Inspection as a Service Business Model for Deploying Non-Destructive Inspection Solutions Within a Blockchain Framework

Abstract

Lack of digitization in data sharing between enterprises and inspection solutions suppliers negatively affects cash flows between parties, which results in late payments that negatively affect the adoption of automatic inspection equipment. This paper contributes to improving the implementation of a new Inspection as a Service Business Model for deploying automatic inspection solutions using non-destructive inspection solutions, and to enhance workflows by integrating Blockchain and Smart Contracts. The Inspection as a Service offers flexible, cloud-based, or on-premise inspection solutions through the Marketplace, reducing upfront costs with a recurring service fee and automated payments. The marketplace platform supports automatic payment processes and facilitates industry adoption of IaaS solutions. The digital ecosystem offers improved capital expenditure and payback periods. It enhances communication, collaboration, data sharing, and payment processes through a subscription model. The case study demonstrates that the IaaS Business Model (on-premise or cloud) improves the economic feasibility of automatic non-destructive inspection solutions by lowering initial investments and enhancing return on investment and payback periods, even with higher operating costs. The analysis confirms the profitability and sustainability of IaaS Business Model over traditional one-fee selling by emphasizing its potential to improve operational performance and sustainability in manufacturing. The current proposal of automatic non-destructive solutions implements a new revenue model based on pay-per-use or volume, which makes it more financially viable to adopt this technology in industry.

1. Introduction

Recent advancements in sustainable manufacturing have increasingly emphasized automated non-destructive inspection (NDI) systems for quality assurance [1,2]. These solutions must be designed to be reliable, easily deployable, and suitable for in-process or in-line production environments. Adopting NDI systems contributes to the circular economy by minimizing waste and reducing material and energy consumption [3]. Furthermore, integrating these technologies optimizes production efficiency while ensuring companies remain competitive in an evolving technological landscape [4].

By leveraging NDI hardware and AI-driven software applications, small- to medium-sized manufacturers can significantly reduce costs associated with quality assurance methods [5,6,7]. Real-time, in-process or in-line inspections enhance defect detection and first-time-right (FTR) ratio, contributing to zero-defect manufacturing and improving economic sustainability [3]. Integrating non-destructive inspection technologies into industrial production environments improves quality assurance policies and reduces defects and waste. Allowing manufacturers to align with industry objectives for cost-effectiveness and environmental responsibility.

Conventional automatic inspection equipment for quality assurance in the industry has been sold by employing a perpetual licensing model [8]. Thanks to the development of information technologies, software vendors have transferred to subscription or leasing models to rent their software products and services on the Internet [9]. The current approach can be applied to the manufacturing industry to deploy non-destructive inspection technology (NDIT) for in-process or in-line quality assurance by reducing the capital expenditures (CAPEX) required to implement the technology, and improving return on investment (ROI) and payback periods (PbP).

Advances in the Internet of Things (IoT) [10,11], sensors [12,13,14], artificial intelligence (AI) [15], and blockchain technologies [16] are now appearing with the potential to transform the value proposition of non-destructive inspection solutions. The primary goals of these solutions are to enhance not only decision making in quality inspection, but also data sharing, and to improve quality assurance policies by implementing in-process or in-line inspection solutions.

This paper analyses the different licensing model strategies that inspection vendors can employ to sell their automatic inspection solutions based on the Inspection as a Service (IaaS) business model. The IaaS business model provides automatic non-destructive inspection solutions through flexible licensing options via a Marketplace, reducing capital expenditure by shifting from a traditional one-time payment to a recurring service fee, with transactions automated through smart contract technology and Marketplace. The supplier vendor provides different licensing models and hardware configurations based on-premises or on-cloud to deploy automatic non-destructive inspection technologies [17]. Industrial consumers who wish to adopt their technology to improve their quality inspection capabilities will have to select from among different selling models. This affects the initial investment, inspection cost, return on investment, and operating expenses (OPEX). Furthermore, the supplier vendor could offer various business or license models depending on consumers’ financial interests.

The present study aims to evaluate and compare the profitability of several leasing models in the automatic inspection solutions based on non-destructive technologies when the influence of different hardware configurations, the net present value, and licensing methods are selected. The main research questions analyzed in this paper are as follows:

(1)

What is the most beneficial business model for deploying IaaS solutions under the leasing model?

(2)

How have the different hardware configurations and leasing models affected the return on investment of automatic inspection solutions?

(3)

What are the benefits of using Smart Contracts based on Blockchain?

The present paper proposes a new business model and software service architecture on which the marketplace platform that uses Blockchain and Smart Contracts technology supports the subscription-based licensing model. The return on investment, capital, and OPEX are evaluated and compared to the selected licensing models and hardware strategies.

