Decoding the digital thread digitalization approach for product design and development: benefits, challenges, and extensions

Abstract

Digital Tools are reshaping how Engineering Design data and information are produced, processed, used, reused, shared, and stored. The Digital Thread prioritizes the flow of design data and information, promoting effective collaboration and process efficiency. While literature showcases the immense application and capability of taking a Digital Thread approach to Product Design, best practices, key features, and benefits of successful implementations remain scarce. Reviewing and understanding successful implementations can assist researchers and practitioners in making informed decisions to effectively implement Digital Threads in their product design processes. This article addresses this gap by reporting a post hoc review of a collaborative Research & Development project that developed and implemented a Digital Thread approach to the design of hydrogen composite pressure vessels. A thematic analysis of the project’s reports and interviews with members of the project team was performed to identify the key features that expedite and improve the design process through an effective Digital Thread implementation. The post hoc review offers valuable insights – in the form of six feature benefits, four potential implementation challenges, three possible extensions, and four best practice recommendations – for companies looking to adopt and implement a Digital Thread approach to their design process.

Introduction

Industry 4.0 marked a significant transformation in design and manufacturing practice. Advancements in digital technology, such as the Digital Threads, Big Data Analytics, and Artificial Intelligence (AI), have paved the way for smart factories, digitally interconnected systems, and automation-driven practices (Lasi et al., 2014; Li et al., 2017; Lee et al., 2018). Industries have been motivated to adopt such digital technologies for a streamlined management of their product design, manufacturing, supply chains, and operations – allowing human operators to effectually interact and work with machines and systems. As a result, Industry 5.0 has emerged to further enhance the digital collaboration between humans and machines with a particular focus on sustainability and personalization (Xu et al., 2021; Leng et al., 2022). This shift is particularly evident in product design processes, where AI and digital tools are crucial in enhancing design efficiency by automating repetitive tasks, optimizing design parameters, exploring more of the design space, facilitating real-time collaboration, and reducing uncertainty (Lasi et al., 2014; Nambisan, 2017; Khanolkar et al., 2023b).

The growing importance of digitalization in product design processes is reshaping engineering design. Digitalization through approaches such as the Internet of Things (IoT) and Digital Twins enables streamlined connectivity and real-time data exchange between devices, providing designers with critical insights into the product design process – aiming for enhanced product performance and optimization (Burnap et al., 2019; McCausland, 2021). These approaches enhance the efficiency of product development and foster innovation by enabling designers to experiment with complex scenarios (Heinis et al., 2017; Wang and Li, 2022). The advancements in digitalization are making these approaches indispensable as companies strive to remain competitive, ensuring that their product design processes are state-of-the-art and reflective of the market needs. However, the careful and strategic implementation of these approaches is essential to fully realize the benefits of digitalization in product design processes.

Roadmaps for guiding digital transformation of businesses exist (Hill, 2018; Zaoui and Souissi, 2020; Aras and Büyüközkan, 2023; Oliveira et al., 2023); however, they focus on a broader organizational scale. The scope of this review is specific to a product design process and how a digital transformation approach enhances its data management. The Digital Thread approach prioritizes the flow of design data and information through the design process and is defined as “A data and/or information flow between systems and/or people in an organization that is systematic, consistent, and auditable, delivering the right information at the right time to the right people through the right mechanism” (Gopsill et al., 2024). Given its ability to effectively connect, access, process, and store data across the entire product lifecycle, this article explores the implementation of the Digital Thread within the context of product design processes.

Digital Thread implementations to date remain bespoke, as they are tailored specifically to the organization’s customized workflows, resources, tools, processes, and compliance requirements for their product lifecycle processes (Kasper et al., 2024; Zhang et al., 2024). Although existing literature highlights effective Digital Thread implementations through literature reviews (Eskue, 2023; Bianchini et al., 2024) and prescriptive studies (Bonnard et al., 2018; Siedlak et al., 2018; Yang et al., 2023), an unexplored gap remains in understanding the process behind such successful implementations, particularly within the context of product design. Exploring this gap can help elicit best practices from such successful Digital Thread implementations, illuminate the decision-making strategies, identify common challenges and solutions, and ultimately supporting the broader adoption of Digital Threads in product design processes. A successful implementation of a Digital Thread approach fosters further digitalization, with the literature suggesting the capability of data-driven techniques to leverage AI to enhance product design processes (Q. Zhang et al., 2024). In summary, there is currently a lack of studies that explore:

1. 1. How Digital Thread approaches have been implemented in product design processes – the development steps, the decision-making, and the challenges faced,

2. 2. How a Digital Thread benefits product design processes, and

3. 3. How it can subsequently aid future AI implementation in product design processes.

Understanding such best practice implementation not only ensures the full realization of the benefits of digitalizing product design processes but also can aid the next step of implementing data-driven tools leveraging state-of-the-art digital technologies, such as AI.

To address this gap, we present a post hoc review of a Collaborative Research & Development (CR&D) project that took a Digital Thread approach to the design of hydrogen composite pressure vessels. Findings were derived through thematic analysis of the qualitative data collected from project reports and interviews of engineers and designers involved. These findings highlight the process behind a successful implementation of a Digital Thread approach to a composite-pressure-vessel (CPV) design process. In addition, we also highlight certain key Digital Thread features that make it an ideal tool for generating, processing, transferring, and storing design data – ultimately accelerating and enhancing the design process and experience. Along with the key features, we discuss the challenges encountered by the project team during their Digital Thread implementation and explain how these were addressed, offering insights and considerations for future, similar implementations. Furthermore, we also discuss the team’s perspectives on how this digitalization endeavor can be leveraged for future AI applications. With these findings, our review can serve as a reference for companies aiming to implement Digital Threads in their product design processes.

This article is structured as follows. In Section 2, we present the importance of the role, the capabilities of digital tools in the realm of engineering design, and the motivation to implement digitalization for AI-driven product design enhancements. In Section 3, we present how we collected qualitative data, highlighting the team’s journey through interviews and project reports, followed by a thematic analysis. Section 4 then presents the project team’s journey to successfully implementing the Digital Thread approach. We present the results in Section 5, which focuses on the benefits of Digital Threads to the design process, the challenges faced by the team in the implementation process, and how the Digital Thread-enabled product design process can be extended for further process enhancements. Section 6 then discusses the findings by highlighting the best practices and benefits of successful Digital Thread implementation. In Section 7, we describe how these results can be leveraged for future AI integration in the product design process. Finally, we highlight our main takeaway of this work and present our conclusion in Section 8.

Background

This section presents relevant background information on Engineering Design processes and how digital tools have assisted through their capabilities in data management, streamlined process workflow, and automation of repetitive tasks.

