Abstract
Companies today face growing demands for customization, necessitating the maintenance of high quality, low cost, and short lead times. Workload management plays a crucial role in achieving these objectives, and it primarily involves two forms: input control and output control. Existing literature has significantly focused on input control while often overlooking output control. This research focuses on the critical lever of workers’ flexibility as an essential output control element and investigates its impact on lead time performance. This research focuses on recognizing workers’ flexibility as a key competitive opportunity for companies, allowing them to cope effectively with temporary job demand imbalances. Companies can optimize direct labor utilization and production lead times by reallocating workers from underloaded stations to overloaded ones, thereby driving higher profit margins and customer satisfaction. To evaluate the benefits of increased flexibility, we employ discrete-event simulation techniques demonstrating a significant reduction in lead time. Additionally, we examine the impact of limiting workers’ movement across workstations. Surprisingly, These results demonstrate that, despite reducing the number of workers’ relocations, the advantages outweigh the negative effect on lead time. Companies can achieve optimal output control strategies that enhance their overall performance by improving the trade-off between lead time and the number of workers’ relocations. Overall, this research underscores the importance of output control in workload management and highlights workers’ flexibility as a critical lever to improve lead time performance. By adopting effective output control strategies, companies can efficiently align capacity and orders, enhancing operational performance and customer satisfaction. This study provides valuable insights into the dynamics of workload management and offers practical recommendations for manufacturing organizations seeking to optimize their processes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Enrique, D.V., Druczkoski, J.C.M., Lima, T.M., Charrua-Santos, F.: Advantages and difficulties of implementing Industry 4.0 technologies for labor flexibility. Procedia Comput. Sci. 181, 347–352 (2021). https://doi.org/10.1016/j.procs.2021.03.130
International Federation of Robotics. (2022). Positive Impact of Industrial Robots on Employment
Pasparakis, A., De Vries, J., De Koster, R.: Assessing the impact of human-robot collaborative order picking systems on warehouse workers. J. Oper. Manag. (2023), Advance online publication.https://doi.org/10.1080/00207543.2023.2183343
Francas, D., Löhndorf, N., Minner, S.: Machine and labor flexibility in manufacturing networks.Int. J. Prod. Econ. (2011).https://doi.org/10.1016/j.ijpe.2010.03.014
Costa, F., Kundu, K., Rossini, M., Portioli-Staudacher, A.: Comparative study of bottleneck-based release models and load-based ones in a hybrid MTO-MTS flow shop: an assessment by simulation. Oper. Manag. Res. 16(1), 33–48 (2023)
Portioli-Staudacher, A., Costa, F., Thürer, M.: The use of labour flexibility for output control in workload controlled flow shops: a simulation analysis. In: Proceedings of the 16th International Conference on Factory Communication Systems, vol. 12543, pp. 429–442 (2020). https://doi.org/10.5267/j.ijiec.2019.11.004
Costa, F., Portioli-Staudacher, A.: Labor flexibility integration in workload control in Industry 4.0 era. Oper. Manag. Res. 14(3–4), 420–433 (2021). https://doi.org/10.1007/s12063-021-00184-6
Askar, G., Sillekens, T., Suhl, L., Zimmermann, J.: Flexibility planning in automotive plants. In: Günther, H.-O., Mattfeld, D.C., Suhl, L. (eds.) Management Logistischer Netzwerke, pp. 235–255. Physica, Heidelberg (2007). https://doi.org/10.1007/978-3-7908-1916-9_14
