The Role of Productization in End-To-End Traceability

Abstract

End-to-end traceability offers significant opportunities for product lifecycle visibility, sustainability enhancement, and regulatory compliance in product management. However, it faces challenges in data integration and management, supplier collaboration, cost and complexity, and the sharing of information across the supply chain. Productization refers to the representation of a product and connects commercial and technical aspects to the systemic perspective of product management. This includes a focus on the engineering lifecycle with inherent linkages to product data. The product management perspective, specifically in relation to the connection between end-to-end traceability and the productization concept, has not been extensively studied. This study explores the role of both productization and traceability in the context of end-to-end traceability. It combines an extensive literature review and an empirical example of applying productization logic across company borders to support end-to-end traceability. The key findings indicate that productization logic with a product structure focus can support end-to-end traceability in product management by providing consistency and a foundation for tracking both technical and operational data across the engineering lifecycle of a product. By focusing on productization, companies can overcome traceability challenges and unlock the benefits of end-to-end traceability.

1. Introduction

End-to-end traceability is essential in product management to ensure quality [1,2], regulatory compliance [3,4], and accountability [5,6] across the entire product lifecycle. This enables companies to manage risks more effectively [3,7], respond quickly to problems [8,9], and meet customer expectations for safety and sustainability [10,11,12]. Although various studies have elaborated on end-to-end traceability, there is a notable lack of focus on the integration of technical and operational traceability.

Technical traceability, or product data management (PDM)/product lifecycle management (PLM) traceability, relates to the ability to accurately capture and recover technical product design and manufacturing information and how it may have changed over the lifecycle of a product. Specifically, the ability to recall and review information when key decisions are made is crucial [13]. In contrast, operational traceability, or supply chain traceability, is the recording of operational data of a specific product and its components’ journeys across the value-adding stages of a supply chain network. This type of traceability requires addressing fundamental questions, such as what information needs to be collected/recorded and, crucially, by whom [14]. Companies within a supply network are independent economic actors and either need to voluntarily agree to participate in a supply chain traceability system or are forced to participate by regulatory or other mandatory requirements [15]. Examples of mandatory requirements include legal compliance in the healthcare and pharmaceutical industries [16] and stakeholder power [17] by a major automotive original equipment manufacturer (OEM) [14]. Nevertheless, organisations could benefit from combining technical and operational traceability to address traceability challenges and from working towards end-to-end traceability.

These two traceability systems, technical traceability and operational traceability, are related yet distinct. Both contribute to ensuring traceability over a product’s engineering lifecycle. However, they deal with different sets of data and information. The lifecycle spans multiple processes carried out by various actors, posing challenges to general-purpose traceability efforts [18]. Technical traceability focuses on a product’s design and development history through PDM/PLM systems [19], whereas supply chain traceability ensures visibility over its manufacturing, logistics, and delivery processes, even if the concepts of traceability and visibility may not be fully interchangeable [20]. Technical traceability data include product design, development, and change data [21,22], such as design documents and computer-aided design (CAD) files, bills of materials (BOMs) and part lists, change orders and revision histories, material specifications, testing and validation data, manufacturing instructions, compliance and certification records, and supplier and component data. On the other hand, operational traceability data involve records of raw material sourcing, manufacturing processes, transportation, storage, and handling activities [14,23,24]. These data may include batch numbers, lot codes, supplier information, production dates, and compliance certifications [25,26,27]. The combination of technical and operational traceability can be considered a key component of end-to-end traceability, with end-to-end traceability emphasising the integrated flow of data over a product’s engineering lifecycle. End-to-end traceability offers potential benefits in areas such as sustainability, product quality, regulatory compliance, supply chain transparency, and informed decision-making; however, its realisation requires addressing significant challenges. A product management-focused approach could serve as a starting point for achieving end-to-end traceability.

Challenges related to product management considerations within companies and traceability include inconsistent product understanding [28], inconsistent product structure [29], unclear or non-defined processes or issues [28], and issues with data and product data ownership [30]. Additionally, batch operating logic, which is often used in manufacturing, does not support linking data to individual units and is a challenge for item-level traceability [31]. The unique identification of components is necessary for item-level traceability [32], which is necessary for identifying the source of any individual component [33]. Scoping traceability requirements and identifying opportunities for end-to-end traceability have also been recognised as significant challenges [34]. A structural approach that enables consistency between products and data can support end-to-end traceability.

Productization can be defined as a concept that focuses on representing a product by aligning and integrating its commercial and technical aspects with systemic product management practices. This approach manages the scope of the offering throughout the engineering lifecycle, ensuring consistency, efficiency, and support for product data.

The significance of product structure and productization has been studied in the context of product management, including product portfolio management [35], product data, business processes and information systems [21], data and fact-based product profitability analysis [29,36], product and supply chain-related data, processes and information systems [22], the engineering lifecycle of products [37], BOM configurations [38], business model orientations [39], and productization strategies [40]. Productization is a concept that helps ensure that products are well articulated and documented with visualised features and a defined structure [41,42,43]. The primary goal of productization is to ensure that products can be efficiently produced, delivered, sold, purchased, and used. It provides consistency in a product’s structural focus, aligning both its commercial and technical representations [44,45]. Productization acknowledges the varying focus along the stages of a product’s engineering lifecycle [37] and can be applied to gain control over products to enable processes to perform [46]. Decisions on products and data management affect vital company processes [47]. However, the significance of productization and product structure logic for traceability has been deficiently studied, particularly in the context of combining technical and operational traceability, leading to end-to-end traceability.

