Development of Digital Transformation Maturity Assessment Model for Collaborative Factory Involving Multiple Companies

Abstract

Recent advancements in digital transformation (DX) in the industrial sector have spotlighted digital collaborative factories, which emphasize relationships with partners, particularly in the manufacturing sector. However, existing DX maturity assessment models primarily focus on evaluating individual companies, lacking consideration of partnerships and thereby failing to reflect the complexities of collaborative systems. To address this limitation, this study aims to develop a DX maturity assessment model tailored to digital collaborative factories while accounting for collaborative relationships. Initially, 25 existing DX maturity assessment models were reviewed, and the evaluation elements related to collaboration were extracted from each. Accordingly, the maturity assessment model was created with 15 evaluation factors, including organizational aspects, process management, quality control, and logistics operations. Finally, to verify the applicability of the model, the DX maturity levels of a lead company—a forklift-manufacturing company—and its partner—an automotive component manufacturer—within the same value chain were assessed, and the model’s suitability was evaluated. The results indicate that the lead company needed to improve on the intelligence, connectivity, and automation aspects, while its partner needs to streamline production process operations and technological connectivity. This approach enables manufacturers to obtain more reliable information in resolving issues arising from collaboration with partners, as well as in establishing future strategies. The findings suggest that strengthening collaboration systems among partners and advancing DX based on digital collaboration will raise competitiveness within the manufacturing sector.

1. Introduction

The rapid advancement of digital technologies is accelerating digital transformation (DX) across industries, establishing it as a crucial element in revolutionizing traditional business models and maximizing operational efficiency [1]. In recent years, DX has established itself as an essential requirement for responding to shifts in market dynamics, enhancing operational efficiency, and promoting innovation. Particularly, in the manufacturing sector, the adoption of digital technologies has revolutionized the traditional production processes, creating new concepts such as smart factories and digital collaborative factories [2,3,4].

The concept of the smart factory leverages advanced technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics, to optimize manufacturing processes and enable real-time decision making [5,6]. As part of DX, smart factories have integrated digital technologies with physical production processes to enhance productivity, flexibility, and quality, making collaboration between companies crucial in driving digital change [7]. Specifically, the manufacturing sector, with its complex production processes and supply chains, requires the integrated coordination of various activities, such as product design, manufacturing, procurement, and distribution. In this context, collaboration among different stakeholders (manufacturers, suppliers, customers, etc.) is essential to improving productivity and efficiency [8]. Manufacturers continually face demands for high quality standards, ongoing product improvements, and rapid market responses, which emphasize the growing importance of collaborative approaches leveraging digital technologies. Against this backdrop, the concept of digital collaboration factories has emerged as an extension of the smart factory. Unlike smart factories, which focus on digitizing and optimizing production processes, digital collaboration factories emphasize enhancing collaboration among various stakeholders, including manufacturers, suppliers, and customers. Their goal is to integrate the data generated during these processes and use them to revolutionize the entire production ecosystem through smooth information exchange and collaboration among stakeholders. Digital collaboration factories go beyond the digitization of production processes, aiming to improve the competitiveness of the manufacturing industry, enhance productivity and quality, and enable swift responses to market changes through a collaborative approach among stakeholders.

Therefore, digital collaboration factories, building upon the smart factory concept, contribute to increasing the overall digital maturity of the manufacturing sector by not only optimizing individual companies’ production processes but also by establishing a collaborative ecosystem. This approach is essential for manufacturers to effectively address current challenges and accelerate innovation [9]. Ultimately, this concept seeks to innovate production processes by digitizing and connecting them, thereby enhancing productivity and strengthening collaboration among stakeholders (manufacturers, suppliers, customers, etc.) to improve the competitiveness of the manufacturing industry. Through such digital collaboration, manufacturers, suppliers, customers, and other stakeholders can strengthen collaborative partnerships, accelerate innovation, respond effectively to industrial challenges, and promote mutual growth and competitiveness by facilitating the exchange of data, insights, and best practices [7,10].

To drive these initiatives effectively, companies must accurately assess their current level of DX and identify areas for improvement. In this regard, existing DX assessment models, such as the Smart Industry Readiness Index (SIRI) by Singapore’s Economic Development Board (EDB) [11], the Digital Transformation Capability Maturity Model (DX-CMM) [12], and the Digital Maturity Model [13], have some limitations. First, these studies did not consider collaboration as a core component, focusing merely on assessment criteria. This results in a lack of sufficient reflection on the importance of inter-organizational collaboration, making it challenging to measure or assess the actual effectiveness of such cooperation. Second, such studies have often overlooked the elements necessary across various types of collaborations for building long-term cooperative relationships. The DX process is not a one-time project but a continuous development. Therefore, long-term strategic partnerships are crucial for its success. However, existing assessment models do not adequately address the factors necessary for building these long-term collaborative relationships, thus failing to provide the insights needed for companies to establish sustainable partnerships. Finally, although many DX assessment models have attempted to cover a range of industries, including manufacturing, few studies have analyzed cases of companies in cooperative relationships. A deep analysis of specific industry cases is necessary to understand and evaluate the practical impact of collaboration. However, existing research is insufficient in this area, presenting a clear limitation.

In this context, this study aims to explore the intersection of DX, collaboration, and manufacturing by developing and applying a DX maturity assessment model tailored to digital collaborative factories. The proposed model evaluates the DX level of manufacturers and their collaborative partners to derive DX collaboration measures and improvement strategies. By integrating the aspect of collaboration into the DX assessment framework, this study elucidates the contribution of collaboration to overall digital maturity and performance in the manufacturing sector.

The remainder of this paper is structured as follows: Section 2 reviews existing DX assessment models and the involvement of collaboration in DX. Section 3 explains the development and application of the proposed DX maturity assessment model considering collaboration. Section 4 describes the evaluation of two companies using the model and presents the strategy development results. Section 5 outlines the advantages and disadvantages of the model, along with its differences from existing models, through real-world application. Finally, Section 6 summarizes the conclusions, future research scope, and practical implications. Through this comprehensive analysis, the study aims to contribute to the advancement of DX and collaboration in the manufacturing sector.

2. Literature Review

2.1. DX Assessment Models

DX refers to the process by which companies innovate traditional production processes and change business models by leveraging new digital technologies [14,15]. The smart factory is a key element in DX, playing a crucial role in improving productivity and enhancing flexibility through the adoption of digital technologies. Examining the relationship between smart factories and DX reveals that smart factories contribute to the digitalization and efficiency of business processes—one of the core goals of DX—by establishing CPS (Cyber–Physical System)-based intelligent systems that optimize production processes and enable real-time decision making [16,17]. Therefore, companies aim to innovate business processes and enhance productivity by adopting digital technologies and establishing smart factories. To drive these changes effectively, companies must accurately assess their current level of DX and identify areas that require improvement. Examples of DX assessment models for this purpose include the following:

The DX-CMM employs the Software Process Improvement and Capability dEtermination (SPICE) concept to determine the maturity of DX across various fields [12]. SPICE is the international standard for software process improvement and capability measurement criteria, integrating various process improvement models under ISO/IEC. The DX-CMM emphasizes the importance of rapid adaptation to changing environments by aligning technology dimensions with strategy, culture, personnel, and processes. It consists of 26 DX processes within four process groups. The model defines six levels of evaluation for each of the 26 assessment criteria, ranging from Level 0 (Incomplete) to Level 5 (Innovating), following a sequential order: Performed, Managed, Established, Predictable, and Innovating. Each evaluation criterion includes process attributes applicable to all processes, as well as general practices and best practices specific to each process. The assessment involves determining the capability level for each evaluation criterion to determine the maturity level of the overall model.

The evaluation system of the Digital Industry Innovation Maturity Assessment Model comprises seven evaluation areas and 28 evaluation criteria from the perspectives of Input, Process, and Outcome in business activities [18]. The evaluation areas from the Input perspective include “organization and strategy”, “data”, “technology”, and “personnel and culture”. The Process perspective includes the evaluation areas of process and collaboration, while the Outcome perspective refers to performance. This assessment model defines six levels of evaluation for each of the 28 criteria and uses survey questions to diagnose these criteria.

2.2. Collaboration for DX

Collaboration for DX is a critical requirement in the business environment. Collaboration with suppliers plays a crucial role in successfully driving DX [19,20] by helping manufacturers efficiently manage their supply chains, promptly adopt new technologies, and improve the quality of products and services. Additionally, collaboration with customers is becoming increasingly important in the realm of DX. Customers now demand personalized services and swift responses, requiring manufacturers to establish close cooperation with their customers to meet these demands [21]. Manufacturers must leverage digital technologies to strengthen customer relationships and secure the flexibility needed to respond swiftly to business-to-consumer (B2C) requirements. This study analyzes the elements of collaboration in the DX process by categorizing the stakeholders into suppliers and customers. To provide a detailed analysis of customer types, both business-to-business (B2B) and B2C concepts are reviewed.

