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Research Article | | Peer-Reviewed

Technical Contributions of the Push Model to the Health Product Supply Chain in Benin in 2025

Lamidhi Salami*,  Colette Azandjemey,  Charles Patrick Makoutode,  Yolaine Glele Ahanhanzo,  Badirou Aguemon,  Edgard-Marius Ouendo
Published in Central African Journal of Public Health (Volume 12, Issue 3)
Received: 20 April 2026     Accepted: 6 May 2026     Published: 21 May 2026
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Abstract

Background: In Benin, two third of health facilities experienced stockouts for at least one tracer product, revealing persistent weaknesses in supply chain management. To address this, the Couffo department adopted the PUSH model in one health zone while maintaining the PULL model in another. Objective: This study aimed to evaluate the technical contributions of the PUSH model to health product supply chain performance through a comparative analysis of both logistics models. Methods: A descriptive cross-sectional study with an evaluative and analytical purpose was conducted across 32 public health facilities in the Couffo department from September 2024 to February 2025. Data on inputs, processes, and outcomes were collected via observations, interviews, and documentary review, then analysed using the Varkevisser scale and comparative statistical tests (Chi-squared, Fisher's exact, Student's t-test) at the 5% significance threshold. Results: The PUSH model demonstrated higher overall performance (86.13% vs. 71.0%). Stockout frequency was significantly lower (31.48% vs. 68.52%; OR=0.21; p<0.001), delivery lead times shorter (8.2 vs. 20.9 days), and distribution (93.3% vs. 36.1%) and quality assurance scores (100% vs. 66.6%) substantially higher. Conclusion: The PUSH model significantly improves supply chain efficiency. Its gradual extension, supported by appropriate technical and financial assistance, is recommended to sustain universal access to quality health products in Benin.

Published in Central African Journal of Public Health (Volume 12, Issue 3)
DOI 10.11648/j.cajph.20261203.16
Page(s) 173-181
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2026. Published by Science Publishing Group
Previous article
Keywords

PUSH Model, PULL Model, Supply Chain, Logistics Efficiency, Technical Contribution, Benin

1. Introduction
Improving health logistics represents a critical strategic challenge in meeting the increasingly numerous and diverse demands required to ensure the availability and quality of health products
[1]
. The continuous growth in demand for care, demographic change, and advances in medical technology are liable to complicate considerably the management of health product flows
[2-4]
. In view of these challenges, the establishment of a high-performing and responsive supply chain appears essential to guarantee access to care and respond effectively to patients' needs.
In Benin, the health product supply chain is characterised by orders from pharmacy or pharmaceutical dispensaries within health facilities to the Zonal Health Depot Distributors (Dépôts Répartiteurs de Zone Sanitaire, DRZS), which are generally triggered by local consumption data. According to data from the A7 form of the national health information and management system, 68.83% of health facilities experienced a stockout for at least one of the seven tracer products, revealing a persistent weakness in supply planning and logistics governance
[5]
. These supply challenges prompted the country to commit to the adoption of differentiated logistics strategies, including a transition towards the PUSH model for health product procurement in the health zones.
These strategic choices led, in the Couffo department, to the coexistence of two logistics supply models: the continued application of the PULL model in the Aplahoué-Djakotomey-Dogbo (ADD) health zone, and the adoption and implementation of the PUSH model in the Klouékanmè-Toviklin-Lalo (KTL) health zone in accordance with the new national guidelines on health product procurement.
The PULL model is demand-driven: products are delivered solely in response to a specific order formulated locally. In the Beninese context, this system requires health facilities to finance their own resupply by mobilising their working capital. Although this model promotes local autonomy and stock optimisation, it remains constrained by limited forecasting capacity, irregular ordering patterns, and fragmentation of distribution circuits
[8]
. The PUSH model, in contrast, is grounded in consumption forecasts and historical demand data to determine the quantities of stock to be distributed. Products are thus 'pushed' through the logistics chain to reach health facilities according to a predefined schedule. This model brings the source of supply closer to the point of need by anticipating requirements and allows intermediate distribution steps to be simplified by delivering health products directly to the source of demand
[8]
.
To date, few studies have simultaneously examined the technical contributions of both models within an integrated framework permitting assessment of the overall performance of supply systems
[10]
. This gap limits the capacity of decision-makers to guide logistics reforms on the basis of evidence
[9]
. The present study aimed to evaluate the contribution of the PUSH model to the effectiveness of supply chain management through a comparison of the technical aspects of the two logistics systems applied in the Couffo department.
2. Materials and Methods
2.1. Study Setting
The study was conducted in the Couffo department (south-west Benin), covering an area of approximately 2,404 km2 with a population of 971,674 inhabitants (2025), who rely primarily on agricultural, commercial, and craft activities
[11, 12]
.
The department has 65 public health centres and two district hospitals distributed across two health zones, each equipped with a DRZS that obtains its supplies from the SoBAPS regional depot in Cotonou. The DRZSs are responsible for the management and distribution of health products.
In accordance with the national last-mile distribution guidelines, the DRZSs constitute the sole point of entry for health products in each zone. However, the Beninese supply chain is currently undergoing a transitional phase characterised by the coexistence of two logistics supply models, as observed in the Couffo: the KTL zone operates the PUSH model with planned, centralised deliveries from the DRZS to health centres, whilst the ADD zone continues to apply the PULL model whereby each centre formulates its own orders and collects products autonomously and in a decentralised manner. This coexistence provides a relevant analytical framework for a comparative assessment of the contributions of each model. The two zones share comparable characteristics (population size, geographical context, common administrative oversight, identical partners, uniform national policies and logistics frameworks), strengthening the assumption that any observed technical logistics differences are attributable to the supply model adopted.
2.2. Technical Comparison of Management Processes
Table 1 presents a detailed comparison of the two models according to the various stages of the management process.
Table 1. Detailed comparison of PUSH and PULL models according to management process stages.

