18 Advances in Production Engineering & Management ISSN 1854-6250 Volume 20 | Number 1 | March 2025 | pp 18–28 Journal home: apem-journal.org https://doi.org/10.14743/apem2025.1.524 Original scientific paper Mass customization in practice: Strategic implementation and insights from Polish small and medium sized enterprises Patalas-Maliszewska, J. a,* , Kowalczewska, K. b , Pajak, G. a a Institute of Mechanical Engineering, University of Zielona Góra, Zielona Góra, Poland b Doctoral School of Exact and Technical Sciences, University of Zielona Góra, Zielona Góra, Poland A B S T R A C T A R T I C L E I N F O Implementing a mass customization (MC) strategy in manufacturing enter- prises presents an ongoing challenge for both managers and researchers. To remain competitive, managers must consider adopting advanced technologies associated with Industry 4.0 and 5.0 (I4.0/5.0). This study seeks to identify so- lutions that support strategic decision-making aimed at enhancing the level of MC implementation. The paper begins with a literature review focused on the adoption of MC strategies within European manufacturing enterprises. It then presents findings from a questionnaire-based survey conducted among more than 100 small and medium-sized enterprises (SMEs) in Poland’s automotive sector. Statistical analysis, including correlation coefficients, was used to eval- uate the data. The results indicate that consumer participation in the product design process is the key driver of successful MC strategy implementation in the surveyed SMEs. Furthermore, managers recognized strong correlations be- tween the adoption of I4.0/5.0 technologies—such as automated machinery and real-time data usage—and higher levels of MC capability. The benefits of implementing MC strategies, including increased production flexibility and waste reduction, were also highlighted. The findings offer general insights ap- plicable to SMEs in the automotive industry. Keywords: Mass castomisation strategy; Small and medium sized manufacturing enterprises; Consumer participation; Production flexibility; Industry 4.0/5.0 technologies; Automotive industry *Corresponding author: J.Patalas-Maliszewska@iim.uz.zgora.pl (Patalas-Maliszewska, J.) Article history: Received 18 December 2024 Revised 14 April 2025 Accepted 19 April 2025 Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. 1. Introduction Designing and implementing an appropriate production strategy requires managers to have ade- quate knowledge about the potential effects and possible scenarios of enterprise development. Mass customization (MC) involves the production of various product variants while reducing the costs of tools and equipment, minimizing changes in production processes, machinery, the number of em- ployees, and improving production flexibility and quality [1]. Currently, the strategy of MC and per- sonalization is one of the challenges related to the need to implement the Industry 4.0/Industry 5.0 (I4.0/5.0) concept in enterprises. MC can be treated as a production strategy that combines push and pull production paradigms to achieve a core competence [2]. Customization can be treated as the process of adapting a product or service to the customer's own needs, most often with support from information systems such as product configurators. Customized products currently respond to customer expectations and enable companies to gain a competitive advantage. An open research question remains: How can the level of MC be defined and measured, and what indicators signal the Analysis and modelling mass customisation strategy realizing in Polish small and medium sized manufacturing enterprises Advances in Production Engineering & Management 20(1) 2025 19 success of implementing this production strategy? According to [2], MC research has been unex- plored, and therefore manufacturing companies have low competence in implementing MC. Cus- tomers increasingly demand personalized, high-quality products at competitive prices [3-4]. To identify the research gap, a preliminary literature review (Table 1) was conducted with the aim of defining key factors influencing the degree of mass customization (MC) strategy implementation, based on empirical evidence from European manufacturing enterprises. Table 1 A literature review of current factors determining a level of MC strategy implementation in European Enterprises MC realizing in the manufacturing enterprises European country Factors Source Small and medium enterprises, Mechanical products Italy Product platform development, IT-based product configuration, Group technology [5] Clothing (Burrberry), Furniture (IKEA) and toy (LEGO) industries UK, Denmark, Sweden Appropriate business and marketing strategy, Operational