Additive manufacturing (AM) has transformed mass customisation by allowing personalised production with remarkable efficiency. This systematic review compiles findings from 61 peer-reviewed articles (2010–2024) to highlight strategies for implementation, technological facilitators, challenges, industry applications, and evaluation frameworks relevant to mass customisation in AM contexts. Utilising the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, the review applies stringent inclusion criteria and thematic analysis to create an in-depth understanding of this developing area. Four major strategies for implementation have been identified: combining AM with conventional manufacturing, integrating customer-centred design, establishing flexible manufacturing networks, and creating adaptive production systems. Key technological facilitators include capabilities for multi-material processing, integration of digital workflows, and advanced monitoring of processes, while obstacles consist of limitations in materials, challenges in quality assurance, and complexities related to digital asset management. Industry applications reveal tailored approaches specific to medical, industrial, and architectural sectors. This analysis presents a multi-tiered implementation framework encompassing strategic, tactical, operational aspects and performance evaluation aspects to assist organisations in embracing AM-based mass customisation. This framework fills a notable gap in existing literature by aligning personalisation goals with operational efficiency. This paper also outlines future research priorities, such as creating standardised evaluation methods, improving system reliability, incorporating sustainability, and leveraging emerging tools like AI for process improvement. Ultimately, this review bridges theory and practice, offering a clearer path forward for mass customisation in the era of AM.

Additive manufacturing (AM) is a revolutionary industrial process, and its integration with artificial intelligence (AI) is becoming a key strategy for manufacturers aiming to gain a competitive advantage through sustainability initiatives. While some studies have explored machine learning in manufacturing optimization, there is a limited understanding of how AI supports sustainable practices in AM across various sectors and contexts. This study analysed peer-reviewed journals from the Scopus database published between 2019 and 2025, following systematic review methods. After screening and quality assessment, 34 relevant studies were selected and analysed using thematic content analysis. The findings reveal three key areas where AI improves sustainable AM: (1) energy management and prediction systems for optimizing energy use, (2) environmental impact assessment frameworks combining life cycle thinking with real-time decision-making, and (3) process optimization tools for improving material efficiency and supporting the circular economy. The study shows that machine learning algorithms, optimization techniques, and hybrid AI methods consistently deliver measurable sustainability benefits across various manufacturing applications. This research contributes to the field by integrating AI applications into sustainable AM, identifying implementation patterns and outcomes, and proposing a conceptual framework that aligns technological capabilities with sustainability goals to guide future research and practice.

The focus of this study was to examine the mediating effects of operational efficiency on the relationship between budgeting practices (budget planning and budget participation) and financial performance among 3D printing SMMEs in South Africa. A quantitative, cross-sectional survey design was adopted for the study. The stratified sampling technique was used to select 3D printing SMMEs in South Africa. A total of two hundred and fifty (250) managers and employees of 3D printing SMMEs formed the sample size of the study. Statistical Package for Social Sciences (SPSS) software versions 26 and Smart PLS 4 were used to analyze the data gathered. PLS-Structural Equation Modelling was used to test the hypothesis for both direct and mediation effects. The results indicate that a significant negative relationship exists between budget planning and financial performance, as well as budget participation and financial performance. Significant positive relationships were found to exist between the following variables: budget planning and operational efficiency, budget participation and operational efficiency, and operational efficiency and financial performance. Operational efficiency was found to mediate the relationship between budget planning and financial performance as well as budget participation and financial performance. The results imply that the implementation of budgeting practices such as budget planning and budget participation in 3D printing SMME is not enough to achieve financial performance unless operational efficiency is given the utmost attention in 3D printing SMMEs in South Africa.

This study examined the mediating effect of managerial commitment on the relationships between budgeting practices and financial performance among 3D printing SMMEs in South Africa. The study used the stratified and purposive sampling techniques to select 250 managers and employees of 3D printing SMMEs in South Africa. The results of the study showed that a significant negative relationship exists between budget planning and financial performance, while a non-significant positive relationship exists between budget participation and financial performance. Significant positive relationships exist between budget planning and managerial commitment, budget participation and managerial commitment and also managerial commitment and financial performance. Managerial commitment mediates the relationship between budget planning and financial performance as well as budget participation and financial performance. The study recommends that 3D printing SMMEs must leverage on managerial commitment to enhance their budgeting practices and financial performance.