Community-Driven Energy Service Data Collection and Six Sigma-Based Transparency Framework for Power Governance

Background of the Study

Power governance plays a crucial role in the economic development, social stability, and environmental sustainability of any nation. Electricity is not only a basic utility but also a backbone of industrial growth, healthcare, education, and daily life activities. However, despite technological advancements in power generation and distribution systems, transparency and accountability in power governance remain major global challenges. Traditionally, power governance systems operate through centralized administrative structures where decisions are made by authorities with limited participation from end users or communities. Although this model allows structured management, it often leads to issues such as lack of transparency, delayed response to service failures, inequitable service distribution, and limited accountability. Citizens, who are the ultimate beneficiaries of power services, frequently experience service grievance redressal. Interruptions, billing inaccuracies, voltage fluctuations, and delayed In recent years, digital transformation and data-driven governance models have gained importance. Governments and regulatory bodies are increasingly recognizing the need to incorporate citizen feedback into governance systems. Community-driven data collection has emerged as a promising approach to improve service transparency and trust. By collecting real-time service data directly from consumers, authorities can better understand actual performance levels of power services.

Related Work

Power governance and energy service transparency have become critical research areas due to increasing energy demand, sustainability concerns, and citizen awareness. Researchers have explored multiple frameworks combining sustainability evaluation, quality management systems, and statistical modeling techniques to improve governance efficiency. However, there remains a gap in integrating community-driven data with structured improvement methodologies in power governance

In the Existing work, top-down, organization-centric-based means of power governance, and sustainability assessment is mostly employed, as nominated in previous Lean Six Sigma and sustainability studies. The information that is employed to assess the energy and power services is primarily gathered via organizational surveys, managerial reports, and regulatory audits, which provide only a small amount of transparency and little community engagement. The Lean, Six Sigma and Lean Six Sigma (LSS) techniques are used to enhance operational efficiency, cost-reduction, and defect-minimization, and economic performance indicators are significantly stressed. Even though analytical techniques like PLS-SEM are applied in determining the impact of sustainability, aspects of social transparency and social accountability are usually underrepresented. Moreover, the governance assessments are not usually done in relation to real-time community feedback, which leads to ineffective feedback loops and low responsiveness in the power governance decisions. Consequently, the current system does not have a unified system that will integrate the citizen level of insights, which will decrease the level of transparency, trust, or inclusiveness in the governance of the power services.

In the Proposed work, it is Community-Driven Energy Service Data collection and Six Sigma-based Transparency Framework of Power Governance to overcome the drawbacks of traditional models of governance. The framework in comparison to the current methods actively takes into account community-sourced information to make the power services more transparent, accountable, and trustworthy. The systematically-defined governance objectives, real-time power services performance, transparency gap analysis, targeted improvements implementation, and continuous monitoring control mechanisms were incorporated in the system by integrating Lean Six Sigma (LSS) concepts with the DMAIC approach. In order to prove the outcomes of governance quantitatively, PLS-SEM is used to measure the complicated relationships among the Six Sigma constructs and transparency indicators within economic, social, and environmental sustainability pillars. The suggested framework establishes a dynamic feedback process between communities and authority of powers, creating the possibility of making data-driven decisions on governance. This would improve the efficiency of the services, equity, reliability, and transparency, which will offer a sustainable and transparent power governance solution that is scalable and evidence-based.

RESEARCH METHODOLOGY

The research methodology describes the systematic approach adopted to design

and develop the proposed transparency framework.

The methodology integrates:

• Community-driven data collection
• Lean Six Sigma principlesSRS Community-Driven Energy Service Data Collection and Six Sigma-BasedTransparency Framework for Power Governance
• DMAIC improvement cycle
• PLS-SEM statistical modeling
• Sustainability-based performance evaluation

The proposed work follows a layered architecture model.

Layer 1: Community Interface Layer

• Collects real-time feedback from electricity consumers.
• Allows submission of service complaints and performance ratings.
• Ensures user authentication and validation.

Layer 2: Data Collection & Validation Layer

• Stores community-generated data.
• Filters duplicate or invalid entries.
• Performs initial quality checks.

Layer 3: DMAIC Processing LayerSRS Community-Driven Energy Service Data Collection and Six Sigma-Based Transparency Framework for Power Governance 20

1. Define – Identify governance transparency goals.

2. Measure – Collect service performance indicators.

3. Analyze – Detect transparency gaps using statistical tools.

4. Improve – Implement targeted governance improvements.

5. Control – Monitor long-term sustainability.

Layer 4: PLS-SEM Analysis Layer

• Models relationships between transparency indicators and sustainability
• outcomes.
• Identifies significant governance constructs.
• Evaluates impact across economic, social, and environmental dimensions.

Layer 5: Governance Decision Support Layer

• Generates reports.
• Provides policy recommendations.
• Supports regulatory authorities.

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