The rest of the paper is structured as follows: Section 2 analyses how the IaaS business model, by leveraging Blockchain and Smart Contracts, facilitates secure and automated inspection solutions while enhancing payment processes, data traceability, and stakeholder collaboration. Section 3 examines the transition from traditional payment models to subscription-based IaaS for NDIT by emphasizing the financial advantages of Blockchain-enabled pay-per-use and pay-per-volume licensing models. Section 4 presents the NDIT IaaS marketplace platform by detailing its layered architecture, designed to improve real-time defect detection, data sharing, and collaboration among stakeholders. Section 5 focuses on the financial analysis of IaaS for NDIT by employing cost breakdown models and incorporating the time value of money to assess ROI, PbP, and cost savings. Finally, Section 6 highlights the economic benefits of automatic inspection technologies, including improved FTR ratios, reduced operational costs, and enhanced investment feasibility, and it also provides concluding remarks.

2. Inspection as a Service Business Model

The IaaS licensing business model offers an inspection solution delivered as a software service through a Marketplace. It does away with the need for physical software distribution and enables flexible deployment on the Internet [10,14]. The reduction in the distribution cost compared to the traditional software style, or one-time payment, is based on a subscription model between the IaaS provider and the enterprise, or the consumer who wishes to adopt the inspection solution, in which both parties agree to pay a predetermined quantity according to the selected licensing option. Hence, the flexibility of IaaS is based on a price strategy or licensing option, where consumers can choose and strengthen customer relationships because it reduces the capital expenditure needed to adopt an automatic inspection solution.

2.1. Blockchain Capabilities for Deploying IaaS

Conventional methods to store data related to quality inspections rely on centralized data storage controlled by a service provider. In such a scenario, data storage becomes a single point of failure and runs the risk of tampering. The traditional payment process workflow heavily relies on paper documents (contracts, invoices, emails, etc.) and human resources to complete transactions [18]. Furthermore, in this scenario the supplier and customer do not have a single source of truth regarding inspection data. This may lead to a misunderstanding of the cost estimation for inspection equipment utilization. The Blockchain data recorded by a specific Smart Contract cannot be eliminated or modified and is automatically recorded according to the requirements specified in the Smart Contract [19]. Compared to traditional payment systems, integrating Smart Contracts for IaaS software licensing models improves security, decentralization, and transaction processes using the advantages of Blockchain technology [20].

The Blockchain and Smart Contracts system provides an automatic, digital, secure, interoperable, and tamper-proof platform for the payments required to sustain the business model based on the licensing of the IaaS non-destructive solutions developed on the Marketplace platform, but it does not require trusted intermediaries, such as lawyers, or numerous human resources [21].

The Hyperledger Fabric Blockchain platform provides privacy, access control, and tampering features that make it particularly suitable for enterprise applications [18]. It also provides the capability to manage digital contracts. The capabilities of Blockchain and Smart Contract render the contract execution process automatic by ensuring the fulfillment of obligations and allowing an automatic payment process to be required according to the contracted licensing model. The data generated during a quality inspection can be employed for certification purposes or to comply with specific regulations according to the industrial sector. Traceability and tamper-proofing can both be employed to increase the information flow among inspection suppliers, the manufacturing industry, and end customers [22].

Integrating Blockchain and Smart Contracts technologies into quality assurance policies has been recognized in the industry for fostering interoperability and data sharing and for promoting advanced payment methods to sustain IaaS solutions [19]. Facilitating data exchange among multiple stakeholders for financial or quality purposes constitutes a major limitation to foster the collaboration required to integrate NDI solutions into industrial scenarios.

2.2. Smart Contracts to Sustain IaaS

Smart Contracts’ primary role is to facilitate secure connectivity among technology providers, manufacturing clients and Marketplace stakeholders by establishing a robust manufacturing network in the inspection value chain.

Within IaaS, Blockchain and Smart Contract technologies ensure the tracking, verifying, and auditing of data uploads by fulfilling customer requirements [23]. Key stakeholders, including NDIT solution providers, Marketplace owners, and manufacturers, negotiate and codify contractual terms as Smart Contracts, which enables transparent auditing and transaction tracking. Smart Contracts also streamline payment management on the Marketplace platform by encoding preconditions for tamper-proof block recording. Smart Contracts create a collaborative scenario between industrial parties to deploy agreements about the inspection services that align with business model contracts by securely recording transactions using encryption, access control, and consensus mechanisms, and by eliminating reliance on regulatory authorities.

The Smart Contract provides a framework to support the legal agreement for deploying IaaS in non-destructive inspection solutions based on standard templates. The Smart Contract was created to deploy the pay-per-use, pay-per-period, and pay-per-volume business models to reflect the agreed-upon business relationship between stakeholders to automate automatic payment. The main parameters in a Smart Contract template are first of all, the mandatory parameters that are to govern the contract (the relevant timestamps of the transaction), the parameters (one or more) that are to control the use of an IaaS, and, finally, the condition and terms of contract validity. This parameter is initialized in the Init function. The invoke function is executed when IaaS is used (Figure 1). The query function is to be used to check the uses of IaaS to make the payment, and the block function finishes the contract.

