Design-as-a-Service Framework for Enabling Innovations in Small- and Medium-Sized Enterprises

The manufacturing sector is the economic backbone of major nations, with about 40% of revenue share derived from Small- and Medium-Sized Enterprises (SMEs) [1]. These enterprises significantly contribute to the Gross Domestic Product (GDP) and employment for most manufacturing-oriented economies. For example, 23.4 million SME units within the non-financial sector across Europe generated €3934 billion in value and 91 million jobs during 2015 [2], and 63.4 million SME units contributed 33.4% to the Indian manufacturing output [3] with 96.4% of manufacturing exports [4]. The manufacturing equipment within SMEs contains a mix of legacy and automatically controlled machines with a primary focus on fabricating component-level items or sub-assemblies for Original Equipment Manufacturers (OEMs). SMEs perform various manufacturing activities ranging from simple to complex component fabrication with superior precision, high productivity, and cost-effectiveness. However, they cannot design or innovate new products, and their sustainability in the market largely depends on work orders from OEMs. Most SMEs work at meager profit margins and do not have the required human or financial resources to systematically set up Research and Development (R&D) activities for mitigating potential solutions.

The primary factors differentiating SMEs from larger enterprises are the lower penetration of Information Technology (IT) and Operational Technology (OT), the inability to employ highly skilled human capital, and the absence of an innovation ecosystem for new products or solutions. The conceptual design inabilities or innovation incompetence leads to sluggish growth of SMEs compared to large enterprises and results in inadequate agility in responding to a sudden change in market requirements, e.g., unforeseen circumstances such as the Covid-19 pandemic or a sudden shift in market conditions caused by geopolitical reasons. It is reported that the Covid-19 pandemic caused massive disruptions for SMEs within several weeks of its onset. Federal and state governments had to provide substantial financial support for the revival [5], e.g., Coronavirus Aid, Relief, and Economic Security (CARES) Act by the USA government [6] or Credit Guarantee Scheme for Subordinate Debt (CGSSD) by the Indian government [7]. The earlier discussion demonstrates the necessity of systematic interventions by implementing new-age technological solutions within SMEs. It is necessary to overcome design or innovation challenges, providing agility and self-sustainability for responding to sudden business environment changes.

In recent years, the manufacturing industries are experiencing significant transformations by adopting Internet-based technologies linked with the newer automation trends or Industry 4.0. Cloud-Based Design and Manufacturing (CBDM) is one solution proposed to provide highly reliable and scalable on-demand pay-per-use services that expand the scope of the traditional manufacturing organization to the global level [8]. CBDM is a service-oriented framework transforming consolidated manufacturing practices into a distributed system. Hitherto, the CBDM framework has comprised various service models: Platform-as-a-Service (PaaS), Infrastructure-as-a-Service (IaaS), Hardware-as-a-Service (HaaS), Software-as-a-Service (SaaS), and Manufacturing-as-a-Service (MaaS). Figure 1 depicts services offered by each service model, the information flow within the model, and linkages. The PaaS is an electronic gateway and means for service amalgamation to implement the CBDM framework effectively. It offers a platform that assists service providers in demonstrating the scope of services and acts as an online negotiation stage for users. The IaaS facilitates users with elementary resources such as high-performance computers, high-speed networking, manufacturing space, logistics, and warehouses required to connect and ensure smooth functioning. The HaaS allows users to lease hardware facilities (e.g., CNC machines, 3-D printers, tools, measurement equipment, and fixtures/jigs) on a pay-for-use basis. The SaaS comprises several software modules related to design, manufacturing, engineering analysis, and resource planning. The users can utilize different software using an online platform instead of procuring exclusive licenses. The MaaS establishes a flexible production line through a seamless, fully connected network of manufacturers on a single digital platform [9]. For example, it enables OEMs to outsource the fabrication of components and sub-assemblies to SMEs and their logistics effectively and efficiently [10].

Although CBDM involves several important aspects of manufacturing and puts forward a commendable framework for implementing digital manufacturing, it still lacks one key aspect, i.e., innovation. As per the innovation management standard ISO 56000:2020, organizational growth and economic viability primarily depend on the ability to innovate [11]. The business component of innovation requires considerable involvement of human intelligence in various activities. The current CBDM framework conversely focuses on combining manufacturing elements with the minimal or needless involvement of human intelligence. For instance, SaaS aims to provide access to software facilities but does not undertake the ability to best exploit the software. The prevailing version of the PaaS model does not provide human resources essential to address fundamental issues such as component manufacturability, strength and cost analysis, end-user feedback, or aesthetic appeal in an optimized manner. Due to this drawback of the current CBDM framework, a disconnection has emerged between the software and manufacturing service model. Therefore, the present work aims to establish a concrete connection between the two service models and embrace the innovations in design and manufacturing by adding a new service-oriented model, namely Design-as-a-Service (DaaS), to the existing CBDM framework.