management in accordance with sustainable production and technological development [6] Machine building industry (FENDT) Germany Highly flexible assembly line [7] Automotive and agricultural industries (Fiat powertrain technologies) Italy Use real-time data [8] Automotive industry (assem- bly a diesel particulate filter (DPF)) - Use augmented reality technology, Dynamic production rescheduling [9] Semiconductor industry - Robot utilization, Simulation and statistical analyses [10] Electronic industry (laptops/PCs) - Product configuration mechanism [11] Assembly industry - Modeling and simulation techniques of production processes [12] Industrial enterprises Czech Republic Industry 4.0 (Flexible processes, Artificial intelligence-based solutions, Automation, Robotics, e-commerce, 3D printing, Flexible manufacturing) [13] Manufacturing sector - Collaboration networks, Business agility, Digital supply chain, Use of I4.0 technologies [14] Electronic industry, Automotive industry EU High level of modular design, Remanufacturing of modular products [15] Automotive industry - Clear description of configuration options provided to customers, Flexible manufacturing system and processes [16] Table 1 summarizes the findings of the literature review concerning the implementation of mass customization (MC) strategies in European manufacturing enterprises. Specifically, it pre- sents (1) the types of European industries analyzed, (2) the countries in which these industries are located, and (3) the key factors identified as influencing the level of MC strategy implementa- tion. To the best of our knowledge, and based on the current state of the literature (as presented in Table 1), no previous studies have explicitly investigated how these factors contribute to in- creasing the level of MC implementation within European manufacturing enterprises in the auto- motive sector. Furthermore, there is a lack of research addressing the impact of emerging tech- nologies—specifically those associated with the Industry 4.0/5.0 paradigm—on the degree of MC implementation. While prior studies (e.g., [2]) suggest that MC is inherently linked to technologi- cal advancement, the reviewed literature (Table 1) does not provide empirical evidence on the relationship between the adoption of Industry 4.0/5.0 technologies and the enhancement of MC capabilities in manufacturing firms. Therefore, in this paper we examine the following research questions: (1) What key factors influence increasing the level of MC in European Manufacturing Enterprises? (2) What kind of Patalas-Maliszewska, Kowalczewska, Pajak 20 Advances in Production Engineering & Management 20(1) 2025 I4.0/5.0 technologies impact the MC level, and (3) How can these relationships influence future research trends in MC research? The I4.0/5.0 technologies in the context of MC strategy realization can be distinguished as ro- botics, simulation and integration, cyber-physical systems (CPSs), Internet of things (IoT), cloud computing, automated machines, AI, augmented reality (AR) and virtual reality (VR), cybersecu- rity, human-machine interaction (HMI), and finally human-robot collaboration (HRC). The usage of robots generally enables flexible and efficient production. But in the case of MC, it requires op- erators to perform responsible tasks to adapt production to individual customer requirements. Simulation of production processes allows presenting different scenarios to improve decision- making in an MC strategy [17]. CPSs integrate technologies with physical systems in order to in- crease the automation of MC production [18]. IoT-based solutions facilitate maintaining the oper- ating state of the system within MC production [19]. Cloud computing allows manufacturers to respond to the fact that more and more customers are willing to participate in the design process, and it enables MC production to be flexible and scalable [20]. Automated machines enable pro- duction tailored to the customer's needs. AI-based tools provide visualization of processes, their monitoring and control, configuration of products, quality assurance, and real-time data classifi- cation to maximize process efficiency [21]. AR and VR for MC production enable product visuali- zation for customers [9]. Cybersecurity is a set of necessary technologies in MC production for the secure collection of customer data [22]. HMI in MC enables more effective delivery of products to market [23]. This paper analyses and models the implementation of the mass customization (MC) strategy in European manufacturing, using Polish small and medium-sized manufacturing enterprises (SMEs) as a representative case study. Furthermore, it aims to identify and define the relation- ships between the adoption of Industry 4.0/5.0 technologies and the achieved level of MC imple- mentation. The originality of this study is as follows: • This study establishes the relationships between the I4.0/5.0 technologies implementation and the increased level of MC strategy for European SMEs in the automotive industry. • It provides practical insights into MC through empirical research in over 100 European manufacturing SMEs from Poland. • This study determines the benefits of implementing the MC strategy in the automotive in- dustry. • Recommendations for managers in the automotive manufacturing industry were formu- lated regarding necessary actions to increase the level of MC strategy. 