Developing a Circular Economy-Based Operational Efficiency Model for the Retail Sector: An Integrated PESTEL and Value Stream Mapping Approach
Bibliographic record
Abstract
The Indonesian retail sector's dominant linear economic model leads to high operational waste, cost inefficiency, and low competitiveness, making the transition to a circular economy (CE) a strategic urgency for resource optimization.However, academic studies on CE implementation in Indonesian retail are limited, particularly those integrating external factor analysis and systemic operational value stream mapping (VSM).This research aims to develop a model for optimizing retail operational efficiency based on CE principles by integrating the PESTEL framework and the VSM method.A qualitative approach was employed, using onsite field visits, interviews, and field observations at four retail entities across Jakarta, Bandung, Bali, and Padang.Current waste management is fragmented and linear, hindered by technological limitations, a lack of sectoral regulation, low consumer awareness, and supplier dependency.VSM analysis identified key inefficiencies, including overstock, delayed detection of expiring products, and limited reverse logistics.The proposed future-state model emphasizes digital integration, multi-actor collaboration, demand-data-driven planning, and proactive reverse logistics schemes.This model offers the potential to significantly reduce waste and carbon emissions, enhance cost efficiency, and strengthen industrial competitiveness.The research contributes an integrative PESTEL-VSM analysis framework and provides relevant implementation strategies for Indonesian industrial policy and practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".