Systems Thinking for Supply Chains: Identifying Bottlenecks Using Process Mapping of a Child Health Intervention in the Democratic Republic of the Congo (DRC)
Bibliographic record
Abstract
The quality of supply chains in public health interventions in low- and middle-income countries can determine how effectively a program is able to treat its intended population group and subsequently achieve its health targets. We aimed to disentangle where challenges exist hierarchically and administratively through the application of process mapping to the supply chain of an integrated community case management (iCCM) intervention in the Democratic Republic of the Congo (DRC). We conducted a document review, semi-structured key informant interviews, and focus group discussions with program agents involved in supply chain processes of the child health intervention. Enterprise architecture was used to map the intervention’s supply chain and its participatory actors, and detailed bottlenecks of the chain through the application of a health systems framework. The results of this study will be used to inform a system dynamics model of the supply chain of iCCM in DRC. The greatest bottlenecks leading to stockouts at the community level occurred upstream (from national to province and from zone to health facility). While the use of local procurement processes was partially attempted to strengthen systems, parallel supply chain activities compromised sustainable system integration and development. Initial delays in stock dispensation were due to international procurement at the supplier, inducing a trickle-down effect. Inadequate quantification of supply needs and subsequent insufficient product procuration were the single most important steps that led to stockouts. This study demonstrated that the community health supply chain would be most impacted by improvements made in processes at the highest administrative strata, while exposing its delicate dependence on activities at the lowest levels. Visibility of inventory at all levels and improved data quality and use through a transparent tracking system have the potential to significantly reduce stockouts. Future interventions should take care to not develop parallel processes or exclude local health system agents to avoid disruption and ensure sustainable health outcome gains. Causal loop studies and system dynamics can further identify the systems interactions and relationships and their underlying causal mechanisms in need of intervention.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".