A Blockchain-Based Quality 4.0 Application for Warehouse Management System

Abstract

In today’s competitive conditions, firms compete in every aspect. It is essential to meet the quality requirements in all processes and to meet customer needs quickly. It should be ensured that all processes in the enterprises, all the technology used, and all the workforce employed are included in the total quality of the enterprise; necessary controls and corrections are made; and the quality is sustainable. In this study, (1) one of the critical processes of an enterprise, the process of a material arriving at the warehouse after its procurement and the process of its storage in the warehouse, is discussed. (2) The basic processes in storing raw materials or finished products have been redesigned based on quality with the help of the Blockchain (BC) method from Industry 4.0 (I-4.0) technologies. (3) A model has been developed for the BC-based Quality 4.0 (Q-4.0). This model was applied to the warehouse management processes of an enterprise and compared with the enterprise’s existing system. (4) As a result of the comparison, it has been seen that the developed Q-4.0 model is more effective and more comprehensive. (5) Due to the originality of the developed model, such a study is not encountered in the literature.

1. Introduction

With developing technologies, competition between businesses increases. Businesses can have a sustainable market share depending on their adaptation to these technologies. For sustainable market share, businesses must be able to manage their value chains carefully [1]. In order to overcome the difficulties that may arise in the meantime, companies need to manage their entire value chains in an agile and sensitive manner [2].

In addition, companies need imaginary structures that will allow for quick adaptation in many processes [3]. Meeting the needs ensures that the process chains of the companies are more effective. In addition, companies will grow more with the development of digitalization, where these processes can progress more smoothly by contributing to each other without harming each other [4,5]. In order to adapt to the changes in the processes, some time-studies should be performed, and unnecessary time losses should be reduced. Therefore, the quality of work will also increase.

The systems and processes used to create value evolve as technology advances. Developing processes and technologies are expected to adapt to I-4.0 to increase value production. The industrial revolution, which we call I-4.0, occurred when product processes in companies operated in a digital environment [6]. I-4.0 can be defined as the digitalization of all physical assets to create an infrastructure and stakeholders that form the e-value chain [7]. With I-4.0, all elements in the production process, namely equipment, management processes, and communication environments, are digitalized [8,9,10].

Having high technologies is the basic condition to ensure digitalization in every field. The most advanced level of technologies is evident in every sector. The continuity of this digitalization must be ensured. In this context, the sustainability of Industry 4.0 must be ensured. Iyer (2018) [11] investigated the developments in sustainable production processes. By making an exemplary application in India, the study examined what path developing economies should follow to transition to I-4.0. In recent years, the increasing environmental problems of consumers have led companies to offer environmentally monitored and certified products. Papetti et al. (2019) [12] state that a structure that can effectively model the complex supply chain process and measure the environmental sustainability of the elements of the process can be created by sharing data among stakeholders.

While new practices continue to ensure sustainability, activities such as ensuring/increasing the product/process/employee quality remain essential. Quality is the degree to which a service or product meets the specifications or possible needs. Quality means trying to ensure customer satisfaction [13]. Increasing the quality is possible by involving the employees in the process at all stages [14]. Participation of senior management employees and all team employees is required at all stages. Employees’ work to achieve business goals in line with a common goal increases the quality of the business in every field [15]. Although the importance of quality has remained the same, the concept of quality is developing and diversifying with industrial revolutions and changes in management philosophies.

1.1. Main Idea of the Study

The Total Quality Management philosophy is based on meeting quality requirements in all processes and involving the contribution of all employees in order to meet customer needs in a timely and accurate manner. Based on this idea, this study was carried out to monitor whether all processes in the enterprises, all the technology used, and all the workforce employed are up to the total quality of the enterprise; to make the necessary controls and corrections; and to ensure that the quality is sustainable. In addition, while all these are achieved, a new model that offers a digital environment with the help of I-4.0 technologies is proposed to treat the economy as a constraint. With the reference model called Q-4.0, the quality of the business processes can be monitored and controlled, and the sustainability of quality can be ensured.

1.2. Originality of the Study

In this study, the Q-4.0 model was implemented in a warehouse management system. General explanations and literature research about I-4.0 and Q-4.0 are included in the first and second parts of this study.

Additionally, how I-4.0 technologies are used sectorally has been researched. The reference model developed for Q-4.0 is explained in detail in the third part of this study, and its operation is explained. I-4.0 includes many technologies. For this study, other technologies, especially IoT, could have been used. However, when the literature was examined, it was seen that the most suitable technology for the developed model was BC. IoT technology can be used when working with real-time data. BC was preferred due to its strong security structure and ease of use. When using BC, the fact that it is easy to update instant data changes and that the tracking of data is secure thanks to the cryptographic structure brought BC technology to the fore.

The role of I-4.0 in ensuring the quality traceability/controllability/sustainability of the technology used in all processes in the enterprises and the workforce employed is explained. The fourth Section shows the proposed Q-4.0 model and BC architectural structure. Based on this architectural structure, the application made in the warehouse management process of a business is included in the fifth chapter. The Pythagorean Fuzzy SWARA (PFS)-CoCoSo method, one of the multi-criteria decision-making methods (MCDMs), was used to demonstrate the model’s effectiveness. The general flow of the study is shown in Figure 1.

Figure 1. Flowchart of the study.

2. Literature Research

Industry 4.0, one of the industrial revolutions, aims to increase the capacity of the processes in a business. The digitalization of the processes in the system and their automatic progress enable the formation of smart factory systems [16]. I-4.0, one of the industrial revolutions, includes many new technologies. With the help of these technologies, data transfers between processes and compatibility of devices with technology have improved [17,18].

An organization located on a specific chain where communication between technologies is at the highest level is referred to as I-4.0 [19,20]. I-4.0 technologies are implemented in many smart factories, from procurement to production, and more efficient and maximum capacity work is supported. With technological developments, the way factories work has also had to change. Smart factories have begun to be used with the use of developing technologies. The leading technologies of I-4.0 are smart activities such as smart production, smart products, and smart supply [16,21]. In smart factories, processes at any stage of production can be renewed and improved using automation [22], and work can be conducted [23] and controllable [24,25].

With the development of technology, keeping data in central systems is no longer necessary. With I-4.0, more reliable and faster communication will be provided between units because when the requested datasets increase, these data are shared only with certain people [26]. By using BC technology, data in units can be shared between departments, and updates can be made when necessary [27,28]. BC consists of a data block based on cryptography theory [29,30]. Blocks are recorded on a distributed ledger according to consensus rules agreed upon by network partners [31].

Additionally, the system allows trade between individuals without needing a trusted third party. All individuals can view the entire transaction history. A complete transaction history also ensures the validity of each virtual transaction, and all virtual transactions can be tracked from the moment they are created. It also prevents modification of existing records. Thus, the need for management decreases [32,33]. BC applications can be encountered in many areas, for example, in the tracking of materials during the supply chain process in production [34], in tracking patients by creating a digital identity in healthcare services [35] and in the intervention of patients in emergencies [36], in storing student grades and protecting personal data in the education sector [37], in ensuring data communication between suppliers and businesses [38], and in securing money transfers using bitcoin in finance [39].

The traditional meaning of quality has recently gained a more significant role. Q-4.0 can be described as the digitalization of Total Quality Management (TQM) and its impact on quality technology, processes, and individuals [40], just as it can be defined as the application of I-4.0 technologies to quality [41]. Their ability to decide how and why information should be used is crucial for quality professionals because the process must drive the use of information rather than the other way around [42]. Q-4.0 is a new concept. For this reason, Q-4.0 studies in the literature are limited. Carvalho et al. [43] conducted a study aiming to analyze the relationship between different applications of quality management and the new technologies of I-4.0, which could lead to improving quality management. Through literature review, this study has led to the development of a table that relates the relationship between quality management practices and quality-improving I-4.0 technologies as intended. Escobar et al. [44], in their study, propose a Q-4.0 initiative and a certification program for quality/production engineers, managers, and managers. The proposed work presents a method that combines six knowledge areas (statistics, quality, production, programming, learning, and optimization) and a seven-step problem-solving strategy. Singh et al. [45] used the Q-4.0 concept to digitalize the traditional Quality Management System (QMS) and demonstrated the effectiveness of the Autonomous Quality Management System (AQMS) in their application. Haleem et al. [46] explored the role of Q-4.0 and how it can be used to meet challenges and maintain healthcare quality during the pandemic.

Additionally, they have implemented essential practices of Q-4.0 in healthcare during the COVID-19 pandemic. With Q-4.0, quality can be maintained using innovative and advanced digital technologies. Christou et al. [47], in their study, created an end-to-end Q-4.0 architecture that includes machine learning and the Internet of Things. Javaid et al. [48] showed how Q-4.0 will significantly impact the production field. In their study, researchers discussed various vital aspects and enablers of Q-4.0 for manufacturing, and finally, eighteen critical applications of Q-4.0 in the field of manufacturing were identified. A summary of the studies in the literature, along with their methods, is given in Table 1.

Table 1. Literature research methods.