This study aims to clarify the role of productization in the end-to-end traceability context by focusing on combining technical and operational traceability. This study specifically focuses on product structure and data as key elements for achieving end-to-end traceability. This study combines an extensive literature review and an empirical example of applying productization logic across company borders to support end-to-end traceability. This aim is described by the following research question (RQ):

RQ. What are the roles and potential of productization and product lifecycle management (PLM) in enabling end-to-end traceability?

2. Methodology

This study combines an extensive literature review and company analyses to clarify end-to-end traceability in conjunction with the application of productization logic. This involves addressing the current reality and related future potential. The literature review covers traceability in general, end-to-end traceability, technical and operational traceability, the traceability of products, productization, product structure logic, and data for traceability. The literature was reviewed by conducting keyword searches using Google Scholar. The keywords included but were not limited to “Productization”, “Traceability”, “End-to-end traceability”, “Product Traceability”, “Technical traceability”, “Product structure”, “Product management”, “Operational traceability”, “BOM”, “Supply chain”, “Value chain”, “Data management”, “ Product data”, and “Master data”. The use of different keywords and their combinations was guided by the identified content. The specific interest was in the link between traceability, productization, product structure, and data. The inclusion criterion was the suitability of the context discussed with the focus of this study. The inclusion of journal articles was favoured over other publication types when possible. Studies without a suitable fit for the chosen focus were excluded. A systematic literature review was not conducted. The content of the included articles was analysed, first through in-article searches and then by reading the articles, first around the relevant content and then the entire article when necessary. Notes were made by linking them to the reference information. The content was organised by themes, and the literature findings were synthesised and distilled. The empirical part of this study contained a real-life exploration of the role of productization in the context of end-to-end traceability. The empirical example involved applying productization logic across company borders to support end-to-end traceability. This included studying the applicability of productization and product structure logic, traceability, product data, and product data management.

The analysis focuses on five companies in a business-to-business environment. The context is a partnered supply chain and projects to develop productization practices and traceability, with a focus on new commercial products and business generation. The focus was on a specific smart windscreen product to which different companies contributed. The product was related to smart transparent surfaces that support digital services with two-directional data and opportunities for infotainment and the display of information for purposes in the automotive sector and elsewhere. The main company is a well-known OEM that provides products to the automotive sector and operates in a highly regulated environment. The third company provides lamination technology, and the fourth and fifth companies provide complementary systems that either provide additional use value or enable functionalities. Cooperation involves motivation for new business generation.

Qualitative research was conducted to focus on productization, traceability management, and related data management. The existing productization focus and traceability management were assessed to construct a combined productization logic for companies to support a combined business perspective and traceability. Relevant data management was also addressed. Semi-structured interviews [48] were used to gain the necessary understanding while allowing interviewees to explain issues as entities. The interviewees were guided to remain within the focus but were allowed to provide responses as entities. Seven interviews were conducted, with one to three interviewees representing each company. The interviewees included new business development, IT, product, and production managers. Snowball sampling was applied to benefit the interviewees by proposing participants [48], simultaneously ensuring that different groups and key people in a variety of product management and supply chain roles were included. New interviewees were introduced until information saturation was achieved. The utilised data also included internal documentation and relevant information from the data systems. Publicly available materials were also used to understand the offerings. The interviews were recorded and transcribed to enable a thorough analysis and focus on the interview and discussion during the interview sessions. The interview data were analysed manually by generating codes while reading the transcripts. The primary target of the initial coding was to support the finding of complementarities in productizing a combined offering. Traceability practices and relevant data management were also considered. As a result, offerings by five different interlinked companies were productised and a common product structure logic was considered. The constructed productization logic and findings were presented to a focus group for validation purposes. Traceability was then further considered in the context of validated productised offerings and product structure logic. Individual component suppliers were not included in this study.

3. Literature Review

3.1. Benefits and Challenges of End-To-End Traceability

The benefits and challenges of end-to-end traceability vary. For example, sustainability reporting is becoming an increasingly timely issue [49] because companies are called on to adopt sustainable behaviour owing to global concerns about the environment and social responsibility [50]. End-to-end traceability enables the tracking of environmental impacts across the product engineering lifecycle, from raw material sourcing to end-of-life disposal [51,52,53]. In addition, other key areas of ESG criteria (Environmental, Social, and Governance) can be tracked [54,55]. However, the current discussion is extremely limited in terms of addressing the data perspective on sustainability. Sustainability-related data enable companies to produce accurate sustainability reports [56] and meet the demands of consumers, regulators, and investors for transparency and accountability [57]. It is necessary to operate based on facts to avoid greenwashing [58]. Companies could benefit from support for complying with environmental regulations, demonstrating commitment to sustainability, and potentially gaining competitive advantage [59,60]. Companies would also gain support in pursuing certifications, such as ISO 14001 [61] or B Corp status, a certification awarded to businesses that meet high standards of social and environmental performance, accountability, and transparency [62].

Improved supply chain transparency enables visibility in supply chain stages [34,63]. This would benefit companies by aiding in identifying inefficiencies and areas for improvement [64]. Better visibility over supplier, manufacturer, and logistics partner activities [65] would allow sustainability and resilience benefits [66] but also support regulatory alignment [3,4,67]. Improved transparency can enhance trust between customers and business partners [68], strengthen supplier relationships [69], and reduce the risk of supply chain disruptions due to non-compliance or unethical practices [15,70].

