A Decision-Support Model for Managing Outbound Logistics: Forecasting, Simulation, and Real-Time Operational Control

Magaletti, Nicola and Caponio, Giancarlo and Amodio, Angelo and Notarnicola, Valeria and di Molfetta, Mauro and Leogrande, Angelo (2025): A Decision-Support Model for Managing Outbound Logistics: Forecasting, Simulation, and Real-Time Operational Control.

Abstract

This article presents a decision support system developed as part of a Research and Development project undertaken by La Logistica srl, a third-party logistics company specializing in the storage and distribution of hydro-sanitary products. The approach is methodological and focuses on the comprehensive analysis of a case study related to freight exit processes with the aim of defining and implementing a software application to support the short-term management of picking and loading operations for product delivery. The developed decision support system integrates past data series analysis and projections, time series simulations, What-If analysis capabilities, and real-time monitoring within a single computational paradigm to anticipate peak points in the freight exit process. The developed decision support system is designed to accumulate and structure operational data from the warehouse management system software, to analyse the periodic rhythms of orders received to generate graphical projections of expected peak points and working hours based on the analysis of past data series and is able to dynamically review projections via real-time monitoring capabilities to adapt projections to actual progress made at any given time. Additionally, What-If analytics capabilities facilitate management's use of various workforce combinations to determine the feasibility of the process at any time, while identifying potential bottlenecks before they occur. Test results conducted with the corporate team indicate improvements in workload visibility and readiness for associated short-term programming strategies, while preventing operational disruptions through advance alerts on operational overload points.

• Item Type: MPRA Paper
• Language: English
• Keywords: Outbound Logistics; Decision Support System; What-If Simulation; Predictive Forecasting; Case Study.
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; L - Industrial Organization > L9 - Industry Studies: Transportation and Utilities > L91 - Transportation: General; M - Business Administration and Business Economics ; Marketing ; Accounting ; Personnel Economics > M1 - Business Administration > M11 - Production Management; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O33 - Technological Change: Choices and Consequences ; Diffusion Processes; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R4 - Transportation Economics > R41 - Transportation: Demand, Supply, and Congestion ; Travel Time ; Safety and Accidents ; Transportation Noise

References

Alnahhal, M., Tabash, M. I., & Ahrens, D. (2021). Optimal selection of third-party logistics providers using integer programming: A case study of a furniture company storage and distribution. Annals of Operations Research, 302(1), 1-22.

Aloini, D., Benevento, E., Dulmin, R., Guerrazzi, E., & Mininno, V. (2025). Unlocking Real-Time Decision-Making in Warehouses: A machine learning-based forecasting and alerting system for cycle time prediction. Transportation Research Part E: Logistics and Transportation Review, 194, 103933.

Anggraeni, M. S., & Amarilies, H. S. (2022). Pureshare Method in Dashboard Development to Monitor Warehouse Performance at PT XYZ Using the Cost Per Case (Cpc) Perspective. Journal of Emerging Supply Chain, Clean Energy, and Process Engineering, 1(1), 19-34.

Baglio, M., Perotti, S., Dallari, F., & Creazza, A. (2022). How can logistics real estate support thirdparty logistics providers?. International journal of logistics research and applications, 25(10), 1334- 1358.

Boonma, C. (2025, May). Logistics Data Analytics and Delay Prediction. In The 15th Benjamit National and International Conference (pp. 72-78).

Daroń, M. (2022). Simulations in planning logistics processes as a tool of decision-making in manufacturing companies. Production Engineering Archives, 28.

Fabianova, J., Janekova, J., & Horbulak, J. (2021). Solving the bottleneck problem in a warehouse using simulations. Acta logistica, 8(2), 107-116.

Ganbold, O., Kundu, K., Li, H., & Zhang, W. (2020). A simulation-based optimization method for warehouse worker assignment. Algorithms, 13(12), 326.

Giuffrida, M., Mangiaracina, R., & Burki, U. (2021). Cloud-based booking platforms in warehouse operations. Sustainability, 13(20), 11547.

