A Cost-Beneﬁt Analysis of Additive Manufacturing as a Service

Igor Ivki

2 a

, Tobias Buhmann

3,4

and Burkhard List

b&mi, Wiesmath, Austria

Lancaster University, Lancaster, U.K.

University of Applied Sciences Burgenland, Austria

Forschung Burgenland, Eisenstadt, Austria

Keywords:

Cloud Crafting Platform, Additive Manufacturing, Manufacturing as a Service, Cost-Beneﬁt Analysis.

Abstract:

The global manufacturing landscape is undergoing a fundamental shift from resource-intensive mass produc-

tion to sustainable, localised manufacturing. This paper presents a comprehensive analysis of a Cloud Crafting

Platform that enables Manufacturing as a Service (MaaS) through additive manufacturing technologies. The

platform connects web shops with local three-dimensional (3D) printing facilities, allowing customers to pur-

chase products that are manufactured on-demand in their vicinity. We present the platform’s Service-Oriented

Architecture (SOA), deployment on the Microsoft Azure cloud, and integration with three different 3D printer

models in a testbed environment. A detailed cost-beneﬁt analysis demonstrates the economic viability of

the approach, which generates signiﬁcant proﬁt margins. The platform implements a weighted proﬁt-sharing

model that fairly compensates all stakeholders based on their investment and operational responsibilities. Our

results show that on-demand, localised manufacturing through MaaS is not only technically feasible but also

economically viable, while reducing environmental impact through shortened supply chains and elimination

of inventory waste. The platform’s extensible architecture allows for future integration of additional manufac-

turing technologies beyond 3D printing.

1 INTRODUCTION

The global manufacturing landscape has long been

characterised by resource-intensive production pro-

cesses, extended supply chains, and a tendency

towards overproduction (Westk

amper and L

ofﬂer,

2016). These practices not only contribute to signiﬁ-

cant environmental impacts, but also create economic

dependencies that can be disrupted by global events,

as demonstrated by the COVID-19 pandemic (Mugu-

rusi and de Boer, 2013) or the Suez Canal blockade

(Chopra and Meindl, 2007; Lee and Wong, 2021). In

addition, there is a growing consumer preference for

fair, local, and sustainable products (Schwilling et al.,

2021), indicating a shift in market demands that tra-

ditional production models struggle to meet.

The integration of cloud computing and addi-

tive manufacturing technologies has opened up new

opportunities to transform traditional manufacturing

processes. This convergence has given rise to the con-

cept of Manufacturing as a Service (MaaS), which

https://orcid.org/0000-0003-3037-7813

has the potential to transform production methods,

supply chains, and customer experiences in the long

term. Our research is focused on developing a cloud-

based platform that connects webshop owners with

small and medium-sized enterprises (SMEs) that op-

erate three-dimensional (3D) printers, enabling on-

demand, localised production of goods (Lu and Xu,

2019).

In this paper, we propose a Cloud Crafting Plat-

form that addresses the challenges of mass produc-

tion and the traditional supply chain by enabling a

paradigm shift in how products are manufactured and

distributed. The proposed platform acts as a bridge

between customers, online retailers, and local 3D

printing facilities, enabling the production of goods

only after they have been purchased. This on-demand

approach not only reduces waste and inventory costs,

but also empowers customers to actively participate in

the manufacturing process by choosing local, sustain-

able production methods.

The platform’s architecture is designed to be scal-

able and ﬂexible, using a serverless approach that can

seamlessly connect web shops (points of sale) with

Ivki

c, I., Buhmann, T. and List, B.

A Cost-Beneﬁt Analysis of Additive Manufacturing as a Service.

DOI: 10.5220/0013361300003950

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 15th International Conference on Cloud Computing and Services Science (CLOSER 2025), pages 219-230

ISBN: 978-989-758-747-4; ISSN: 2184-5042

219

3D printer operators (points of manufacture). This

approach not only stimulates local economic growth,

but also signiﬁcantly reduces the environmental im-

pact associated with long-distance shipping and tradi-

tional mass production techniques.

Our research goes beyond the theoretical frame-

work to include a comprehensive cost-beneﬁt analy-

sis that evaluates the economic viability of this MaaS

approach. By comparing different 3D printer mod-

els and analysing metrics such as print time, material

usage, and energy consumption, we provide insights

into the operational efﬁciencies and potential cost sav-

ings of on-demand, localised manufacturing.

