Forecasting the Feasibility of a Sales Budget Expansion Target in Shipping Logistics: Evidence from PT XYZ

Wilujeng Dewi Ariyanti; Timotius FCW Sutrisno

doi:10.37034/jems.v9i1.584

Authors

Wilujeng Dewi Ariyanti Universitas Ciputra
Timotius FCW Sutrisno Universitas Ciputra

DOI:

https://doi.org/10.37034/jems.v9i1.584

Keywords:

Forecasting, Feasibility, Sales Budget, Expansion Target, Shipping Logistics

Abstract

Annual sales budgets in shipping logistics are often set by managerial ambition rather than evidence. This study tests whether a 100% incremental sales target is quantitatively feasible, using 36 months of revenue and operational data (June 2023 to May 2026) from PT XYZ, an Indonesian freight forwarding provider. Ten forecasting models in three groups (baseline, statistical and decomposition-based, including Prophet-type and empirical wavelet transform hybrids) were compared on a 12-month hold-out year. The chosen model was re-evaluated to predict revenue for FY2027, and a bootstrap simulation was used to determine the likelihood of achieving the USD 21.99 million target. The simple model performed best, with a MAPE of 9.62%, and none of the decomposition models surpassed the basic benchmarks. It was found that the length of the forecasting period had a greater impact on accuracy than the complexity of the model. The forecast for FY2027 revenue is USD 12.57 million, which is an increase of 14.3%, but this only meets 57% of the target. None of 30,000 simulated outcomes reached the target, which would require operational volumes to grow by 100% to 184%, against a record of 82.7%. An evidence-based committed budget of USD 12.74 million and a stretch budget of USD 13.10 million are proposed. The study links forecasting research with budget target-setting theory by measuring target difficulty before the budget year and offers a transparent procedure that firms with short data histories can replicate.

References

Lee, H. L., Padmanabhan, V., & Whang, S. (1997). Information distortion in a supply chain: The bullwhip effect. Management Science, 43(4), 546–558. https://doi.org/10.1287/mnsc.43.4.546

Syntetos, A. A., Babai, Z., Boylan, J. E., Kolassa, S., & Nikolopoulos, K. (2016). Supply chain forecasting: Theory, practice, their gap and the future. European Journal of Operational Research, 252(1), 1–26. https://doi.org/10.1016/j.ejor.2015.11.010

Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, 136. https://doi.org/10.1016/j.tre.2020.101922

Notteboom, T., Pallis, T., & Rodrigue, J.-P. (2021). Disruptions and resilience in global container shipping and ports: The COVID-19 pandemic versus the 2008–2009 financial crisis. Maritime Economics & Logistics, 23(2), 179–210. https://doi.org/10.1057/s41278-020-00180-5

UPS. (2026). UPS releases 4Q 2025 earnings and provides 2026 guidance. Business Wire. Retrieved from https://www.businesswire.com/news/home/20260127727273/en/UPS-Releases-4Q-2025-Earnings-and-Provides-2026-Guidance

Anugranov, D. (2026). Indonesia pushes multimodal logistics to cut costs at 14.29 percent GDP. Radio Republik Indonesia. Retrieved from https://rri.co.id/en/national/2379761/indonesia-pushes-multimodal-logistics-to-cut-costs-at-1429-percent-gdp

Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). The M4 Competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting, 36(1), 54–74. https://doi.org/10.1016/j.ijforecast.2019.04.014

Petropoulos, F., Apiletti, D., Assimakopoulos, V., et al. (2022). Forecasting: Theory and practice. International Journal of Forecasting, 38(3), 705–871. https://doi.org/10.1016/j.ijforecast.2021.11.001

Gneiting, T., & Katzfuss, M. (2014). Probabilistic forecasting. Annual Review of Statistics and Its Application, 1, 125–151. https://doi.org/10.1146/annurev-statistics-062713-085831

Merchant, K. A., & Manzoni, J.-F. (1989). The achievability of budget targets in profit centers: A field study. The Accounting Review, 64(3), 539–558.

Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705–717. https://doi.org/10.1037/0003-066X.57.9.705

Bouwens, J., & Kroos, P. (2011). Target ratcheting and effort reduction. Journal of Accounting and Economics, 51(1–2), 171–185. https://doi.org/10.1016/j.jacceco.2010.07.002

Arnold, M. C., & Artz, M. (2015). Target difficulty, target flexibility, and firm performance: Evidence from business units' targets. Accounting, Organizations and Society, 40, 61–77. https://doi.org/10.1016/j.aos.2014.12.002

Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PLoS ONE, 13(3). https://doi.org/10.1371/journal.pone.0194889

Bates, J. M., & Granger, C. W. J. (1969). The combination of forecasts. Operational Research Quarterly, 20(4), 451–468. https://doi.org/10.1057/jors.1969.103

