1. Introduction
Production industries have been struggling a great deal for a long time to develop and support their products throughout their lifecycle. Throughout this duration, their prime focus in product development has been to optimize their product design and improve its intended functions. However, in today’s competitive environment, the aim is not just design optimization and function improvements, but the ability to connect and accordingly update their products to conform to the current market trends. This is only possible if the industry grabs all the opportunities coming along with breakthrough technologies and, hence, properly manages the complete lifecycle of its products. To this aim, several software systems have been implemented in industries. According to one of the recent surveys, a total of 135 such software are currently implemented in different sectors for managing the various aspects of the product lifecycle from conceptualization to its disposal stage [
1]. This existing software provides both on-cloud and on-premises services and contains various capabilities and challenges.
Product lifecycle management (PLM) focuses on data creation, storage, and retrieval throughout the lifecycle of a product from its conceptualization all the way to its ultimate disposal or retirement [
2]. Its purpose is to provide an integrated view of what is happening with the products across their lifecycle. In this strategy, efficient integration of the products, associated stakeholders, working processes, and data storage systems is required to obtain the best results in product data management. PLM evolution has played an essential role in high-tech industry leaders’ decisions to change their way of work to fulfil critical requirements due to high market changes [
3]. The benefits obtained through the implementation of the PLM solution have encouraged industries to invest in PLMs [
4]. J.G. Enríquez et al. developed a quality evaluation model for evaluating the different aspects of the PLM systems implemented in industries [
5]. The introduction of PLM systems into the industry has both short-term benefits, i.e., reduction of time for each routine activity, reduction of overhead activities, and improvements to the way of work, as well as long-term benefits, i.e., improved competitive position, reduction of time to market, etc. [
6].
The existing PLMs, i.e., Teamcenter, Windchill, Enovia, SAP, etc., are implemented based on a standalone and centralized strategy. They are mainly developed for in-house use (mostly manufacturing) and hence are incapable of accessing, processing, and analyzing data across other stages of the product lifecycle. For instance, an industry producing a product, for example, a production machine or equipment, and using the existing PLM can access and analyze the product data only in the manufacturing stage and is unable to access the data of that product when it goes to another stage, i.e., operations and maintenance where it performs its intended function. Hence, as the product information chain along the lifecycle spans enterprise boundaries, these PLMs are unable to meet all the data management requirements of the product [
7]. Interoperability, data transparency and openness, decentralization, credibility, collaborative data provision, and data security are some of the broadly discussed major challenges faced by the currently implemented PLM systems in the industry [
1,
6,
7,
8,
9]. To meet all these challenges and expectations of the production industry, novel blockchain-based frameworks and solutions have been proposed by researchers to manage the entire lifecycle of a product [
1,
4,
10]. Nowadays, an increasing number of initiatives in the blockchain are opening new horizons for its implementation in various sectors. Although most of the current work on the blockchain in the real business environment is still in an early stage [
11], it is strongly expected by experts that blockchain will considerably target every industry and significantly change existing practices [
12,
13,
14].
Blockchain is a distributed and immutable ledger that facilitates the process of recording transactions and tracking assets in a business network [
15]. The transactions are recorded in a data format called block and added to a shared database in chronological order to form a chain [
12,
16]. The transaction data in the block are validated by the pre-defined nodes in the network and then added and stored on the chain [
13,
17,
18]. The uses of blockchain have been investigated for different purposes across various industries. Three basic themes, i.e., impact, functions, and configuration, of blockchain implementation have been discussed and expanded by various researchers and practitioners [
19,
20]. In parallel, a substantial amount of research is in progress on identifying blockchain implementation enablers and challenges. Samuel and Babak developed a fuzzy-based analytic model to identify the key blockchain adoption enablers and analyze their impact on supply chain performance [
21]. The identification and evaluation of blockchain adoption challenges is not an easy task and, therefore, needs an integrated approach. In this context, Karuppiah et. al. used fuzzy the Delphi-assisted grey-DEMATEL analytic method to identify the eminent challenges in blockchain adoption [
22].
Four generations of blockchain, i.e., blockchain 1.0 to 4.0, have been recognized since the inception of Bitcoin by Satoshi Nakamoto in 2008 [
23,
24,
25].
