Improving Delivery Performance in High-Mix Low-Volume Manufacturing by Model-Based and Data-Driven Methods
Source abstract
Abstract
In a high-mix and low-volume (HMLV) manufacturing environment where demand fluctuation is the rule rather than the exception, daily production management in face of conflicting key performance indicators such as high delivery precision, short lead time, and maximal resource utilization is a most challenging task. This situation may even be hampered by unreliable supplier performance. This paper presents a generic decision support workflow, which first identifies the most critical external and internal factors which have a serious impact on delivery performance. Next, it suggests a method which combines traditional manufacturing system simulation with advanced machine learning techniques to support the improved daily routine lot-sizing and production scheduling activities in a HMLV company. Argumentation is motivated and illustrated by a detailed industrial case study.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Flexible and Reconfigurable Manufacturing Systems · Digital Transformation in Industry · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Emerson Automation FCP Ltd., 3300 Eger, Hungary
References (28)
Show source-provided references
1. Cyber-physical systems in manufacturing10.1016/j.cirp.2016.06.005 ↗
2. Beregi, R., Pedone, G., Háy, B., and Váncza, J. (2021). Manufacturing execution system integration through the standardization of a common service model for cyber-physical production systems. Appl. Sci., 11.10.3390/app11167581 ↗
3. Renaissance of group technology: Reducing variability to match lean production prerequisites
4. Leveling of a low volume and high mix production based on a Group Technology approach10.1016/j.cirpj.2011.06.003 ↗
5. Work in process control for a high product mix manufacturing system10.1016/j.procir.2017.03.352 ↗
6. Kanban for lean production in high mix, low volume environments
7. To pull or not to pull: What is the question?10.1287/msom.1030.0028 ↗
8. Improving the overall equipment effectiveness in high-mix-low-volume manufacturing environments10.1016/j.cirp.2015.04.126 ↗
9. Dynamic modelling of impact of lean policies on production levelling feasibility10.1016/j.cirp.2014.03.108 ↗
10. Job shop control: In search of the key to deliver improvements10.1016/j.ijpe.2015.07.007 ↗
11. Lot splitting scheduling procedure for makespan reduction and machine capacity increase in a hybrid flow shop with batch production10.1007/s00170-011-3525-x ↗
12. A review of lot streaming10.1080/00207543.2013.774506 ↗
13. Lot splitting under load-limiting order release in high- variety shops: An assessment by simulation10.1016/j.jmsy.2018.06.003 ↗
14. Aligning workload control theory and practice: Lot splitting and operation overlapping10.1080/00207543.2016.1143134 ↗
15. Data analytics-based decision support workflow for high-mix low-volume production system10.1016/j.cirp.2019.04.001 ↗
16. A catalogue of digital solution areas for prioritising the needs of manufacturing SMEs10.1016/j.compind.2021.103532 ↗
17. Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook10.1016/j.compind.2016.02.004 ↗
18. A survey of the advancing use and development of machine learning in smart manufacturing10.1016/j.jmsy.2018.02.004 ↗
19. A survey on knowledge transfer for manufacturing data analytics10.1016/j.compind.2018.07.001 ↗
20. Big data analytics in supply chain management between 2010 and 2016: Insights to industries10.1016/j.cie.2017.11.017 ↗
21. Substitution and complementation of production management functions with data analytics10.1016/j.procir.2018.03.145 ↗
22. Simulation-based decision support tool for in-house logistics: The basis for a digital twin10.1016/j.cie.2020.107094 ↗
23. Gödri, I., Kardos, C., Pfeiffer, A., and Váncza, J. (2018, January 17–19). Scenario-based analysis of a high-mix low-volume production environment. Proceedings of the 17th International Conference on Modelling & Applied Simulation (MAS 2018), Budapest, Hungary.
24. Applying value stream mapping in manufacturing: A systematic literature review
25. Hastie, T., Tibshirani, R., and Friedman, J. (2008). The Elements of Statistical Learning, Springer Series in Statistics. [2nd ed.].
26. Systematic procedure for leveling of low volume and high mix production10.1016/j.cirpj.2012.10.003 ↗
27. A new method for autonomous control of complex job shops–integrating order release, sequencing and capacity control to meet due dates10.1016/j.jmsy.2016.10.006 ↗
28. Lessons learned from the COVID-19 pandemic and their possible consequences on manufacturing10.1520/SSMS20200063 ↗