Previous research has considered collaboration with suppliers. For instance, the Digital Transformation Competitiveness Analysis Model [13] highlighted the limitations of existing digital maturity models, such as unreliability due to frequent changes in enterprise-specific measurement items, and it aimed to redefine DX through an integrated framework. To establish key indicators for DX based on this framework, four core areas were identified: (1) Customer Experience, (2) Operations, (3) Strategy, and (4) Organization. By examining recent theories and trends in each of these areas, three key indicators were extracted for each field, and the resulting DX maturity measurement model comprised 12 core factors for South Korean public enterprises. The aforementioned study analyzed inter-enterprise collaboration from an operational perspective, focusing on technological innovation within supply chains and networks. In this context, the integration of supply chains and networks is considered a fundamental prerequisite for DX [7,22], whereby digitalization within supply chains advances alongside DX, facilitating information sharing and decision coordination. With regard to technological innovation within supply chains, DX converts supply chains into “complex adaptive systems” capable of adapting autonomously to environmental changes [23]. For this purpose, technological innovation is crucial, with notable examples being the use of drones and IoT [24,25]. The Digital Transformation Competitiveness Analysis Model assesses the degree of integration between information systems with cooperative partners in terms of 12 core factors for analysis; it also examines the levels of advanced connectivity and integration for IoT applications, smart devices, drones, sensors, and related technologies within the area of technological innovation in supply chains. Previous research models for measuring an organization’s digital capabilities often involved selecting and modifying measurement items based on a basic framework provided, leading to frequent changes in indicators and a lack of consistency. This has made it challenging to compare DX maturity evaluations across different organizations [26]. For instance, retail companies might emphasize Customer Engagement and Customer Experience, while manufacturing firms might focus on Technology and Operations in their DX evaluations. This approach, which varied the weighting of evaluation criteria based on industry or company characteristics, has created difficulties in directly comparing DX maturity across organizations. Additionally, these models have often concentrated on technological factors and failed to integrate long-term collaborative relationships or operational details, limiting their practical utility.

Lim et al. [13] addressed these limitations by conducting multiple case studies with 10 public enterprises in South Korea to develop standardized key indicators. This approach enhanced the measurement capability by providing a framework that allowed for consistent evaluation across different organizations and facilitated quantitative comparison and analysis. Furthermore, the model improved practical relevance by incorporating long-term collaborative relationships and operational details, which had been overlooked by previous models. This research emphasizes the importance of collaboration while also considering the strengths of measurement capability and practicality, proposing a model that can be applied and validated with real-world cases.

In the context of Industry 4.0 technologies and digital supply chain management (DSCM), Kim and Park [22] empirically examined and validated the effectiveness of DSCM, leveraging information technology and key aspects of the Industry 4.0 to achieve SCM objectives. Their study underscored the necessity of establishing intelligent systems based on effective communication capabilities through big data processing, collaboration, digital hardware and software, and networking to support and synchronize activities among SCM participants. Regarding the capability of supply chain processes, integration of information flow, physical flow, and financial flow were identified as critical requirements. In terms of internet-based supply integration, the study highlighted the importance of inventory management coordination with cooperative partners, order processing, and the sharing of purchase order information. Finally, from the perspective of DSCM, the study emphasized the importance of shared activities, such as collaborative demand forecasting with partners, sharing plans for introducing new products or services, and support services. Based on these key elements, three hypotheses were proposed and verified: First, the execution of DSCM will positively impact corporate performance. Second, the use of AI in enterprises will positively moderate the relationship between DSCM and performance. Third, leveraging big data in enterprises will positively moderate the relationship between DSCM and performance. To validate these hypotheses, a survey was conducted on 353 companies, and empirical validation was performed through regression analysis and moderated regression analysis targeting respondents at managerial levels and above. The results confirmed the validity of the first hypothesis, verifying that the execution of DSCM enhances corporate performance.

The literature on collaboration for DX also includes studies that have considered differences between B2B and B2C contexts. For B2B companies, providing product and service models based on “customer experience” poses specific challenges, since their customers are other businesses [27]. Traditional approaches for DX in B2B companies have leaned more toward operational-based innovations that strengthen core business capabilities rather than consumer-facing innovations based on mobile and internet technologies that directly enhance the user experience. However, recent trends indicate more cases of consumers driving industrial change, expanding the B2B domain into a B2B2C model, where both businesses and consumers are transaction counterparts. This shift has underscored the necessity for B2B companies to swiftly gather market information, including consumer spending patterns and responding rapidly with effective strategies, such as (1) acquiring market and customer information, (2) synchronizing processes with suppliers and cooperative partners, (3) implementing process automation (smart factories), and (4) fostering an immersive organizational environment.

In addition, for B2C companies, which directly interface with end users, numerous business models have been developed based on new data and user experiences. The diversification of business and the promotion of cross-industry cooperation by leveraging the core competitive advantages of existing companies have been actively explored. Particularly, the realm of B2C DX is undergoing a shift toward rapidly expanding business domains and partnerships, with “customer experience innovation” as a core strategy. In this context, a customer-centric approach is recognized as being crucial during the DX process in the B2C industry. Specifically, it is essential to consider strategies such as providing personalized products and services, minimizing customer inconvenience beyond cost reduction, and maximizing customer satisfaction.

3. DX Maturity Assessment Model for Digital Collaboration Factory

3.1. Research Method

In this study, we reviewed existing assessment models to develop a specialized DX maturity assessment model tailored for factories involved in digital collaboration. The principles governing the model establishment in this study are as follows:

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Stepwise approach and systematicity: The study consisted of four stages, with clear objectives and corresponding tasks at each stage. In the first and second stage, the need for a clear roadmap for DX processes was recognized, which led to the proposal of developing a maturity assessment model to fulfill this need. The third stage involved systematically developing the model based on a maturity model development framework [28]. De Bruin et al. [28] proposed a methodology to generalize the major stages of maturity model development, which are (1) Scope, (2) Design, (3) Populate, (4) Test, (5) Deploy, and (6) Maintain. Finally, the model was validated through multiple case studies involving the lead company and seven partner companies.

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Research motivation and future orientation: Evaluating existing research revealed deficiencies in DX roadmap areas, prompting a proposal to develop a maturity model based on established frameworks such as KS X9001-3.

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Case studies and industrial application: Multiple case studies were conducted to validate the model, and DX levels were measured in two organizations operating in different industries and company sizes.

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Expert participation and consensus: A panel of experts was convened before developing this model to define DX processes and measurement frameworks. The panel consisted of smart factory experts from academia, industry, and research institutes, who jointly defined DX processes.

Drawing inspiration from various maturity model development methods, this study aimed to follow a process similar to that outlined in Figure 1. The maturity model development process involves defining objectives through current-state assessment, designing the model, developing detailed assessment criteria, and finally applying and validating the model within collaborative factories:

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Step 1. Defining the Concept of Maturity Model through Current-State Assessment

In the first step, the scope of DX was determined based on the definition of DX itself. Before this step, we compared 25 DX-related maturity assessment models and constructed a preliminary model through expert-based brainstorming. The initial design of the model referenced seven indicators to consider in maturity model development, as outlined by De Bruin et al. [28]. We determine the scope of the assessment of the maturity model and define which factors the maturity model should consider in the first step. Thus, classification divides these two steps into scope and context information (

Table 1. Decisions Made during Model Development (Adapted from De Bruin et al. [28]).

Classification	Criterion	Characteristic
Scope	Focus of Model	① Domain-Specific ② General
	Development Stakeholders	① Academia ② Practitioners ③ Government ④ Combination
Context Information	Audience	① Internal ② External ③ Executives, Management ④ Auditors, Partners
	Method of Application	① Self Assessment ② Third-Party Assisted ③ Certified Practitioner
	Driver of Application	① Internal Requirement ② External Requirement ③ Both
	Respondents	① Management ② Staff ③ Business Partners ④ Combination
	Application	① 1 entity/1 region ② Multiple entities/single region ③ Multiple entities/multiple regions

). Criteria exist within each classification: the scope identifies which model is being focused on and which stakeholders are considered. Context information determines to whom the model is provided, how it is measured, which requirements are reflected, who responds, and which environment is applied. The characteristic column in Table 1 lists the possible alternatives that each criterion can have. The initial scope of this model was considered specific to the manufacturing industry, focusing on Company A and its collaboration with suppliers; accordingly, this model was tailored for industry professionals. We aimed to develop a more comprehensive model specialized in the domain of “collaborative factories engaged in vehicle manufacturing and assembly”, making it unlike existing models targeting more general subjects. Therefore, the objectives can be explicitly stated as follows. The first objective was to develop a domain-specific model that can accurately capture the complexity of collaboration between Company A and its suppliers, thereby enhancing domain capabilities. The second objective was to develop an assessment tool that can assess or describe the current-state domain position of the evaluation target, enabling the development of a roadmap to improve the domain position from the current state to the target state. Considerations during model development included the need for content related to collaborative factories between Company A and its supply chain partners, taking into account that the industry of Company A (other machinery and equipment manufacturing) focuses more on assembly than self-production. Additionally, the possibility of evaluation areas/items being self-identified through surveys and field assessments was considered. Accordingly, it was decided that the model assessment should highlight the strengths and areas of improvement for each company. Thus, the ultimate aim was to build a model that can assess the status and identify improvement areas for DX-based collaboration with suppliers within the automotive parts industry (or industrial vehicle industry). The specific considerations and outcomes for model development are listed in Table 1, with the parts considered in this model highlighted among the various characteristics.

