Figure 3 illustrates the first key aspect of a DPP: its connectivity type. It can identify three categories of connectivity: (a) local, (b) cloud, and (c) hybrid. This aspect pertains to the method, frequency, and volume of data that are incorporated into the DPP, underlining the need for different DPP types to meet the various needs of manufacturers. Specifically, a cloud-based DPP is a dedicated cloud data space housing various data types concerning the product. The information can either be automatically or manually updated on the cloud, but the most important feature of this type of DPP is the fact that the DPP data can be accessed from anywhere. Conversely, a local DPP requires physical proximity or contact for access. Data updates to the local DPP can also be automatic or manual. In this case, data is stored in a physical drive attached to the product. For example, an elementary form of a local DPP is an RFID tag holding key information ^[24]. The size of the storage required for the DPP is dependent of the type of information and data the manufacturer needs to store to the DPP. Last, the hybrid connectivity type entails having a DPP instance on the cloud and another locally on the product. These instances do not store identical information. If a manufacturer opts for a hybrid DPP, it suggests that some of the DPP data should be universally accessible for use by other manufacturers or applications, while some data should be locally stored on the product’s DPP instance.

Another key feature of the DPP that creators should consider is the frequency of data storage. Generally, there are three categories: (a) no data update, which implies that once the DPP is created and populated with initial data, this information neither changes nor becomes enriched over time; (b) fixed time period updates, where data is stored in the DPP at intervals defined by the manufacturer; (c) individual data updates as and when available, meaning the DPP is updated whenever there is new data.

Data can be input into the DPP manually, semi-automatically, or automatically. Manual data entry entails someone physically inputting the data into the DPP. As such, when manual data update type is selected, the volume of data in the DPP will be considerably lower compared to the other two types and typically involves only basic information. Semi-automated data update still requires human involvement, but, unlike manual update, data can be directly uploaded from other systems or data sources, subject to user approval. This method offers control over the data uploaded to the DPP without concerns about data volume. Automatic data storage implies the DPP is connected to various data sources, and the DPP updates autonomously when data becomes available.

The DPP plays a crucial role in storing data from various stages of a product’s life cycle. Using the product lifecycle steps as a guide ^[9], the researchers have identified eight stages where DPP can be effectively deployed, as illustrated in Figure 4. The DPP’s owner, typically the OEM, can decide at which stages the DPP will be utilized, depending on its intended purpose. The DPP is responsible for storing data for a single instance of a product, hence the design or prototype stages included in the product life cycle are omitted ^[9][25]. The recommended steps cover the product from its manufacturing process until its end of life, culminating in recycling.

Another feature of the DPP is information inheritance, which determines whether some or all of the data from the DPP will be passed on to another product at the end of its life or if the information will be lost. This characteristic is bidirectional, implying that a DPP should facilitate the inheritance of information and, conversely, the receiving DPP should be capable of inheriting data from other DPPs; the notion of information sharing/inheritance is well-known in the applications of cloud manufacturing and manufacturing as a service ^[26][27]. The subject of information inheritance will be explored further in this paper.

The scope of DPP is to accompany a product throughout its entire life cycle, as illustrated in Figure 3. The purpose of the DPP is to assist at each corresponding stage. This means that the information available in the DPP from earlier stages can be used in future steps to enhance the associated process or simply to inform the user about a product’s crucial details. Depending on the life cycle stage, different actors interact with the DPP. In total, seven distinct actors have been identified and are listed below. These actor categories encompass all individuals or digital systems that can interact with the DPP.

The actors represent overarching entities, Implying that the actor interacting with the DPP could be either a human or a digital system corresponding to that actor. The Original Equipment Manufacturer (OEM) is both the owner and creator of the DPP. A value chain partner is defined as any node in the product’s value chain that adds value through various manufacturing processes. In contrast, the distributor’s role is to bring the product to market, hence the separation of these actors, which can correspond to the different access rights to the DPP.

Each actor is responsible for either retrieving or adding different types of data to the DPP. Consequently, different actors will have varying access rights to the DPP. Specifically, the DPP’s creator and manager will define the access rights for each actor who will access the DPP. The access options include either “read only” or “read/write”. By doing so, the DPP’s owner can effectively control and manage it. The access rights should not apply to the actor level but to specific information within the DPP.

