Mass Customisation Strategies in Additive Manufacturing: A Systematic Review and Implementation Framework

Abstract

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.

1. Introduction

The intersection of additive manufacturing (AM) and mass customisation marks a transformative shift in modern manufacturing practices [1]. Lacroix et al. [2] claim that traditional manufacturing has always had to select between easy manufacturing of standard goods in a low price range or production of individually customised products in a high price range and low efficiency. This fundamental manufacturing dilemma, however, is being resolved through AM technologies. The AM approach could eliminate the traditional trade-off between customisation and cost efficiency [3]. AM-enabled mass customisation represents systematic approaches that encompass design methodologies, production workflows, software solutions, and organisational structures that allow companies to leverage AM capabilities while balancing customisation with operational efficiency [4]. Through these strategies, manufacturers can develop products with standardised bases but customisable features, implement digital workflows that efficiently process individual specifications, and create production systems capable of handling significant product variation without proportional increases in cost. Implementing AM-enabled mass customisation strategies in organisations has been found to maintain quality while reducing lead times in different customisation scenarios [5,6]. Other benefits include resource efficiency, improved facility operation flexibility, and adding customer value due to personalisation [7,8]. Within the context of the medical field, Wazeer [9] and Shidid [10] indicate that AM-produced patient-specific devices have shown improved treatment outcomes. Paoletti [11] further states that AM-enabled mass customisation has also been used in architectural applications to create complex geometric designs previously impossible through conventional manufacturing methods. Despite the transformative potential of AM for enabling mass customisation, there is a critical absence of comprehensive, cross-industry implementation frameworks that address enablers, barriers, and evaluation methodologies of mass customisation strategies in AM, leaving organisations without adequate guidance to successfully adopt and measure AM-enabled mass customisation strategies across diverse manufacturing contexts.

Given the immense potential benefits and associated challenges with implementing AM-enabled mass customisation, several scholars have explored different aspects of this manufacturing technology from various perspectives. Yu et al. [12], for instance, assess the application of 3D and 4D printing technologies in the fashion industry for mass customisation, and the potential of AM to produce tailored products efficiently was revealed in a review. Pajonk et al. [13] explore the role of multi-material AM in architecture and construction, emphasising its customised building components. In another study, Korne et al. [14], through a systematic review, explore trends in the integration of the AM in the fourth industrial revolution, highlighting the AM to create customisable products in line with the tenets of the 4.0, thus enhancing mass customisation. These studies, however, present several limitations. First, there is a lack of comprehensive understanding of the different customisation implementation strategies in AM and frameworks guiding the effective implementation of AM-enabled mass customisation across various industries. Second, these studies do focus on evaluating the mass customisation strategies and performance metrics, creating obstacles for organisations attempting to determine implementation success. Finally, the absence of a comprehensive analysis of technological enablers and inhibitors of these studies hinders stakeholders from gaining insights into the drivers of success or failure regarding mass customisation strategies in AM. This systematic review therefore seeks to address these knowledge gaps by offering a comprehensive evaluation of implementation strategies, alongside technological enablers and inhibitors. The review aims to explore industry-specific applications and integrative frameworks essential for the successful implementation of AM-enabled mass customisation. This systematic review seeks to answer the following research questions:

• RQ1: What are the different mass customisation implementation strategies in AM (2010–2024)?
• RQ2: What are the technological ecosystems including technological enablers and barriers in AM-enabled mass customisation?
• RQ3: What are the evaluation methodologies and performance metrics for assessing the viability of AM-based mass customisation implementations?
• RQ4: What are the industry-specific applications and outcomes of AM-based mass customisation?
• RQ5: How can a comprehensive multi-layered implementation framework for AM-based on mass customisation be developed that integrates strategic, operational, and evaluative dimensions while addressing industry-specific considerations and technological factors?

This systematic review makes several significant contributions to the understanding and implementation of AM-based mass customisation. Analysing 61 peer-reviewed studies spanning 2010–2024 addresses a critical knowledge gap in balancing customisation capabilities with production efficiency. The study identifies four key implementation strategies: integration with traditional manufacturing, customer-centric design integration, flexible manufacturing networks, and adaptive production systems, each offering distinct advantages across various industrial contexts. The primary contribution of this research is a multi-layered implementation framework that integrates strategic, tactical, and operational dimensions, providing organisations with a structured approach to AM-based mass customisation. Unlike previous reviews that focused on isolated dimensions such as technological capabilities or business models, this study employs a comprehensive analytical approach that simultaneously addresses technological, organisational, and market factors. Practically, it provides actionable guidance for organisations across healthcare, industrial, and architectural sectors by identifying critical success factors, implementation barriers, and evaluation methodologies. The study also establishes a foundation for future research by identifying key gaps in standardised evaluation methods, long-term reliability assessment, and integrating emerging technologies like artificial intelligence.

The remainder of this paper is organised as follows: Section 2 details the methodological approach employed in this systematic review, including search strategy, selection criteria, and analytical procedures. Section 3 presents the findings around four major themes: implementation strategies, technological enablers and barriers, industry-specific applications, and evaluation methodologies. Section 4 proposes a comprehensive implementation framework for AM-based mass customisation, integrating insights from the systematic review. Section 5 discusses practical implications, limitations, and directions for future research.

2. Materials and Methods

The systematic review followed the PRISMA guidelines, an in-depth methodological approach that ensures transparency, completeness, and reproducibility in systematic review studies [15]. The PRISMA was adopted in this study due to the unique advantages in terms of methodological rigor. First, the PRISMA checklist provides a comprehensive 27-item framework that systematically covers all the critical steps for protocol design and synthesis, thereby reducing selective bias and data omission in the review [16]. Second, the requirement of the PRISMA flow diagram enables comprehensive documentation of the inclusion and exclusion procedures of the study, thus increasing transparency where the reasons for decisions are highlighted [15]. Third, through the use of systematic framework, PRISMA enables other scholars to confirm our methodology and try to repeat our findings [17]. Fourth, for intricate topics such as AM-enabled mass customization that transcend multiple disciplines, the framework provides comprehensive coverage in engineering, business, and technology sectors without selection bias [17]. This systematic review was conducted according to PROSPERO guidelines and registered in the PROSPERO International Prospective Register of Systematic Reviews (registration number: CRD420251058742). The protocol was registered prior to study selection and data extraction.

The systematic review followed the PRISMA guidelines of selecting papers on mass customisation strategies in AM from the Scopus database searched on 31st November 2024, covering publications from 2010 to 2024 [18,19]. Scopus was chosen due to its comprehensive multidisciplinary coverage [20], rigorous quality control processes [21], superior search functionality [22], robust citation analysis tools [23], and excellent coverage of recent technological literature [24]. The search string was as follows: TITLE-ABS-KEY ((“Additive Manufacturing” OR “3d Printing”) AND (“Mass Customisation” OR “Personalisation” OR “Co-creation”) AND (“Strategy” OR “Approach” OR “Method” OR “Framework”)). This search strategy defines the main keywords employed in related research papers. The search was temporally constrained to cover technological innovations while being as up to date as possible with modern industry, yielding the first results of 559 articles (see Figure 1). The temporal dimension was beneficial given the dynamism of AM technologies and their uses in mass customisation as noted by Singh et al. [25].

2.1. Study Selection and Eligibility Criteria

The research selected publications through a multi-step selection and screening procedure demonstrated in Figure 1. The initial search yielded 559 articles, which were reduced to 303 after 256 articles were excluded (duplicates). The remaining articles were sorted using the titles and abstracts, and inclusion and exclusion criteria were set. The inclusion criteria specifically focused on studies that addressed the application or evaluation of mass customization initiatives in industries utilizing additive manufacturing technology; provided real-world experiences in industries to support conceptual frameworks; and examined specific sectors, technologies, or business infrastructures. After the screening process, 124 articles were excluded, those not focused on AM mass customization (n = 78) and with insufficient empirical evidence (n = 46), leaving a total of 179 full articles for further consideration. Of the 179 articles sought for retrieval, 38 could not be retrieved, leaving 141 records that were assessed for eligibility. Of these, 77 were excluded for lacking focus on mass customisation (n = 27), quality control details (n = 28), digital infrastructure requirements (n = 10), and technological enablers/inhibitors of AM mass customization (n = 12). The final systematic review included 61 articles that met all criteria.

2.2. Data Extraction and Quality Assessment

A standardized data extraction form was developed to systematically extract relevant information from the selected studies. The form captured bibliographic details, study objectives, methodological approach, key findings, implementation strategies, technological factors, industry context, performance metrics, and limitations. Quality assessment was conducted using adapted criteria from the Critical Appraisal Skills Programme (CASP) [26] modified to accommodate qualitative and quantitative research designs. The synthesis process integrated findings from various studies to identify patterns, relationships, and contradictions in the synthesis theme. This involved a constant comparison between studies, an amalgamation of similar concepts, and identification of overarching themes [27]. In synthesising the research questions, the research was guided, and it focused on developing an understanding of the implementation strategy, technological barriers, and the evaluation methods for mass customisation within an AM environment. All 61 articles were subjected to quality assessment using adapted CASP criteria, which were modified to accommodate both qualitative and quantitative research designs present in the sample.

2.3. Data Analysis and Synthesis

Qualitative thematic evaluation was combined with a quantitative methodological approach evaluation, integrated research analysis per Creswell and Plano Clark [28]. The qualitative methodological approach incorporated a systematically constructed coding framework for data extraction and thematic organization by predetermined conceptual domains, adhering to the established protocols for thematic analysis as delineated by Braun and Clarke [28]. The data analysis commenced with an initial previously defined code structure based on theoretical requirements and then included new themes which emerged from the information [29]. Two investigators employed coding methodologies on a representative article sample to assess intercoder reliability by reconciling coding discrepancies [30]. The investigators achieved robust reliability, evidenced by a Cohen’s kappa coefficient of 0.83 for the initial coding process, indicating substantial agreement, [31]. In addition to intercoder reliability, we utilised a methodological triangulation of cross comparisons of our coding findings among various data sources: empirical papers, conceptual studies, and industry case studies as a means of validating the consistency of identified themes. This triangulation approach showed convergence processes across varying study types, which further confirmed the validity of our thematic framework.

In order to ensure a high degree of methodical rigor and validate the thematic domains, we used a comprehensive multi-stage validation approach. Internal validation involved independent review by two more researchers who were not part of the coding process, resulting in an inter-coder reliability of 0.78 on validation themes. To empirically validate, we cross-validated key themes against alternative coding approaches, as well as performed a sensitivity analysis to ensure the stability of the thematic framework we developed [32]. Practical validation was carried out in the form of validation meetings with professionals from the healthcare, automotive, and construction industries, which confirmed the practical relevance of each thematic domain and established three more sub-themes brought into our final framework. Finally, thematic structure was reviewed by two academic campus experts in manufacturing strategy and systematic review methodology.

Table 1. Thematic Domains on mass customisation strategies in AM.