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Step 2. Designing the DX Maturity Assessment Model for Digital Collaborative Factory

In the second step, the model was designed to establish the assessment items for DX maturity evaluation. To create a model that considers DX and collaboration status in production, distribution, and sales within the automotive manufacturing industry, existing DX-CMMs were reorganized and restructured. Five evaluation domains were delineated based on considerations specific to the automotive manufacturing industry and collaboration aspects: strategy, data, collaboration, process, and technology. Among these, the evaluation items for collaboration were defined more elaborately than in existing models, as previous research often did not assign sufficient importance to the unique characteristics of collaborative manufacturing partners.

A review of DX maturity assessments revealed that assessment domains such as strategy, data, process, and technology have been extensively covered in previous studies. From an overall perspective, the evaluation domains were categorized into strategy/organization, process, technology/data, and collaboration, as summarized in

Table 2. Evaluation Domains in DX and Smart Factory Maturity Models.

No.	Model Name (Year of Release)	Refer	Domain/Area Classification
			Strategy/Organization	Process	Technology/Data	Collaboration
1	Industrie 4.0 Readiness Model (2015)	[29]	○	○	○	-
2	Industrie 4.0 Maturity Index (2017)	[30]	○	○	○	-
3	Digital Transformers-Digital Maturity Assessment (2016)	[31]	○	○	○	○
4	Smart Industry Readiness Index (2020)	[11]	○	○	○	-
5	Digital Maturity Assessment Tool (2021)	[32]	○	○	○	○
6	The Digital Maturity Model 4.0 (2016)	[33]	○	-	○	-
7	Digital Acceleration Index (2020)	[34]	○	○	○	○
8	The five Digital Business Aptitude domains (2016)	[35]	○	○	-	○
9	Industry 4.0 Maturity Model (2019)	[36]	○	○	○	○
10	Digital REadiness Assessment MaturitY model (2017)	[37]	○	○	○	-
11	Digital Services Capability Mode (2017)	[38]	○	○	○	○
12	Global Digital Maturity Status (2017)	[39]	○	○	-	-
13	Asia Pacific SMEs Digital Maturity Study (2020)	[40]	○	○	○	-
14	Organizations digital readiness framework (2018)	[41]	-	○	-	○
15	Digital transformation framework (2015)	[42]	○	○	○	-
16	Digital Business Transformation in the Context of Knowledge Management (2015)	[43]	○	○	○	-
17	Obstacles and possibilities of digitalization (2018)	[44]	○	○	-	○
18	Key changes preceding digital transformation (2020)	[45]	○	-	○	○
19	e-Transformation Level Assessment Model (2005)	[46]	-	-	○	○
20	Digital Maturity Model (2019)	[47]	○	○	○	-
21	Smart Factory Level Confirmation System	[48]	○	○	○	-
22	DX-CMM (2021)	[12]	○	○	○	-
23	Digital Transformation Maturity Model (2020)	[49]	○	○	○	○
24	Development of DT Competitiveness Analysis Model (2021)	[13]	○	○	-	○
25	DX Competency Model (2017)	[50]	○	-	○	○

. In this table, the No. provides the serial number for each model, and the Model Name provides the model name and year of creation. The Refer column shows the reference number, and the Domain/Area Classification shows whether each model considers four areas of assessment: Strategy/Organization, Process, Technology/Data, and Collaboration.

After reviewing existing models, appropriate assessment items for strategy, data, process, and technology were compiled based on the findings. While some models consider collaboration, this study focuses specifically on collaborative partners in automotive manufacturing; this necessitates the consideration of not only relationships with end consumers—as has been commonly observed in prior research—but also interactions with suppliers and various types of consumers. To achieve this, collaboration factors must encompass supplier collaboration, as well as customer collaboration within the value chain, accounting for both B2B and B2C interactions.

Collaboration with suppliers refers to the process where organizations share information with external suppliers or partners, pursuing mutual benefits in achieving their goals [2,51]. Collaboration with suppliers constitutes a foundational aspect of DX [7,10]. As the stages of DX progress, the impact of strengthening collaboration with suppliers and external organizations tends to increase [9]. Various types of collaboration with suppliers have been mentioned in studies [22,52]:

• Information-sharing collaboration: This type of collaboration focuses on enhancing organizational competitiveness by aligning innovation objectives [53]. It emphasizes seamless exchange and the transmission of information among partner companies during the collaboration process [54,55].
• Technological innovation–based collaboration: This involves continuous optimization within business processes and innovating existing technologies through collaboration [56]. The focus is on augmenting and advancing technological capabilities collaboratively with suppliers [24,25].
• Human resources and cultural collaboration: This involves building trust between organizations and fostering a collaborative atmosphere [57].
• Performance-based collaboration: This type of collaboration is intended to achieve common performance and innovation-related goals [58,59].

Continuous performance evaluation is crucial for developing and enhancing collaborative relationships [60]. When assessing the level of collaboration with suppliers, factors such as demand fluctuations, inventory levels, manufacturing completion data, and others should be considered. Additionally, interactions among members, the sharing of information and data, real-time insights into changes, automation technology, and performance should all be taken into account [61]. In terms of collaboration with suppliers, the assessment criteria in existing digital maturity assessment models do not adequately reflect critical aspects such as information and data sharing, the exchange of technological capabilities, and performance monitoring and improvement. Therefore, these indicators must be integrated into the model for diagnosing DX levels, particularly for collaborative factories. Collaboration with suppliers encompasses strategic, process-oriented, and communicative aspects for information exchange, technological and operational aspects for on-site operations, and performance-oriented aspects for future improvements. By aligning these considerations with the parameters of existing DX maturity models, three evaluation criteria have been established for the proposed DX collaboration maturity model (Figure 2).

Collaboration with customers is one of the key elements of DX, because customer experience in the digital age is shaped by interactions between businesses and customers, as well as among customers themselves [13]. Customers (demand) are classified into corporate customers (B2B) and end consumers (B2C). As these two types of customers have different purchase volumes and requirements, the nature of collaboration differs as well. The B2B market, characterized by numerous transactions, has a complex structure [62,63]. B2B transactions typically involve significantly higher values than B2C transactions, and there exists a distinction in terms of the independence in transactions [64]. B2B transactions typically rely on long-term contracts or strategic partnerships, leading to a higher level of interdependence between the trading parties and often involving complex negotiations. In contrast, B2C transactions are generally independent, short-term engagements with individual consumers, and the terms of these transactions are relatively simpler. In addition to the concept of scale, the differences between B2B and B2C can affect collaboration factors acting in digital collaboration factories, and this study attempted to identify such factors. Basically, in the case of the B2C scenario, as an end user, it aims to increase customer satisfaction by optimizing a large number of unspecified customer experiences and facilitating communication with customers [65]. However, from the B2B scenario’s point of view, since there are specific requirements—because there is a customer who accurately reaches the customer—and the company needs to provide or cooperate with information, that means these characteristics should be considered. The content introduced in the next paragraph introduces the characteristics of B2B and B2C cooperation and describes the selection of cooperation evaluation items with customers.

Collaboration with customer firms (B2B) refers to the relationship between business partners engaged in B2B exchange activities, where the customer firm is viewed as the enterprise from the partner’s perspective. This definition implicitly includes trust in the partner’s expertise, relevant authority, collaboration readiness, and risks and contractual conditions [66]. Achieving the highest performance through real-time business activities and information sharing among B2B partners is crucial in competitive strategies [67]; this involves voluntary participation in decision-making and information-sharing outcomes to achieve mutual goals [68]. The following types of collaboration with customer firms have been identified in studies [69,70]:

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Collaboration through product/service exchange involves the supplier evaluating quality factors for the customer firm, which is critical for assessing and maintaining the business relationship between the supplier and the customer firm [71].

