Governing Enterprise AI/ML Roadmaps for Supply Chain Forecasting: A Cross-Functional Framework for Improving Forecasting Performance
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I2P130Keywords:
Artificial Intelligence, Machine Learning, Supply Chain Forecasting, AI Governance, Product Roadmaps, Cross-Functional Integration, Longitudinal Analysis, Dynamic Capabilities, Supply Chain DisruptionAbstract
Enterprise investments in artificial intelligence and machine learning (AI/ML) for supply chain forecasting are frequently evaluated as if adoption were a discrete technological event. This treatment obscures a central managerial problem: forecasting capability develops through time as data pipelines stabilize, models are recalibrated, planners learn how to use probabilistic outputs, and governance routines determine which experiments are scaled, redesigned, or retired. This article develops a longitudinal empirical design for examining how cross-functional governance of enterprise AI/ML roadmaps affects forecasting performance before, during, and after major supply-chain disruption. The proposed study uses a firm-quarter panel covering 2015Q1–2024Q1, ending before May 2024. Publicly listed retail, consumer-goods, manufacturing, and distribution firms constitute the unit of analysis. AI/ML roadmap governance is measured through reproducible textual coding of annual and quarterly filings and related public disclosures; operational forecasting performance is proxied by inventory-demand alignment, inventory turnover stability, abnormal inventory growth, and forecast-related disclosure indicators. Global Supply Chain Pressure Index values provide an exogenous temporal stress measure. The empirical strategy combines firm and time fixed effects, distributed lags, dynamic panel estimation, structural-break tests, and shock-period interactions. The central theoretical proposition is that AI/ML adoption does not generate an immediate performance premium. Instead, governance quality converts technical capability into delayed and cumulative forecasting gains by aligning model ownership, data stewardship, planning cadence, exception management, and business accountability. The article specifies hypotheses, measurement rules, identification tests, robustness procedures, and a complete results architecture without inventing numerical findings. Its contribution is a temporal theory of governed AI/ML capability in which performance depends on sequencing, organizational learning, and adaptation to disruption rather than adoption alone.
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