Performance Measurement in LVHM Manufacturing: Kpis, Technologies, and Startup Gaps

Wickramanayake Pathirannahalage Sajith Dilshan; Andrea Matkó; Domicián Máté; Jolita Vveinhardt

doi:10.15544/mts.2026.06

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Performance Measurement in LVHM Manufacturing: Kpis, Technologies, and Startup Gaps

Management Theory and Studies for Rural Business and Infrastructure Development

Volume 48 (2026): Issue 1 (March 2026)

By: Wickramanayake Pathirannahalage Sajith Dilshan, Andrea Matkó, Domicián Máté and Jolita Vveinhardt

Open Access

|May 2026

Abstract

The high degree of product variability and the ever-changing demands of customers make performance monitoring a constant struggle in low-volume high-mix (LVHM) production settings. This research provides a systematic literature review in accordance with PRISMA principles, examining 42 peer-reviewed papers (2021–2025) sourced from Scopus and Web of Science. Research has indicated that lead time and makespan are the predominant performance indicators. Moreover, digital twins, reinforcement learning, and discrete-event simulation are being increasingly used to increase scheduling efficiency and cost effectiveness. A bibliometric study using R Studio indicates a significant focus on job shop scheduling and AI-driven optimisation. Critically, there is a substantial gap in the literature, as no study explicitly addresses startup LVHM manufacturing. This review lays the foundation for performance frameworks specifically designed for LVHM manufacturers in the early stages of development.

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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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).
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.15544/mts.2026.06 | Journal eISSN: 2345-0355 | Journal ISSN: 1822-6760

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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

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Low-Volume High-Mix Manufacturing,

Startup,

Performance Metrics,

KPI Framework

Related subjects:

Business and economics,

Business management,

Management, organization, corporate governance,

Business development, change management, cooperation,

Business management, other,

Industries,

Agribusiness, food industry

© 2026 Wickramanayake Pathirannahalage Sajith Dilshan, Andrea Matkó, Domicián Máté, Jolita Vveinhardt, published by Vytautas Magnus University
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 License.

Volume 48 (2026): Issue 1 (March 2026)