Counting the Cost of Supply Errors: an Analysis of Product Fulfilment Failures in Public Healthcare
Year: 2026
Type: article
Abstract: Background: Incomplete product fulfilment is a persistent operational challenge in pharmaceutical supply chains, producing discrepancies between ordered and supplied commodities. These failures including under-supply, over-supply, wrong commodity delivery, and invoice inconsistencies impose direct and indirect economic costs on health systems and, where they result in stock-outs, carry consequences that extend to patient access and health equity. Objective: To assess the economic implications of incomplete product fulfilment in a public pharmaceutical supply chain using routine supply error records. Methods: A retrospective descriptive analysis was conducted using supply error records documented between December 2025 and May 2026 at the Plateau State Drugs and Medical Commodities Management Agency (PS-DMCMA), Jos, Nigeria. Data were extracted from a centralised, institutionally maintained supply error tracker and analysed for frequency, error typology, and monetary value of fulfilment discrepancies at transfer price. Results: More than 21 discrete supply error incidents were documented across at least nine health facilities. Under-supply was the predominant error type, with a cumulative transfer price value of ₦2,945,457.60, accounting for approximately 91% of the combined discrepancy total of ₦3,235,407.10. The highest monthly under-supply value was recorded in January 2026 (₦1,132,203.10). Primary documented causes were pick-and-pack errors, confirmation errors, and loading errors. Conclusion: Supply fulfilment errors impose substantial and quantifiable economic costs on a public pharmaceutical supply chain. These costs are compounded by indirect burdens including administrative workload, emergency redistribution, and downstream stockout effects that are not captured in transfer price valuations. Dual-verification protocols, digital proof-of-delivery, and structured staff training are recommended as priority interventions.
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7224008905