Measures for auditing performance and integration in closed‐loop supply chains
Bibliographic record
Abstract
Purpose In a growing number of competitive sectors with closed‐loop supply chains, the reverse component has become an inherent part of the business, not to mention a core competence; hence the need to have performance measures that can be used to provide an accurate diagnosis of the state of the supply chain by addressing both its forward and its reverse components. It is also important to identify the level of existing integration between parties, as this has been associated with supply chain performance. This paper seeks to address this issue. Design/methodology/approach Elements gathered from the literature reviewed are used to present a set of measures that can be applied for auditing purposes in: the forward supply chain; product returns and reverse logistics; flows of materials and information and integration between supply chain tiers. To illustrate the use of the proposed set of measures for auditing purposes a case study involving a major European mobile phone network operator was analysed using the operator's own brand of handsets characterised for having a closed‐loop supply chain. Findings The proposed set of measures for auditing purposes provide an overall picture of the performance of a closed‐loop supply chain by revealing high levels of stock for the products analysed, consequence of the difficulty to generate accurate forecasts and the accumulation of high quantities of product prior to launch. Also the methodology presented in this paper identifies links between product returns (faulty and non‐faulty) to operations in the forward component of the supply chain (design, sourcing, manufacturing and forecasting) and also indicates how performance is affected because of integration. Research limitations/implications The proposed set of measures for auditing purposes is relevant to closed‐loop supply chains which are related to products with short life cycles and during their lifetime can experience faulty and non‐faulty returns. The scope of the study presented may look limited; however, the application of the performance measures presented in this research can become a fundamental component of larger audit exercises. Further research should be carried out with supply chains on products with lifetime cycles that span long periods of time. Practical implications For industry sectors with closed‐loop supply chains, the availability of a set of measures that address the forward and reverse components plus integration can provide a detailed picture of the performance of value streams over traditional approaches to measurement that focus on only one component of the supply chain. The set of measures has the potential to be used to achieve better customer service and reduction in costs involving shipping, warehousing, labour and call centres. Originality/value The contribution of this research on closed‐loop supply chains is a methodology that defines performance measures for auditing purposes of the forward and reverse components of supply chains and assists in assessing the importance of integration between different tiers of supply chains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".