There are seven distinct generic data type categories involved in the DPP, as illustrated in Figure 5. These categories cover the entire spectrum of the product and all the steps of its life cycle. Some data may fit into more than one category, or different data may depend on data from other categories. Each DPP data category corresponds to specific stages in the product’s lifecycle, though some categories may contain information from across the entire product life cycle. The DPP’s owner is responsible for deciding the level of detail that enters the DPP. The various types of DPP detail levels will be discussed further in this section.

Material data: This category pertains to any information about the materials involved in the respective product that the DPP will store. More specifically, the basic information should include the types of materials used in the product (e.g., steel, aluminum, etc.) and details on the material specifications. Also, the amount of material is important to note because such information can be beneficial at a later stage of the product, such as the recycling process or otherwise. Additionally, this specific DPP data type could function as the bill of materials for the product and beyond.

Environmental data: This category of the DPP is tasked with storing any information related to the environmental aspects of the product. More specifically, it includes data about the environmental friendliness of the different materials involved in the product, or the environmental characteristics of the manufacturing processes applied to the product. This information can serve two purposes: monitoring the environmental impact of the manufacturing process, and determining the environmental implications of the product’s usage once it is manufactured. In this manner, the product’s entire life cycle can be monitored in terms of its environmental impact.

Manufacturing data: This category stores all manufacturing-related data. Manufacturing data encompasses all the different information generated during a product’s manufacturing process. This includes data from various sensors, different machine PLC systems and operators’ reports or notes, etc. This information could be highly useful for manufacturers for traceability and optimization purposes. Generally, all data generated during the manufacturing process should be categorized under this heading. As one can understand, there is some overlap between each category. For instance, the power consumption of a machine during manufacturing is both manufacturing and environmental information. Furthermore, re-manufacturing data will also be part of the manufacturing data.

Value network data: This category is tasked with storing all information generated by the various nodes in the product’s value network. Some of this data might be related to the manufacturing processes for the product, so such information would be present in both categories. Furthermore, details regarding logistics and product tracking until it reaches the end user will be stored under this category.

Maintenance data: This category will house all information related to maintenance. Once the product has begun being used by its owner, any form of maintenance or intervention on the product is stored under this category.

Circularity data: This category is tasked with storing data related to the implementation of circularity, such as product re-use, re-purposing, or recycling data. More specifically, when a change occurs in the product’s life cycle after its initial use, this change should be logged in order to keep track of the exact steps that were followed for each product. Such data should aid in both the better and more efficient implementation of circularity but also assist in the precise evaluation of the effectiveness of circularity. Additionally, the manufacturer should include information regarding protocols or procedures for the re-use, re-purposing, or recycling of a product here. For example, such data could be a disassembly sequence for more efficient disassembly. Also, the manufacturer should specify which components should be returned to the manufacturer after the product’s end of life for re-use purposes.

End user data: In this category, all data produced by the use of the product or by the owner of the product are stored. Additionally, the manufacturer should include information regarding the product meant for the end user in this section, such as product instructions, maintenance requirements, and any other information that the manufacturer wishes to communicate to its final customers.

The previous list explains in detail the different types of data that the DPP should contain. Figure 6 shows when each set of data could be generated or is being generated. Material, environmental, and value network data will be populated with information across the entire product life cycle. Manufacturing data are generated only during the manufacturing process, as well as during the product’s re-use and re-purposing. Maintenance occurs from the beginning of a product’s use until the recycling stage.

As explained in the previous sections, there are multiple potential data sources for the DPP. The critical question DPP owners should answer is what level of detail of data should be stored in the DPP. Defining this is not a straightforward process. Again, it refers to the different product life cycle steps defined in Figure 3. All these steps can be categorized into three major groups: the manufacturing stage, the product operation stage, and the product circularity stage.

The manufacturing stage involves all the different manufacturing steps until the product reaches the end user and begins to operate. The operation stage refers to the period during which the product operates. The product circularity stage refers to all the stages after the use of the product and involves actions such as product reuse or repurposing actions in order to fully exploit the remaining useful life of the products or some of their components. This step also includes the recycling process.

This classification into different stages of the product life cycle is used to determine the DPP’s level of detail. These categories are independent and can have varying levels of detail, depending on the DPP owner’s requirements. The researchers have defined the specific levels of detail for each category to aid DPP owners in understanding the potential of using the available information, as detailed in Table 1 for the manufacturing stage, Table 2 for the product operation stage, and Table 3 for the product circularity stage. All categories include a Level 0, indicating that no information is being collected for the selected topic, thus rendering the application of DPP impractical. In such cases, additional investment in data collection infrastructure is required before DPP deployment. The defined levels serve a dual purpose: they help existing data collection infrastructures identify deployable DPP levels, and for those without such infrastructures, they act as a guide for selecting the appropriate equipment to collect the necessary data.