Table 1 illustrates this evolution of blockchain from the first generation, i.e., cryptocurrency, until the fourth generation, i.e., application in industry 4.0. In Blockchain 1.0, the focus is on finances concerning cryptocurrency [
26]. Blockchain 2.0 supports the creation of smart contracts for many agreement terms through autonomous computer programs and commands that execute automatically [
24,
27]. Blockchain 3.0 emphasizes decentralized applications (DApps). It uses decentralized storage and communication, hence its backend code runs on a decentralized P2P network. Blockchain 4.0 focuses on applications and the impact of blockchain implementation in different industries, specifically in industry 4.0 [
28]. In other words, Blockchain 4.0 refers to making blockchain 3.0 usable and satisfying industry 4.0 requirements by making blockchain promises true in the industry. At present, various challenges still exist, leading to the evolution of blockchain 5.0. In this generation, blockchain would be applied together with artificial intelligence, hyper-converged infrastructure, and other advanced data analytics and industry 4.0 technologies for high security, reliability, scalability, etc. [
29].
To date, several blockchain-based platforms have been developed by various developers and organizations. Depending on different applications, all these platforms can be divided into three types of networks, i.e., public, private, and consortium blockchains. Public blockchains are permissionless in nature and open and anyone in the world can access, perform transactions, and participate in the consensus process [
31]. Private blockchains are permissioned blockchains controlled by a single central authority. The central authority permits the nodes that can join and perform transactions. Consortium blockchains are also permissioned blockchain but instead of a single authority, it is controlled by a group of preselected nodes [
32]. Blockchain-based platforms are technically mature with sufficient community support to make sure future maintenance [
33]. All the permitted members of the network control each other, therefore, no intermediary or central point of the account is needed [
34]. Furthermore, the use of blockchain technology is especially beneficial in the case of high-value products with low trading volume [
35]. In general, the traditional PLMs manage the data from conceptual design to product release. However, blockchain-based platforms have the capability to manage the below-mentioned data through the entire product lifecycle, i.e., design to final disposal or recycling.
Provenance Management: Provenance management refers to the tracking of information about people, processes, and methodologies involved in producing data from design to disposal or recycling. It simply shows where, when, how, and by whom the data are generated. The purpose of provenance management is to provide greater visibility and better efficiency by creating records in the network [
36].
Bill of Materials (BOM) Management: BOM management refers to the capturing, configuring, and management of data, i.e., raw materials, sub-components, constituent parts, and quantities of each required for the end products. Effective BOM management is critical for the success of any manufacturer irrespective of the complexity of their products.
Manufacturing data and Process Management: Securely managing and validating the data generated through the entire manufacturing and post-manufacturing as well as material handling. It also accurately connects process planning to production to ensure the transfer of correct data to and from the production setup.
Collaborative Document Management: Collaborative document management refers to capturing, storing, and making the documents available throughout the product lifecycle in a controlled and secure network. It allows for linking the related documents to products, processes, or any other document for easy and fast reference and retrieval.
Suppliers and Materials data Management: It enables the procurement teams to track and communicate with their suppliers in various matters, i.e., quality of raw material, selection and purchasing of parts, revision of already placed orders, and so on.
Change Management: Due to changes in customer demand and availability of raw materials, the most frequent changes occur in design and production. Change management ensures that these changes are clearly defined, documented, and managed so that the other stakeholders can access the updated data.
Quality Management: Critical quality documents such as PCP, PFD, fishbone diagram, fault tree analysis, etc., can be managed across the organization. It helps in ensuring that the quality of the products is as per the organizational goals and regulatory needs.
Requirements Management: Requirements management involves collecting, analyzing, and managing what the different stockholders want from the product or service. It also includes requirements traceability, product configuration to requirements, collaboration with different stakeholders, and approval of requirements.
Operational and maintenance data management: Collecting and managing the real-time operational data, i.e., product operational conditions, running time, etc., and maintenance history. It helps in ensuring better decision-making within and even after the end of the product lifecycle, i.e., disposal or recycling.
Product Distribution data Management: Product distribution data management includes packaging, transportation, and delivering the product to the customer or facility where it is intended to be used. It helps in answering the question of when, how, and under what conditions the product is delivered.
In this paper, the analytic hierarchy process (AHP) has been applied for selecting the best possible blockchain-based platform for PLM. As blockchain is a new technology, a clear comparison of the available platforms based on performance-based metrics and key performance indicators is still not possible. This is the reason for using an AHP-based approach for the selection of a blockchain-based platform. AHP is one of the multi-criteria decisions making (MCDM) tools broadly accepted because of its provision for converting complex problems into a form of hierarchy [
37]. It involves matrix algebra for measurements based on an expert’s judgment on pairwise comparison. In AHP, both qualitative and quantitative criteria can be compared using informed judgments to derive weights and priorities. AHP is commonly used in selection, prioritization, and forecasting where it is assumed that the decision-makers implicitly or explicitly know the objectives and the associated alternatives [
38]. The research question that this paper tries to address are:
The rest of the paper is organized as follows: The available and top blockchain-based platforms are explored in
Section 2.
