POLITANI SAMARINDAPOLITANI SAMARINDA

TEPIANTEPIAN

The coffee shop industry in Indonesia is experiencing rapid growth, along with the increasing demand for more innovative and efficient customer experiences. To maintain competitiveness and improve operational efficiency, coffee shop companies need to utilize technology in a structured manner. This study aims to design and develop an Enterprise Architecture (EA) model that can be applied to Big Coffee Signature in order to align business strategy with the technology used. In this study, the TOGAF and ArchiMate frameworks are used to design an enterprise architecture that is integrated with online ordering systems, digital payments, customer management, and raw material stock management. Through the implementation of EA, Big Coffee Signature can optimize the use of technology to improve operations and provide better customer experience. The application systems used, such as the Smart Ordering System, Multichannel Inventory Management System, are integrated to improve efficiency, transparency, and convenience for customers. This study also shows that by integrating third-party platforms such as GoFood and GrabFood, companies can expand their market reach and provide greater convenience for customers in placing orders. The results of the study show that the implementation of a structured EA can provide significant benefits in terms of operational efficiency, aligning technology with business needs, and improving customer service. The suggestions provided, such as further development of the customer loyalty system, utilization of AI for service personalization, and development of a cloud-based POS system, are expected to help Big Coffee Signature to increase the competitiveness and sustainability of their business.

The study successfully designed an Enterprise Architecture (EA) model for Big Coffee Signature using the TOGAF and ArchiMate frameworks.The findings demonstrate that EA implementation can integrate key systems such as the Smart Ordering System, Payment Gateway, Inventory Management, and real-time order tracking, enhancing operational efficiency and customer satisfaction.By adopting cloud-based infrastructure and integrating with third-party platforms, Big Coffee Signature can align its business strategy with technology and unlock opportunities for further development.

Based on the findings, future research could explore the implementation of Artificial Intelligence (AI) to personalize customer service and optimize menu recommendations, potentially leading to increased customer loyalty and sales. Furthermore, investigating the feasibility of a blockchain-based supply chain management system could enhance transparency and traceability of coffee bean sourcing, addressing growing consumer concerns about ethical and sustainable practices. Finally, a comparative study analyzing the effectiveness of different cloud-based POS systems in the coffee shop sector could provide valuable insights for businesses seeking to upgrade their technology infrastructure, ultimately improving operational efficiency and customer experience. These research directions build upon the current studys findings and aim to address emerging trends and challenges in the coffee shop industry, contributing to its continued innovation and sustainability.

  1. Pengaruh Digital Marketing dan Kualitas Pelayanan terhadap Keputusan Pembelian Konsumen Coffe Shop pada... doi.org/10.33087/jmas.v7i2.564Pengaruh Digital Marketing dan Kualitas Pelayanan terhadap Keputusan Pembelian Konsumen Coffe Shop pada doi 10 33087 jmas v7i2 564
  2. Enterprise Architecture Planning for the Coffee Shop Sector Using ADM at Big Coffee Signature | TEPIAN.... doi.org/10.51967/tepian.v6i3.3444Enterprise Architecture Planning for the Coffee Shop Sector Using ADM at Big Coffee Signature TEPIAN doi 10 51967 tepian v6i3 3444
  3. Analytics-Enabled Adaptive Business Architecture Modeling | Complex Systems Informatics and Modeling... doi.org/10.7250/csimq.2020-23.03Analytics Enabled Adaptive Business Architecture Modeling Complex Systems Informatics and Modeling doi 10 7250 csimq 2020 23 03
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