Another potential benefit is the improvement in product quality [1,2] and safety [10,11,12] through the traceability of components and materials throughout the engineering lifecycle [51,52,53]. End-to-end traceability helps companies identify defects or quality issues early and trace them back to their source [71]. End-to-end traceability capability is highlighted in regulated industries such as the pharmaceutical, automotive, and food industries [72]. It reduces the likelihood of costly recalls [1], improves product quality [1,2], and enhances customer trust [72].

Support for risk management [3,63] and regulatory compliance [6,73] revolve around end-to-end traceability, which provides a full record of a product’s journey over its lifecycle [2]. This is highlighted in industries with strict compliance standards [74], such as the automotive and pharmaceutical industries. Traceability can ensure compliance with safety, environmental, or other relevant laws [3,75]. This will reduce the risk of non-compliance penalties [76], improve the ability to respond to audits [77], and increase confidence in the integrity of the product [78], among other benefits.

End-to-end traceability provides support for circular economy initiatives [79,80]. Businesses can implement circular economy practices by tracking products and materials throughout their lifecycles [81]. This can involve reuse, refurbishment, and recycling [82,83]. The design for disassembly is a related example that supports tracking the flow of materials to ensure that they are properly recycled or reused [84]. End-to-end traceability can help reduce waste, conserve resources, and support sustainability goals by closing the product lifecycle loop [85].

The contribution to improved decision-making is also among the benefits [86,87]. Integrated data can enable informed decision-making [88]. Companies can analyse the product lifecycle and optimise factors such as costs, efficiency, and sustainability [89]. This can enhance operational efficiency, reduce costs, and provide analytical opportunities to support decision-making, potentially enhancing competitiveness [90].

The challenges in end-to-end traceability include data integration and interoperability. This is because end-to-end traceability requires the integration of multiple data sources across different systems and deals with different sets of data [18]. This can include technical traceability data on a product’s design and development history through PDM/PLM systems [19,21,22] and operational traceability data involving records of raw material sourcing, manufacturing processes, transportation, storage, and handling activities [14,23,24] over the supply chain. This can be challenging because of legacy systems [2,91], inconsistent data formats [92,93], and technological gaps between suppliers and manufacturers [94]. In addition, the complexity of managing and unifying data from different stages and actors in the product lifecycle and supply chain can result in data integration and interoperability challenges [95].

Reluctance to share information is a significant challenge because companies within a supply network are independent economic actors [15]. Achieving end-to-end traceability necessitates organisations to collaborate with multiple stakeholders in the supply chain, including suppliers, manufacturers, logistics providers, and distributors [63]. Companies are reluctant to share detailed information because of competitive concerns and confidentiality [96]. Data availability and information-sharing among actors may pose significant challenges [97]. Thus, deficient transparency and data-sharing across the supply chain can hinder traceability and challenges in achieving sustainability and compliance goals [12].

End-to-end traceability can be costly because its implementation may involve significant investments in technology, infrastructure, and personnel [12]. Traceability is costly and time-consuming because it requires long-term investments in technologies and information systems, company engagement and commitment, and coordination between supply chain actors [98].

Complex regulatory landscapes can also be viewed as a challenge [99]. Different industries and regions have varying regulatory requirements for traceability [100]. Companies operating in global supply chains must comply with diverse standards [101]. Managing compliance with complex regulations can be resource-intensive and time-consuming. Non-compliance can lead to fines, legal consequences, and reputational damage [102].

Finding 1.

End-to-end traceability combines technical and operational traceability to cover the entire product lifecycle and can offer benefits in terms of sustainability, supply chain transparency, product quality, risk management, and regulatory compliance. In addition, support for circular economy initiatives and improved decision-making are among the potential benefits. The challenges include but are not limited to reluctance to share information, cost–value balance, data integration and interoperability, and complex regulatory landscapes.

3.2. Product Perspective on Traceability

The product perspective on traceability is vital, as the products and services drive revenue, are the source of competitive advantage, and are fundamental to companies’ success. The technical traceability of a product’s design and development history through PDM/PLM systems [19,21,22] and the operational traceability of products involving records of raw material sourcing, manufacturing processes, transportation, storage, and handling activities [14,23,24] over the supply chain are focal to end-to-end traceability, focusing on the stages of the engineering lifecycle of products [37].

Traceability is important for smooth product flow through the supply chain and product safety [103]. It enables the verification of product origins, integrity of components, and sustainability of production practices, while also ensuring high product quality [104]. Traceability relates to the availability of product information throughout the product lifecycle from raw material sourcing to disposal [105,106]. The rise in counterfeit products further underscores the need for robust traceability systems [107]. The importance of traceability is emphasised, particularly in safety-critical industries such as food production, medical industries, and aviation, while traceability is often mandated by industry standards [108,109,110,111] and enforced by government regulations.

However, for comprehensive traceability, technical and operational traceability must be combined to ensure end-to-end coverage of the supply chain [112]. Definitions vary, but traceability generally refers to the ability to document and track a product and its components as they move along the production chain, including stages such as transport, storage, processing, distribution, and sales [108,109,110,111,112,113]. Excessive traceability data can also be counterproductive, highlighting the need for a balanced approach that ensures an acceptable cost while maintaining the right level of traceability [114]. Moreover, without proper management, traceability systems can backfire because of misconduct, such as the sale of product labels to unregulated parties. Therefore, it is essential to carefully evaluate the suitability of any traceability system [115].