(2)

Collaboration via information exchange involves assessing the supplier’s ability to provide requested information appropriately for the customer firm [71].

(3)

Collaboration through financial exchange requires the supplier to provide timely payments and accurate records according to the customer firm’s demands, which highlights the importance of convenient payment methods and swift invoice processing [71].

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Collaboration via social exchange involves building trust and understanding between the customer firm and the supplier [71].

When collaborating with customer firms, it is crucial to consider aspects such as real-time adaptation to changes, information technology capabilities, maintaining the reliability of provided information, and the contribution of the supplier to the customer firm’s profitability [72,73,74]. These considerations help assess the collaboration in terms of trust levels, changes in supply situations, the information technology capabilities of the supplier, and the supplier’s contribution to the customer firm’s profitability.

The evaluation criteria for collaboration with customers (B2C) are intended to capture customer value through interactions and communication with customers [65]. Based on a review of collaboration with customers, three categories are proposed herein:

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Collaboration from the perspective of analyzing customer requirements emphasizes understanding the requirements of customers and the ability to deliver excellent customer experience [75].

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Collaboration from the perspective of product/service development involves using customer feedback to create products and services that enhance customer satisfaction; this defines collaboration as a means for product and process innovation [76].

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Collaboration from the perspective of post-purchase management emphasizes continuously understanding customer demands and focusing on actions that transform digital customer experience [77].

Factors traditionally used as indicators of collaboration with consumers are categorized into customer requirement collection, product/service development, and customer management. Based on these indicators, customer data utilization and analysis, customer-centric product development, and customer relationship management (Figure 2) are set as the evaluation items in the digital maturity assessment model. These aspects are considered common to both B2C and B2B customer collaborations. The interactions between businesses and customers involve analyzing customer requirements, developing and monitoring products/services based on these requirements, and post-purchase management. However, B2B collaboration involves complexities related to long-term contracts and strategic partnerships that are distinct from B2C interactions. Therefore, when considering the characteristics of B2B collaboration, elements such as the integration of business models between suppliers and clients, the sharing of long-term DX goals, problem-solving processes, and information sharing are crucial. To account for these complex factors, the proposed model evaluates B2B collaboration in detail by assessing aspects such as the integration of production planning (both long-term and short-term), collaboration in problem-solving processes (including the complementing of technological capabilities and performance monitoring), and the level of information sharing. In contrast, the B2C perspective emphasizes prompt feedback and real-time responses, with customer communication being the primary evaluation metric (Figure 2).

These evaluation area selection results can address different requirements by taking into account differences between specific customers in the digital maturity assessment model that integrates B2B and B2C cooperation. Overall, it is possible to consider data collection and the requirements of both end users and corporate customers from a customer’s point of view, and at the same time, it is possible to check the level at which the quality information of the collaboration factory can be shared with corporate customers and the information in the area can be provided in a timely manner. This takes into account important factors to consider in the DX maturity assessment model in the collaboration factory, which was not confirmed in the previous DX maturity model.

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Step 3. Deriving Detailed Evaluation Criteria

In the third step, the maturity levels for each evaluation criterion are defined. This model is benchmarked against SIRI, which provides the most extensive and reliable results for DX maturity. Therefore, for the criteria related to strategy, data, collaboration, processes (process standardization, process integration, and business process automation), and technology, a 6-level scale based on SIRI’s standards has been used. Additionally, for items ranging from product development to logistics operations, the maturity levels are based on the KS X 9001-3 Smart Factory Operations Management System Diagnostic Evaluation Model. These criteria are outlined in

Table 3. Evaluation Levels by Evaluation Criteria.

Evaluation Area	Evaluation Item	Evaluation Levels
Strategy	Clarity of Purpose and Plan, Digital Strategy	① Informal, ② Structured, ③ Continuous, ④ Integrated, ⑤ Adapted, ⑥ Forward looking
	Agile Change Management, Digital Dedicated Organization	① None, ② Formalization, ③ Development, ④ Implementation, ⑤ Scaling, ⑥ Adaptive
	Business Model Innovation	① Same as past, ② Improved, ③ Firm First, ④ Domestic First, ⑤ World First, ⑥ Change Paradigm
	Leadership of Executives	① Unfamiliar, ② Limited Understanding, ③ Informed, ④ Semi-dependent, ⑤ Independent, ⑥ Adaptive
Data	Data Management, Data Quality, Data Security	① None, ② Formalization, ③ Development, ④ Implementation, ⑤ Scaling, ⑥ Adaptive
	Data Utilization	① None, ② Systematic collection, ③ Data processing, ④ Data analytics, ⑤ Internally Shared, ⑥ Used for Decision-making
Collaboration	Communication with Customers	① None, ② Attempted Introduction, ③ Problem Sharing, ④ Sharing Planning Information, ⑤ Joint Work Introduction, ⑥ Decision making
	Collaboration with Suppliers	① Informal, ② Communicating, ③ Cooperating, ④ Coordinating, ⑤ Collaborating, ⑥ Integrated
Process	Product Development, Production Planning, Process Management, Quality Management, Equipment Management, Logistics Operations	① None, ② Manual/Automated, ③ Systematized and Monitoring, ④ Automatic Monitoring and Exception Control, ⑤ Integration and Optimization, ⑥ Autonomous Operation
	Process Standardization, Process Integration	① None, ② Planned, ③ Defined, ④ Partially Applicated, ⑤ Entirely Applicated, ⑥ Improved
	Business Process Automation	① None, ② Basic, ③ Advanced, ④ Full, ⑤ Flexible, ⑥ Converged
Technology	Automation	① None, ② Introduction of Automation Technology, ③ Advanced Automation Technology Introduction, ④ Highest-Level Automation Technology Introduction, ⑤ Situation Improvement and Flexibility, ⑥ Interaction and Integration
	Connectivity	① None, ② Introduction of Connectivity Technology, ③ Interoperable, ④ Information Security, ⑤ Real-time Service, ⑥ Task Management Service
	Intelligence	① Presence/Absence of Enterprise System Technology, ② Computerization of Work Processes, ③ Change Detection through Monitoring, ④ Assessment Results, ⑤ Data Prediction, ⑥ Data and Environmental Change Adaptability

. In this table, the Evaluation Area classifies the five evaluation areas (Strategy, Data, Collaboration, Process, and Technology) of the maturity model presented in this study. The Evaluation Item presents between two and four evaluation elements for each evaluation area. Evaluation Levels provide criteria for a six-step scale. The evaluation questions within each evaluation item were constructed with consideration of the evaluation levels.

Based on these criteria, the digital assessment model was generalized and adapted for easy application in the field. A total of 62 self-assessment survey items were constructed based on the evaluation content, forming a comprehensive guideline for on-site assessment with a total of 100 detailed evaluation criteria. Examples of the evaluation criteria are presented in

Table 4. Evaluation Guideline for On-site Assessment: (Example) Strategy—Digital Dedicated Organization.

Evaluation Criteria	Evaluation Content	Question Guideline	Expected Answers and Conditions
0. None	Confirm the presence or absence of a dedicated organization for promoting digital strategy	Does a collaboration system exist for each organization to promote the digital strategy?	(Yes) A collaboration system and a dedicated organization exist (No) No collaboration system exists for promoting digital strategy
1. Formalization	Confirm the establishment of plans to form a dedicated organization for promoting digital strategy	Are you planning to form a dedicated organization for the digital strategy?	(Yes) Planning to form a dedicated organization (No) Not planning to form a dedicated organization.
2. Development	Confirm whether the dedicated organization has been formed and official work has begun	Have you formed a dedicated organization and assigned specific tasks?	(Yes) A dedicated organization has been formed, and tasks have been assigned (No) No specialized tasks exist
3. Implementation	Confirm whether the dedicated organization is performing its tasks	Is the dedicated organization performing assigned tasks after being formed?	(Yes) Tasks are being performed with periodic restructuring (No) There are no clearly assigned tasks
4. Scaling	Confirm whether the dedicated organization is being expanded company-wide	Is the dedicated organization being expanded company-wide?	(Yes) Company-wide expansion is in progress with periodic restructuring (No) Company-wide expansion is not in progress
5. Adaptive	Confirm whether the dedicated organization is being continuously restructured to adapt to the environment	Is the dedicated organization being continuously restructured to adapt to the environment?	(Yes) Periodic restructuring is in progress (No) Progressing without specific restructuring efforts

. This table presents an evaluation guideline for on-site assessments, focusing on the establishment and operation of a dedicated organization for promoting digital strategy. The table outlines six evaluation criteria, ranging from the presence or absence of a dedicate organization for promoting digital strategy (Stage 0) to its adaptive restructuring processes (Stage 5). Each criterion includes an evaluation content, a question guideline, and expected answers and conditions. For example, Stage 0 evaluates whether a collaboration system and a dedicated organization exist to promote the digital strategy, with the expected answers being either “Yes” (indicating their existence) or “No” (indicating their absence). The provided questions serve as a tool for evaluators to gauge the organization’s current status in relation to its digital transformation efforts.