Section 3 provides a detailed framework and uses an analytic hierarchy process-based selection of platforms. Results are presented and discussed in
Section 4. Finally, this work is summarized and future directions are presented in
Section 5.
2. Available Blockchain-Based Platforms
A systematic procedure has been followed to explore the available blockchain-based platforms for managing the entire product lifecycle. In the literature, the existing research publications consider a limited number of available platforms, therefore relying only on research publications would not be a wise approach. In this context, parallel to published research articles, different open search engines and research blogs published by well-known publishers, i.e., Capterra, LeewayHertz, G2, Tech Target, HFS, etc., could be considered as a more sophisticated method, and hence is adopted in this exploration process. Furthermore, as this study focuses on the selection of a platform for managing the entire lifecycle of the product, several important points that have been considered in this identification and exploration process can be pointed out as follows:
Maturity Status: With the rapid increase in demand, the development of blockchain-based platforms by different organizations is also increasing day by day. Some of the well-known platforms are now fully developed and matured while the code of other developed platforms is still being tested in different scenarios. In the initial exploration of the available platforms, we consider all the developed platforms irrespective of their maturity level. However, in the analytic process for selection, we only considered the fully matured platforms as alternatives.
Type of Blockchain: Although any type of blockchain-based platform can be used to manage the product lifecycle, as data privacy, transaction speed, and scalability are the prime concern in PLM, the focus in the exploration and selection processes is only on the permissioned blockchain.
Programming Languages: So far, many programming languages, i.e., C++, Python, Java Script, Solidity, etc., have been used in the development of blockchains. In addition to their native languages, these blockchain platforms also have the capability to support other languages. In this study, we considered all the blockchain platforms irrespective of their native and supported languages.
Consensus Protocols: The available platforms use different consensus protocols or algorithms, i.e., proof of work (PoW), proof of stake (PoS), practical byzantine fault tolerance (PBFT), and so on. This consensus protocol enables all the parties of the blockchain network to come to a common agreement on the present data state of the ledger. The business can define its own or can choose from the well-known available protocols. In one way or the other, these consensus protocols distinguish the platforms from one another. In the initial exploration, we considered platforms irrespective of their consensus protocols. However, the final analytics process considers the platforms with the most suitable protocols.
Smart Contract: Smart contracts are computer programs or transaction protocols stored on a blockchain that intend to execute automatically once a predefined condition is fulfilled. These smart contracts are responsible for the process of validation and enforcing the action on the blockchain. However, not all blockchain platforms need to support the concept of smart contracts. This study does not consider this factor and, hence, takes all the platforms irrespective of their case and whether they support the concept of smart contracts or not.
Scalability: Today’s product lifecycles are usually complex and decentralized in nature and hence, many stakeholders are involved in data generation and sharing. Therefore, blockchain platforms should be scalable enough to grow and manage an increase in the number of participants and transaction data. In the initial survey, we do not consider this factor; however, only highly scalable platforms are considered in the final selection through AHP.
A number of blockchain-based platforms introduced by different organizations have been explored as illustrated in
Table A1 of
Appendix A. Most of the presented platforms are generic, i.e., they are not limited to just financial applications and, hence, can be used as an alternative to the traditional PLM systems to provide a framework for organizing and securing the data generated at different phases of the product lifecycle. However, based on certain metrics, different search engines, i.e., G2.com [
39], Capterra.com [
40], Gartner.com [
41], Hacker noon [
42], and Value coders [
43], as well as research blogs published by LeewayHertz [
44], Tech Target [
45], HFS [
46], etc., have considered some of them as top blockchain-based platforms as presented in
Table A2 of
Appendix A. These top platforms have versatile capabilities and can be applied in any production industry to improve the management of the entire product lifecycle.
4. Results and Discussion
Although any permissioned blockchain platform can be used for data creation, storage, and retrieval throughout the lifecycle of a product, this study was carried out with the intention to select the platform that would not only manage the entire lifecycle of a product but also fulfill the requirements of industry 4.0. In this context, this study mainly consists of two parts, i.e., the identification of available blockchain platforms and the selection of the best platforms with the help of the AHP technique. The identified and top platforms are provided in
Table A1 and
Table A2, respectively, while the final results obtained via the AHP technique are summarized in
Table 5. The percentage data for criteria in the first row of
Table 5 are obtained from the eigenvector of the criteria, while the corresponding column data are based on the eigenvector of alternatives with respect to each criterion. The last two columns of
Table 5 represent the final weightage and ranking of alternatives, respectively.