Study	Method
Li et al. [1]	Explains the I-4.0 revolution with technological drivers
Schumacher et al. [2]	A maturity model for assessing I-4.0 and maturity of manufacturing
Gligor and Holcomb [3]	Antecedents and consequences of supply chain agility
Fatorachian and Kazemi [4]	A critical investigation of I-4.0 in manufacturing
Cottyn et al. [5]	A method to align a manufacturing execution system
Weking et al. [6]	Archetypes for I-4.0 business model innovations
Bibby and Dehe [7]	Defining and assessing I-4.0 maturity levels
Alcacer and Cruz-Machado [8]	A literature review for I-4.0
Ku et al. [9]	Digital transformation to empower smart production for I-4.0
Longo et al. [10]	Smart operators in I-4.0
Iyer [11]	Moving from I-2.0 to I-4.0
Papettti et al. [12]	Web-based platform for eco-sustainable supply chain management
Budianto [13]	Searching the quality of service for customers
Frank et al. [16]	I-4.0 technologies: implementation patterns
Ozsoylu [17]	Explaining the I-4.0 technologies
Picarozzi et al. [18]	A systematic literature review for I-4.0 in management
Fox and Subic [19]	An I-4.0 approach to the 3D printing of composite materials
Tao et al. [20]	Digital twins and cyber-physical systems toward smart manufacturing
Brettel et al. [21]	Virtualization, decentralization and network building in I-4.0
Wang et al. [22]	BC for the IoT and industrial IoT
Radziwon et al. [23]	The smart factory, exploring adaptive and flexible manufacturing
Wang et al. [24]	Towards smart factory for I-4.0
Barreto et al. [25]	Industry 4.0 implications in logistics
Kshetri [26]	BC’s roles in strengthening cybersecurity and protecting privacy
Rao and Clarke [27]	Perspective on emerging directions in using IoT devices in BC
Gupta and Chow [28]	An overview for networked control system
Nakamoto [20]	Explaining bitcoin with I-4.0 technologies
Agrawal et al. [30]	BC-based framework for supply chain traceability
Swan [31]	BC technology in economy
Beck et al. [32]	Cryptographic transactions for BC
Risius and Spohrer [33]	A BC research framework
Ducas and Wilner [34]	The security and financial implications of BC technologies
Kuo et al. [35]	BC technologies in healthcare
Tandon et al. [36]	A literature review for BC in in healthcare
Chen et al. [37]	Exploring BC technology and its potential applications for education
Thoben et al. [38]	Industry 4.0 and smart manufacturing
Pisa and Juden [39]	BC and economic development
Dan Jacob [40]	Defining the Q-4.0 impact and strategy
Bowers et al. [41]	Big data tools zoom in Q-4.0
Carvalho et al. [43]	A literature review for Q-4.0
Escobar et al. [44]	Researching the Q-4.0 towards quality management
Singh et al. [45]	Using Q-4.0 concept in automotive industry
Haleem et al. [46]	Q-4.0 technologies in healthcare
Christou et al. [47]	End-to-end industrial IoT platform for Q-4.0 applications
Javaid et al. [48]	Significance of Q-4.0 in manufacturing sector

Within the scope of this study, many studies in the literature have been examined. Table 1 analyzes the main studies conducted in the fields related to our study. As can be seen, I-4.0 technologies are frequently encountered in studies conducted in recent years. In our study, the BC technique from I-4.0 technologies was applied to the warehouse management system. There is no study conducted in this field using this method in the literature. Despite this, in our study, the originality of our study was increased by using different methods. The evaluation was also made with the Pythagorean Fuzzy SWARA-CoCoSo method. In addition, the study with the developed Q-4.0 architectural structure has become original among the studies conducted in the relevant field in the literature. As can be seen from Table 1, studies on Q-4.0 have generally focused on the inputs and outputs of Q-4.0. Research has been conducted on the areas in which Q-4.0 can be applied. Studies have been conducted on how Q-4.0 can be useful for different sectors. There is no Q-4.0 application in the literature as in this study.

3. Quality 4.0 Model

With the development of technology, it is becoming increasingly important to integrate these technologies into businesses and achieve a certain quality. In order to say to what extent a process or situation is of high quality, its quality must be measurable. Some criteria can be used to measure quality. Quality criteria are criteria used to determine the process’s quality and measure how well the process meets relevant standards. With the Q-4.0 model proposed in this study, the quality of the business will be evaluated in terms of both processes, the technology used in the processes, and the personnel operating the processes by using traceability–controllability–sustainability criteria to evaluate the level of quality met in business processes. Since cost is an essential factor when making this evaluation, it will also be considered whether all the improvement works to be carried out for the transition to Q-4.0 are economically worthwhile. The developed Q-4.0 model is shown in Figure 2.

Figure 2. Quality 4.0 model.

In the proposed Q-4.0 model, four components must be evaluated for the business: process, technology, human resources management, and economy. Quality criteria have been determined to determine the quality of the processes, technology used, and workforce in the business (traceability, controllability, and sustainability). That is, each component will be evaluated in terms of three criteria. While ensuring traceability, controllability, and sustainability of the quality of technology in the business, it should also be monitored whether the practices and improvements made for quality are economically appropriate.

Quality Process: If the processes are controlled, repeatable, reliable, and stable, we can discuss a quality process [49]. Increasing the quality of processes will positively affect the quality of the business. However, to comment on the increase or decrease in the quality of the process, the quality must be measurable. There are studies in the literature that include the measurement [50], structuring [51], and design [52] of the process. However, studies have yet to be found in the literature that measure the quality of the process using I-4.0 technologies. The Q-4.0 model proposed in this study aims to ensure the traceability, controllability, and sustainability of the quality of the processes operated in the enterprise and, therefore, to measure the process quality.

Quality of Technology: Along with the industrial revolutions, technological development also accelerated. The use of high technology directly affects production quality. With advanced technology, demands can be responded to faster and better. Many studies in the literature include the integration of I-4.0 technologies with the technology used in production and management activities in enterprises (Internet of Things technology in product development in R&D) [53] and the use of big data in the processing of suppliers. Cloud-computing technology is used for information in purchasing [54], for keeping personnel information records in human resources [55], and for remote access to production information in production [56]. However, studies have yet to be found that use I-4.0 technologies to measure the traceability, controllability, and sustainability of the quality of the technology used in the business. The Q-4.0 model aims to ensure traceability, controllability, and sustainability of the quality of technology by using I-4.0 technologies and, therefore, to measure the quality of technology.

Quality of Personnel: For the products and services offered by businesses to be high, more is needed for the technology to be of high quality and all processes to be defined. In order to obtain more efficiency from the technology used and to run the processes more efficiently, the quality of the personnel who use the technology and run the processes must also be suitable for the technology and the process. In the literature, there are studies in which I-4.0 technologies are used to store employee information (such as using cloud-computing technology to keep the information of R&D employees [55]). The proposed Q-4.0 model aims to measure how effectively and efficiently the personnel employed in the enterprise use the technology and also to what extent it contributes to the quality of the processes in which they are involved by using I-4.0 technologies for the traceability, controllability, and sustainability of the quality of the employee.

Quality of Economy: Costs must be considered when producing quality products or services that meet customer expectations and ensure sustainable market share. Costs also increase while improving the quality of processes, employees, and technology used in the business. The economic dimension of business activities has the opposite effect on other quality components (technology, process, and employee). Economic components should also be considered when improving the quality of technology, processes, and employees. Trying to obtain more quality than the customer wants will result in a sales price that will reduce the sales rate of the product/service. This result will not comply with the general objectives of the business. A balance must be established between the process, technology, and the quality of the employees and the economy. For this reason, while trying to improve the quality of the processes, technology, and employees of the products/services produced with the Q-4.0 model, the “economy” component is considered a constraint. With I-4.0 technologies, the traceability, controllability, and sustainability of the economy component will be ensured.

The Q-4.0 Model can be applied to all business functions in the enterprise (manufacturing, procurement, planning, sales and marketing, R&D, human resources management). In the proposed Q-4.0 model, traceability, controllability, and sustainability criteria are used to evaluate the level of quality met in businesses.

Traceability of Quality: With traceability of the quality improvement process, businesses will be able to notice any coordination problems [57]. In these criteria, whether the process is run with the right method/at the right time/at the right cost is monitored, considering the quality expected from the process. In the Q-4.0 model, I-4.0 technologies are used to ensure traceability of quality. By ensuring quality traceability, problems that may arise will be responded to promptly. In any business function where the Q-4.0 model is applied, the appropriate I-4.0 technologies can be used depending on the complexity of the processes–technology or the competence of the employees. This study applied the Q-4.0 model to the warehouse management system. BC technology, one of the I-4.0 technologies, was used to ensure traceability of the processes, technology, and employee quality within the warehouse management system within the scope of the economy component. With BC technology, accessibility between authorities and relevant stakeholders is also determined, and confidentiality is ensured through information protocols. Since BC records all data, the businesses and relevant stakeholders will be able to monitor the quality of the products and ensure the traceability of products [58].

Controllability of Quality: The traceability criterion of the Q-4.0 model ensures traceability of the quality of processes, technology, and employees in the enterprise. However, it is also necessary to control the quality of processes, technology, and employees and intervene when necessary. With the criterion of controllability of quality, processes, technology, and employees will be checked in terms of quality. At the same time, they will be checked in terms of economic components. Definitions will be made to intervene if productivity decreases. In the proposed Q-4.0 model, I-4.0 technologies will be used to ensure quality controllability. As with traceability, creating an information protocol for controllability is essential. Quality controllability can be achieved with BC technology.

Sustainability of Quality: The desired quality will be achieved by ensuring the continuity of assets in a business [59]. The Q-4.0 model aims to ensure the sustainability of quality in all business functions in the enterprise. Siva et al. [60] examined studies using sustainable product development and quality management approaches. In terms of quality management support to sustainable product development, they have identified four areas: supporting sustainability through the integration of management systems, supporting the implementation of quality and environmental management systems, supporting the integration of sustainability issues, and supporting stakeholder management and customer focus. Bastas and Liyanage [61] identified critical themes for the sustainability of product quality. These include leadership, customer focus, supply chain integration, relationship management, and evidence-based decision-making. Hattinger and Stylidis [62] conducted a study to translate Q-4.0 into resilient operator 5.0 needs. Antonino et al. [63] prepared a Q-4.0 model for architecting I-4.0 systems. Quality is an important criterion when measuring the effectiveness of I-4.0 technologies [64]. Aichouni et al. [65] also presented a systematic literature review for integrating quality to I-4.0. Sader et al. [66] conducted a literature review for Q-4.0 to investigate which technologies could be implemented and how. Maganga ve Taifa [67] developed a quality management system by conceptualizing Q-4.0. Saihi et al. [68] conducted a systematic Q-4.0 literature review on what can be performed to implement quality management more effectively by utilizing I-4.0 technologies. Liu et al. [69] conducted a study on how to transition from quality management to Q-4.0. With the Q-4.0 model proposed in this study, while performing the functions in the business, it focuses on targets such as adapting to the economy, adapting to the environment, adapting to/guiding customer expectations, and improving knowledge in order to ensure environmental/social assurance, as well as ensuring the sustainability of processes, technology, and employee quality. I-4.0 technologies will be used to ensure sustainability.