Thürer, M., Zhang, H., Stevenson, M., Costa, F., Ma, L.: Worker assignment in dual resource constrained assembly job shops with worker heterogeneity: an assessment by simulation. In: Dornberger, R., Jung, R., Steinmetz, R. (Eds.) Proceedings of the 16th International Conference on Factory Communication Systems, pp. 6336–6349. Springer Lecture Notes in Computer Science, vol. 12543 (2020). https://doi.org/10.1007/978-3-030-55184-2_492
Kher, H., Malhotra, M.: Acquiring and operationalizing worker flexibility in dual resource constrained job shops with worker transfer delays and learning losses. Omega 22, 521–533 (1994). https://doi.org/10.1016/0305-0483(94)90032-9
Malhotra, M.K., Narasimhan, R., Giri, B.C.: The impact of learning and labor attrition on worker flexibility in dual resource constrained job shops. Decis. Sci. 24, 641–664 (1993). https://doi.org/10.1111/j.1540-5915.1993.tb01296.x
Kingsman, B., Hendry, L.: The relative contributions of input and output controls on the performance of a workload control system in make-to-order companies. Prod. Plann. Control. 13, 579–590 (2002). https://doi.org/10.1080/0953728021000026285
Thürer, M.J., Land, M., Stevenson, M., Fredendall, L.D.: On the integration of due date setting and order release control. Prod. Plann. Control. 28, 420–430 (2017). https://doi.org/10.1080/09537287.2017.1302102
Huang, Y.: Information architecture for effective workload control: an insight from a successful implementation. Prod. Plann. Control. 28, 351–366 (2017). https://doi.org/10.1080/09537287.2017.1288278
Gupta, S., Jain, S.K.: A literature review of lean manufacturing. Int. J. Manag. Sci. Eng. Manag. 8, 241–249 (2013). https://doi.org/10.1080/17509653.2013.825074
Sammarco, M., Fruggiero, F., Neumann, W.P., Lambiase, A.: Agent-based modeling of movement rules in DRC systems for volume flexibility: human factors and technical performance. Int. J. Prod. Res. 52(3), 633–650 (2014). https://doi.org/10.1080/00207543.2013.807952
Araz, Ö.U., Salum, L.: A multi-criteria adaptive control scheme based on neural networks and fuzzy inference for DRC manufacturing systems. Int. J. Prod. Res. 48(1), 251–270 (2010). https://doi.org/10.1080/00207540802471256
Małachowski, B., Korytkowski, P.: Competence-based performance model of multi-skilled workers. Comput. Ind. Eng. 91, 165–177 (2016). https://doi.org/10.1016/j.cie.2015.11.018
Boysen, N., Fliedner, M., Scholl, A., Bock, S.: A classification of assembly line balancing problems. Jenaer Schriften zur Wirtschaftswissenschaft (2006). Available at: https://pdfs.semanticscholar.org/c9c5/1dd66ddf14b0a0af0fa62e86ff49fb2dac5d.pdf
Wang, X.V., Wang, L.: Augmented reality enabled human–robot collaboration. Adv. Human-Robot Collaboration Manuf. 395–411(2021). https://doi.org/10.1007/978-3-030-69178-3_16
Esengün, M., Üstündağ, A., İnce, G.: Development of an augmented reality-based process management system: the case of a natural gas power plant. IISE Trans. 55, 201–216 (2023). https://doi.org/10.1080/24725854.2022.2034195
Moreira, M.R.A., Alves, R.A.F.S.: Input-output control order release mechanism in a job-shop: how workload control improves manufacturing operations. Int. J. Comput. Sci. Eng. 7(3), 214–223 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 IFIP International Federation for Information Processing
About this paper
Cite this paper
Costa, F., Ahmadi, A., Portioli-Staudacher, A. (2023). Optimizing Performance-Allocation Trade-Off: The Role of Human-Machine Interface Technology in Empowering Multi-skilled Workers in Industry 4.0 Factories. In: Alfnes, E., Romsdal, A., Strandhagen, J.O., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures. APMS 2023. IFIP Advances in Information and Communication Technology, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-031-43662-8_51
Download citation
DOI: https://doi.org/10.1007/978-3-031-43662-8_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43661-1
Online ISBN: 978-3-031-43662-8
eBook Packages: Computer ScienceComputer Science (R0)