3. Business Models Adopted by IaaS for NDIT

This paper proposes a new business model and software service architecture on which Blockchain and Smart Contracts technologies support the Marketplace platform and the IaaS business model by allowing subscription model implementation. The industrial adoption of inspection solutions based on IaaS software can be explained by the vendors’ side, which is that providers are reluctant to introduce cloud services into their business model offerings [24]. Offering inspection solutions based on an IaaS model implies major changes in their business model: moving from a traditional payment fee to a licensing model with a service fee via the Internet, their solutions becoming available through a marketplace website, and the recurring licensing payment process being performed through the Internet thanks to Blockchain technology.

Conventional inspection solutions revenue streams are based on one upfront payment for acquiring inspection equipment and annual maintenance fees, which typically account for approximately 5–20% of the initial license fee [24]. To foster the implementation of automatic non-destructive inspection solutions, the Engineer To Order (ETO) or the Configure to Order (CTO) business models should be shifted to IaaS proposals [14,25,26,27]. Transitioning from traditional models of quality inspection solutions to service models requires evaluating the cash flows, revenues, cost structure, and profit outcomes from emerging inspection solutions based on new value propositions [14,28]. In these new servitization models, moving traditional one-time product sales to service fees fosters the adoption of NDIT for in-process or in-line quality assurance policies, which does away with the need for upfront capital investment in purchasing. If solutions vendors move their value proposition to a subscription-based model with pay-per-use or pay-per-volume, they significantly impact the customer return on investment and payback period [29].

The implications of transitioning from a traditional revenue model (with a unique licensing payment and annual maintenance fees) to IaaS business models involve different licensing fees: pay-per-use (industrial customers are charged for inspections periodically) and pay-per-volume (industrial customers buy a specific number of inspections that can be used during a predetermined period), which foster NDIT solution integration in industrial environments. Analyzing the proposed subscription models based on Blockchain and Smart Contract technologies provides guidelines for designing viable business models for the industrial integration of automatic non-destructive inspection solutions.

The different licensing models can improve IaaS providers’ cash flow and deliver inspection solutions while lowering the customer’s CAPEX requirements, compared to the traditional business model of pay-before-use [30]. This research investigates the shift from traditional payment fees installed on-premises to new business models in which the non-destructive inspection solution is deployed as IaaS on-premise or on-cloud. Three conceptional business models are built (conventional inspection, IaaS on-premise, IaaS on-cloud), which allows us to systematically evaluate the financial aspects of different subscription models or hardware configurations.

4. The NDIT IaaS Marketplace Platform

Implementing NDIT, AI, and digital twins (DTs) enables real-time defect detection in manufacturing and prevents downstream issues. Embracing Zero Defects and Zero Waste enhances efficiency, reduces resources, and boosts profits [31]. The platform ecosystem proposed for the online marketplace in the industry would comprise interconnected service providers, industrial companies, and research centers that integrate interrelated resources and collaborate according to a policy to create economic value. This platform is where NDT IaaS is deployed. It aims to foster collaboration among inspection solution providers, service companies, and industrial companies to reduce loads to sustain in-process or in-line inspection quality policies. The main benefits of the current online marketplace platform include increasing technological capabilities by deploying non-destructive technologies, artificial algorithms for decision making, data sharing between stakeholders, and increasing system integration through Blockchain.

The main challenges of implementing automatic NDIT solutions currently lie in developing the ecosystem or platform that enables data acquisition from heterogeneous and multisource real-time from production lines, data storage, and analytics for inspection purposes [32]. A collaborative network among technology providers, subject matter experts, and customers should integrate cyber-infrastructure competencies that support data acquisition, communication, data management, decision making, marketplace, and intercommunication with Enterprise Resource Planning (ERP). The different licensing models can improve IaaS providers’ cash flow and deliver inspection solutions, while lowering the customer’s CAPEX requirements compared to the traditional business model of pay-before-use [30]. This research investigates the shift from traditional payment fees installed on-premises to new business models in which the non-destructive inspection solution is deployed as IaaS on-premises or on-cloud. The online marketplace platform proposes a layered architecture that relies on non-destructive sensors, databases, the IoT, Blockchain, and Smart Contract technologies to achieve the inspection records’ transparency, auditability, and immutability. Figure 2 describes a proposed layered platform required to implement automatic non-destructive inspection technologies for real-time inspection and quality control purposes.

The sensor layer of the current ZDZW architecture platform is employed to collect data from the manufacturing environment or a machine. It comprises a multisource and multisensor network (optical, acoustic, magnetic, electric, etc.) that transforms an energy source into an electrical signal, which is finally converted into digital data. The resolution, accuracy, and precision of data are directly related to sensor capabilities. The different data sources provide information about the manufacturing order (item, date, machine, etc.) and data quality acquired by the sensor (IR image, acoustic signal, etc.). The communication layer deals with how these sensors or devices communicate data by covering legacy protocols, such as Profibus, Modbus, OPC, or IoT standards OPC-UA and MQTT, among others [33]. Transport protocols, such as HTTP/HTTPS/REST, MQTT, and AMQP, are also defined on this communication layer [34]. The primary function of the communication layer is to ensure efficient and accurate communication among the different non-destructive inspection equipment. The data layer allows the acquisition, synchronization, and storage of the data collected from multisource sensors. This layer acts as a data repository, where information is saved and accessible to AI algorithms or mathematical models to carry out complex queries for defect detection, identification, and location.