With the developed Q-4.0 model, a model that can be used in six essential business functions in businesses (manufacturing, procurement, planning, sales and marketing, R&D, human resources management) is proposed. The model can be applied to all systems belonging to these functions in the enterprise. Four components (process, technology, employee, and economy) were determined to evaluate and measure quality in each function. In order to measure quality, three quality criteria were determined: traceability, controllability, and sustainability. It is recommended that I-4.0 technologies be used for these criteria.

4. Blockchain-Based Quality 4.0 Architecture

In this study, the Q-4.0 model was applied to the warehouse management system, one of the supply functions of a company that buys and sells sheet metal and profiles. An application was made for a 100 × 100 × 4 mm sheet metal profile, which is one of the product groups of the enterprise. An architecture for the Q-4.0 model has been developed using Blockchain (BC), one of the I-4.0 technologies. The architecture is shown in Figure 3.

Figure 3. Blockchain-based Quality 4.0 architectural structure.

While the Q-4.0 model was applied to the enterprise’s warehouse management system, the sub-processes of the warehouse management system were first determined. There are four participants in the warehouse management system: product supplier, worker, warehouse manager, and purchasing manager. Afterward, a BC-based Q-4.0 architecture was created for the determined processes. With the support of BC, the participants were given the necessary authorization.

In this system, in addition to the architectural structure in the figure, CreateProductRecord, GrantAccessToProvider, GrantAccessToWarehouse, RevokeAccess, and RevokeAccessToWarehouse commands will also be included. With these commands, transactions in the blockchain algorithm will be grouped. However, various assets and smart contracts will also be defined without being limited to these. According to the architecture shown in Figure 3, system participants first request a registration certificate from the certification authority through the Membership Service Provider (MSP). They register to the system via the client application when their requests are met. The certification authority then issues the participant a new identity with their certificate and private key. All transactions are distributed through Product Blockchain Network Records. It is also recorded in the ESR (Electronic Supply Records) book. Participants have different roles in the system, and due to the BC structure, they can only access the records to which they have access rights. To register the products in the system, the warehouse manager uses the client application by calling the relevant chain code and registering the products. Once the registration process is transferred to the BC network, the updated transactions are distributed to all participants over the network. Records in the BC network are constantly updated, and updates are visible to all users. Therefore, this distribution process ensures that unauthorized users cannot change and delete every transaction. Each new transaction is added to the previous block with just a timestamp. Therefore, the network is entirely secure. Providers with high authority can query the necessary data over the network. Suppose providers with more authority (purchasing manager and warehouse manager for the application) allow providers with less authority (workers and suppliers) to view and update the records in the ESR ledger. In that case, these providers can also view and update the products’ records.

In this architectural structure, an architectural structure has been created for the use of a single warehouse and multiple users. If there is more than one warehouse in the firm, the data will be evaluated in terms of traceability, controllability, and sustainability in the developed mathematical model and will be processed in the address book in the architectural structure. Adding more users and warehouses to the system in terms of scalability will not create a problem in terms of the functioning of the architectural structure.

In cases where there are more warehouses, when real-time data are used, the variability of the data must be constantly monitored. The amount of products in each warehouse must be kept up-to-date, and users must be able to view them up-to-date when needed. Due to the flexible structure of BC, when working with real-time data, there will be no incompatibility despite the dynamic structure of the data.

5. Application

5.1. Blockchain Application

For the BC architectural structure to work, the BC algorithm must be created for each participant. The BC algorithm developed for a manager with high authority is shown in Algorithm 1. The administrator’s registration certificate is requested from the certification authority. The administrator has full access to the system, including writing, reading, updating, and removing participants. Administrators can give other participants an ID to access the BC network. If it is determined that one of the participants is making inappropriate use, the administrator can remove this participant by making a statement on the BC network. In Algorithm 2 is the BC algorithm created for the cycle of products coming from the supplier; in Algorithm 3 is the BC algorithm of a participant with less authority (worker for this application); in Algorithm 4, the BC algorithm prepared for the relevant work functions (warehouse work for this application) is shown. (The algorithms are developed for warehouse management system application, such as PHL: product registry, WHL: labor register, SHL: warehouse manager registry, NAdmin: network administrator, BN: blockchain network, WID: worker ID, PID: product ID, SID: warehouse manager ID, PK: private key, UName: user name, PREC_I: product registrations, and MPID: product movement records).

Algorithm 1: BC algorithm in manager work

Input: Enrollment Certificate (EC) requested from Certification Authority (CA) Output: Access to PHL, WHL and SHL transactions for all (PHL, WHL, SHL) E BN Initialization: NAdmin should be valid node. NAdmin can Write/Read/Update/Remove nodes WID, PID, SID 1: procedure ADMIN(PID, WID, SID) 2:     while (True) do 3:        if (WID is valid) then 4:           Add_Worker to the blockchain Network 5:           Add_Worker(BN, WID) 6:           Grant_access(WID, UName, PK) 7:        else 8:           Not_exist(WID) 9:        end if 10:        if (PID is valid) then 11:           Add Product to the blockchain Network 12:           Add_Worker(BN, PID) 13:           grant_access(PID, UName, PK) 14:        else 15:           Not_exist(PID) 16:        end if 17:      if (SID is valid) then 18:           Add Storage to the blockchain Network 19:           Add_Storage(BN, SID) 20:           grant_access(SID, UName, PK) 21:        else 22:           Not_exist(SID) 23:        end if 24:   end while 25:   int N; (0 means bad behaviour, 1 means good behaviour) 26:   for all (,,) do 27:     if (behaviour_node(N)) then 28:           Not update(WID, PID, SID) 29:   else 30:           Remove_update(WID, PID, SID) 31:   end if 32:     end for 33: end procedure

Algorithm 2: BC algorithm in product work

Input: ID and key requested from NAdmin Output: Get access to PHL transactions Initialization: PHL should be valid node. PHL can Read/Write/Grant/Revoke ESR records. 1: procedure PRODUCT(PID) 2:   while (True) do 3:      if (PID E BN) then 4:         if (PREC_I not BN) then 5:           Create_records(PID, PREC_I, BN) 6:         else 7:           Update_records(PID, PREC_I, BN) 8:           Read_records(PID, PREC_I, WID, SID, BN) 9:         end if 10:      else 11:         Not_exist(PID) 12:      end if 13:      if Visit(PID, WID, SID, BN) then 14:         MPID= Moverecord(PID) 15:         if then MPID E PHL(BN) 16:           Grant_records(MPID, WID, SID, BN) 17:         else 18:           (WID, SID) <<< NOTIFY(“Movement record does not exist”) 19:         end if 20:         if (MPID E WID, SID Treatment_completed(PID)) then 21:           Revoke_records(MPID, PREC_I, WID, SID, BN) 22:         else 23:           (CID, LID) <<< NOTIFY(“PID voluntary revoke MPID”) 24:           Revoke_records(MPID, PREC_I, WID, SID, BN) 25:         end if 26:      else 27:           Not Visit 28:      end if 29:   end while 30: end procedure

Algorithm 3: BC algorithm for workers’ work

Input: ID and key requested from NAdmin Output: Get access to WHL transactions Initialization: WHL should be valid node. WHL can Read/Write Permissioned ESR records by the workers and write movement records of the workers. 1: procedure WORKER(WID) 2:    while (True) do 3:      if (WID E BN) then 4:        if (Granted MPID E WID) then 5:           Read_records(WID, PREC_I, MPID, BN) 6:           Update_records(WID, PREC_I, MPID, BN) 7:        else 8:         Write_records(WID, MPID, BN) 9:           Read_records(WID, SID, BN) 10:       end if 11:       else 12:        Not_exist(CID) 13:      end if 14:    end while 15: end procedure

Algorithm 4: BC algorithm in warehouse operation

Input: ID and key requested from NAdmin Output: Get access to SHL transactions Initialization: SHL should be valid node. SHL can Read/Write Permissioned ESR records by the workers. 1: procedure STORE(SID) 2:    while (True) do 3:      if (SID E BN) then 4:         if (Granted MPID E SID) then 5:            Read_records(SID, PREC_I, MPID, BN) 6:            Write_records(SID, PREC_I, MPID, BN) 7:         else 8:            Read_records(SID, MPID, BN) 9:         end if 10:      else 11:        Not_exist(SID) 12:      end if 13:   end while 14: end procedure

When applying the proposed Q-4.0 model in businesses, support can be obtained from different I-4.0 technologies depending on the business function to which the model is applied. In this study, the Q-4.0 model was applied in the warehouse management system of a commercial enterprise engaged in buying and selling activities. The BC technology was preferred due to the structure of the processes in the warehouse management system.

For the warehouse management system, four participants (purchasing manager, warehouse manager, worker, and supplier representative) monitor and control the quality of all processes run from the time the product is ordered to the supplier until the business receives the product, the quality of the technology used, and the quality of the employees in the processes, and to ensure their quality is sustainable. BC algorithms are needed. Additionally, an interface was created for the participants. Thanks to this interface, all activities regarding the quality of the purchased product can be easily monitored. Participants will be able to log in to the interface using their system login information and measure the quality of activities in the warehouse management system in terms of the four quality components of the Q-4.0 model (process, technology, employee, economy) and quality criteria (traceability, controllability, and sustainability) according to their authority levels.

Participants will see the “product selection” menu after logging in from the main screen with their username and password. In this menu, participants will be able to make four different selections: product selection, warehouse selection, supplier selection, and customer selection. When the product is selected, the participant will be presented with the “main operational process” tab (supply process, product acceptance process, product placement process, and product delivery process) and the “quality component selection” tab (process, technology, employee, economy). When the quality component is selected, the participant will be able to see the quality criteria (traceability, controllability, and sustainability) of that component within their authority. When the relevant quality criterion is selected, the participant will be able to see the results of the quality measurements of that quality criterion within their authority.

The BC-based Q-4.0 model allows for many operations to be carried out efficiently and quickly. Thanks to the traceability, controllability, and sustainability criteria offered by the Q-4.0 model, any problems that may occur in quality will be noticed in a shorter time, and a solution can be provided in a shorter time. Running the Q-4.0 model based on BC will also increase the security of the warehouse management system.