Process element

PUSH model

PULL model

Planning

Centralised; carried out by the DRZS on the basis of forecasts and historical demand and/or consumption data

Local; initiated by health facilities according to consumption data available in real time

Implementation

Quasi-automatic: allocations are defined in advance and scheduled within the distribution plan

Manual: each facility prepares and submits its orders using available resources and tools

Execution

Products are delivered directly by the DRZS without prior request

Facilities monitor their own orders and travel to collect products

Communication

Unidirectional (top-down): the DRZS informs facilities of forthcoming deliveries

Bidirectional: health centres liaise with the DRZS to clarify, monitor, and adjust

Evaluation

Managed centrally on the basis of global indicators

Depends on the local capacity to produce and analyse reports

Control

Quality control integrated into the logistics process (per-delivery control, audits, monitoring of lead times and compliance)

Variable; primarily self-monitoring by the facility itself, limited to verification of delivery notes and stock inventories
2.3. Study Participants and Eligibility Criteria
The study was conducted in public health facilities and amongst stakeholders involved in pharmaceutical logistics management across the two health zones of the Couffo: Aplahoué-Djakotomey-Dogbo (ADD) and Klouékanmè-Toviklin-Lalo (KTL). The entities observed comprised operational public health facilities in both zones, the DRZSs, and those directly involved in logistics activities, including DRZS managers, health centre directors, and staff responsible for stock management and health product procurement.
Inclusion criteria
Health facilities (health centres, depot distributors) that had been managing health inputs for a minimum of six months were eligible, as were managers or individuals responsible for health product management in health facilities within the Couffo department's health zones, who had been in post for at least six months and were available during the study period.
Exclusion criteria
Facilities established less than one year before the study or that had experienced an interruption in functioning of three months or more during the preceding twelve months were excluded, as were any manager or individual responsible for health product management who had not provided free and informed consent.
2.4. Study Design
This was a descriptive cross-sectional study with an evaluative and analytical purpose, using a controlled before-and-after comparison of the intervention zone (KTL) applying the PUSH model versus the control zone (ADD) applying the PULL model. The observation period spanned six months, from September 2024 to February 2025. The study employed a probabilistic method, with simple random sampling of approximately 50% of the 65 public health facilities in the department, yielding 34 health facilities. This number was then distributed equally between the two health zones concerned. An exhaustive approach was applied to the selection of zone depot distributors (n = 2), zone logisticians (n = 2), health centre managers (n = 34), and stock managers at health centre level.
2.5. Components and Data Collection Tools
The effectiveness of the supply chain was assessed across three components: inputs (comprising the sub-components of human, material, financial, and informational resources), processes (procurement, storage, distribution, and quality assurance), and outcomes (health product availability, order integrity and compliance, stakeholder and community satisfaction, reduction in logistics costs, and reduction in losses).
Data were collected through a documentary review of logistics and financial management tools available in health facilities. A standardised data collection grid was used to extract cost-related information from purchase orders, stock cards, reception and distribution reports, and financial registers covering the period September 2024 to February 2025.
2.6. Data Processing and Analysis
Collected data were entered and cleaned using KoboCollect software, then analysed using Stata version 14. Comparative tests (Chi-squared, Fisher's exact test, Student's t-test) were performed at a 5% significance threshold. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated for categorical variables, and regression coefficients (β) for continuous variables.
Each component was assessed against specific criteria with scores attributed (1 for criterion present; 0 for criterion absent), with major criteria weighted. The total score for each component was expressed as a percentage of the expected score and interpreted according to the Varkevisser scale: <60% = Very poor; 60–79% = Poor; 80–89% = Good; ≥90% = Very good. Results were expressed as means, standard deviations, and ratios for the comparison of PUSH and PULL models.
2.7. Ethical Considerations
The study was conducted following validation by a panel of scientific and ethical experts mandated by the Regional Institute of Public Health. Administrative authorisations were subsequently obtained from the Ministry of Health and the departmental directorate of the Couffo. Each respondent was enrolled following provision of free and informed consent. Anonymity and confidentiality were guaranteed, and only aggregated data are made publicly available. No conflicts of interest are associated with this manuscript.
3. Results
3.1. General Characteristics of the Sample and Operational Context
The study involved 32 health centres distributed equally across the two health zones of the Couffo department. The two depot distributors of these zones, their zone logisticians, and the health centre managers (n = 34) were also surveyed.
3.2. Inputs
3.2.1. Resources
Significant logistics disparities existed between KTL (PUSH) and ADD (PULL). The KTL health zone had more rolling stock (47.1% versus 5.9%), more functional cold-storage refrigerators (94.1% versus 88.2%), more generators (29.4% versus 17.6%), and more compliant storage infrastructure (100% versus 76.5%).
Both zones achieved 100% compliance with standards and guidelines and were fully integrated into the e-SIGL system; however, the PUSH model ensured more systematic utilisation through centralised planning, supervision, and reporting, in contrast to the decentralised use characteristic of the PULL model.
Financial resources were deemed sufficient in 76.5% of KTL facilities compared with 29.4% of ADD facilities.
3.2.2. Availability and Stockouts
Table 2 presents comparative results for the main logistics performance indicators. The PUSH model (KTL) was associated with a significantly lower stockout frequency than the PULL model (ADD) (31.48% versus 68.52%; OR = 0.21; p < 0.001). It also showed greater product availability (58.33% versus 41.67%; OR = 2.00; p = 0.002). Finally, the mean stockout duration was markedly shorter in KTL facilities (2.1 days versus 11.7 days; β = −9.61; p = 0.039).
Table 2. Paired comparison of performance indicators related to health product availability and stockouts in health zones in 2025.

