Sustainable Supply Chain: Data Gaps & ESG
Budget: ₹750 – ₹1,250 INR
Project Title: Circular Supply Chain Data Gaps & TNFD Integration: A Multi-Region Case Study
Deliverable: An academic article
Project Overview: We are seeking a qualified researcher to analyze missing data across supply chain boundaries in relation to circular economy initiatives and ESG frameworks like the TNFD (Taskforce on Nature-related Financial Disclosures). The project aims to uncover data-driven strategies that can make supply chains more circular and sustainable, mitigate nature-related risks, and identify opportunities for AI-enabled process improvements. The project data will be collected through an ongoing study at three different organizations.
Objectives
1. Identify Data Gaps: Map and document the specific data points missing in supply chains (raw materials to production) that hinder circular economy strategies and sustainable inventory management. Build the right kind of interview questions and surveys to identify the data challenge.
2. Identity and Integrate Framework: identify which would be the best ESG framework for this work and Investigate how the ESG framework applies to these data gaps, and propose ways organizations can align with nature-related risk disclosures.
3. Assess AI Opportunities: Explore how AI and advanced analytics can fill identified data gaps, improve traceability, and reduce inventory waste. Future scope for AI.
4. Develop Practical Value Propositions: Provide benchmarking insights and clear value propositions to organizations aiming to improve their circular supply chain and nature disclosure practices.
Deliverables:
Project Plan & Kickoff Presentation – Outline of methodology, timelines, and stakeholder engagement plan.
Literature & Benchmark Report – Summary of key findings from academic/industry sources and leading organizations.
Data Gap Analysis Matrix – Detailed mapping of current vs. missing data across each supply chain tier.
Framework Integration Guide – Explanation of how organizations can embed TNFD-related metrics into existing data flows.
AI Readiness & Value Proposition – Assessment of potential AI solutions, with cost-benefit insights.
Deliverable: An academic article
Project Overview: We are seeking a qualified researcher to analyze missing data across supply chain boundaries in relation to circular economy initiatives and ESG frameworks like the TNFD (Taskforce on Nature-related Financial Disclosures). The project aims to uncover data-driven strategies that can make supply chains more circular and sustainable, mitigate nature-related risks, and identify opportunities for AI-enabled process improvements. The project data will be collected through an ongoing study at three different organizations.
Objectives
1. Identify Data Gaps: Map and document the specific data points missing in supply chains (raw materials to production) that hinder circular economy strategies and sustainable inventory management. Build the right kind of interview questions and surveys to identify the data challenge.
2. Identity and Integrate Framework: identify which would be the best ESG framework for this work and Investigate how the ESG framework applies to these data gaps, and propose ways organizations can align with nature-related risk disclosures.
3. Assess AI Opportunities: Explore how AI and advanced analytics can fill identified data gaps, improve traceability, and reduce inventory waste. Future scope for AI.
4. Develop Practical Value Propositions: Provide benchmarking insights and clear value propositions to organizations aiming to improve their circular supply chain and nature disclosure practices.
Deliverables:
Project Plan & Kickoff Presentation – Outline of methodology, timelines, and stakeholder engagement plan.
Literature & Benchmark Report – Summary of key findings from academic/industry sources and leading organizations.
Data Gap Analysis Matrix – Detailed mapping of current vs. missing data across each supply chain tier.
Framework Integration Guide – Explanation of how organizations can embed TNFD-related metrics into existing data flows.
AI Readiness & Value Proposition – Assessment of potential AI solutions, with cost-benefit insights.
Related categories:
Scientific Research
Research Writing
Business Analysis
Supply Chain
Environmental Consulting