The location of the products can also be tracked during transportation through this system. The locations of the vehicles on the map can be determined through this application during procurement from the supplier and delivery to the customer.

BC-based application of the proposed Q-4.0 model for the warehouse management system was made in a commercial enterprise that buys and sells sheet metal and profiles. The company has five different warehouses in Ankara. Warehouses are far from each other, and different products are stored.

The company’s warehouse management system’s first operational process is the “ supply process.” Orders come from supplier companies at certain time intervals using company-owned vehicles. Products purchased from two different suppliers are transported to five warehouses belonging to the company. The second operational process after procuring products from supplier companies is the “product acceptance process”. When the product acceptance process is positive, the product entered into the warehouse is included in the “product placement process” operational process. Appropriate and planned placement of the warehouse is essential to ensure rapid shipment of products in line with the customer’s needs. The last operational process of the warehouse management system is the “delivery process.” Knowing in advance how much of which product is in the warehouse is very important for the timely delivery. Figure 4 shows the main operational processes of the warehouse management system.

Figure 4. Main operational processes.

In this study, the Q-4.0 model was applied on a BC basis to ensure the traceability, controllability, and sustainability of the quality of the technology and process employees used in the four main operational processes of the warehouse management system of the enterprise within an acceptable economic framework for the enterprise. Each primary process has sub-processes and activities that need to be performed in these sub-processes. Figure 5 shows the sub-processes of the warehouse management system.

Figure 5. Sub-processes.

The business has an existing warehouse management system where the application is made. In the current warehouse management system, product tracking can only monitor shelf status and quantity. Other systems are used to access financial, employee, and supply vehicle status information. Obtaining all this different information in an integrated manner through a single system will speed up business processes and, therefore, increase the efficiency of the business. With the BC-based Q-4.0 model, all processes of the warehouse management system, the quality of the process, the quality of the technology used, and the quality of the employee can be monitored and controlled sustainably within the economic framework determined by the business. BC algorithms will also ensure the security of the system. The application steps of the BC-based Q-4.0 model developed for the enterprise’s warehouse management system are shown in Figure 6.

Figure 6. Blockchain-based Quality 4.0 implementation.

The proposed Q-4.0 model transfers the quality information (belonging to the product’s process, technology, and employee) of the products that the business purchases and sells to the BC network structure in a broader context. With the model, traceability, controllability, and sustainability of all information regarding quality measurement will be ensured.

5.2. Application of the Pythagorean Fuzzy SWARA(PFS)–CoCoSo Method

In this article, a PFS-CoCoSo method was also applied to evaluate the comparison of our developed method with the system used by the company where the application was made and with other stock control systems used in the sector. The reason for using this method is that it provides a more comprehensive analysis than other MCDMs in the literature. The criteria were evaluated based on expert opinions, and a selection was made among the alternatives using the CoCoSo method, considering the calculated benefit values.

The PFS method has been widely applied to various disciplines. It can be used to estimate subjective criterion weights or the importance of attributes. SWARA is a method that works on weighting criteria, depending on priorities. There are very few studies using PFS in the literature. In this study, originality was added to the study by using the PFS-CoCoSo method. When used with PFS, the CoCoSo approach can be practical in applications with I-4.0 technologies, such as the Internet of Things. This method was preferred in this study because I-4.0 technologies were used. The application area of the CoCoSo method will be more advanced when used with PFS. A flowchart of the PFS-CoCoSo method is given in Figure 7.

Figure 7. Flowchart of the PFS-CoCoSo method.

There are four alternatives in total, three of which are the most widely used in our country and the method we developed. Alternative methods are shown in Table 2.

Table 2. PFS-CoCoSo method alternatives.

Alternatives	Application
Alternative-1 (S1)	Developed Q-4.0 Model
Alternative-2 (S2)	Software used by the application company
Alternative-3 (S3)	Software company operating in Turkey
Alternative-4 (S4)	Software company operating in Turkey

Here, the S₁ alternative is the developed BC-based Q-4.0 model. The S₂ alternative refers to the warehouse-tracking system used by the company where the application is made. In the system used by the business, the entry and exit of the products are tracked through the warehouse management software they use.

With this software, information such as the purchase price, sales price, quantity of units purchased, and purchase date of the materials purchased by the business from suppliers can also be tracked. The S₃ alternative refers to the software many businesses operate in our country use.

With the S₃ alternative, information such as actual warehouse quantity of products, actual warehouse quantity, product codes, stock type, buying–selling prices, and supply dates can be tracked. The S₄ alternative is a software preferred by many businesses in our country, especially those that store metal and tube sheets.

With the S₄ alternative, transactions such as buying and selling prices, product quantities in the warehouse, tracking with QR coding, and monitoring transfers between warehouses can be followed.

The 41 criteria were preferred as criteria in this study. While creating these criteria, the processes of a warehouse management system and the criteria and components in our model were used. The criteria are shown in Table 3.

Table 3. PFS-CoCoSo method criteria.

Criteria No.	Criteria
Criteria-1 (B1)	Use of Technology when Obtaining Supplier Information
Criteria-2 (B2)	Cost Calculation When Obtaining Supplier Information
Criteria-3 (B3)	Competence of Personnel Obtaining Supplier Information
Criteria-4 (B4)	Using Technology When Calculating Transportation Costs
Criteria-5 (B5)	Competency of the Personnel Calculating the Transportation Cost
Criteria-6 (B6)	Use of Technology in Determining Supply Quantity
Criteria-7 (B7)	Cost of Determining Supply Quantity
Criteria-8 (B8)	Competency of the Personnel Determining the Supply Amount
Criteria-9 (B9)	Use of Technology in Determining Lead Time
Criteria-10 (B10)	Cost Calculation in Determining Supply Time
Criteria-11 (B11)	Competence of Personnel to Determine Supply Time
Criteria-12 (B12)	Using Technology When Obtaining Product Information
Criteria-13 (B13)	Cost Calculation in Obtaining Product Information
Criteria-14 (B14)	Competence of Personnel Obtaining Product Information
Criteria-15 (B15)	Use of Technology in Product Quality Control
Criteria-16 (B16)	Cost Calculation of Product Quality Control
Criteria-17 (B17)	Competency of the Personnel Performing Product Quality Control
Criteria-18 (B18)	Use of Technology in Creating Product QR Codes
Criteria-19 (B19)	Cost Calculation of Creating a Product QR Code
Criteria-20 (B20)	Competency of Personnel Creating Product QR Code
Criteria-21 (B21)	Using Technology When Determining Product Placement
Criteria-22 (B22)	Cost Calculation While Product Placement
Criteria-23 (B23)	Competency of Product Placement Personnel
Criteria-24 (B24)	Using Technology When Determining Product Shelf Time
Criteria-25 (B25)	Cost Calculation When Determining Product Shelf Time
Criteria-26 (B26)	Competency of Personnel Determining Product Shelf Time
Criteria-27 (B27)	Using Technology While Maintaining Product Conditions
Criteria-28 (B28)	Cost Calculation of Maintaining Product Conditions
Criteria-29 (B29)	Personnel Working to Maintain Product Conditions
Criteria-30 (B30)	Use of Technology when Determining Product Delivery Conditions
Criteria-31 (B31)	Cost Calculation When Determining Product Delivery Conditions
Criteria-32 (B32)	Competence of Personnel Determining Product Delivery Conditions
Criteria-33 (B33)	Use of Technology in the Product Transportation Process
Criteria-34 (B34)	Cost Calculation in the Product Transportation Process
Criteria-35 (B35)	Competence of Personnel Working in the Product Transportation Process
Criteria-36 (B36)	Use of Technology in the Product Tracking Process
Criteria-37 (B37)	Cost of Using Technology in the Product Tracking Process
Criteria-38 (B38)	Competence of Personnel Working in the Product Tracking Process
Criteria-39 (B39)	Use of Technology in Obtaining Customer Information
Criteria-40 (B40)	Cost Calculation of Obtaining Customer Information
Criteria-41 (B41)	Competence of Personnel Working in Obtaining Customer Information

Among these criteria, B₁ is how to contribute to the quality of supplier information by using any I-4.0 in obtaining supplier information; criterion B₂ examines the impact of the cost of obtaining supplier information at appropriate levels on the economic quality of the business; criterion B₃ determines the effect of the experience and competence of the personnel involved in obtaining supplier information on the quality of information acquisition; criterion B₄ examines the impact of technology use on transportation quality during the transportation of raw materials, semi-finished products, and finished products purchased from suppliers; criterion B₅ determines whether the competence of the personnel working during the transportation of raw materials, semi-finished products and finished products purchased from suppliers affects the carrying capacity; criterion B₆ examines the impact of technology use on order quality when determining the quantity of orders to be ordered from suppliers; criterion B₇ includes the contribution of the cost of determining the order quantity to be ordered from suppliers to the economic quality of the enterprise; criterion B₈ examines the impact of the personnel working on order quality in determining the quantity of orders to be ordered from suppliers; criterion B₉ determines the impact of technology use on the quality of time in determining lead time; criterion B₁₀ examines the impact of the cost of determining the lead time at appropriate levels on the quality of business economics; criterion B₁₁ determines the effect of the competence of the personnel working in determining the supply time on the quality of the time; criterion B₁₂ determines the impact of the use of technology in obtaining information about the products provided on the quality of information acquisition; criterion B₁₃ determines the effect of the cost of obtaining information about the products supplied at appropriate levels on the economic quality of the enterprise; criterion B₁₄ determines the effect of the competence of the personnel working in obtaining information about the products supplied on the quality of information acquisition; criterion B₁₅ includes quality control of the use of technology in product quality control; criterion B₁₆ examines the impact of the cost of product quality control at appropriate levels on the economic quality of the enterprise; criterion B₁₇ determines the effect of the competence of the personnel performing product quality control on quality control; criterion B₁₈ includes the use of technology in creating product QR codes; criterion B₁₉ determines the impact of the cost of creating a product QR code at appropriate levels on the economic quality of the business; B₂₀ criterion determines the competence of the personnel working in creating the product QR code; B₂₁ criterion includes the use of technology when determining the placement of products in the business warehouse; B₂₂ criterion determines the impact of the cost of determining the placement of products in the business warehouse at appropriate levels on the economic quality of the business; B₂₃ criterion determines the competence of the personnel who place the products in the business warehouse; B₂₄ criterion includes the use of technology in determining the shelf time of products in the warehouse; B₂₅ criterion determines the impact of the cost of determining the shelf time of products in the warehouse at appropriate levels on the quality of business economy; B₂₆ criterion determines the competence of the personnel working in determining the shelf time of the products in the warehouse; criterion B₂₇ includes the use of technology while maintaining product conditions; criterion B₂₈ examines the impact of the cost of maintaining product conditions at appropriate levels on the economic quality of the enterprise; criterion B₂₉ determines the competence of personnel in maintaining product conditions; B₃₀ criterion includes the use of technology when determining product delivery conditions; B₃₁ criterion, the effect of appropriate cost levels in determining product delivery conditions on the economic quality of the enterprise; B₃₂ criterion determines the competence of the personnel working in determining product delivery conditions; criterion B₃₃ examines the impact of technology use in the product transportation process on the quality of the transportation process; criterion B₃₄ determines the impact of the cost of the product transportation process at appropriate levels on the economic quality of the enterprise; B₃₅ criterion determines the effect of the competence of the personnel working in the product transportation process on the quality of the transportation process; criterion B₃₆ examines the impact of technology use in the product-tracking process on the quality of product tracking; criterion B₃₇ determines the impact of the cost of the product-tracking process at appropriate levels on the economic quality of the business; criterion B₃₈ determines the impact of the competence of the personnel working in the product-tracking process on the quality of the tracking process; criterion B₃₉ examines the impact of technology use in obtaining customer information on the quality of information acquisition; criterion B₄₀ examines the impact of the cost of obtaining customer information at appropriate levels on the economic quality of the business; criterion B₄₁ also measures the impact of the competence of the personnel working in obtaining customer information on the quality of information acquisition.