Thematic Area	Sub-Dimensions	Papers
Mass customisation implementation strategies in AM	- Integration of AM with - Traditional Manufacturing - Customer-Centric Design Integration - Flexible Manufacturing Networks - Adaptive Production Systems	Zawadzki and Żywicki [33], Jiang et al. [34], Kang and Lee [3], Yuan et al. [35], Hafenecker et al. [36], Paoletti [11], Lacroix et al. [2], Kim and Lee [3], Rajamani et al. [37], Rousseau et al. [38], Rayna et al. [39], Zanetti et al. [40]
Technological ecosystem for AM mass customisation	- Enablers - Barriers and Implementation Challenges - Process Control - Digital Infrastructure	Narendran et al. [41], Lunetto et al. [42], Kumar and Jeong [43], Sun et al. [44], Kumar et al. [45], Lin et al. [46], Pesce et al. [47], Schubert et al. [48], García-Dominguez et al. [49], Turner et al. [50], Shen et al. [51], Bos et al. [52], Gao et al. [53], Lacroix et al. [54], Li et al. [55], da Silva et al. [56], Gulisano et al. [57], Sivabalakrishnan et al. [58], Olakanmi et al. [59], Giunta et al. [60]
Industry-specific applications and outcomes	- Medical and Dental Applications - Industrial and Engineering Applications - Architectural and Construction Applications	Leong et al. [61], Solaimani et al. [62], Bilalis et al. [63], Rana et al. [45], Du et al. [64], Kromoser and Pachner [65], Lacava et al. [66], Bayraklilar [67], Górski et al. [68], He and Han [69], Hauser et al. [70], Alabbasi et al. [71]
Evaluation methodologies and performance metrics	- Cost-Benefit Analysis Frameworks - Quality Assessment Methods - Performance Evaluation Systems	Deradjat and Minshall [72], Huang et al. [73], Basak et al. [74], Martinez-Marquez et al. [75], Frohn-Sörensen et al. [76], Rajamani et al. [37], Zanetti et al. [40], Koop et al. [77], Richter et al. [78], Büscher et al. [79], Sæterbø and Solvang [80], Wang et al. [81], Abdulhameed et al. [82], Bellens et al. [83], Kim and Jeong [43], Huang et al. [73], Lin et al. [84], García-Dominguez et al. [85]

provides the results of the thematic analysis. In the quantitative component of the analysis, descriptive statistical measures were utilised to examine the temporal distribution of the articles and the identification of methodological preferences within the field.

3. Results

3.1. Descriptive Analysis of Included Studies

Analysis of the 61 included studies reveals a clear trajectory of growing research interest in mass customisation strategies in AM across three distinct periods. The temporal distribution shows an initial exploratory phase (2010–2014) with limited output of just two publications, followed by steady development (2015–2019) of 14 publications, and culminating in a significant acceleration phase (2020–2024) that produced 45 publications, representing 73.8% of the total research output (see Figure 2). Rapid research growth directly follows advancement of AM technologies and widespread industrial implementation of mass customisation solutions in fields requiring complex product personalisation [79]. The descriptive analysis reveals that 46 of 61 papers (75.4%) adopt empirical methods, whereas conceptually oriented papers make up only 15 (24.6%). Researchers employ the empirical approach as the dominant methodology within this academic field. Multiple scientific investigations have researched theoretical and practical experiences in mass customisation implementation in various industrial settings. The studies apply mass customisation methodologies to solve genuine manufacturing challenges such as production flow control, quality assurance, and customer involvement.

3.2. Thematic Analysis

3.2.1. Mass Customisation Implementation Strategies in AM

Integration of AM with Traditional Manufacturing

Integration of AM with traditional manufacturing emerged as one of the four key mass customisation implementation strategies identified in our systematic review. This strategy represents a sophisticated hybrid production approach that leverages the complementary advantages of both manufacturing paradigms to overcome traditional barriers to mass customisation. AM provides critical customisation capabilities within this strategy through its unique ability to economically produce complex, variable geometries without tooling changes or setup costs. While traditional manufacturing excels at producing standardised components with high efficiency and consistent quality, AM specifically contributes to geometric freedom, tool-less production, and digital-to-physical conversion, enabling customised product aspects [86]. The organisations using this strategy introduce combined manufacturing line configurations that use conventional manufacturing for standardised items alongside additive manufacturing for customised parts [37,75], creating a flexible production ecosystem where mass customisation becomes economically viable. This integration manifests through several key mechanisms. First, digital thread implementation allows direct transmission of data from AM technology to standard processes while Product Lifecycle Management (PLM) systems protect design goals across platforms to perform automated quality assessment [35,36]. This integration is particularly valuable because the digital workflow of AM can accommodate infinite design variations, while traditional manufacturing requires extensive tooling changes for each variant. By reserving AM specifically for the customised components, organisations can achieve mass customisation economics that would be impossible with either manufacturing approach alone. This digital continuity is essential for mass customisation, enabling efficient translation of customer requirements into production specifications without disrupting manufacturing workflows. Advanced simulation tools and digital twins optimise hybrid manufacturing processes by predicting process interactions and potential quality issues pre-production, reducing iterative cycles and material waste while maintaining the efficiency needed for mass customisation at industrial scales. The role of AM is particularly crucial because its layer-by-layer building process enables complex internal structures, conformal features, and integrated functionality that would be impossible to customize with traditional manufacturing methods [87]. Second, hybridization has created distinct post-processing methods that close the performance difference between products produced by AM and conventional manufacturing processes. Organisations use automated finishing operations to exact precision among components while keeping the output quality similar to conventional methods [49]. This ensures that customised components meet the same quality standards as mass-produced parts, a critical requirement for customer acceptance of mass-customised products. Material scientists have formulated composite materials optimised for hybrid manufacturing environments, maintaining consistent mechanical properties across manufacturing techniques [42,87], enabling seamless integration of customised and standardised components in final products.

Organisations have now created intricate supply chains for distributed resource management between various levels of production technology. By strategically deploying AM capabilities closer to customers while maintaining centralised traditional manufacturing for standard components, organisations can provide rapid, localised customisation without sacrificing the economies of scale that make mass customisation economically viable. Companies often establish networks that unite centre-based traditional manufacturing and localised AM technology to offer quick customised resources using standardised component economies [2]. According to Büscher et al. [79] and Song et al. [50], taking into consideration manufacturing challenges such as geometric complexity, production volume, material requirements, and delivery times, advanced production planning systems employ artificial intelligence (AI) systems to maximise work distribution between AM and traditional methods. This is to optimise the balance between customisation capabilities and production efficiency, which is the central challenge of mass customisation. This integration has also created a new group of hybrid manufacturing experts who can handle complex multi-modal production settings by combining traditional manufacturing knowledge with AM-specific skills. This has helped create specialised training programs for the workforce [80].

The hybrid manufacturing paradigm has created the need for sophisticated cost modelling and performance assessment systems. These systems specifically address the unique cost structures of AM (where complexity is virtually free but volume is expensive) and traditional manufacturing (where tooling is expensive but volume is cheap), enabling organisations to optimise which elements to produce with each technology for maximum customisation value at minimum cost. Activity-based costing systems accurately show the economic effects of combining traditional and AM. These systems look at things like how much equipment is used, how many workers are needed, how much material is required, and how quality control is handled in each mode of production [72,74]. Some new performance measures, like process switching efficiency, hybrid material performance indices, and multi-modal quality consistency measures, have been added because they are helpful in hybrid manufacturing settings [73,76]. Research institutes and industry partners are collaborating to establish standardised testing and certification processes for hybrid manufactured components, thereby establishing new quality assurance protocols that address the unique characteristics of combined AM and traditional manufacturing techniques [35,79]. This is anticipated to create the quality infrastructure to scale mass customisation while maintaining regulatory compliance and customer confidence in customised products.

Customer-Centric Design Integration

Customer-centric design integration represents a fundamental shift from traditional customisation approaches, emerging as an AM-enabled mass customisation strategy. This approach is characterised by sophisticated digital platforms that allow real-time co-creation experiences. This approach focuses on placing customer inputs and preferences at the centre of the design process while ensuring manufacturability and structural integrity through AM capabilities [88]. Organisations that adopt this method use advanced parametric design software, which converts customer selection data into production specifications without compromising design requirements or structural stability [34,84], leveraging the geometric freedom of AM to realise customer-specific requirements that would be impossible with conventional manufacturing. In Kim and Lee [3], in their study on personalising the customisation experience, it was shown that customer satisfaction with and likelihood of purchasing the product increased by matching the interface design to customers’ culture-specific processing style, a capability uniquely enabled by the tool-less production approach of AM. This strategy is applied in medical applications by developing more advanced biomechanical modelling and simulation tools that take advantage of the ability of AM to produce complex, patient-specific geometries. Automated design platforms that integrate patient-specific anatomical data with finite element analysis are used by healthcare providers to optimise custom implants and prostheses [84,89], creating mass customisation opportunities in medical devices that were previously limited to costly one-off productions. Patient gait analysis, pressure mapping, and dynamic loading patterns are used by machine learning algorithms to generate optimal internal structure for functional performance while satisfying previously constrained manufacturing limitations [61,63], utilising the unique ability of AM to create complex internal structures impossible with traditional manufacturing methods. When machine learning is combined with medical imaging technologies, automated feature detection and design adaptation are made possible design while significantly improving anatomical fit accuracy [75].

In the industrial domain, organisations embrace topology optimisation systems to balance customer requirements and performance constraints. Manufacturing companies use generative design systems to explore multiple design iterations automatically based on customer functional criteria and optimise material distribution and structural efficiency [45,64]. To achieve a Pareto optimal design solution (a set of solutions where no objective can be improved without degrading another objective) for both customer and production capabilities, manufacturing constraints, material properties, and cost are integrated into multi-objective optimisation algorithms [76], enabling economically viable mass customisation through AM. Advanced simulation techniques provide regulatory compliance using real-time evaluation of tailored designs against industry-specific performance criteria, preserving design freedom while assuring regulatory compliance [81].

Advanced encryption techniques and secure data management approaches have enhanced intellectual property protection in collaborative designs. Organisations use distributed ledger solutions to establish transparent modification records, which protect confidential data and intellectual property [69,82], for AM-enabled mass customisation. Organisations can safely handle design rights and manufacturing permissions through smart contracts between customers, designers, and manufacturers working on distributed production networks [80]. This facilitates the complex digital supply chains needed for successful AM-enabled mass customisation.

