research-article
Authors: Alexandros Xanthopoulos and Ioannis Kostavelis
Published: 02 July 2024 Publication History
- 0citation
- 0
- Downloads
Metrics
Total Citations0Total Downloads0Last 12 Months0
Last 6 weeks0
New Citation Alert added!
This alert has been successfully added and will be sent to:
You will be notified whenever a record that you have chosen has been cited.
To manage your alert preferences, click on the button below.
Manage my Alerts
New Citation Alert!
Please log in to your account
- View Options
- References
- Media
- Tables
- Share
Abstract
The importance of supply chain management is paramount nowadays and this research seeks ways for better supply chain coordination. We present a roadmap for implementing true digital replicas of physical supply chains based on a disruptive simulation modeling approach. These digital replicas can enable the comparative evaluation of existing Just-In-Time (JIT) control policies for the first time without the unrealistic assumptions of existing studies in the literature. These realistic supply chain simulation models have the prospect of giving us better understanding of their behavior and of their complex dynamics. We provide guidelines for using the digital replicas and advanced optimization approaches to derive novel supply chain coordination policies pushing the field of JIT control beyond the state-of-the-art. An equally important contribution of this research is that it advances the field of hybrid discrete event-system dynamics (DES-SD) simulation with the prospect of pushing Systems Engineering to the next level. This research discusses theoretical foundations for mixing DES with SD simulation, and explores technical aspects and implementation details. The implications of this extent well beyond the supply chain field since both DES and SD have dozens of application areas including manufacturing, service systems and economics, social sciences, respectively. The paper is concluded with a discussion on the potential impact of the proposed approach including insights for practitioners, applicability of scientific results in industry and prerequisites for business process re-engineering.
References
[1]
Martin. Cristopher, Logistics and Supply Chain Management, Pearson Education Ltd, 2023.
[2]
Michael. Hugos, Essentials of Supply Chain Management, John Wiley & Sons, Inc, 2018.
[3]
Satie L.T. Berger, Guilherme L. Tortorella, Enzo M. Frazzon, Simulation-based analysis of inventory strategies in lean supply chains, IFAC PapersOnLIne 51 (11) (2018) 1453–1458.
[4]
Mansoor Shekarian, Seyed V.R. Nooraie, Mahour M. Parast, An examination of the impact of flexibility and agility on mitigating supply chain disruptions, International Journal of Production Economics 220 (2020) article.
[5]
Wenming Chung, Srinivas Talluri, Gyongyi Kovacs, Investigating the effects of lead-time uncertainties and safety stocks on logistical performance in a border-crossing JIT supply chain, Computers & Industrial Engineering 118 (2018) 440–450.
[6]
Dumitrita I. Apafaian, Diana M. Egri, Cristina Veres, Case study regarding the implementation of one-piece flow line in automotive company, Procedia Manufacturing 46 (2020) 244–248.
[7]
Pedro L. Gonzalez-R, Jose M. Framinan, Rafael Ruiz-Usano, A methodology for the design and operation of pull-based supply chains, Journal of Manufacturing Technology Management 24 (3) (2013) 307–330.
[8]
Pedro L. Gonzalez-R, Jose M. Framinan, Henry Pierreval, Token-based pull production control systems: an introductory overview, Journal of Intelligent Manufacturing 23 (2012) 5–22.
[9]
Gustavo Bagni, Moacir G. Filho, Matthias Thurer, Mark Stevenson, Systematic review and discussion of production control systems that emerged between 1999 and 2018, Production Planning & Control 32 (7) (2021) 511–525.
[10]
Alexandros S. Xanthopoulos, Dimitrios E. Koulouriotis, Multi-objective optimization of production control mechanisms for multi-stage serial manufacturing-inventory systems, International Journal of Advanced Manufacturing Technology 74 (2014) 1507–1519.
[11]
Alexandros S. Xanthopoulos, Dimitrios E. Koulouriotis, Antonios Gasteratos, Adaptive card-based production control policies, Computers & Industrial Engineering 103 (2017) 131–144.
[12]
Lorena S. Belisario, Henri Pierreval, Using genetic programming and simulation to learn how to dynamically adapt the number of cards in reactive pull systems, Expert Systems with Applications 42 (6) (2015) 3129–3141.
[13]
Onur Golbasi, Merve O. Turan, A discrete-event simulation algorithm for the optimization of multi-scenario maintenance policies, Computers & Industrial Engineering 145 (2020) article.
[14]
Alexandros S. Xanthopoulos, Georgios Chnitidis, Dimitrios E. Koulouriotis, Reinforcement Learning-based adaptive production control of pull manufacturing systems, Journal of Industrial and Production Engineering 36 (5) (2019) 313–323.