The previously mentioned tables detail the different levels of specificity for each of the three defined categories: manufacturing, product use, and circularity information. When an OEM designs a DPP, it must pay close attention to the dependencies that exist among these levels. This means that a selection from one category will influence the available compatible options in the other two categories. More specifically, Table 4 outlines the dependencies among the different levels. The table utilizes three symbols: “o”, “-”, and “X”. The symbol “o” signifies that the selected level (row, first column) incorporates the information from the level in the first row and corresponding column. The symbol “-” signifies that the selected level does not support the corresponding level, and finally, the “X” symbol indicates compatibility. Furthermore, Table 5 showcases the different aspects defined in the previous sections, such as DPP connectivity, data update frequency, and involved actors, in relation to the different levels of detail for each of the three categories. In Table 5, the minimum requirements are outlined. Specifically, for connectivity, the hybrid approach is considered the most advanced, as it merges both concepts. While the hybrid approach can be implemented in all cases, it may not be cost-effective if it is not necessary.

All information saved by the different actors to the DPP is intended to serve as an assistive tool when an actor requires some data for the product and for improving the traceability of changes to the product. As has been made clear by now, there are many different variations of a DPP according to the needs of the OEM who owns the DPP. Once the OEM has defined all the above information, the selection of the DPP access method should be the next step. Since the DPP, as the name suggests, is “digital”, it contains digital information, which implies that it must be accessible by digital means. More specifically, there are six different ways to access the DPP:

• Local access to the DPP using some of the product’s systems.
• Local access to the DPP using a wireless or wired connection to a laptop, smart phone, or tablet.
• The product has a QR code or similar, and, by using the DPP application (laptop, smart phone, or tablet), the QR code is scanned, and basic information about the product appears.
• The product has a QR code or similar, and the user is redirected to the product’s DPP online.
• The OEM has developed a DPP platform for the product, and the corresponding actor enters some credentials (e.g., serial number, etc.) and they access the DPP online.
• If the product is using an RFID, the user scans the RFID and reads the information saved in the DPP.

The design of a DPP should be executed in a standardized manner to ensure repeatability and consistency across different products. This consistency will be significantly beneficial in cases where assembly occurs and multiple DPPs must be combined into one. Therefore, in this section, a DPP design template is proposed to be used during the design and documentation of a DPP. The proposed template aims to appropriately document the various pieces of information that will be stored in the DPP. Two different templates have been developed: one that will contain all the generic information of the DPP (Table 6). Table 6 is organized into three sections. The first section covers fundamental DPP information related to the product, including who will have access to the DPP and to which specific information. It also indicates whether the DPP inherits information from other DPPs. The second section defines the variables to be used and stored in the DPP, detailing their shareability and the potential recipients. The final section addresses variables inherited from other DPPs at previous stages in the product lifecycle.

In the DPP model, blockchain technology plays a pivotal role in ensuring the security, integrity, and transparency of product data. Blockchain’s decentralized and immutable ledger system allows for the secure and verifiable tracking of product information across the supply chain, enhancing trust among stakeholders. This technology is particularly crucial in scenarios where data authenticity and protection against tampering are paramount.

Furthermore, data carriers, such as QR codes or RFID tags, are essential components of the DPP system. They facilitate the easy storage and retrieval of product data. By encoding product information, data carriers enable quick access to the DPP, thus streamlining the product identification and information retrieval processes across different stages of the product lifecycle.

The scope of the current chapter is to set the foundations for the explanation and the standardization of the DPP concept. Currently, DPP domain is very immature and no clear path exists. The proposed model seems that can bring light and unification among the researchers and industries in this domain.

3.1. Implications of the DPP Model

The proposed DPP model stands to profoundly impact the industrial manufacturing ecosystem by offering a digitized, centralized, and standardized product-information management system. This new model aims to transform the way manufacturers, consumers, and stakeholders interact with the product and its associated data throughout its lifecycle.

With the DPP model, the researchers can expect a remarkable improvement in manufacturing efficiency. The model allows for an enhanced decision-making process based on the accurate and detailed data it provides. As such, manufacturers could drastically reduce waste and improve product quality. In the automobile industry, for instance, DPPs could provide real-time information on each vehicle component, enabling manufacturers to anticipate potential issues, streamline production lines, and enhance overall output.

