Analyzing environmental sustainability methods for use earlier in the product lifecycle

Area of Science:

• Manufacturing Engineering
• Environmental Science
• Data Science

Background:

• Environmental sustainability information is fragmented across manufacturing product lifecycles.
• Designers lack reliable manufacturing data for early-stage sustainability assessments, relying on outdated or analogous information.
• Smart manufacturing and the Internet of Things (IoT) offer potential solutions for data and knowledge sharing.

Purpose of the Study:

• To analyze environmental sustainability assessment methods for earlier design integration.
• To identify opportunities for improving data representation standards in manufacturing.
• To bridge the gap between product design and manufacturing stages for sustainability.

Main Methods:

• Categorization of sustainability assessment techniques by product lifecycle stage using the SIMA reference architecture.
• Analysis of data and knowledge sharing opportunities enabled by smart manufacturing and IoT.
• Identification of standard data representation alignments for sustainability assessment.

Main Results:

• A structured overview of sustainability assessment methods applicable to different product lifecycle stages.
• Identification of specific data sharing challenges and opportunities.
• Recommendations for aligning data representation to support early design sustainability decisions.

Conclusions:

• Improved data sharing through smart manufacturing and IoT can significantly enhance early-stage environmental sustainability assessment in product design.
• Standardizing data representation is crucial for enabling accurate and timely sustainability evaluations.
• Integrating sustainability considerations earlier in the design process leads to more environmentally responsible products.