This paper extends on our previous work (Ivki

et al., 2024), which introduced the concept of a MaaS

platform for on-demand, localised manufacturing us-

ing 3D printing technology. Building on this founda-

tion, this paper presents a comprehensive analysis of

our Cloud Crafting Platform, presenting its architec-

tural design, addressing implementation challenges,

and providing detailed results of our cost-beneﬁt anal-

ysis. This paper provides a deeper exploration of the

platform’s potential to transform manufacturing pro-

cesses and traditional supply chains.

By exploring the intersection of cloud comput-

ing (Xu, 2012), additive manufacturing (Wong and

Hernandez, 2012), and on-demand production (Lu

and Xu, 2019), our research contributes to the on-

going dialogue about the future of manufacturing.

We discuss how MaaS can not only improve pro-

duction efﬁciency and reduce environmental impact,

but also support local economic development and in-

crease supply chain resilience. This work has impli-

cations for a range of stakeholders, including manu-

facturers, retailers, consumers, and policymakers, as

we move towards a more sustainable and responsive

manufacturing ecosystem.

The remainder of this paper is structured as fol-

lows: Section 2 identiﬁes related work in the ﬁeld and

presents the theoretical foundations and technological

advances underlying additive MaaS. Next, in Section

3, the Cloud Crafting Platform prototype design, ar-

chitecture, and testbed setup are presented. Based on

that, Section 4 presents the results of the cost-beneﬁt

analysis, highlighting key metrics and their implica-

tions for the feasibility of the approach including a

discussion of the results. Finally, Section 5 concludes

with a summary of the contributions and directions

for future work.

2 RELATED WORK

This chapter provides a comprehensive overview of

the current state of research in three key areas rel-

evant to our study: (1) cloud-based manufacturing

systems, (2) additive manufacturing, and (3) the as-

sociated business models, sustainability, and social

aspects. By examining these interrelated areas, we

aim to provide a solid foundation for our research and

identify the gaps that our work will address.

2.1 Cloud-Based Manufacturing

Cloud-based manufacturing systems represent a sig-

niﬁcant advancement in the manufacturing landscape,

often using a combination of advanced technologies

such as cloud computing, the Internet of Things (IoT)

and artiﬁcial intelligence (AI) to improve the ﬂexi-

bility and efﬁciency of traditional manufacturing pro-

cesses. Thames and Schaefer (2017) provide an

overview of the technological foundations for cloud

manufacturing, discussing both the challenges and

opportunities presented by these advanced technolo-

gies. Caiazzo et al. (2022) demonstrate the potential

for improved process monitoring and control through

AI-assisted monitoring and risk classiﬁcation in man-

ufacturing environments.

Zhang et al. (2014) explore the feasibility of

cloud manufacturing architectures, presenting a prac-

tical prototype that provides a comprehensive and

integrated platform for the manufacturing industry.

Building on this, Cui et al. (2022) propose a model

for 3D printing in cloud manufacturing, outlining four

different roles: cloud operators, 3D printing service

providers, demanders, and logistics service providers.

Skulj et al. (2017) discuss a distributed network

architecture that provides a more ﬂexible and scal-

able alternative to centralised systems, supported by

compute and knowledge clouds. Lu et al. (2014) ex-

plore the concept of a hybrid manufacturing cloud,

which combines traditional manufacturing processes

with cloud-based technologies to enable efﬁcient in-

tegration and resource optimisation.

Wang et al. (2019) and Rudolph and Emmel-

mann (2017) explore the integration and optimisation

of manufacturing resources through cloud computing,

enabling the creation of interoperable cloud manufac-

turing concepts that support the global optimisation of

manufacturing resources. They highlight how cloud

manufacturing can enhance collaborative manufactur-

ing environments through virtualisation and services.

Adamson et al. (2017) and Wu et al. (2013) ad-

dress the challenges associated with cloud manufac-

turing, including cybersecurity concerns and the need

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220

for intelligent monitoring and control systems. They

also discuss future developments and potential solu-

tions to fully realise the full potential of cloud manu-

facturing.

Giunta et al. (2023), Vedeshin et al. (2020) and

Simeone et al. (2020) present innovative applications

and forward-looking technologies in cloud manufac-

turing. Their work illustrates how cloud-based tech-

nologies are transforming the manufacturing industry

by offering ﬂexibility and personalised manufacturing

capabilities.

