DOI : https://doi.org/10.18517/ijaseit.15.3.20835

Optimal Lot-Sizing Decisions in Production Systems Using the Wagner-Whitin Algorithm for Data-Driven Inventory Control

Bela Pitria Hakim (1), Muhammad Nashir Ardiansyah (2)
(1) Faculty of Industrial Engineering, Telkom University, Jl. Telekomunikasi.1 Dayeuhkolot, Bandung, Indonesia
(2) Faculty of Industrial Engineering, Telkom University, Jl. Telekomunikasi.1 Dayeuhkolot, Bandung, Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
[1]
B. P. Hakim and M. N. Ardiansyah, “Optimal Lot-Sizing Decisions in Production Systems Using the Wagner-Whitin Algorithm for Data-Driven Inventory Control”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 3, pp. 848–856, Jun. 2025.
Citation Format :
The intersection of automation, visualization, and lot sizing, particularly through the Wagner-Whitin algorithm, plays a crucial role in optimizing production processes and improving decision-making efficiency. Determining optimal lot sizes in multi-period production systems is complex due to fluctuating demand, setup costs, holding costs, and capacity constraints. Effective solutions must dynamically address these variables to ensure optimal resource utilization and minimize waste. This study aims to develop an automated calculator that streamlines lot-sizing computations by integrating advanced mathematical models, such as the Wagner-Whitin algorithm, and innovative data visualization techniques. To design and evaluate this calculator, the study compares its effectiveness with traditional methods, such as lot-for-lot, with a focus on enhanced usability and user satisfaction. The study uses historical production data, including demand forecasts, setup costs, holding costs, and capacity constraints, to validate the model. The tool integrates the Wagner-Whitin algorithm for optimal lot sizing and incorporates sensitivity analysis to assess various scenarios. A comparative analysis is performed, testing the automated calculator against conventional methods. Performance metrics, including accuracy, calculation speed, scalability, and error reduction, are evaluated in simulated multi-period production environments. The results demonstrate that the automated calculator significantly improves calculation accuracy, decision-making, and error reduction compared to traditional methods. This research highlights the transformative potential of automated solutions in enhancing manufacturing operations. Future studies could expand the tool to address complex constraints, such as supply chain disruptions and multi-echelon inventory systems, and incorporate machine learning for improved demand forecasting accuracy and adaptability.

Y. Li, F. Saldanha-da-Gama, M. Liu, and Z. Yang, "A risk-averse two-stage stochastic programming model for a joint multi-item capacitated line balancing and lot-sizing problem," Eur. J. Oper. Res., vol. 304, no. 1, pp. 353-365, Jan. 2023, doi: 10.1016/j.ejor.2021.09.043.

L. Mönch and L. Shen, "Parallel machine scheduling with the total weighted delivery time performance measure in distributed manufacturing," Comput. Oper. Res., vol. 127, p. 105126, Mar. 2021, doi: 10.1016/j.cor.2020.105126.

R. Bin Hasan, H. Osman, A. Azab, and F. Baki, "Improvement to an existing multi-level capacitated lot sizing problem considering setup carryover, backlogging, and emission control," Manuf. Lett., vol. 35, pp. 28-39, Aug. 2023, doi: 10.1016/j.mfglet.2023.07.012.

C. Li, P. Zheng, Y. Yin, B. Wang, and L. Wang, "Deep reinforcement learning in smart manufacturing: A review and prospects," CIRP J. Manuf. Sci. Technol., vol. 40, pp. 75-101, Feb. 2023, doi:10.1016/j.cirpj.2022.11.003.

M. H. Jabeur et al., "Optimizing integrated lot sizing and production scheduling in flexible flow line systems with energy scheme: A two level approach based on reinforcement learning," Comput. Ind. Eng., vol. 190, p. 110095, Apr. 2024, doi: 10.1016/j.cie.2024.110095.

V. Suhandi and P.-S. Chen, "Closed-loop supply chain inventory model in the pharmaceutical industry toward a circular economy," J. Clean. Prod., vol. 383, p. 135474, Jan. 2023, doi:10.1016/j.jclepro.2022.135474.

M. M. Pereira and E. M. Frazzon, "A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains," Int. J. Inf. Manage., vol. 57, p. 102165, Apr. 2021, doi:10.1016/j.ijinfomgt.2020.102165.

S. Beraudy, N. Absi, and S. Dauzère-Pérès, "Timed route approaches for large multi-product multi-step capacitated production planning problems," Eur. J. Oper. Res., vol. 300, no. 2, pp. 602-614, Jul. 2022, doi: 10.1016/j.ejor.2021.08.011.

J. Chen, B. Panigrahi, and M. Khojasteh, "Spreadsheet model for activity-on-node project using Excel evolutionary solving method," J. Acad. Bus. Econ., vol. 23, no. 1, pp. 41-52, Mar. 2023, doi:10.18374/jabe-23-1.3.

