Short answer

Incorporate IoT and DSS into the design of products and systems to enable data-driven decision-making that supports circularity, optimizes resource use, and enhances economic and environmental outcomes.

Field
Sustainability
Source
American Journal of Scholarly Research and Innovation (2025)
Method
Systematic Review (PRISMA 2020 guidelines)
Sample
68 articles
Evidence
Strong effect

Integrating Internet of Things (IoT) with Decision Support Systems (DSS) significantly enhances both economic performance and environmental sustainability within Circular Economy (CE) business models. This sustainability research insight is drawn from a 2025 study published in American Journal of Scholarly Research and Innovation. Using Systematic review (prisma 2020 guidelines) with 68 articles, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Incorporate IoT and DSS into the design of products and systems to enable data-driven decision-making that supports circularity, optimizes resource use, and enhances economic and environmental outcomes.

Study
SustainabilityNew This WeekStrong effect

IoT-enabled Decision Support Systems drive economic efficiency and sustainability in Circular Economy models

Integrating Internet of Things (IoT) with Decision Support Systems (DSS) significantly enhances both economic performance and environmental sustainability within Circular Economy (CE) business models.

American Journal of Scholarly Research and Innovation · 2025

01

Key Findings

  • 01IoT-enabled DSS improve economic performance through reduced costs, optimized labor, predictive maintenance, and efficient material use.
  • 02Environmental performance is enhanced via real-time monitoring of emissions, water, and energy, supporting CE principles like product lifecycle extension and reverse logistics.
  • 03Challenges include fragmented infrastructure, high costs, lack of interoperability, organizational resistance, and underdeveloped applications in non-industrial sectors.
  • 04Gaps exist in longitudinal studies, stakeholder research, and a unified theoretical framework.
02

Application

Design takeaway

Incorporate IoT and DSS into the design of products and systems to enable data-driven decision-making that supports circularity, optimizes resource use, and enhances economic and environmental outcomes.

How to apply

When designing products or services intended for circular economy models, consider how IoT sensors and data analytics platforms can provide actionable insights for resource management, maintenance, and end-of-life processes.

Project actions

  • 01When designing a product, think about how it can be tracked and managed throughout its life using IoT.
  • 02Consider how data from IoT devices can inform decisions about repair, refurbishment, or recycling.
03

Method & Evidence

AimTo systematically review the literature on IoT-enabled DSS for Circular Economy business models, focusing on their economic efficiency and sustainability outcomes.
MethodSystematic Review (PRISMA 2020 guidelines)
ProcedureA systematic review was conducted, analyzing 68 peer-reviewed articles published between 2013 and 2024, covering interdisciplinary research in environmental economics, industrial engineering, and information systems.
Sample68 articles
ContextCircular Economy business models, IoT, Decision Support Systems

Variables

IVIntegration of IoT with DSS
DVEconomic efficiency outcomes, Sustainability outcomes
CVCircular Economy business models, specific industry sectors
04

Strengths & Limitations

Strengths

  • +Comprehensive systematic review methodology (PRISMA 2020).
  • +Covers a broad range of interdisciplinary research.

Limitations

The complexity and cost of implementing IoT and DSS can be a barrier for smaller design projects or businesses.

Reliability & validity

The systematic review methodology, adhering to PRISMA guidelines, enhances the reliability and validity of the findings by ensuring a transparent and reproducible research process.

Think critically

To what extent can the identified challenges (e.g., high upfront costs, lack of interoperability) be overcome through innovative design solutions for IoT-enabled DSS in CE?

05

Design Principles

"Leverage digital technologies for real-time monitoring and adaptive management to achieve circular economy objectives."

This integration offers practical pathways for businesses to optimize resource utilization, reduce operational costs, and minimize environmental impact, aligning with growing demands for sustainable practices and regulatory compliance.

06

What This Means for Your Design

Using smart sensors (IoT) connected to computer systems (DSS) helps businesses make better decisions to reuse materials, save money, and protect the environment, which is key for 'circular economy' ideas.

How to use in your project

  • 1.Reference this study when discussing the role of technology in achieving sustainability goals or implementing circular economy principles in your design project.
07

Add to My Project

08

Quick Cite

Paragraph starter

The integration of Internet of Things (IoT) with Decision Support Systems (DSS) offers a powerful approach to enhancing both economic efficiency and environmental sustainability within Circular Economy (CE) business models. Research indicates that these integrated systems can lead to significant improvements through optimized resource use, reduced operational costs, and better management of product lifecycles, thereby supporting the core principles of CE.

09

Source

American Journal of Scholarly Research and Innovation

AN IOT-ENABLED DECISION SUPPORT SYSTEM FOR CIRCULAR ECONOMY BUSINESS MODELS: A REVIEW OF ECONOMIC EFFICIENCY AND SUSTAINABILITY OUTCOMES

journal · 2025

View source

Questions About This Research

What does the research say about iot-enabled decision support systems drive economic efficiency and sustainability in circular economy models?
Incorporate IoT and DSS into the design of products and systems to enable data-driven decision-making that supports circularity, optimizes resource use, and enhances economic and environmental outcomes. Evidence: American Journal of Scholarly Research and Innovation (2025).
Why does "IoT-enabled Decision Support Systems drive economic efficiency and sustainability in Circular Economy models" matter for design?
This integration offers practical pathways for businesses to optimize resource utilization, reduce operational costs, and minimize environmental impact, aligning with growing demands for sustainable practices and regulatory compliance.
How can designers apply this research?
Incorporate IoT and DSS into the design of products and systems to enable data-driven decision-making that supports circularity, optimizes resource use, and enhances economic and environmental outcomes.
What were the main findings?
IoT-enabled DSS improve economic performance through reduced costs, optimized labor, predictive maintenance, and efficient material use.. Environmental performance is enhanced via real-time monitoring of emissions, water, and energy, supporting CE principles like product lifecycle extension and reverse logistics.. Challenges include fragmented infrastructure, high costs, lack of interoperability, organizational resistance, and underdeveloped applications in non-industrial sectors.. Gaps exist in longitudinal studies, stakeholder research, and a unified theoretical framework.
What research method was used?
Systematic Review (PRISMA 2020 guidelines) with 68 articles.
How strong is the evidence?
Evidence strength is rated Strong effect, based on a 2025 journal from American Journal of Scholarly Research and Innovation.
What should I do differently in my next project?
When designing products or services intended for circular economy models, consider how IoT sensors and data analytics platforms can provide actionable insights for resource management, maintenance, and end-of-life processes.
What are the limitations?
The review's findings are based on published literature and may not capture all emerging or proprietary solutions. Gaps in research for specific sectors and longitudinal data were identified.