Abstract

Ensuring cost-effective logistics operations is a critical factor in sustaining competitiveness within Indonesia’s manufacturing and distribution industries. The textile chemical distribution sector is particularly challenged by escalating rental charges for third-party trucks, which have risen by approximately 15% annually, thereby creating long-term financial pressure and reducing control over operations. To address this concern, the company evaluated the feasibility of acquiring its own truck as an alternative.

This study applies a descriptive-quantitative approach to compare leasing with truck ownership, employing Net Present Value (NPV), Payback Period (PP), Internal Rate of Return (IRR), and Modified Internal Rate of Return (MIRR) as evaluation indicators. Distribution demand from 2020 to 2027 was projected using seasonal decomposition, achieving a MAPE of 12.4%, which indicates reliable accuracy for cash flow forecasting. Although both options resulted in negative NPVs, truck ownership demonstrated more favorable outcomes. The investment’s payback period of 4.5 years remains within the truck’s eight-year economic life. Furthermore, the IRR of 16.21% surpasses the firm’s Minimum Attractive Rate of Return (MARR) of 15%, with a MIRR of 15.9% confirming feasibility. Overall, truck acquisition is more advantageous than leasing, offering financial efficiency, improved operational control, and strengthened supply chain resilience.

Keywords

  • Investment feasibility
  • Net Present Value (NPV)
  • Internal Rate of Return (IRR)
  • Payback Period (PP)
  • seasonal decomposition
  • logistics.

References

  1. 1. S. B. Bhandari, “Discounted Payback Period—A Viable Complement to Net Present Value for Projects with Conventional Cash Flows,” The Journal of Cost Analysis, vol. 7, no. 1, pp. 43–53, 1989, doi: 10.1080/08823871.1989.10462376.
  2. 2. G. Barnes and P. Langworthy, “Per-mile costs of operating automobiles and trucks,” Transportation Research Record, vol. 1864, no. 1, pp. 64–71, 2004, doi: 10.3141/1864-10.
  3. 3. H. Bierman and S. Hass, “The Payback Method Revisited,” The Engineering Economist, vol. 34, no. 4, pp. 275–283, 1989, doi: 10.1080/00137918908902905.
  4. 4. J. Bilau and J. St-Pierre, “Microcredit repayment in a European context: Evidence from Portugal,” The Quarterly Review of Economics and Finance, 2018, doi: 10.1080/0015198X.2018.1470902.
  5. 5. A. Engholm, A. Pernestal, and I. Kristofferson, “Cost analysis of driverless truck operations,” Transportation Research Record, vol. 2674, no. 9, pp. 616–627, 2020, doi: 10.1177/0361198120930228.
  6. 6. L. Gan, C.-Y. C. Lin, and S. Pouliot, “Vehicle Cost per Mile and the Economics of Automobiles,” Transportation Economics, vol. 22, no. 3, pp. 45–61, 2003.
  7. 7. N. D. Go and O. F. Jin, “Analisis kelayakan finansial terhadap pembangunan infrastruktur pada dua port di tepi Sungai Barito – Kalimantan Tengah,” Jurnal Teknik Sipil, vol. 27, no. 2, pp. 169–176, 2020, doi: 10.5614/jts.2020.27.2.7.
  8. 8. A. Gunardi, R. Suryadi, and L. Rahayu, “Investment Feasibility Analysis: Concepts and Practical Application in Business Decision,” Journal of Business and Management Research, vol. 12, no. 2, pp. 55–68, 2022.
  9. 9. E. Hermawan and U. Sudjadi, “Integrated nuclear-renewable energy system for industrialization in West Nusa Tenggara Province, Indonesia: Economic, potential site, and policy recommendation,” International Journal of Energy Economics and Policy, vol. 12, no. 4, pp. 146–159, 2022, doi: 10.32479/ijeep.13067.
  10. 10. R. J. Hyndman and Y. Khandakar, “Automatic time series forecasting: The forecast package for R,” Journal of Statistical Software, vol. 27, no. 3, pp. 1–22, 2008, doi: 10.18637/jss.v027.i03.
  11. 11. M. Ibrahim and A. Rinienta, “Analisis Investasi Armada Transportasi dan Faktor-faktor Penentunya,” Jurnal Logistik dan Transportasi, vol. 5, no. 1, pp. 34–42, 2020.
  12. 12. H. E. Kierulff, “MIRR: A better measure,” Business Horizons, vol. 51, no. 4, pp. 321–329, 2008, doi: 10.1016/j.bushor.2008.02.005.
  13. 13. F. Lestari, A. Nugroho, and R. Pratiwi, “Forecasting Demand Using Decomposition Method in Time Series Analysis,” Indonesian Journal of Statistics and Its Applications, vol. 3, no. 2, pp. 99–108, 2019.
  14. 14. S. Makridakis and M. Hibon, “The M3-Competition: Results, conclusions and implications,” International Journal of Forecasting, vol. 16, no. 4, pp. 451–476, 2000, doi: 10.1016/S0169-2070(00)00057-1.
  15. 15. G. A. Ndubizu, “A Comparative Analysis of Payback and Discounted Payback Methods,” The Engineering Economist, vol. 45, no. 1, pp. 1–14, 2000, doi: 10.1080/00137910008967546.
  16. 16. A. Pranoto, H. Hermawan, and N. Albart, “A systematic literature review: Business feasibility analysis using Net Present Value (NPV) and Internal Rate of Return (IRR) methods in the automotive industry,” Jurnal Indonesia Sosial Sains, vol. 6, no. 1, pp. 171–184, 2025, doi: 10.59141/jiss.v6i1.1577.
  17. 17. R. Ridwan Margana and O. Kusumah, “Analisis Kelayakan Investasi Truk Menggunakan Net Present Value, Payback Period, dan Internal Rate of Return pada PT Swisstex Naratama Indonesia,” Journal of Technology and Engineering, vol. 2, no. 2, pp. 87–98, 2024, doi: 10.59613/journaloftechnologyandengineering.v2i2.161.
  18. 18. M. V. Sokolov, “NPV, IRR, PI, PP, and DPP: A unified view,” arXiv preprint, 2023, doi: 10.48550/arXiv.2302.02875.
  19. 19. T. A. Weber, “On the (non-)equivalence of IRR and NPV,” Journal of Mathematical Economics, vol. 53, pp. 74–91, 2014, doi: 10.1016/j.jmateco.2014.03.006.
  20. 20. S. Yard, “Developments of the payback method,” International Journal of Production Economics, vol. 67, no. 2, pp. 155–167, 2000, doi: 10.1016/S0925-5273(00)00003-7.