Flexible Manufacturing Networks

Flexible manufacturing networks represents another AM implementation strategy which enables mass customisation as identified in this systematic review. This strategy provides an organisational format for distributed AM-based mass customisation using coordinated production facilities across different locations. AM technology is central to this strategy because its digital-to-physical production process eliminates the need for physical tooling transfers across locations. Unlike traditional manufacturing, which requires physical moulds, dies, or fixtures to be replicated or transferred between facilities, AM allows digital design files to be instantly transmitted to any location with compatible equipment [2]. This digital nature of AM enables geographically distributed production without sacrificing consistency—the same STL file can be printed at multiple locations while maintaining dimensional accuracy and material properties. Organisations utilising this strategy create decentralised network models with centralised design oversight and production sites positioned close to end-users, particularly when local production results in market advantages [2], allowing mass customisation through AM to be scaled geographically while maintaining consistency. The unique material capabilities of AM systems at each production node allow customisation tailored to local requirements—for example, different material properties for varied climates or regulatory environments—while maintaining the same underlying design architecture [87]. Digital platforms maintain consistent product standards while coordinating design distribution, production scheduling, and quality control across multiple manufacturing sites [45], enabling standardised processes for AM-based mass customisation across diverse locations. These platforms protect intellectual properties through dedicated measures and simultaneously support distributed manufacturing needs, especially when manufacturers handle design changes at various production sites [39], addressing a critical concern in the digital distribution of customisable designs for AM production. This sophisticated digital framework automatically allows companies to allocate their production resources at various distribution centres. Cloud-based information technology systems enable organisations to handle production scheduling, logistics, and quality control within all their facilities through a single system with standardised quality standards [2], ensuring consistent quality of mass customised products regardless of which network node handles the AM production. In customised consumer goods production, flexible manufacturing networks have demonstrated significant value through their ability to rapidly adapt to local market needs and reduce supply chain lead times, resulting in higher customer satisfaction and market responsiveness [90]. Notably, this is a key advantage for AM-enabled mass customisation that can respond to regional preferences while maintaining global design integrity. This adaptability is further enhanced as IoT-enabled inventory management systems track material consumption and quality across multiple manufacturing locations. This enables organisations to automatically replenish and ensure quality consistency [58], critical for maintaining consistent AM material properties across distributed production sites. Digital twin technology applications, which model material movement patterns, assist in identifying upcoming points of congestion so organisations can take preventative steps to optimise their logistics systems [74]. For AM networks specifically, these digital twins simulate material consumption rates for different powder, resin, and filament types across the network. This ensures an adequate stock of specialised materials needed for customised production. Advanced systems for detecting and recovering network faults have contributed to better network resilience. AI-powered monitoring systems in organisations lead to quick responses and production distribution reallocations when they detect quality problems or equipment breakdowns at various manufacturing points [81,86]. These systems capitalise on the digital workflow of AM by redirecting production to alternate facilities with compatible equipment when failures occur, often without requiring physical transfers of tools or equipment—something impossible with traditional manufacturing methods. Digital process twins maintain virtual replicas of production capabilities across the network, enabling dynamic workload allocation in case of equipment failures or capacity limitations [37], allowing mass customisation orders to be rerouted to the most appropriate AM systems based on current network status. Flexible manufacturing networks are associated with reduced delivery times, improved customisation capabilities, and enhanced supply chain resilience [55], making this strategy particularly valuable for scaling AM-enabled mass customisation beyond the limitations of centralised production facilities.

Adaptive Production Systems

Adaptive production systems represent another key implementation strategy for AM-enabled mass customisation identified in our systematic review. This technological strategy makes it possible to provide dynamic adaptation to dynamically varying requirements for customisation through sophisticated sensor networks [91], enabling AM systems to adjust processing parameters in real time to meet diverse customisation requirements. The layer-by-layer building process of AM uniquely suits adaptive production systems in ways traditional manufacturing cannot match. With AM, each layer’s processing parameters can be modified during production, adjusting laser power, scan speed, material flow rates, or energy density based on the specific geometry built at that moment [92]. This parametric adaptability allows a single AM system to produce highly varied customised products without the extensive tooling changes, setup adjustments, or production line reconfigurations required in traditional manufacturing. This strategy is typically implemented by organisations that continuously monitor multi-modal sensor arrays for critical process parameters, material properties, and environmental variables for dynamic manufacturing parameters [3,43]. These sensor arrays monitor AM-specific processes like melt pool dynamics, powder bed temperature distribution, recoating uniformity, and layer adhesion, which are factors unique to additive processes that must be controlled precisely to maintain quality across diverse customised parts [93]. According to Giunta et al. [60], machine learning algorithms are used to analyse real-time sensor data and predict potential quality issues, as well as automatically adjust process settings, ensuring consistent product quality even as the number of customised options increases, a critical capability for maintaining quality consistency across diverse AM-produced customised variants. Digital process controllers allow the application of control algorithms that optimise several objectives simultaneously while maintaining process stability, as Buscher et al. [78] described, enabling AM systems to adapt to different customisation requirements without manual reconfiguration dynamically. These controllers specifically manage AM process variables like layer thickness, hatch spacing, and energy density—parameters that would require extensive manual setup in traditional manufacturing but can be continuously adjusted during AM production. Systems of quality have evolved to include automatic fault recovery and predictive maintenance. According to Martinez-Marquez et al. [75] and Rajamani et al. [37], many inspection technologies enabled with AI work in an organisation quality monitoring system to locate and categorise the defects in all product arrangements, ensuring that mass-customised AM products meet quality standards despite their unique geometries. Through the simultaneous combination of staffing and process technology, advanced process control systems that automatically re-optimise setting parameters according to quality data feedback [81], maintain production efficiency, and reduce waste address a key challenge in scaling AM-enabled mass customisation. For AM systems specifically, these controls adjust for material variations and chamber environmental conditions and build plate thermal gradients that would otherwise cause quality variations across different customised designs. Advanced systems that schedule operations and make capacity plans benefit resource optimisation. Different product varieties are distributed optimally across organisation resources, materials inventory, production machine abilities, and delivery deadlines [2,80]. Modern simulation systems assist businesses in real-time estimation of several production scenarios that help them reach optimal production efficiency with quality norms spread among various customisation offerings [71], allowing organisations to predict and optimise AM performance across diverse customisation requirements. Intelligent automation interfaces between people and the systems improve system flexibility. Organisations with automated collaborative robot systems optimise their operational patterns based on product specifications and operator feedback to perform flexible manufacturing operations [50], combining human expertise with automation to handle the complexity of AM-based mass customisation. By employing augmented reality and gesture recognition features, the augmented human machine interfaces can also be used by users to control advanced production systems for safe performance and operational excellence [79], providing intuitive interfaces for managing the complexity of AM-enabled mass customisation production. The adaptive production system strategy is particularly valuable for organisations seeking to accommodate diverse and evolving customisation requirements through AM technologies, as it enables the dynamic process adaptations needed to maintain quality and efficiency across varied product configurations without extensive manual intervention between production runs.

3.2.2. Technological Ecosystem for AM Mass Customisation

Technical Enablers for Mass Customisation

Modern AM systems excel at creating complex geometrical structures that are unbounded by traditional manufacturing constraints. This capability enables the design and production of customised parts with internal features and organic shapes that were previously unattainable through conventional methods [39]. The technological enablers facilitating AM-based mass customisation can be organised into several key categories.

Researchers have significantly expanded options for AM technology in materials, particularly for medical and aerospace applications. These advancements include polymers, metals, and ceramics [42], establishing material diversity as a critical enabler of mass customisation. The broader material portfolio enables manufacturers to precisely align specific customer requirements with suitable material properties, whether biocompatible polymers for personalised medical implants, high-strength aluminium alloys for lightweight aerospace components, or ceramics for heat-resistant custom parts. This material flexibility, combined with the geometric freedom of AM, creates a dual customisation capability where both form and material composition can be adjusted simultaneously. The capability to process such diverse materials through a single manufacturing paradigm overcomes traditional limitations, where material selection often restricts design possibilities, thus fundamentally enabling mass customisation by optimising each product for both function and user needs without prohibitive tooling or process changes [84].

Digital workflow integration is another technological fundamental enabler, as seamless interfaces between CAD systems and AM platforms allow quick design changes and instantaneous production adaptation without tooling changes or major setup periods [43]. Such integration creates a continuous digital thread from customer requirements capture through design iteration to final manufacturing, eliminating communication barriers and reducing translation errors between systems. Data show that companies with completely integrated digital workflows reduce production time while decreasing costs compared to organisations that maintain separate digital systems [84]. This digital continuity is particularly critical for mass customisation, where each product variant must move efficiently from concept to production without manual intervention.

The elimination of traditional tooling requirements has transformed the economics of customised production, particularly in the medical and consumer sectors. Factory flexibility, which allows quick product variation with no extra tooling expenses, readjusts customisation cost structures to support affordable low-quantity manufacturing [47]. Unlike conventional manufacturing, where tooling costs must be amortised across production volumes, AM shifts the economic equation by making the unit cost relatively consistent regardless of quantity. This cost structure democratises customisation by removing the financial penalties traditionally associated with producing unique items, allowing companies to offer personalised products at price points comparable to mass-produced alternatives.

The convergence of multi-material AM techniques with computational design optimisation has created unprecedented opportunities for functional grading and property customisation. When combined with topological optimisation methodologies and multi-physics simulation tools, manufacturers can achieve user-specific optimisation of internal structures. This enables user-specific optimisation of internal structures and the subsequent targeted adjustment of mechanical and thermal properties alongside electromagnetic behaviours per area of the product [41,44]. These advanced design capabilities allow for the creation of components where material composition and structural properties vary continuously throughout the part, optimising performance for specific loading conditions, thermal requirements, or user ergonomics. Such tailored functional properties represent a higher dimension of customisation beyond mere geometry.

In mass customising contexts, the development of digital twin technology has transformed process control and quality assurance. Organisations employ physics-based digital twins to conduct simulations of complex material-to-process relationships, which forecast necessary manufacturing settings for different customised solutions [49,50]. These virtual replicas of physical manufacturing systems enable pre-production validation of customised designs, identifying potential manufacturing issues before committing resources to production. Machine learning algorithms enhance simulation processes across the production line using new data inputs from the manufacturing environment [51]. The closed-loop system created between physical production and digital simulation enables manufacturers to maintain consistent quality across highly varied product offerings, addressing one of the traditional challenges of customisation.

The combination of adaptive slicing methods with multi-axis deposition has produced major enhancements to the ability to create geometries using AM techniques. While early AM systems were limited to simple layer-by-layer construction, modern systems employ sophisticated path planning with rotational freedom and variable layer thicknesses optimised for specific part geometries. Path planning algorithms with advanced capabilities use live process monitoring data to adjust deployment patterns in real time, which enhances mechanical properties alongside surface quality for sophisticated customised designs [55,56]. Organisations that utilise complete technological enablers obtain considerably higher customisation capabilities and operational efficiency than organisations with incomplete implementations [49]. This underscores the importance of adopting a holistic approach to AM-enabled mass customisation rather than focusing on isolated technologies. Companies realising the full potential of AM-enabled mass customisation typically integrate these various enablers into cohesive systems where digital design tools, material capabilities, process controls, and verification technologies work in concert to deliver customised products with consistency, efficiency, and economic viability that rivals traditional mass production approaches.

Technical Barriers and Implementation Challenges

Technology advancements have not eliminated the several ongoing barriers that impede the implementation of mass customization in AM environments. Material property complexity presents a major implementation challenge for AM-enabled mass customization. The final components exhibit anisotropic behaviours and show diverse mechanical properties which vary depending on both construction orientation and process settings [49,50]. This variability complicates quality control and makes it difficult to ensure consistent performance across customized products. Advanced characterization studies reveal complex connections between processing parameters, microstructural evolution, and mechanical properties, particularly in metal AM. The integration of various materials in multi-material AM systems introduces additional difficulties in interface bonding and property matching.