2. Materials and methods 2.1 Questionnaire surveys on MC The usage of a tool such as a questionnaire enables the collection of research data among many manufacturing companies in a short time, as the respondents provide answers to specific questions in writing, either traditionally or electronically. The questionnaire used in this study concerned the implementation of the MC strategy in manufacturing enterprises, factors determining the level of MC strategy realization, and the influence of the implementation of I4.0 technologies on the level of the MC strategy. Firstly, the level of the MC strategy was defined. In our research, the MC strategy implementation level is classified according to the Technology Readiness Levels (TRL) classification. TRL is a classi- fication that allows the determination of the technological maturity of a product, process, or ser- vice—from the creation of an idea and basic research, through conceptual and laboratory work cor- responding to industrial research, to creating a prototype as part of development work, and finally, to a ready-made solution applicable in practice. TRL determines what validation activities have al- ready been performed and what still need to be done [24]. According to TRL, five levels of imple- mentation of the MC strategy were defined, depending on the answers given in the conducted sur- veys. Level I (TRL levels 1-2) is achieved if the respondent declares knowledge of the MC definition. Analysis and modelling mass customisation strategy realizing in Polish small and medium sized manufacturing enterprises Advances in Production Engineering & Management 20(1) 2025 21 Level II (TRL levels 3 and 4) is achieved if, in a company, customized orders are tailored to the indi- vidual needs of customers. Level III (TRL level 5) concerns analytical and experimental confirmation of critical functions or conscepts of the technology. Level IV (TRL levels 6 and 7) refers to the stage when the technology components or its basic subsystems have been integrated, and the company plans to implement the MC strategy. Level V (TRL levels 8 and 9) is achieved when the company defines the methods for realizing the MC strategy. Next, based on the literature research results (Table 1), it was possible to define the factors for European Manufacturing Enterprises that influence the level of the MC strategy (Table 2) in our questionnaire. Table 2 Factors describing MC strategy Factors describing MC strategy Abbreviations Consumer participation in the design process C1 Customer participation in the product design C2 Integration of customer preferences C3 Product configuration mechanisms C4 The ability to handle multiple process variations at different stages of production C5 Highly flexible assembly line C6 Using modeling and simulation techniques of production processes C7 The ability to use real-time data to make efficient, quick decisions to assembly line C8 Table 3 Industry 4.0/5.0 Technologies – Factors of implementation Industry 4.0 technology Abbreviations Robotics I1 Product simulations I2 Material simulations I3 Production processes simulations I4 Cyber-physical systems (CPSs) I5 Systems integration I6 Industrial Internet of Things (IIoT) I7 Cloud computing I8 Automated machines I9 Artificial Intelligence tools I10 AR and VR I11 HMI and HRC I12 Cybersecurity I13 Subsequently, to address the second research question, the survey incorporates items related to the implementation of selected I4.0/5.0 technologies, as outlined in Table 3. To investigate how the identified relationships may shape future research directions in the field of mass customization (MC), empirical research was conducted among the surveyed enter- prises to evaluate the benefits associated with the implementation of the MC strategy within the automotive industry (Table 4). The questionnaire is structured to include an introductory section outlining the objectives and scope of the survey, followed by four distinct modules. Table 4 Benefits of MC strategy implementation Factors Abbreviations Annual sales increase up to 10 % E1 Annual sales increase from 11 to 25 % E2 Annual sales increase above 25 % E3 Increasing the flexibility of the work surface E4 Increasing production flexibility E5 Reducing the amount of waste E6 Reducing electricity consumption E7 More effective analysis of large data sets E8 Decentralization of decision-making E9 Patalas-Maliszewska, Kowalczewska, Pajak 22 Advances in Production Engineering & Management 20(1) 2025 The first module comprises a set of open- and closed-ended questions designed to collect es- sential company-related information from the respondent. This includes the company’s regis- tered office and operational location, the number of employees (enabling enterprise size classi- fication), the primary product portfolio, and the respondent’s position within the organization. These elements allow for an assessment of the respondent’s familiarity with the company’s man- agement strategy and development policy. The second module is completed by respondents whose companies have implemented a mass customization (MC) strategy. It covers questions related to expenditures incurred during imple- mentation, methods of gathering customer preferences and requirements, approaches and out- comes of MC implementation, the level of satisfaction associated with MC deployment, the I4.0/5.0 technologies utilized to support MC, and future plans concerning the extension of MC applications within the enterprise. The third module targets respondents from companies that have not adopted the MC strategy. It includes a multiple-choice question addressing the reasons for non-implementation, alongside the possibility to provide an open-ended explanation and indicate whether implementation is planned for the future. The fourth module explores the perceived significance of I4.0/5.0 technologies in achieving the intended outcomes of MC strategy implementation. 2.2 Research group and data collection The empirical study was conducted using a structured questionnaire composed of closed, multi- ple-choice questions. Data collection took place between January 10 and August 31, 2023, through both face-to-face interviews (29 %) and telephone surveys (71 %). The survey followed a sample-based research design and gathered responses from 153 Euro- pean manufacturing enterprises operating in Poland within the automotive sector. The sample consisted of 117 small and medium-sized enterprises (SMEs; defined as employing up to 249 in- dividuals) and 36 large enterprises. The selection of the automotive industry as the focus of analysis is justified by its strategic importance to the European economy, accounting for nearly 7 % of the region’s gross domestic product (GDP). Furthermore, the automotive sector employs approximately 13.8 million individuals in the European Union, which represents 6.1 % of the total workforce. Notably, collaboration among partners within the automotive industry remains an open re- search question [25], further validating the relevance of this sector for investigation. This study places particular emphasis on SMEs, as they constituted over 76 % of the total re- search sample. The research sample may be considered representative. According to data from the Polish Cen- tral Statistical Office, 3,954 enterprises were registered in 2022 as operating in the automotive industry. The obtained sample size of 153 exceeds the minimum required number of 145 enter- prises, calculated at a 95 % confidence level, with a proportion (p) of 0.5 and a maximum margin of error of 8 %. The required minimum sample size was determined using the standard formula (Eq. 1): 𝑁𝑁 𝑚𝑚 𝑚𝑚𝑚𝑚 = 𝑁𝑁 𝑝𝑝 ( 𝛼𝛼 2 ∙ 𝑓𝑓 (1 − 𝑓𝑓 )) 𝑁𝑁 𝑝𝑝 ∙ 𝑒𝑒 2 + 𝛼𝛼 2 ∙ 𝑓𝑓 (1 − 𝑓𝑓 ) (1) where 𝑁𝑁 𝑚𝑚 𝑚𝑚𝑚𝑚 – minimum sample size, 𝑁𝑁 𝑝𝑝 – population size, α – confidence interval, 𝑓𝑓 – fraction size, 𝑒𝑒 – assumed maximum error. Moreover, the research was conducted across the entire territory of Poland, ensuring repre- sentation proportional to the number of companies registered in each voivodeship. 2.3 Research model Based on the results of in-depth interviews with 117 SMEs from the automotive industry, a re- search model (Fig. 1) was developed and analysed using the correlation and regression method to estimate the level of Mass Customization (MC) strategy implementation in manufacturing en- terprises. The survey instrument used for testing the model was constructed by defining Analysis and modelling mass customisation strategy realizing in Polish small and medium sized manufacturing enterprises Advances in Production Engineering & Management 20(1) 2025 23 appropriate measurement scales to assess both the impact of MC strategy implementation and the influence of Industry 4.0/5.0 technologies on its realization. The factors describing the level of MC