Complexities of engineering design processes and the role of digital tools

Engineering design processes are inherently complex due to the myriad factors that need to be considered simultaneously, such as interdisciplinary requirements, collaborative efforts, and technical constraints (material properties, manufacturability, design economics, and performance) (Ulrich and Eppinger, 2012; Dym et al., 2013). Design practitioners, who traditionally follow the double-diamond product design process model as described in Figure 1 (Design Council, 2015), must consider these factors during effort-and-time intensive design space exploration during the low-fidelity modeling stage in the first diamond and computationally intensive simulations and optimization endeavors during the high-fidelity modeling stage in the second diamond (Subramanya and Chakravarthy, 2019; Viviani et al., 2024). The complexity is further exacerbated by Engineering Design’s iterative nature, where multiple revisions within and across design stages are necessary to achieve the desired solution (Vrolijk et al., 2023). One of the major challenges in Engineering Design is managing the vast amounts of data generated throughout the process (Begoli and Horey, 2012). Examples of data include design requirements, design artifacts such as computer-aided design (CAD) models and their finite element analysis (FEA) evaluations, and final design reports (Cantamessa et al., 2020). Studies have indicated that many design tasks use knowledge and information from previous activities and projects (Hou and Ramani, 2004; Bracewell et al., 2009; Schacht and Mädche, 2013). As such, designers aim to store and access such knowledge and information in the form of data for effective reference and reuse – avoiding the additional time and effort spent on recreating similar designs catered to design requirements (Vasantha et al., 2022; Regenwetter et al., 2024). Thus, the implementation of appropriate digital systems that facilitate effective data management and storage is necessary for organizations aiming to streamline their design workflow.

Figure 1. The double-diamond design process (Design Council, 2015).

Digital tools offer powerful analytical capabilities, enabling designers to perform complex calculations and simulations iteratively that would be effort-and time-intensive to do manually (Marion and Fixson, 2020). Hence, digital tools play a crucial role in managing the complexities of engineering design processes and improving efficiency, accuracy, and collaboration. Tools such as Product Data/Lifecycle Management, Enterprise Resource Planning, and Manufacturing Resource Planning systems are increasingly leveraged to streamline the data management process, allowing designers to create, modify, share, and store digital representations of their designs efficiently (Gulotta et al., 2013). Moreover, these digital tools can facilitate better collaboration among team members using cloud-based platforms, which enable real-time sharing and editing of design documents and ensure data synchronicity between the team members (Wu et al., 2015; Menold et al., 2024).

Capabilities and applications of digitalization in product design processes

Digital tools are an integral part of the product design process, from CAD and computer-aided manufacturing (CAM) to Design Exploration tools, Application Programming Interface (API), and recently, generative design – all enabling effective generation, processing, transferring and storage of design data. CAD assists designers in generating and visualizing designs in 3D space, followed by evaluating these designs to assess their performance in physics-based simulations (Lee, 1999). CAM leverages design data to assist with the manufacturing of the designs generated by CAD software through the automation of manufacturing processes, resulting in optimized toolpaths that reduce resource use, such as materials, tools, and machinery (K. Lee, 1999). Design Exploration tools, such as Siemens HEEDS (High-Performance Engineering Exploration and Design Optimization Software), support designers through the automatic and iterative exploration of design space for effective design optimization by providing quick analysis of multiple design variations and multidisciplinary optimization objectives (Holzer, 2016). APIs are interfaces embedded in design software that allows users to manipulate the backend of the software environment and promote process automation (Ofoeda et al., 2019). These APIs facilitate connections between multiple design software applications, enabling the flow of data between the applications, ultimately leading to improved efficiency and data connectivity. Generative design leverages AI methods to learn from previous design data to create new designs using inputs as requirements, constraints, and all these digital tools support designers and design teams to navigate through complex workflows with effective collaboration (Regenwetter et al., 2022).

Digitalization has revolutionized product design processes by integrating advanced technologies that streamline and enhance every stage of design and development (Marion and Fixson, 2020; Khanolkar et al., 2023b). One approach that has effectively promoted digitalization is the Digital Thread. Considering its nascence as an effective product digitalization tool, a recent study by Gopsill et al. (2024) highlighted the different perspectives of how a Digital Thread is interpreted within academia and industry – providing seven types of Digital Threads, as described in Table 1. Each of these seven Digital Thread types provides a unique data management capability to the product’s lifecycle within the organization. As such, Digital Threads can be collectively termed as information systems embedded within a product’s lifecycle – structured to effectively and efficiently manage the flow of data between users and systems in an organization.

Table 1. Types of Digital Threads and their capabilities, as stated by Gopsill et al. (2024)

Digital Threads will become a fundamental element that will need to be managed by all Digitally Enabled Engineering organizations, ranging from start-ups, small-to-medium enterprises, and Research & Development to large and global engineering organizations that feature extensive supply chains, as well as across various sizes of engineering projects (Margaria and Schieweck, 2019). As such, the promise of the capabilities and applications of digitalization enabled by tools, such as Digital Threads, in product design processes across industries is vast and transformative.

Current Digital Thread literature comprises studies highlighting the benefits, challenges, and implementations of Digital Threads in various application-based contexts. Table 2 highlights certain research works that have proposed and implemented a Digital Thread-based framework to assist the data management of the product or system lifecycle. However, these implementations only focus on service composition, production, quality control, and service life monitoring through the product’s lifecycle. Studies depicting the Digital Thread implementation in a product design context remain scarce – especially ones that describe how a team implements a Digital Thread in an existing product design workflow and how it can be leveraged to integrate AI for future enhancement. Exploring this gap can help researchers and practitioners realize the benefits of utilizing Digital Threads to generate, store, process, and transfer design data across their product development workflow – potentially streamlining their data management system and ultimately leading to creating and supplying products efficiently.

Table 2. Examples of Digital Thread-based frameworks applied for enhancing product/systems lifecycle (the application-context and purpose are bolded)

Digitalization as a prerequisite for AI in product design

AI has the potential to significantly transform the product design process by providing powerful data-driven tools for automating repetitive tasks and optimizing design parameters (Khanolkar et al., 2023b). AI can analyze large datasets to identify patterns and trends that might not be apparent to human designers. This capability can lead to innovative design solutions and optimization of existing designs; for example, generative design software uses AI for design space exploration and optimization (Oh et al., 2019; Yonekura and Hattori, 2019). However, the common factor behind such innovative AI applications is the availability and use of large-scale design data. Lack of historical data in the appropriate format can hinder attempts made to implement AI for product design enhancement (Williams et al., 2022; Khanolkar et al., 2023a). Amini et al. (2023) highlighted the lack of large-scale compatible data as a significant barrier to applying AI-based machine learning models in mechanical engineering domains. Furthermore, Picard et al. (2023) present the guidelines focusing on compatibility, diversity, completeness, and realism to create appropriate AI-applicable design datasets. As such, researchers and practitioners must initially ensure that their product design processes are suitably digitalized to generate compatible data – subsequently enabling AI and its transformational benefits to their design processes.

From Digital Threads to AI, digitalization enables more efficient, accurate, and innovative design solutions. However, the appropriate development and integration of these digital tools and technologies in product design processes are required to leverage their immense capabilities and benefits to create products efficiently. Although there are research studies that focus on digital tools in the context of product design (Javaid et al., 2023; Marion and Fixson, 2020; Zhou et al., 2022), few examine the intricate process and decision-making involved in implementing digitalization in product design processes, in which well-informed decisions can result in significant benefits in productivity, collaboration, and design experience. In contrast, poorly implemented digitalization can lead to suboptimal outcomes, introduce new complexities, and hinder future technological advancements, such as AI implementation (Haug et al., 2011; Batz et al., 2025). Ultimately, digitalization is essential for streamlining design processes and maintaining a competitive edge in an increasingly data-driven world where the right technology can assist industries to thrive.

Methods

To understand the best practices for implementing a Digital Thread approach and how it enhances the product design process, we have taken a qualitative post hoc review approach. Post hoc reviews are conducted – similar to exploratory case studies – to identify the key variables and generate insights on a phenomenon using real-world examples to gain a deeper understanding of underexplored topics (Yin, 2018). Hence, such post hoc reviews are deemed a suitable approach to effectively understand the underexplored topic of the Digital Thread and its implementation.