The three decision experts (DEs) who evaluated this method were selected from various businesses according to their work and profession. The number of years that the DEs had worked in their fields of expertise was also considered. Data obtained from the business were used for evaluations.

The three DEs selected for the method were evaluated through an online survey using the linguistic variables given in Table 4 below. The weights given by DEs are shown as Pythagorean Fuzzy Numbers (PFN). Then, the PFS method was applied to calculate the criterion weight, considering the weights specified by the DEs. The CoCoSo method was used to rank the alternatives according to weighted criteria. Below, the PFS-CoCoSo method is explained in detail.

Table 4. Linguistic values of options and degrees of decision experts.

Linguistic Values	Pythagorean Fuzzy Numbers
Extremely Important (EI)	Y(0.90, 0.15, 0.409)
Very Very Important (VVI)	Y(0.75 0.40, 0.527)
Very Important (VI)	Y(0.70, 0.55, 0.456)
Important (I)	Y(0.60, 0.70, 0.387)
Less Important (LI)	Y(0.40, 0.80, 0.447)
Very Less Important (VLI)	Y(0.30, 0.90, 0.316)

The decision-making process is scientifically and logically choosing the best option. When decision experts make such decisions, it is necessary to evaluate the alternatives by considering essential criteria from various aspects for the decisions to be considered multi-criteria. In this study, decision experts used linguistic variables to apply Pythagorean fuzzy sets against uncertainties. Below are the steps of the PFS-CoCoSo method.

Step 1: Creating an MCDM problem:

S = {S₁,S₂,…,S_m} set of alternatives, B = {B₁,B₂,…,Bn} set of criteria, P = (${\eta }_{ij}^{\left(k\right)}$), and each i,j denotes the decision experts. The set of decision experts $\iota DEsE=${E₁, E₂, …, Eι} is shown.

Step 2: Calculating the weights of decision experts:

In the MCDM, evaluating the weights decision experts allocate is very important. k. In order to evaluate the weight of the decision expert, (b_k, n_k) shows the rating of experts given by a decision expert. In this case, the weight value is calculated as follows:

$${\varpi }_k={\displaystyle \frac{\left(b_k^2+{\pi }_k^2\times \left(\frac{b_k^2}{b_k^2+n_k^2}\right)\right)}{{\sum }_{k=1}^{\iota }\left(b_k^2+{\pi }_k^2\times \left(\frac{b_k^2}{b_k^2+n_k^2}\right)\right)}}, \mathrm{k}=1(1)\mathcal{l}, {\varpi }_k\ge 0,{\sum }_{k=1}^{\mathcal{l}}{\varpi }_k=1$$

(1)

Step 3: Creating the collective Pythagorean fuzzy decision matrix:

Linguistic values are used to form the collective Pythagorean fuzzy decision matrix. These linguistic values are given in the table below.

$$\mathrm{A}={({\xi }_{ij})}_{m\times n}$$

(2)

$${\xi }_{ij}=(\sqrt{1-{\prod }_{k=1}^{\mathcal{l}}{\left(1-b_k^2\right)}^{{\varpi }_k}, {\prod }_{k=1}^{\mathcal{l}}{\left(n_k\right)}^{{\varpi }_k})}$$

(3)

Step 4: PFS model for calculating weights:

The following tables are used to estimate weights. As we mentioned, the first step in making a decision is to create the decision matrix.

Step 5: Exact degrees are estimated. Point degrees of Pythagorean fuzzy numbers are calculated with Equation (2).

Step 6: Criteria are selected. Criteria are prioritized from most important to least important according to the decision experts’ preferences.

Step 7: The relative importance of the average value is evaluated. The importance is estimated from the criterion listed in the second position, and the relative importance is calculated by comparing the B_j criterion with the B_j−1 criterion.

Step 8: The relative coefficient K_j can be calculated as follows:

$$K_j=\left\{\begin{array}{rr}1,& j=1\\ s_j+1,& j>1\end{array}\right.$$

(4)

Step 9: Weights are calculated. The recalculated weight is defined as ρ_j.

$${\rho }_j=\left\{\begin{array}{rr}1,& j=1\\ {\displaystyle \frac{{\rho }_j-1}{K_j}},& j>1\end{array}\right.$$

(5)

Step 10: Normalized weights are calculated. Criterion weights are normalized as follows:

$$w_j={\displaystyle \frac{{\rho }_j}{{\sum }_{j=1}^q{\rho }_j}}$$

(6)

Step 11: Normalized Pythagorean fuzzy decision-making is evaluated.

$${\xi }_{ij}=\left\{\begin{array}{cc}\hfill {\xi }_{ij}=\left(b_{ij},n_{ij}\right),& for benefit criterion\hfill \\ \hfill {\left({\xi }_{ij}\right)}^c=\left(n_{ij},b_{ij}\right),& for cost criterion\hfill \end{array}\right.$$

(7)

Step 12: Evaluation of the weighted sum model, weighted product model:

It is calculated with the weighted sum model C_i(1) and the weighted product model C_i(2) as follows:

$$\mathrm{Ci}(1)={\sum }_{j=1}^n{w}_j{\xi }_{ij}$$

(8)

$$\mathrm{Ci}(2)={\sum }_{j=1}^n{w}_j{\xi }_{ij}$$

(9)

Step 13: Balancing the scores of the alternatives:

This step involves evaluation scores to evaluate the balanced consensus score of the alternatives, which is derived as follows:

$$Q_i^{\left(1\right)}={\displaystyle \frac{S^{*}\left(C_i^{\left(1\right)}\right)+S^{*}\left(C_i^{\left(2\right)}\right)}{{\sum }_{i=1}^m(S^{*}\left(C_i^{\left(1\right)}\right)+S^{*}\left(C_i^{\left(2\right)}\right)}}$$

(10)

$$Q_i^{\left(2\right)}={\displaystyle \frac{S^{*}\left(C_i^{\left(1\right)}\right)}{min{S}^{*}\left(C_i^{\left(1\right)}\right)}}{\displaystyle \frac{+S^{*}\left(C_i^{\left(2\right)}\right)}{min{S}^{*}\left(C_i^{\left(2\right)}\right)}}$$

(11)

$$Q_i^{\left(3\right)}={\displaystyle \frac{\gamma S^{*}\left(C_i^{\left(1\right)}\right)+\left(1-\gamma \right)S^{*}\left(C_i^{\left(2\right)}\right)}{\gamma max{S}^{*}\left(C_i^{\left(1\right)}\right)+\left(1-\gamma \right)S^{*}\left(C_i^{\left(2\right)}\right)}}$$

(12)

Here, γ is the decision strategy coefficient.

Step 14: The general situation is evaluated.

The consensus index Qi is evaluated to indicate the importance of the alternative as follows:

$$Q_i={\left(Q_i^{\left(1\right)}+Q_i^{\left(2\right)}+Q_i^{\left(3\right)}\right)}^{1/3}+{\displaystyle \frac{1}{3}}\left(Q_i^{\left(1\right)}+Q_i^{\left(2\right)}+Q_i^{\left(3\right)}\right)$$

(13)

The priorities of the alternatives are calculated by increasing the degrees of the consensus index Q.

In applying the PFS-CoCoSo method, Table 4 and Table 5 show the weights given by decision experts as linguistic values. The information in Table 6 was created using the values in Table 7 and using Equation (1). The weights of three decision experts on the alternatives for different criteria are shown in Table 8. The weight of each criterion created by decision experts using PFS is given in Table 9. Each decision expert was asked to select the importance of each criterion. Using Equations (4)–(6), decision experts ranked all available features from first to last. Based on PFS, the criterion with the highest importance was given first place, while the criterion with the most minor importance was given last place. Overall rankings obtained by decision experts were estimated. The weights of all criteria are shown as wj in Table 10. Using Equations (10)–(13), the results of the PFS-CoCoSo model were estimated and shown in Table 11. According to the results obtained according to the consensus index Qi value, the order was S₁ > S₂ > S₃ > S₄. In line with these results, it was seen that the best option was the Q-4.0 model (S₁) we developed. The software used by the company where the application was made (S₂) ranked second.

Table 5. Linguistic values of options and degrees of performance.