Regarding the collaboration part that this study focused on, from the supplier’s point of view, collaboration related to information sharing includes information and data sharing, and collaboration related to technological innovation includes performance monitoring and improvement, as well as the exchange of technical expertise. In addition, collaboration related to information sharing in collaboration with customers includes the sharing of product/service quality information and the timeliness of information provision, and customer relationship management, customer-centric product development, and the utilization and analysis of customer data were selected as collaboration metrics for technological innovation. Collaboration related to information sharing specifically included the timeliness of information provision and information sharing within the company, and timeliness was composed of items emphasizing real-time information provision (

Table 5. Evaluation Guideline for On-site Assessment: (Example) Collaboration—Timeliness of Information Provision.

Evaluation Criteria	Evaluation Content	Question Guideline	Expected Answers and Conditions
0. None	Confirm infrastructure is in place to provide timely information	Are there platforms and systems for information provision, and communication devices prepared? If not, are attempts being made to do this?	No information is provided separately/all work is done by hand
1. Attempted Introduction	Confirm that company is attempting to build an infrastructure that can provide timely information	Are you planning to form a dedicated organization for the digital strategy?	There is no infrastructure yet to provide information, but plans are in place for future introduction and development
2. Problem Sharing	Confirm that problem sharing and solutions are provided to customers in a timely manner	Have you formed a dedicated organization and assigned specific tasks?	Professional servers provide the necessary information through digitized systems
3. Sharing Planning Information	Confirm that company can deliver business plan to customers in a timely manner	Is company providing customers with their business plans in a timely manner, such as production schedules, product/service launches, etc.?	(Yes) Provide business plans to customers in a timely manner to ensure that their plans are not disrupted (No) We do not share our business plans
4. Joint Work Introduction	Confirm that that company is working with customers in real time	Can customers share production plans or business plan modifications and supplements in real time, and is this possible to collaborate?	(Yes) Customers can modify their product changes and plans through the platform (No) The ability to work with customers is not ready
5. Decision making	Confirm that business management decisions are being made in collaboration with customers in a timely manner	Based on the customer’s modifications, the production schedule, and plan are re-established, and are these done in real time?	(Yes) Production plans are quickly re-established to meet customer requirements (No) Customer requirements are reflected through a series of administrative processes after confirmation by the company

); information sharing within the company was composed of items to check whether joint work can be performed at the same time as running the platform (

Table 6. Evaluation Guideline for On-site Assessment: (Example) Collaboration—Sharing Product/Service Quality Information.

Evaluation Criteria	Evaluation Content	Question Guideline	Expected Answers and Conditions
0. None	Product/service quality sharing is performed through the platform	Do company provide products/services to corporate (B2B) customers? Are product/service quality information being produced shared through the platform to corporate (B2B) customers? If not, are any attempts being made to do so?	Unable to check quality information
1. Attempted Introduction	Confirm that attempts are being made to share product/service quality sharing through the platform	Are you planning to form a dedicated organization for the digital strategy?	We are developing a platform to share quality information
2. Problem Sharing	Confirm that the platform provides product/service quality issues	Have you formed a dedicated organization and assigned specific tasks?	Basic quality information and problems of products and services are shared through our site
3. Sharing Planning Information	Confirm that the platform provides product/service planning information	Can the platform provide product/service planning information so that enterprise (B2B) customers can check the production schedule or quality information of their products?	(Yes) Enterprise (B2B) customers are providing on the platform to track their products and know their production schedules (No) Not prepared at that level
4. Joint Work Introduction	Confirm that company is operating a platform that allows you to collaborate by sharing product/service information	Do company operates a platform that allows you to collaborate by sharing product/service information?	(Yes) Corporate (B2B) customers can understand their product situation and communicate through the platform when a problem arises (No) Only check the production schedule and quality information of the product, but there is no platform function for communication
5. Decision-making	Confirm decisions are being made to integrate platform quality information and use analytics to improve mature product/service quality	Is company collecting and analyzing quality information data shared within the platform to improve quality standards and quality?	(Yes) The feedback provided within the platform is collected and analyzed to define major problems and include them in the quality standards (No) Not collecting or analyzing data within a separate platform

). In the case of technological innovation, it was composed of items to check the extent to which new digital services and products can be developed or technical capabilities can be supplemented by collecting data from customers and suppliers (

Table 7. Evaluation Guideline for On-site Assessment: (Example) Collaboration—Customer-centric Product Development.

Evaluation Criteria	Evaluation Content	Question Guideline	Expected Answers and Conditions
0. None	Confirm that activities related to customer-based product development are carried out through digitized systems	Can customers experience and comment on their products in advance through a digitized system?	(Yes) Virtual model or simulation of the product provides customers with the opportunity to experience the actual product (No) Does not support product experience and opinion presentation through digitized systems
1. Attempted Introduction	Confirm that customers can experience the product through digitized systems	Can the digitalized system allow customers to experience the product?	(Yes) Digitalized systems (e.g., VR) enable customers to simulate and experience products in a virtual world (No) Does not provide an experience through a digitized system, focusing on the actual experience of a product or service
2. Problem Sharing	Confirm that the digitized system can recognize and comment on the customer’s experienced products	Can the digitized system recognize and comment on the products the customer has experienced?	(Yes) Digital simulation or virtual experience enables the customer to identify problems or inconveniences while using the product, records them through the system or provides feedback (No) Experience is possible, but does not discuss issues
3. Sharing Planning Information	Confirm that a digitized system can suggest a product development plan based on customer experience	With the digitized system, can the customer suggest a product development plan based on the customer’s experience?	(Yes) Customers are proposing improvement ideas based on their experience in product design, functionality, usability, etc. (No) Collect and analyze data indirectly, but no direct development plan proposals are made by the customer
4. Joint Work Introduction	Confirm that the digitized system allows customers to collaborate on product development	Can customers jointly develop products through digitalized systems?	(Yes) Customers can collaborate with companies to develop products at various stages, including product idea presentation, design suggestion, feedback provision, prototype testing, and QA testing (No) Product development does not perform development tasks directly to customers due to security, intellectual property rights, confidential information, etc.
5. Decision making	Confirm that the customer is making product development decisions with a digitized system	Are customers making product development decisions through digitized systems?	(Yes) We are developing customer engagement products that collect customer feedback and analyze it in real time to make decisions about product development. Participate in decision making about product characteristics, design, functionality, price, release schedule, etc. (No) Consider various factors and derive decisions by considering security, technical constraints, cost-effectiveness, and strategic goals. Customer participation does not have a decisive effect on decision making

and

Table 8. Evaluation Guideline for On-site Assessment: (Example) Collaboration—Exchange of Technical Expertise.

Evaluation Criteria	Evaluation Content	Question Guideline	Expected Answers and Conditions
0. Informal	Confirm that exchange activity processes are introduced with suppliers to complement technical capabilities	Is company conducting exchange activities to supplement technical capabilities with suppliers using digital technology?	(Yes) Remote control between computers to complement technical capabilities is carried out (No) Does not engage in exchange activities to supplement technical capabilities with suppliers
1. Communicating	Confirm formal performance of exchange activity process to complement supplier and technical capabilities	Is company officially conducting exchange activities to complement technical capabilities with suppliers using digital technology?	(Yes) conducting exchanges to complement technical capabilities through formal communication with suppliers (No) No formal communication is in progress
2. Cooperating	Confirm cooperative performance of exchange activity processes to complement supplier and technical capabilities	Does company mutually cooperate in exchange activities to supplement technical capabilities with suppliers using digital technology? Is company working on any improvements?	(Yes) Organizational improvement is carried out through exchanges to complement technical capabilities in cooperation with suppliers (No) No additional improvements are made
3. Coordinating	Check whether the results of exchange activities to complement supplier and technical capabilities reflect the business plan	Will the results of exchange activities to complement technical capabilities with suppliers using digital technology be reflected in the establishment of the company’s business plan?	(Yes) The results of exchanges to complement the technology capabilities with suppliers are reflected in the establishment of the company’s business plan (No) Not reflected in business plan
4. Collaborating	Confirm that the exchange activity process is collaborated with the supplier to complement the technical capabilities	Is the exchange activity to complement technical capabilities with suppliers using digital technology done through collaboration?	(Yes) Technical capability supplement exchange is carried out through collaboration with suppliers (No) No separate collaboration is performed
5. Integrated	Confirm integrated organization of exchange activity process to complement supplier and technical capabilities	Are the exchange activities to complement technical capabilities with suppliers using digital technology carried out through the integrated organization?	(Yes) Technical capability supplement exchange is carried out (No) No separate integrated organization

). Table 5, Table 6, Table 7 and Table 8, similar to Table 4, include evaluation content, question guidelines, and expected answers, and they provide explanations aligned with each evaluation item.