Except the implication of the DPP in the manufacturing stage of a product it has many other implications in the steps after the manufacturing. The distribution actors have will be able to monitor and manage in a better and more efficient way their products and also will allow them to implement more sophisticated optimization strategies. At the same time, the end-users of the products will be able to have better overview of the product and using the potential services from the manufactures be able to take full advantage of their product and avoid unnecessary costs and contribute to the minimization of the environmental impact.

3.2. Enhanced Product Lifecycle Management

The lifecycle of a product, from inception to disposal, could be significantly improved through the application of DPPs. Service personnel would have immediate access to essential information about the product, allowing for more effective maintenance and repair strategies ^[28]. This could result in extended product longevity, reducing the frequency of new product purchases, and thereby, contributing to a more sustainable economy ^[9].

The DPP model also shines in the context of recycling. Effective recycling or repurposing strategies are possible by leveraging the information stored within the DPP. This fosters a circular economy, especially pertinent given the increasing complexity of materials in modern manufacturing.

3.3. Potential Challenges and Mitigation Strategies

Despite the evident benefits, implementing a DPP system is not without hurdles. Data privacy and security pose a significant challenge ^[29]. With sensitive consumer and product information at stake, robust cybersecurity measures must be in place to prevent unauthorized access or misuse. This can be tackled through encryption, strict access control mechanisms, and regular security audits ^[30].

Integrating the DPP system into existing processes is another considerable challenge. It demands substantial coordination among all stakeholders. Resistance could stem from those viewing the DPP as an unwelcome complexity or a cost burden ^[31]. Here, the key lies in transparently communicating the DPP system benefits, such as improved efficiency, cost reduction, product longevity, and its role in fostering sustainability.

For the concept of DPP to work properly and companies be able to harvest its advanced capabilities there is the need that in a value chain all nodes are utilizing the DPP concept otherwise the capabilities of the DPP implementation in a global scale are reduced. Also from the business perspective, DPP is not a technology that aims at providing its benefits at short terms but rather in mid and long term.

3.4. Guidelines for Implementing DPP

For manufacturers aiming to transition into this new era of data-centric operations by implementing DPP, the following detailed guidelines should be thoroughly considered:

1. Standardization: Begin by ensuring the DPP model follows current industry standards for data management, security, and privacy. This is a prerequisite to ensure interoperability between different systems and stakeholders, while concurrently safeguarding sensitive data against potential threats. Additionally, conforming to standardized practices can assist in attaining certifications or qualifications that may be required in certain industries or regions, further enhancing the product’s marketability.
2. Integration: DPP implementation should be planned and executed as a seamless extension of the existing manufacturing and operational processes. To accomplish this, one needs to map out the current processes, identify the points where DPP interactions would occur, and design systems to minimize disruption and maximize value addition. Remember, the DPP is not meant to replace existing processes but to augment them by providing rich, comprehensive, and accessible data.
3. Training: Ensuring that employees across the organization, from shop-floor workers to top-level management, understand the DPP system and its benefits is crucial. Training programs must be comprehensive and ongoing, providing instruction on how to access, input, interpret, and secure data. By fostering a deep understanding and comfort with the system, employees will be able to harness the DPP’s full potential.
4. Stakeholder Communication: Effective and transparent communication with all stakeholders, including suppliers, distributors, and customers, is paramount. This entails highlighting the benefits that the DPP system offers, such as improved product traceability, quality assurance, and potential for cost reduction. Open dialogues can mitigate resistance and encourage adoption, fostering a shared vision for a more efficient, sustainable manufacturing landscape.
5. Gradual Implementation: Implementing a DPP system is a complex process that involves significant changes to existing operations. To manage this transition effectively, it could be beneficial to phase in the DPP, starting with a pilot project on a single product line or component. This allows for real-time adjustments and fine-tuning of the system before full-scale implementation, minimizing potential disruptions to the operation.
6. Continuous Evaluation: After implementation, it is critical to continuously evaluate and improve the system based on performance metrics and stakeholder feedback. Leveraging the metrics proposed in this paper can provide insights into the DPP’s effectiveness and guide its optimization.

Implementing these guidelines will facilitate a smoother transition to a DPP system, equipping manufacturers to leverage the potential of data in fostering efficiency, sustainability, and value creation in their operations.

In addition to the guidelines outlined, the following strategies are proposed to further enhance the adoption and effective use of DPP among stakeholders:

Partnership Development: Forge partnerships with industry leaders and influencers to showcase successful DPP integrations, creating case studies that highlight tangible benefits.