2.2 Additive Manufacturing Systems

Additive manufacturing (or commonly known as 3D

printing) has seen signiﬁcant technological advances

in recent years and has the potential to transform the

way how products are made across multiple industries

by challenging and complementing traditional manu-

facturing approaches.

Shahrubudin et al. (2019) and Rauch et al. (2018)

provide a comprehensive overview of the transforma-

tive impact of additive manufacturing on industrial

applications such as automotive, aerospace, and me-

chanical engineering. They highlight the ability of 3D

printing techniques to enable the production of com-

plex parts that are difﬁcult or impossible to manufac-

ture using traditional methods. The authors also dis-

cuss the beneﬁts in terms of reducing material waste

and accelerating design cycles, leading to shorter iter-

ation and innovation cycles.

The application of additive manufacturing in mil-

itary contexts demonstrates the use of this technology

to overcome logistical challenges and increase efﬁ-

ciency. Jagoda et al. (2020) describe the use of 3D

printing for rapid on-site production of parts, which

is particularly beneﬁcial in conﬂict and crisis areas.

Fiske et al. (2018) discuss the possibility of using

additive manufacturing to construct building struc-

tures through additive manufacturing in remote areas.

Rankin et al. (2014) highlight the cost-effectiveness

and functionality of 3D printed surgical instruments

produced through 3D printing, which can improve

medical care on the battleﬁeld.

In the medical ﬁeld, Url et al. (2022) discuss the

production of personalised medical implants and sur-

gical instruments through 3D printing. They high-

light how 3D printing services are being integrated

into hospitals, leading to improved, patient-speciﬁc

treatment strategies. Ghilan et al. (2020) provide a

comprehensive overview of developments in 3D and

4D printing, highlighting the importance of machine

learning in improving design efﬁciency and optimis-

ing the functionality of medical devices.

Panda et al. (2023) discuss technical limitations

such as quality assurance in additive manufacturing

processes and challenges in material selection. They

emphasise that scaling up production and integration

into existing manufacturing systems must be strategi-

cally planned to achieve effective results.

2.3 Business Models, Sustainability and

Social Aspects

The rapid development of digital technologies and

their integration into the manufacturing industry is

leading to fundamental changes in traditional busi-

ness models. The additive MaaS approach exem-

pliﬁes this shift, allowing companies to control and

scale their production processes via cloud-based plat-

forms. Goldhar and Jelinek (1990) emphasise that

Computer Integrated Manufacturing (CIM) improves

product variety and customisation by promoting the

integration of information technology into production

processes. This more ﬂexible production environ-

ment allows companies to respond quickly and cost-

effectively to individual customer requirements.

Nie et al. (2023) discuss the ﬂexibility of 3D print-

ing in monopolistic markets and its impact on cus-

tomer loyalty and market dominance of companies.

Ivanov et al. (2022) further explain that cloud supply

chain models enable seamless integration and man-

agement of physical and digital assets, increasing op-

erational ﬂexibility and more dynamic adaptation of

resources to changing market conditions.

Rauch et al. (2018) and Smith et al. (2013) dis-

cuss the impact of the introduction of mass customi-

sation and adaptive manufacturing systems on tra-

ditional production paradigms. The introduction of

these technologies implies not only a change in man-

ufacturing processes, but also a redesign of customer

interactions and the entire value chain.

Pahwa and Starly (2021) show how the use of deep

reinforcement learning can improve decision making

on MaaS platforms, increasing adaptability and re-

sponsiveness. Sun et al. (2024) provide a detailed

analysis showing that, under certain conditions, ad-

ditive manufacturing processes can outperform tradi-

tional methods in terms of cost and speed, particularly

for rapid product iterations and complex designs.

Chaudhuri et al. (2021) evaluate the impact of dif-

ferentiated pricing strategies on MaaS platforms on

proﬁtability and market penetration. Strategic pricing

decisions allow companies to tailor their offerings to

speciﬁc market segments and thereby achieve optimal

market positioning.

Dhir et al. (2023) and Bulut et al. (2021) exam-

ine the challenges and potential of MaaS for SMEs

A Cost-Beneﬁt Analysis of Additive Manufacturing as a Service

221

in different geographical regions. Tao et al. (2017)

and Fisher et al. (2018) discuss the environmen-

tal aspects of sustainability in manufacturing. They

show how cloud manufacturing can improve resource

efﬁciency, minimise waste, and increase energy ef-

ﬁciency through optimised manufacturing processes

and improved material usage.

