Integrating data-driven mechanisms for enhancing efficiency in business administration through biomechanics and bio-inspired modeling
Abstract
This paper explores integrating biomechanics data and bio-inspired models to enhance efficiency in business administration, focusing on task scheduling, resource allocation, and workflow optimization. Biomechanics, traditionally applied in fields such as healthcare and sports, is used to analyze human movement and physical strain in business processes, particularly in physically demanding environments like manufacturing and logistics. Bio-inspired models, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), are applied to solve complex optimization problems in resource management and task scheduling. The study presents three case studies to demonstrate the practical application of these methodologies: (1) workflow optimization in a manufacturing environment using biomechanics data to reduce physical strain and improve task completion times; (2) resource allocation in Supply Chain Management (SCM) using PSO to minimize transportation and labor costs while improving warehouse utilization and delivery times; and (3) task scheduling in an office environment using GA to enhance task efficiency, workload distribution, and employee satisfaction. The case study results demonstrate the practical application of these methodologies: (1) a 21.6% reduction in shoulder joint strain and an 18.2% improvement in task completion time in a manufacturing setting; (2) a 16.1% reduction in transportation costs and an 18.6% improvement in warehouse utilization in SCM using PSO; and (3) a 17.6% decrease in makespan and a 29.8% improvement in workload distribution through GA-based task scheduling in an office environment. These findings underscore the potential of combining human-centered biomechanics data with bio-inspired optimization models to improve operational efficiency, employee well-being, and cost-effectiveness significantly.
References
1. Kolasani, S. (2023). Leadership in business innovation and transformation, navigating complex digital landscapes and enterprise technology ecosystems and achieving sustainable growth in today’s rapidly evolving market. International Journal of Holistic Management Perspectives, 4(4), 1–23.
2. Porath, U. (2023). Advancing managerial evolution and resource management in contemporary business landscapes. Modern Economy, 14(10), 1404–1420.
3. Kuhn, K. M., Meijerink, J., and Keegan, A. (2021). Human resource management and the gig economy: Challenges and opportunities at the intersection between organizational HR decision-makers and digital labor platforms. Research in personnel and human resources management, 39, 1–46.
4. Eden, L., and Nielsen, B. B. (2020). Research methods in international business: The challenge of complexity. Journal of International Business Studies, 51, 1609–1620.
5. Leppänen, P., George, G., and Alexy, O. (2023). When do novel business models lead to high performance? A configurational approach to value drivers, competitive strategy, and firm environment. Academy of Management Journal, 66(1), 164–194.
6. Nicoloso, L. G. D. V. (2021). Data-Driven Systems Engineering for Bioinspired Integrative Design. University of California, San Diego.
7. De Vivo Nicoloso, L. G. (2021). Data-driven systems engineering for bioinspired integrative design (Doctoral dissertation, UC San Diego).
8. Azeez, T. O., Lamina, S., and Chukwunenye, O. Human biomechanics: basic and applied.
9. Alderink, G. J., and Ashby, B. M. (2023). Clinical kinesiology and biomechanics: a problem-based learning approach. Springer Nature.
10. Kamala, V., Yamini, S., and Gajanand, M. S. (2024). Ergonomic risks affecting the performance of work-from-home employees in IT industry: a comprehensive analysis. International Journal of Productivity and Performance Management.
11. Indumathi N et al., Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach, Sensors, 2023, 23 (9), 4365; DOI: 10.3390/s23094365.
12. Parkavi K et al., Effective Scheduling of Multi-Load Automated Guided Vehicle in Spinning Mill: A Case Study, IEEE Access, 2023, DOI: 10.1109/ACCESS.2023.3236843.
13. Ran Q et al., English language teaching based on big data analytics in augmentative and alternative communication system, Springer-International Journal of Speech Technology, 2022, DOI: 10.1007/s10772-022-09960-1.
14. Ngangbam PS et al., Investigation on characteristics of Monte Carlo model of single electron transistor using Orthodox Theory, Elsevier, Sustainable Energy Technologies and Assessments, Vol. 48, 2021, 101601, DOI: 10.1016/j.seta.2021.101601.
15. Huidan Huang et al., Emotional intelligence for board capital on technological innovation performance of high-tech enterprises, Elsevier, Aggression and Violent Behavior, 2021, 101633, DOI: 10.1016/j.avb.2021.101633.