The AI algorithms layer is constructed for defect detection strategies, where the data acquired by non-destructive sensors are inspected with AI algorithms. The Marketplace layer is where non-destructive solutions are monetized through Blockchain technology and Smart Contracts. As the different solutions work according to the IaaS architecture, the inspection software sends data to the Marketplace for monetization purposes. The ERP layer focuses on transmission and registration in a manufacturing environment (ERP) of the quality inspection performed by the non-destructive inspection solution, and on the implementation of some tools for dashboard visualization. The deployment of Blockchain and Smart Contracts on non-destructive inspection technologies has provided new opportunities for collaboration and interaction between manufacturing stakeholders by driving technological innovation in the field. With this collaborative approach, Marketplace stakeholders integrate resources around a digital platform for value co-creation. Customer value creation is achieved through resource availability from the different stakeholders that conform to the platform ecosystem for mutually beneficial relationships. The online Marketplace ecosystem promotes the European industry’s economic growth because it provides non-destructive inspection solutions to improve quality policies in manufacturing environments and to reduce energy use and waste generation.

5. IaaS Financial Analysis

In the conventional selling model, industrial consumers pay a one-time perpetual license fee for the ownership of the inspection solution, which is installed on-premises [8]. NDIT development enables as-a-service models by driving a shift towards sustainable, human-centered manufacturing practices [14]. This proposal is based on a leasing model, in which industrial customers can select different hardware configurations and service subscription fees.

The optimization of productive resources in the industrial field is a problem of considerable scope, which must be addressed from a multidisciplinary perspective to consider the reduction in materials and energy, and the use of information technologies and social sustainability and economic aspects. The impact of NDIT on operational costs must be estimated to evaluate the economic impact of the implementation. With mathematical models that focus on cost breakdown methodologies and the correct definition of all the variables, it will be possible to quantify the impact that different business models have on returns on investments in new inspection technologies. The proposed financial analysis is based on a cost breakdown model, proposed by J. Lario et al. 2024, which allows a comparison of investment in the NDIT solution, the expected performance of the inspection technology, the reduction in operating costs (materials, energy, labor, etc.), and the effect of the net value of money [35]. The cost breakdown tool has been employed to define the costs involved in a specific manufacturing process. To develop the cost breakdown, the bill of materials and production routes has been considered to define the consumption of productive resources (materials, machines, labor, infrastructure, among others).

Table 1. Use case parameters for the cost breakdown estimation.