Internet-based technologies such as CBDM are essential for expanding the global footprint of manufacturing enterprises. It can result in an enormous shift by replacing conventional manufacturing with IT-empowered intelligent manufacturing and assisting in tackling global challenges such as sustainability, resource/energy efficiency, competitive strength, and long-term economic advantage [2]. The literature has reported several attempts to highlight the CBDM framework benefits and the extension of service models to SMEs. It has been shown that the CBDM framework has great potential to provide agility to SMEs if implemented effectively. The framework enables the integration and sharing of the infrastructure and hardware resources from collaborators across different domains to minimize financial burdens and risks [12]. Taylor et al. [13] and Christauskas and Miseviciene [14] outlined the benefits and fundamental limitations of implementing CBDM for SMEs. The benefits are reduced overall cost, a smoother adaptation of business requirements, easier administration, maintenance, and quick global access to information. At the same time, security and privacy issues, performance and connectivity criteria, loss of control, and dependency were considered significant limitations. Raut et al. [15] emphasized an essential benefit of distributed technology to reduce energy consumption and e-waste generation. The studies of Gupta et al. [16] and Shetty and Panda [17] presented reviews highlighting the utility, readiness, and adoption level of cloud computing in SMEs. It was underlined that the ease of use, convenience, cost reduction, and affordable delivery of SaaS software are significant factors motivating SMEs to migrate to CBDM.

Small- and medium-sized enterprises have effectively exploited the MaaS model of the CBDM, resulting in a complete paradigm shift for manufacturing activities. SMEs could secure work orders from OEMs by highlighting manufacturing resources and capabilities through the MaaS model of CBDM [10]. The MaaS significantly improved networking, resource distribution, and process planning capabilities [18]. The platforms such as Xometry² and Fast Radius³ emerged as entrepreneurial ventures in recent years that allowed inputs in the form of a Computer-Aided-Design (CAD) model of the product, material requirements, and quality specifications. The online platform seamlessly integrated OEMs and SMEs to offer work estimates comprising price, lead time, and manufacturability feedback. SMEs transformed into service providers using these online platforms but became heavily dependent on OEMs for work orders and utilization of facilities. The integration through the MaaS platform streamlined order flow, capacity utilization, and revenues for SMEs, as shown in Fig. 2. However, it did not facilitate designs or innovations linked to the new product and process development.

Based on the previous discussions, it is realized that implementing internet-based design engineering can enable nimbleness to industries, especially SMEs, which are not characterized by high product varieties. Recently, Jiao et al. [19] presented a review on the evolution of design engineering and concluded that a new concept of Design Engineering 4.0 (DE4.0) is required for a successful transition toward industry 4.0. It re-conceptualizes the design process by realizing a seamlessly integrated Cyber-Physical-human ecosystem for the need-based product design. The literature shows recent attempts in the direction of DE4.0 wherein Wang et al. [20] presented a Knowledge-Based Design Guidance System (KBDGS) framework to assist the decision-making process of cloud-based technologies. It guides and perceives a design solution through an iterative process by integrating design complexity, uncertainty, and knowledge in the decision workflow. Zhou et al. [21] analyzed human emotional experiences while designing a product in a collaborative environment to establish a tool that correlates emotions with specific design activities.

Online platforms such as avidpd⁴ and rabbit⁵ have been developed recently to allow user interactions during CAD model development. These platforms are developed as standalone modules that lack coordination and compatibility with other elements of the CBDM framework. Several cloud-based design communities (e.g., GrabCAD⁶ or Pinshape⁷) are developed where an individual can share and retrieve CAD models of standard components. However, these communities do not facilitate customization abilities or on-demand development of CAD models based on user requirements. The cloud-based versions of existing CAE software (e.g., SIM 360 of Autodesk) are also available for computational studies such as structural analysis, weight optimization, and fluid dynamics. These solutions are more appropriate for large enterprises as a dedicated workforce is required for performing engineering analysis. Further, the component design stage must ensure that the end-user functional needs are met, which can be realized by analyzing user feedback on functional prototypes [22]. Although prototyping facilities can be provisioned through the MaaS model, it does not ensure user feedback, which is essential in determining the utility of new products. Therefore, it is necessary to develop a central entity addressing these shortcomings of the existing CBDM framework.