The review investigated a project that developed and implemented a Digital Thread for an exemplar CPV design process as part of Digital Engineering Technology & Innovation (DETI)¹ research initiative. This project comprised of 12 academic and industry collaborators – referred to as “the team” – from the University of Bristol, the Centre for Modeling and Simulation (CFMS), and the National Composites Centre (NCC). The review explored how the team progressed through this project, which focused on implementing the Digital Thread catered to the CPV design process. Through this review, we elicited best practices, identified and listed the challenges encountered by the team, and scoped the future potential of implementing innovative technologies, such as AI – all of which could serve as a reference for readers aiming to digitalize their product design processes. We present the overview of the qualitative methodology used for this post hoc review in Figure 2.

Figure 2. Post hoc review method – Overview.

In this post hoc review, we conducted four interviews with three team members, representing the three collaborating organizations – the University of Bristol, CFMS, and NCC – to represent the diversity of stakeholder perspectives. These team members included the Digital Thread developer, a principal researcher who worked on the CPV design process, and a graduate researcher. The graduate researcher was primarily involved in the early stages of the project, contributing to the groundwork of defining and applying the Digital Thread within the project’s context. With extensive experience in the design and manufacture of CPVs, the principal researcher played a major role in the project’s final stages, which focused on the deployment, application, and evaluation of the Digital Thread in the CPV design process. The Digital Thread developer not only led the development of the Digital Thread architecture but also had a supervisory role throughout the project – from defining the Digital Thread to overseeing its application in the CPV design process. Given their unique and leading roles in this project, we aimed to capture their insights and gain an in-depth understanding of the Digital Thread implementation process. Since the Digital Thread developer was indicated to be significantly involved in the project by the other two team members, we conducted two interviews with the developer to extract detailed information regarding the project. Approved by the University of Toronto’s Ethics Review Office, the interviews were conducted in a semi-structured format over an hour-long Zoom online video conferencing platform – with only the audio being recorded. The interview questions were focused on the team’s journey on this project, with details focused on the motivation for starting the project, the key decisions taken, the challenges observed and addressed, proposed extensions, and future scope of the project, and its outcome. The interviewees were asked to provide answers as elaborately as they could to the interview questions – some of which are provided below, as examples.

• • What was the motivation behind implementing the Digital Thread in the CPV design process?

• • What were the key decisions taken during the Digital Thread implementation?

• • Could you (the interviewee) describe the key challenges faced in the Digital Thread implementation process?

• • How much time/effort/financial is being saved by implementing this Digital Thread?

• • Retrospectively, could you (the interviewee) have made any changes/improvements on any aspects within this project?

Collectively, the interviews provided the decision-making and the journey behind the project’s commencement, progress, and completion. We also reviewed the team’s project reports to explore and understand the technicalities of developing and deploying Digital Threads in the CPV design process. As such, our dataset for this study comprised interview transcripts and the team’s detailed project reports. The data collected throughout the study were thoroughly reviewed and validated by the team that worked on this project.

The qualitative data comprised 192 pages of four project reports [PR1, PR2, PR3, and PR4] and 48 pages of four interview transcripts [IT1, IT2, IT3, and IT4]. Supplementary details regarding this qualitative data and the corresponding notations are provided in Appendix A. NVivo coding software was used for qualitative coding and thematic analysis. The qualitative data were coded by a single coder, who is the first author of this article with experience conducting thematic analyses and cases studies based on computational tools and technologies. The coding was done in an inductive manner; the codes and subsequent themes were generated from the data itself and were iteratively processed through multiple rounds of coding (Auerbach and Silverstein, 2003). In the coding process, we extracted key aspects of the project’s initiation, development, and implementation. These aspects included the project’s background, motivation, timeline, use/modification of certain software, improvements from digitalization, future scope, and so forth Next, the coder categorized these codes into four themes: (1) Digital Thread Implementation Process, (2) Digital Thread Features, (3) Challenges of Implementing Digital Threads, and (4) Extensions to the Digital Thread-Enabled Product Design Process. We present the codes and the themes obtained from the thematic analysis on this post hoc review’s qualitative data in Appendix B. These codes and themes were reviewed and validated by the Digital Thread developer, who was involved in each step of this project. The first theme describes the company’s journey in developing and implementing in a Digital Thread in the CPV design process. The remaining three themes correspond to the key Digital Thread features that enhance the CPV design process, the challenges faced by the team in this project, and the potential extensions to the Digital Thread to further enhance the CPV design process. Collectively, these four themes aim to highlight the best practices on how digitalization can be effectively implemented in a product design process and how the Digital Thread features can be leveraged to develop AI tools to potentially enhance such product design processes.

The process of Digital Thread implementation

The post hoc review was conducted on a Digital Thread digitalization project as part of the DETI – a multiyear CR&D initiative funded by the West of England Combined Authority and Industry². The initiative fostered collaboration of engineering companies, universities, and digital technology researchers to identify, develop, and implement digital tools and technologies for accelerating digital engineering capabilities and promoting skills needed to apply such tools. This initiative comprised five Engineering Capabilities Work Packages (ECWPs), with each focused on accelerating the development of digital engineering capabilities and identifying the skills required to apply such capabilities. To address the research gap presented in this work, the scope of this review is only focused on one of the ECWPs – the Digital Thread Work Package – that concentrated on the digital transformation of product design processes using Digital Threads. The following subsections correspond to the first theme that describes the team’s Digital Thread implementation journey as narration.

Motivating the Digital Thread implementation

Motivated by the emerging need to use hydrogen as a renewable energy source, this initiative focused on digitally enabled design and manufacturing processes for the development of composite hydrogen storage solutions as an exemplar use case [PR4, IT1, IT3]. NCC’s principal engineer highlighted the importance of hydrogen fuel and stated,

“…there’s a lot of interest in hydrogen and then if you trying to store hydrogen in aircraft you want it light which is where the composites comes in…” [IT1].

Since hydrogen fuel is primarily used in the aerospace industry, composites – being lightweight – are considered ideal for building pressure vessels for storing hydrogen fuel [IT1, IT3]. However, the NCC realized the current methods for designing such CPVs were time-consuming and effort-intensive [PR4, IT2, IT4]. While some digital tools were used, designers needed to manually operate and share data, which made capturing the options evaluated and iterations challenging and time-consuming [IT2, IT3]. Considering the combinatorial nature of parameters, such as vessel geometry, materials used, and winding configurations, the possible number of designs may result in a very large design space. [PR4, IT2, IT4]. The team also realized that the computational requirement for exploring such a large design space has an impact on carbon footprint; thereby emphasizing the need to develop efficient yet sustainable design exploration methods [PR1, PR2, PR3, IT2, IT3, IT4]. As such, the project started to digitalize designing such storage vessels to alleviate these challenges and improve the automated data flow between the digital tools and design processes. For the project’s outcome, the team envisioned the final digital CPV design system to enable the designer to set the problem statement and for the digitalized design-and-manufacturing process to generate an optimal solution [PR3, PR4, IT3, IT4]. Additionally, the current process of designing CPVs was ready to be technologically matured from Technology Readiness Level (TRL) 4 to TRL 6 (Mankins, 2009) [PR4, IT1, IT4]. In their project report, the team stated the emergence and growing importance of Digital Threads in organizations:

“…The Digital Thread will become a critical feature of an engineering organizations Digital Transformation exercise and is critical to ensure maximum value is attained from their investment into Digital…” [PR2].

