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Integrating Artificial Intelligence into Product Life Cycle Value and Activity Value Management: A Case Study
of P Channel Agent

5. Originality / Contribution

This research advances management accounting theory and practice by integrating
Artificial Intelligence (AI) into the Product Life Cycle Value (PLCV) framework and

combining it with Activity Value Management (AVM) to establish a data-driven decision
support system for channel distributors. While traditional Activity-based Costing (ABC)
emphasizes cost accuracy and process efficiency, it often lacks predictive capability and
cross-level linkage between product-level and activity-level values. By embedding AI

models like XGBoost and Neural Network algorithms into the PLCV-AVM integration,
this study enables more accurate forecasting of product performance and enhances
managerial decision quality across the value chain.
The originality of this paper lies in its multi-layer integration of product life cycle

analytics, activity-based management logic, and AI-based predictive modeling. This
hybrid framework provides a quantitative mechanism that connects B2B distributor data
with B2C consumer-level behaviors, bridging marketing and accounting perspectives. The
PLCV-AVM system also incorporates demographic and transactional variables, offering a

comprehensive lens for analyzing how activity drivers, customer structures, and life cycle
stages jointly affect long-term product profitability.
From a theoretical stand point, the study extends the foundational works of Cooper
and Kaplan (1988) and Kaplan and Anderson (2004) by embedding dynamic learning

algorithms within value management systems. This integration transforms static cost
management into an adaptive and forward-looking value management model. Furthermore,
incorporating AI interpretability strengthens managerial trust and transparency and
provides a basis for future research at explainable accounting analytics.

From a practical perspective, the PLCV-AVM system functions as an effective AI-
assisted management tool that helps firms optimize product portfolios, allocate marketing
resources more efficiently, and evaluate channel-specific product performance. The
framework provides actionable insights into strategic investment planning and operational

alignment across product life cycle stages. While the current study does not incorporate
ESG indicators directly, the data-driven structure of the PLCV-AVM approach offers
a promising foundation for future integration with sustainability-oriented performance
metrics. Thus, it helps support the development of management accounting systems that

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