Number	Pythagorean Fuzzy Numbers
Incredibly High (IH)	Y(0.95, 0.20, 0.387)
Very Very High (VVH)	Y(0.85, 0.30, 0.432)
Very High (VH)	Y(0.80, 0.35, 0.488)
High (H)	Y(0.70, 0.45, 0.552)
Medium-High (MH)	Y(0.60, 0.55, 0.580)
Medium (M)	Y(0.50, 0.60, 0.623)
Medium Low (ML)	Y(0.40, 0.70, 0.593)
Low (L)	Y(0.30, 0.75, 0.588)
Very Low (VL)	Y(0.20, 0.85, 0.488)
Incredibly Low (IL)	Y(0.10, 0.95, 0.295)

Table 6. Decision experts’ weights for evaluating options.

Decision Expert	Linguistic Values	Pythagorean Fuzzy Numbers	Weight
E1	Important (I)	Y(0.60, 0.70, 0.387)	0.2325
E2	Very Very Important (VVO)	Y(0.75, 0.41, 0.518)	0.4370
E3	Very Important (VI)	Y(0.70, 0.55, 0.456)	0.3398

Table 7. Linguistic values determined by decision experts for the criteria.

	S1	S2	S3	S4
B1	(L,L,M)	(L,VL,VL)	(L,M,ML)	(VL,L,L)
B2	(ML,L,VL)	(VH,M,M)	(H,VH,H)	(LH,MH,L)
B3	(ML,L,ML)	(ML,M,ML)	(H,MH,M)	(MH,MH,M)
B4	(ML,L,ML)	(L,ML,L)	(ML,M,ML)	(ML,NL,ML)
B5	(ML,M,H)	(ML,MH,H)	(MH,VVH,MH)	(M,VH,H)
B6	(VL,MH,L)	(L,H,M)	(ML,L,H)	(M,M,MH)
B7	(ML,L,VL)	(M,L,VL)	(H,L,H)	(MH,M,H)
B8	(ML,ML,M)	(L,VL,ML)	(H,L,ML)	(MH,L,M)
B9	(MH,VH,H)	(M,VH,H)	(M,VL,ML)	(M,VL,ML)
B10	(ML,M,H)	(MH,VVH,H)	(ML,MH,M)	(ML,H,M)
B11	(MH,H,H)	(M,MH,M)	(ML,VL,M)	(ML,L,M)
B12	(MH,L,H)	(M,M,M)	(ML,L,M)	(L,M,MH)
B13	(H,H,MH)	(MH,L,H)	(L,L,H)	(VL,L,H)
B14	(H,MH,MH)	(ML,L,L)	(M,H,ML)	(VH,VH,ML)
B15	(V,MH,VH)	(VL,VH,H)	(H,M,MH)	(H,VL,VH)
B16	(VH,VH,H)	(VH,H,VH)	(VL,M,H)	(ML,M,MH)
B17	(ML,M,H)	(VH,M,H)	(VL,VL,VH)	(VL,M,H)
B18	(ML,MH,H)	(ML,H,H)	(MH,ML,M)	(VH,M,M)
B19	(VL,MH,L)	(M,VL,L)	(VH,H,M)	(VVH,H,ML)
B20	(ML,MH,L)	(L,VL,L)	(M,MH,M)	(H,MH,M)
B21	(H,VH,VH)	(M,H,VH)	(VL,M,MH)	(ML,M,H)
B22	(H,M,H)	(M,H,VH)	(M,ML,H)	(L,ML,H)
B23	(L,L,M)	(L,VL,L)	(L,M,ML)	(L,ML,ML)
B24	(ML,L,L)	(MH,L,ML)	(MH,H,M)	(MH,MH,M)
B25	(MH,VL,L)	(ML,M,ML)	(H,VH,M)	(H,H,M)
B26	(ML,L,L)	(VL,L,ML)	(ML,M,ML)	(L,VL,VL)
B27	(ML,M,H)	(L,MH,H)	(MH,VVH,MH)	(ML,MH,VH)
B28	(H,VH,ML)	(L,VH,H)	(M,ML,VH)	(M,M,MH)
B29	(L,L,VL)	(M,L,VL)	(MH,M,VH)	(H,ML,VH)
B30	(L,L,M)	(L,VL,ML)	(MH,ML,M)	(MH,VL,ML)
B31	(H,H,H)	(M,VH,H)	(M,VL,ML)	(M,ÇVL,ML)
B32	(ML,M,H)	(MH,VVH,H)	(ML,MH,M)	(ML,H,M)
B33	(H,VH,H)	(VH,H,VH)	(VL,M,H)	(ML,M,MH)
B34	(ML,M,H)	(VH,M,H)	(VL,ML,MH)	(ML,M,H)
B35	(VL,MH,H)	(VL,H,H)	(VH,VL,M)	(VH,M,M)
B36	(VH,MH,L)	(M,VL,L)	(VH,H,M)	(VVH,H,MH)
B37	(VL,VH,L)	(L,VL,L)	(M,MH,M)	(H,MH,M)
B38	(H,VH,VH)	(M,H,VH)	(ML,M,MH)	(ML,M,H)
B39	(VH,ML,VH)	(M,VH,H)	(M,ML,H)	(L,ML,H)
B40	(ML,ML,MH)	(L,VL,VL)	(L,M,ML)	(L,ML,ML)
B41	(M,VL,VL)	(MH,L,L)	(MH,H,M)	(H,H,ML)

Table 8. Pythagorean fuzzy values of criteria and alternatives determined by decision experts.

	S1	S2	S3	S4
B1	(0.385, 0.695, 0.607)	(0.228, 0.826, 0.516)	(0.430, 0.666, 0.610)	(0.380, 0.711, 0.591)
B2	(0.327, 0.738, 0.590)	(0.402, 0.698, 0.593)	(0.622, 0.520, 0.585)	(0.570, 0.567, 0.595)
B3	(0.362, 0.721, 0.591)	(0.447, 0.655, 0.609)	(0.706, 0.446, 0.550)	(0.687, 0.467, 0.557)
B4	(0.263, 0.812, 0.520)	(0.347, 0.728, 0.591)	(0.447, 0.655, 0.609)	(0.333, 0.761, 0.558)
B5	(0.570, 0.564, 0.598)	(0.607, 0.543, 0.580)	(0.743, 0.424, 0.517)	(0.721, 0.432, 0.542)
B6	(0.694, 0.462, 0.552)	(0.712, 0.448, 0.541)	(0.622, 0.534, 0.573)	(0.538, 0.583, 0.609)
B7	(0.301, 0.770, 0.563)	(0.338, 0.743, 0.578)	(0.661, 0.490, 0.569)	(0.605, 0.533, 0.591)
B8	(0.438, 0.664, 0.606)	(0.306, 0.773, 0.556)	(0.491, 0.628, 0.604)	(0.463, 0.647, 0.606)
B9	(0.733, 0.423, 0.533)	(0.711, 0.452, 0.552)	(0.366, 0.734, 0.572)	(0.366, 0.715, 0.596)
B10	(0.570, 0.564, 0.598)	(0.765, 0.396, 0.507)	(0.530, 0.599, 0.600)	(0.589, 0.550, 0.592)
B11	(0.680, 0.471, 0.562)	(0.547, 0.578, 0.606)	(0.380, 0.722, 0.579)	(0.404, 0.684, 0.607)
B12	(0.558, 0.587, 0.587)	(0.500, 0.600, 0.624)	(0.404, 0.684, 0.607)	(0.455, 0.664, 0.593)
B13	(0.638, 0.514, 0.573)	(0.461, 0.641, 0.614)	(0.438, 0.664, 0.606)	(0.358, 0.733, 0.578)
B14	(0.605, 0.533, 0.591)	(0.379, 0.721, 0.579)	(0.599, 0.541, 0.590)	(0.647, 0.496, 0.579)
B15	(0.721, 0.432, 0.542)	(0.607, 0.543, 0.580)	(0.592, 0.545, 0.594)	(0.564, 0.582, 0.586)
B16	(0.749, 0.404, 0.525)	(0.763, 0.390, 0.516)	(0.570, 0.564, 0.598)	(0.520, 0.604, 0.604)
B17	(0.607, 0.522, 0.599)	(0.654, 0.499, 0.569)	(0.490, 0.627, 0.605)	(0.500, 0.588, 0.636)
B18	(0.455, 0.676, 0.579)	(0.331, 0.751, 0.571)	(0.681, 0.468, 0.563)	(0.722, 0.454, 0.521)
B19	(0.481, 0.646, 0.592)	(0.263, 0.791, 0.552)	(0.547, 0.578, 0.606)	(0.599, 0.541, 0.590)
B20	(0.781, 0.371, 0.503)	(0.710, 0.442, 0.548)	(0.520, 0.604, 0.604)	(0.570, 0.564, 0.598)
B21	(0.631, 0.509, 0.585)	(0.721, 0.432, 0.542)	(0.556, 0.581, 0.594)	(0.527, 0.612, 0.590)
B22	(0.385, 0.695, 0.607)	(0.228, 0.826, 0.516)	(0.430, 0.666, 0.610)	(0.380, 0.711, 0.591)
B23	(0.327, 0.738, 0.590)	(0.402, 0.698, 0.593)	(0.622, 0.520, 0.585)	(0.570, 0.567, 0.595)
B24	(0.362, 0.721, 0.591)	(0.447, 0.655, 0.609)	(0.706, 0.446, 0.550)	(0.687, 0.467, 0.557)
B25	(0.263, 0.812, 0.520)	(0.347, 0.728, 0.591)	(0.447, 0.655, 0.609)	(0.333, 0.761, 0.558)
B26	(0.570, 0.564, 0.598)	(0.607, 0.543, 0.580)	(0.743, 0.424, 0.517)	(0.721, 0.432, 0.542)
B27	(0.694, 0.462, 0.552)	(0.712, 0.448, 0.541)	(0.622, 0.534, 0.573)	(0.538, 0.583, 0.609)
B28	(0.301, 0.770, 0.563)	(0.338, 0.743, 0.578)	(0.661, 0.490, 0.569)	(0.605, 0.533, 0.591)
B29	(0.438, 0.664, 0.606)	(0.306, 0.773, 0.556)	(0.491, 0.628, 0.604)	(0.463, 0.647, 0.606)
B30	(0.733, 0.423, 0.533)	(0.721, 0.432, 0.542)	(0.366, 0.734, 0.572)	(0.366, 0.715, 0.596)
B31	(0.570, 0.564, 0.598)	(0.765, 0.396, 0.507)	(0.530, 0.599, 0.600)	(0.589, 0.550, 0.592)
B32	(0.680, 0.471, 0.562)	(0.547, 0.578, 0.606)	(0.380, 0.722, 0.579)	(0.404, 0.684, 0.607)
B33	(0.558, 0.587, 0.587)	(0.500, 0.600, 0.624)	(0.404, 0.684, 0.607)	(0.455, 0.664, 0.593)
B34	(0.638, 0.514, 0.573)	(0.461, 0.641, 0.614)	(0.438, 0.664, 0.606)	(0.358, 0.733, 0.578)
B35	(0.605, 0.533, 0.591)	(0.379, 0.721, 0.579)	(0.599, 0.541, 0.590)	(0.647, 0.496, 0.579)
B36	(0.721, 0.432, 0.542)	(0.607, 0.543, 0.580)	(0.592, 0.545, 0.594)	(0.564, 0.582, 0.586)
B37	(0.749, 0.404, 0.525)	(0.763, 0.390, 0.516)	(0.570, 0.564, 0.598)	(0.520, 0.604, 0.604)
B38	(0.607, 0.522, 0.599)	(0.654, 0.499, 0.569)	(0.491, 0.628, 0.604)	(0.500, 0.588, 0.636)
B39	(0.455, 0.676, 0.579)	(0.331, 0.751, 0.571)	(0.681, 0.468, 0.563)	(0.722, 0.454, 0.521)
B40	(0.481, 0.646, 0.592)	(0.263, 0.791, 0.552)	(0.547, 0.578, 0.606)	(0.599, 0.541, 0.590)
B41	(0.781, 0.371, 0.503)	(0.710, 0.442, 0.548)	(0.520, 0.604, 0.604)	(0.570, 0.564, 0.598)