Gkanatsas, E., & Krikke, H. (2020). Towards a pro-silience framework: a literature review on quantitative modelling of resilient 3PL supply chain network designs. Sustainability, 12(10), 4323. 11

González-Vidal, A., Gómez-Bernal, P., Mendoza-Bernal, J., & Skarmeta, A. F. (2021, December). BIGcoldTRUCKS: a BIG data dashboard for the management of COLD chain logistics in refrigerated TRUCKS. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 2894-2900). IEEE.

Hamidy, F., & Yasin, I. (2023). Implementation of Moving Average for Forecasting Inventory Data Using CodeIgniter. Journal of Data Science and Information Systems, 1(1), 17-23.

Kirchhoff, D., Kirberg, M., Kuhnt, S., & Clausen, U. (2023). Metamodel‐based optimization of shift planning in high‐bay warehouse operations. Quality and Reliability Engineering International, 39(2), 590-608.

Klumpp, M. (2018). Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements. International Journal of Logistics Research and Applications, 21(3), 224-242.

Kmiecik, M. (2022). Automation of warehouse resource planning process by using a cloud demand forecasting tool. Scientific Papers of Silesian University of Technology.

Kmiecik, M. (2022). Logistics coordination based on inventory management and transportation planning by third-party logistics (3PL). Sustainability, 14(13), 8134.

Minashkina, D., & Happonen, A. (2023). A systematic literature mapping of current academic research linking warehouse management systems to the third-party logistics context. Acta Logistica (AL), 10(2).

Mirbagheri, S. (2023, September). Leveraging data warehousing and decision support systems for effective supply chain management. In 2023 IEEE 8th international conference on smart cloud (SmartCloud) (pp. 111-115). IEEE.

Osho, G. O., Omisola, J. O., & Shiyanbola, J. O. (2020). An Integrated AI-Power BI Model for Real- Time Supply Chain Visibility and Forecasting: A Data-Intelligence Approach to Operational Excellence. Unknown Journal.

Santos, C. H. D., Lima, R. D. C., Leal, F., de Queiroz, J. A., Balestrassi, P. P., & Montevechi, J. A. B. (2020). A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods. Production, 30, e20200018.

Sodiya, E. O., Umoga, U. J., Amoo, O. O., & Atadoga, A. (2024). AI-driven warehouse automation: A comprehensive review of systems. GSC Advanced Research and Reviews, 18(2), 272-282.

Steinbacher, L. M., Düe, T., Veigt, M., & Freitag, M. (2024). Automatic model generation for material flow simulations of Third-Party Logistics. Journal of Intelligent Manufacturing, 35(8), 3857- 3874.

Swari, M. H. P., Qusyairi, M., Mandyartha, E. P., & Wahanani, H. E. (2021, May). Business Intelligence System using Simple Moving Average Method (Case Study: Sales Medical Equipment at PT. Semangat Sejahtera Bersama). In Journal of Physics: Conference Series (Vol. 1899, No. 1, p. 012121). IOP Publishing.

Tamás, P. (2025). New Dimensions in the Study of Outsourcing Logistics Services: The Role of Digitalization in Enhancing Efficiency. Logistics, 9(2), 44. 12

Tang, Y. M., Ho, G. T. S., Lau, Y. Y., & Tsui, S. Y. (2022). Integrated smart warehouse and manufacturing management with demand forecasting in small-scale cyclical industries. Machines, 10(6), 472.

Tikwayo, L. N., & Mathaba, T. N. (2023). Applications of industry 4.0 technologies in warehouse management: A systematic literature review. Logistics, 7(2), 24.

Tufano, A., Accorsi, R., & Manzini, R. (2022). A machine learning approach for predictive warehouse design. The International Journal of Advanced Manufacturing Technology, 119(3), 2369- 2392.

Watanabe, W. C., Wichaisri, S., & Patitad, P. (2023). Outbound logistics resilience considering customer participation level: A case study of Thailand’s sugar factory. Engineering and Applied Science Research, 50(4), 382-390.

Wolny, M., & Kmiecik, M. (2025). Unveiling Patterns in Forecasting Errors: A Case Study of 3PL Logistics in Pharmaceutical and Appliance Sectors. Sustainability, 17(1), 214.

Zhai, X. (2024). Visualizing Walmart’s supply chain management: A case study on detailed warehouse management practices. Transactions on Economics, Business and Management Research, 10, 37-41.