(4)

Step 4. Applying and Validating the DX Maturity Assessment Model for Digital Collaborative Factory

The final step involved obtaining reviews from three experts to validate the model. The experts included the director of the Smart Manufacturing Research Center, the head of the Innovation Planning Division at the Industrial Intelligence Association, and the team leader of the Smart Manufacturing Consulting Center. They were selected for the validation of the models and the review of the evaluation results because they had the experience of smart factory development and consulting service delivery. The structure and evaluation criteria of the proposed digital maturity assessment model were presented for review. To validate the model’s generalizability, results from self-assessment at eight actual sites and evaluations from two companies in the field were provided for joint review. According to the expert reviews, the model constructed for DX level assessment and its assessment results were deemed largely appropriate. The experts confirmed that the evaluation criteria and scoring standards were validly designed for comparison with the results of the SIRI model for generalizability. The assessment results were also specific and did not reveal significant errors in the scoring. Furthermore, the model was recognized as valid for deriving new items in the process and collaboration areas, which represent additional perspectives over previous models.

3.2. Proposal of DX Maturity Assessment Model for Digital Collaborative Factory

In this study, multiple case studies and expert evaluations were conducted to validate the effectiveness of the model. Multiple case studies involved an in-depth analysis of several cases to assess the validity of a particular theory or model. This research method was used to determine how the model operates in various situations and whether the results are consistent.

First, for the multiple case studies, data from the lead company and its partners were collected. DX self-assessment results were gathered from eight companies, and for a more detailed analysis, one lead company and one partner company were selected for on-site visits to assess their DX levels. The lead company is a forklift assembly firm that assembles items delivered by its partners without processing them, whereas the partner company directly processes the items it delivers, indicating that the two firms operate in different sectors. For each case, the DX levels were identified through surveys, interviews, and on-site observations, focusing on both the organization and production sectors. The DX assessment results were then shared with the stakeholders of each company to discuss the accuracy of the analysis. As a result, both the lead company and the partner company could clearly identify their respective DX levels. It was found that the lead company had made significant progress in digital transformation, particularly in its collaboration with customers and suppliers. However, the partner company, while cooperating digitally with the lead company as a customer, showed little digital collaboration with its own suppliers. This highlighted the need to promote digital transformation among companies that are not directly connected to the lead company. In all areas, the lead company demonstrated a higher level of digital transformation compared to the partner company, which showed relatively lower progress. When comparing the results of these case studies, it was confirmed that the model could be applied to both assembly and processing firms, and the issues related to the position within the collaboration chain were identified.

Based on the reviewed indicators, a DX maturity assessment model was created by comparing 25 assessment models, as shown in Figure 3. The assessment w categorized into two levels aimed at evaluating different aspects.

(1)

Organizational Level: Managers represent the evaluation subjects for assessing the DX status and comparing levels with other companies.

(2)

Production Process Level: Field stakeholders constitute the evaluation subjects for deriving detailed improvement suggestions.

However, collaboration-related aspects were evaluated within the collaboration-specific assessment items, while other evaluation areas focused on assessing the organization itself, without considering collaboration.

4. Illustration

4.1. Overview of Evaluation Method

We evaluated the DX maturity of actual enterprises using the developed digital maturity assessment model. The evaluation involved sending assessment questionnaires to the companies, where internal personnel conducted self-assessments, and experts visited the sites to assess the levels. Subsequently, strategies for successful DX were explored based on the evaluation results. This evaluation utilized a DX maturity assessment model specialized in collaborative relationships, involving eight companies in partnership with an assembly manufacturing company. Initially, self-assessment was conducted based on 26 organizational and 62 production process items, along with example assessment criteria provided to the nine companies, including the assembly manufacturing firm. These items were delivered to IT or production managers within the companies. For two key partner companies (the main contractor and component-manufacturing partner), on-site assessments were conducted by the model developers. They interviewed DX-related practitioners and comprehensively assessed the site to complete the evaluation. Strategies for DX were proposed based on the evaluation questionnaire. The scores were reorganized based on the DX framework proposed by SIRI and Correani et al. [78], and examples were provided to lead strategies for areas requiring improvement. The actual companies targeted for self-assessment and on-site evaluation are detailed in

Table 9. Companies Analyzed.

No.	Company Type	Business Description	Analysis Type
1	Lead Company	Manufacturing and retail of forklifts, industrial machinery, and electronic machinery	On-site assessment and self-assessment
2	Component Manufacturer Partner	Production of forklift overhead guards	On-site assessment and self-assessment
3	Component Manufacturer Partner	Production of excavator cabins and covers	Self-assessment
4	Component Manufacturer Partner	Production of automotive parts and molds	Self-assessment
5	Component Manufacturer Partner	Production of forklift frames (chassis floors), masts, fork movers, etc.	Self-assessment
6	Component Manufacturer Partner	Production of forklift cabins	Self-assessment
7	Component Manufacturer Partner	Production of forklift frames (chassis floors), masts, and carriages	Self-assessment
8	Component Manufacturer Partner	Production of transmissions, pumps, and travel motors	Self-assessment

. This table provides an overview of the companies analyzed in the study, classifying them by company type, business description, and the type of analysis conducted. The table lists eight companies, with one identified as the lead company, responsible for the manufacturing and retail of forklifts, industrial machinery, and electronic machinery. The remaining seven companies are classified as component manufacturer partners, each specializing in various aspects of forklift and automotive component production. The analysis type varies between the companies, with the Lead Company undergoing both on-site assessment and self-assessment, while the Component Manufacturer Partners primarily conducted self-assessments.

To analyze the results of the on-site assessment, we compared them with those from the existing SIRI model. The SIRI model categorized Process, Technology, and Organization as the top layers, which were subdivided into eight core elements: Operations, Supply Chain, Product Lifecycle, Automation, Connectivity, Intelligence, Talent Readiness, and Structure and Management. It employed 16 dimensions for assessing maturity levels [11].

The Organization dimension, along with Process and Technology, plays a significant role in the Fourth Industrial Revolution. In this context, Organization focuses on two major components:

(1)

The Talent Readiness pillar emphasizes the ability of organization members to lead and deliver the Fourth Industrial Revolution for value creation. It connects to the digitalization strategies in the proposed model’s strategy section—including digital organizational structures, leadership, and agile change management—and their alignment with capturing new opportunities and innovations in business models.

(2)

The Structure and Management pillar pertains to the system elements that regulate the allocation and control of roles within an organization. It highlights how interactions among members contribute to achieving organizational objectives. This can be linked to the collaboration section of the proposed model, encompassing communication with customers and collaboration with suppliers, as well as the clarity of objectives and plans, digitalization strategies, and business model innovation in the strategy section.

The Technology dimension encompasses processes for creating a highly connected industrial environment using digital equipment and by linking physical assets with corporate systems. Within the SIRI model, this dimension consists of the Automation; Connectivity; and Intelligence pillars. Automation refers to the technology used for monitoring and controlling the production and delivery of products and services to adapt quickly to changing market demands. This can be connected to the sub-assessment items within the automation technology section of the proposed model. Connectivity signifies the state of interconnection between necessary equipment and computer-based systems to enable asset communication and data exchange. This can be linked to the sub-items within the connectivity technology section of the proposed model. Intelligence pertains to the analytics required for data processing and decision making for DX execution. It assesses how intelligent systems can accurately and diligently perform tasks in enterprises; shop floors; and facilities. This can be linked to the sub-items within the intelligence technology section of the proposed model. The model comprises nine evaluation items, with Shop Floor referring to where goods are produced and managed; Enterprise specifying administrative tasks; and Facility denoting factory-related items for each pillar. This evaluation model connects shop floors and enterprises to operations and links facilities to equipment-related items.

The Process dimension evaluates how effectively companies have designed digital processes during DX. It comprises the following pillars: (1) Operations; (2) Supply Chain; and (3) Product Lifecycle. Operations are assessed based on whether production and service provision are designed to save costs efficiently. This can be connected to the sub-assessment items within the production planning; process management; quality management; and equipment management sections of the proposed model. Supply Chain refers to whether the supply chain model is digitized to handle the entire value chain end-to-end. This can be linked to the sub-assessment items within the logistics operations section of the proposed model. Product Lifecycle assesses whether stages from initial product development to completion can be digitally managed. This can be linked to the sub-assessment items within the product development section of the proposed model

4.2. Self-Assessment Results

Before actual on-site assessments, online self-assessments were conducted for each company, and the results are as follows. The average levels of the organization/production processes were compared according to industry structure (lead company–component manufacturing partner) to visualize the assessment levels of these processes. According to the results, the lead company recorded a high level of digital maturity across all organizational assessment items, while the component-manufacturing partner experienced the lowest overall level of DX (Figure 4). Specifically, in the production process segment, the lead company demonstrated strength across all assessment items but scored relatively low in connectivity technology.