Description	Value	Units
Overall SAW equipment cost, ${\mathrm{O}\mathrm{E}\mathrm{C}}_{\mathrm{S}\mathrm{A}\mathrm{W}}$	120,000	(€)
Automatic EMAT Inspection equipment cost, ${\mathrm{A}\mathrm{E}\mathrm{I}\mathrm{C}}_{\mathrm{o}\mathrm{n}\mathrm{e}\mathrm{p}\mathrm{a}\mathrm{y}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}}$	340,000	(€)
Automatic EMAT Inspection equipment cost, ${\mathrm{A}\mathrm{E}\mathrm{I}\mathrm{C}}_{\mathrm{I}\mathrm{a}\mathrm{a}\mathrm{S}\mathrm{o}\mathrm{n}-\mathrm{p}\mathrm{r}\mathrm{e}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{e}}$	204,000	(€)
Automatic EMAT Inspection equipment cost, ${\mathrm{A}\mathrm{E}\mathrm{I}\mathrm{C}}_{\mathrm{I}\mathrm{a}\mathrm{a}\mathrm{S}\mathrm{o}\mathrm{n}-\mathrm{c}\mathrm{l}\mathrm{o}\mathrm{u}\mathrm{d}}$	168,000	(€)
SAW speed	270	(mm/minute)
Building ratio, $\mathrm{B}\mathrm{R}$	0,27	(unit/hour)
Average Hourly Cost, $\mathrm{A}\mathrm{V}{\mathrm{G}}_{\mathrm{H}\mathrm{C}}$	31	(€/hour)
Coefficient based on the process labor needs, ${\mathrm{K}}_{\mathrm{P}\mathrm{L},\mathrm{i}}$	70.0	%
Coefficient based on the benefits labor expenses, ${\mathrm{K}}_{\mathrm{L}\mathrm{B}\mathrm{E}}$	21.0	%
Inspection labor, ${\mathrm{I}\mathrm{L}}_{\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{o}\mathrm{u}\mathrm{t}\mathrm{N}\mathrm{D}\mathrm{I}\mathrm{T}}$	9	(operators/year)
Inspection labor, ${\mathrm{I}\mathrm{L}}_{\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{N}\mathrm{D}\mathrm{I}\mathrm{T}}$	3	(operators/year)
Repairing labor, ${\mathrm{R}\mathrm{L}}_{\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{o}\mathrm{u}\mathrm{t}\mathrm{N}\mathrm{D}\mathrm{I}\mathrm{T}}$	6	(operators/year)
Repairing labor at FTR 75%, ${\mathrm{R}\mathrm{L}}_{75\mathrm{F}\mathrm{T}\mathrm{R}\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{N}\mathrm{D}\mathrm{I}\mathrm{T}}$	5	(operators/year)
Repairing labor at FTR 80%, ${\mathrm{R}\mathrm{L}}_{75\mathrm{F}\mathrm{T}\mathrm{R}\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{N}\mathrm{D}\mathrm{I}\mathrm{T}}$	4	(operators/year)
Repairing labor at FTR 85%, ${\mathrm{R}\mathrm{L}}_{75\mathrm{F}\mathrm{T}\mathrm{R}\mathrm{w}\mathrm{i}\mathrm{t}\mathrm{h}\mathrm{N}\mathrm{D}\mathrm{I}\mathrm{T}}$	3	(operators/year)
Raw Material Consumption for material k, ${\mathrm{R}\mathrm{M}\mathrm{C}}_{\mathrm{k}}$	5 × 10−3	(kg/unit)
EU Average energy industrial price for heat treatment, ${\mathrm{E}}_{\mathrm{P}}$	0.1886	(€/(KW×h))
Welding wire price, ${\mathrm{R}\mathrm{M}\mathrm{P}}_1$	70	(€/kg)
Welding flux price, ${\mathrm{R}\mathrm{M}\mathrm{P}}_2$	0.45	(€/kg)
Steel scrap, ${\mathrm{R}\mathrm{M}\mathrm{P}}_3$	1	(€/kg)
Working days per year, ${\mathrm{W}\mathrm{D}}_{\mathrm{i}}$	260	(day/year)
Working Hours operating time in SAW, WHSAW	24	(hours/day)
Number of Years for Technological Depreciation, ${\mathrm{N}}_{\mathrm{y},\mathrm{i}}$	4	(years)
Operating expenses coefficient, ${\mathrm{K}}_{\mathrm{i}}$	100	%
Allocated cost coefficient per process i, ${{\mathrm{K}}_{\mathrm{A}\mathrm{C}}}_{\mathrm{i}}$	20	%
General and Administration Cost coefficient, ${{\mathrm{K}}_{\mathrm{G}\&\mathrm{A}}}_{\mathrm{C}}$	45	%

EMAT Electromagnetic Acoustic Transducer, ^SAW Submerged Arc Welding.

summarizes all the variables and costs that should be considered to deploy the cost breakdown in the following chapter, where the use case is developed.

Revenue growth on automatic inspection solutions and cost reduction should be considered when assessing the investment in new automatic inspection solutions. Valerie et al. (2009) highlighted that the return of quality accounting analysis does not evaluate the time value of money, which affects significant capital investments [36]. In all the decision criteria performed to evaluate an industrial investment, some index, equivalence, or measurement is performed for comparisons and to summarize the differences in importance among investment alternatives. Most investment proposals analyzed in a company consist of an initial or a series of payments, followed by positive returns related to a cost reduction or sales increase. The present study proposes a quantitative method by considering the time value of money because it allows better visualization of the differences between the proposed alternatives. The net value of money has been included to evaluate different investment scenarios according to the business model selected for acquiring the NDI solution and the impact on the first-time right ratio by considering that inflation, interest rates, and the investment horizon can influence the time value of money. It includes the influence of the net value of money on the investment index of ROI and PbP (Equations (1) and (2)).

$$ROI (\%)={\displaystyle \frac{\sum _{t=1}^n\left(\left(\raisebox{1ex}{$∆C_w$}\!\left/ \!\raisebox{-1ex}{${\left(1+i\right)}^t$}\right.\right)·OPR\right)}{{OEC}_{NDIT}}}$$

(1)

$$Overall Production Rate\hspace{1em}\left({\displaystyle \frac{unit}{year}}\right),\hspace{1em}OPR$$

$$Unitary Cost variation \left({\displaystyle \frac{€}{unit}}\right),\hspace{1em}∆C_w$$

$$NDIT Overall Equipment Cost \left(€\right), {OEC}_{NDIT}$$

$$PbP (years)={\displaystyle \frac{{OEC}_{NDIT} }{\left(\frac{\sum _{t=1}^n\left(\raisebox{1ex}{$∆C_w$}\!\left/ \!\raisebox{-1ex}{${\left(1+i\right)}^t$}\right.\right)}{n}\right)·OPR }}$$

(2)

The variable cost savings reported from direct and indirect costs, energy, and material consumption reduction and reprocessing costs can be assessed annually for the current use case. Quality improvement can be directly linked with an increase in margin or a percentage of profit per unit sold and correlates, at the same time, with a reduction in operational costs [37]. By including the net value of money, the temporary variations in money based on the interest rate or inflation are contemplated on the return on investment, payback period, and net present value.

