This study investigates work order scheduling for product portfolios at the enterprise level by extending the traditional α|β|γ This study investigates work order scheduling for product portfolios at the enterprise level by extending the traditional α|β|γ scheduling framework to support production–marketing coordination. While the triplet structure is widely applied to single-system scheduling, its role in portfolio-level decision-making remains limited, particularly when allocating multiple products across diverse manufacturing environments. To address this gap, a universal scheduling model is proposed to integrate appropriate scheduling criteria into enterprise-wide planning. The model accommodates various machine environments in the α field, including single machine, parallel machines, flow shop, flexible flow shop, job shop, flexible job shop, and open shop systems, ensuring high flexibility and general applicability. In addition, a Python-based application is developed to convert work order data from Excel into the proposed model, enabling systematic scheduling and analysis. The approach supports the evaluation of production line efficiency, prioritization of product portfolios, and generation of feasible or near-optimal schedules. This study provides a scalable and adaptable decision-support tool that enhances coordination between production and marketing in complex manufacturing systems.

Keywords 

Scheduling, ERP, MRP, Product Portfolios framework to support production–marketing coordination. While the triplet structure is widely applied to single-system scheduling, its role in portfolio-level decision-making remains limited, particularly when allocating multiple products across diverse manufacturing environments.

To address this gap, a universal scheduling model is proposed to integrate appropriate scheduling criteria into enterprise-wide planning. The model accommodates various machine environments in the α field, including single machine, parallel machines, flow shop, flexible flow shop, job shop, flexible job shop, and open shop systems, ensuring high flexibility and general applicability.

In addition, a Python-based application is developed to convert work order data from Excel into the proposed model, enabling systematic scheduling and analysis. The approach supports the evaluation of production line efficiency, prioritization of product portfolios, and generation of feasible or near-optimal schedules.

This study provides a scalable and adaptable decision-support tool that enhances coordination between production and marketing in complex manufacturing systems.