Additive manufacturing (AM), also known as 3D printing, is considered a renaissance of the manufacturing industry. Its unique capability of manufacturing 3D objects with intricate geometrical configurations has been used to produce hospital equipment and personal protective equipment (PPE) in an attempt to curb the spread of the COVID-19 pandemic in South Africa. The technology has been used by different research units to produce ventilators, respirator face masks, oscillating respiratory devices, oxygen connectors, oxygen splitters, non-invasive ventilation helmets, reusable clinician PPE, visor frames for face shields, etc. Despite the efforts of the AM community in South Africa, COVID-19 infections have continued to increase in the country. It came to light that technological interventions (including AM) alone cannot prevent the spread of the virus without the corresponding adaptive behavioural changes, such as adhering to COVID-19 prevention protocols (washing of hands, social distancing, etc.). It could be postulated that the spread of COVID-19 can only be prevented by inter-marrying the technological interventions (AM) with adaptive behavioural changes.

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.

Purpose: This paper examined supply chain disruptions in the manufacturing industry in South Africa and how additive manufacturing technology was used to help deal with disruptions encountered.Design/methodology/approach: The study adopted a qualitative research approach in order to meet the objectives of the study. A total of ten (10) managers of conventional manufacturing firms and ten (10) managers of 3D printing firms in Free State, South Africa participated in the study through the use of a purposive sampling technique. Interview guides were used in the collection of data. Data transcriptions and thematic analysis were used to analyze data gathered from the interviews.Findings: The results of the study showed that 3D printing contributed positively in dealing with manufacturing disruptions that were encountered by manufacturing firms in Free State, South Africa through the production of components and spare parts that were broken down in manufacturing machinery/plants. From the study, the researchers recommend that 3D printing technology should be adopted by manufacturing firms because of its practicality in providing manufacturing support and production continuity even in supply chain disruptive experiences caused by pandemics such as Covid-19. Originality/value: The study proves that due to the versatility of the 3D printing technology it could be used to minimize the effect of supply chain disruption during cries such as the Covid-19 pandemic. One of the unique contributions of the current study is the realization that additive manufacturing was not of much relevance to the generic supply chain challenges encountered in supply chain activiteis, but rather very relevant in helping to prevent disruptions of the manufacturing process by improvising spare parts at the peak of the Covid-19 pandemic.

Purpose: The purpose of this study was to assess both the direct effect of supply chain integration on firm's operational performance and the mediating effect of external integration (customer integration and supplier integration) on the relationship between internal integration and operational performance among manufacturing firms. Design/methodology/approach: Quantitative survey research design was adopted. A total of thirty (30) manufacturing firms were sampled using cluster sampling. The data was collected using questionnaires from 317 employees in the procurement, account and administration units of the 30 manufacturing firms. Structural Equation Modeling was used to analyze the direct and mediation relationships. Findings: The results showed that the relationship between internal integration and operational performance was significantly mediated by external integration. It was established that operational performance could only be achieved when manufacturing firms' managers pay critical attention to internal and external integration. A Significant positive relationship also exists between supplier integration and operational performance as well as customer integration and operational performance. Originality/value: The unique contribution of the current study is the assessment of the mediating effect of external integration constructs (customer integration and supplier integration) as the mediating variable on internal integration and operational performance.

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.