3.2.1. Information and Architecture for Traceability

Product lifecycle management information is not always integrated or automatically shared among cooperating parties in the supply chain, which poses challenges for achieving true traceability [116]. Additionally, the structural differences between supply chains across industries and the varying legal and customer requirements for traceability must be understood and considered [100]. In some cases, traceability is facilitated by extracting bill-of-material (BOM) data from enterprise resource planning (ERP) systems. BOM data representing the components that constitute a product can be viewed along with the product’s information requirements [91]. Effective traceability systems are essential for tracking products, verifying their authenticity, and monitoring components or locations during manufacturing and distribution [117]. Furthermore, linking traceability data to the as-built BOM configuration has been considered for lifecycle traceability [118]. For effective and scalable traceability solutions, a framework based on blockchain has been presented that specifies the type and granularity of information to be collected at each value-adding stage of the supply chain [14].

It is product structure that supports efficient product configuration management and helps one understand the evolution when managed in a PDM/PLM system [119]. Product structure is also necessary to consider data models that provide a clear structure for organising, managing, and understanding data [120]. Product structure can help improve IT integration and play a role in supporting traceability [121]. However, product structures are typically not addressed beyond company borders. Some efforts have been made to link traceability data and product structure in the context of an extended enterprise [122], without demonstrating the role of product structure in a generic manner, while providing links to IT systems. In addition, bills of materials have been linked to production batch numbers in the manufacturing context [123]; however, product structures have not been discussed in detail. Nevertheless, some studies have realised the significance of product structures in the context of IT systems and their relevance to traceability [124,125,126]. However, establishing capabilities for traceability and structure management across engineering disciplines has been suggested [127]. Traceability and its role in evolving from a company’s internal paradigm of technical traceability to a collaborative paradigm in the extended enterprise are seen as important [128]. However, inconsistent identification and limited traceability remain as challenges [129].

A well-designed data flow architecture is essential for visualising end-to-end traceability, considering the product structure, and expanding traceability across the entire product lifecycle. Various traceability architectures with different abstraction levels have been proposed. For example, traceability has been discussed across four layers: the application layer (data usage and processing), network layer (data exchange), integration layer (data collection, compression, analysis, and system integration), and infrastructure layer (data identification and processing infrastructure) [130]. In addition, data readers and sensors have been linked to IoT contexts, with layers focusing on interconnection (IoT integration), data (storage, conversion, and intelligence), and services (managing processed data) [131].

Another proposed architecture features a data generation layer (physical world), data gathering and preprocessing, data management and application, and service layers [132]. Data-driven approaches have also highlighted cyber–physical visibility and traceability using smart technologies [133]. However, one of the most straightforward models is the four-layered data flow architecture proposed by [134], which includes a data carrier layer (using suitable technologies to carry product-related data), data capture layer (involving data readers and middleware), data-sharing layer (manipulation and exchange of product-identifying information among stakeholders), and application layer (communication protocols and interfaces for applications).

Traceability relates to many considerations relevant to product management, business processes, IT systems, and data and how their combinations are addressed. This can be a challenging development target from a technical traceability perspective alone, but more so if operational traceability is considered. Consequently, a clear methodology for achieving traceability would be valuable to many actors, particularly if possible to automate [135]. However, the field is evolving as digital technologies are being applied for traceability in modern supply chains to enable data sharing and the availability of relevant data with end-to-end visibility over products and components [134]. In addition, recent technological developments and the potential of AI to support traceability deserve attention [136,137]. Therefore, system perspectives and systematics are necessary to support end-to-end traceability, effectively address challenges, and ensure compatibility with product management. For example, solid data management benefits from technical and operational traceability.

3.2.2. Technologies for Traceability

Various technologies have been used for traceability in industry, such as barcodes, Radio Frequency Identification (RFID), and Quick Response (QR) codes, with the primary purpose of identifying products or groups of products (e.g., production lots) [138]. However, it is challenging to implement these technologies to achieve effective traceability. Barcodes, for example, assign the same Universal Product Code (UPC) to all stock-keeping units (SKU), making it difficult to distinguish between individual items. In contrast, RFID provides each item with a unique identification number, allowing for differentiation between similar items and avoiding double counting [139]. In addition, the potential for automatic information collection contributes to more timely traceability [140]. Despite its potential, RFID has not fully lived up to its promise and remains costly, with various challenges [141]. Nonetheless, it has been applied to tracking trade items [28]. Similarly, other barcode systems such as the European Article Number (EAN) are inadequate for situations in which the product structure or component interdependence varies [142]. RFID can provide information on an item’s origin, packaging location, packaging date, best-before date, current location, and time spent at specific points in the supply chain [139]. Recent discussions in the traceability field have highlighted the role of RFID in smart digital networks [143]. QR codes are useful for internal processes and customer interaction but have limitations, often requiring other methods such as barcodes for external use. In recent years, cloud technologies that rely on object identification have begun to supersede older technologies in the traceability context [144,145]. As a result, understanding the strengths and weaknesses of different technologies is essential for effective traceability, and it may be that no single technology suits end-to-end traceability needs [138]. Therefore, the suitability of specific technologies must be carefully assessed.