Demonstration Projects: Implement DPPs in pilot projects or flagship products, allowing stakeholders to witness firsthand the advantages and operational improvements.

Regulatory Alignment: Engage with policymakers to align DPP implementation with emerging regulations and standards, ensuring compliance provides a direct incentive for adoption.

Technology Integration Support: Provide technical assistance and toolkits to integrate DPPs with existing enterprise systems, lowering the barrier to adoption due to technological challenges.

Feedback Mechanisms: Establish channels for regular feedback from stakeholders, using their insights to continuously refine the DPP system.

By implementing these additional promotional tools alongside the guidelines previously mentioned, manufacturers can create a robust ecosystem that not only supports the technical implementation of DPPs but also fosters a culture of adoption and sustained use throughout the product lifecycle.

3.5. Application Framework

The application of a DPP system could be systematically executed following the expanded framework below:

1. Requirement Analysis: The first step involves a comprehensive understanding and documentation of product information requirements. This includes identifying necessary product data, desired functionalities of the DPP system, and all potential stakeholders. Additionally, it's essential to assess the current operational workflow, IT infrastructure, and compatibility requirements to ensure seamless integration of the DPP system later on.
2. System Design: Once the requirements are clear, designing the DPP system can commence. This involves creating an architecture that meets the requirements identified in the analysis phase while aligning with industry standards. The design should prioritize usability and security, facilitating efficient and safe data handling. A user-friendly interface can encourage stakeholder acceptance and expedite the learning curve.
3. Integration: The implementation of the DPP system into the existing manufacturing process is a critical phase. Given the magnitude of the change, it's recommended to adopt a gradual approach. Start by integrating the DPP system in less critical operations or smaller product lines. This approach allows for real-time feedback and adjustments without major disruptions to existing operations.
4. Testing: Once integrated, extensive testing should be conducted to validate the system's functionality. This step involves stress testing, regression testing, and user acceptance testing to identify and fix bugs, validate data integrity, and ensure that the system aligns with user expectations and requirements.
5. Training and Rollout: After thorough testing, training programs should be established for all relevant employees and stakeholders. A clear understanding of the system's functionality and benefits can mitigate resistance and promote usage. The system can then be officially launched, starting with smaller scales or pilot areas before a full-scale rollout, thus ensuring that the system functions effectively under operational conditions.
6. Monitoring and Evaluation: Post-launch, the DPP system should be continuously monitored and evaluated against set performance metrics and user feedback. This iterative process will guide necessary tweaks and enhancements to optimize the system over time.
7. Continuous Improvement: The application framework does not end at launch. Given the dynamic nature of data and technologies, it's crucial to maintain an attitude of continuous improvement. Regular system reviews should be conducted, technological advancements should be continually integrated, and user training should be refreshed periodically to ensure the DPP system remains relevant and beneficial.

Following this application framework provides a systematic roadmap for manufacturers to implement a DPP system effectively, ensuring they are well-prepared to navigate the challenges and harness the immense potential that DPP offers.

3.6. Real-world Applications

To further illustrate the applicability and benefits of the DPP model, let's delve into the electronics manufacturing industry, particularly, the smartphone segment. A smartphone is a perfect exemplar of a complex product with numerous components sourced from various suppliers across the globe.

3.6.1. Supply Chain Transparency

Implementing a DPP system in this context could provide unprecedented transparency into the supply chain. With each component assigned a digital passport detailing its origins, manufacturers, consumers, and regulators would have access to a product's complete backstory. Such visibility can allow manufacturers to better manage their supply chain, reducing the risk of delays and promoting ethical sourcing by avoiding suppliers associated with unsustainable practices or unfair labor conditions ^[32].

3.6.2. Process Optimization and Quality Assurance

The detailed product information derived from DPP could aid manufacturers in optimizing processes. For instance, if a component regularly fails quality checks, the DPP could help trace the issue back to a specific batch or supplier, allowing the manufacturer to address the issue directly and efficiently. Such preventive and corrective actions could lead to improved product quality, decreased waste, and enhanced customer satisfaction. The adoption of DPP will significantly affect positively the performance of Zero Defect Manufacturing (ZDM) implementation, as more information will be available which is the key for the implementation of approaches like ZDM to be implemented ^[32][33]. Furthermore, the data in the DPP could also significantly help on the maintenance of all the different artificial intelligence, machine learning, digital twin models in order to adapt to the changes over time ^[34].