All these aspects contributed to the three-year-long project of implementing a Digital Thread in the CPV design process [IT1, IT3]. The project was a “proof-of-concept” for reviewing, developing, and implementing Digital Threads in product design processes [IT1, IT4]. The intended aim for this endeavor was to realize the transformational benefits of having an apt digitalized product design process; these benefits include increased productivity, effective data traceability, decision-making, establishing a single source of truth, and process maturity [PR3, PR4, IT3]. The plan was to use virtual machines (VMs) to alleviate the burden on local computational devices [PR4]. With this newfound knowledge and understanding, the team targeted the threading of a CPV design process [IT3, IT4]. Before implementing Digital Threads in the CPV design process, the team had to ensure that Digital Threads were properly defined to guarantee that the correct methods and techniques were implemented in the CPV design process.

Contextualizing the Digital Thread

The team’s process of contextualizing the Digital Threads was motivated by an academic gap in what the term “Digital Thread” meant and how it fits together to provide an overall framework in the context of Digital Engineering and their project [PR1]. The team discussed that defining such digital tools could be a prerequisite for the industry to understand their current technological maturity and subsequently determine if, where, how, and when to deploy Digital Threads to commence their digital transformation [PR1, IT1]. According to the researcher who worked on contextualizing the Digital Thread types,

“… when we were creating this framework, we, one of the largest problems with anticipated having was actually trying to draw some sort of consensus of all these different concepts and ideas and what these companies were touting Digital Thread was being and trying to condense that into a cohesive set …” [IT2].

As such, the team decided to examine the academic and industry perspectives on how the term “Digital Thread” has been explored, understood, interpreted, and applied [PR1, IT1, IT3]. The team conducted a thematic analysis of both academic and industry literature and contextualized the Digital Thread as an interconnected information system to support synchronous and traceable flow of data – providing a single source of truth through the Unified Data Model (UDM) [PR1]. As stated in the project report,

“The Digital Thread, from the perspective of the UDM, will provide the relationships that connects the rationale together and enable it to be queried as a whole. This should then enable an organization to produce a full narrative of design rationale across product development and beyond.” [PR4]

This contextualization of Digital Thread as a UDM would facilitate all employees to access and use the data for their respective roles within the CPV’s design process and beyond within the organization [PR1, PR4].

Developing and Implementing the Digital Threads

Once the team had contextualized Digital Thread for the CPV design process, they initiated the development of these Digital Threads within the design process. The first step in this phase included carefully studying the traditional method of designing and manufacturing the CPV design process and identifying the requirements needed to implement a Digital Thread [PR4]. The team reviewed the traditional design toolchain, which included Excel sheets and Python scripts for conceptual design in the low-fidelity modeling stage and then Abaqus and WoundSim for detailed design optimization in the high-fidelity modeling stage [PR4, IT1, IT4]. The process produced the full Design & Manufacture digital definition required by the on-site manufacturing to produce a vessel using a composite filament winding process [PR1, PR4, IT2].

First, the team focused on developing a Digital Thread in the design toolchain and decided to use the Python programming language to develop the Digital Threads [PR4, IT3]. The reason behind this decision was the compatibility of using Python with the APIs of the existing design tools in the toolchain to provide a seamless flow of data and information [PR4, IT3]. In addition, the team also reasoned that Python contained a suite of built-in optimization algorithms (libraries and packages) that could serve as effective tools for design optimization [IT4]. The team’s next step was to extract and map the steps within the traditional CPV design process to identify elements that can be coded into a standalone Python package using the Python programming language, as well as those requiring standalone proprietary software to function [PR4]. Next, the developer moved on to the next step of transferring all those elements by,

“…wrapping all that [backend elements] mathematics in Python … use git repository to make that version control so going forward …. [monitoring] any design process were there, exactly which version they were using, when they were doing that…” [IT3].

As such, this step allowed the team to reduce the dependency and the number of tools within the Digital Thread-enabled workflow, thereby minimizing complexity by providing version control and management of the mathematics for traceability [PR4, IT3]. According to the developer, this step was particularly challenging as the backend mathematics of the CPV design process needed to be meticulously accounted for to avoid subsequent rework [IT3]. Once these elements were determined, the team initialized a GitLab repository where the Python code would reside and be used to operate the Digital Thread and the subsequent data management of the design toolchain [PR3, PR4]. According to their project report,

“The Python code would perform some of the necessary calculations for evaluating a design option but more importantly, orchestrate the entire design workflow, initiating all other programs where and when required (e.g., Woundsim and Abaqus).” [PR4]

After setting up the GitLab repository, the team installed the necessary packages to build the toolchain [PR4]. Next, the team created method stubs, interfaces, and functions with other software and tools to ensure seamless integration [PR4]. As such, the team designed the digitalized toolchain to allow the users to operate the threaded design toolchain using a command line interface, Python script and/or Graphical User Interface [PR3, PR4, IT4].

Once the Digital Thread was implemented in the design toolchain, the team focused on how this digitalized toolchain could be operated by the design team. Realizing the computational requirements of running the high-fidelity software, licensing, and the requirements of enabling effective collaboration, the team decided to deploy VMs to create a digital design environment for the project [PR4, IT3, IT4]. VMs are the technology that leverages cloud data management and processing to satisfy the user’s software and hardware requirements without needing a local computational device [PR4]. Understanding the benefits of moving the traditional design process from the local devices to the cloud, the developer highlighted:

“…one of the advantages of going to the cloud is that it [cloud computing] opens up a lot of freedom in terms of specifying the compute requirements you need…one of the things that we demonstrated is you are fixed when you buy a desktop workstation that might be your workstation for the next five years or so…if software develops over that time and yeah supersedes all requires more resources, you might have to upgrade it…” [IT3].

The team realized that within VMs, users could log and monitor all events, providing traceability of who has done what and when [PR4, IT4]. Moreover, VMs enable the design engineers to manage the computational resources that are being used, thereby monitoring the energy usage and its impact on the carbon footprint of the organization [PR4]. In addition, the team implemented Git version control systems to effectively track and manage changes made to the designs and also enable the users to roll back to any point in the project timeline and review the designs and data at that particular point [PR3, PR4, IT3]. As per the developer,

“…virtual machine provides a nice, hosted environment for the design options that gave us that full traceability in terms of the managing the output of the data…” [IT4].

The team also configured the systems to allow the users to access the VMs through a secure Virtual Private Network-enabled browser window requiring two-factor authentication [PR4]. After setting up the VMs, the team held workshops with design engineers to discuss the opportunities the new system presented in supporting their design workflows [PR4]. The team realized that the affordance of being able to track activity through the VM seemed powerful, and feedback from the workshops suggested that review meetings and collation of data for reviews could be drastically reduced [PR4].

Evaluating the Digital Threads

The team held workshops to evaluate the Digital Thread-enabled CPV design process and how it supports six different roles at the NCC organization [PR4]. These six roles are described in Table 3. As stated in Table 3, the workshops proved useful to these stakeholders in understanding how the Digital Thread supported their roles and enhanced the design process:

“Workshops with stakeholders highlight the value-added by the Digital Thread productivity, traceability, decision-making, single source of truth and process maturity.” [PR4]

The team next conducted a sensitivity analysis on the digitalized toolchain to evaluate how a change in the design can successfully trigger real-time updates within the Digital Thread to verify and validate metrics such as performance, manufacturability, cost and sustainability in near real time [PR3, PR4, IT1]. In terms of cost-saving, the principal engineer stated:

“… [the new digital thread-enabled CPV design process can] save you four days but in those four days you’re actually getting 30 times the optimization…and… £3000 would be the four-day saving…” [IT1].