2.4 Summary

The identiﬁed literature reveals a growing body of

research on cloud-based manufacturing systems, ad-

ditive manufacturing, and their associated business

models and sustainability aspects (as shown in Table

1). However, there are still signiﬁcant gaps in our un-

derstanding of how these technologies can be effec-

tively integrated and implemented in real-world sce-

narios, particularly in the context of MaaS.

While existing research explores the beneﬁts and

challenges of cloud-based manufacturing systems and

additive manufacturing, few studies focus on the inte-

gration of these technologies within the MaaS ecosys-

tem and their practical applicability in different in-

dustrial contexts. In particular and to the best of our

knowledge, the area of designing and implementing

a prototype that provides additive MaaS has not been

explored in detail.

This paper aims to address these gaps by devel-

oping and analysing a MaaS-based prototype that in-

tegrates cloud-based systems with additive manufac-

turing. Our work contributes to the ﬁeld by (1) pro-

viding a comprehensive analysis of the architectural

and technical requirements for implementing additive

manufacturing as a cloud-based service, (2) perform-

ing a direct comparison of cost structures between

different 3D printers within a MaaS approach, (3)

evaluating the impact of an additive MaaS approach

on reducing environmental impact and promoting re-

gional economic development in the context of sus-

tainable production practices, and (4) developing and

analysing a functional prototype to demonstrate the

practical implementation of these concepts.

3 CLOUD CRAFTING

PLATFORM

Customer attitudes have changed to the point where

they want to know more than ever where their prod-

ucts come from and how they are made. The de-

sire to actively inﬂuence the manufacturing process

is greater than ever. Many prefer to support local

economies and buy regionally and sustainably pro-

duced products rather than rely on imported mass-

produced goods. Thanks to technological advances in

additive manufacturing (Wong and Hernandez, 2012),

it is now possible to produce a wide range of products

(Shahrubudin et al., 2019) locally and cost-effectively

using three-dimensional (3D) printing technologies

(Mai et al., 2016). At the heart of the MaaS approach

is a Cloud Crafting Platform that can be used by 3D

printer operators to integrate their 3D printers online

and offer 3D printing as a service.

With this Cloud Crafting Platform, any 3D printer

operator can become a local on-demand producer, ca-

pable of handling a certain amount of customisation

during 3D printing (or manufacturing). This strength-

ens the local economy and offers customers the op-

portunity to buy products that have been made in the

nearby region. Another aim of the Cloud Crafting

Platform is to break up traditional supply chains and

produce products where they are bought. This elimi-

nates long transport routes and has a positive impact

on the environment.

The Cloud Crafting Platform follows a server-

less architecture approach that can connect both web

shops and 3D printer operators. the following sections

outline the overall use case, prototype architecture,

and experimental testbed setup of the Cloud Crafting

Platform, which aims to provide an alternative to tra-

ditional manufacturing processes by enabling local,

sustainable production.

3.1 Overall Use Case

As shown in Figure 1, the Cloud Crafting Platform

orchestrates a comprehensive ecosystem connecting

ﬁve key stakeholders in an innovative additive MaaS

approach. At its core, the platform acts as a bridge

between the point of sale (web shops) and the point

of manufacture (3D printer operators), enabling a

seamless on-demand production process. Similar to

how mobile app stores connect developers and users,

the Cloud Crafting Platform maintains a repository of

Computer Aided Design (CAD) models created by

3D model designers. These digital blueprints form

the basis of the platform’s product catalogue. Before

the products can be offered in the web shop, the CAD

model designers upload their 3D designs to the plat-

form, where they become available for commercial

use. The web shop operators can then integrate these

designs into their online stores, offering products that

do not yet exist in physical form, but can be manufac-

tured on demand.

When a customer makes a purchase, it triggers an

order workﬂow that initiates the manufacturing pro-

cess through the web shop, which forwards the re-

quest to the Cloud Crafting Platform. The platform

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222

Table 1: Summary of Related Work on Additive MaaS.