Traceable objects, trade units, and items are often referred to as traceable resource units (TRUs) [146]. However, differences exist between technical and operational traceability because traceable objects outside specific companies must be identifiable by multiple parties along the supply chain. In technical traceability, units or items are typically assigned internal codes that are meaningful to the company but may not be easily understood by external actors [147]. The identification of traceable objects across the supply chain can be supported by technologies such as barcodes, RFID, QR codes, bulk numbers, or raw material markers [26]. However, traceability is not always transparent because products may carry the same identification, whereas their raw material origins or manufacturing locations differ [123]. However, this study does not provide a definitive position regarding the technologies that should be used.

End-to-end traceability offers significant benefits to the supply chain, where the traceability of individual components can be ensured through a chain of trust among manufacturers, distributors, and users [148]. Blockchain, as an ordered list of blocks containing transactions, holds the potential for traceability because transactions on a blockchain cannot be deleted or altered, providing strong defences against data tampering [76,149,150]. However, blockchain-based traceability also faces challenges owing to the reluctance of independent actors to share data [96]. Many studies have explored the deployment of blockchain for various purposes [151], and it is often presented as a solution to traceability issues in supply chain management (SCM) [117].

A key issue in using blockchain for traceability is that private data cannot be publicly stored on the blockchain, raising concerns about the information that should remain on or off the chain [116]. This challenge has been addressed by proposing a framework that integrates product lifecycle management (PLM) with blockchain, IT systems, and other traceability methods while considering off-chain activities [116]. Such frameworks may provide opportunities for application-independent data usage [152,153]. Additionally, the Internet of Things (IoT) and blockchain can potentially work together by utilising sensor data, although the integration faces challenges related to scalability, security, and data privacy [154]. Despite these hurdles, IT systems, processes, and traceability solutions can be integrated to achieve comprehensive data integration [155].

Artificial intelligence (AI) is also gaining attention because of its potential role in traceability, particularly in handling large datasets and enabling advanced analytics [156,157]. Smart technologies can further enhance architecture design and cross-industry collaboration [158], although low-cost, reliable, and efficient interconnectivity is seen as crucial from the Industry 4.0 perspective [159], making them particularly relevant for developing traceability frameworks. Ultimately, traceability holds business value and benefits product management and other stakeholders in the supply chain.

3.3. Productization and Potential Support for Traceability

Productization is an offering-centric concept that can be linked to a holistic product management perspective. The necessary whole can be seen as involving products with commercial and technical representation [37,44,45]; product data, where master data play a key role [21,30,160,161,162]; business processes [21,46,47,163]; IT systems [21,22,29,36,47], such as PDM/PLM, CAD, and ERP; and customer relationship management (CRM). Productization can be viewed as having the objective of transforming an ad hoc offering into a well-defined one [42,44,45,164], meeting key requirements cost-effectively [37], managing the scope of the offering [46], increasing efficiency, reducing inefficiencies [43,165,166], and increasing understanding of the offering [167]. Productization has been seen to benefit active product portfolio management over the engineering lifecycle of products [35,37], which is a wider focus than traditional new product development [168]. Productization benefits data analytics by providing consistency [22,29,152]. It ensures that products that are produced, delivered, sold, purchased, and used remain the same throughout the lifecycle [37,44,45]. This is the structural focus along with the commercial and technical representation of the product [38,44,45], which is necessary to gain control over the product. The products must be controlled before the processes are able to perform [46]. Productization is also necessary to gain control over a product’s master data [21]. The revenue model is a potential starting point for considering a commercial offering to clarify the core of the product in terms of what a customer is willing to pay [169]. The revenue model supports a structural approach to a product [170,171]. Understanding product architecture at the company level is also beneficial before focusing on product structure [172,173]. The technical product structure is related to modules and components [172,173,174,175,176,177] or processes and resources [43,45,46,178], depending on the product type. From an engineering lifecycle perspective, productization acknowledges a varying focus along the stages of the lifecycle [37]. Traceability has been referred to in relevant productization discussions [45,173,175,179] but has not been elaborated upon further. The significance of productization and product structure logic on traceability has not been studied in detail or considered when combining technical and operational traceability for end-to-end traceability. The level of detail in describing product structure as correlating with the delivery structure and logistics model [180] is a potential point of connection between productization and traceability, but it has not been studied from this perspective.

It can be assumed that focusing on productization and product structure logic helps bridge the gap between technical traceability and operational traceability by creating a unified framework for tracing components, parts, and materials from the design phase through production and delivery until disposal. Figure 1 illustrates the potential connection between productization and end-to-end traceability. Productization and logistics models are interlinked through the product structure. Product design and the applied productization model impact the delivery process and model, and vice versa. The selected delivery model affects product design. Productization logic is important because it affects the entire engineering lifecycle of a product. Therefore, it can be assumed that productization logic affects end-to-end traceability, as well as both technical and operational traceability.

Finding 2.

The interface between the product and delivery structures creates a key reference point for operational traceability and productization.

3.4. Literature Synthesis

End-to-end traceability requires the integration of multiple data sources across different traceability systems, involving the technical traceability of a product’s design and development history through PDM/PLM systems and the operational traceability of products involving records of raw material sourcing, manufacturing processes, transportation, storage, and handling activities over the supply chain. Productization supports end-to-end traceability throughout the product structure by aligning commercial and technical product representations with systemic product management practices and linking them to product data. This approach enhances the data perspective and supports traceability over the product lifecycle.

Productization can be linked to the technical traceability relevant to individual actors in the value chain through product structure and systemic product management (Figure 2). Systemic product management combines products, business processes, IT, and data in the engineering lifecycle. The value chain links the product structures of multiple actors, meaning that productization logic is relevant for connecting the actors within the value chain. Operational traceability relies on the data framework and lifecycle support provided by productization.