16. Sudhakar S, et al., Cost-effective and efficient 3D human model creation and re-identification application for human digital twins, Multimedia Tools and Applications, 2021. DOI: 10.1007/s11042-021-10842-y.
17. Prabhakaran N et al., Novel Collision Detection and Avoidance System for Mid-vehicle Using Offset-Based Curvilinear Motion. Wireless Personal Communication, 2021. DOI: 10.1007/s11277-021-08333-2.
18. Balajee A et al., Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest, J Ambient Intell Human Comput, 2021, DOI: 10.1007/s12652-020-02869-0.
19. Omnia SN et al., An educational tool for enhanced mobile e-Learning for technical higher education using mobile devices for augmented reality, Microprocessors and Microsystems, 83, 2021, 104030, DOI: 10.1016/j.micpro.2021.104030 .
20. Firas TA et al., Strategizing Low-Carbon Urban Planning through Environmental Impact Assessment by Artificial Intelligence-Driven Carbon Foot Print Forecasting, Journal of Machine and Computing, 4(4), 2024, doi: 10.53759/7669/jmc202404105.
21. Shaymaa HN, et al., Genetic Algorithms for Optimized Selection of Biodegradable Polymers in Sustainable Manufacturing Processes, Journal of Machine and Computing, 4(3), 563–574, https://doi.org/10.53759/7669/jmc202404054.
22. Hayder MAG et al., An open-source MP + CNN + BiLSTM model-based hybrid model for recognizing sign language on smartphones. Int J Syst Assur Eng Manag (2024). https://doi.org/10.1007/s13198-024-02376-x
23. Bhavana Raj K et al., Equipment Planning for an Automated Production Line Using a Cloud System, Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, 565, 707–717, Springer, Singapore. DOI: 10.1007/978-981-19-7455-7_57.
24. Patel, V., Chesmore, A., Legner, C. M., and Pandey, S. (2022). Trends in workplace wearable technologies and connected‐worker solutions for next‐generation occupational safety, health, and productivity. Advanced Intelligent Systems, 4(1), 2100099.
25. Kamarudin, W. F. W., Khir, N. H. M., Zulkifli, N. I. H. M., Azman, Y. M., and Ruslan, Q. A. (2024). Ergonomic Risk Assessment and Mitigation Strategies for Retail Workers: Insights from a Case Study in Dungun. International Journal of Business and Technology Management, 6(3), 48–57.
26. Brunner, S., Yuching, C. K., and Bengler, K. (2024). Streamlining operations management by classifying methods and concepts of Lean and Ergonomics within a sociotechnical framework. Operations Management Research, 1–25.
27. Bharathi, P., Ramachandran, M., Ramu, K., and Chinnasamy, S. (2022). A study on various particle swarm optimization techniques used in current scenario. Des. Model. Fabr. Adv. Robot, 1, 15–26.
28. Alam, T., Qamar, S., Dixit, A., and Benaida, M. (2020). Genetic algorithm: Reviews, implementations, and applications. arXiv preprint arXiv: 2007.12673.
29. Tang, J., Liu, G., and Pan, Q. (2021). A review on representative swarm intelligence algorithms for solving optimization problems: Applications and trends. IEEE/CAA Journal of Automatica Sinica, 8(10), 1627–1643.
30. Darvishpoor, S., Darvishpour, A., Escarcega, M., and Hassanalian, M. (2023). Nature-inspired algorithms from oceans to space: A comprehensive review of heuristic and meta-heuristic optimization algorithms and their potential applications in drones. Drones, 7(7), 427.
31. Sengan Sudhakar and S. Chenthur Pandian, (2016), ‘Hybrid Cluster-based Geographical Routing Protocol to Mitigate Malicious Nodes in Mobile Ad Hoc Network, InderScience-International Journal of Ad Hoc and Ubiquitous Computing, vol. 21, no. 4, pp. 224-236. DOI:10.1504/IJAHUC.2016.076358.
32. Hu, J., Wu, H., Zhong, B., and Xiao, R. (2020). Swarm intelligence-based optimisation algorithms: an overview and future research issues. International Journal of Automation and Control, 14(5–6), 656–693.
Copyright (c) 2025 Zilian Li, Guixian Tian
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