N. Kumar, B. J. Singh, and P. Khope, "Unleashing an ML-based selection criteria for economic lot sizing in a smart batch-type production system," TQM J., vol. 36, no. 1, pp. 90-107, Jan. 2024, doi:10.1108/tqm-05-2022-0166.

M. Lujak, A. Fernández, and E. Onaindia, "Spillover algorithm: A decentralised coordination approach for multi-robot production planning in open shared factories," Robot. Comput.-Integr. Manuf., vol. 70, p. 102110, Aug. 2021, doi: 10.1016/j.rcim.2020.102110.

B. Beykal, S. Avraamidou, and E. N. Pistikopoulos, "Data-driven optimization of mixed-integer bi-level multi-follower integrated planning and scheduling problems under demand uncertainty," Comput. Chem. Eng., vol. 156, p. 107551, Jan. 2022, doi:10.1016/j.compchemeng.2021.107551.

T. Hamadneh et al., "Application of stork optimization algorithm for solving sustainable lot size optimization," Comput., Mater. Contin., vol. 80, no. 2, pp. 2005-2030, 2024, doi: 10.32604/cmc.2024.052401.

I. Slama et al., "Stochastic program for disassembly lot-sizing under uncertain component refurbishing lead times," Eur. J. Oper. Res., vol. 303, no. 3, pp. 1183-1198, Dec. 2022, doi: 10.1016/j.ejor.2022.03.025.

P. K. Mutyala and A. Kaur, "Inventory optimization of multi-echelon supply chain under market uncertainties," in Proc. 1st Int. Conf. Pioneering Develop. Comput. Sci. Digit. Technol. (IC2SDT), Aug. 2024, pp. 257-262, doi: 10.1109/IC2SDT62152.2024.10696052.

M. Bertolini et al., "Machine learning for industrial applications: A comprehensive literature review," Expert Syst. Appl., vol. 175, p. 114820, Aug. 2021, doi: 10.1016/j.eswa.2021.114820.

A. Kallmuenzer et al., "Adoption and performance outcome of digitalization in small and medium-sized enterprises," Rev. Manag. Sci., pp. 1-28, 2024, doi: 10.1007/s11846-024-00744-2.

P. Suryawanshi and P. Dutta, "Optimization models for supply chains under risk, uncertainty, and resilience: A state-of-the-art review and future research directions," Transp. Res. E, Logist. Transp. Rev., vol. 157, p. 102553, Jan. 2022, doi: 10.1016/j.tre.2021.102553.

L. L. Lorente-Leyva, M. M. E. Alemany, and D. H. Peluffo-Ordóñez, "A conceptual framework for the operations planning of the textile supply chains: Insights for sustainable and smart planning in uncertain and dynamic contexts," Comput. Ind. Eng., vol. 187, p. 109824, 2024, doi: 10.1016/j.cie.2023.109824.

S. Ramingwong et al., "Factory logistics improvement: A case study analysis of companies in Northern Thailand, 2022-2024," Logistics, vol. 8, no. 3, p. 88, Sep. 2024, doi: 10.3390/logistics8030088.

R. Kumar and S. Pande, "Medical inventory cost reduction: A survey based study," AIP Conf. Proc., p. 020022, 2023, doi:10.1063/5.0162741.

M. F. Zia et al., "Energy management system for a hybrid PV-wind-tidal-battery-based islanded DC microgrid: Modeling and experimental validation," Renewable Sustain. Energy Rev., vol. 159, p. 112093, May 2022, doi: 10.1016/j.rser.2022.112093.

M. Koot, M. R. K. Mes, and M. E. Iacob, "A systematic literature review of supply chain decision making supported by the Internet of Things and Big Data Analytics," Comput. Ind. Eng., vol. 154, p. 107076, Apr. 2021, doi: 10.1016/j.cie.2020.107076.

M. Arampatzis, M. Pempetzoglou, and A. Tsadiras, "Two lot-sizing algorithms for minimizing inventory cost and their software implementation," Information, vol. 15, no. 3, p. 167, Mar. 2024, doi:10.3390/info15030167.

Y. Tadayonrad and A. B. Ndiaye, "A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality," Supply Chain Anal., vol. 3, p. 100026, Sep. 2023, doi: 10.1016/j.sca.2023.100026.

O. Abbaas and J. A. Ventura, "A flexible combinatorial auction bidding language for supplier selection and order allocation in a supply chain with price sensitive demand," Comput. Ind. Eng., vol. 194, p. 110373, Aug. 2024, doi: 10.1016/j.cie.2024.110373.

E. M. D. O. Sánchez, L. L. Hernández, and A. L. Hernández, "Supply of raw materials in an automotive manufacturing company," Int. J. Manag. Decis. Making, vol. 20, no. 4, p. 323, 2021, doi:10.1504/ijmdm.2021.118541.

K. Tamssaouet, E. Engebrethsen, and S. Dauzère-Pérès, "Multi-item dynamic lot sizing with multiple transportation modes and item fragmentation," Int. J. Prod. Econ., vol. 265, p. 109001, Nov. 2023, doi: 10.1016/j.ijpe.2023.109001.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).