Table 9. Linguistic values of the criteria given by decision experts.

	E1	E2	E3	Pythagorean Fuzzy Number	Net Worth (S*)
B1	L	M	ML	(0.535, 0.590, 0.612)	0.4688
B2	L	ML	MH	(0.468, 0.652, 0.593)	0.3952
B3	ML	M	MH	(0.521, 0.614, 0.624)	0.4528
B4	MH	ML	L	(0.437, 0.688, 0.594)	0.3652
B5	Y	ML	ML	(0.512, 0.652, 0.581)	0.4266
B6	MH	MH	L	(0.538, 0.612, 0.580)	0.4927
B7	L	MH	ML	(0.498, 0.652, 0.582)	0.4132
B8	ML	MH	MH	(0.644, 0.489, 0.579)	0.5894
B9	M	ML	MH	(0.514, 0.632, 0.589)	0.4335
B10	ML	M	MH	(0.652, 0.528, 0.586)	0.5658
B11	MH	ML	MH	(0.540, 0.620, 0.588)	0.4547
B12	ML	MH	H	(0.627, 0.553, 0.570)	0.5367
B13	ML	MH	ML	(0.522, 0.651, 0.581)	0.4267
B14	M	M	MH	(0.548, 0.593, 0.629)	0.4751
B15	L	ML	L	(0.357, 0.718, 0.581)	0.2953
B16	M	M	ML	(0.480, 0.652, 0.626)	0.4105
B17	VH	L	ML	(0.341, 0.755, 0.583)	0.2673
B18	ML	H	H	(0.670, 0.481, 0.572)	0.6201
B19	ML	M	H	(0.580, 0.574, 0.588)	0.5033
B20	H	M	ML	(0.478, 0.634, 0.608)	0.4128
B21	MH	ML	MH	(0.540,0.620, 0.579)	0.4547
B22	ML	MH	H	(0.617, 0.533, 0.570)	0.5367
B23	ML	MH	ML	(0.512, 0.621, 0.581)	0.4267
B24	M	M	MH	(0.558, 0.573, 0.619)	0.4751
B25	L	ML	L	(0.357, 0.748, 0.581)	0.2953
B26	M	M	ML	(0.480, 0.652, 0.626)	0.4105
B27	VH	L	ML	(0.341, 0.764, 0.583)	0.2673
B28	ML	H	H	(0.690, 0.481, 0.572)	0.6201
B29	ML	M	H	(0.580, 0.574, 0.588)	0.5033
B30	H	M	ML	(0.418, 0.624, 0.618)	0.4128
B31	H	M	ML	(0.526, 0.581, 0.622)	0.4689
B32	L	ML	MH	(0.462, 0.645, 0.582)	0.3952
B33	ML	M	MH	(0.525, 0.624, 0.621)	0.4528
B34	MH	ML	L	(0.442, 0.665, 0.586)	0.3654
B35	H	ML	ML	(0.517, 0.628, 0.588)	0.4266
B36	MH	MH	L	(0.535, 0.622, 0.586)	0.4927
B37	L	MH	ML	(0.489, 0.672, 0.522)	0.4134
B38	ML	MH	MH	(0.654, 0.499, 0.569)	0.5894
B39	M	ML	MH	(0.504, 0.622, 0.599)	0.4335
B40	ML	M	MH	(0.632, 0.518, 0.576)	0.5658
B41	MH	ML	MH	(0.530, 0.610, 0.589)	0.4547

Table 10. Determining the importance criteria using the PFS method.

	Net Worth	Importance Comparison of Criteria (sj)	Coefficient (kj)	$\mathbf{Calculation} \mathbf{of} \mathbf{Weights} ({\mathit{\rho }}_{\mathit{j}})$	Final Weight (wj)
B6	0.6118	-	1.0000	1.0000	0.0537
B8	0.5891	0.0303	1.0308	0.9716	0.0517
B10	0.5658	0.0232	1.0233	0.9475	0.0517
B32	0.5365	0.0295	1.0292	0.9234	0.0491
B7	0.5031	0.0337	1.0331	0.8922	0.0475
B16	0.4927	0.0105	1.0108	0.8824	0.0470
B22	0.4752	0.0179	1.0179	0.8668	0.0462
B39	0.4689	0.0064	1.0063	0.8613	0.0459
B21	0.4547	0.0142	1.0145	0.8496	0.0452
B22	0.4528	0.0017	1.0014	0.8471	0.0452
B15	0.4525	0.0004	1.0007	0.8477	0.0452
B19	0.4333	0.0007	1.0003	0.8468	0.0451
B1	0.4267	0.0065	1.0062	0.8415	0.0448
B14	0.4266	0.0004	1.0004	0.8403	0.0448
B17	0.4132	0.0133	1.0131	0.8296	0.0442
B5	0.4128	0.0005	1.0007	0.8297	0.0442
B4	0.4101	0.0027	1.0026	0.8275	0.0441
B11	0.3958	0.0155	1.0154	0.8148	0.0434
B9	0.3859	0.0101	1.0101	0.8067	0.0421
B13	0.3655	0.0199	1.0199	0.7910	0.0421
B3	0.3257	0.0714	1.0702	0.7394	0.0394
B2	0.3074	0.0285	1.0281	0.7191	0.0383
B40	0.2980	0.0135	1.0135	0.7097	0.0377
B31	0.2888	0.0108	1.0108	0.6995	0.0371
B33	0.2651	0.0687	1.0687	0.6979	0.0369
B34	0.2607	0.0254	1.0254	0.6881	0.0362
B24	0.2602	0.0173	1.0173	0.6874	0.0359
B27	0.2598	0.0021	1.0021	0.6794	0.0355
B25	0.2588	0.0045	1.0045	0.6759	0.0350
B26	0.2565	0.0132	1.0132	0.6728	0.0341
B29	0.2458	0.0068	1.0068	0.6697	0.0338
B28	0.2433	0.0165	1.0165	0.6687	0.0332
B18	0.2428	0.0008	1.0008	0.6668	0.0324
B20	0.2389	0.0681	1.0681	0.6460	0.0317
B23	0.2261	0.0004	1.0004	0.6263	0.0304
B36	0.2244	0.0018	1.0018	0.6233	0.0298
B37	0.2217	0.0104	1.0104	0.6219	0.0292
B12	0.2205	0.0278	1.0278	0.6196	0.0280
B28	0.2194	0.0188	1.0188	0.6189	0.0274
B30	0.2154	0.0130	1.0130	0.6160	0.0268
B41	0.2133	0.0098	1.0098	0.6143	0.0257

Table 11. General ranking of alternatives.

Alternatives	Ci(1)	Ci(2)	S* (Ci(1))	S* (Ci(2))	Qi(1)	Qi(2)	Qi(3)	Qi	Arrangement
S1	(0.612, 0.519, 0.585)	(0.568, 0.545, 0.588)	0.555	0.522	0.2617	2.1662	1.0000	1.9733	1
S2	(0.601, 0.512, 0.576)	(0.512, 0.596, 0.601)	0.548	0.484	0.2478	2.0685	0.9512	1.8667	2
S3	(0.564, 0.542, 0.587)	(0.544, 0.573, 0.589)	0.512	0.479	0.2439	2.0355	0.9340	1.8340	4
S4	(0.584, 0.540, 0.544)	(0.542, 0.567, 0.602)	0.528	0.481	0.2470	2.0678	0.9488	1.8660	3

6. Discussion

When the current warehouse management system of the business and the quality criteria (traceability, controllability, and sustainability) of the proposed BC-based Q-4.0 model are compared according to the quality components (process, technology, employee, and economy), the proposed BC-based Q-4.0 model will be much more beneficial for the business. It was observed that it met the criteria more. The comparison of the two systems according to the process quality component is in Table 12; the comparison according to the technology quality component is in Table 13; the comparison according to the employee quality component is in Table 14; the comparison according to the economy quality component is shown in Table 15.

Table 12. Benchmarking for process criteria.