The lead company exhibited a high level of digital maturity due to recent investments and the proactive adoption of DX initiatives aimed at enhancing productivity and the reducing costs associated with assembly operations. These strategic measures are believed to have elevated the average scores of DX maturity. In contrast, the component-manufacturing partner has historically managed its manufacturing processes using traditional methods, leading to initial hurdles in adopting digital technologies, as reflected in its lower maturity scores. Additionally, the component manufacturer’s processes often require considerable standardization or sophisticated technologies, which inhibits digitalization. The consequent delays or difficulties in technology adoption may have contributed to the lower maturity levels.

Moreover, the level of collaboration with supply partners in component manufacturing (0.29) is lower than the level of collaboration with suppliers evaluated by the lead companies (1.5), showing the largest discrepancy between the two types. This suggests that component-manufacturing partners, especially those closer to the raw materials in the supply chain, may face difficulties in digital transformation due to their DX maturity, as well as the nature of their products. Appropriate and effective communication and collaboration strategies are needed, as these differences in digital transformation maturity appear to significantly influence the relationship between component manufacturers and assembly lead companies.

4.3. On-Site Assessment and Strategic Formulation Results

A collaboration-focused DX maturity model was employed to conduct field assessment at the lead company and the component-manufacturing partner. The lead company is a large-scale enterprise in Korea engaged in forklift manufacturing and wholesale, employing 610 staff members. Interviews were conducted with two operational managers. The component-manufacturing partner is a small-to-medium-sized enterprise in Korea that specializes in automotive components, employing 83 personnel. Interviews were conducted with five individuals—the vice president, product development manager, production manager, quality manager, and IT manager.

4.3.1. Lead Company

During the field assessment, the lead company in the assembly industry demonstrated a largely strong standing in terms of DX within the enterprise, as well as a systematically formulated strategy for establishing collaboration platforms with partners. The lead company was observed to maintain adequate standards and processes for data management/security, actively utilizing collected data for decision-making related to product development and quality enhancement. A well-established system for collecting, analyzing, and utilizing data through various systems such as MES (Manufacturing Execution System), SAP (Systems, Applications, and Products in Data Processing), and SRM (Supplier Relationship Management) successfully facilitated overall digitalization. Additionally, in logistics operations, digital picking systems were implemented to reduce human error by employing identification systems and management devices, which reflects a high level of overall management proficiency.

Despite these strengths, areas for improvement and strategies for enhancing DX were identified. From a technological perspective, while automation and intelligence initiatives are underway, the connectivity levels were found to be sub-optimal. Moreover, while the logistics operations and product development are at par with or better than those at the top 10% companies globally, many evaluation criteria showed relatively low levels, indicating the need for comprehensive enhancement efforts. Although information systems are deployed across various processes, the requirement for manual input remains with several systems and processes; this highlights the potential for automation through the expanded use of IoT technologies. Therefore, accumulated data should be analyzed to optimize operational and management tasks intelligently.

When concretizing strategy proposals, the setting of production goals through DX was noted to be challenging due to operational complexity. With regard to DX in equipment management, while the need for digitalization was not immediately apparent, establishing objectives is crucial for achieving factory-wide digitalization. The digitalization of manufacturing processes focuses on managing equipment through data, emphasizing the need for enhancing productivity by implementing Overall Equipment Effectiveness goals for key equipment. The datafication of standard information in product development can enhance product development efficiency, necessitating the digitalization of product development stages based on CAD (Computer-Aided Design), PLM (Product Lifecycle Management), and other product development data. Setting goals for equipment management, defining data collection and system requirements, and establishing DX processes necessitate AI-based management processes. To this end, automating worker management and securing connectivity are necessary. The integration of data-based systems requires seamless connectivity between different data sources and business management systems (BMs). Specifically, this involves integrating data from various sources (e.g., production data, worker performance data, etc.) with business management processes to ensure that all relevant information is consistently and accurately shared across the system.

4.3.2. Component-Manufacturing Partner

A field assessment was conducted for the component-manufacturing partner as well. The overall strengths of this company include real-time updates of production and shipment plans through integration with ERP (Enterprise Resource Planning) systems based on a cloud platform for multi-company operations. It was evident that the component manufacturer has plans for MES system implementation and a strong desire for DX. Additionally, standard definitions for processes and products, along with the computerization of product information management, are being utilized to reduce human intervention, with ERP, SRM, and robotic devices being leveraged to improve work environments and ensure smart manufacturing systems. The establishment of a rack system for storage efficiency, defined code systems and identification tags for raw materials, and a system for entering shipment information via mobile devices represent a strong foundation for establishing data management frameworks to monitor production processes.

However, areas for general improvement and DX enhancement strategies were also identified. Primarily, from a technological perspective, automation, infrastructure connectivity, and the level of intelligence were found to be sub-optimal. Compared to the top 10% companies worldwide, lower levels were observed across most evaluation criteria, indicating the need for comprehensive enhancements. Although information systems are deployed across various processes, challenges in utilization necessitate exploring avenues for reusing existing programs that require manual input.

When preparing specific strategic proposals for this company, it was found that the DX in product development and production planning was not underway; this is because the component-manufacturing partner’s operations simply replicate the parent company’s plans. Should demands increase due to business expansion, prioritizing goal setting through DX to achieve objectives pertaining to relationships with various companies would necessitate active improvement in DX maturity. The prior assessment and management of key performance indicators at the current levels were deemed necessary before establishing DX goals. In terms of logistics operations, no DX goals were set, and periodic operations were not considered to be significantly inconvenient; accordingly, collecting and digitizing data for future goal setting in logistics operations and production planning were confirmed to be necessary. The implementation of MES and the formulation of data target strategies through ERP integration were also deemed necessary. Furthermore, recognizing the current data collection level in the established information systems and elevating it to the level of the Medium-sized Business Support Project’s Middle 1st Stage for collecting real-time status data are essential. Although abstract DX concepts and plans are envisioned by the component-manufacturing partner, ongoing conceptualization at the C level and the absence of dedicated DX professionals or organizations were noted. Once DX goals are clarified, preparing a dedicated organization to seamlessly establish top-down DX strategy objectives will be crucial. Successfully deploying and utilizing the MES system under construction is vital, which emphasizes the importance of strengthening MES specialization. In equipment management, no DX activities were identified, necessitating a comprehensive review from a company-wide perspective on the changes needed in processes when introducing digital technologies, as well as the data to be collected, analyzed, and utilized (e.g., as-is and to-be comparisons). Figure 5 compares the on-site assessment results for the lead company and the component-manufacturing partner, along with the SIRI evaluation results for the top 10% companies.

4.3.3. Comparison of Strategic Planning Results between Lead Company and Component-Manufacturing Partner

This section compares the strategic planning results between the lead company and the component-manufacturing partner from a factor perspective. Figure 6 illustrates the evaluation scores of the lead company and the component-manufacturing partner, along with the scores of SIRI top 10% companies. The vertical axis represents the balance of factors for top companies, while the horizontal axis denotes the level of each entity. A baseline was established using averages based on which Quadrant II represents Strategic Group I, Quadrant I represents Strategic Group II, and Quadrants III and IV represent Strategic Group III.

Strategic Group I comprises entities with evaluation scores below the company average and with significant score differences from SIRI top 10% companies, highlighting the need for immediate improvement. Strategic Group II includes entities scoring above the company average but with notable differences from the SIRI TOP 10% companies, suggesting additional improvement areas with respect to the top-performing benchmarks. Strategic Group III encompasses factors where the maturity level gaps are lower than the average.

For the lead company, Strategic Group I includes Shop Floor Intelligence, Enterprise Connectivity, Facility Automation, and Facility Connectivity. In contrast, for the component-manufacturing partner, this group includes Facility Connectivity and Operations. Both entities indicate a strong requirement for facility connectivity, with the assembly company displaying deficiencies in automation and connectivity relative to the target levels of strategy and goal setting for each product and facility. Conversely, the component manufacturer demonstrates a primary need for the integration of resources and production planning.

In Strategic Group II, the entity for the lead company is Shop Floor Connectivity, while the entities for the component manufacturer are Shop Floor Connectivity, Enterprise Connectivity, Talent Readiness, and Organizational Structure and Management. Unlike the assembly company, the component manufacturer requires improvements not only in technical evaluation factors but also in organizational dimensions concerning strategy, goal setting, and knowledge level management.