The productization of each individual company along the value chain, whose participation is necessary for end-to-end traceability, is significant because consistency in productization and product structure logic affect traceability. The product structure provides a frame for the data and enables data consistency. End-to-end traceability leverages data from multiple companies, and each participating company is part of the value chain in which each actor plays a role. Productization logic and product structure connect the actors (Figure 2).

Value chain actor-specific business processes define how products are developed, sold, supplied, manufactured, ordered, delivered, invoiced, installed, and serviced. Additionally, a specific actor’s role in the value chain may influence business processes. Productization logic connects actors within a value chain. Product definition is necessary so that processes can be defined and developed. Product data are linked to product structure and enriched in the value chain. If parts of the product are supplied by an actor lower in the value chain, the company higher in the chain must manage that item, and the company lower in the chain must manage the content. This should influence traceability. What is considered a product varies according to the company’s position in the value chain.

The product structure provides a frame for the master data within each actor. Master data, being critical business information and relating to products, also make its quality significant for traceability. Nevertheless, other data assets, including transactional and interactional assets, can be beneficial for traceability. Operational traceability necessitates the voluntary sharing of data between actors, motivated by the benefits, regulations, or requirements of a powerful stakeholder. Hence, end-to-end traceability entails considering the data to be shared and addressing any data availability-related challenges.

Finding 3.

Technical traceability, relevant to individual actors in the value chain, can be supported by a systemic product management perspective that combines products, business processes, IT, and data in the context of the engineering lifecycle. Productization is linked to the whole through commercial and technical product representation. Productization logic and traceability can be interlinked in the technical traceability context. Operational traceability relates to a collaborative paradigm among companies that can provide benefits in lifecycle visibility and prominent matters such as sustainability but is challenged by issues such as reluctance to share information.

Finding 4.

Understanding the value chain, nature of products, and product flow is necessary for effective end-to-end traceability. Traceability also necessitates considering how to combine different traceability systems, what data need to be shared, the benefits of end-to-end traceability, and the motivations for sharing data among independent economic actors. Consistent productization logic may support item-level traceability, whereas traceability data, data flow, data integration, and suitable technologies are necessary for end-to-end traceability.

4. Empirical Study

Figure 3 illustrates the interlinked product structure of the combined offerings of five different companies constructed based on interviews and product information. The dotted line separates the commercial and technical product structures. The main product sold by Company 1 to car manufacturers was a type of smart windscreen that could contain a display and the windscreen itself, including a heating system and camera system. Product configurations were car manufacturer-specific, owing to differences in design and desired functionalities. The LED displays were built into the windshield with options regarding the display colour. The sub-parts of the products of Company 1 were provided by Companies 2–5 (main suppliers), with each of their offerings being a version item from the perspective of Company 1. Version item (VI) relates to the specific product versions that may change due to, for example, quality improvements or cost reductions and, hence, affect product structure. The required LED technology was provided by Company 2. Company 3 provided the technology related to the required glass, with options in terms of glass colour and shading. (The red dotted line divides the commercial and technical structure). The windscreen colour was based on the colour of the glass, and shading could be integrated into lamination. Company 4 provided a heating system to protect the windscreen from icing. The built-in camera system provided by Company 5 provided various innovative possibilities for utilising glass surfaces. The products sold were not limited to windshields; the offering was generally possible on glass surfaces.

Figure 4 illustrates a more detailed product structure for Company 2. The company provided a built-in screen. One such product is a screen built within glass. The LED product consisted of LED foil, wired foil, and LEDs, depending on the colour options needed. A solder mask was also necessary and had colour-based options. Delivery options were based on the type of adhesive used. LED foil was customised in terms of shape. The wired foil, LED colour, and solder mask had delivery links to module variants. Several suppliers provided the necessary materials and components. (The red dotted line divides the commercial and technical structure).

The companies involved had shortcomings in their traceability, many of which were related to the integrity of the related information and data management. Some of these shortcomings were related to IT tools and responsibilities regarding traceability and data. The regulatory side did not affect all companies equally. Some were obliged to comply with stringent industry standards and legal implications. Those who were more regulated placed heavier emphasis on technical traceability with corresponding activities. Those who were more regulated had better-defined responsibilities and processes. Not all companies originally considered product structure and its role from a traceability perspective. Quality and related standards are drivers of traceability. Only some utilised unique numbers for shipments. The numberings enabled traceability to a certain extent to link the materials used. Customer requirements were mentioned as the driving force behind these practices. The traceability information relating to the order–delivery process was recorded and integrally linked to the product data. Technical traceability was linked to PDM/PLM for those that had adopted it. Traceability codes related to shipments were recorded in the ERP. Barcodes were applied to shipments, enabling them to link data internally and backtrace internally. Some other technologies, such as RFID, were considered by companies, but, generally, no bulletproof technologies or their combinations were applied to ensure the integrity of the supply chain.

In a supply chain sense, the original situation was challenged by a variety of practices and levels of maturity in terms of productization in companies. Some lacked an official product structure and systematic logic for products and their variations. Certain types of informalities prevailed. Some utilised a product structure motivated by the PDM/PLM, and certain rules and practices were deliberate. Consistency in logic could not be confirmed. The supply chain was challenged by weaker links (companies) lacking product definitions and logic of variations and revisions. This further contributed to deficiencies in instruction and product management. Suppliers and customers were seen to be in their own information silos, and the visibility of product data was mostly limited to an internal perspective.