Indicator

ADD (PULL)

KTL (PUSH)

Statistical value

p-value

Stockout at patient visit (n = 55 patients/zone)

37/55 (68.52%)

17/55 (31.48%)

OR = 0.21 [0.10–0.45]

0.000

Product availability (n = 55 patients/zone)

35/55 (41.67%)

49/55 (58.33%)

OR = 2.00 [1.30–3.50]

0.002

Mean stockout duration (n = 17 facilities/zone)

11.71 days (SD: 4.15)

2.1 days (SD: 1.67)

β = −9.61 [−11.94 to −7.28]

0.039
3.3. Processes
3.3.1. Quality of the Procurement Process
This was assessed through delivery lead times, delivery quality, and average supply indicators. Compared with the PULL model (ADD), the PUSH model (KTL) exhibited shorter delivery lead times (8.23 ± 1.34 days versus 20.88 ± 1.59 days; β = −12.65; p = 0.001). The proportion of facilities reporting a reduction in losses was higher in KTL (56.52% versus 43.47%).
KTL facilities placed significantly more orders per month (2.81 ± 0.14 versus 1.77 ± 0.15; p = 0.0000), with no statistically significant difference in mean order cost (p = 0.71) or mean distance travelled (p = 0.964).
Table 3. Assessment of delivery times, delivery quality, and average supply indicators for health products in health zones in 2025.

Indicator

ADD (PULL)

KTL (PUSH)

Statistical value

p-value

Delivery times and quality

Mean delivery lead time (n = 17 facilities/zone)

20.88 days ± 1.59

8.23 days ± 1.34

β = −12.65 [−16.90 to −8.39]

0.001

Reduction in reported losses (n = 17 facilities/zone)

43.47%

56.52%

OR = 2.28 [0.52–9.99]

0.000

Average supply indicators

Mean number of orders per health centre/month

1.77 ± 0.15

2.81 ± 0.14

–

0.000

Mean order cost per health centre/month

1,364,871 FCFA ± 364,669.8

1,506,264 FCFA ± 106,452.5

–

0.71

Mean distance travelled/month

10.74 km ± 0.95

10.7 km ± 0.63

–

0.964
3.3.2. Detailed Analysis of Processes
The PUSH model (KTL) showed higher overall performance than the PULL model (ADD) across all logistics processes. Differences were particularly pronounced for distribution processes (overall score: 93.27% versus 36.13%) and quality assurance (100% versus 66.6%). Procurement and storage processes were broadly satisfactory under both models, but remained more effective in KTL, particularly with regard to adherence to order lead times, application of standard operating procedures, and warehouse organisation.
Table 4. Technical aspects of processes applied under the PUSH and PULL models in 2025.

Indicator

ADD (PULL)

KTL (PUSH)

Procurement process

Planned orders

16/17 (94.1%)

17/17 (100%)

Orders submitted to correct supplier

17/17 (100%)

17/17 (100%)

Orders submitted on time

12/17 (70.6%)

17/17 (100%)

Procurement process compliance

17/17 (100%)

17/17 (100%)

Availability of tracer products

17/17 (100%)

17/17 (100%)

Single supply circuit

17/17 (100%)

17/17 (100%)

Overall procurement score

83.95%

98.4%

Storage process

Twice-daily temperature recording of medicines

2/17 (11.8%)

3/17 (17.6%)

Functional air-conditioning unit available

1/17 (5.9%)

2/17 (11.8%)

Controlled access to depot

16/17 (94.1%)

17/17 (100%)

Stock rotation (FIFO/FEFO)

17/17 (100%)

17/17 (100%)

Storage organised in accordance with standards

14/17 (82.4%)

17/17 (100%)

Warehouse cleanliness

17/17 (100%)

17/17 (100%)

Application of SOPs for storage

10/17 (58.8%)

17/17 (100%)

Overall storage score

78.57%

82.77%

Distribution process

Distribution plan in place

3/17 (17.6%)

16/17 (94.1%)

Products delivered to correct recipients

17/17 (100%)

17/17 (100%)

Adequate transport conditions

1/17 (5.9%)

15/17 (88.2%)

Overall distribution score

36.13%

93.27%

Quality assurance process

Audits/quality checks conducted

0/17 (0%)

17/17 (100%)

Application of quality SOPs

17/17 (100%)

17/17 (100%)

Inventory verification

17/17 (100%)

17/17 (100%)

