Supply chain delivery performance improvement: a white-box perspective

Abstract

This paper proposes a white-box perspective that portrays a supply chain delivery process as a network of related activities which remains to be improved. It addresses a critical disadvantage of supply chain delivery performance models, namely considering a delivery process as a whole and ignoring characteristics and relationships between activities in the delivery process. A delivery process is modeled using the Graphical Evaluation and Review Technique based on the characteristic function (CF-GERT). Based on the CF-GERT model, a framework for applying managerial effort to activities to improve overall delivery performance is proposed. Then, particle swarm optimization (PSO) based on the penalty function is used to solve the delivery performance improvement framework. Finally, a numerical case shows how applying efforts to activities can effectively improve delivery performance and demonstrates the influence of several parameters on the related costs.

KEYWORDS:

• Supply chain management
• delivery performance
• delivery window
• graphical evaluation and review technique
• particle swarm optimization

Acknowledgements

This research was previously published as conference proceedings (Tao et al. 2022). This paper differs from the proceedings in several ways. First, section 3 is extended to provide additional details about the supply chain delivery performance model. Second, section 5 was added. The section includes an illustrative case with model validation, sensitivity analysis, and comparison of PSO to other algorithms. Third, section 6 was extended to discuss the results of the illustrative case analysis, provide additional managerial insights, and outline shortcomings of the proposed methodology.

Disclosure statement

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

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Additional information

Funding

This work was supported by China Postdoctoral Science Foundation: [Grant Number 2019TQ0150]; China Scholarship Council: [Grant Number 202106835003]; Nanjing University of Aeronautics and Astronautics: [Grant Number xcxjh20210909]; National Natural Science Foundation of China: [Grant Number 71801127]; Support Program for Longyuan Youth and Fundamental Research Funds for the Universities of Gansu Province [Grant Number NS2022075].

Notes on contributors

Liangyan Tao

Liangyan Tao is an associate professor of Industrial Engineering at Nanjing University of Aeronautics and Astronautics, College of Economics and Management. His current research focused on supply chain performance improvement, project scheduling and monitoring.

Ailin Liang

Ailin Liang is currently pursuing her MD degree at the Department of Economics and Management, Nanjing University of Aeronautics and Astronautics of China. Her research interests include supply chain network design, supply chain scheduling optimization.

Maxim A. Bushuev

Maxim Bushuev is an Associate Professor in the Department of Information Science and Systems at Morgan State University. His research interests include supply chain coordination, supply chain delivery, digital supply chains, production planning, and interface of operations management and other business disciplines.