This indicated that the proof-of-concept successfully demonstrated that the Digital Thread could bring automation to the CPV design process by efficiently navigating through the design space and providing rapidly optimized design solution. In such a manner, the team successfully implemented a Digital Thread in the CPV design process.

Table 3. The use of Digital Threads by different roles in the organization [PR4]

Findings

Through qualitative coding of the project reports and interview transcripts, we identified a number of features, challenges, and extensions, as observed in the team’s journey to implement a Digital Thread in the CPV design process. We summarize these features, challenges, and extensions in Table 4, and expand on them in the following subsections. We recommend readers and practitioners review these aspects to ensure their own attempts at digitalizing products provide the desired results, in the form of a streamlined data management structure.

Table 4. Summary of the findings from the qualitative coding of the interview transcripts and project reports

Key features of Digital Threads

Here, we highlight some key features observed in the Digital Thread implementation process of the CPV design process.

Uniform data model

CPV design iterations and corresponding evaluations can be stored in one data management model – represented as UDM. Such UDM leverages a cloud storage system to enable full traceability, accessibility, and version control by not only the design team but also by any employee in the organization [PR1, PR3, IT2]. Application of such UDMs in the CPV design process can be attributed to the shift of the traditional CPV design process’s information systems to cloud-based data management services, VMs, and the utilization of APIs that enable organization to query and access these design digital assets from these systems [PR4, IT4].

Single source of truth

Implementing the Digital Thread in the CPV design process has been demonstrated to have value in increasing the accessibility of the data and information being stored in an organization. Such accessibility is achieved through a single endpoint to access the data, which can be extended as new data points are added [PR1, PR3, PR4, IT2]. Threading IoT enabled the integration of new data points into existing databases by connecting design data in the digital space with manufacturing data from physical equipment and resources [PR2, PR4]. By threading the IoT, the CPV design process has shown promising value in the accessibility of the data and information being stored – enabling the team to achieve and maintain CPV design data synchronicity automatically in real time [PR4].

Synchronicity

In terms of the Digital Thread in the CPV design process, operational and twinning threads (refer to Table 1) were incorporated to mainly maintain synchronicity of data between systems e.g., CAD files and their simulation models) and automatically update information [PR2, PR4]. The team recognized the importance of ensuring data connectivity and information flows in assessing the dynamics of the CPV’s engineering processes and the ability to model and monitor how changes propagate through the system [PR2]. In such a data-driven product design process, Digital Threads typically ensure synchronicity between CAD and/or parametric definition files and the simulation models [PR2]. This is achieved through real-time computing solutions that can perform the transform within the data rate timeframe [PR2]. Industries can leverage such threads to ensure that the models are executed, and the results are recorded to enable the real-time evaluation of design options and variants [PR1, PR2, IT1].

Traceability

The team configured the Digital Thread to track all of CPV’s digital activity through the VMs and to trace how different design data and information flows are connected, specifically how digital changes propagate through the system [PR1, PR2]. Such traceability of data can enable the designers to query the VM thread to check whether certain design operations were performed [PR1]. According to the developer, VMs provided full traceability in terms of design data management [IT4]. The advantage of having VMs with data traceability is that one can log and monitor all events within it, providing traceability of who has done what and when [PR2, PR4, IT4].

Version control

The team realized that if the Digital Threads managing the design data are improperly implemented, the threads and the data can be prone to misuse and errors, leading to increased design iterations and process inefficiency [PR3]. As such, the team implemented a system that tracks file ownership and manages version control by checking in and out the files from the digital asset or document vault every time a document is changed [PR1, PR3, IT4]. This system allowed different design groups to effectively communicate design changes as they occur, manage different design variations, and respond immediately to the variations in an efficient manner, as the amount of rework in response to other design changes is vastly reduced [IT2].

Interoperability

The team preconfigured the digital design tools to reduce or eliminate interoperability issues, by enabling automatic storage backup and facilitating users to rollback to any point in the CPV design project’s timeline to obtain the design data at that particular point [PR1, PR2]. In addition, the team configured the system such that each project could have its own design environment (i.e., a user can log into multiple VMs) [PR4]. Such a configuration enabled the design data to be stored, tested, processed, and validated in their own design environment such that they can be beneficial in terms of compliance [PR4].

Challenges of implementing Digital Threads

Here, we highlight the challenges observed during the development and implementation of the Digital Thread in the CPV design process.

Process of contextualizing the Digital Thread

One of the challenges that the team faced was gathering and analyzing the literature to get clarity on how Digital Threads were defined and interpreted [IT2]. During this process, the team realized that the term “Digital Thread” is in its nascent stage, and thereby, the confusion lies in how it is defined and applied [PR1, IT2, IT3]. According to the researcher who worked on defining Digital Threads, the challenge in this step was to collect and consolidate the different academic and industry perspectives of what Digital Threads are and condense them into a finite number of interpretations based on functionality [IT2]. As such, the team spent several iterations on consolidating what a Digital Thread is, how it works, and how it can be applied in the context of a product design process [IT2].

Ensuring effective interoperability

Another key challenge that the team faced was ensuring that all the different software managed by the Digital Thread are interoperable with each other [IT1, IT2, PR4]. According to the researcher, the anticipated challenge was architecting the Digital Thread to establish smooth connectivity and transferability of design data across different design software [IT2]. The team was successful in tackling this interoperability challenge by reviewing the different design software, their API functionality, and preconfiguring these software tools to ensure that the Digital Thread effectively connects and stores data at every point of the CPV project’s timeline [PR4].

Mapping the backend of the design process

One of the most meticulous tasks required in implementing the Digital Thread was mapping the mathematics of the design process into the environment wherein the Digital Thread could effectively capture, process, and store the data [IT3]. In addition, as the design process deals with various measurements and dimensions, the developers had to cautiously account for the standards and units of these measurements, especially when transferring these dimensional metrics from one platform to another [IT3]; for example, using inches or millimeters to measure and evaluate CPV shape dimensions between CAD and FEA software [IT3]. Accurate mapping and implementation of backend calculations with appropriate measurement units could potentially save time and effort spent on error finding and sensitivity analysis during the development stage.

Licensing review

This “proof-of-concept” identified challenges in the license structure of the engineering software [PR3, IT3]. Many of the license structures were created in the days of single-user machines, thereby posing a challenge to effectively deploy in today’s multiuser cloud infrastructures [PR4, IT3]. As such, these license infrastructures need to be revised to be exploited for the opportunities they bring to today’s collaborative cloud infrastructures in a commercial context. For this challenge, the team dedicated most time to handling the activation of licenses while setting up the VMs [IT3]. In the VMs, the team implemented “dynamic licensing,” in which the licenses and associated costs occurred only when the particular CPV design project was being worked on [PR4].

Extensions to the Digital Thread-enabled product design process

Here, we highlight the how this digitalized product design process can be extended – as stated by the interviewees – to further enhance the product design process such that they are promote effective design data processing, visualization, and automation.

