Zhang Wenye, Sergey Barykin
Decision-making in omnichannel logistics networks requires balancing multiple performance dimensions under dynamic and uncertain operating conditions. In practice, key performance indicators (KPIs) are frequently applied for ex-post evaluation, while their direct integration into decision formulation remains limited, leading to a structural separation between performance management and alternative selection. This study proposes a KPI-driven decision framework that embeds multidimensional performance indicators at the modeling stage. Alternatives are represented within a unified multi-criteria structure, where KPI dimensions are normalized and aggregated into a weighted composite index. The decision problem is formulated as the maximization of expected composite performance over discrete alternatives under scenario uncertainty. A simulation-based operationalization is developed to incorporate stochastic variation in key indicators while preserving a consistent comparison logic across scenarios. The resulting structure enables robust ranking and selection of alternatives based on expected multidimensional performance. The study provides an interpretable and computationally reproducible framework for multi-indicator decision support in omnichannel logistics networks.