PPC. Secondly, recent applications seldom couple PPC to logistics as well as to design of products and processes. Finally, two key pitfalls are identified in the implementation of ML-PPC models: the complexity of using Internet of Things technologies to collect data and the difficulty of updating the ML model to adapt it to the manufacturing system changes." @default.
• W3000441679 created "2020-01-23" @default.
• W3000441679 creator A5027312442 @default.
• W3000441679 creator A5055023069 @default.
• W3000441679 creator A5066725212 @default.
• W3000441679 creator A5076659352 @default.
• W3000441679 creator A5091297342 @default.
• W3000441679 date "2020-01-11" @default.
• W3000441679 modified "2023-10-05" @default.
• W3000441679 title "Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0" @default.
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