Economic barriers significantly restrict the widespread adoption of AM-enabled mass customisation. The relatively high material expenses coupled with machinery costs prevent small and medium enterprises from feasible implementation. The exact expense of AM technology has been researched extensively through multiple studies, which show that expensive metal printing equipment and precision upgrade systems demand higher investments than traditional manufacturing methods [51,52]. According to Gao et al. [53] and Lacroix et al. [54], there are complex interactions between equipment utilisation, material prices, and production volumes, particularly in cases requiring high-end systems for metal printing or precision applications. Additionally, specialised post-processing complications demand and quality control, which require major capital investment, have become a barrier for many organisations.

Post-processing complexities further challenge mass customisation implementation. These processes demand advanced techniques that create difficulties in maintaining standardised quality across personalised products [53,54]. Post-processing requirements for achieving specific surface finishes or mechanical properties often introduce additional variability into the manufacturing process. The unique characteristics of AM techniques combined with the broad spectrum of possible material combinations complicate the establishment of standardised testing methods and certification processes for customised components.

Maintaining constant quality among tailored components is much challenged by material property anisotropy and process-induced variability. Many factors affect how a material behaves, especially in metal AM [50,51], and advanced characterisation studies show how processing parameters, microstructural evolution, and mechanical properties are all connected in complex ways. While integrating various materials in multi-material AM systems presents difficulties in interface bonding and property matching [52], post-processing needs for achieving specific surface finishes or mechanical properties often introduce additional variability. The special qualities of AM techniques and the broad spectrum of conceivable material combinations challenge the creation of standardised testing methods and certification processes for bespoke components.

Production volume and size limitations constrain mass customisation capabilities in AM environments. Specific obstacles include functional restrictions in build volume capacity and production speed limitations that affect more significant component production and high-volume sectors. These build volume restrictions and manufacturing speed limits directly impact throughput capacities [55], making AM less suitable for specific customisation scenarios requiring large components or high production volumes.

Integration challenges emerge when organisations transition from conventional manufacturing to AM-enabled mass customisation. Companies face exclusive difficulties when integrating AM systems with their existing industrial frameworks due to data management and process control issues [55]. Particularly in companies changing from conventional manufacturing techniques, the integration of AM systems with current industrial infrastructure creates challenges in workflow optimisation and resource allocation [56]. Limited understanding of process–structure–property relationships and the computational requirements for real-time decision-making impede the development of closed-loop control systems that can automatically adjust process parameters based on quality feedback [59,83].

Quality Control and Process Optimisation

Successful implementation of mass customisation through AM requires specialised quality control and process optimisation approaches that differ significantly from traditional manufacturing. The layer-by-layer building process of AM creates unique opportunities for mass customisation by enabling complex geometries and internal features impossible with conventional methods, but it also introduces specific quality challenges that must be addressed. Organisations establish thorough frameworks to implement real-time monitoring systems designed explicitly for AM-based mass customisation, tracking critical parameters like thermal conditions, powder bed uniformity, and layer-by-layer verification [57,60]. These monitoring systems are crucial for mass customisation through AM, as each customised product may require different process parameters despite being produced on the same equipment—a capability that conventional manufacturing cannot match. Prior to production, organisations, especially those operating within controlled sectors such as medical device manufacturers, put in place processes and procedures to ensure that customised designs are accurately verified. The processes include the strict adherence to guidelines and procedures, such as automated checks for manufacturing, compliance with material-specific design, and structural integrity. Several studies [94,95,96] have established that there is an association between process parameters and the quality of the final product in customised AM applications. According to Giunta et al. [60], this aids in creating predictive models that harness manufacturing parameters in alignment with the needs and preferences of customers. Furthermore, some studies have revealed that adopting machine learning algorithms for optimising the processes, among other things, automatically changes manufacturing parameters to ensure that quality is maintained throughout customisation conditions while reducing waste and production time [59,97]. In order to preserve consistency in mass-customised production environments, quality control systems now integrate multi-modal sensor arrays with real-time analytics systems to offer complete monitoring of the build process, which has the ability to identify and fix deviations in real time.

The integration of the multi-modal sensor arrays and real-time analytics, advanced in situ monitoring systems specifically developed for AM processes, has been found to provide critical transformation to quality control in AM mass customisation. Gulisano et al. [57] and Giunta et al. [98] indicate that the use of thermal imaging systems, machine vision, and acoustic monitoring are combined to track the AM building process for customised components, identifying deviations that would lead to quality issues in complex geometries specific to customised designs. Unlike adaptive control systems which change the processes automatically to maintain ideal manufacturing conditions, multi-sensor data are assessed by machine learning algorithms in real time, aiding in identifying any quality problems before they become severe [99].

Process optimisation frameworks for AM-enabled mass customisation now use multi-dimensional objective optimisation techniques to manage conflicts that specifically balance the geometric complexity enabled by AM with production efficiency. Computational models optimise AM process parameters through thermal–mechanical simulation and microstructural prediction [38,77]. This allows organisations to efficiently produce customised designs with different geometries in the same build—a capability unique to AM that dramatically enhances mass customisation economics. Multiple builds of inspection data undergo statistical process control analysis to expose systematic deviations and spot process drift. Machine learning algorithms develop proper inspection approaches by analysing product features and essential design criteria [74,82]. Automated systems for classification of defects allow quick inspections of unique components to sustain both production throughput and unyielding quality requirements [69,80].

Combining automated inspection systems designed for AM with statistical process control has also improved mass customisation quality assurance. Through the advanced metrology systems that incorporate measuring devices, optical scanning, and computed tomography, it is possible to carry out comprehensive dimensional verification and identification of internal flaws [74,82]. While machine learning models anticipate ideal inspection tactics based on component geometry and essential feature requirements, statistical process control systems examine inspection data over several builds to find consistent variances and process drift. The presence of automated defect classification systems aids in the rapid evaluation of the tailored components, which enhances the effect production flow that confirms that quality standards achieve efficacy in manufacturing [69,80].

Digital Integration and Data Management

Digital integration and data management abilities establish fundamental requirements for efficient mass customisation possibilities within AM operations. The unique layer-by-layer nature of AM processes generates vastly more process data than traditional manufacturing, necessitating sophisticated data handling approaches. Organisations use complete digital thread architectures that transform how they manage data throughout their AM mass customisation structures, from initial capture of customer requirements to final part delivery and lifecycle support. Modern PLM systems unite AM design tools with simulation platforms as well as quality control tools to maintain continuous data flow throughout the product life duration [74,81], enabling seamless transition from customised digital designs to final AM-produced parts without the data translation issues that often occur in traditional manufacturing. These systems incorporate AM-specific parameters such as build orientation, support structures, part nesting, and thermal distortion prediction, which are critical for efficient customisation. Complex systems that control versioning maintain design consistency with production standards, essential when managing thousands of unique part variations, while blockchain deployments protect data authenticity during distributed manufacturing operations. This is a critical capability for AM-enabled mass customisation where design files must be securely transferred to various AM systems without corruption or unauthorised modification. Digital rights management controls how design and process information is shared in controlled ways between organisations to support their collective development of customised products [82,86], protecting the valuable intellectual property in AM build files and parameter settings that enable successful customisation. This allows secure outsourcing of custom AM production while safeguarding intellectual property embedded in designs and process parameters—a critical concern when mass customisation leads to dispersed manufacturing. The analysis of extensive production data by big data analytics systems now enables complete process enhancement and quality improvement. A lot of sensor system data undergo extensive analysis by advanced algorithms, which detect elaborate relationships between AM process variables such as laser power, scanning strategy, and build chamber conditions [81,83].

Real-time sensor data quality issues are automated for identification through pattern recognition systems that use machine learning models with historical production data to predict optimal process parameters for new customisation needs. Data processing through edge computing solutions takes place across distributed AM production networks, which allows rapid decisions to be made and reduces transmission requirements [67,78], enabling local control of AM processes while maintaining centralised design integrity critical for mass customisation. Intelligent knowledge management systems use advanced processes to improve organisational learning and manufacturing improvement in mass customisation applications. By using advanced knowledge bases which include design rules alongside process parameters and quality data obtained from various manufacturing contexts [73,85], new customisation requirements can be addressed speedily. These systems codify critical AM expertise, such as support structure generation rules, optimal part orientation strategies, and post-processing techniques for different geometries and materials, transforming tribal knowledge into digital assets.

The combination of automated documentation systems and machine learning techniques enables comprehensive design decision records, maintenance, and manufacturing parameter tracking alongside automated process best practice definition and improvement identification from production histories. The integration of artificial intelligence tools with knowledge management systems automatically generates AM process specifications for new customisation scenarios, which shortens development periods and upholds quality consistency standards [82,99], a capability that eliminates the extensive manual process planning typically required when switching between different customised designs in traditional manufacturing. Electronic systems supporting AM-enabled mass customisation receive complete security protection through advanced cybersecurity methods to defend design and manufacturing information from sensitive exposure. The combination of cutting-edge encryption methods along with access management protocols protects distributed manufacturing data through network-wide security measures to monitor potential security breaches [57]. Secure architectures in combination with communication technologies enable safe teamwork between multiple participants during custom development, whereas digital signatures alongside audit trails, AM design modifications, and build parameter changes throughout the production process [63,77], ensuring traceability critical for regulated industries adopting AM-enabled mass customisation.

3.2.3. Industry-Specific Applications and Outcomes

Medical and Dental Applications

AM-based mass customisation has revolutionised medical applications by enabling the economical production of highly personalised medical devices at a scale previously impossible with traditional manufacturing methods. Documented evidence demonstrates significant clinical benefits from these customised devices [59,60,83,89], leveraging the unique ability of AM to produce complex patient-specific geometries without the prohibitive tooling costs that would make such mass customisation economically unfeasible with conventional techniques. Advanced orthotics and prosthetic technology have reached new heights with digital scanning technology combined with automated manufacturing approaches. According to Pallari et al. [89], precise digital scanning together with automated manufacturing techniques results in enhanced patient comfort and improved fit accuracy. AM uniquely enables this mass customisation by directly converting digital anatomical scans to physical products without moulds or tools, making each device economically viable despite being one of a kind [100]. Studies demonstrate successful deployment in ankle–foot orthoses, where the capability of AM to produce complex metamaterial structures enables clinicians to integrate patient-specific anatomical data with metamaterial structures to enhance comfort and functionality [61]. Advanced biomechanical modelling and topological optimisation have also transformed patient-specific implant and prosthesis design. Unlike traditional manufacturing, where each design variation requires new tooling, AM enables mass customisation of implants where every device can be unique without cost penalties. These are, however, structures that are impossible to produce with conventional manufacturing but are ideally suited to the layer-by-layer building process of AM. Finite element analysis combined with machine learning algorithms accurately predicts mechanical stress levels and physiological load patterns, enhancing the optimisation of internal lattice structures for optimal osseointegration and mechanical performance [84,89]. Improved material models accurately predict tissue–implant interactions and biological responses, while multi-physics simulation systems combine fluid dynamics with structural analysis to optimise vascular implant designs for individual patient anatomies. These automated optimisation systems develop patient-specific implant designs that modify shape during periods of tissue adjustments by employing individual adaptation models for growth prediction [63,75].

