Performance Measurement in LVHM Manufacturing: Kpis, Technologies, and Startup Gaps
References
- Albayrak, E., & Önüt, S. (2024). Energy-efficient scheduling for a flexible job shop problem considering rework processes and new job arrival. International Journal of Industrial Engineering Computations, 15(4), 871–886 https://doi.org/10.5267/j.ijiec.2024.7.004
- Aljinović, A., Gjeldum, N., Bilić, B., & Mladineo, M. (2022). OptimisationOptimisation of Industry 4.0 implementation selection process towards the enhancement of a manual assembly line. Energies, 15(1). https://doi.org/10.3390/en15010030
- Antons, O., & Arlinghaus, J. C. (2022). Data-driven and autonomous manufacturing control in cyber-physical production systems Computers in Industry, 141 https://doi.org/10.1016/j.compind.2022.103711
- Aslan, A., Vasantha, G., El-Raoui, H., Quigley, J., Hanson, J., Corney, J., & Sherlock, A. (2023) Hierarchical ensemble deep learning for data-driven lead time prediction International Journal of Advanced Manufacturing Technology, 128(9–10), 4169–4188 https://doi.org/10.1007/s00170-023-12123-4
- Bao, B., Duan, Z., Xu, N., Zhang, H., Luo, Y., Wang, W., Yu, X., Luo, Y., & Liu, X. (2023) A new algorithm of the scheduling of a flexible manufacturing system based on genetic algorithm Manufacturing Review, 10 https://doi.org/10.1051/mfreview/2023010
- Behzad, K., & Seyed Taghi, A. N. (2020) Multiobjective optimisation of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm Journal of Risk and Reliability https://doi.org/10.1177/1748006X20946521
- Brochado, A. F., Rocha, E. M., Almeida, D., de Sousa, A., & Moura, A. (2023). A data-driven model with minimal information for bottleneck detection - application at Bosch thermotechnology International Journal of Management Science and Engineering Management, 18(4), 318–331 https://doi.org/10.1080/17509653.2022.2116121
- Carl May, M., Nestroy, C., Overbeck, L., & Lanza, G. (2024) Automated model generation framework for material flow simulations of production systems International Journal of Production Research, 62(1–2), 141–156 https://doi.org/10.1080/00207543.2023.2284833
- Chen, B., Zhang, J., Xiong, J., Tang, W., & Jiang, S. (2025) An explainable multilayer graph attention network for product completion time prediction in aircraft final assembly lines Journal of Manufacturing Systems, 80, 1053–1071 https://doi.org/10.1016/j.jmsy.2025.04.018
- Danishvar, M., Danishvar, S., Katsou, E., Mansouri, S. A., & Mousavi, A. (2021) Energy-Aware Flowshop Scheduling: A Case for AI-Driven Sustainable Manufacturing IEEE Access, 9, 141678–141692 https://doi.org/10.1109/ACCESS.2021.3120126
- Digalwar, A. K., & Sangwan, K. S. (2011) An overview of existing performance measurement frameworks in the context of world class manufacturing performance measurement In Int. J. Services and Operations Management (Vol. 9, Number 1).
- Ding, L., Guan, Z., Luo, D., & Yue, L. (2025) Data-driven hierarchical multipolicy deep reinforcement learning framework for multi-objective multiplicity dynamic flexible job shop scheduling Journal of Manufacturing Systems, 80, 536–562 https://doi.org/10.1016/j.jmsy.2025.03.019
- Filho, I. R., de Souza, F. B., & Ikeziri, L. M. (2023). Analysis of a support method for offering delivery promises in environments managed by S-DBR system Production, 33 https://doi.org/10.1590/0103-6513.20230023
- Gan, Z. L., Musa, S. N., & Yap, H. J. (2023a) A Review of the High-Mix, Low-Volume Manufacturing Industry Applied Sciences (Switzerland), 13(3) https://doi.org/10.3390/app13031687
- Gan, Z. L., Musa, S. N., & Yap, H. J. (2023b) A Review of the High-Mix, Low-Volume Manufacturing Industry Applied Sciences (Switzerland), 13(3) https://doi.org/10.3390/app13031687
- Ghaleb, M., Taghipour, S., & Zolfagharinia, H. (2021) Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance Journal of Manufacturing Systems, 61, 423–449 https://doi.org/10.1016/j.jmsy.2021.09.018
- Gödri, I. (2022). Improving Delivery Performance in High-Mix Low-Volume Manufacturing by Model-Based and Data-Driven Methods Applied Sciences (Switzerland), 12(11) https://doi.org/10.3390/app12115618
