Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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All Journal Community Engagement and Emergence Journal (CEEJ) Journal of Applied Business, Taxation and Economics Reseach
Mustofa, Firzainy Jiddan
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Humanities Economics, Econometrics & Finance Education Environmental Science Health Professions

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Improving ERP Implementation Phase through BPM and BPMN: A Case from Indonesia Mustofa, Firzainy Jiddan; Okdinawati, Liane; Widjaja, Fransisca Budyanto
Journal of Applied Business, Taxation and Economics Research Vol. 4 No. 6 (2025): August 2025
Publisher : PT. EQUATOR SINAR AKADEMIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54408/jabter.v4i6.465

Abstract

This study aims to evaluate and improve the business processes involved in the ERP implementation phase of a local Indonesian provider, with a specific focus on the master data preparation stage. Utilizing an integrated framework combining Business Process Management (BPM), Business Process Model and Notation (BPMN), the Activity-Based Model, and the ESIA method, this research identifies key bottlenecks caused by sequential data input processes and lack of integration among activities. The proposed business process redesign emphasizes parallel data input, document simplification, and automation of verification and validation, which are expected to accelerate implementation time, enhance data accuracy, and optimize operational efficiency. This study contributes empirically to an in-depth business process analysis within the Food and Beverage (F&B) sector and highlights the importance of proper process modelling and targeted automation to address common challenges faced by local ERP providers. Limitations include a focus on a single ERP implementation phase and a single case study, with the redesign not yet tested through simulation. Future research is recommended to expand the scope to other implementation phases, integrate quantitative performance metrics, and utilize simulation software to evaluate the effectiveness of the proposed process redesign.
The Role of Artificial Intelligence–Based Decision Support Systems in Managerial Decision-Making in the Hospitality Industry: A Systematic Literature Review Gultom, Rafly Aqsha; Mustofa, Firzainy Jiddan; Tanaga, Selwin Malta
Community Engagement and Emergence Journal (CEEJ) Vol. 7 No. 3 (2026): Community Engagement & Emergence Journal (CEEJ)
Publisher : Yayasan Riset dan Pengembangan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/ceej.v7i3.10497

Abstract

The advancement of Artificial Intelligence (AI) has accelerated the adoption of Decision Support Systems (DSS) to assist managerial decision-making in the increasingly complex and dynamic hospitality industry. This study aims to systematically examine how AI-based DSS are utilized to support managerial decision-making in the hospitality sector, with a particular focus on the types of decisions supported, the AI techniques employed, the benefits obtained, and the challenges of implementation. This research adopts a Systematic Literature Review (SLR) approach guided by the PRISMA framework. A comprehensive literature search was conducted using the Scopus database, resulting in 32 peer-reviewed journal articles that met the inclusion criteria within the publication period of 2017–2026. The findings indicate that AI-based DSS are predominantly used to support operational and tactical decisions, particularly in demand and occupancy forecasting, dynamic pricing and revenue management, workforce scheduling, and service quality management. Machine learning and predictive analytics emerge as the most widely applied AI techniques, while rule-based systems are used to a more limited extent. The literature also highlights key benefits, including improved decision accuracy, enhanced operational efficiency, and better service quality. However, these benefits are accompanied by challenges related to data quality, system transparency, and organizational readiness. This study provides a structured synthesis of the role of AI-based DSS in managerial decision-making within the hospitality industry and offers a foundation for future research and managerial practice.
Co-Authors Fransisca Budyanto Widjaja, Fransisca Budyanto Gultom, Rafly Aqsha LIANE OKDINAWATI, LIANE Tanaga, Selwin Malta