The earlier discussion establishes the need for effective implementation of DE4.0 that requires a cyber-physical-human ecosystem conceptually similar to the CBDM framework. Also, the design process requires human experts with specific skill sets (CAD, CAE, physical validation, and user feedback analysis) to work collaboratively while realizing an entity meeting specific functional requirements. However, it is beyond the realm of possibilities for SMEs to invest in developing a cyber-physical-human ecosystem and endure the higher cost of retaining full-time human resources with these skill sets. Therefore, it is required to create a service-based model that can provide competent and experienced human resources to SMEs through the existing CBDM framework to accomplish various product design steps and realize innovations. The proposed service model can help SMEs save on the recurring cost of employing competent and experienced full-time human resources.

The DaaS model presented in this paper aims to simplify the new product design process with an improved innovation process through online communications and collaborations using CBDM. It envisions a service-based model aiming to arrange the skilled and experienced human resources essential in accomplishing different design stages without full-time employees. Figure 3 illustrates an overall architecture of the service-based DaaS model for SMEs that can be further integrated with the CBDM framework. The DaaS model proposes the empowerment of SMEs to search for competent and experienced human resources in designing, analyzing, and prototyping based on their profile, reviews, and service cost. A human resource profile sheet can be customized, including strengths, examples, and feedback on previous work orders. The subsequent subsections discuss the significance of human resource and their provisioning to SMEs through various elements of the DaaS model presented in this paper.

SMEs require human resources with design expertise that can transform the product idea into a virtual model using CAD software as a first step. The framework allows SMEs to see various design expert profiles available on the platform as PaaS users. The DaaS framework allows submitting brief product requirements, functionality, overall physical constraints, and other relevant information. The registered design experts can view the submitted requirements and provide work estimates. The information exchange between design experts and SMEs is permitted through discussion or feedback modules. The PaaS summarizes estimates of design experts and provides relevant information to SMEs for subsequent actions. The desirable CAD software and resources can be provisioned upon selecting the design expert through the SaaS model. The design expert envisages preliminary designs and suggests modifications ensuring functionality, ease of manufacturing, cost-effectiveness, comfort, aesthetic value, and other relevant features. The framework allows SMEs to evaluate various design options against outlined requirements and facilitates selecting an appropriate design. The design expert also accomplishes the material selection task by considering factors such as resistance to wear and corrosion, mechanical properties, suitability with other system components, reliability, availability, cost of material and fabrication, and visual aspects. The module permits design iterations between SMEs and feedback from the analysis or validation experts in the subsequent stages. The design expert generates component CAD models and material specifications as output during each iteration till the product is realized.

The second element requiring human resources is the engineering analysis that evaluates component performance under various loading conditions and performs design optimization as a Design Expert. SMEs can view analysis expert profiles registered on the PaaS, similar to the previous step. The module allows the requirement submission as working constraints (e.g., nature of loading and its magnitude, flow conditions, boundary conditions) and targeted attributes (e.g., maximum permissible deformations, desired weight, dynamic, and thermal behavior). The DaaS model facilitates analysis experts to see the requirements stipulated by SMEs and provide work estimates and the stipulated time frame. Once SMEs finalize an analysis expert, the module provides a CAD model developed by the design expert and a linkage for suitable CAE software through the SaaS. The analysis expert performs engineering analysis and generates a detailed report on targeted attributes such as strength, weight, geometric features, and ergonomics. The report can be viewed by SMEs and circulated among design and analysis experts to achieve trade-offs on various design parameters and derive an optimized design through iterations. The involvement of engineering analysis expertise assists SMEs in optimizing the virtual designs derived in a previous step based on product requirements.

Finally, human expertise must validate the design and compliance with functional needs to meet end-user requirements. It necessitates transforming the product from the virtual domain to a functional prototype to comprehend practical needs and visualization. It requires connections with prototyping facilities such as 3-D printing, CNC machines, material casting, and other similar resources to build prototypes and conduct validation tests for feedback. The DaaS model allows the registration of design validation experts on the PaaS with information related to capabilities that SMEs can view to appreciate suitability. The SME can appoint a prototype expert through the PaaS model and provide the final CAD model. The PaaS also allows validation experts to choose prototyping facilities registered on the MaaS model of the CBDM framework. The validation tests (e.g., structural, functional, reliability, and user feedback) are conducted under conditions identified as per the requirement of SMEs, and test reports are generated in specific formats. It also signifies the design sensitivity to assumptions made during the analysis. If a prototype lacks its intended functionality or specifications, the DaaS model allows iterations among SMEs and design and analysis experts to obtain the revised configuration. The iterative process helps SMEs realize the product within a much lesser period and without significant financial commitments.