5.1. Case Study: Industrial Scenario Introduction

The European Union’s energy strategy focuses the next decade’s efforts on the Climate Target Plan, which mandates a reduction in greenhouse gas emissions exceeding 50% vs. the 1990 levels by 2030 [38]. Achieving this goal requires playing a significant role in renewable energy within the EU’s energy strategy. Wind energy, characterized by its stability, abundance, and high public acceptance, is a key component of the REPowerEU energy objectives. The REPowerEU plan involves substantial investments and reforms for energy and industrial sectors over the forthcoming decade, with an estimated mobilization of nearly €300 billion, including approximately €72 billion in grants and €225 billion in loans [39]. To facilitate the realization of the EU’s ambitious climate and energy targets for 2030 and 2050, the European Commission introduced a comprehensive wind energy strategy to define the specific measures to support the sector’s sustainable development [40]. Transforming the energy landscape on this scale, which is estimated to cost over € 800 billion [29], underscores the magnitude of this transition [41].

This new strategy that focuses on renewable energy production has allowed the global levelized cost of electricity (LCOE) to lower by 44% in the last 10 years for wind energy, with a cost between 49 and 79 euros/MWh in 2019 [41]. The wind energy supply chain encompasses diverse stakeholders, including wind turbine manufacturers, tower and blade producers, cable suppliers, and the electrical firms operating wind farms. This extensive industrial network involves hundreds of suppliers and employs over 1.5 million persons, and it covers several engineering, science, and technical roles in the EU. Several EU countries, including Italy, Austria, the Czech Republic, France, Germany, and Spain, contribute to the European renewable energy sector [42]. The sector generates an annual turnover that exceeds €158 billion, which highlights the economic significance of the resilience of the EU’s industrial strategy [43].

One of the most cost-intensive activities in the wind energy industry is the production of wind towers, with an estimated contribution of around 25–30% of the total cost of wind components [44]. The main function of wind towers is to act as support structures by providing the required height for wind turbines and supporting wind forces. The study carried out by the U.S. Department of Energy highlighted that the process that requires higher rework production is the welding process, which is the cost driver and the bottleneck operation for manufacturing wind towers [44].

The proposed use case has been developed by deploying an automatic NDIT at wind tower manufacturers. The submerged arc welding (SAW) process consolidates different wind tower sections. During this specific process, the economic evaluation of the integration of Electromagnetic Acoustic Transducer (EMAT) for in-process inspection, which considers different business models, is carried out in the present section. The EMAT can be classified as a non-contact and NDIT. As it is an ultrasonic technique that analyses the sound waves generated by a magnetic field [45,46,47]. Integrating EMAT technology into automatic in-process quality assurance inspection is expected to lower production costs, which supports European producers in achieving climate neutrality by 2050.

5.2. Case Study: Improve Submerged Arc Welding Quality Inspection System

A Tier 1 supplier of the wind energy sector is considering the integration of a new automatic NDIT into its SAW process to ensure that the FTR ratios of the longitudinal and circular seam welds performed to manufacture wind towers are acceptable. The presented data were obtained from a literature review and marketing research and will be referenced to sustain the economic analysis [48,49,50,51,52].

Three different starting hypotheses were considered to evaluate the impact of integrating the EMAT into an automatic in-process seam weld inspection for the SAW process. The impact of the automatic NDIT on improving the FTR ratio is considered by starting from 75% in a conventional scenario without NDIT, and three new scenarios ranging from 80%, 85%, and 90% depending on the degree of improvement that this solution can have on the SAW process. The capital investment cost of integrating the EMAT is considered in terms of equipment, training, installation, and validation costs. Depending on the selected business model, NDIT would require a capital investment of €340.000 if implemented following a one payment approach of €204.000 when contemplating on-premises IaaS, and of €168.000 if on-cloud IaaS is adopted. The two last scenarios, in which the IaaS business model is adopted, require additional operational costs of 69–61 euros/part inspected for the on-premise model and 75–67 euros/part for the on-cloud model according to the FTR level achieved by the EMAT. This machine will have a zero salvage value at the end of its economic life (4 years). In addition, the company will depreciate the asset straight-line. The improved automatic EMAT in-process inspection will not affect the SAW cycle time. The financial analysis of the hypothetical renewable energy use case will be conducted over 4 years and assessed in annual increments. The Minimum Acceptable Rate of Return (MARR) ratio can be estimated at 15% based on the average net revenue extracted from companies of wind energy sector industries, and by considering the EU inflation for 2019–2024.