The extent of the companies’ data models and a lack of such thinking could not be confirmed. However, product data management received considerable attention. Product data were understood by the companies, but the structure of product data was lacking in some cases. There were also deficiencies in data collection for some companies. The same variety was visible in the applied processes, with clear deficiencies in definitions in some and people’s centricity in others. IT infrastructure was also considered better by more advanced companies. Some companies used PDM/PLM but focused on product data in an ERP-centric manner. Other companies that used PDM/PLM focused on traceability and had foundations for product structure but were driven towards unnecessary manual work in traceability. The versatility of the IT used and the product data being distributed to multiple sources hindered traceability, indicating a lack of a data model for companies. Training personnel for PDM/PLM use were considered necessary to ensure the technical traceability and timeliness of the product data.

The constructed productization logic that formed a combined structure for the developed offering was seen to have potential by the focus group, validating the construction. Some adjustments were made to the initial proposal based on these comments. The focus group seemed to understand the benefits of combining structural logic with productization systematics. They seemed to understand the benefits of consistency, specifically those of using more systematic technical traceability. The scalability potential and modularity were particularly appreciated. Company 1 realised the role of industrial standards in traceability and the possibility of introducing systematics for compliance. They also understood the significance of deliberately considering structures to support traceability. The others did not follow similar systematics to the same extent but applied traceability more from the perspective of “customer requirements”. Nevertheless, they seemed to be enlightened by their potential to improve their systematics. Company 1 had traceability-related processes and roles, defined procedures, and an embedded traceability information model for technical traceability. Operational traceability was highlighted as a means of leveraging complementarity and synergies between relevant actors. However, other companies seemed to have operational traceability in a better order than technical traceability. The potential reason for this seemed to involve customers, who played a strong role in their traceability. There had been ongoing efforts to improve the formalisation of technical traceability. Data management processes in general, and specifically traceability, were seen to benefit from the partnership and combined productization logic by focusing on the data that would be created, distributed, and shared.

The interviewees viewed productization and product data as having further potential in terms of traceability in all analysed companies individually, but also across the supply chain. This entails careful consideration of company processes, IT use, and product data. These factors influence traceability-related processes and responsibilities. Nevertheless, the “synchronised” productization was seen as beneficial by aligning the product focus. Synchronised productization among key companies was beneficial for providing consistency in logic. However, it was not seen to be enough, but data from the entire supply chain were seen as needing to be “synchronised” to allow the necessary integrity in traceability. This was understood to necessitate the use of additional technologies aside from the currently used ones that enable technical traceability to further address the processes, IT, and data and cover the whole supply chain. The availability of data was not a major issue for the five studied companies, as the example involved a partnered supply chain with a common interest in new business generation. Figure 5 illustrates the need to synchronise productization and data for supply chain traceability. (The arrows (↑) indicate suppliers contributing to the technical offering, which necessitates data availability to enable synchronised data).

Finding 5.

Consistent productization and product structure may support traceability. Traceability is supported by consistency in the product structure, which provides consistency in the data. This consistency can extend beyond company borders and support supply chain traceability.

5. Discussion

The key contribution of this study is that productization with a product structure focus can benefit end-to-end traceability by creating a structured and scalable approach to commercial and technical product definitions to manage products from concept to end-of-life. This can be achieved by addressing the varying focus at different life-cycle stages. Productization involves the formalisation of the product structure, which can help formalise business processes and supply chain workflows to ensure scalability and traceability. Through this formalised product structure logic—including components, modules, and processes—productization ensures that products are consistently traceable from design to end-of-life. A clear structure is provided to manage product definitions across different lifecycle stages, reduce uncontrolled variability, and allow better control over products. This formalisation enables PDM/PLM systems to capture and manage data related to products, their development, and changes throughout the lifecycle more effectively, benefitting technical traceability. Simultaneously, operational traceability is simplified by standardising the processes, modules, and components. The linkage between productization logic and the delivery process is established through a product’s structural level, providing a touchpoint for approaching both operational traceability and productization. Overall, it appears that the focus on product structure through productization has the potential to support the integration of technical and operational traceability by promoting data consistency and facilitating seamless traceability across different traceability systems. This is a well-considered product structure logic that relates to the representation of modules, assemblies, components, and raw materials within the systems used and acts as a foundation for managing data. The structured approach focuses on product structure levels and related data across the engineering lifecycle and supports both technical traceability and operational traceability integration.

Productization can help reduce inefficiencies and support a focus on productivity by promoting the standardisation of components, materials, and processes. This involves defining and using a fixed set of parts, modules, and processes to create scalable products and further support traceability by promoting unified data in terms of technical and operational traceability. Productization logic involves considering what is allowed to change in different lifecycle stages. Process standardisation can promote the use of approved suppliers and validated methods to enhance operational traceability by simplifying processes. Operational traceability is also simplified by scalability through consistent productization logic, product structure, and data. Nevertheless, it is productization that promotes the perspective of the engineering lifecycle and its stages, consisting of commercial and technical aspects that potentially mediate the view of both technical and operational data. Hence, productization may encourage the integration of technical and operational traceability systems, PDM/PLM managing product lifecycle data, and supply chain traceability systems that manage operational data. This creates a digital thread for end-to-end traceability from raw materials to end-of-life disposal.

