Simulation and process mining in a cross-docking system: a case study

Abstract

The increasing development of the competitive market has forced organisations to make great efforts in the processes of supply, production, and distribution to meet customer demand in the shortest time and at the lowest cost. A cross-docking (CD) system is one of the successful and practical strategies in this field considered by researchers in various fields. Also, business process management plays a key role in continuous improvement and increased productivity. In today’s digital age, due to the ability to record all activities, process mining is an important method to identify the current situation and improve productivity. In this research, a newly established CD belonging to a chain store is studied to improve the current situation, in which different goods enter and then exit after different processes. The purpose of this study is to obtain the optimal number of doors and loaders as sources. First, helping an RFID system, all activities are recorded, and the current situation of the processes is monitored, and then, the real process model is identified using heuristic and inductive miner algorithms. After adapting to the event log by using the simulation process in Arena software, different scenarios are examined, and the best possible case is presented.

KEYWORDS:

• Cross-docking
• process mining
• data mining
• automated process discovery
• simulation

SDG 9:

• industries
• Innovation and infrastructure

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Notes

1 https://www.omg.org/spec/BPMN/2.0.2

2 https://pm4py.fit.fraunhofer.de

3 www.okcs.com

4 https://fluxicon.com/disco

Additional information

Notes on contributors

Sadaf Shams-Shemirani

Sadaf Shams-Shemirani held her M.Sc. in Industrial Engineering from Sharif University of Technology, Tehran, Iran. Her main fields of interests are machine learning, data mining, data-driven optimisation & decision making, process mining, discrete-event & agent-based simulation, and heuristic/meta-heuristic algorithms. In addition, inventory and supply chain modeling and optimisation, replenishment optimisation in inventory systems and modeling & optimisation of healthcare systems make up an important part of her research interests.

Reza Tavakkoli-Moghaddam

Reza Tavakkoli-Moghaddam is a Professor of Industrial Engineering at the College of Engineering, University of Tehran in Iran. He obtained his Ph.D., M.Sc. and B.Sc. degrees in Industrial Engineering from Swinburne University of Technology in Melbourne (1998), University of Melbourne in Melbourne (1994), and Iran University of Science and Technology in Tehran (1989), respectively. He serves as the Editor-in-Chief of Journal of Industrial Engineering published by the University of Tehran and the Editorial Board member of nine reputable academic journals. He is the recipient of the 2009 and 2011 Distinguished Researcher Awards and the 2010 and 2014 Distinguished Applied Research Awards at University of Tehran, Iran. He has been selected as the National Iranian Distinguished Researcher in 2008 and 2010 by the MSRT (Ministry of Science, Research, and Technology) in Iran. He has obtained the outstanding rank as the top 1% scientist and researcher in the world elite group since 2014. Also, he received the Order of Academic Palms Award as a distinguished educator and scholar for the insignia of Chevalier dans l'Ordre des Palmes Academiques by the Ministry of National Education of France in 2019. He has published 5 books, 39 book chapters, and more than 1000 journal and conference papers.

Alireza Amjadian

Alireza Amjadian is a Ph.D. candidate at Dalhousie University in Canada, where he is conducting research in the field of Remanufacturing and Pricing, with a focus on Machine Learning. He obtained his M.Sc. in Industrial Engineering from Kharazmi University in Tehran, Iran. His research interests are Inventory and Supply Chain Modeling, Pricing, Machine Learning, and Decision Making. His research endeavours focus on the application of Exact, Heuristic, and Meta-heuristic algorithms to optimise complex mathematical models such as MINLP, NLP, MILP, and MIP within the context of supply chains and inventory systems. His work extends to integrated inventory systems, where he investigates optimal lot-sizing and replenishment strategies, including EPQ, EOQ, and EGQ models.

Bahar Motamedi-Vafa

Bahar Motamedi-Vafa is a master's candidate at the University of Ottawa's Telfer School of Management in Canada, with a specialization in the field of Management and Business Analytics. She completed her bachelor's degree in industrial engineering from Kharazmi University in Tehran, Iran. She is interested in improving the operations of systems and policies through data-driven methods. Her research interests are centered on modern analytics methodologies such as the utilization of data-driven optimization, machine learning, and simulation techniques to inform the design of systems and policy development.