Overall quality assurance score

66.6%

100%
3.4. Detailed Structure of Outcomes
3.4.1. Stakeholder Satisfaction and Perception
Stakeholder satisfaction differed significantly between the two models. In the KTL zone (PUSH), 12/17 managers reported satisfaction with logistics functioning, compared with 6/17 in ADD (PULL) (OR = 4.40; 95% CI [1.04–18.60]; p = 0.044). Interviews with stakeholders revealed that although challenges persisted in both zones, particularly regarding staffing, PUSH model stakeholders more frequently raised issues related to internal monitoring and management, whilst PULL model stakeholders cited systemic dysfunctions linked to delays, stockouts, and mismatches between orders and deliveries:
"Deliveries are often delayed; this creates problems for us. We cannot plan our care properly. (Physician, ADD health zone)"
"We lack staff to manage products effectively. (Nurse, ADD health zone)"
"The quantity of medicines requested is not always available; one must often adapt, which is not easy. (Nurse, ADD health zone)"
"We do not have enough time to conduct proper inventory follow-up. (Physician, KTL health zone)"
3.4.2. Reduction in Losses and Waste
The PUSH model was associated with better preservation of product integrity. The proportion of facilities reporting a reduction in losses was 13/17 in KTL compared with 7/17 in ADD.
3.4.3. Overall Component Performance
The PUSH model (KTL) demonstrated higher overall performance than the PULL model (ADD) (86.13% versus 71.0%, a difference of +15.13 percentage points), with particularly pronounced differences at the level of processes (+22.38) and outcomes (+24.44). The largest gains were observed in distribution (+57.14), quality assurance (+33.4), and cost reduction (+47.05). Inputs showed more moderate differences (+5.61), driven primarily by financial (+16.83) and material resources (+6.45), whilst human and informational resources remained broadly comparable across both zones.
Table 5. Overall performance of components for assessing the technical contribution of PUSH and PULL models in the two health zones in 2025.

