Planning improvements sometimes add cost without clear benefit
When Does an Added Planning Capability Create Operational Value? An Observable Evidence Standard
Artificial Intelligence
Summary
It is hard to know which new features in AI planning systems actually help operations and which just add complexity. The authors offer a way to tell if a new planning feature truly changes what happens and if that change is helpful or costly. They tested their method using real data from retail and supply chains and found that simply changing plans doesn’t always lead to better outcomes. Their approach helps organizations decide if new features are worth the ongoing effort to use and maintain.
What this means in practice
- •For supply chain planners: Assess the real impact of added planning features on executed supply plans and operational costs before deployment.
- •For retail operations teams: Decide which AI-driven planning capabilities to maintain based on observable changes in plan execution and cost-effectiveness under varying conditions.
Authors
Yihua Xu, Yan Lyu, Jize Li, Jiani He, You Lin, Yidan Hu, Rubing Qiang
Abstract
Industrial artificial intelligence (AI) planning systems combine predictive models, optimization logic, controls, and governance rules, but improvement in the complete policy does not show whether one addition made an independent contribution or whether that contribution justifies its burden. We address this attribution problem with an observable evidence standard that separates opportunity to act, executed-plan change, operational consequence, equivalence, uncertainty, and operating scope. Using forecast-to-plan replay on Favorita, M5, and Walmart data, we apply the standard to heterogeneous planning additions. Three limits of single-indicator evaluation emerge. First, a composite policy can conceal negligible component contribution: one control changed only 15 of 681 executed plans. Second, changing execution does not guarantee stable benefit: the evaluated multi-period capability helped in several long-lead settings but was 7.6% more costly under tight capacity. Third, indirect evidence can prioritize review but cannot complete attribution. Independent runs produced different paths in 19 of 599 cases that a rule-based check predicted would be unchanged. Although a lower-cost diagnostic matched the full-evaluation winner in only 55.0% of cases, it selected an option within the $10^{-6}$ cost-equivalence tolerance in 79.5%; the remaining cases prevented the proxy from authorizing acceptance. These experiments establish capability-level contribution evidence; the organization completes the value decision by weighing it against implementation and sustaining burden. The resulting capability evidence record links the observations to validation, deployment scope, fallback, monitoring, redesign, and retirement decisions.