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By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

3D printing has been warmly welcomed by clothing enterprises for its customization capacity in recent years. However, such clothing enterprises have to face the digital transformation challenges brought by 3D printing. Since the business model is a competitive weapon for modern enterprises, there is a research gap between business model innovation and digital transformation challenges for 3D-printing garment enterprises. The aim of the paper is to innovate a new business model for 3D-printing garment enterprises in digital transformation.

A business model innovation canvas (BMIC), a new method for business model innovation, is used to innovate a new 3D-printing clothing enterprises business model in the context of digital transformation. The business model canvas (BMC) method is adopted to illustrate the new business model. The business model ecosystem is used to design the operating architecture and mechanism of the new business model.

First, 3D-printing clothing enterprises are facing digital transformation, and they urgently need to innovate new business models. Second, mass customization and distributed manufacturing are important ways of solving the business model problems faced by 3D-printing clothing enterprises in the process of digital transformation. Third, BMIC has proven to be an effective tool for business model innovation.

The new mass deep customization-distributed manufacturing (MDC-DM) business model is universal. As such, it can provide an important theoretical reference for other scholars to study similar problems. The digital transformation background is taken into account in the process of business model innovation. Therefore, this is the first hybrid research that has been focused on 3D printing, garment enterprises, digital transformation and business model innovation. On the other hand, business model innovation is a type of exploratory research, which means that the MDC-DM business model’s application effect cannot be immediately observed and requires further verification in the future.

The new business model MDC-DM is not only applicable to 3D-printing garment enterprises but also to some other enterprises that are either using or will use 3D printing to enhance their core competitiveness.

A new business model, MDC-DM, is created through BMIC, which allows 3D-printing garment enterprises to meet the challenges of digital transformation. In addition, the original canvas of the MDC-DM business model is designed using BMC. Moreover, the ecosystem of the MDC-DM business model is constructed, and its operation mechanisms are comprehensively designed.

The main issue in the mass customization of apparel products is how to efficiently produce products of various sizes. A parametric pattern-making system is one of the notable ways to rectify this issue, but there is a lack of information on the parametric design itself and its application to the apparel industry. This study compares and analyzes three types of parametric clothing pattern CAD (P-CAD) software currently in use to identify the characteristics of each, and suggest a basic guideline for efficient and adaptable P-CAD software in the apparel industry.

This study compared three different types of P-CAD software with different characteristics: SuperALPHA: PLUS(as known as YUKA), GRAFIS and Seamly2D. The authors analyzed the types and management methodologies of each software, according to the three essential components that refer to previous studies about parametric design systems: entities, constraints and parameters.

The results demonstrated the advantages and disadvantages of methodology in terms of three essential components of each software. Based on the results, the authors proposed five strategies for P-CAD development that can be applied to the mass customization of clothing.

This study is meaningful in that it consolidates and organizes information about P-CAD software that has previously been scattered. The framework used in this study has an academic value suggesting guidelines to analyze P-CAD systems.

Customers today expect businesses to cater to their individual needs by tailoring the products they purchase to their own preferences. The term “Industry 5.0” refers to a new wave of manufacturing that aims to meet each customer's unique demands. Even while Industry 4.0 allowed for mass customization, that wasn't good enough before, customers today demand individualized products at scale, and Industry 5.0 is driving the transition from mass customization to mass personalization to meet these demands. It caters to the individual needs of each consumer by meeting their demands. More specialized components for use in medicine are made possible by the widespread customization made possible by Industry 5.0. These individualized parts are included into the medical care of the patient to meet their specific needs and preferences. In the current medical revolution, an enabling technology of Industry 5.0 can produce medical implants, artificial organs, bodily fluids, and transplants with pinpoint accuracy. With the advent of AI-enabled sensors, we now live in a world where data can be swiftly analyzed. Machines may be programmed to make complex choices on the fly. In the medical field, these innovations allow for exact measurement and monitoring of human body variables according to the individual's needs. They aid in monitoring the body's response to training for peak performance. It allows for the digital dissemination of accurate healthcare data networks. In order to collect and exchange relevant patient data, every equipment is online.

Customization, a marketing strategy through providing personalized products, might be a new solution to motivate consumer feedbacks in electronic commerce (e-commerce) websites. Taking the dual-value of customization (emotional involvement and uniqueness expression) as the theoretical basis, this study aims to investigate the impact of customization on consumer's word-of-mouth (WOM) behaviors and contents by motivating: (1) more, (2) faster, (3) positive at first and then negative, (4) longer and (5) more helpful WOMs.

A field study was conducted with multi-sourced data: customer order data from a Chinese retailer and WOM data from Amazon.com. The two datasets were matched to filter out 463 online reviews among 6,892 customers who placed customized orders. Heckman's two-stage model, logistic regression, Ordinary least squares regression, Tobit regression, analysis of covariance and Lind–Mehlum U Test were used in the data analysis.

This study has found that (1) customization level motivates WOM behaviors including WOM posting and WOM speed, (2) an inverted U-shaped relationship exists between customization level and consumer rating and (3) customization level has a significantly positive impact on WOM helpfulness but not on WOM length.

This study advances theoretical development in the area of WOM motivators by proposing a new product-centric approach, customization, to stimulate voluntary WOMs. Empirical field research that analyzes consumer's real responses to customization is in scarcity. The dual-value of customized products is proposed as the underlying mechanism to explain the impact of customization level on consumer's WOM behaviors/contents. An interesting inverted U-shaped relationship is found between customization level and customer rating. This research provides nuanced practical guidance for websites, companies and consumers.

Customization allows brands to provide goods that match customers’ preferences, but its impact on consumer–brand relationships is unclear. This study aims to examine the impact of two key moderators on the effectiveness of customization to enhance brand’s perceived partner quality, which mediates the relationship between customization and brand attitude.