Components / Sub-components

ADD zone (PULL)%

KTL zone (PUSH)%

Difference

INPUTS

Human resources

69.9%

69.28%

−0.62

Material resources

67.50%

73.95%

+6.45

Financial resources

76.47%

93.30%

+16.83

Informational resources

100%

100%

0

Inputs score

74.60%

80.21%

+5.61

PROCESSES

Procurement

83.95%

98.4%

+14.45

Storage

78.57%

82.77%

+4.2

Distribution

36.13%

93.27%

+57.14

Quality assurance

66.6%

100%

+33.4

Processes score

70.16%

92.54%

+22.38

OUTCOMES

Product availability

100%

100%

0

Order integrity

100%

100%

0

Stakeholder satisfaction

35.3%

70.6%

+35.3

Cost reduction

35.3%

82.35%

+47.05

Reduction in losses

41.2%

76.47%

+35.27

Outcomes score

61.53%

85.97%

+24.44

OVERALL PERFORMANCE

71%

86.13%

+15.13
4. Discussion
4.1. Technical Contributions of the PUSH and PULL Models to Processes
The results demonstrated the superiority of the PUSH model for the processes component (92.54% versus 70.16%). This difference may be explained by several technical mechanisms. The centralised planning inherent in the PUSH model enables vertical coordination of deliveries and visibility over product flows, reducing the risk of delays or errors. Unlike the PULL model, in which each facility manually prepares its own orders, the PUSH model defines allocations in advance within an automated distribution plan. This centralisation translates into 100% compliance with procurement procedures in the KTL zone, alongside markedly superior performance in distribution (93.27% for KTL versus 36.13% for ADD). These findings are consistent with studies on the effectiveness of the PUSH model in vaccine supply chains, demonstrating better product availability, greater procedural compliance, and reduced stockouts attributable to centralised planning and flow visibility
[1, 6, 7]
.
Storage also benefited from the PUSH model: 94.1% of KTL facilities adhered to storage standards compared with 64.7% in ADD. This difference may be explained by the availability of appropriate infrastructure (100% in KTL versus 76.5% in ADD) and regular stock monitoring, which was carried out in 88.2% of KTL facilities compared with 58.8% in ADD. The centralised supervision characteristic of the PUSH model facilitates more frequent checks and harmonised planning. This aligns with findings from studies demonstrating that the PUSH model improves compliance with storage standards and vaccine cold-chain quality through centralised monitoring and better-adapted infrastructure
[1]
.
Quality assurance revealed the most significant disparity: 100% in KTL versus 66.6% in ADD. The PUSH model integrates quality control into the logistics process through per-delivery checks, audits, and monitoring of lead times and compliance. By contrast, the PULL model relies on variable self-monitoring conducted by the facilities themselves, limited to verification of delivery notes. This superiority of the PUSH model is corroborated by work demonstrating that centralised supervision and systematic control mechanisms improve the reliability and compliance of vaccine supply chains
[3, 6]
.
These results should, however, be interpreted with caution. The high performance of the PUSH model observed in this study is closely linked to the existence of supervision, quality control, and regular monitoring of logistics indicators. In the absence of such mechanisms, centralisation could instead generate rigidity or mismatches between supply and local needs, as has been highlighted in evaluations of centralised systems poorly adapted to specific contexts
[9, 10]
.
4.2. Impact on Availability and Stockouts
