Short answer

Embrace data analytics to inform and optimize circular economy strategies within manufacturing processes, leading to improved efficiency, reduced environmental impact, and enhanced market competitiveness.

Field: Commercial Production
Source: Journal of Computational Informatics & Business (2025)
Method: Quantitative research using Partial Least Squares Structural Equation Modelling (PLS-SEM).
Evidence: Strong effect

Integrating Big Data Analytics (BDA) with Circular Economy (CE) practices significantly enhances the resilience and sustainability performance of manufacturing firms, particularly in the post-pandemic landscape. This commercial production research insight is drawn from a 2025 study published in Journal of Computational Informatics & Business. Using Quantitative research using partial least squares structural equation modelling (pls-sem)., researchers explored how this design variable affects real-world outcomes. The key design takeaway: Embrace data analytics to inform and optimize circular economy strategies within manufacturing processes, leading to improved efficiency, reduced environmental impact, and enhanced market competitiveness.

Study

Commercial ProductionNew This WeekStrong effect

Big Data Analytics and Circular Economy Drive Firm Performance in Post-Pandemic Manufacturing

Journal of Computational Informatics & Business · 2025

Key Findings

01BDA implementation augments decision-making processes.
02CE practices contribute to environmental impact mitigation and cost reduction.
03Digital marketing, driven by changing consumer preferences, boosts customer engagement and financial performance.
04The combined focus on BDA and CE enhances resilience and sustainability.

Application

Design takeaway

How to apply

Implement data collection and analysis systems to track resource usage, waste generation, and product lifecycle data. Use these insights to redesign products for disassembly, reuse, and recycling, and to optimize manufacturing processes for minimal environmental impact.

Project actions

01Consider how data can inform your design choices for sustainability.
02Explore how product end-of-life scenarios can be integrated into the initial design phase.

Method & Evidence

AimTo investigate the impact of Big Data Analytics (BDA) and Circular Economy (CE) practices on the performance of manufacturing firms in the post-pandemic era.

MethodQuantitative research using Partial Least Squares Structural Equation Modelling (PLS-SEM).

ProcedureCross-sectional data was collected and analyzed using PLS-SEM to establish the relationships between BDA, CE, digital marketing, and firm performance.

ContextManufacturing sector in China during the post-pandemic era.

Variables

IV["Big Data Analytics (BDA) implementation","Circular Economy (CE) practices","Digital Marketing"]

DVFirm Performance (resilience, sustainability, financial performance, customer engagement)

Strengths & Limitations

Strengths

+Utilizes a robust statistical modelling technique (PLS-SEM).
+Addresses a timely and relevant topic concerning post-pandemic business strategies.

Limitations

The specific context of China's manufacturing sector might influence the applicability of these findings to other regions or industries.

Reliability & validity

The use of PLS-SEM provides a framework for assessing the reliability and validity of the proposed model. However, the cross-sectional nature of the data may limit the ability to establish strong causal claims, impacting external validity.

Think critically

To what extent can the findings regarding BDA and CE be generalized to industries outside of manufacturing, or to economies with different regulatory frameworks?

Design Principles

"Data-driven circularity enhances operational efficiency and sustainability."

In today's volatile market, understanding and leveraging data is paramount. This research highlights how BDA can inform and optimize CE strategies, leading to tangible benefits like cost reduction and improved environmental impact. For design and manufacturing professionals, this means a strategic shift towards data-informed, resource-efficient production models.

What This Means for Your Design

Using big data and 'circular economy' ideas (like reusing and recycling) helps factories work better and be more environmentally friendly, especially after tough times like the pandemic.

How to use in your project

1.Reference this study when discussing the integration of data analytics and sustainable practices in your design project.
2.Use the findings to justify the importance of data-driven decision-making for resource efficiency.

Add to My Project

Quick Cite

Paragraph starter

The integration of Big Data Analytics (BDA) with Circular Economy (CE) practices has been shown to significantly enhance firm resilience and sustainability performance in manufacturing. For instance, research by Rafi and Sulman (2025) highlights how BDA augments decision-making, while CE contributes to environmental mitigation and cost reduction, collectively boosting performance in the post-pandemic era. This underscores the importance of data-driven approaches in designing for resource efficiency and waste reduction.

Source

Journal of Computational Informatics & Business

Post-Pandemic Insights: Evaluating the Impact of Big Data Analytics, Circular Economy Practices, and Digital Marketing on Firm Performance

journal · 2025

View source

Questions About This Research

What does the research say about big data analytics and circular economy drive firm performance in post-pandemic manufacturing?: Embrace data analytics to inform and optimize circular economy strategies within manufacturing processes, leading to improved efficiency, reduced environmental impact, and enhanced market competitiveness. Evidence: Journal of Computational Informatics & Business (2025).
Why does "Big Data Analytics and Circular Economy Drive Firm Performance in Post-Pandemic Manufacturing" matter for design?: In today's volatile market, understanding and leveraging data is paramount. This research highlights how BDA can inform and optimize CE strategies, leading to tangible benefits like cost reduction and improved environmental impact. For design and manufacturing professionals, this means a strategic shift towards data-informed, resource-efficient production models.
How can designers apply this research?: Embrace data analytics to inform and optimize circular economy strategies within manufacturing processes, leading to improved efficiency, reduced environmental impact, and enhanced market competitiveness.
What were the main findings?: BDA implementation augments decision-making processes.. CE practices contribute to environmental impact mitigation and cost reduction.. Digital marketing, driven by changing consumer preferences, boosts customer engagement and financial performance.. The combined focus on BDA and CE enhances resilience and sustainability.
What research method was used?: Quantitative research using Partial Least Squares Structural Equation Modelling (PLS-SEM)..
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2025 journal from Journal of Computational Informatics & Business.
What should I do differently in my next project?: Implement data collection and analysis systems to track resource usage, waste generation, and product lifecycle data. Use these insights to redesign products for disassembly, reuse, and recycling, and to optimize manufacturing processes for minimal environmental impact.
What are the limitations?: The study is cross-sectional, limiting the ability to establish causality definitively. Findings are specific to the Chinese manufacturing sector and may not be universally generalizable.