The findings in
Table 5 reveal that Hyperledger Fabric (relative weightage = 14.84%) is ranked first and is thus the best possible option followed by EOS.IO (relative weightage = 11.80%) and Quorum (relative weightage = 11.36%). Hence, these blockchain platforms should be the top priorities of the industries for the said purpose. On the contrary, OpenChain (relative weightage = 5.01%) is considered the worst option for industries to select among the alternatives. Furthermore, the overall low numeric values obtained for relative weightages are just because of the high number of alternatives considered in AHP. Therefore, consequently, it can be stated that the lower the number of alternatives considered in AHP, the higher the relative weightage each alternative would obtain or vice versa. These results are purely based on the defined importance of each criterion and the comparative weightage of alternatives in terms of criterion. Furthermore, for the purpose of high transaction speed, data privacy, and scalability, this work only considered the permissioned blockchains among the dedicated platforms.
In AHP, both qualitative and quantitative criteria could be considered and compared to derive weights and priorities. However, blockchain platforms have not yet been qualitatively compared on an exact scale in terms of different factors, i.e., interoperability (the ability of a platform to communicate and exchange the data with another platform), level of decentralization (transfer of control to a distributed network or nodes), etc. However, it is evident from the studies that the presented top blockchain-based platforms perform equally well when compared for those qualitative factors, i.e., interoperability, data transparency, scalability, ubiquitous access, level of decentralization, collaborative data provision, and data security. Hence, in addition to the quantitative criteria, this study considers only the built-in capabilities of the platforms as decision criteria, i.e., throughput (number of transactions processed per second), block confirmation time (the amount of time for the creation of a new block), etc.
5. Conclusions and Future Directions
This study identified and selected the most suitable blockchain-based platform that can be used for product lifecycle management. Initially, a total of 140 blockchain platforms were identified through various search engines. However, taking certain specific requirements into consideration, various platforms, i.e., those permissionless in nature or third-party service providers, were dropped from further analysis. It was further observed that among the available permissioned blockchain platforms, some are still not fully matured, and their consensus mechanism is still in the testing and validation stage. Therefore, top software reviews and selection websites were used to identify and consider the fully matured, highly scalable, and secured blockchain platforms. Out of those platforms, 10 were finalized as the best available alternatives for consideration in the final selection process.
A number of criteria were identified through different sources as given in
Table 2. Among these defined criteria, seven decision criteria were finally settled on for the selection of the best blockchain platforms. Accordingly, based on the available data on different search engines and thorough discussion with experts from various research groups and industries, each criterion as well as alternatives were assigned relative importance as illustrated in
Table 4. An analytical hierarchy process-based multi-criteria decision support system was then applied to rank and select the top 10 available alternatives.
To conclude our findings, we could say that Hyperledger Fabric has the highest relative score of 14.76% and is the best possible option that can be selected for the purpose of PLM. EOS.IO and Quorum are the second and third best options with relative scores of 11.80% and 11.36%, respectively. All the results obtained in this work are solely based on the defined importance of criteria and the comparative weightage of alternatives in terms of each criterion. However, the developed approach is generic and can be applied for any similar selection purpose with a range of criteria, alternatives, and their relative importance. To our knowledge, this work is the first and could be considered a pioneer in the selection of blockchain platforms for the purpose of product lifecycle management.
In continuation of the obtained results, immediate future work could be the development and implementation of a Hyperledger Fabric-based blockchain platform for product lifecycle management in a real production environment. This intended work could first identify and describe the basic components of the Hyperledger fabric blockchain for a complete product lifecycle management system, i.e., peers, channels, chain codes, ordering services, and so on. Then, various policies would be developed, i.e., what peers would have the authority to endorse the transaction, what peers just commit the transaction, and what the consensus mechanism would be. Although Hyperledger fabric has a built-in crash fault tolerance consensus, due to its modular architecture, one can just plug in and out their own consensus mechanism. Furthermore, this work could also emphasize how the data generated through different sources in a real production setup could be extracted, secured, and transferred to the Hyperledger fabric blockchain. This work could keenly focus on hash storing and transactions among the various stakeholders involved throughout the product lifecycle and a common cloud database for storing a huge amount of design, manufacturing, and other data.