Acquiring logistics process intelligence: Methodology and an application for a Chinese bulk port

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Abstract

The processes of logistics service providers are considered as highly human-centric, flexible and complex. Deviations from the standard operating procedures as described in the designed process models, are not uncommon and may result in significant uncertainties. Acquiring insight in the dynamics of the actual logistics processes can effectively assist in mitigating the uncovered risks and creating strategic advantages, which are the result of uncertainties with respectively a negative and a positive impact on the organizational objectives.

In this paper a comprehensive methodology for applying process mining in logistics is presented, covering the event log extraction and preprocessing as well as the execution of exploratory, performance and conformance analyses. The applicability of the presented methodology and roadmap is demonstrated with a case study at an important Chinese port that specializes in bulk cargo.

Introduction

Logistics is an industry consisting of process-oriented businesses that focuses on managing the flow of resources, both material and abstract resources, between the point of origin and the point of destination (Chow et al., 2007, Langley and Holcomb, 1992). Special attention is being paid to achieve the best comparative net value for the customer. This can be observed in the efforts to optimize the processes, improve the availability or guaranteeing timely and consistent deliveries. Research has also demonstrated that the logistic operations of the service providers remain highly human-centered processes and demonstrate high degrees of flexibility and complexity, which commonly results in a series of uncertainties (Myers, Griffith, Daugherty, & Lusch, 2004).

Standard operating procedures in the form of business processes are commonly set up by logisticians in order to control the operations and maintain a satisfactory level of service. However, research has demonstrated the existence of a discrepancy between the procedures encoded in the designed processes and the actual executions of the processes (Chow et al., 2007). This discrepancy might result in serious operational risks. As highly knowledge driven organizations (Chow et al., 2005, Huang, 2009), logistic companies could benefit from acquiring a full insight in the actual process executions in order to enable logisticians to improve and revise the designed processes. Several contributions have indicated the potential value of process knowledge as a strategic asset for logistics organizations (Arvis et al., 2007, Law and Ngai, 2008, van der Aalst et al., 2011). These contributions describe how competitive advantage could be achieved by enhancing process transparency, improving the logistics performance and strengthening the internal control of the logistics firms. This implies the following question: how to extract process knowledge?

Contemporary logistics information systems record detailed information about the events happening in the environment. These events are occurrences of importance in the context of logistics management, e.g. the arrival of a certain ship. Consequently, the collection of events, i.e. the event log, contains an untapped reservoir of knowledge about the logistics processes. Process mining refers to the set of techniques that analyzes these event logs to acquire insights into the real business processes (van der Aalst and van Dongen, 2002, van der Aalst, 2004). Therefore, process mining can be considered as a set of techniques suitable for acquiring knowledge from the real-world logistics processes.

This paper contributes to the literature on logistics process intelligence by:

  • Providing intelligence support for logisticians using a variety of process mining techniques that enable knowledge discovery within specific logistics processes. This results in a focused analysis leading to recommendations for logistics process improvement.

  • Proposing a comprehensive methodology for process mining in logistics that fits well with logistics processes.

  • Elaborating on an extensive case study at an important Chinese bulk port.

The paper is structured as follows: Section 2 provides the state of the art of logistics intelligence and process mining techniques as well as the motivation for process mining in logistics, followed by the introduction of the methodology in Section 3 and the extensive elaboration of the case study in Section 4. Section 5 provides the discussion and future work. Finally, Section 6 concludes the paper.

Section snippets

Logistics processes & process mining: motivation & state of the art

Logistics can be considered as a process that is highly human-centric with large diversity, complexity, and flexibility. This creates the need for knowledge acquisition in the specific context of logistics processes. In this respect, process mining offers a promising way to provide logistics intelligence supporting process improvement.

Methodology

In this part, we propose a methodology for acquiring knowledge within particular logistics processes using process mining techniques. The methodology consists of five distinctive steps: logistics event log extraction, preprocessing, logistics explorative analysis, logistics performance analysis, and logistics process conformance analysis. The result will provide intelligence support for logisticians with helpful insights for logistics process improvement. Fig. 1 schematically illustrates the

Application at a Chinese bulk port

Ports are considered as potential logistics centers (Bichou and Gray, 2004, Stank et al., 2001). For the application of process mining in logistics, we obtained the cooperation of a Chinese port, which is one of the largest comprehensive hub ports in China. With its throughput ranking among the top 5 in mainland China, this port deals with both containers and bulk cargoes including oil, coal, grain, chemical fertilizer, steel, ore, automobiles, etc. The data used for the application is from the

Opportunities

Applying process mining in logistics provides opportunities for intelligence support for logisticians. The result of process mining is able to help logistics process improvement in enhancing logistics process transparency, strengthening the internal control of logistics firms, and improving logistics performance.

Conclusion

The processes of logistics service providers can be considered highly human-centric, diverse, flexible and complex. As a result it has been demonstrated that actual process executions may significantly deviate from the designed processes, which incorporate the standard operation procedures. As a result of these process deviations important legal and operational risks may occur. In this contribution we proposed a methodology with a roadmap for obtaining a unique and complete insight in the real

Acknowledgments

This research is supported by the Natural Science Foundation of China under Grant Nos. 71132008. We would like to thank the KU Leuven research council for financial support under grant OT/10/010: Business Process Mining: New Techniques and Evaluation Metrics.

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