Sustainable manufacturing: trends and research challenges

By Marco Garetti, Marco Taisch. 2012

863 citations recorded by the source corpus.

Subfields: Environmental Engineering, Industrial and Manufacturing Engineering, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/1398179b1776ada239f007ec5837a8384d07babc

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In Environmental Engineering

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Citations in the corpus

The value of Big Data in servitization (2015, background): “Finally, such a new strategic opportunity of Big Data exploitation in relation to servitization could enable the manufacturer to revise business models that are based on the idea to sell as many products as possible, hence not being adequate in terms of sustainability (Garetti and Taisch, 2012).”
The impact of big data on world-class sustainable manufacturing (2015, background): “Despite the growing interest in WCSM, there is still lack of consensus in current literature with regards to its definition and implication for organizations [37].”
Electrostatic atomization minimum quantity lubrication machining: from mechanism to application (2022, background): “However, this lubrication method should be abandoned because of the environmental pollution, carbon emission, resource depletion, and similar challenges faced by humans today [10, 11], especially under the drastic evolution of the greenhouse effect [12–23].”
Industrial Internet: A Survey on the Enabling Technologies, Applications, and Challenges (2017, methodology): “For example, in aerospace assembly, riveting is the major joining methods and required to be done precisely [52], [56].”
Energy-efficient scheduling in manufacturing companies: A review and research framework (2016, background): “Garetti & Taisch (2012) give AC CE PT ED M AN US CR IP T a broad overview of sustainable manufacturing and identify energy-aware production planning and control as a suitable instrument for energy-efficient manufacturing, but they give no further insights into this topic.”
Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain (2017, background): “Garetti and Taisch [50] advocate the need for methods that can process a large volume of data to gain actionable insights into economic, social and environmental sustainability.”