Dental applications have also evolved through the linking of intraoral scanning devices and automated design platforms. AM has transformed dental prosthetics from labour-intensive craftsmanship to digital mass customisation, enabling consistent quality across unique designs. Patented image processing techniques merge different scan technologies, which construct exact digital models of patient teeth structures and plate designs through automated machine learning protocols [61]. The combination of digital design automation with tool-less production of AM creates an economically viable mass customisation workflow where each dental restoration is unique yet produced with industrial efficiency. Real-time visualisation software allows instant confirmation of fitted prostheses along with their tooth relationships, and automated manufacturing sequence programs optimise printing orientation and support frameworks for achieving the best results regarding dimensional precision and surface quality. Printed dental restorations now utilise biocompatible resins to produce custom-made permanent restorations while multi-material printing grants prosthetic manufacturing with artificial structure mechanics suited to mimic natural teeth [63,75].

Orthopaedic medicine has experienced major advancements in bespoke implants, together with specialised surgery instruments for individual patients. AM enables mass customisation of surgical guides and implants that would be prohibitively expensive with traditional manufacturing methods, making patient-specific approaches economically viable for mainstream adoption. Machine learning algorithms analyse medical imaging data to automatically generate optimal cutting guides and positioning tools, while advanced registration systems ensure precise alignment during surgery [45]. This end-to-end digital workflow, combined with AM’s geometric freedom, enables mass customisation at a scale and economic feasibility impossible with conventional manufacturing techniques. Computational models simulating postoperative biomechanics and predicting long-term implant performance help optimise implant designs for specific patient activity and lifestyle requirements. Additive manufacturing combined with surface texturing enables precise control of implant–tissue interfaces, resulting in improved biological fixation and reduced infection risk [89]. Employing hybrid manufacturing methods that integrate additive and subtractive techniques, complex internal structures and exact matching surfaces may be produced, therefore maximizing both mechanical and biological performance. Customer-centric design integration has been adopted by the medical and dental fields as the key method of implementing AM, including gait analysis, pressure mapping, and dynamic loading patterns, as well as manufacturing, enabling the production of highly customisable prostheses in both fields. The use of patient-specific anatomical data in combination with finite element analysis allows medical prosthetics to produce custom made prostheses and orthoses [68,75] (see Figure 3a) (

Table 2. AM Strategies (Customer-Centric Design) in Medical and Dental Applications.

Application Area	AM Tools and Method	Specific Products	Clinical Benefits	Cost and Time Benefits	Key Challenges	Supporting Research
Medical Prosthetics	- Digital scanning - Finite element analysis - Machine learning algorithms - Biomechanical modeling	- Ankle-foot orthoses - Custom implants - Patient-specific prostheses - Complex metamaterial structures	- Enhanced patient comfort - Improved fit accuracy - Better osseointegration - Optimal mechanical performance	- No tooling costs for unique designs - Direct digital-to-physical conversion - Economically viable customization	- Complex regulatory approval - Material biocompatibility certification costs - Data privacy protection - Skills gap in clinical settings	[63,89]
Dental Prosthetics	- Intraoral scanning - Automated design platforms - Image processing techniques - Multi-material printing	- Custom dental restorations - Prosthetic crowns - Digital dental models - Biocompatible resin products	- Consistent quality - Precise dimensional accuracy - Natural teeth mechanics - Real-time fit verification	- Labour-intensive to digital workflow - Unique yet industrial efficiency - Optimized printing orientation	- Standard reimbursement models - Technical training requirements - Digital supply chain integration - Quality standardization across sites	[61,63]
Orthopedic Medicine	- Advanced registration systems - Machine learning imaging analysis - Computational biomechanics - Hybrid manufacturing	- Surgical guides - Positioning tools - Patient-specific implants - Surface textured implants	- Precise surgical alignment - Improved biological fixation - Reduced infection risk - Optimized long-term performance	- Economically viable patient-specific tools - End-to-end digital workflow - Complex geometries possible	- Regulatory requirement - Healthcare financing gaps	[75,89,90]

). Healthcare providers take advantage of automated design tools that make use of vast patient data including gait analysis, pressure mapping, and dynamic loading patterns as well as machine learning techniques to formulate the best internal structures for optimum function. Dental prosthetics are a prime example of this approach that uses interactive visualization tools for the fast confirmation of prosthesis fit and dental alignment (see Figure 3b) (Table 2). Medical specialists take advantage of automated production planning tools that tweak build orientations and support design to ensure key functional integration with the underlying dental anatomy. Customer-centric design integration has, to a large extent, been successful in prosthetics with the personalized befit helping the patient feel comfortable, in use, and in treatment at large.

Despite the impressive gains that the technology has achieved, the medical sector still struggles with challenges that limit its wide adoption of AM-supported mass customization (Table 2). Regulatory barriers are the most striking challenge because, for individualised medical devices, the FDA or CE marking approval process is longer than for standardized devices, as stated by Ventola [104]. Protocols for material biocompatibility verification based on variation are expensive and time-consuming. The existing healthcare financing structure usually fails to consider the benefits of customization, thereby making it difficult for healthcare providers to charge an appropriate price for patient-specific implants. Major problems also arise from the area of data privacy, requiring protection measures for patient-specific data within the electronic supply chain without compromising efficient integration between scanning, design, and manufacturing technologies [105]. The lack of AM-trained professional resources in the clinical setting contributes to the challenge of the AM-enabled customer-centric design approach in medical and dental applications.

Industrial and Engineering Applications

The industrial and engineering sectors demonstrate a broad and fast-growing environment for mass customisation through AM practices, which deal with vital performance requirements and intricate operational needs. Unlike traditional manufacturing that requires expensive tooling changes for each design variant, AM uniquely enables mass customisation in industrial applications through its tool-less, direct digital-to-physical production process [84]. Several studies have been conducted on industrial implementations of AM-based mass customisation, mainly in the automotive and aerospace industries. Kumar et al. [45] showcased techniques for producing custom-made components built on the complex geometry with particular functional needs, which demonstrates successful implementations in both prototype and end-use production. This leverages the unparalleled geometric freedom of AM to produce customised industrial components economically unfeasible with conventional manufacturing methods. This employs a mass customisation approach uniquely enabled by the ability of AM to produce parts without tooling investments, making “lot size one” economically viable for industrial components [76]. Through the creation of specific systems for using AM in spare part manufacture, organisations have enabled on-demand manufacturing of tailored components, minimising inventory equipment while enhancing supply chain responsiveness [64]. According to Frohn-Sörensen et al. [76], extant literature has revealed successful instances of rapid tool manufacturing, which has resulted in a significant reduction in setup times and enhancement in production flexibility. This demonstrates how AM enables mass customisation of production tooling itself—an approach impossible with traditional tool-making methods that require extensive machining operations. They further indicate that customised manufacturing aids and fixtures through AM in tooling applications, where the ability of AM to economically produce unique jigs and fixtures for each product variant creates a secondary level of mass customisation support in industrial environments. Additionally, Wang et al. [81] demonstrated how the use of AM provides significant benefits in the development of structural parts. This approach affords manufacturers the capacity to construct highly performing lightweight parts that maintain their mechanical properties while also allowing for customisation, utilising the unique ability of AM to create internal structures and variable densities impossible with traditional manufacturing, enabling mass customised optimisation for specific loading conditions of each component variant. The work by Górski et al. [90] reports that the AM technologies enable the manufacture of cost-effective personalised consumer products via specific methodologies that maintain efficiency in the manufacturing process while customising items. This is fundamentally changing the economics of mass customisation by eliminating the tooling costs that typically make small-batch customisation prohibitively expensive [68]. The relatively high success in the implementation of AM-based mass customisation in industrial and engineering uses has been attributed to advances in material characterisation techniques, process control systems, and design optimisation tools, which aid in achieving efficiency with consistent quality [80].

The aerospace industry now designs components with enhanced methods such as engineering, generative design, and topological optimisation. AM uniquely enables mass customisation in aerospace by making these optimised designs manufacturable regardless of their complexity, unlike traditional manufacturing, where geometric constraints limit customisation options. Multi-objective optimisation software allows engineers to create simple integrated products that keep assembly requirements basic and weight minimal through performance, structural efficiency, and thermal control goal optimisation [63]. Computer simulations of fluid dynamics systems, along with structural models, optimise the design of cooling channels and flow pathways as advanced material models predict mechanical changes due to thermal cycling conditions. The combination of artificial intelligence with design optimisation tools enables users to discover new solutions beyond traditional constraints by exploring complex designs through fast design space exploration and attaining novel solutions that comply with regulations [74,79]. Mass customisation techniques have transformed automotive elements by improving both structural components and attractive features. The integration of advanced design automation systems creates a rapid behavioural generation of personalised interior parts that fulfil crash safety standards and ergonomic comfort needs [90]. In this context, AM serves as the manufacturing technology that makes this level of individualisation economically viable at production scales. Combined material printing, along with topology optimisation, evolves into a process that delivers strong structural components that minimise vibration and suppress sound while maintaining maximum lightness. Special automotive materials allow for the direct manufacturing of final products with superior thermal characteristics and chemical durability through automated quality testing that maintains batch consistency [78,79].

Mass customisation techniques used in structural and cosmetic components have revolutionised automotive applications. Through advanced design automation systems, organisations in the automotive industry are able to rapidly create tailored interior components that meet crash safety criteria while accommodating individual preferences for comfort [43,90]. While the use of topology optimisation techniques builds structures that are lightweight yet very strong for complex loads, multi-material printing creates integrated parts that minimise noise and vibration. Wang et al. [80] and Saeterb and Solvang, ref. [78] further contend that the use of advanced quality control systems ensures consistent production while adopting specialised automotive materials allows direct manufacturing of durable end-use parts with design algorithms optimising customisable components within manufacturing constraints.

Modern industrial tooling applications benefit considerably from conformal cooling implementation, along with topology optimisation implementations. AM uniquely enables mass customisation of industrial tooling by rendering complex internal cooling channels physically producible—features impossible to manufacture with traditional methods, regardless of cost [34]. Complete thermal management in injection moulding equipment relies on computational fluid dynamics, which optimise cooling channel geometry, while structural analysis protects against high-pressure conditions in the equipment [50,74]. The combination of additive and subtractive techniques in hybrid manufacturing systems allows for the production of tools that demonstrate superior thermal performance and precise dimensions by achieving complex internal structures. This enables a level of functional customisation in industrial tooling that is impossible with conventional manufacturing methods alone. Specific moulding conditions prompt machine learning algorithms to analyse process data, which helps predict optimal cooling topologies for automated design methods based on part geometry and material properties [63,78]. This supports the creation of a mass customisation workflow where each tooling design is uniquely optimised for its specific application—an approach that is economically viable only through AM’s tool-less production paradigm.

Table 3. AM Strategies Applied to Industrial Applications.

