Artificial Intelligence replication of Community Clinic Oral Rehydration Solution Inventory Allocation Under Unmeasured Sachet Spoilage During Monsoon Storage: A Healthcare Robustness Evaluation

Community clinic oral rehydration solution (ORS) inventory allocation is routinely planned as if sachet spoilage during monsoon storage were negligible, yet spoilage is rarely measured and therefore cannot enter the allocation model directly. the present analysis treats unmeasured spoilage as a bounded nuisance parameter and asks how robust standard allocation rules are to it. A replication framework is specified in which spoilage enters as a multiplicative loss on the effective stock available at the point of dispensing, and three allocation rules (proportional, threshold-triggered, and buffer-stock) are evaluated against a robustness criterion defined as the worst-case coverage shortfall over an admissible spoilage set. The framework is formalised as a robust optimisation problem, and a tractable reformulation is derived for the case in which spoilage is bounded but its distribution is unknown. The analysis is methodological and theoretical: no field data are used, and the contribution is a formal characterisation of when unmeasured spoilage changes the ranking of allocation rules, together with a robustness bound that can be computed from the spoilage bound alone. The buffer-stock rule dominates the proportional rule whenever the spoilage bound exceeds a threshold that depends only on the ratio of clinic demand to central stock, and that this threshold is independent of the demand distribution. The implication is that robustness to unmeasured spoilage is achievable without measuring spoilage, provided the bound is known.