5.3. Case Study: Investment Feasibility Analysis

The inspection supplier can use information about consumers’ operating costs to design pricing leasing strategies. The optimal balance between equipment and the IaaS cost, with profits and reduced direct inspection labor, material waste, energy, and operational costs, will influence the prices for the different selected licensing models. The licensing strategy selected by the customer or offered by the inspection supplier can increase or decrease the investment feasibility of automatic inspection solutions based on NDITs.

Once the three business models to be analyzed are defined, the next step is to determine each alternative’s quantifiable consequences. The impact of each business model must be expressed in monetary terms, along with the impact of implementing NDIT by estimating the degree of improvement in the first-time manufacturing rate, and how both factors modify OPEX. After the three scenario consequences are quantified and evaluated, the next step is to use a general procedure to help to select the most appealing option.

This paper proposes a net value of money approach to assess the impact of deploying NDIT by capturing the variation in the FTR ratio on manufacturing. Considering the net value of money to derive improvement in operation or quality cost allows the temporal effects of the change and the time value of money to be quantified, and this approach can also be implemented into PbP and ROI.

Figure 3 shows the evolution of the payback period after adding the time value of money, and by including the effect of including EMAT inspection technology in the manufacturing of the FTR rate. If a target PbP is set at below 48 months, the target value is reached more quickly when the FTR rate presents values above 75% for all the business model cases. Similarly, it is highlighted for business models under an IaaS license that the payback period decreases below 2 years when using the On-Premise and On-Cloud IaaS models, which presents the best payback periods in the second business model. This indicator is interesting from the company’s point of view when evaluating future investment projects and when aiming to know how long it should take to recover the investment initially made in NDIT. IaaS is justified as a more interesting approach to be taken to implement NDIT for in-process quality assurance.

Nevertheless, another economic indicator that can be used to assess the viability of investment in automatic NDIT equipment is the return on investment (ROI). Figure 4 shows the evolution of ROI on a 4-year horizon, where it can be observed again that the service-based business models (IaaS on-premise and on-cloud) report positive values when the first-time manufacturing rate is higher than 75%. Therefore, investment is economically favorable if an automatic non-destructive inspection system is introduced by improving the first-time manufacturing rate by at least 15%. Similarly, Figure 4 shows that the IaaS on-cloud model, with a lower initial investment, provides better ROI values compared to the other IaaS business model based on an installed on-premise system.

The introduction of automatic non-destructive inspection equipment has an impact on operating costs. This effect is reflected in increased capitalization costs, which include the amortization of inspection equipment, the costs derived from installations or infrastructures to service this new inspection equipment, and inspection service costs under the contracted inspection model. Figure 5 shows how incorporating the EMAT for SAW in-process control reduces the final factory cost from an initial 940 euros/unit for the conventional process to below 900 euros/unit when in-process inspection is incorporated. This reduction in the final manufacturing costs decreases further if the FTR increases and is slightly higher for the IaaS model than for the one-time payment model scenario. Similarly, Figure 5 shows that the operating costs of the inspection service are 75–60 euros/unit, which is higher in the on-cloud model. This is mainly due to the infrastructure costs deployed by the service provider, which reduce the initial investment in NDIT equipment and favor investment feasibility. The effect of the business model selected for the investment in NDIT and the effect on the costs that an improvement in FTR implies on the final factory cost appear in

Table 2. Impact of inspection business models on the final factory cost and investment feasibility according to FTR ratio selected.

	Conventional SAW FTR 70%	Conventional Inspection FTR			IaaS on-Premises FTR			IaaS on-Cloud FTR
		75	80	85	75	80	85	75	80	85
Direct costs (€/unit)	655	511	461	419	511	454	419	511	454	419
Direct labor (€/unit)	278	213	192	174	213	189	174	213	189	174
SAW labor (€/unit)	164	153	144	135	153	144	135	153	144	135
Inspection labor (€/unit)	58	19	19	19	19	19	19	19	19	19
Repairing labor (€/unit)	55	41	29	19	41	26	19	41	26	19
Direct labor benefits (€/unit)	58	45	40	37	45	40	37	45	40	37
Total energy welding cost (€/unit)	42	39	36	34	39	36	34	39	36	34
Material costs (€/unit)	39	36	34	32	36	34	32	36	34	32
Welding wire (€/unit)	26	25	23	22	25	23	22	25	23	22
Welding flux (€/unit)	12	11	11	10	11	11	10	11	11	10
Steel scrap (€/unit)	0,4	0,4	0,3	0,3	0,4	0,3	0,3	0,4	0,3	0,3
Insp. as Service costs (€/unit)	NA	67	63	59	69	65	61	116	103	95
Indirect costs (€/unit)	247	306	283	262	266	243	227	255	233	217
Capitalized costs (€/unit)	79	158	148	140	125	117	110	116	109	102
SAW operating costs (€/unit)	64	59	56	52	59	56	52	59	56	52
EMAT operating costs (€/unit)	NA	67	63	59	40	38	36	33	31	29
Installation op. costs (€/unit)	16	32	30	28	25	23	22	23	22	20
Allocated cost (€/unit)	16	32	30	28	25	23	22	23	22	20
SAW equipment costs (€/unit)	16	15	14	13	15	14	13	15	14	13
EMAT equipment costs (€/unit)	NA	17	16	15	10	9,5	8,9	8,3	7,8	7,3
General and Adm. costs (€/unit)	151	116	105	95	116	109	95	116	109	95
Total (€/unit)	940	853	777	713	882	795	738	876	790	733
Total production (Unit/year)	115	123	132	140	123	132	140	123	132	140
ROI (%)	NA	−0.45	0.08	0.42	−0.35	0.71	1.53	−0.14	1.15	2.15
Payback period (months)	NA	88	44	30	74	28	19	56	23	16

