Optimization Techniques for Improving Economic Profitability Through Supply Chain Processes: A Systematic Literature Review

Ricardo Jarquin-Segovia; José Antonio Marmolejo-Saucedo

doi:10.3390/math14010185

Optimization Techniques for Improving Economic Profitability Through Supply Chain Processes: A Systematic Literature Review

Journal

Mathematics

ISSN

2227-7390

Publisher

MDPI AG

Date Issued

2026-01-04

Author(s)

Ricardo Jarquin-Segovia

José Antonio Marmolejo-Saucedo

Type

text::journal::journal article

DOI

10.3390/math14010185

URL

https://scripta.up.edu.mx/handle/20.500.12552/12760

Abstract

In today’s dynamic and global business landscape, economic profitability is essential for creating and sustaining competitive advantage. Nevertheless, a critical gap persists in the literature regarding the application of advanced optimization techniques that systematically link operational improvements in the supply chain with strategic financial indicators. Accordingly, this study aims to identify and synthesize the optimization techniques applied to supply chain processes and their impact on economic profitability. To achieve this objective, the PRISMA methodology was employed. A systematic literature review covering the last ten years (2015–2025) was conducted using the Web of Science database. After applying inclusion and exclusion criteria, 35 studies were selected, revealing a growing methodological diversity. Nature-Inspired Algorithms (NIAs) and hybrid approaches (such as MILP combined with Simulation) demonstrate greater capacity to address complex and multi-objective scenarios. Notably, hybrid techniques have been successfully applied to the maximization of Economic Value Added (EVA), a key strategic value indicator. Despite the sophistication of these optimization techniques, the predominant objective remains total cost minimization, often sidelining the direct optimization of strategic indicators such as EVA or the Cash Conversion Cycle (CCC). Additionally, a key research gap was identified in the development of adaptive and resilient models that integrate technologies such as Digital Twins, Blockchain, and Artificial Intelligence to dynamically manage physical and financial disruptions in supply chains. The study concludes by emphasizing the need for a theoretical shift toward models that go beyond cost minimization and focus on real value metrics, as well as the exploration of more accessible solutions for SMEs. This review contributes a reference framework for academics and practitioners to align the most suitable optimization techniques with strategic financial objectives in supply chain management.