strategy realization in Polish automotive SMEs were de- rived from structured feedback and are listed in Table 2. Respondents assessed the importance of each factor (c1-c8) using a binary scale: factor0 – not very important, factor1 – very important for increasing the level of MC implementation. Similarly, the perceived benefits of MC strategy implementation (Table 4) were evaluated based on company experiences in 2022, with respondents indicating whether each benefit (E1- E9) was considered not very important (factor0) or very important (factor1). The overall level of MC strategy implementation in the surveyed companies was classified into five levels according to the Technology Readiness Level (TRL) framework. Subsequently, our research also examined the implementation of Industry 4.0/5.0 technolo- gies (Table 3). The assessment of these technologies was based on structured survey responses. Respondents were asked to indicate whether their company applies specific I4.0/5.0 technologies (I1-I12), and to evaluate the perceived significance of each technology in the context of MC strat- egy implementation. The following binary scale was applied: factor0 – not very important, factor1 – very important. Fig. 1 Research model 3. Results 3.1 Results from the surveyed enterprises When addressing the first research question, it can be confirmed that the factors identified in the relevant literature (Table 1) were also recognized by the surveyed enterprises. Among Polish manufacturing SMEs, 55.56 % of companies enable customer participation in the product design process, while 52.99 % of enterprises report both customer participation and integration of cus- tomer preferences. Product configuration mechanisms and the ability to utilize real-time data for efficient and rapid decision-making on the assembly line are implemented by 44.44 % of surveyed companies. A highly flexible assembly line is employed by 41.88 % of enterprises, simulation tech- niques for production processes are utilized by 30.77 %, and the ability to manage multiple pro- cess variations across different production stages is reported by 18.80 % of SMEs. Furthermore, the empirical research confirms that I4.0/5.0 technologies (as listed in Table 3) are actively implemented among the surveyed manufacturing SMEs. Automated machines are utilized by 41.88 % of the surveyed enterprises, while systems integration is present in 35.04 % of enter- prises. Product simulations, production process simulations, and cybersecurity solutions are adopted by 29.06 % of respondents. Material simulations are implemented by 23.93 % of enter- prises. HMI and HRC are utilized by 22.22 % of SMEs, robotics by 21.37 %, and CPSs by 17.09 %, Realisation MC strategy in manufacturing SMEs c1 c4 c5 c8 c2 c3 c6 c7 E1 E2 E3 E6 E5 E4 E7 E8 E9 Increasing the level of implementation of MC strategy in manufacturing SMEs Patalas-Maliszewska, Kowalczewska, Pajak 24 Advances in Production Engineering & Management 20(1) 2025 respectively. However, cloud computing is employed by only 7.69 % of SMEs, IIoT by 5.98 %, artifi- cial intelligence tools by 3.42 %, and AR and VR technologies by 1.71 %, respectively. These findings indicate that the most commonly adopted I4.0/5.0 technologies among European SMEs in Poland include automated machines, systems integration, simulations of products and pro- duction processes, as well as cybersecurity. Therefore, to address the first and second research questions, statistical analysis was conducted using correlation coefficients to identify the strength of relationships between the identified factors and the level of MC strategy implementation. 3.2 Analysis MC strategy realizing in Polish manufacturing SMEs The correlation analysis is a statistical method used to examine the strength and direction of a linear relationship between two variables, quantified by the correlation coefficient r, which takes values in the interval ⟨−1, 1⟩. A value of −1 indicates a perfectly negative linear relationship, while +1 denotes a perfectly positive linear relationship. A value of 0 signifies the absence of a linear correlation (Bobko, 2001). The Pearson correlation coefficient is calculated according to the for- mula Eq. 2: 𝑟𝑟 = Σ( 𝑥𝑥 𝑚𝑚 − 𝑥𝑥 ̅)( 𝑦𝑦 𝑚𝑚 − 𝑦𝑦 � ) � Σ( 𝑥𝑥 𝑚𝑚 − 𝑥𝑥 ̅) 2 Σ( 𝑦𝑦 𝑚𝑚 − 𝑦𝑦 � ) 2 (2) where 𝑥𝑥 𝑚𝑚 and 𝑦𝑦 𝑚𝑚 are the values of the variables 𝑥𝑥 and 𝑦𝑦 , respectively, and 𝑥𝑥 ̅, 𝑦𝑦 � are the mean values of these variables. During the statistical analysis, the correlation between the factors related to the realization of the MC strategy and the increase in the level of MC strategy implementation was examined. The analysis was conducted using Statistica version 13.3 (StatSoft Polska Sp. z o.o., Kraków, Poland). The results of the