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EPR Reference Database

Publication type: Conference Paper

Optimizing Product Lifecycle Management with Omnichannel Strategies, Social Media Insights, and Extended Producer Responsibility: ...Read more Optimizing Product Lifecycle Management with Omnichannel Strategies, Social Media Insights, and Extended Producer Responsibility: A Case Study on Efficient Plastic Waste Recycling

Abstract/summary

In today’s dynamic business environment, effective Product Lifecycle Management (PLM) is crucial for maintaining market competitiveness. Traditional PLM faces challenges related to product quality, design, recycling, and marketing delays, resulting in lost sales, order backlog, increasing pollutant and environmental impacts. This paper proposes a contemporary approach by integrating Extended Producer Responsibility (EPR) into the PLM cycle, focusing on the recycling phase to address environmental concerns, particularly plastic waste. The study evaluates the impact of incorporating Omnichannel (OC) strategies and social media insights into the PLM framework. OC strategies foster collaboration among stakeholders, addressing challenges associated with lost sales and order backlog and social responsibility concerns. Social media analytics provide valuable customer insights, enabling prompt issue identification and resolution. The research compares a system using traditional PLM methods against one incorporating EPR and OC strategies with system dynamics approach. It aims to demonstrate how this integrated approach enhances awareness, encourages collaboration, and streamlines plastic waste collection. The findings emphasize the potential to improve environmental sustainability, operational efficiency, and market competitiveness. This paper advocates for a sustainable PLM framework, offering insights to businesses navigating environmental responsibility, customer engagement, and operational excellence.

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Author(s)
Noushin Mohammadian
Yilmaz Uygun
Omid Fatahi Valilai
Year
2024
Conference name
International Conference on Flexible Automation and Intelligent Manufacturing
Publisher
Springer
DOI
10.1007/978-3-031-74485-3_40
URL
https://doi.org/10.1007/978-3-031-74485-3_40
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