3.6.3. Improved After-sales Services

For after-sales service providers, DPPs can serve as comprehensive product histories, outlining the precise specifications, the history of repairs or modifications, and even the conditions under which the product was used ^[35]. This can expedite the troubleshooting process, making repairs more efficient, and extending the product's lifespan.

3.6.4. Streamlined Recycling Processes

Furthermore, the DPP model could dramatically simplify the recycling process by providing accurate and detailed material composition information. For example, smartphones contain precious metals like gold and rare-earth elements that could be recovered and reused. Currently, identifying and extracting these materials can be a complex and costly process. With DPP, recyclers could access a precise blueprint of the materials in a given product, enabling more effective and profitable recycling.

3.6.5. Consumer Engagement

For consumers, DPP can offer insights into a product’s entire lifecycle - from the sourcing of its components, the conditions of its assembly to the product maintenance and condition of use. This level of transparency could empower consumers to make more informed, sustainable purchasing decisions, favoring products with responsible supply chains, and high recycling potential. By extrapolating from this smartphone example, the benefits of DPP implementation could extend across industries, contributing to more efficient and sustainable manufacturing landscapes [44]. In conclusion, while the DPP model poses some challenges, the benefits, which range from enhanced manufacturing efficiency to improved lifecycle management and a strengthened circular economy, present a strong case for adoption. With detailed planning, strict security measures, and open communication among all stakeholders, the DPP system could be a game-changer for product lifecycle management.

3.7. Practical Challenges and Barriers in DPP Adoption

While the DPP offers transformative potential for the manufacturing industry, its implementation is not without challenges. Key barriers include:

• Integration Complexity: Incorporating DPPs into existing manufacturing systems can be technically complex and costly, particularly for smaller enterprises. Developing user-friendly, scalable DPP platforms could mitigate this challenge.
• Stakeholder Resistance: Resistance from various stakeholders, including manufacturers and suppliers, may arise due to perceived complexity or cost implications. Targeted educational campaigns and demonstrable benefits can be instrumental in overcoming this resistance.
• Data Privacy and Security: Handling sensitive data in DPPs raises concerns about privacy and cybersecurity. Implementing robust encryption and strict access controls is crucial for maintaining data integrity and trust.
• Standardization and Interoperability: The lack of standardized DPP formats may hinder interoperability between systems. Collaborative efforts towards standardization are essential for seamless integration across different platforms and industries.
• Cost Implications: The initial setup and maintenance costs of DPP systems could be a significant barrier, especially for smaller manufacturers. Financial incentives or support from regulatory bodies may encourage wider adoption.

To overcome these barriers, a multi-faceted approach involving technological innovation, stakeholder engagement, policy support, and education is necessary. By addressing these challenges proactively, the manufacturing industry can fully leverage DPPs to achieve enhanced transparency, efficiency, and sustainability.

3.8. DPP's Relevance to Modern Manufacturing

The DPP model is particularly relevant in the context of modern manufacturing, which is increasingly characterized by digitalization, sustainability, and interconnected supply chains. The DPP framework aligns with these contemporary trends by providing a digital means to track and manage product lifecycle data efficiently. This capability is crucial for modern manufacturing processes that require high levels of transparency, compliance with sustainability standards, and efficient resource utilization. By integrating DPPs, manufacturers can not only enhance operational efficiency but also contribute to more sustainable and circular manufacturing practices.

3.9. Proposed Methodology for Industry-Specific DPP Development

Recognizing the critical role of DPPs in various sectors, the researchers propose a generalized methodology to guide the development of industry specific DPPs. This approach encompasses the following steps:

Industry Analysis: Conduct a thorough analysis of the target industry to understand unique operational processes, supply chain dynamics, and regulatory requirements.

Stakeholder Identification: Identify all relevant stakeholders within the industry, including suppliers, manufacturers, regulators, and end-users, to understand their data needs and interactions.

Data Requirements Mapping: Map out the specific data requirements for each stakeholder, ensuring the DPP captures all necessary information for a product's lifecycle within the industry.

Technology Assessment: Evaluate the current technology landscape of the industry to determine the best data carriers and platforms for DPP integration.

Regulatory Compliance: Ensure that the DPP framework aligns with industry-specific regulations and standards, facilitating compliance and adoption.

Pilot Testing: Develop and test a pilot DPP with a select group of industry participants to gather feedback and refine the DPP model.

Implementation Guidelines: Create detailed guidelines for full-scale DPP implementation, including integration strategies, training programs, and evaluation metrics.

This proposed methodology provides a blueprint for future research and practical applications, allowing for the adaptation of the DPP framework to meet the nuanced needs of different industries.