Study 1 (n = 219) tests the moderated–mediation relationship, the effect of customization (IV) on perceived partner quality (mediator), and its indirect effect on brand attitude (DV), which is moderated by consumers’ self-construal orientation (Moderator 1). Study 2 (n = 416) extends the model tested in Study 1 by including an additional moderator, shopping task context (Moderator 2).

Results empirically demonstrate the impact of self-construal and shopping task context on the effectiveness of customization in improved customer–brand partner quality and eventual brand attitude.

Customization may not appeal to every customer for the same reason. Marketers must target customers’ individual traits (independents vs interdependents) and understand the context of the shopping task context (self-purchase vs gift-giving).

The work reveals how brands can enhance consumers’ perceptions of the brand by allowing them to customize the product. It is novel in demonstrating that customization is not just a fine-grained segmentation strategy but a brand building tool. It highlights contextual factors affecting the outcome of customization by demonstrating the conditions under which it is most effective.

The fifth industrial revolution, known as Industry 5.0, envisions an industry that is innovative, resilient, socio-centric, and competitive while minimizing negative environmental and social impacts, respecting people, the planet, and prosperity. Industry 5.0 is replacing earlier advancements and it is successful because it reaches the pinnacle of perfection. Additionally, machine work saves human workers time and effort. It is built on the concept of fusing digitalization elements from the fourth industrial revolution with Sustainable Development Goals through human-centric solutions, bio-inspired technologies, and secure data transfer. Industry 5.0 mentions about the various opportunities, constraints, and potential directions for future research. Industry 5.0 places less emphasis on technology and focus on human collaboration for progress, it supposed to have a shift in existing paradigm. Industry 5.0 is necessary in contemporary business with the paid technology advancements in order to get competitive advantages as well as economic growth for the manufacturing and it has three drivers: “green transition”, “digital transition”, and “competitive transition”. The goal of green transition is to prevent climate change and environmental degradation. This necessitates changes to current economic growth strategies. The goal of digital transition is to support the circular economy by modernizing digital strategies in terms of digital skills, data, technologies, and infrastructure. Competitive transition aims to convert marketing policies, regulations, standards to increase people's prosperity and business value. It focuses on business and marketing rules that are fair, competitive, innovative, and adaptable.

The internationalization of business has grown the production value chains and created performance challenges for industrial production. Industry 4.0, the digital transformation of industrial processes, promises to deliver performance improvements through smart functionalities. This study investigates how digital transformation translates to performance gain by adopting a systems perspective to drive smartness.

This study uses qualitative research to collect data on the lived experiences of digital transformation practitioners for theory development. It uses semi-structured interviews with industry experts and applies the Gioia methodology for analysis.

The study determined that enterprise smartness is an organizational capability developed by digital transformation, it is a function of integration and the enabler of organizational performance gains in the Industry 4.0 context. The study determined that performance gains are experienced in productivity, sustainability, safety and customer experience, which represents performance metrics for Industry 4.0.

This study contributes a model that inserts smartness in the linkage between digital transformation and organizational outcomes to the digital transformation and production management literature.

The study indicates that digital transformation programs should focus on developing smartness rather than technology implementations, which must be considered an enabling activity.

Existing studies recognized the positive impact of technology on performance in industrial production. The study addresses a missing link in the Industry 4.0 value creation process. It adopts a systems perspective to establish the role of smartness in translating technology use to performance outcomes. Smart capabilities have been the critical missing link in the literature on harnessing digital transformation in organizations. The study advances theory development by contributing an Industry 4.0 value model that establishes a link between digital technologies, smartness and organizational performance.

This study provides an up-to-date review of additive manufacturing (AM) technologies and guidance for selecting the most appropriate ones for specific applications, taking into account the main features, strengths, and limitations of the existing options.

A literature review on AM technologies was conducted to assess the current state-of-the-art. This was followed by a closer examination of different AM machines to gain a deeper insight into their main features and operational characteristics. The conclusions and data gathered were used to formulate a classification and decision-support framework.

The findings indicate the building blocks of the selection process for AM technologies. Furthermore, this work shows the suitability of the existing AM technologies for specific cases and points to opportunities for technological and decision-support improvements. Lastly, more standardization in AM would be beneficial for future research.

The proposed framework offers valuable support for decision-makers to select the most suitable AM technologies, as demonstrated through practical examples of its utilization. In addition, it can help researchers identify the limitations of AM by pinpointing applications where existing technologies fail to meet the requirements.

The study offers a novel classification and decision-support framework for selecting AM technologies, incorporating machine characteristics, process features, physical properties of printed parts, and costs as key features to evaluate the potential of AM. Additionally, it provides a deeper understanding of these features as well as the potential opportunities for AM and its impact on various industries.

The purpose of this paper is to study the profit optimization of manufacturers who provide personalized products to customers, thus avoiding additional operational costs and response times in the production process of personalized product design.

First, the authors present an integrated model for optimizing profit where the design and production of personalized products are both considered. The authors propose the concept of personalization level and four cases of personalization level are considered including top, high, medium and low levels. Polynomial-time optimal rules are provided for each level by analyzing 17 subcases where all possible personalized products are considered. Then, the authors compare the obtained profit of personalized products with the optimal profit of standardized products.

At low and high levels of personalization, personalized manufacturing is more profitable for specific products with specific characteristics. At the top and middle level of individuation, assuming that the product has certain characteristics, whether the best choice to produce personalized products depends on the level of individuation chosen by customers.

An important decision issue for manufacturers is whether to produce personalized or standardized products. In this study, the authors consider this decision problem and analyze the operational functions of personalized products. The authors hope to provide theoretical support in the operational management of manufacturers offering personalized products.