The reduction in mean delivery lead time in the KTL zone (β = −12.65 days) was accompanied by a significant improvement in product availability (OR = 2.00). The PUSH model organises deliveries directly to health facilities according to a predefined schedule, eliminating the need for centres to place orders or travel to collect products. This efficiency is confirmed by several studies demonstrating that centralised planning and scheduled deliveries improve the availability of medicines and vaccines whilst reducing lead times and stockouts
[6, 13]
.
The frequency of stockouts at patient visits fell from 68.52% in the PULL zone to 31.48% in the PUSH zone (OR = 0.21), representing a reduction of more than half. Mean stockout duration was reduced from 11.71 days to 2.1 days. These results are consistent with the experience in Senegal, where the PUSH model reduced contraceptive stockouts to below 2%
[14]
.
4.3. Study Limitations
This study does not permit conclusions to be drawn regarding the superiority of the PUSH model across all dimensions of logistics performance. For certain indicators, notably costs related to stockouts and time spent on logistics activities, the absence of repeated measurements precluded inferential statistical analysis, thereby limiting the scope of the conclusions.
5. Conclusions
The PUSH model significantly improves the efficiency of the health product supply chain in the Couffo department. The observed gains are reflected in greater product availability, a reduction in stockouts, shorter delivery lead times, and a substantial decrease in logistics costs. These results support the case for a progressive extension of the PUSH model to other health zones in Benin, whilst highlighting the need for appropriate technical and financial support to ensure its sustainability.
Abbreviations

ADD

Aplahoue-Djakotomey-Dogbo

DRZS

Zonal Health Depot Distributors

FCFA

Franc de la Communauté Financière Africaine [Central African CFA Franc]

FEFO

First Expired First Out

FIFO

First In First Out

KTL

Klouekanme-Toviklin-Lalo

OR

Odds Ratio

PULL

Demand-driven Supply Model

PUSH

Forecast-driven Supply Model

SoBAPS

Beninese Health Product Supply Company

SOP

Standard Operating Procedure
Acknowledgments
The authors express their gratitude to the team at the IRSP, the staff of the Couffo departmental health directorate, the members of the KTL and ADD zone supervisory teams, and the personnel and users who contributed to the conduct of the study.
Author Contributions
Lamidhi Salami: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing
Colette Azandjemey: Conceptualization, Formal Analysis, Methodology, Supervision, Validation, Visualization, Writing – review & editing
Charles Patrick Makoutode: Conceptualization, Formal Analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing
Yolaine Glele Ahanhanzo: Data curation, Formal Analysis, Investigation, Methodology, Software, Visualization, Writing – original draft
Badirou Aguemon: Conceptualization, Formal Analysis, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – review & editing
Edgard-Marius Ouendo: Conceptualization, Formal Analysis, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing
Data Availability Statement
The data supporting the outcome of this research work has been reported in this manuscript.
Conflicts of Interest
The authors declare no conflicts of interest.
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    Salami, L., Azandjemey, C., Makoutode, C. P., Ahanhanzo, Y. G., Aguemon, B., et al. (2026). Technical Contributions of the Push Model to the Health Product Supply Chain in Benin in 2025. Central African Journal of Public Health, 12(3), 173-181. https://doi.org/10.11648/j.cajph.20261203.16

