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This paper introduces a two-stage data-driven supplier selection and procurement strategy (SSPS) model that integrates multi-objective optimization with real-world supplier performance data. The model uses augmented max–min fuzzy multi-objective linear programming to simultaneously consider procurement costs, delivery delays, defect rates, sustainability performance, and disruption risks. Results demonstrate that a simulation-based supplier negotiation mechanism, exploring capacity adjustments, can improve overall procurement performance by reallocating orders and reducing supplier management complexity.
Boosting top suppliers' capacity by 45% can lift procurement performance by 6.5% across multiple objectives, without increasing costs, by using a data-driven negotiation model.
Data-driven decision-making has become a pivotal approach to enhancing processes in supply chain management (SCM), particularly in complex supplier selection and procurement strategy (SSPS). This study develops a novel data-driven SSPS model integrating multi-objective optimisation and real-world supplier performance data to improve supply chain efficiency, sustainability, and resilience against disruptions. Unlike traditional models focusing on limited or static scenarios, this framework simultaneously considers procurement costs, delivery delays, defect rates, sustainability performance, and disruption risks in multi-product procurement planning. The model employs augmented max–min fuzzy multi-objective linear programming, leveraging historical data to balance conflicting objectives and achieve a globally optimised solution. Furthermore, an innovative supplier negotiation mechanism supported by simulation-based analyses explores capacity adjustments, enabling win-win procurement outcomes. Experimental results show that, through capacity negotiation, procurement orders could be reallocated to only three suppliers instead of four, thereby reducing supplier management complexity. Notably, when the top-performing suppliers agreed to increase capacity by 45%, the average overall procurement performance improved from 81.77% to 88.28%, enhancing all objectives without additional costs. These results also demonstrate meaningful improvements in five objectives without incurring additional procurement costs. The findings underscore the practical potential of data-driven modelling in developing intelligent, sustainable, and resilient procurement decision-making frameworks.