The scientific implications include providing original contributions to the context of end-to-end traceability by exploring the role of productization in a context that combines technical and operational traceability. This should provide new perspectives on end-to-end traceability [1]. This study contributes by directly combining technical and operational traceability to achieve an end-to-end perspective. Nevertheless, it can be argued that the coverage of combined technical and operational traceability is equal to that of end-to-end traceability. Studies focusing on technical traceability [13,19,21,22] provide ideas on the role and potential of productization to support traceability in terms of technical traceability and potential linkages to operational traceability. Similarly, studies focusing on operational traceability [14,23,24,90] provide ideas on the role and potential of productization for operational traceability and potential linkages to technical traceability. Naturally, it is possible that the discussion on technical or operational traceability contains elements that may have purposes similar to those of productization, but the perspective of productization inherently involves the product management perspective, which has been insufficiently discussed. This study contributes to previous product management research [21,22,28,29,30,35,36,37,38,39,40] by providing a direct discussion on traceability through a productization lens. Previous discussions of productization [21,22,29,35,37,41,42,43,44,45,152,164,173,175,179] gain a new contribution with this study, which explored the role and potential of productization in supporting traceability. Previous product structure discussions in the productization context [21,22,29,35,36,37,39,40,45,46,47,152,170,172,173,174,175,176] are expanded to a discussion of the traceability context. Nevertheless, the role of product structure is also further understood, especially for technical traceability [91,118,120,121,122]. However, the discussion has not been as extensive and has not covered an end-to-end perspective. Specifically, the inclusion of commercial product representation is lacking. Additionally, a productization logic focus has not yet been applied. Earlier studies have focused on the IT system context and its relevance to traceability by applying a structural approach [124,125,126]. The applied productization perspective also contributes to the earlier related focus on the engineering lifecycle [21,35,36,37] by including the traceability perspective. The lifecycle perspective is not new to traceability [2,18,19,51,52,53,81,85,89,95]; however, this study provides new perspectives through commercial and technical focus and logic and emphasises varying focus over lifecycle stages. This study contributes to previous discussions on traceability data in different forms [12,14,18,19,23,27,56,88,92,93,95], pointing out how productization can aid in standardising data structures and provide consistency to facilitate traceability, ultimately facilitating end-to-end traceability.

5.1. Managerial Implications

Managers can benefit from understanding the potential benefits of productization logic over the engineering lifecycle in general and specifics relating to end-to-end traceability. Productization supports addressing the challenges of end-to-end traceability. For example, the reluctance to share information might be alleviated by productization logic promoting the formalisation of product structure, the standardisation of components and materials, and the establishment of standardised data structures, potentially enabling selective information sharing. Productization may also promote further clarity on data ownership and control over product data, enabling the building of the necessary trust for sharing data. The modularity of a product structure may also enable separate data-sharing structures to maintain confidential details. The following implications 1–4 can be particularly valuable:

(1) Addressing the cost–value balance could be supported by productization, which promotes economies of scale in product designs that reduce complexity, thereby lowering the cost of implementing traceability solutions. Implementing traceability within the product architecture during productization is also a possibility. When implemented consistently, this can lead to cost reductions in traceability, while providing value. Data integration from technical and operational systems may be supported by productization by potentially standardising product data models so that all stakeholders can align. This supports the unification of data from different systems.

(2) Productization focusing on the engineering lifecycle may support cross-functional collaboration and the alignment of technical and operational traceability data. Additionally, navigating the variety in regional and industry-specific regulations might be facilitated by productization logic by supporting embedding compliance into product designs and regulatory reporting aided by a formalised product structure. This approach would provide consistency in data collection, thereby aiding compliance reporting.

(3) Managers can benefit from understanding how motivations for end-to-end traceability can be promoted through considerations such as sustainability. Sustainability reporting is becoming an increasingly timely issue for companies in many parts of the world and could act as a driving force for improving traceability, should the business perspective provide sufficient support. A productization focus may play a crucial role in supporting sustainability reporting and compliance through a product structure-centric approach, which may support the ability to collect, analyse, and report relevant sustainability information. This approach promotes the consistent connection of sustainability data to products and may support related traceability over the engineering lifecycle.

(4) In summary, productization can enhance the integration of technical and operational traceability, leading to improved product-related insights that can inform decision-making.

5.2. Limitations and Future Studies

The limitations include the conceptual nature of the findings and the limited empirical focus on a specific sector, analysing a limited number of companies with a limited number of interviewees. The methodological approach of combining an extensive literature review with a limited number of examples sets the boundaries for generalisability. These findings can be considered conceptual, as the full application of this concept has not been studied for end-to-end traceability. In addition, this study did not consider the suitability of different technologies for traceability or their dynamics when combined while considering the productization approach and end-to-end traceability. However, the chosen approach appears to be sufficient to indicate the role and potential of productization for end-to-end traceability. This study did not delve into the intricate details of buyer–supplier relationships or consider the contractual arrangements necessary for traceability between separate companies.

Some industry-specific factors are expected to require further research. Future empirical studies are needed to confirm the benefits and applicability of productization logic for traceability. In addition, the potential and limitations of different technologies for combining technical and operational traceability should be clarified. Future studies can clarify potential application-independent and data-centric approaches to traceability by focusing on the potential of cloud technology and productization support for traceability. Furthermore, the intersection of sustainability reporting, productization, and end-to-end traceability deserves further investigation. The suitability of product structures in different contexts should be studied further from a traceability perspective.