AM Strategy	Industrial Products	Key Applications	Cross-Industry Implementation	Supporting Evidence
Integration with Traditional	- Hybrid manufacturing tooling - Advanced moulds with AM cooling channels - Combined subtractive–additive fixtures	- Conformal cooling implementation - High-precision tooling - Hybrid process optimisation	Tooling: Primary strategy for hybrid manufacturing and optimised cooling channel design Automotive: Critical for safety-critical components requiring high precision	[37,63,75]
Customer-Centric Design	- Personalised automotive interiors - Custom structural components - Tailored safety-critical parts	- Crash safety compliance - Performance optimisation - Individual specifications	Aerospace: Primary strategy for topology optimisation and lightweight high-performance components Automotive: Emphasis on personalised interiors and ergonomic customisation	[45,47]
Flexible Networks	- Distributed spare parts - Regional custom manufacturing - Supply chain components	- Inventory reduction - Rapid response - Distributed capacity	Spare Parts: Primary strategy for distributed on-demand manufacturing All Industries: Enables regional customisation and reduced lead times	[64]
Adaptive Systems	- Variable density structures - Self-optimising components - Quality-adaptive production	- ML-driven optimisation - Real-time quality control - Automated adaptation	Aerospace: Combined with Customer-Centric Design for real-time optimisation Tooling: Used for optimised cooling channel design based on process data	[82]

shows that four distinct AM strategies enable mass customization across industrial sectors, each finding specific applications based on industry requirements. Integration of AM with traditional manufacturing dominates tooling applications, where hybrid approaches combine AM’s geometric capabilities with conventional precision. Injection moulds with conformal cooling channels exemplify this integration, producing superior thermal performance while maintaining dimensional accuracy [39,40] (see Table 3 and Figure 4b). Building on this foundation of integrated manufacturing, customer-centric design integration extends customization possibilities in the aerospace and automotive sectors. While tooling focuses on process improvement, aerospace manufacturers leverage this strategy to create topology-optimized, light-weight components for specific aircraft requirements [45,106]. An example of this is GE Aviation LEAP Engine Fuel Nozzle found in Figure 5. Similarly, automotive applications produce personalized interiors that meet individual preferences while maintaining safety standards [39], with automated design systems bridging customer requirements and manufacturing specifications.

Moving beyond localized production, flexible manufacturing networks transform spare parts manufacturing through geographically distributed capabilities. Unlike the centralised approach of traditional integration, these networks digitally distribute design files to regional production nodes [102], enabling custom components to be manufactured locally. This strategy eliminates inventory while reducing lead times and maintaining quality consistency across diverse locations. Daimler’s Spare Parts On-Demand Network is an excellent example of the flexible manufacturing network AM strategy in action (see Figure 5). Complementing these distributed networks, adaptive production systems provide real-time optimisation capabilities, particularly valuable for aerospace components and advanced tooling. Through dynamic parameter adjustments during manufacturing, these systems enable variable density structures and optimized cooling channels based on specific geometries and materials [82], ensuring consistent quality across diverse customised variants without manual intervention between production runs. Together, these strategic implementations demonstrate how different sectors tactically employ AM capabilities to address specific customisation requirements while balancing performance, economics, and regulatory considerations in delivering the next generation of personalised industrial products.

However, uptake of AM-enabled mass customization in industries is shaped by sector-specific factors and thus limits its potential for widespread use. In mass-producing applications, layer-by-layer AM is still beaten by conventional manufacturing; conventional production typically uses commercial AM machinery that produces 10–50 parts per hour compared with thousands from injection moulding [107]. Material cost, especially in the case of AM grade metal powders, which is three to ten times pricier than traditional materials, prevents mass customization from becoming cost-effective for most industries [73]. The ability to maintain consistent quality in customized variants is particularly problematic in aerospace and automotive sectors, where the certification requirements stipulate flawless manufacture. Achieving 99.9% of consistency during repeated part production for various geometries requires high process control, increasing costs [50]. Traditional supply models still create hurdles because the existing manufacturing infrastructure lacks the ability to support the digital file sharing process as well as networked production required for AM mass customization [39]. Further, the protection of intellectual property often narrows down the scope of collaboration, especially in areas that necessitate the protection of designs while still allowing for the use of distributed production chains [104].

Figure 4. Injection mould with AM-enabled conformal cooling channels demonstrating integration with traditional manufacturing. Source: [108] (Open Access CC-BY 4.0).

Figure 4. Injection mould with AM-enabled conformal cooling channels demonstrating integration with traditional manufacturing. Source: [108] (Open Access CC-BY 4.0).

Figure 5. (a): GE aviation LEAP engine fuel nozzle [109]. (b): Daimler spare part on-demand network [110] (customer-centric design integration) (Courtesy GE Aviation) (flexible manufacturing networks). Sample industrial and engineering applications of AM-enabled mass customisation.

Figure 5. (a): GE aviation LEAP engine fuel nozzle [109]. (b): Daimler spare part on-demand network [110] (customer-centric design integration) (Courtesy GE Aviation) (flexible manufacturing networks). Sample industrial and engineering applications of AM-enabled mass customisation.

Architectural and Construction Applications

Architectural and construction applications represent an emerging domain for AM-based mass customisation, characterised by large-scale components and integration with conventional building systems. Computational design tools adopted by architecture professionals now optimise structures while analysing environmental conditions. The simulation tools combine multiple physics analyses where structural evaluation joins forces with heat and sound models to design optimised building elements across various environmental factors [52,66]. The benefits of this include the creation of customisation opportunities that AM can directly translate to physical components without the economic penalties that traditional construction methods would impose for non-standard elements. Generative design algorithms generate new geometric designs that increase performance potential and aesthetic value, and large-scale printing systems equipped with automatic reinforcement placement systems create structural designs with optimised stress distribution while minimising material requirements. This enhances mass customisation of structural elements where each component can be optimised for its specific loading conditions and location within the building—an approach economically unfeasible with traditional casting methods [76]. Different machine learning algorithms process environmental and occupant behaviour data to develop optimal building components for specific climate regions and usage scenarios [70,71].

Construction applications have undergone transformation through the combination of advanced material science with enhanced process control mechanisms, which resulted from concrete printing technologies. AM enables mass customisation in concrete construction by eliminating the formwork costs that typically make customised concrete elements economically unfeasible [54]. Contemporary material compositions with speciality additives and woven fibres boost rheological attributes as well as mechanical features of printed products. Additionally, in-process property monitoring systems maintain uniform material characteristics across the printing operation [65]. Enhancements in multi-axis printing technologies eliminate formwork constraints, a breakthrough capability that fundamentally enables mass customisation in construction by removing the economic barrier of creating unique moulds for each design variant. Automated tool path generation algorithms optimise stability by optimally distributing material deposition, allowing AM systems to adapt their building strategy for each unique architectural element being produced. Printing parameter adjustments occur in real time based on material and environmental conditions through a combination of process control algorithms and monitoring systems, which lead to high-quality outcomes in large-scale components [52,71]. Modern facade element manufacturing advances because of integrated design processes being combined with manufacturing operations. The integration of automated manufacturing plans increases industrial production effectiveness and waste reduction and enables multi-substance printing to build facade elements that optimise heat and sound resistance, leveraging the unique ability of AM to create functionally graded materials and complex internal structures impossible with conventional facade manufacturing techniques. Existing installation performance data processed by machine learning programs generate optimised design parameters for future projects through digital twin modelling [65,71]. This creates a continuous improvement cycle for AM-enabled mass customisation where each new facade element benefits from the performance data of previously installed customised components.

AM strategies are fundamentally reshaping architecture and construction through diverse implementation approaches that enable unprecedented mass customization.

Table 4. AM Strategy Implementation in Architecture and Construction.

AM Strategy	Construction Products	Key Applications	Challenges	Supporting Evidence
Customer-Centric Design Integration	- Functionally graded structures - Optimised load-bearing elements - Customised connection nodes	- Computational design - Multi-physics simulation - Generative algorithms	- Regulatory approval - Performance validation - Digital skills gap	[52,66,71]
Adaptive Production Systems	- Large-scale concrete elements - Multi-axis printed structures - In situ material optimisation	- Real-time parameter adjustment - Property monitoring - Automated tool path generation	- Weather sensitivity - Environmental control - Process monitoring complexity	[52,65,71]
Integration with Traditional Manufacturing	- Large-format structural elements - Reinforced printed components - Hybrid structural systems	- Multi-substance printing - Conventional system integration - Enhanced performance	- Size limitations (10 × 10 × 10m) - Building code integration - System compatibility	[64,70,71]
Flexible Manufacturing Networks	- On-site construction printing - Networked production planning - Iterative optimisation systems	- Performance data collection - Continuous improvement - Digital twin modeling	- Industry fragmentation - Coordination requirements - Digital infrastructure gaps	[65,71]

illustrates how each strategy addresses specific industry challenges while creating new possibilities. Customer-centric design integration leverages computational power to create responsive building components that would be economically prohibitive using traditional methods [11,52]. Apis Cor’s 3D-printed house (Figure 6b) exemplifies the capabilities of adaptive production systems for dynamic adjustment during construction, allowing their mobile technology to respond to changing site conditions while maintaining quality regardless of environment [111]. Harvard GSD’s ceramic panels (Figure 6a) showcase integration with traditional manufacturing by combining complex 3D-printed geometries with conventional mounting systems, bridging innovative design with established practices [112]. Flexible manufacturing networks distribute construction capabilities through digital coordination, reducing transportation costs while enabling localization. These contrasting applications demonstrate how AM overcomes traditional constraints, eliminates formwork costs, and enables efficient production of non-standard elements.

Architectural and construction industries face significant barriers to AM-enabled mass customization. Regulatory challenges include absent standards for 3D-printed structural parts, requiring expensive custom approvals [11]. Environmental factors like temperature, dust, and moisture compromise print quality and increase equipment costs. Size limitations (10 × 10 × 10 m maximum) necessitate hybrid strategies that reduce customization benefits [112]. Workforce challenges exist with several firms lacking qualified AM personnel. The fragmented nature of construction supply networks complicates implementation, while specialized weather-resistant materials cost up to three times more than conventional alternatives, limiting applications to financially justified projects [104].

3.2.4. Evaluation Methodologies and Performance Metrics

Cost–Benefit Analysis Frameworks

Strategic decision-making depends on complete frameworks to assess the economic feasibility of mass customisation implementations based on AM technologies. This review captures distinctive financial assessment procedures specifically developed for evaluating AM in mass customization contexts, integrating new approaches that suit the unique characteristics of such production environments. Deradjat and Minshall [72] in their work provided a comprehensive model for assessing the direct and indirect costs linked with the implementation of the AM by evaluating the materials, labour, equipment and quality control within the context of mass customization, in ways that account for the unique cost structure of AM where complexity is virtually free but volume carries premium costs. The evaluation of production efficiency in hybrid manufacturing systems uses specific metrics such as the calculation of the use of resources, setup times, and throughput rates across different AM-enabled customization scenarios [73]. Organisations have developed cost models that focus on the cost structure that factors in various production volumes and customization levels, drawing comparisons with that of conventional manufacturing approaches [74]. According to Frohn-Sörensen et al. [76], the use of decision support systems to determine the most appropriate means of using resources in AM-enabled mass customisation settings has demonstrated that this system enables real-time analysis of manufacturing costs and resource utilisation across diverse customisation scenarios.

