AI-Enabled Enterprise Scorecards for Reducing Operational Errors and Enhancing Supply Chain Consistency

Abstract

This study examined why supply chains still face preventable execution errors and inconsistent KPI performance despite dashboards, because reporting visibility does not consistently translate exceptions into coordinated corrective action. The purpose was to test whether AI-enabled enterprise scorecard capability improves operational error reduction and supply chain consistency, and whether AI alert trust and actionability act as mechanisms in an enterprise case setting. A quantitative, cross-sectional, case-based design used a five-point Likert survey of N = 210 scorecard users from planning, procurement, warehouse, logistics, and BI or reporting functions. Key variables were Scorecard Capability (SC_CAP), Alert Trust (TRUST), Alert Actionability (ACT), Operational Error Reduction (OER), and Supply Chain Consistency (SCC). Analysis combined descriptive statistics, reliability testing, Pearson correlations, and multiple regression for hypotheses and mechanisms. Reliability was acceptable to excellent (α = .86–.91; SC_CAP α = .89; OER α = .91; SCC α = .90), supporting composite-index modeling. Respondents reported moderately high capability (SC_CAP M = 3.86, SD = 0.62) and broad availability of KPI dashboards (82%), drill-down traceability (74%), and automated exception alerts (69%). Correlations supported the expected direction: SC_CAP related to OER (r = .62, p < .001) and SCC (r = .58, p < .001), and OER related to SCC (r = .55, p < .001). In regression, SC_CAP predicted OER (β = .47, p < .001, R² = .43) and SCC (β = .34, p < .001, R² = .38), and OER predicted SCC (β = .29, p < .001, R² = .31). Adding TRUST and ACT increased explained variance (OER R² = .51; SCC R² = .49) and showed partial mediation: SC_CAP’s OER effect reduced to β = .36 while TRUST (β = .18, p = .004) and ACT (β = .21, p = .001) remained significant, and ACT was especially influential for SCC (β = .24, p < .001). Implications are that organizations should manage AI scorecards as operational control infrastructure by strengthening KPI governance and drill-down traceability and by improving alert credibility and usability through clear ownership and response playbooks, so exceptions lead to repeatable actions that reduce errors and stabilize execution.