Including CAD in the scope

According to the principal engineer, the project scope could have included CAD generation and updates [IT1]. Although the current project scope included the development of Python scripts for processing information from Excel spreadsheets and interacting with Abaqus models for FEA, it did not cover how the Digital Thread leveraged these scripts to generate or update CAD models [PR4]. The principal engineer further stated that the CAD modeling and assembly provided an essential visualization of the CPV fitting, and assembly mounting takes place with the rest of the system, ultimately leading to the manufacturability of the CPV [IT1]. Accounting for these aspects within the project scope could have been beneficial, and future extensions could focus on CAD integration [IT1].

Automating the mapping process

As indicated in the challenges, the team spent time and iterations on effectively mapping the design process into Python scripts [PR4, IT3]. As such, the Digital Thread developer stated that a certain automation program could have helped to speed up the mapping process. This program could also account for the correct measurement units and effectively capture and translate the backend calculations from the Excel spreadsheets to the new Python programming scripts [IT3].

Integrating advanced technologies

Although all three interviewees stated that integrating AI was not included in the scope of their project during the start of their project, they recognize the possibilities of leveraging AI within the Digital Thread-enabled product design process [IT1, IT2, IT3, IT4]. The developer and the principal engineer introspectively indicated the possibility of using AI or machine learning algorithms for evaluating a multitude of possible design solutions for a given set of design requirements and automatically retrieving the top best ones [IT1, IT4]. In addition, the researcher also stated the potential for integrating state-of-the-art immersive technologies, such as augmented reality and virtual reality, within the Digital Thread-enabled design process for effective design visualizations [IT2]. However, developing and deploying such technologies within the existing design process could be an effort-intensive endeavor [IT2].

Discussion

The CPV design process was significantly enhanced by the streamlined data management service and features provided by the Digital Thread. We observed that the Digital Thread provided effective low-fidelity modeling by streamlining the design space exploration, followed by integrating different CAD and FEA software for high-fidelity modeling, simulation, and optimization. The Digital Thread enabled the creation and storage of each data point during the low-fidelity or high-fidelity modeling stages. Additionally, the Digital Thread features ensured that each data point – be it a CAD model, stored requirements, design specifications, or simulation results of any design – was transferred, processed, and accessed effectively by any member of the design team. We conjecture that any product’s design process would follow an approach similar to the team’s double diamond model of the CPV design process, specifically the low-fidelity and then high-fidelity modeling stages (Subramanya and Chakravarthy, 2019). As illustrated in Figure 3, implementing Digital Threads in the product design process enables streamlined data connectivity, storage, and access through UDM and VMs – effectively assisting design teams as they navigate through the low- and high-fidelity modeling stages to achieve the desired solution. As such, the thematic findings of this study can be generalized to other products’ design processes in which Digital Threads are to be implemented to digitally enhance the low- and high-fidelity modeling stages of their product design processes. Using a thematic analysis of the evidence of the team’s journey in defining, developing, and deploying Digital Threads in the CPV design process, we identified the best practices and benefits of implementing Digital Threads in product design processes.

Figure 3. The double diamond design process (Design Council, 2015), integrated with the Digital Thread (bold lines), leveraging virtual machines (VMs) through a Uniform Data Model (UDM).

Best practices for implementing Digital Threads in product design processes

Using our post hoc review findings, we recommend firms and practitioners consider the benefits of Digital Threads, leverage their features, and learn from the challenges to effectively digitalize their product design processes. In addition, we recommend the following key points to consider before and after developing and implementing Digital Threads.

Understand the traditional design tools and processes

As stated in Section 4.3, the very first and crucial step of digitalizing the existing product design process is to understand how products are designed traditionally, identify specific aspects to be digitalized, ensure that the backend calculations are well-accounted, and benchmark the improvements made after digitalizing. This process includes reviewing the traditional design software and its corresponding functioning to ensure that the digital elements are properly embedded and realize the full benefits and features of digitalization. This step was also observed in all the Digital Thread implementations stated in Table 2. In these implementations, the understanding and reviewing of current processes, processes, and tools – such as existing systems (Pang et al., 2021; Vodyaho et al., 2022; Eskue, 2023), software (Bonnard et al., 2018; Kim et al., 2017; Koo et al., 2024; Siedlak et al., 2018), programming languages (Vodyaho et al., 2022; Koo et al., 2024), and backend architecture of the system or software (Pang et al., 2021; Gery, 2023; Bianchini et al., 2024) – were done prior to the development and successful deployment of Digital Thread frameworks in corresponding products’ lifecycles. In this case, the team found the process of mapping the traditional CPV design process to its digitalized counterpart to be a challenging endeavor, as indicated in Section 5.2. Such a challenge could also be observed for other product design processes, as different industry design teams may use different software, subsystems, or technologies for designing their products (Jadhav and Sonar, 2009; Lin and Lin, 2023; Wu et al., 2020). In order to address such a challenge, industry teams could leverage use of systems mapping approaches and tools such as IDEF0, to map the inputs and outputs of different digital activates and connect them to understand the workflow effectively. Ultimately, it ensures that digitalization enhancements are relevant, targeted, and integrated smoothly.

Review and implement the different Digital Thread frameworks, its tools, and features

As described in Section 4.2 and Table 1, reviewing different Digital Thread types can be beneficial to understand their functionalities in enhancing design data management and connectivity across different stages of the product design process. Literature on successful Digital Thread implementations, as stated in Table 2, has also demonstrated the need for careful considerations on selecting the right Digital Thread architectures (Pang et al., 2021; Bianchini et al., 2024), software (Vodyaho et al., 2022), tools (Vodyaho et al., 2022; Eskue, 2023; Gery, 2023), and features (Gery, 2023) to assess how the proposed Digital Thread framework can be applied to the existing product lifecycle. After this assessment, the next step is the development and implementation of these Digital Thread types, leveraging apt tools such as VMs and Git. As stated in Section 4.3, the use of tools such as VMs and Git facilitates effective design computations and design data management, storage, and tracking – ensuring all design iterations are effectively monitored, stored, and can be easily retrieved wherever required. The VMs can be configured and deployed for designers to realize the full potential of Digital Thread features, including UDM, data synchronicity, interoperability, traceability, and version controlling – all resulting in an enhanced product design process. In addition to the Digital Thread features, the acknowledging and addressing challenges – stated in Section 5.2 – can serve as an apt proactive approach to ensure a smooth and successful Digital Thread implementation. As such, companies and organizations aiming to incorporate Digital Threads should ensure that the functionalities of different Digital Thread types are understood, followed by the implementation of appropriate tools that enable effective deployment and application of the Digital Thread and its features.

Consider the sustainability of operating the Digital Threads

As indicated in Section 4.1, project reports and interviewees have emphasized the need for sustainable and energy-efficient Digital Threads. Design processes can be inherently energy-consuming, especially during simulations, which require high computational power (Cox et al., 2022). This necessitates that the tools and technology be sustainable by considering that the Digital Thread processes the information in a computationally efficient manner, and the designs are generated with optimum use of resources. Literature highlights that researchers have considered the sustainability impact of product design processes with the aim to control the carbon footprint produced by their computational tools and processes (Peng et al., 2019; Acharya et al., 2020; Parolin et al., 2024; Vidal et al., 2024). As such, Digital Thread developers should consider including apt design optimization methods that efficiently navigate the design space and reach the optimal solution using minimal computational effort for effective sustainability. For companies aiming to utilize Digital Threads, appropriate research must be done to ensure that the digitally enabled design processes are conducted with minimal impact on the carbon footprint.

