Studying the sustainability of third party logistics growth using system dynamics
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Bibliographic record
Abstract
Purpose In this Industry 4.0 era, third-party logistics (3PL) industries face huge cost pressure to deliver their service. With increase in competition among the players, constant mergers and acquisitions (M&A) have been taking place to sustain competitive advantage. Therefore, this study aims to investigate the growth dynamics among the 3PL service providers. Design/methodology/approach In this research, the system dynamics methodology was applied to the study of the growth of 3PL industry in Singapore. A population growth model incorporating the predator–prey interaction is developed to account for growth through M&As among 3PLs and their interaction phenomenon are modeled through modified Lotka–Volterra method. The two-species system model consisting of small and medium logistics service providers (SMLSPs as the prey) and the lead logistics providers (LLPs as the predator) are gauged according to the firm size. Findings Results from the baseline model indicates that Singapore’s logistics industry looks very optimistic for SMLSPs for another 6 years from 2018, while the LLP population will achieve a peak at about 12 years from 2018. Further sensitivity analysis through macroeconomic and microeconomic changes reveals increase in trend of M&As. By varying competitive pressures between firms, results indicate that the LLP population experiences a decreasing rate of increasing SMLSP population falls. Research limitations/implications The research provides guidance for logistics and supply chain managers to better understand the critical factors that impact and determine competitive dynamics. The paper further recommends managers to build sustainable logistics strategies to retain competitive advantages. Originality/value The research contributes to both economic and social dimensions of logistics sustainability of how resilient the industries are during uncertain conditions. Some of the limitations of this research include the geographic coverage of the study region and other methodological aspects. The research value thus helps policymakers for developing strategic policies for sustainable industrial growth.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it