When Does an Added Planning Capability Create Operational Value? An Observable Evidence Standard

A new arXiv paper (2610.27305v1) proposes an observable evidence standard for attributing operational value to individual planning capabilities added to industrial AI planning systems, tested via forecast-to-plan replay on the Favorita, M5, and Walmart datasets. The study found that a composite policy can hide negligible component contribution — one control changed only 15 of 681 executed plans — and that an evaluated multi-period capability was 7.6% more costly under tight capacity despite helping in several long-lead settings. Independent runs diverged in 19 of 599 cases a rule-based check predicted would be unchanged, and a lower-cost diagnostic matched the full-evaluation winner in only 55.0% of cases but fell within the $10^{-6}$ cost-equivalence tolerance in 79.5%.

arXiv:2610.27305v1 Announce Type: new 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.