Wang, X., Hyndman, R. J., Li, F., & Kang, Y. (2023). Forecast combinations: An over 50-year review. International Journal of Forecasting, 39(4), 1518–1547. https://doi.org/10.1016/j.ijforecast.2022.11.005

Gardner, E. S., Jr. (2006). Exponential smoothing: The state of the art—Part II. International Journal of Forecasting, 22(4), 637–666. https://doi.org/10.1016/j.ijforecast.2006.03.005

Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3), 1–22. https://doi.org/10.18637/jss.v027.i03

Taylor, S. J., & Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37–45. https://doi.org/10.1080/00031305.2017.1380080

Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3), 324–342. https://doi.org/10.1287/mnsc.6.3.324

Gilles, J. (2013). Empirical wavelet transform. IEEE Transactions on Signal Processing, 61(16), 3999–4010. https://doi.org/10.1109/TSP.2013.2265222

Tashman, L. J. (2000). Out-of-sample tests of forecasting accuracy: An analysis and review. International Journal of Forecasting, 16(4), 437–450. https://doi.org/10.1016/S0169-2070(00)00065-0

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001

Davydenko, A., & Fildes, R. (2013). Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts. International Journal of Forecasting, 29(3), 510–522. https://doi.org/10.1016/j.ijforecast.2012.09.002

Armstrong, J. S., & Collopy, F. (1992). Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of Forecasting, 8(1), 69–80. https://doi.org/10.1016/0169-2070(92)90008-W

Harvey, D., Leybourne, S., & Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13(2), 281–291. https://doi.org/10.1016/S0169-2070(96)00719-4

Smith, J., & Wallis, K. F. (2009). A simple explanation of the forecast combination puzzle. Oxford Bulletin of Economics and Statistics, 71(3), 331–355. https://doi.org/10.1111/j.1468-0084.2008.00541.x

Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18(3), 439–454. https://doi.org/10.1016/S0169-2070(01)00110-8

Chatfield, C. (1993). Calculating interval forecasts. Journal of Business & Economic Statistics, 11(2), 121–135. https://doi.org/10.1080/07350015.1993.10509938

Indjejikian, R. J., Matějka, M., Merchant, K. A., & Van der Stede, W. A. (2014). Earnings targets and annual bonus incentives. The Accounting Review, 89(4), 1227–1258. https://doi.org/10.2308/accr-50732

Hewamalage, H., Bergmeir, C., & Bandara, K. (2021). Recurrent neural networks for time series forecasting: Current status and future directions. International Journal of Forecasting, 37(1), 388–427. https://doi.org/10.1016/j.ijforecast.2020.06.008

Xie, G., Wang, S., Zhao, Y., & Lai, K. K. (2013). Hybrid approaches based on LSSVR model for container throughput forecasting: A comparative study. Applied Soft Computing, 13(5), 2232–2241. https://doi.org/10.1016/j.asoc.2013.02.002 [33] Liu, J., Chu, N., Wang, P., Zhou, L., & Chen, H. (2024). A novel hybrid model for freight volume prediction based on the Baidu search index and emergency. Neural Computing and Applications, 36(3), 1313–1328. https://doi.org/10.1007/s00521-023-09106-7

Claeskens, G., Magnus, J. R., Vasnev, A. L., & Wang, W. (2016). The forecast combination puzzle: A simple theoretical explanation. International Journal of Forecasting, 32(3), 754–762. https://doi.org/10.1016/j.ijforecast.2015.12.005

Weitzman, M. L. (1980). The “ratchet principle” and performance incentives. The Bell Journal of Economics, 11(1), 302–308. https://doi.org/10.2307/3003414

Leone, A. J., & Rock, S. (2002). Empirical tests of budget ratcheting and its effect on managers' discretionary accrual choices. Journal of Accounting and Economics, 33(1), 43–67. https://doi.org/10.1016/S0165-4101(01)00044-1

Hansen, S. C., Otley, D. T., & Van der Stede, W. A. (2003). Practice developments in budgeting: An overview and research perspective. Journal of Management Accounting Research, 15(1), 95–116. https://doi.org/10.2308/jmar.2003.15.1.95

Covaleski, M. A., Evans, J. H., III, Luft, J. L., & Shields, M. D. (2003). Budgeting research: Three theoretical perspectives and criteria for selective integration. Journal of Management Accounting Research, 15(1), 3–49. https://doi.org/10.2308/jmar.2003.15.1.3

Libby, T., & Lindsay, R. M. (2010). Beyond budgeting or budgeting reconsidered? A survey of North-American budgeting practice. Management Accounting Research, 21(1), 56–75. https://doi.org/10.1016/j.mar.2009.10.003

Ekholm, B.-G., & Wallin, J. (2000). Is the annual budget really dead? European Accounting Review, 9(4), 519–539. https://doi.org/10.1080/09638180020024007