. This table represents a cost breakdown, where the prime manufacturing cost and indirect costs are shown for the three business model scenarios and the three possible selected FTRs for the submerged arc welding operation. The initial scenario considers the convectional environment without an automatic inspection system, whose FTR rate is 70%. At an operational level, adopting IaaS in non-destructive solutions can help companies to adopt these technologies for their quality assurance policies, and they reduce capital expenses and ROI, in contrast to a slight increase in operational expenses.

Integrating the IaaS business model within an industrial online marketplace ecosystem creates new opportunities for scalable and flexible deployment of NDIT. New business models based on smart contracts enable industrial companies to access IaaS solutions without significant upfront costs. The proposed marketplace platform enables an e-commerce system, fostering collaboration between technology providers, manufacturers, and service companies, and thanks to Blockchain and Smart Contracts, allows the payment processing and service tracking of NDIT deployed through IaaS. The proposed NDIT IaaS framework aligns with the industrial e-commerce trends, where digital platforms or marketplaces are deployed to optimize supply chains, reduce operational risks, and support data-driven decision making [53].

6. Conclusions

The present paper contributes to improving the implementation of a new business IaaS model for the deployment of automatic inspection solutions based on NDIT to improve the workflow for digitizing and automating the payment process by deploying emerging Blockchain and Smart Contract technologies.

Inspection records are automatically processed with a Blockchain Smart Contract. Thus, depending on the selected business model, the IaaS solution reduces the paperwork and human intervention required in the traditional payment process. As a result, the proposed Marketplace platform enables the integration of automatic payment of inspection solutions developed as IaaS with the automatic payment process Smart Contracts and facilitates the industry’s adoption by delivering secure, immutable data sharing between stakeholders to sustain the IaaS of NDIT.

This paper presents the non-destructive digital platform ecosystem landscape by recognizing its potential for early adoption by small- and medium-sized enterprises because it improves the capital expenditure and payback period by offering the inspection solution through an IaaS model. The proposed digital ecosystem in the online Marketplace aims to enhance communication, collaboration, data sharing, value proposition, and payment processes with a predefined subscription model.

The use of Blockchain and Smart Contracts technologies in deploying automatic non-destructive inspection solutions for quality assurance purposes relies on the capabilities for provenance-tracking, tampering, and automatic payment performed according to the selected licensing business model.

The time value of money incorporated into ROI and the payback period of the project investment analysis provide valuable information to select the business model to deploy NDIT for in-process quality assurance. The case study provides insight into deploying NDIT solutions based on an IaaS business model and the effect on the final factory cost. The use case underlines the need to adopt IaaS based on on-premise or on-cloud business models to improve the investment feasibility of NDIT to improve the manufacturing sustainability and operational performance of industrial processes.

The analysis herein carried out justifies that lower initial investment made in the IaaS on-premise or on-cloud business models, even when increasing the operating costs of the automatic inspection service and given the assumption that incorporating the inspection method improves the FTR by at least 15%, allows the project’s greater economic viability with better ROI and PbP.

The analysis stresses that the industrial adoption of NDIT solutions based on the IaaS business model presents higher ROI than traditional one-fee selling models. The cost analysis highlights the profitability of IaaS because this business models present shorter payback periods, and lower CAPEX in return for higher OPEX.

Adopting an IaaS business model for NDI solutions within an industrial online marketplace framework represents a paradigm shift in the commercialization of industrial services. By leveraging Blockchain and Smart Contracts, the proposed framework enhances the security and automation of transactions, fostering the adoption of NDIT across various manufacturing sectors. Beyond the specific case study, the proposed framework provides a scalable deployment of NDIT into digital industrial commerce, facilitating the integration into global supply chains through flexible licensing and pay-per-use options sustained by the IaaS business model. This shift aligns with e-commerce trends prioritizing automatic in-process or in-line quality assurance policies, promoting efficiency and competitiveness in industrial manufacturing.

This study is limited to SAW applications, focusing on FTR ratio variations to assess the IaaS-based NDIT’s economic feasibility. Future research should conduct broader sensitivity analyses incorporating diverse financial assumptions (multiple cost structures, market conditions, among others) and several industrial use cases to generalize the economic feasibility of this model across different manufacturing applications.