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    ACS Style

    Salami, L.; Azandjemey, C.; Makoutode, C. P.; Ahanhanzo, Y. G.; Aguemon, B., et al. Technical Contributions of the Push Model to the Health Product Supply Chain in Benin in 2025. Cent. Afr. J. Public Health 2026, 12(3), 173-181. doi: 10.11648/j.cajph.20261203.16

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    AMA Style

    Salami L, Azandjemey C, Makoutode CP, Ahanhanzo YG, Aguemon B, et al. Technical Contributions of the Push Model to the Health Product Supply Chain in Benin in 2025. Cent Afr J Public Health. 2026;12(3):173-181. doi: 10.11648/j.cajph.20261203.16

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  • @article{10.11648/j.cajph.20261203.16,
  author = {Lamidhi Salami and Colette Azandjemey and Charles Patrick Makoutode and Yolaine Glele Ahanhanzo and Badirou Aguemon and Edgard-Marius Ouendo},
  title = {Technical Contributions of the Push Model to the Health Product Supply Chain in Benin in 2025},
  journal = {Central African Journal of Public Health},
  volume = {12},
  number = {3},
  pages = {173-181},
  doi = {10.11648/j.cajph.20261203.16},
  url = {https://doi.org/10.11648/j.cajph.20261203.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cajph.20261203.16},
  abstract = {Background: In Benin, two third of health facilities experienced stockouts for at least one tracer product, revealing persistent weaknesses in supply chain management. To address this, the Couffo department adopted the PUSH model in one health zone while maintaining the PULL model in another. Objective: This study aimed to evaluate the technical contributions of the PUSH model to health product supply chain performance through a comparative analysis of both logistics models. Methods: A descriptive cross-sectional study with an evaluative and analytical purpose was conducted across 32 public health facilities in the Couffo department from September 2024 to February 2025. Data on inputs, processes, and outcomes were collected via observations, interviews, and documentary review, then analysed using the Varkevisser scale and comparative statistical tests (Chi-squared, Fisher's exact, Student's t-test) at the 5% significance threshold. Results: The PUSH model demonstrated higher overall performance (86.13% vs. 71.0%). Stockout frequency was significantly lower (31.48% vs. 68.52%; OR=0.21; p<0.001), delivery lead times shorter (8.2 vs. 20.9 days), and distribution (93.3% vs. 36.1%) and quality assurance scores (100% vs. 66.6%) substantially higher. Conclusion: The PUSH model significantly improves supply chain efficiency. Its gradual extension, supported by appropriate technical and financial assistance, is recommended to sustain universal access to quality health products in Benin.},
 year = {2026}
}

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  • TY  - JOUR
T1  - Technical Contributions of the Push Model to the Health Product Supply Chain in Benin in 2025
AU  - Lamidhi Salami
AU  - Colette Azandjemey
AU  - Charles Patrick Makoutode
AU  - Yolaine Glele Ahanhanzo
AU  - Badirou Aguemon
AU  - Edgard-Marius Ouendo
Y1  - 2026/05/21
PY  - 2026
N1  - https://doi.org/10.11648/j.cajph.20261203.16
DO  - 10.11648/j.cajph.20261203.16
T2  - Central African Journal of Public Health
JF  - Central African Journal of Public Health
JO  - Central African Journal of Public Health
SP  - 173
EP  - 181
PB  - Science Publishing Group
SN  - 2575-5781
UR  - https://doi.org/10.11648/j.cajph.20261203.16
AB  - Background: In Benin, two third of health facilities experienced stockouts for at least one tracer product, revealing persistent weaknesses in supply chain management. To address this, the Couffo department adopted the PUSH model in one health zone while maintaining the PULL model in another. Objective: This study aimed to evaluate the technical contributions of the PUSH model to health product supply chain performance through a comparative analysis of both logistics models. Methods: A descriptive cross-sectional study with an evaluative and analytical purpose was conducted across 32 public health facilities in the Couffo department from September 2024 to February 2025. Data on inputs, processes, and outcomes were collected via observations, interviews, and documentary review, then analysed using the Varkevisser scale and comparative statistical tests (Chi-squared, Fisher's exact, Student's t-test) at the 5% significance threshold. Results: The PUSH model demonstrated higher overall performance (86.13% vs. 71.0%). Stockout frequency was significantly lower (31.48% vs. 68.52%; OR=0.21; p<0.001), delivery lead times shorter (8.2 vs. 20.9 days), and distribution (93.3% vs. 36.1%) and quality assurance scores (100% vs. 66.6%) substantially higher. Conclusion: The PUSH model significantly improves supply chain efficiency. Its gradual extension, supported by appropriate technical and financial assistance, is recommended to sustain universal access to quality health products in Benin.
VL  - 12
IS  - 3
ER  -