Figure 4 depicts the revised configuration of the CBDM framework and detailed information flow after the inclusion of the DaaS model. The augmentation of the DaaS model enables the generation of an optimized product design and embraces innovations effectively. The DaaS model is implemented before the MaaS for providing essential inputs, i.e., product design for effective execution of the CBDM. The final product design obtained through the DaaS can be readily input into the MaaS for manufacturing. The aggregation of the feedback received from manufacturers and end-users on the manufacturability, functionality, aesthetics, and human factors is displayed on the PaaS Discussion Platform and utilized for future reference of human resources. The cognitive actions are communicated to human resources to improve the component design. The practical implementation of the DaaS enables a paradigm shift for SMEs from mere service providers of OEMs to potential beneficiaries from the CBDM framework. It also assists SMEs in reducing their dependence on OEMs for work orders, increasing profitability and business continuity.

The augmentation of the proposed DaaS model into the CBDM framework can assist manufacturing industries, particularly SMEs, to realize new functional products without needing to employ skilled humans and invest upfront in computational resources. The DaaS model enables these enterprises to achieve global competitiveness through a parallel innovation ecosystem based on shared resources. It provisions computational infrastructure and human resources as consultants in managing innovation tasks efficiently. The model offers communication and feedback from various stakeholders as essential features for product realization through a centralized platform. If the model is implemented successfully, it can streamline communication among stakeholders (design expert, analysis expert, prototyping expert, and manufacturers) in the entire innovation process. The DaaS model also provides an employment opportunity to freelancers with design, analysis, prototyping, and manufacturing expertise. The freelancers can register as experts for providing remote services on the CBDM platform irrespective of their physical location and without resource ownership. The proposed model is generic and can be applied to realize complex products through effective integration with other service models of the CBDM framework.

The CBDM is a complex computing technique, and its implementation in a highly dynamic manufacturing environment results in several challenges, as presented in the previous literature [23,24]. Similarly, the DaaS model is also vulnerable to challenges due to the continuous sharing of design information among various human experts. The first step requires sharing the idea or concept between users (SME or individual), design experts, and a platform service provider to derive a virtual CAD model. It requires well-defined Intellectual Property (IP) protection guidelines and sharing agreements between associated parties. In the case of simple products, lesser stakeholders are involved in the innovation process, and sharing benefits would be more straightforward. However, a complex product requires detailed descriptions of financial or tangible benefits for inventions, artistic work, symbols, or images developed by the design expert. For example, the overall product concept or idea can be formulated by an SME or an individual, but features derived while achieving the design are the IP of a design expert resulting in IP conflicts. Successfully implementing the framework requires various options and dispute-resolution mechanisms for both parties involved in the process.

The skillset assessment and reliability of human experts involved at each stage of the DaaS model are also essential. The feedback of human experts is vital regarding the utility and guiding the SMEs or individuals in making an appropriate selection. It will be necessary for the platform developer to build a dynamically evolving utility that assists the user in selecting an appropriate human expert for the given task. The integrity commitments of validation experts also play a critical part in deciding the appropriateness of the feedback. Formulating the feedback questionnaire also poses challenges, as the output can be subjective in multiple cases. As DaaS is an Internet-enabled framework, it also experiences challenges similar to a cloud computing environment. It includes cybersecurity, pricing policies, information security, and data lock-in. The data storage and information flow occur through a central cloud; data integrity and breaches can be handled efficiently.

This paper introduced a DaaS model for augmentation in the existing CBDM framework to quickly transform ideas or concepts into products without upfront human and resource commitments. The DaaS model aims to provide on-demand human resources for transforming the conceptual idea into a functional product through seamless integration on a central platform. It bridges the gap between the SaaS and MaaS service models of the existing CBDM framework. It is realized that adopting the DaaS model could assist manufacturing industries, especially SMEs, in new product discovery and customization of the product. The proposed framework presents a preliminary model with multiple limitations, such as IP protection and benefit-sharing, reliability of human experts, the integrity of the feedback, and other common challenges associated with internet-based cloud platforms. The subsequent studies consider evolving strategies to resolve some of these challenges associated with the successful model implementation.

There are no conflicts of interest.

The data sets generated and supporting the findings of this article are obtainable from the corresponding author upon reasonable request.