Economic modelling for AM mass customisation now incorporates comprehensive approaches for multi-dimensional value evaluation. Digital twin simulations use accurate projections to show resource needs and production planning, along with machine learning algorithms that examine historical production information for cost forecasts regarding different customisation approaches [72,73]. Monitoring operations in real time helps activity-based costing systems distribute overhead expenses exactly between different product variations through precise tracking of energy consumption and material usage together with labour resource usages specific to AM processes, while automated quality control systems evaluate cost implications of various inspection strategies for geometrically complex AM-produced customized parts [74,76].

Life cycle cost analysis systems have added sustainability evaluation elements, together with environmental assessment capabilities, to their framework. The combination of advanced simulation tools allows humans to estimate environmental impacts across production scenarios using energy modelling alongside material flow analysis. Machine learning methods discover efficient process parameters and minimise resource usage [39,77]. Open supply chain optimisation methods allow businesses to analyse complete logistics expenses together with inventory needs, which results in well-founded decisions regarding product batches and production sites. The technology enables companies to measure work-related community needs and social benefits through integrated systems that automatically monitor regulatory compliance across multiple geographic regions [11,78].

Mutual integration of dynamic optimization models, along with real options theory, has generated radical changes to investment analysis frameworks. Advanced financial modelling incorporates numerous possibilities for market demand and technological evolution to make complete evaluations about investment timing and scale decisions [79,80]. Digital twin simulations, along with machine learning algorithms, are combined to predict future customisation needs by tracking market trends and competitive dynamics. These methods offer both accurate assessments of equipment specifications and capacity requirements. Investment strategies built with modular structures allow organisations to introduce AM capabilities at different times, and risk management solutions provide complete analysis of market uncertainties and technology expiration risks [81,82].

Quality Assessment Methods

Research has produced specific approaches for assessing product quality in mass customising systems based on AM. With a specific focus on preserving consistency in important characteristics while allowing for varied design needs, Rajamani et al. [37] establish exact methods to evaluate dimensional accuracy and surface quality across many customizing situations. Organisations have put in place complete quality management systems, including in-process monitoring and post-production verification, with particular procedures for matching bespoke components against predetermined quality criteria specifically designed by the layer-by-layer production of customized components in AM [11]. Koop et al. [77] talk about the creation of automated inspection systems for mass-tailored parts. They show how these systems make it possible to quickly confirm important features while keeping the efficiency of manufacturing. Research has revealed particular strategies for applying statistical process control in AM-based mass customization, including real-time monitoring of process parameters and product characteristics to preserve consistent quality across various customization scenarios, enabling consistent quality across all customizing scenarios. Büscher et al. [79] record specific methods for applying quality assurance procedures that address geometric accuracy and material properties in bespoke components, thereby addressing both geometric accuracy and material properties. These methods are particularly relevant in applications that require certain functional performance characteristics.

Quality evaluation in AM mass customization has been transformed by advanced metrology systems which integrate multi-modal measuring methods. While machine learning algorithms automatically identify critical dimensions and geometric features, high-resolution computed tomography combined with laser scanning and structured light systems allows a thorough evaluation of internal features and surface characteristics [37,79]. While automated inspection planning systems optimize measurement tactics based on component geometry and essential feature requirements, real-time monitoring systems track dimensional stability throughout the manufacturing process. Digital twin simulations can estimate measurement uncertainty for complex geometric features [40,77]. However, the development of in situ calibration techniques ensures that measurement accuracy is always checked. Integration of automated defect classification systems helps quickly evaluate tailored components in terms of quality.

Advanced non-destructive testing techniques are now included in material characterisation systems. While machine learning algorithms automatically find possible flaws and material anomalies, integration of ultrasonic testing with thermal imaging and acoustic emission monitoring enables comprehensive evaluation of the internal structure and material properties [75,83]. Automated data analysis tools can track how properties change over several builds. Creating specific test methods for additively manufactured materials ensures an accurate characterisation of uneven features and changes caused by the process. Through the use of digital twin simulations, it is possible to anticipate property distributions for various process parameters [80,81]. However, adopting statistical process control techniques helps in the early identification of material property drift.

The execution of performance validation frameworks became possible by merging accelerated testing methods with computational modelling approaches. Test systems featuring advanced technologies combine equipment that applies mechanical force with environmental simulation to simulate long-term performance under service scenarios [37]. These testing approaches use machine learning processes to optimize test parameter settings according to specific application needs. Data analysis automation monitors how performance shifts during testing, simultaneously with newly developed methodologies that allow testing of additively manufactured components under complicated loading requirements. Test systems operated by computers optimize acceptance methods according to device specifications and digital twin technology projects equipment longevity during different operational conditions [79,82].

Performance Evaluation Systems

System performance assessment frameworks that cover complete mass customization systems based on AM serve as essential methods to boost strategic planning and continuous improvement activities. Widespread frameworks and methodological improvements resulting from the systematic review help organizations to assess comprehensive performance across all dimensions, specifically for these unique manufacturing settings. The implementation of advanced production monitoring systems enables performance assessment through predictive modelling while enhancing real-time analytics capabilities [115]. Several sensor devices that merge process tracking with quality assessment capabilities provide an extensive system for analysing both production operational effectiveness and product quality standing [80,81]. The system autonomously detects operational weaknesses and structural improvement opportunities. Integration produces performance metrics based on productivity standards and flexibility, as well as customizability aspects, using automated data reporting systems that monitor production performance in various scenarios. The use of digital twin technology for the performance prediction in the system simulation allows the automated optimisation algorithms to assess the production system performance across different configuration domains [83,110]. Advanced optimisation methods are employed with sustainability evaluation criteria in resource utilisation models. With the energy monitoring systems in combination with material tracking tools, companies are able to quantify complete resource efficiency measurements, and machine learning algorithms optimise manufacturing parameters to minimise waste production [43,73]. When managing different customised products from manufacturing, automated schedulers use the resources better, whereas digital twins calculate resource use for new variations. They can also be used for enabling waste reduction measurement with material recycling efficiency, along with automated systems enforcing environmental regulations between different control areas [49,84]. Monitoring of customer satisfaction throughout the years has experienced some major changes, largely due to the use of sophisticated analytics in conjunction with a feedback system. Machine learning algorithms can be parallel to evidence from product performance and customer feedback to detect necessary satisfaction factors to measure different product customisation options [3] by means of automated surveys [75]. Digital twin technologies allow companies to estimate customer reaction to design adjustments while stakeholders develop complete quality indicators that use both objective conditions and subjective user perceptions. Customers can receive immediate feedback through time-sensitive systems, and automated analysis platforms track consumer satisfaction through diverse market distributions [79].

4. AM-Enabled Mass Customisation Implementation Framework

In lieu of review, an implementation framework is constructed specifically for AM-enabled mass customisation, comprising four steps that guide organisations from planning through execution (Figure 7). This hierarchical structure addresses the unique challenges and opportunities of using AM technologies for cost-effective, scalable mass customisation. The framework synthesises insights from 61 research studies across diverse industries and technological systems, providing a coherent implementation roadmap that addresses both the technical capabilities unique to AM and the organisational transformations required for successful mass customisation deployment. The framework unites insights from 61 research studies across diversifying industries and technological systems to deliver an organised implementation system that links customisation options to production efficiency.

This framework differs significantly from current models in the literature in a number of significant ways. Unlike other frameworks, which generally concentrate on single, distinct areas like technology choice [72] or business models [116], our framework is a unique construct integrating the strategic decision-making, operational implementation, and measurement aspects, which are usually described separately in prior research [117]. In addition, whereas current frameworks tend to operationalise theoretical premises in isolation, our integrated perspective offers concrete mechanisms of how these four theoretical perspectives interrelate in the context of AM-supported mass customisation.

This framework builds upon four complementary theoretical foundations, each carefully applied to the specific context of AM-enabled mass customisation. Configuration theory guides organisations to maintain alignment between strategic objectives, AM technological capabilities, organisational structures, and mass customisation market requirements [118]. The resource-based view directs strategic resource allocation and capability development specific to AM technologies, recognising these specialised manufacturing capabilities as sources of competitive advantage in mass customisation markets [119,120]. Dynamic capabilities theory informs the Performance Evaluation Layer [121,122], enabling organisations to continuously adapt their AM processes and customisation offerings as technologies evolve and customer preferences change. The framework adopts a socio-technical systems approach in the Tactical Integration and Operational Excellence Layers [123,124], recognising the critical interdependence between AM technologies and workforce capabilities required for successful mass customisation implementation. A key innovation of this framework is its systematic integration of four theoretical foundations specifically applied to AM-enabled mass customisation, a synthesis not present in existing implementation models. This multi-theoretical approach provides organizations with more robust guidance compared to frameworks that rely on single theoretical perspectives.

The framework organises its four interconnected elements—Strategic Foundation, Tactical Integration, Operational Excellence, and Performance Evaluation—in a progressive structure where each layer addresses specific aspects of AM-enabled mass customisation (Figure 3). Every implementation tier focuses on separate implementation aspects through defined coordination and feedback systems between tiers. This multi-layered approach enables organisations to systematically address both strategic and operational aspects of AM-based mass customisation while maintaining alignment between business objectives and implementation activities. The framework introduces several novel elements not found in previous implementation models: (1) explicit feedback loops and validation checkpoints to ensure continuous verification of alignment during the implementation process, (2) specific considerations for AM processes (build orientation, support structures, nesting efficiency) incorporated into resource planning, as well as (3) adaptive control algorithms that specifically address variable customisation requirements unique to AM environment, and (4) performance metrics specifically tailored to AM-enabled mass customisation that extend beyond traditional manufacturing metrics.