- Gödri, I., Kardos, C., Pfeiffer, A., & Váncza, J. (2019) Data analytics-based decision support workflow for high-mix low-volume production systems CIRP Annals, 68(1), 471–474 https://doi.org/10.1016/j.cirp.2019.04.001
- Gunasekaran, A., & Kobu, B. (2007) Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications International Journal of Production Research, 45(12), 2819–2840 https://doi.org/10.1080/00207540600806513;PAGE:STRING:ARTICLE/CHAPTER
- Hammedi, S., Elmeliani, J., & Nabli, L. (2025) Optimising resource allocation in job shop production systems with seasonal demand patterns International Journal of Reconfigurable and Embedded Systems, 14(1), 12–25 https://doi.org/10.11591/ijres.v14.i1.pp12-25
- Heo, C. Y., Seo, J., Kim, Y., Kim, Y., & Kim, T. (2025). Estimated Tardiness-Based Reinforcement Learning Solution to Repeatable Job-Shop Scheduling Problems Processes, 13(1). https://doi.org/10.3390/pr13010062
- Jamwal, A., Agrawal, R., Sharma, M., & Giallanza, A. (2021) Industry 4.0 technologies for manufacturing sustainability: A systematic review and future research directions In Applied Sciences (Switzerland) (Vol. 11, Number 12) MDPI AG https://doi.org/10.3390/app11125725
- Jyothi, K., & Dubey, R. B. (2023) Minimising non-processing energy consumption/total weighted tardiness earliness, and makespan into typical production scheduling model-the job shop scheduling problem Journal of Intelligent and Fuzzy Systems, 45(4), 6959–6981 https://doi.org/10.3233/JIFS-222362
- KOUIDER, A., & AIT HADDADÈNE, H. (2021). A bi-objective branch-and-bound algorithm for the unit-time job shop scheduling : A mixed graph coloring approach Computers and Operations Research, 132 https://doi.org/10.1016/j.cor.2021.105319
- Kusrini, E., & Miranda, S. (2021) Determining Performance Metrics of Supply Chain Management in Make-to-Order Small-Medium Enterprise Using Supply Chain Operation Reference Model (SCOR Version 12.0) Mathematical Modelling of Engineering Problems, 8(5), 750–756 https://doi.org/10.18280/mmep.080509
- Lame, G. (2019) Systematic literature reviews: An introduction Proceedings of the International Conference on Engineering Design, ICED, 2019-August, 1633–1642 https://doi.org/10.1017/dsi.2019.169
- Lee, H., & Yang, H. (2023) Digital Twinning and Optimization of Manufacturing Process Flows Journal of Manufacturing Science and Engineering, 145(11) https://doi.org/10.1115/1.4063234
- Lee, Y., Shin, J., & Lee, W. (2025). Manufacturing process analysis framework for process mining: case study of fully automated factory applications International Journal of Advanced Manufacturing Technology, 136(11), 5641–5664 https://doi.org/10.1007/s00170-025-15029-5
- Li, Y., Gu, W., Yuan, M., & Tang, Y. (2022). Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network Robotics and Computer-Integrated Manufacturing, 74 https://doi.org/10.1016/j.rcim.2021.102283
- Li, Z., & Chen, Y. (2023). Dynamic scheduling of multimemory process flexible job shop problem based on digital twin Computers and Industrial Engineering, 183 https://doi.org/10.1016/j.cie.2023.109498
- Liang, Z., Liu, M., Zhong, P., & Zhang, C. (2023) Application research of a new neighbourhood structure with adaptive genetic algorithm for job shop scheduling problem International Journal of Production Research, 61(2), 362–381 https://doi.org/10.1080/00207543.2021.2007310
- Lindberg, C. F., Tan, S., Yan, J., & Starfelt, F. (2015) Key Performance Indicators Improve Industrial Performance. Energy Procedia, 75, 1785–1790. https://doi.org/10.1016/j.egypro.2015.07.474
- Ling, L., Song, Z. M., Zhang, X., Cao, P. Z., Wang, X. Q., Liu, C. H., & Liu, M. Z. (2024) Manufacturing task data chain-driven production logistics trajectory analysis and optimisation decision making method Advances in Manufacturing, 12(1), 185–206 https://doi.org/10.1007/s40436-023-00454-0
- Liu, P., Zhang, Q., Wang, A., Wen, S., & Pannek, J. (2023) Operator-Based Adaptive Tracking Capacity Control in Complex Manufacturing Processes Applied Sciences (Switzerland), 13(1) https://doi.org/10.3390/app13010449
- May, M. C., Oberst, J., & Lanza, G. (2024) Managing product-inherent constraints with artificial intelligence: production control for time constraints in semiconductor manufacturing Journal of Intelligent Manufacturing https://doi.org/10.1007/s10845-024-02472-6