Process	Product Transactions	Current Stock System	Quality 4.0–Stock System
Traceability of Quality	Waiting period of products in the warehouse	The product is kept in the warehouse until the order arrives.	The product is kept in the system for the time allowed, depending on the order status. It is necessary to respond quickly to ensure customer satisfaction.
	The placement process of products in the warehouse	The products are barcoded and placed in the warehouse with the help of lifts.	QR codes are created for the products and placed in the warehouse with the investment of lifts.
	Processes of products entering and exiting the warehouse	The entry of the products is instantly recorded in the system. At the exit, the barcode of the products is scanned, and the products are issued.	All product entry–exit movements can be tracked with the QR code.
Controllability of Quality	Waiting period of products in the warehouse	Manual checks are made to ensure that the product’s waiting time in the warehouse is not exceeded.	When the waiting period for the products in the warehouse is exceeded, will be informed.
	The placement process of products in the warehouse	Control of the placement of products in the warehouse can only be noticed during relocation.	Layout control in the warehouse is listed with QR codes. Regular shelf checks are carried out at specific intervals.
	Processes of products entering and exiting the warehouse	The product is checked with barcodes at entry and exit.	In controls made with QR codes, timeout information can be checked.
Sustainability of Quality	Comparison with the previous period	-	A comparison of the duration of the processes can be made through the system.
	Ensuring continuity of processes	For the continuity of the processes, monitoring must continue outside the system.	Periodic checks are carried out regularly to ensure process continuity.

Table 13. Benchmarking for technology criteria.

Technology	Product Transactions	Current Stock System	Quality 4.0–Stock System
Traceability of Quality	Tracking of materials entering and leaving the warehouse	The current system uses a barcode system. When the products are entered, shelf information and product information are pasted on the product.	A QR code is created for each product, and this information is processed into the blockchain network structure.
	Information inquiry of products in the warehouse	Inquiries can be made using a barcode query or the serial number of the products.	Inquiries can be made using the product name, serial number, and QR code.
	Tracking of material transport vehicles	-	Vehicle and driver information is recorded and tracked for each order in the relevant chain book.
	Monitoring the movements of products leaving the warehouse	-	After the product leaves the warehouse, its information can be viewed via GPS until it reaches the customer.
	Measuring the efficiency of the warehouse	-	The current capacity and occupancy rate of the warehouse can be monitored.
Controllability of Quality	Tracking of materials entering and leaving the warehouse	A complete list of product locations needs to be made. Only the shelf where the products are located is indicated.	Cross-examination can be made since the product list on the shelves can also be viewed.
	Information inquiry of products in the warehouse	Checking the availability of products in the warehouse can be conducted with barcodes.	Product information in the warehouse can be made with a QR code and serial number.
	Tracking of material transport vehicles	-	Vehicle information carrying the products can be queried and checked.
	Monitoring the movements of products leaving the warehouse	-	After the product leaves the warehouse, the route is followed, and a notification is received when there is a deviation or transportation time is exceeded.
	Measuring the efficiency of the warehouse	-	The efficiency percentage can be determined by how much current capacity is used. Current occupancy is displayed.
Sustainability of Quality	Comparison of the current situation with the previous period	-	The system’s error rate is calculated, reported, and recorded in the ledger.
	Ensuring technological continuity	The system continues to be used without modification.	If an error is detected in the system, it is immediately corrected, and continuity is ensured.

Table 14. Benchmarking for personnel criteria.

Human	Product Transactions	Current Stock System	Quality 4.0–Stock System
Traceability of Quality	Personnel movements in the warehouse	-	All personnel movements in the warehouse are tracked with a chain structure.
	Productivity calculation of employees in the warehouse	-	Since the time worked by the personnel in the warehouse will be visible in the system, the work conducted during the day and their productivity can be followed.
	Making job assignments for drivers	-	Before the transportation process, vehicle and personnel assignments are made and displayed on the system.
Controllability of Quality	Personnel movements in the warehouse	-	Personnel movements and assignments in the warehouse can be viewed and controlled.
	Productivity calculation of employees in the warehouse	-	By calculating the time required to work and the work to be carried out during the day, the daily productivity of the personnel can be calculated.
	Making job assignments for drivers	-	It can be checked by viewing whether the personnel assigned for transportation is correct.
Sustainability of Quality	Comparison with the previous period	-	The list of personnel assigned to a product can be compared across periods.
	Ensuring employee continuity	-	Continuity is ensured by checking human performance in previous periods.

Table 15. Benchmarking for economic criteria.

Economy	Product Transactions	Current Stock System	Quality 4.0–Stock System
Traceability of Quality	Purchasing a product from a supplier	Notifications are sent through the application used for orders from suppliers. Anyone who enters the system can see this and make changes.	Orders from suppliers can be transmitted to authorized persons with the cryptographic structure created by blockchain, and only authorized persons can take action regarding the approval, cancelation, and change in this order.
	Account for keeping products in the warehouse	The current application needs to provide this information. The business itself keeps a separate account for this in its accounting unit.	The costs incurred between the time the products enter and exit the warehouse are recorded and tracked in the product blockchain network ledger.
	Tracking the expenses of employees in the warehouse	-	Since the cost information of the personnel working in the warehouse is recorded in the network structure, it can be viewed at any time.
	Monitoring the environmental conditions in the warehouse	-	Since the environmental conditions are recorded as product information in the blockchain ledger at the first entry for each product, it can be tracked.
	First entrance to the warehouse quality control process	More information about this needs to be provided in the current application. The quality control unit keeps this information separate.	Quality control information made at the first entrance to the warehouse and quality control information made by the supplier company after production can be monitored.
Controllability of Quality	Purchasing a product from a supplier	With the current application, past purchase information can be filtered and queried. Such a comparison can be made.	After the product selection is made and the relevant product information is included in the chain structure, all transactions can be viewed from the same place.
	Account for keeping products in the warehouse	This information cannot be accessed in the current application. Information is accessed manually from the accounting unit.	The standard period for each product to be kept in the warehouse is certain. When the time is exceeded, the system administrator is notified.
	Tracking the expenses of employees in the warehouse	-	The budget limit allocated for the expense control of employees in the warehouse is checked. If the limit is exceeded, are notified.
	Monitoring the environmental conditions in the warehouse	-	Environmental conditions information is monitored. The system administrator is informed when there is a change in values.
	First entrance to the warehouse quality control process	Information is provided from the quality control unit.	Product quality limit values are kept in the product information chain network. Information about products of inadequate quality is reported to the manager.
Sustainability of Quality	Comparison with the previous period	It can be conducted manually.	Since the chain network structure is interconnected, filtering can provide access.
	Ensuring economic continuity	Economic information on the relevant products can be accessed	by entering the period. Economic information on the relevant products can be accessed by entering the period.

According to the process quality component comparison between the current system and the proposed system given in Table 12, the main difference is in the warehouse’s monitoring and control of waiting times. While monitoring and control are carried out manually in the current system, these times can be viewed and controlled from the network structure with the BC algorithms of the Q-4.0 model. If there is a problem and it is necessary to intervene in subsequent processes, it can intervene quickly.

According to the technology quality component comparison between the current and proposed systems in Table 13, the two systems are similar regarding product entry, exit, and product inquiry. However, while the movements of the products after leaving the warehouse cannot be tracked in the current system, they can be tracked with the recommended Q-4.0 model. In addition, while efficiency calculations cannot be made in the current system, real-time efficiency calculations can be made in the proposed Q-4.0 model, as the current capacity and occupancy rate can be seen instantly.

Table 14 compares the employee quality component between the current and proposed systems. Work has yet to be conducted on the employee quality component in the current system. The employee list prepared for product purchase, sale, and shipment is recorded in a document separate from the warehouse management system. The list has yet to be transferred to the existing warehouse management system. Information about all employees is available in the BC base of the proposed Q-4.0 model. Employee movements and changes can be viewed and reported in real-time.

The main difference, according to the economy quality component comparison between the current system and the proposed system shown in Table 15, is that in the current system, there is only economic data about the product, while in the proposed Q-4.0 model, there is also information about other components related to the product. While cost calculations cannot be made for products in the current warehouse management system, real-time cost calculations can be made in the Q-4.0 model.

In the company’s existing warehouse management system, anyone in the warehouse management system can view and make changes to the purchase. In the proposed BC-based Q-4.0 model, product purchase information can only be viewed by authorized users, thanks to the cryptographic structure provided by BC. There are limited permissions for changes to the system, and changes can only be made by authorized users. With the BC base, the security of the warehouse management system will increase.

7. Conclusions

This study describes the Q-4.0 model, which was developed to ensure the traceability, controllability, and sustainability of all processes in a business; the technology used in the processes; and the quality of the employees in the processes within an acceptable economic framework for the business. The model can be used in all business functions (manufacturing, procurement, planning, sales and marketing, R&D, and human resources management) by utilizing I-4.0 technologies. By using augmented-reality technology from I-4.0 technologies in the production department, a suitable production plan can be prepared for a company with the developed model. By using cloud technology in the supply department, the traceability of all suppliers can be ensured. A suitable plan can be created for a company by using IoT for planning. By using augmented reality for sales and marketing, the most suitable potential customers in the sector can be determined. A secure data control can be provided for R&D by using BC technology. By integrating cloud technology into the developed model for human resources management, the most effective management of employees can be provided. The business application of the proposed Q-4.0 model was made with the support of BC technology for the warehouse management system.

The products in the warehouse are tracked in the warehouse management system currently used by the company that buys and sells sheet metal and profiles. More is needed to measure the quality of processes–technology–employees. With the Q-4.0 model developed based on BC, the quality of all sub-processes in the warehouse management system, the quality of the technology used in the processes, and the quality of the employees in the processes can be monitored and controlled according to the economic constraints of the business. What needs to be carried out to ensure the sustainability of quality will be known.

With BC, a cryptographic structure is created in the system, and all system components are connected, ensuring a secure information flow. Thanks to the chain structure, product information can be viewed instantly. Thanks to the certification in the system, data/information security has increased significantly compared to the current system. No unauthorized person can view or make changes to the system.

With the use of the PFS-CoCoSo method, the effectiveness of the developed Q-4.0 model has been seen. Along with the model, a comparison of three different leading methods in the industry was made. As a result, the most comprehensive and suitable option was the Q-4.0 model developed.