Implement designer training to operate such digitalized design processes

As highlighted in Section 4.4, the team held workshops to demonstrate the applicability of the Digital Threads with the design engineers. Prior to the adoption of Digital Threads, design engineers were well-versed in operating their traditional design tools and processes by gaining hands-on experience designing their products and their variations multiple times. Any update to any major aspect of the product design process – whether it’s a design software, a subsystem connecting multiple software, or a new technology – necessitates the designer to be familiarized with the new workflow for quick adaptation and application of the Digital Thread-enabled design process. Hence, it is crucial to train designers to aptly operate digitalized product design processes. Training helps designers fully understand and leverage the capabilities of new digital tools. Without proper training, designers may not use these tools to their full potential, thus not realizing the productivity gains of digitalization. Training templates, documentation, and training sessions with developers can prove beneficial in ensuring designers receive the appropriate training for operating the newly digitalized design process. This step is also evident in the case study presented by Pang et al. (2021), who emphasized the necessity of training programs for users to achieve high operational performance. Additionally, implementing a Digital Thread-enabled design process requires a multidisciplinary skill set. It will likely motivate firms to hire and/or train their design workforce with the required skills to sustain such a workflow. Doing so essentially maximizes the benefits of using new technologies to ensure consistency, facilitate effective collaboration, improve problem-solving skills, maintain a competitive edge, enhance job satisfaction, and ensure compliance and security. This investment in training ultimately leads to more successful and innovative product design outcomes.

Table 5 summarizes the best practice considerations proposed in this study, as well as those observed from the literature on successful Digital Thread applications. As stated in Table 5, understanding the traditional design process and reviewing the Digital Thread frameworks and corresponding tools required for effective development are key requirements for successful Digital Thread implementation – as evident in the literature highlighting successful Digital Thread applications. However, considerations on environmental sustainability and upskilling seem to be lacking in the literature and, hence, should be emphasized by industries using Digital Threads to thrive in Industry 5.0. Thus, it is important to consider all these considerations to operate Digital Threads to their maximum potential, realize the full benefits of digitalization, improve digital literacy in industries, and enable future technological developments sustainably.

Table 5. Summary of the best practices of implementing and applying Digital Threads proposed in this post hoc review, and the references that support them

Additionally, design researchers and practitioners aiming to implement Digital Threads in their processes, could consider the three extensions outlined in Section 5.3. With a clear understanding of the backend API functionalities and data compatibility, direct integration between CAD software and other design software, connected by the Digital Thread, can be practically achievable. Such a real-time integration of CAD, orchestrated by the Digital Thread, could result in improved synchronicity between the CAD files and their dependencies, such as design parameters and simulations. Next, the automated mapping of the software backend may require deep software expertise to develop programs that effectively translate the API scripts from one software to another, further accelerating the threading process. Finally, integrating advanced technologies – such as AI models, virtual reality, and augmented reality – in the Digital Thread can enhance design processing and visualization (Rane et al., 2023). However, their effective use depends on the availability of compatible hardware and software tools, proper technical integration with the Digital Thread, and adequate training for the designers of these technologies (Williams et al., 2022).

Benefits of implementing Digital Threads in product design processes

An essential aspect of digitalization is its affordance to enable users to query information such as product requirements, manufacturing processes, logistical information, compliance checks, raw materials, and processing of those materials, as well as reports/review meetings. Moreover, a persistent challenge in the creation of threads is to link different digital information and assets – such as reports, CAD models, simulation models, communications, and presentations – across their respective information silos (e.g., Product Lifecycle Management, Enterprise Resource Planning, Manufacturing Resource Planning, and Simulation Lifecycle Management systems). To address these requirements and challenges, organizations need to leverage the significant benefits provided by the key features of the Digital Thread, as presented in Section 5.1.

As described in Section 4.2, linking all seven Digital Thread types together leads to a UDM that the business can refer to, query, and gain insights from. Single source of truth is a key aspect of building or implementing an information system, such that every data element can be stored and accessed from only one location. Organizations can leverage a single source of truth architecture to effectively digitalize their product design processes, as the Digital Threads facilitate real-time synchronization of design data and information flows. Maintaining data synchronicity between different systems can ultimately provide effective collaboration within and beyond the engineering organizations, as it reduces the likelihood of stale information and rework. Data synchronicity is crucial to ensure that the digital assets of the designs are updated simultaneously in multiple systems and software – such as CAD modeling, simulation, and optimization software – while being consistently stored in a Product Data Management system. On a similar note, such threads that enable efficient data synchronicity also provide effective traceability of design events – like design requirements consolidation or simulation updates – that have occurred in a project. Traceability is crucial in assessing the dynamics of the organization’s design and engineering processes. Organizations – aiming to digitalize their product design processes – need to utilize the full potential of data traceability as it moves through the system to meet compliance requirements and change control so that only the correct version of the design is approved. Using version control, each employee of the organization can access the full history of the files representing the digital design assets, myriad design variations, and revision updates, vastly reducing the amount of rework and iterations in response to other design changes. Digital Threads provide the affordance to enable effective interoperability, ensuring different software communicate and function harmoniously in the organization’s design workflow. As such, the Digital Thread and its features can provide streamlined data management for the design space exploration and the design optimization stages of the double-diamond design process (Design Council, 2015) to assist designers in obtaining the final design solution.

Potential for future AI implementation

Digital Threads and its features not only provide effective data management services for a product’s design process, but can also pave the way for future advancements as the market and industries continue to technologically evolve. In this section, we present the benefits and support that the Digital Threads can provide researchers and practitioners to build AI frameworks for enhancing their product design processes. We also discuss the future directions in terms of the requirements, challenges, and enhancements potentially brought forth by AI integration in the product design processes that are enabled by Digital Threads.

Digital Thread support to AI implementation

AI integration stands out as one of such advancements, already having proven its immense capabilities in the realm of product design processes. By leveraging the Digital Thread features, such as a single source of truth, synchronicity, traceability, version control, unified data management systems, and interoperability, firms can enhance their design processes and unlock the full potential of AI, as described in Table 6.

Table 6. How Digital Thread features can support future AI implementation in the Digital Thread-enabled product design process

As highlighted in this review, VMs and cloud data management systems with all the aforementioned features, enhance the productivity and collaborative structure of product design processes. By managing large datasets, VMs can provide scalable computational resources to run complex AI algorithms, such as generative-adversarial-networks, neural networks, and large language models (LLMs). Cloud data management offers the flexibility of having remote data storage with real-time access, traceability, and version control, leading to streamlined collaboration and data sharing. Leveraging these technologies and the Digital Thread features can enable organizations to expedite effective AI implementation in product design. In such a manner, Digital Threads streamline workflows, improve process efficiency, and provide a robust foundation for future AI developments, ensuring that firms remain competitive in an increasingly data-driven world.