correlation analysis are presented in Table 6. The table includes the following indicators: 𝑟𝑟 2 – coefficient of determination, 𝑡𝑡 – value of the t-statistic testing the significance of the correlation coefficient, and 𝑝𝑝 – probability value (significance level). A very strong positive correlation was observed between the increase in the level of MC strat- egy and consumer participation in the design process ( 𝑟𝑟 = 0.8278). Additionally, significant cor- relations were identified between the increase in MC strategy and both customer involvement in product design ( 𝑟𝑟 = 0.7861) and the integration of customer preferences ( 𝑟𝑟 = 0.7861). In contrast, a weak correlation was found between the increase in MC strategy and the automation of produc- tion process planning as well as the use of simulation techniques. These findings clearly indicate that the key factors influencing the enhancement of MC strategy in Polish manufacturing SMEs include consumer participation in the design process and product design, the ability to understand customer preferences, the utilization of product configuration mechanisms, production process control, and the application of real-time data. In response to the second research question concerning the impact of advanced technologies on the advancement of MC strategy implementation, the relationships between Industry 4.0 technolo- gies adopted and the increase in MC strategy level were analyzed and are presented in Table 6. A strong correlation was observed between the increase in the level of MC strategy and the use of automated machines, such as 3D printers or autonomous processing stations ( 𝑟𝑟 = 0.6285), as well as with systems integration ( 𝑟𝑟 = 0.5438). Consequently, one of the key challenges in advanc- ing the MC strategy is undoubtedly the automation of unique and customized processes. Table 5 Correlations between the factors describing the realization of the MC strategy and the increase in the level of MC strategy in the automotive industry Relations Correlation 𝑟𝑟 2 𝑡𝑡 𝑝𝑝 c1/MC 0.8278 0.6853 15.8271 0.0000 c2/MC 0.7861 0.6180 13.6419 0.0000 c3/MC 0.7861 0.6180 13.6419 0.0000 c4/MC 0.6622 0.4386 9.4792 0.0000 c5/MC 0.3563 0.1269 4.0896 0.0000 c6/MC 0.6285 0.3950 8.6666 0.0000 c7/MC 0.4936 0.2436 6.0871 0.0000 c8/MC 0.6622 0.4386 9.4792 0.0000 Analysis and modelling mass customisation strategy realizing in Polish small and medium sized manufacturing enterprises Advances in Production Engineering & Management 20(1) 2025 25 Table 6 Correlations between Industry 4.0/5.0 technologies implemented and the increase in the level of MC strategy in the automotive industry Relations Correlation 𝑟𝑟 2 𝑡𝑡 𝑝𝑝 I1/MC 0.3859 0.1489 4.4870 0.0000 I2/MC 0.4739 0.2246 5.7715 0.0000 I3/MC 0.4153 0.1724 4.8961 0.0000 I4/MC 0.4739 0.2246 5.7715 0.0000 I5/MC 0.3362 0.1130 3.8285 0.0002 I6/MC 0.5438 0.2957 6.9500 0.0000 I7/MC 0.1867 0.0348 2.0390 0.0437 I8/MC 0.2137 0.0456 2.3464 0.0206 I9/MC 0.6285 0.3950 8.6666 0.0000 I10/MC 0.1393 0.0194 1.5086 0.1341 I11/MC 0.0976 0.0095 1.0522 0.2949 I12/MC 0.3957 0.1566 4.6218 0.0000 I13/MC 0.4739 0.2246 5.7715 0.0000 In addressing the third research question, the survey also investigated the benefits of imple- menting the MC strategy within the automotive industry. Among Polish SMEs, 36.75 % reported an increase in production flexibility, 33.33 % noted a reduction in waste, and 28.21 % observed an annual sales increase of up to 10 %. Additionally, 26.50 % of SMEs declared improvements in workforce flexibility and decentralization of decision-making. A reduction in electricity consump- tion was reported by 24.79 % of enterprises, while 20.51 % highlighted more effective analysis of large data sets. Subsequently, the relationship between these outcomes of MC strategy implemen- tation and the increase in the level of MC strategy was analyzed (see Table 7). The primary relationships identified in Polish SMEs between the implementation of the MC strategy and increased production flexibility (0.5644), as well as waste reduction (0.5235), were found to be significant. Table 7 Correlations between the outcomes of MC strategy implementation and the increase in the level of MC strategy in the automotive industry Relations Correlation 𝑟𝑟 2 𝑡𝑡 𝑝𝑝 E1/MC 0.4641 0.2153 5.6188 0.0000 E2/MC 0.2729 0.0745 3.0431 0.0029 E3/MC 0.1564 0.0244 1.6986 0.0920 E4/MC 0.4445 0.1976 5.3223 0.0000 E5/MC 0.5644 0.3186 7.3328 0.0000 E6/MC 0.5235 0.2741 6.5904 0.0000 E7/MC 0.4250 0.1806 5.0360 0.0000 E8/MC 0.3761 0.1414 4.3535 0.0000 E9/MC 0.4445 0.1976 5.3223 0.0000 4. Discussion The research findings provide comprehensive answers to the three posed research questions. The primary factors driving the enhancement of the Mass Customization (MC) strategy implementa- tion level