A Novel Approach for Strategic Partner Selection in the Vietnamese Logistics Industry Using Two-Stage Non-Parametric DEA Model of Super SBM and Resampling Forecasting Technique
| dc.contributor.advisor | Nguyễn, Phi Hùng | |
| dc.contributor.author | Vu, Duc Quang | |
| dc.contributor.author | Trần, Thị Lụa | |
| dc.contributor.author | Hoang, Thi Ha | |
| dc.contributor.author | Ngo, Minh Tan | |
| dc.contributor.author | Nguyen, Huu Giao Huy | |
| dc.date.accessioned | 2025-03-11T02:10:49Z | |
| dc.date.available | 2025-03-11T02:10:49Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | http://ds.libol.fpt.edu.vn/handle/123456789/4119 | |
| dc.description.abstract | The rapid expansion of international trade is propelling significant growth within the logistics industry. While this growth presents numerous opportunities, it also introduces competitive challenges for logistics enterprises worldwide. In this dynamic landscape, the strategic selection of alliances emerges as a crucial solution for enhancing operational efficiency and achieving sustainable success. However, despite notable instances of influential and successful strategic alliances on a global scale, the adoption of such collaborations remains limited in Vietnam. Many businesses within the Vietnamese logistics sector continue to function independently, lacking the necessary connectivity and synergy. This research aims to evaluate the performance trajectory of 22 logistics enterprises in Vietnam across historical, current, and future periods to identify the most suitable strategic alliance. To achieve this goal, we introduce a novel methodology that combines a two-stage Data Envelopment Analysis (DEA) Model, the Super Slack-Based Measure Model (SuperSBM), and the Resampling technique. Employing the Super-SBM method, we assess the operational efficiency of these 22 logistics enterprises for ten years (2013-2022). Furthermore, through applying Resampling, we forecast performance pre-and postimplementation of strategic alliances for the subsequent five years (2023-2027). Our findings reveal that 19 out of the 22 decision-making units (DMUs) demonstrated effective operations from 2013 to 2022. Notably, Vicem Joint Stock Company (JSC) (DMU7) emerged as the target DMU due to its consistently lower operational efficiency. By leveraging accurate and suitable estimates from the forecasting method, specifically Hybrid, DMU7 can judiciously select a partner that aligns with its strategic goals, fostering operational effectiveness over the fiveyear horizon. It is essential to underscore that selecting a strategic alliance necessitates a dual perspective, considering the interests and aspirations of both partners to ensure the optimal choice. This study offers an initial portrayal of the operational landscape within Vietnam's logistics industry, equipping enterprises with insights to recognize and evaluate their own performance. Moreover, it presents viable strategies forsustained development. Our research also delivers dependable forecasting outcomes, providing managers and strategists with actionable plans to enhance operational efficiencies. Investors are also poised to benefit, armed with a robust foundation for making informed investment decisions. Ultimately, this study contributes to the broader knowledge landscape, supporting the overall success of the global logistics sector, particularly within Vietnam's context. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | FPTU Hà Nội | en_US |
| dc.subject | International Business | en_US |
| dc.subject | Kinh doanh Quốc tế | en_US |
| dc.subject | VIETNAMESE LOGISTICS | en_US |
| dc.subject | LOGISTICS | en_US |
| dc.subject | NON-PARAMETRIC | en_US |
| dc.title | A Novel Approach for Strategic Partner Selection in the Vietnamese Logistics Industry Using Two-Stage Non-Parametric DEA Model of Super SBM and Resampling Forecasting Technique | en_US |
| dc.title.alternative | Phương pháp mới để lựa chọn đối tác chiến lược trong ngành Logistics Việt Nam sử dụng mô hình bao dữ liệu phi tham số hai giai đoạn DEA (Super SBM - Resampling) | en_US |
| dc.type | Thesis | en_US |
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