Future direction for AI-driven product design processes

Digitalization and AI have proven to be reliable computational supports to product design processes – helping designers make better products more efficiently (Cantamessa et al., 2020). Digital tools and AI go hand-in-hand as data suppliers and data processors, with the synergistic flow of information from the data generated by Digital Threads to state-of-the-art AI tools, effectively expediting the compositionally intensive aspects of the product design processes. As such, the data must be generated and processed in a compatible format to be effectually used as inputs to the AI framework. This motivates the need for a strategic approach to ensure that the data generated, processed, and stored by the Digital Threads is compatible with AI-based methods to enhance product design processes. Data are recognized as the key requirement for developing, training, and implementing AI-based methods and needs to be compatible with such applications (Williams et al., 2022; Amini et al., 2023; Khanolkar et al., 2023a); incompatible data can lead to errors, inefficiencies, and increased time and efforts toward achieving compatibility. Compatibility provides a solid foundation for reliable and robust AI systems supporting product design processes. In addition, addressing data compatibility issues early in the digitalization process can be more cost-effective – preventing the later need for costly data migrations, conversions, and system redesigns when integrating AI in the future. As such, the future direction of AI integration should first ensure that the Digital Thread can facilitate the generation and management of compatible data for effective training of AI frameworks.

Once the Digital Thread has successfully facilitated compatible design data for AI usage, the next step is developing AI tools for design task automation. Although the literature has showcased the application of AI to enhance the design process by automating different tasks across different design stages (Khanolkar et al., 2023b), there are certain challenges that need to be addressed for effective AI integration in product design processes. First, the data quantity and quality generated and stored by the Digital Thread must be carefully assessed so that the AI can capture all the necessary features for a proper design-based application (Wang et al., 2021; Witkowski and Wodecki, 2024). Different design tasks have different data, for example, text-based customer reviews and requirements, 3D CAD models of previous designs, FEA simulation results, and final design drawings. Hence, it can be an effort-and-time-intensive task to consolidate all such different data types and convert them into a format compatible with AI training. Second, the data generated by the Digital Thread, or the AI needs to be thoroughly validated before its subsequent use (Witkowski and Wodecki, 2024). This step is crucial to ensure the reliability of the results that would be generated by the AI, especially for products such as hydrogen-fuel-storing CPVs that are used for high-stakes applications like energy storage, aerospace, and defense. Third, applying AI algorithms in Digital Thread-enabled product design processes will require significant computational resources and thereby have an impact on the environment and carbon footprint (Bhaskar and Seth, 2024; Kneese and Young, 2024; Liu and Yin, 2024). As researchers and practitioners aim to apply state-of-the-art AI tools like generative AI and LLMs to leverage their immense automation capabilities, evaluating the computational cost of running these tools and their impact on carbon footprint can be challenging. Thus, all the aforementioned challenges of generating, validating, processing, and applying Digital Thread data for AI must be strategically assessed and addressed through appropriate decision-making while planning AI integration in product design processes.

This post hoc review presented how Digital Thread features can help integrate AI into product design. Data generated and stored at every step of the product design process can be leveraged to develop and apply specific AI tools, such as using natural language processing (NLP) to parse customer reviews and extract relevant design information or use machine learning methods for performance-based design evaluations. AI-based design literature showcases the exceptional capabilities of AI tools such as deep learning-based algorithms for effective design space exploration (Mudgal et al., 2018; Botero and Smart, 2024; Çakmak and Öngün, 2024) and machine learning-based optimization methods for design space exploitation (Kim et al., 2018; Owoyele et al., 2021; Zhou et al., 2009) – proving AI as an effective computational support for enhancing product design processes. These AI-based methods have proven capable of streamlining the product design process by reducing design times (Khanolkar et al., 2020; Plathottam et al., 2023), providing automation for effort-intensive tasks (Yoo et al., 2021; Hwang et al., 2025), and eliminating bottlenecks (Subramaniyan et al., 2021). As such, future research could explore how the design data managed by Digital Threads and its features could be leveraged to support design space exploration and exploitation using AI for composite-based products like the CPV or beyond. A recent literature study has highlighted how researchers can leverage myriad AI methods based on the type of design data available in each stage of the design process (Khanolkar et al., 2023b). The Digital Thread can effectively manage design data from different stages of the design process, thereby motivating researchers to apply different AI methods, including NLP for text-based data (Anwar et al., 2020; Park and Kim, 2021; Wu et al., 2022), GANs for 3D CAD models (Chen and Fuge, 2019; Shu et al., 2020; Zhang et al., 2019), and deep learning-based models for processing simulation data (Singaravel et al., 2018; Yonekura and Hattori, 2019). However, such AI-based tools must be carefully applied to cater to that particular product’s design process and the associated Digital Thread network. Furthermore, such AI tools must be deployed only after rigorous testing on the design data managed by the Digital Threads. The Digital Thread-enabled product design data can be effectively segregated into training, validation, and testing datasets for assessing AI performance based on prediction time and accuracy. In such a manner, the Digital Thread features can enable researchers to compare different AI tools by generating and storing multiple types of design data in real time, which is useful for assessing which AI tool is best suited for their product design application.

Conclusion

Digitalization has become an essential strategy for accelerating processes that are too computationally intensive for human designers to manage. One way to digitalize a design process is to take a Digital Thread approach. Through qualitative analysis on project reports and interviews, our post hoc review reveals how firms can define, develop, and implement Digital Threads catered to their product’s design processes. The thematic analysis conducted in this study illuminates six features, four challenges, and three extensions related to the development, implementation, and application of Digital Thread in the product design process. These features, challenges, and extensions could provide readers with the benefits of implementing a Digital Thread and the best practices to make informed decisions in their own product’s digitalization journey with an eye on future AI implementation.

The Digital Thread benefits, features, and implementation challenges presented in this post hoc review could potentially serve as a reference for similar firms aiming to digitalize the design processes of their products. We highlight that these Digital Thread features, including data synchronicity, transferability, traceability, and version controlling could potentially enhance product design processes by facilitating effective data management, streamlining iterative tasks, and enabling design collaboration. We conjecture that such digital tools can be used to develop future AI methods, such as machine learning and deep learning, to automate the product design process further, providing seamless human–computer collaboration. Although these features of the Digital Thread can be generalized and applied to other product design processes if properly implemented, the applicability of the Digital Thread could depend on the type of product or system being digitalized. The CPV design process is a parametric single-part design process with specific evaluation criteria for structure- and performance-based assessment. Design processes for various products – ranging from single-part products to assembly-based products and systems – can involve different design parameters, modeling techniques, simulation-based evaluations, supported by a range of software tools. Thus, future studies could explore how Digital Threads are incorporated into different types of products’ design processes, including systems and assemblies and their software tools – thereby further contributing to the generalized benefits and applications of the Digital Threads.

The main limitation of this post hoc review is that it is based on a single project with a limited number of interviews. While the applicability of this study’s findings has been discussed within the broader context of the product design process, additional case studies and post hoc reviews on Digital Thread implementations in other product design processes are needed to validate the results and support their generalizability. Future studies can also benefit from involving multiple coders for reduced bias, enhanced validation, reliability, and improved generalizability of the findings.

In an era where digitalization is transforming industries, Digital Threads and tools are becoming indispensable for firms aiming to implement AI in their product design processes. These digital technologies facilitate the seamless integration of AI, paving the way for innovative design solutions, enhanced efficiency, and competitive advantage. Key data-centric concepts such as synchronicity, traceability, version control, unified data management systems, VMs, cloud data management, product data management, and interoperability play crucial roles in this transformative journey. By emphasizing the insights gained from the progress and decision-making throughout such real-world scenarios, companies can comprehensively understand effective strategies and key considerations for digitalizing their product design processes. This practical information is a valuable guide and source of inspiration, enabling firms to make informed decisions and advance their respective design digitalization initiatives within their product design processes.