in Polish manufacturing SMEs within the automotive sector include active customer in- volvement in both the MC product design process and the utilization of real-time configuration tools tailored to meet customer requirements. A notable example of such an approach is the Cus- tomer-Product Interaction Life Cycle (CILC) model [26], which facilitates cost reduction and en- hances customer satisfaction. Furthermore, the results demonstrate that the adoption of Industry 4.0/5.0 (I4.0/5.0) technologies, particularly automated machinery, contributes significantly to the advancement of MC strategy implementation. The study also delineates future research directions in MC, emphasizing: (1) the development of enterprise strategies that integrate customer participation in new product and process design; (2) the incorporation of automation and robotics in alignment with MC objectives; and (3) the transformative impact of I4.0/5.0 technology adoption on MC practices. Patalas-Maliszewska, Kowalczewska, Pajak 26 Advances in Production Engineering & Management 20(1) 2025 Nevertheless, certain limitations warrant attention and suggest avenues for further investiga- tion. First, the current study is confined to the automotive industry, focusing on customized or- ders. Extending this research to other industrial sectors, such as metal manufacturing, could re- veal whether observed patterns are generalizable or industry-specific. Second, the study does not address manufacturer performance metrics. Financial constraints typical of SMEs restrict the de- ployment of I4.0/5.0 technologies, thereby limiting MC strategy realization in production pro- cesses. The substantial investment required for implementing I4.0/5.0 solutions represents a sig- nificant barrier to enhancing MC production capabilities [27]. Future research should, therefore, integrate analyses of investment expenditures with assessments of both tangible and intangible benefits derived from these technologies, as such transformations are imperative for sustaining competitive advantage. Third, the present investigation centers on SMEs, which constitute the majority (over 90 %) of enterprises in Poland. Subsequent research should explore the interplay between Industry 4.0/5.0 adoption (i.e., acquisition and utilization of advanced technologies) and the simultaneous increase in customer satisfaction and profit margins across small, medium-sized, and large enter- prises. It is crucial to recognize that these categories possess distinct financial strategies and cap- ital limitations. This article represents the initial phase of a broader research initiative on MC strategy imple- mentation in manufacturing enterprises. Building on the collected data, an AI-based predictive model will be developed to validate the empirical findings and uncover potential additional or non-linear relationships undetectable through traditional correlation analyses [28]. The model will employ artificial neural networks trained and tested on survey data filtered by significance analysis. The subsequent research stage will leverage this model to simulate and identify strate- gies that optimize the desired level of product customization. 5. Conclusion This paper presents a diagnostic analysis of the situation regarding Mass Customization (MC) re- alization on the example of Poland in the European market. In enterprises across European Union countries, notable similarities can be observed in the functioning of the market, partly due to the regulations of the European Commission. Therefore, our research results in MC can be treated as a reference framework for activities aimed at enhancing the MC level in European manufacturing enterprises. We hope that the results of our research will inspire further studies to explore these areas within other countries with varying geopolitical and economic conditions. Recommenda- tions for managers in the automotive manufacturing sector were formulated regarding necessary actions to increase the level of MC strategy. Firstly, it is advised to implement tools that will enable customers to participate in the design of the product and process, and secondly, to develop mech- anisms that will enable the analysis of real-time data and the implementation of automatic solu- tions, even when dealing with individual projects. This is undoubtedly a challenge and a promising avenue for further research in the field of improving MC strategy implementation. Acknowledgments This work was supported by a program of the Polish Ministry of Science under the title ‘Regional Excellence Initiative’, project no. RID/SP/0050/2024/1. Declaration of interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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