- Miqueo, A., Gracia-Cadarso, M., Torralba, M., Gil-Vilda, F., & Yagüe-Fabra, J. A. (2023). Multi-Model In-Plant Logistics Using Milkruns for Flexible Assembly Systems under Disturbances: An Industry Study Case Machines, 11(1). https://doi.org/10.3390/machines11010066
- Mousavipour, S. H., Farughi, H., & Ahmadizar, F. (2022) A novel bi-objective model for a job shop scheduling problem with consideration of fuzzy parameters, modified learning effects, and multiple preventive maintenance activities. Scientia Iranica, 29(6 E), 3418–3433. https://doi.org/10.24200/SCI.2021.54614.3839
- Patil, P. S., Sudhir Patil, S., Patil, S. M., & Dhanvijay, M. R. (2024) Development of MS Excel and Power BI Integrated Production Scheduling System for an MSME ENGINEERING ACCESS, 10(2), 124–142 https://doi.org/10.14456/mijet.2024.15
- Rohaninejad, M., Janota, M., & Hanzálek, Z. (2023) Integrated lot-sizing and scheduling: Mitigation of uncertainty in demand and processing time by machine learning Engineering Applications of Artificial Intelligence, 118 https://doi.org/10.1016/j.engappai.2022.105676
- Sit, S. K. H., & Lee, C. K. M. (2023). Design of a Digital Twin in Low-Volume, High-Mix Job Allocation and Scheduling for Achieving Mass Personalization Systems, 11(9). https://doi.org/10.3390/systems11090454
- Tarek, N., Algarni, A. D., El-Hefnawy, N. A., Abdel-Kader, H., & Abdelatey, A. (2025) Knowledge Graph-Enhanced Digital Twin Framework for Optimized Job Shop Scheduling in Smart Manufacturing IEEE Access https://doi.org/10.1109/ACCESS.2025.3532600
- Wang, H., Peng, T., Nassehi, A., & Tang, R. (2023) A data-driven simulation-optimisation framework for generating priority dispatching rules in dynamic job shop scheduling with uncertainties Journal of Manufacturing Systems, 70, 288–308 https://doi.org/10.1016/j.jmsy.2023.08.001
- Wang, Y., Wang, R., Sun, J., Deng, F., Wang, G., & Chen, J. (2025). Attention enhanced reinforcement learning for flexible job shop scheduling with transportation constraints Expert Systems with Applications, 282 https://doi.org/10.1016/j.eswa.2025.127671
- Xue, Z., Li, T., Peng, S. tong, Zhang, C. yong, & Zhang, H. chao (2022) A data-driven method to predict future bottlenecks in a remanufacturing system with multivariant uncertainties Journal of Central South University, 29(1), 129–145 https://doi.org/10.1007/s11771-022-4906-z
- Yang, Y., Altarawneh, L., Alattar, M. S., Farrag, A., Kwon, S., & Jin, Y. (2025). A threshold- and priority-based dispatching rule for the simulation-based dynamic scheduling optimisation in automated manufacturing systems Simulation https://doi.org/10.1177/00375497251328047
- Yuan, M., Li, Z., Zhang, C., Zheng, L., Mao, K., & Pei, F. (2023). Research on real-time prediction of completion time based on AE-CNN-LSTM Computers and Industrial Engineering, 185 https://doi.org/10.1016/j.cie.2023.109677
- Zhang, L., Hu, Y., Tang, Q., Li, J., & Li, Z. (2021). Data-driven dispatching rule mining and real-time decision-making methodology in an intelligent manufacturing shop floor with uncertainty. Sensors, 21(14). https://doi.org/10.3390/s21144836
- Zhang, L., Yan, Y., Yang, C., & Hu, Y. (2024). Dynamic flexible job-shop scheduling by multi-agent reinforcement learning with reward-shaping. Advanced Engineering Informatics, 62 https://doi.org/10.1016/j.aei.2024.102872
- Zheng, P., Zhang, P., Wang, M., & Zhang, J. (2021) A data-driven robust scheduling method integrating particle swarm optimisation algorithm with kernel-based estimation Applied Sciences (Switzerland), 11(12) https://doi.org/10.3390/app11125333
- Zhou, T., Tang, D., Zhu, H., & Wang, L. (2021) Reinforcement Learning with Composite Rewards for Production Scheduling in a Smart Factory IEEE Access, 9, 752–766 https://doi.org/10.1109/ACCESS.2020.3046784
Language: English
Page range: 51 - 66
Submitted on: Feb 3, 2026
Accepted on: Feb 26, 2026
Published on: May 5, 2026
Published by: Vytautas Magnus University
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© 2026 Wickramanayake Pathirannahalage Sajith Dilshan, Andrea Matkó, Domicián Máté, Jolita Vveinhardt, published by Vytautas Magnus University
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