The Strategic Foundation Layer provides a clear direction for organisations with respect to clear targets and key performance indicators for the implementation of their AM-enabled mass customising projects, which align with corporate strategy and the unique capabilities of AM technologies. This involves assessing the practicality and financial feasibility of many mass customisation techniques [71,73] and conduction extensive market research to determine client preferences and customisation demands that specifically leverage the geometric freedom and tool-less production advantages of AM. Based on the particular customisation needs and manufacturing limits of the company, the strategic level should also handle the choice and use of suitable AM technologies and materials. The Strategic Foundation Layer establishes critical organisational infrastructure through three interconnected elements, which comprise the strategic alignment, technological infrastructure, and organisational readiness (see Figure 3). The strategic alignment mechanisms need to ensure synchronisation between mass customisation initiatives and market opportunities [34], establishing clear targets and KPIs aligned with corporate strategy while conducting market research to determine customer preferences and customisation demands [76]. The technological architecture components guide should appropriate AM technology and material selection based on specific customisation requirements [42], evaluating how different AM processes (powder bed fusion, material extrusion, vat photopolymerisation) align with various customisation scenarios [34]. Organisational readiness elements focus on developing AM-specific competencies and managing cultural transformation toward customisation mindsets. The Strategic Foundation Layer provides clear direction for organisations regarding specific targets and key performance indicators for implementing their AM-enabled mass customising projects, which align with corporate strategy and the unique capabilities of AM technologies. This involves assessing the practicality and financial feasibility of various mass customisation techniques [72,74] and conducting extensive market research to determine client preferences and customisation demands that specifically leverage the geometric freedom and tool-less production advantages of AM. Based on the particular customisation needs and manufacturing limits of the company, the strategic level should also address the choice and use of suitable AM technologies and materials. The Strategic Foundation Layer establishes critical organisational infrastructure through three interconnected elements: strategic alignment, technological infrastructure, and organisational readiness (see Figure 3). The strategic alignment mechanisms need to ensure synchronisation between mass customisation initiatives and market opportunities [34], establishing clear targets and KPIs aligned with corporate strategy while conducting market research to determine customer preferences and customisation demands [74]. Technological architecture components should guide appropriate AM technology and material selection based on specific customisation requirements [42], evaluating how different AM processes (powder bed fusion, material extrusion, vat photopolymerisation) align with various customisation scenarios [32], a specificity lacking in previous implementation frameworks. Organisational readiness elements focus on developing AM-specific competencies, managing cultural transformation toward customisation mindsets, and implementing knowledge management systems that capture implementation learning from AM-based customisation initiatives [2].

The Tactical Integration Layer orchestrates relationships between strategic objectives and operational execution of AM-enabled mass customisation (see Figure 3). This layer has three interconnected elements, which include process integration, production planning, and quality management. Process integration should enable seamless collaboration between design, production, and quality control functions [35], while standardised protocols for customer engagement and design validation ensure integrity and traceability of customised product information throughout the AM-enabled customisation workflow [82,83]. Production planning needs to incorporate resource optimisation algorithms and dynamic scheduling systems that balance efficiency with adaptability to varied requirements [31], accounting for AM-specific considerations like build orientation, support structures, and nesting efficiency. Quality management systems incorporate real-time monitoring capabilities specifically designed for AM processes to ensure consistent product quality across different customisation variants [79], addressing the unique quality challenges of producing varied designs through additive processes.

The Operational Excellence Layer focuses on execution optimisation through three interconnected components, which are production execution, customer integration, and quality assurance (see Figure 7). Advanced manufacturing systems need to provide constant performance through real-time process monitoring, adaptive control algorithms that adjust parameters for different customized geometries, and predictive maintenance [36,37]. Customer integration systems facilitate collaborative customisation through advanced requirement capture systems optimised for the design freedom of AM and automated validation tools that ensure manufacturability of customised designs within AM process constraints [50]. Quality assurance combines automated inspection technologies specifically developed for complex AM geometries, testing procedures adapted for AM-produced parts, and comprehensive documentation to maintain product quality while enabling process traceability across varied customisation scenarios [60].

The Performance Evaluation Layer, which is the last in the mass customisation implementation framework, ensures continuous optimisation of the AM-enabled mass customisation system through three components: performance metrics, continuous improvement, and validation mechanisms. Performance measurement systems track key indicators specific to AM-based mass customisation, including production lead times across different customised variants, AM resource utilisation, customer satisfaction with customised products, and quality consistency across varied AM-produced parts [73,124]. This layer is a significant improvement over present frameworks in that it uses machine learning concepts for identifying patterns in the data for the AM process and setting AM-specific metrics for the performance of the mass customisation, something absent in previous implementation models that use traditional manufacturing metrics. Continuous improvement frameworks need to incorporate machine learning techniques for identifying patterns in AM process data, benchmarking studies comparing different AM-enabled customisation approaches, and cost–benefit analyses specific to AM economics [124]. Knowledge-sharing platforms support ongoing learning about AM capabilities and applications, while validation systems ensure continued alignment between implementation outcomes and organisational objectives for AM-enabled mass customisation [39]. In addition, our framework is unique in that it responds to the new technological developments in AM that were not included in previous implementation models, especially in the sphere of multi-material printing and AI-based design optimization that emerged in 2020. This temporal specificity, combined with our systematic review methodology covering 61 studies through 2024, provides organizations with guidance that reflects the current state-of-the-art in AM-enabled mass customisation.

In order to achieve implementation success in the mass customisation implementation framework, there is a need for effective coordination of advancement at every layer to guarantee continuous alignment between elements. Prior to progressing to the next layer, there is a need to build strong foundations in AM technologies and customisation strategies at every level, as early advancement might damage the general system efficacy. It is important to retain alignment with changing organizational objectives, market requirements, and evolving AM capabilities.

5. Conclusions and Prospects

This systematic review of 61 studies provides a comprehensive understanding of mass customization strategies in additive manufacturing environments. We identify four key implementation strategies: integration with traditional manufacturing, customer-centric design integration, flexible manufacturing networks, and adaptive production systems—each offering distinct advantages when aligned with organizational capabilities and market requirements. The analysis reveals critical technological enablers (advanced AM systems, digital workflow integration, elimination of tooling requirements) and barriers (material limitations, post-processing requirements, quality assurance challenges) that significantly influence implementation success. Industry-specific investigations demonstrate how strategies must be adapted across healthcare, industrial/engineering, and architectural contexts to achieve optimal outcomes. The proposed multi-layered implementation framework integrates strategic, tactical, and operational dimensions, providing organizations with a structured yet adaptable approach to AM-based mass customization. Despite these advances, important research gaps remain in standardized evaluation methodologies, long-term reliability assessment, sustainability integration, quality control enhancement, economic modelling, digital workflow management, and emerging technology applications. Addressing these gaps will further advance the potential of AM-based mass customization to transform manufacturing paradigms and create competitive advantages.

5.1. Practical Implications

The implementation framework derived from our systematic review offers significant practical benefits for organizations implementing AM-based mass customization. The multi-layered approach provides a comprehensive roadmap that addresses strategic, tactical, and operational dimensions simultaneously, helping organizations avoid focusing exclusively on technological aspects. By identifying specific technological enablers and barriers, organizations can prioritize technologies aligned with their customization objectives while developing mitigation strategies for common implementation challenges. Industry-specific findings enable organizations in healthcare, industrial, and architectural sectors to adapt implementation approaches to their particular contexts. The evaluation methodologies and performance metrics provide practical tools for assessing implementation effectiveness and guiding continuous improvement. Additionally, the identification of critical success factors supports more effective risk management and change management strategies, increasing the likelihood of implementation success.

5.2. Research Limitations

Despite including 61 studies, several limitations should be acknowledged. The relative newness of AM-based mass customisation means longitudinal studies examining long-term outcomes are limited, with most capturing experiences over only 1–3 years. Geographical biases exist, with studies predominantly from Europe, North America, and parts of Asia limiting understanding of implementation in developing regions where different conditions may influence outcomes. Publication bias may result in over-representing successful implementations. Finally, the rapid evolution of AM technologies means some findings may become outdated as new capabilities emerge.

5.3. Future Research Directions

The systematic review of AM-enabled mass customisation has identified significant knowledge gaps in both theoretical frameworks and practical implementations. This section outlines critical research directions from our analysis, each addressing specific barriers and limitations identified in the literature. These interconnected research priorities collectively form a roadmap for advancing the scientific understanding and industrial adoption of AM-enabled mass customisation strategies:

1. Performance Evaluation Frameworks

Our review revealed a critical absence of standardised metrics for evaluating mass customisation implementations across different industrial contexts [72]. Without consistent evaluation methodologies, organisations cannot reliably assess the effectiveness of their customisation strategies or benchmark against industry standards. Future research should develop and validate comprehensive evaluation frameworks that accommodate industry-specific requirements while enabling meaningful cross-sector comparisons. Such frameworks would facilitate evidence-based decision-making and provide clearer pathways to successful implementation.

2. Long-term Performance and Reliability

While short-term performance characteristics of AM-produced components are increasingly well documented, their long-term durability remains inadequately investigated [50]. This knowledge gap presents a significant barrier to adoption in critical applications requiring extended service life or operation in harsh environments. Longitudinal studies examining ageing behaviours, fatigue characteristics, and maintenance requirements of customised AM components would address a fundamental uncertainty in the literature. Such research is particularly crucial for high-consequence sectors like medical implants and aerospace components, where reliability directly impacts safety and regulatory compliance.

3. Material Development and Characterization

The literature reveals substantial variability in material properties depending on build orientation, process parameters, and post-processing technique [44,51]. This inconsistency undermines the reliability of customised components and limits application scope. Future research should focus on developing specialised materials for AM mass customisation that demonstrate more consistent mechanical, thermal, and chemical properties across varying production conditions. Materials science advancements in this area would significantly expand the practical boundaries of mass customisation and enable more predictable quality outcomes.

4. Sustainable Mass Customisation

Our review identifies a significant gap in understanding how sustainability considerations can be integrated into mass customisation decision frameworks [73]. As environmental regulations tighten globally, research must develop methodologies for systematically evaluating the sustainability impacts of customisation strategies, particularly regarding material utilisation efficiency and energy consumption. Future studies should investigate multi-objective optimisation approaches that balance customisation capabilities with environmental performance and economic viability, creating a more holistic understanding of value creation in AM-enabled mass customisation.

5. Economic Models for Hybrid Manufacturing

The economic implications of various mass customization strategies remain poorly understood, particularly in hybrid manufacturing environments that combine traditional and additive approaches [75]. Current economic models fail to capture the complex relationships between customization levels, production efficiency, and value creation. Research should develop more sophisticated economic frameworks that account for the unique cost structures, resource optimization opportunities, and value propositions in AM-enabled customization. Such models would enable organizations to make more informed implementation decisions based on comprehensive cost–benefit analyses rather than technological determinism.

6. Artificial Intelligence Applications

The literature indicates that artificial intelligence and machine learning technologies hold significant yet largely unrealized potential for advancing mass customization capabilities [81]. Research should systematically explore how these technologies can enhance process optimization, quality control, and design automation in AM environments. Particular attention should be given to developing AI approaches that improve production adaptability and system intelligence while maintaining human oversight of critical decisions. This direction represents a convergence of digital twin technology with computational intelligence that could address multiple barriers identified in our review.

7. Post-Processing Automation

Post-processing operations remain a significant bottleneck in AM-enabled mass customization workflows, often requiring substantial manual intervention [53,54]. This limitation constrains scalability and introduces quality variations that undermine customization advantages. Future research should investigate integrated manufacturing cells that seamlessly combine AM processes with automated post-processing operations. Studies should focus on developing standardized approaches that maintain consistency across customized components while reducing labour intensity and variability, thereby enabling more efficient scaling of mass customization strategies.

8. Cross-Industry Implementation Guidelines

Our systematic review reveals a notable absence of structured implementation guidelines tailored to specific industry contexts [49,56]. Organizations face significant uncertainty when transitioning from traditional manufacturing to AM-enabled mass customization. Research should develop sector-specific implementation frameworks that account for unique industry requirements, constraints, and opportunities. These practical roadmaps would help organizations navigate the complex technological, organizational, and market considerations involved in successful implementation, bridging the gap between theoretical potential and practical realization.