[15]
Bin Hu, Zhankun Sun, Managing self-replicating innovative goods, Management Science 68 (1) (2021) 399–419.
[16]
Yonit. Barron, The continuous (S, s, Se) inventory model with dual sourcing and emergency orders, European Journal of Operational Research 301 (1) (2022) 18–38.
[17]
Dariush Z. Dadaneh, Sajad Moradi, Behrooz Alizadeh, Simultaneous planning of purchase orders, production, and inventory management under demand uncertainty, International Journal of Production Economics 265 (2023) article.
[18]
Martin Kunath, Herwig Winkler, Integrating the digital twin of the manufacturing system into a decision support system for improving the order management process, Procedia CIRP 72 (2018) 225–231.
[19]
Dangerfield, Brian (ed). (2020) System Dynamics, Springer.
[20]
Antuela A. Tako, Stewart Robinson, The application of discrete event simulation and system dynamics in the logistics and supply chains context, Decision Support Systems 52 (2012) 802–815.
[21]
Anna P.G. Scheidegger, Tabata F. Pereira, Mona L.M.de Oliveira, Amarnath Banerjee, Jose A.B. Montevechi, An introductory guide for hybrid simulation modelers on the primary simulation methods in industrial engineering identified through a systematic review of the literature, Computers & Industrial Engineering 124 (2018) 474–492.
[22]
Jennifer Morgan, Susan Howick, Valerie Belton, Designs for the complimentary use of system dynamics and discrete-event simulation, in: Proceedings of the 2011 Winter Simulation Conference, 2011, pp. 2710–2722.
[23]
Jennifer S. Morgan, Susan Howick, Valerie Belton, A toolkit of designs for mixing discrete event simulation and system dynamics, European Journal of Operational Research 257 (2017) 907–918.
[24]
Alexandros S. Xanthopoulos, Dimitrios E. Koulouriotis, Cluster analysis and neural network-based metamodeling of priority rules for dynamic sequencing, Journal of Intelligent Manufacturing 29 (1) (2018) 69–91.
[25]
Panagiotis D. Paraschos, Alexandros S. Xanthopoulos, Georgios K. Koulinas, Dimitrios E. Koulouriotis, Machine learning integrated design and operation management for resilient circular manufacturing systems, Computers & Industrial Engineering 167 (2022).
[26]
Dimitrios Katsios, Alexandros S. Xanthopoulos, Dimitrios E. Koulouriotis, Athanasios Kiatipis, A simulation optimisation tool and its production/inventory control application, International Journal of Simulation Modelling 17 (2) (2018) 257–270.
[27]
Sanjeev Kumar, K. Mishra, Role of information technology in successful implementation of business process re-engineering, Tactful Management Research Journal 2 (7) (2014) 1–8.
[28]
Jaime A. Palma-Mendoza, Hybrid SD/DES simulation for supply chain analysis, Encyclopedia of Business Analytics and Optimization, 2014, pp. 1139–1144.
[29]
Martin Kunc, Michael J. Mortenson, Richard Vidgen, A computational literature review of the field of System Dynamics from 1974 to 2017, Journal of Simulation 12 (2) (2018) 15–127.
Recommendations
- Robust dynamic schedule coordination control in the supply chain
We extend the literature by representing the robust coordination approach.We consider disruptions in capacities and supply.The application of attainable sets to supply chain coordination. Coordination plays crucial role in supply chain management. In ...
Read More
- System Dynamics Modeling and Simulation of Multi-stage Supply Chain under Random Demand
ICEE '10: Proceedings of the 2010 International Conference on E-Business and E-Government
The paper starts with the Casual Loop Diagram (CLD) of each supply chain node. The system dynamics model of supply chain node is given. Based on this, the model of multi-stage supply chain consisting of manufacturer, distributor, wholesaler and retailer ...
Read More
- Supply chain and hybrid simulation in the hierarchical enterprise
Read More
Comments
Information & Contributors
Information
Published In
Procedia Computer Science Volume 232, Issue C
2024
3296 pages
ISSN:1877-0509
EISSN:1877-0509
Issue’s Table of Contents
Copyright © 2024.
Publisher
Elsevier Science Publishers B. V.
Netherlands
Publication History
Published: 02 July 2024
Author Tags
- supply chain
- logistics
- hybrid simulation
- system dynamics
- reinforcement learning
- business process re-engineering
Qualifiers
- Research-article
Contributors
Other Metrics
View Article Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
Total Citations
Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Other Metrics
View Author Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in
Full Access
Get this Publication
Media
Figures
Other
Tables