This comparative analysis aims to provide insights into the strategic planning disparities between the lead company and the component-manufacturing partner using SIRI top 10% scores as benchmarks.

Technologies that impact digital collaborative companies, such as IoT, artificial intelligence, and big data analytics, enhance collaboration opportunities between companies or between companies and consumers, enabling the development of better products, services, and solutions in the context of Industry 4.0. Wang et al. [79] proposed a smart factory framework composed of four layers: physical resources, industrial network, cloud, and terminals for supervision and control. The four layers are described as follows: physical resources refer to various types of smart artifacts such as smart products and smart conveyors that can communicate with each other via industrial networks. The industrial network serves as the infrastructure connecting the physical resource layer and the cloud layer, where high-speed industrial wireless network (IWN) protocols are superior to wired ones in manufacturing environments. The cloud represents the network of servers providing services. Finally, terminals for supervision and control connect humans with the smart factory, allowing remote access to statistics provided by the cloud via terminals and the internet, as well as performing maintenance and diagnostics. Li et al. [80] developed DroidPerf, which is a tool for optimizing memory inefficiencies in the Android Runtime (ART), which can be seen as a technology to reduce inefficiencies in real-time communication between various IoT devices, sensors, and robots within smart factories.

The strategies of the two companies based on the DX implementation process are compared. The DX implementation process suggests that successful DX results from a framework adapted from an analysis of exemplary cases of DX [78]. This framework is depicted in Figure 7.

Based on the evaluation of each process area using the model, the following results were obtained: (1) In terms of objectives, the lead company demonstrated strong momentum in advancing DX, as it had implemented specific DX goals. However, the component-manufacturing partner displayed the need for prioritizing goals and strategy formulation for DX. (2) During the data collection phase, both companies needed to define the categories of data that align with their DX goals. This phase also highlighted the necessity for collaboration among partners. (3) Through the data utilization system, the potential for discussing DX goal setting together and exchanging opinions on data collection between the two companies has been demonstrated. (4) To achieve DX goals, the component-manufacturing partner must focus on cultivating specialized DX expertise. (5) Both companies have recognized the necessity of partner collaboration. (6) Data analysis signifying the need to acquire data analysis skills tailored to the tasks once all DX functionalities are defined has been demonstrated.

To summarize, the main company has largely formulated and implemented DX strategies and should now focus on advancing DX through data collection and acquiring data analysis skills via partner collaboration. The component-manufacturing partner, with an understanding of DX based on goal setting, must improve by defining objectives and strategies for DX and securing DX specialists.

5. Discussion

In this study, we reviewed existing DX maturity assessment models, selected evaluation criteria applicable to digital collaborative factories, and constructed a new DX maturity assessment model tailored to the collaboration aspect. The proposed model explicitly emphasizes the importance of collaborative relationships as a core element of DX, which is an aspect that is less highlighted in existing models. This is crucial, especially for achieving successful digital collaborative factories leveraging DX. Additionally, our model distinguishes evaluation criteria based on the types of collaborations among suppliers and customers (B2C/B2B) to account for the perspectives of various stakeholders.

The model subdivides evaluation areas into the Organizational Level and Production Process Level. This enables a more detailed evaluation of the various aspects of an enterprise. Accordingly, by targeting different evaluators, the enterprise’s level of DX can be accurately diagnosed. Furthermore, outcomes derived from evaluations at these levels enable organizations to identify departments and capabilities for implementing DX strategies from both organizational and operational perspectives, providing a systematic and effective basis for management and improvement at specific levels.

The proposed model sets separate evaluation domains for collaboration with customers (B2C) and suppliers (B2B), allowing for consideration of the nature of collaboration. Depending on the stakeholders, the nature of collaboration varies. For example, with suppliers, evaluations include production schedule, strategy, and performance sharing for supplied products, while for customers (B2C), evaluations include the timely provision of product/service quality information. By distinguishing the types of collaboration and the evaluation criteria, the model provides guidance for improving DX maturity by maintaining collaboration processes while ensuring prompt exchange of products and services with customers (other companies or individual customers). Furthermore, as the model subdivides evaluation areas into the Organizational Level and Production Process Level, as mentioned earlier, this broader perspective allows for a better understanding of the enterprise’s DX capabilities compared with existing models. This demonstrates the potential to utilize the model results alongside the strategic formulation processes described in Section 5.

However, the model has certain drawbacks as well. Given its diverse evaluation criteria and multi-dimensional structure, more time and resources are required to understand and implement the model consistently. Moreover, this complexity may hinder straightforward comparisons with simpler evaluation models, although the evaluation tables can be restructured, as demonstrated herein based on those of the SIRI model. Regarding the model’s structure, its reliability may be limited by the lack of analyses of factors such as CFA and EFA, which were not considered in the development of the proposed maturity assessment items. Additionally, the proposed model is tailored to collaborative factories where products are delivered, potentially hindering its application to other scenarios (service provision). Despite these limitations, the developed DX assessment model supports enterprises in efficiently managing resources and making economically effective decisions during the DX process. The model comprises evaluation items that target factors related to automation, intelligence, as well as the connectivity of facilities, tasks, and work; thus, it can assess the maturity levels of DX processes ranging from product development to distribution logistics. This facilitates informed investment-related decision making tailored to each DX target, promoting economic efficiency and reducing the costs of inspection and education, as evidenced by the development of a new model to compare with existing models. These economic benefits provide significant advantages, particularly to small- and medium-sized enterprises, enabling easier participation in DX and enhancing the potential for model utilization. Furthermore, comparing pre-system and post-system implementation will allow enterprises to practically grasp the economic effects of DX and receive effective feedback on related investments. Such feedback would provide a foundation for tracking economically efficient DX elements, thereby enhancing competitiveness and contributing to sustained growth by supporting the successful implementation of DX.

6. Conclusions

This paper emphasizes the importance of DX in industries and proposes a new digital maturity assessment model targeting digital collaborative factories, highlighting the significance of collaboration. We validated this model through self-assessment and on-site assessment in actual corporate environments. The proposed evaluation model enables the assessment of digital collaborative factories, as it was developed based on a comprehensive review of existing digital maturity assessment models while incorporating new elements that emphasize collaborative relationships. This underscores the importance of inter-company collaboration and enhances the effectiveness of DX in industrial ecosystems. Applying this model in real corporate environments allowed us to quantitatively assess the level of DX within enterprises. Multi-criteria decision making (MCDM) is often employed to assess cases from various viewpoints. However, this paper utilized the maturity model to examine the degree of DX maturity and identify areas for improvement in order to enhance the levels of excellence.

This study contributes to future research and practical applications in the field of DX in the following ways. First, the model provides guidance on which areas to activate for collaboration between or within companies and helps formulate concrete strategies to ensure successful digital transformation through comparative analysis (e.g., comparing industry averages and analyzing the DX levels of lead and partner companies). Second, the evaluation criteria were expanded to include both the organizational and production process levels, broadening the scope of DX evaluation. This is significant in that the research extends beyond digital supply chains to cover organizational DX, suggesting that the model can eventually encompass equipment, on-site operations, and organizational tasks. Finally, the model was applied to both a large enterprise (lead company) and a small-to-medium enterprise (partner company), demonstrating its applicability regardless of company size. This allowed both large enterprises, which need to educate their partners, and small-to-medium enterprises, which face challenges in adopting advanced technologies due to a lack of manpower and time, to share DX issues.

From an academic perspective, we conducted an in-depth analysis of 15 existing DX assessment models, integrating evaluation factors and theories on DX to develop a new DX level assessment model. This process involved extracting key factors considering the value chain of manufacturing and partner situations, which assists in addressing the limitations of existing models and developing more effective assessment tools. Furthermore, we pursued the development of models tailored to the characteristics of the manufacturing industry by subdividing evaluation criteria into organizational and production process levels, enhancing both the scholarly understanding and practicability of DX. However, the proposed evaluation model has certain limitations. First, while the model considers various aspects, it may not encompass every facet, potentially not being suitable for specific industries or unique corporate requirements. When using this model to conduct evaluations, an additional review of the characteristics of the given enterprise may be necessary. Moreover, the subjective judgments of the evaluators in this study may have influenced the on-site assessment process, which highlights the need for further efforts to ensure consistent evaluation results. Nevertheless, this study makes significant contributions by emphasizing the importance of DX in industries, raising awareness among enterprises and advocating for the formulation of DX strategies. Future research directions include developing the proposed model further into a more flexible and robust tool applicable to diverse industries and enterprises. Additionally, effective DX strategies must be formulated and implemented based on the evaluation results obtained using the model.