Effectiveness of an automated billing management system for invoice generation in a logistics company
DOI:
https://doi.org/10.35335/ijafibs.v14i1.505Keywords:
Billing Management System, Effectiveness, Invoicing, Revenue AssuranceAbstract
Efficient revenue management is crucial for PT Jalur Nugraha Ekakurir (JNE) amidst surging delivery volumes. To optimize billing and reduce manual errors from the legacy MyOrion system, JNE migrated to the RAISE Billing Management System. However, implementation faces operational constraints including synchronization gaps requiring manual reconciliation, lengthy maintenance downtimes, and rigid administrative validations that hinder cash flow. This study evaluates RAISE's operational effectiveness at JNE Gedebage's Revenue Assurance unit, mapping billing procedures, identifying systemic bottlenecks, and formulating strategic solutions. Utilizing a Qualitative Descriptive approach, data was gathered through interviews with four purposively selected informants and triangulated with observations, document analysis, and literature reviews. Findings reveal that RAISE significantly optimizes workflows via 24-hour automated synchronization, accelerating high-volume processing, reducing human error, and offering billing flexibility. Despite an 8.7 staff rating, technical barriers like early-month maintenance disrupting peak cycles, filter failures on cash transactions, and reliance on external communication for troubleshooting prevent maximum efficiency. Conclusively, while RAISE enhances speed and accuracy, technical instability limits its potential. Strategic recommendations include shifting maintenance to off-peak mid-month periods, mandating tax ID validation during onboarding, refining cash transaction filters, and developing real-time reconciliation dashboards. Future research should quantitatively measure workflow efficiency, evaluate user satisfaction, explore predictive error-detection analytics, and assess the macroeconomic impacts of technical billing inefficiencies.
References
Akbar, U., Jain, A. A., & Bråthen, S. (2024). Sustainability assessment of inter-urban crowdshipping - A case study approach. Research in Transportation Economics, 103(February), 101409. https://doi.org/10.1016/j.retrec.2024.101409
Burney, V., Arnold-Saritepe, A., & McCann, C. M. (2024). How Can Qualitative Methods Be Applied to Behavior Analytic Research: A Discussion and Suggestions for Implementation. Behavior Analysis in Practice, 17(2), 431–441. https://doi.org/10.1007/s40617-024-00917-1
Chen, Y., Wu, F., Wang, Y., Shi, J., Wang, K., & Zhang, B. (2025). Research on the optimization of intelligent electricity billing consolidation and the expansion of refund services based on deep learning. International Journal of Low-Carbon Technologies, 20(October 2024), 66–72. https://doi.org/10.1093/ijlct/ctae261
Engesser, V., Rombaut, E., Vanhaverbeke, L., & Lebeau, P. (2023). Autonomous Delivery Solutions for Last-Mile Logistics Operations: A Literature Review and Research Agenda. Sustainability (Switzerland), 15(3), 1–17. https://doi.org/10.3390/su15032774
Hall, S., & Liebenberg, L. (2024). Qualitative Description as an Introductory Method to Qualitative Research for Master’s-Level Students and Research Trainees. International Journal of Qualitative Methods, 23, 1–5. https://doi.org/10.1177/16094069241242264
Jintana, J., Sopadang, A., & Ramingwong, S. (2021). Idea selection of new service for courier business: The opportunity of data analytics. International Journal of Engineering Business Management, 13, 1–20. https://doi.org/10.1177/18479790211042191
Khan, T. H., & MacEachen, E. (2022). An Alternative Method of Interviewing: Critical Reflections on Videoconference Interviews for Qualitative Data Collection. International Journal of Qualitative Methods, 21, 1–12. https://doi.org/10.1177/16094069221090063
Lagin, M., Håkansson, J., Nordström, C., Nyberg, R. G., & Öberg, C. (2022). Last-mile logistics of perishable products: a review of effectiveness and efficiency measures used in empirical research. International Journal of Retail and Distribution Management, 50(13), 116–139. https://doi.org/10.1108/IJRDM-02-2021-0080
Liu, Q. (2024). Logistics Distribution Route Optimization in Artificial Intelligence and Internet of Things Environment. Decision Making: Applications in Management and Engineering, 7(2), 221–239. https://doi.org/10.31181/dmame7220241072
Magboul, I., Jebreel, M., Shawtri, F., Qabajeh, M., Shanti, A., Alqudah, M., & Abu Huson, Y. (2024). Factors influencing accounting information system usage by oil companies & performance outcomes. Cogent Business and Management, 11(1). https://doi.org/10.1080/23311975.2024.2369211
Mirkovic, M., Lolic, T., Stefanovic, D., Anderla, A., & Gracanin, D. (2022). Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data. Applied Sciences (Switzerland), 12(10). https://doi.org/10.3390/app12105001
Mohamed, S. A., Mahmoud, M. A., Mahdi, M. N., & Mostafa, S. A. (2022). Improving Efficiency and Effectiveness of Robotic Process Automation in Human Resource Management. Sustainability (Switzerland), 14(7). https://doi.org/10.3390/su14073920
Prakash, P. (2021). Efficacy, Effectiveness, and Efficiency. National Journal of Community Medicine, 12(2), 33–39. https://njcmindia.com/index.php/file/article/view/290
Prapinit, P., Kafi, A., Osman, N. H., & Melan, M. (2024). IoT in Courier Services: Impact on Customer Satisfaction and Supply Chain Sustainability. International Journal of Sustainable Development and Planning, 19(2), 671–679. https://doi.org/10.18280/ijsdp.190224
Prayitno, A. (2026). Defense business administration optimization: A secondary data approach with dynamic models. Social Sciences & Humanities Open, 13(January), 102317. https://doi.org/10.1016/j.ssaho.2025.102317
Rajendran, S. (2021). Improving the performance of global courier & delivery services industry by analyzing the voice of customers and employees using text analytics. International Journal of Logistics Research and Applications, 24(5), 473–493. https://doi.org/10.1080/13675567.2020.1769042
Sawik, B. (2024). Optimizing Last-Mile Delivery : A Multi-Criteria Approach and Crowdshipping.
Shalash, A., Abu-Rmeileh, N. M. E., Kelly, D., & Elmusharaf, K. (2022). The need for standardised methods of data collection, sharing of data and agency coordination in humanitarian settings. BMJ Global Health, 7, 1–5. https://doi.org/10.1136/bmjgh-2021-007249
Silva, V., Amaral, A., & Fontes, T. (2023). Sustainable Urban Last-Mile Logistics: A Systematic Literature Review. Sustainability (Switzerland), 15(3), 1–27. https://doi.org/10.3390/su15032285
Sorooshian, S., Khademi Sharifabad, S., Parsaee, M., & Afshari, A. R. (2022). Toward a Modern Last-Mile Delivery: Consequences and Obstacles of Intelligent Technology. Applied System Innovation, 5(4), 1–16. https://doi.org/10.3390/asi5040082
Yoshikuni, A. C., Dwivedi, R., Dultra-de-Lima, R. G., Parisi, C., & Oyadomari, J. C. T. (2023). Role of Emerging Technologies in Accounting Information Systems for Achieving Strategic Flexibility through Decision-Making Performance: An Exploratory Study Based on North American and South American Firms. Global Journal of Flexible Systems Management, 24(2), 199–218. https://doi.org/10.1007/s40171-022-00334-9
Zohry, A. F., & Al-Dhubaibi, A. A. S. (2024). Optimizing Business Performance Through Effective Accounting Information Systems: The Role of System Competence and Information Quality. Journal of Risk and Financial Management, 17(11). https://doi.org/10.3390/jrfm17110515
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