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Author Information

  • Lamidhi Salami

    Regional Institute of Public Health, University of Abomey-Calavi, Ouidah, Benin

    Biography: Lamidhi Salami is a public health and health economics researcher and academic at the Regional Institute of Public Health (IRSP), University of Abomey-Calavi, Benin. His research focuses on health systems performance, health economics, health policy and system, vaccination programme management, and supply chain logistics in sub-Saharan Africa.

    Research Fields: Public health evaluation, health economics, health policy, vaccine supply chain management, health systems performance, logistics information systems, immunisation programme assessment.

    Contact Email

    http://orcid.org/0000-0002-3261-3173

  • Colette Azandjemey

    Regional Institute of Public Health, University of Abomey-Calavi, Ouidah, Benin

    Research Fields: Nutrition and Public Health, health system, health promotion, nutrition epidemiology.

    http://orcid.org/0000-0002-1606-7002

  • Charles Patrick Makoutode

    Regional Institute of Public Health, University of Abomey-Calavi, Ouidah, Benin

    Biography: Charles Patrick Makoutode is a Senior Lecturer in Health Systems and Environment at the Regional Institute of Public Health (IRSP-CAQ), University of Abomey-Calavi, Benin, where he heads the Department of Health Policies and Systems. His research addresses health policy and systems health, environmental management, health economics and financing, universal health coverage.

    Research Fields: health policy and systems health, environmental management, health economics and financing, universal health coverage.

    http://orcid.org/0009-0004-1585-2553

  • Yolaine Glele Ahanhanzo

    Regional Institute of Public Health, University of Abomey-Calavi, Ouidah, Benin

    Biography: Yolaine Glele Ahanhanzo is a Professor in the Department of Epidemiology and Biostatistics and acting Director at the Regional Institute of Public Health (IRSP-CAQ), University of Abomey-Calavi, Benin. Her methodological expertise spans epidemiological survey design, biostatistics, health information systems quality, and cohort study coordination, health system.

    Research Fields: epidemiological survey design, biostatistics, health system, health information systems quality, and cohort study coordination.

    http://orcid.org/0000-0003-3378-4844

  • Badirou Aguemon

    Faculty of Health Sciences, University of Abomey-Calavi, Cotonou, Benin

    Biography: Badirou Aguemon is a Professor in Public Health at the Faculty of Health Sciences (FSS), University of Abomey-Calavi, Cotonou, Benin. His research focuses on infectious disease epidemiology, malaria prevention and control, community health, and health system strengthening in Benin.

    Research Fields: infectious disease epidemiology, malaria prevention and control, community health, and health system strengthening.

  • Edgard-Marius Ouendo

    Regional Institute of Public Health, University of Abomey-Calavi, Ouidah, Benin

    Biography: Edgard-Marius Ouendo is a full professor and former director at the Regional Institute of Public Health (IRSP), University of Abomey-Calavi, Benin. His research focuses on health systems performance, health policy, and global health in West Africa. His research focuses on epidemiology, public health policy evaluation, and health logistics.

    Research Fields: systems performance, health policy, and global health, public health policy evaluation, and health logistics, epidemiology.

    http://orcid.org/0000-0003-3832-939X

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    1. 1. Introduction
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