Cloud-based Manufacturing Systems Sources

Technological foundations and advanced technologies

Thames and Schaefer (2017),

Caiazzo et al. (2022)

Feasibility and integrated platforms in the context of Cloud Manufacturing

Zhang et al. (2014),

Cui et al. (2022)

Decentralised and hybrid cloud network architectures to increase ﬂexibility

Skulj et al. (2017),

Lu et al. (2014)

Interoperability and service-oriented Cloud Manufacturing systems

Wang et al. (2019),

Rudolph and Emmelmann (2017)

Cybersecurity challenges and development of monitoring systems

Adamson et al. (2017),

Wu et al. (2013)

Innovative applications and future-oriented technologies

Giunta et al. (2023),

Vedeshin et al. (2020),

Simeone et al. (2020)

Additive Manufacturing Sources

Transformative impact on industrial applications through additive manufacturing

Shahrubudin et al. (2019),

Rauch et al. (2018)

Additive manufacturing and its beneﬁts in military applications

Jagoda et al. (2020),

Fiske et al. (2018),

Rankin et al. (2014)

Additive manufacturing and its beneﬁts in medical applications

Url et al. (2022),

Ghilan et al. (2020)

Challenges in implementing additive manufacturing

Panda et al. (2023),

Url et al. (2022),

Ghilan et al. (2020)

Business Models, Sustainability and Social Aspects Sources

Business model innovations and their impact on market structures

Goldhar and Jelinek (1990),

Nie et al. (2023)

Cloud supply chain models and mass customisation

Ivanov et al. (2022),

Rauch et al. (2018),

Smith et al. (2013)

Optimisation of MaaS platforms and economic analyses

Pahwa and Starly (2021),

Sun et al. (2024),

Chaudhuri et al. (2021)

Regional adaptations and sustainability approaches for Cloud Manufacturing

Dhir et al. (2023),

Bulut et al. (2021),

Tao et al. (2017),

Fisher et al. (2018)

Figure 1: Overall Use Case where a buying customer initiates the on-demand MaaS process (adapted from Ivki

c et al., 2024).

A Cost-Beneﬁt Analysis of Additive Manufacturing as a Service

223

then identiﬁes the nearest available SME with ap-

propriate 3D printing capabilities and transmits the

production speciﬁcations. This SME, acting as the

3D printer operator, manufactures the product locally,

signiﬁcantly reducing transport distances and sup-

porting regional economic development. The archi-

tecture of the Cloud Craftin Platform enables ﬁve dif-

ferent stakeholders to interact seamlessly within the

additive MaaS ecosystem, namely (Stakeholder 1)

the customer, who initiates the process by purchas-

ing a product, (Stakeholder 2) the web shop operator

who provides the point of sale, (Stakeholder 3) the

Cloud Crafting Platform operator who manages the

overall service and enables the interaction of all stake-

holders involved, (Stakeholder 4) the CAD model

designer who creates the digital product designs, and

(Stakeholder 5) the 3D printer operator (SME) who

produces the physical products.

The Cloud Crafting Platform goes beyond simply

connecting the identiﬁed stakeholders; it also creates

a proﬁt-sharing ecosystem where revenue from each

product sold is shared among the four key service

providers (web shop operator, Cloud Crafting Plat-

form operator, CAD model designer, 3D printer op-

erator). This ensures that each stakeholder receives

fair compensation for their contribution to the manu-

facturing process (Ivki

c et al., 2024).

Furthermore, while the initial implementation of

the Cloud Crafting Platform focuses on 3D printing

technology, its architecture is designed to accommo-

date a wider range of manufacturing technologies.

The system can be extended to integrate various man-

ufacturing machines such as Computerised Numerical

Control (CNC), laser cutting, plotter cutting, robotics,

and augmented reality technologies, making it a ver-

satile solution for different manufacturing needs. This

extensibility ensures that the platform can evolve with

technological advances and adapt to changing market

demands while maintaining its core principle of con-

necting customers with local manufacturers.

3.2 Prototype Architecture

The Cloud Crafting Platform follows a Service-

Oriented Architecture (SOA) designed to ensure scal-

ability, reliability, and seamless integration between

web shops and 3D print shops. The architecture con-

sists of two gateways, a central load balancer, and ﬁve

core services that work together to enable the MaaS

ecosystem. Together, all the building blocks provide

the necessary endpoints to connect all the web shops

and 3D print shops, including all the services, to pro-

cess a purchased product and route it to the nearest

available MaaS production site. To achieve this goal,

the platform provides two gateways for external shop

and production site integration and external commu-

nication:

• API Gateway: this gateway acts as an entry point

for web shops, handling all incoming requests and

ensuring secure communication protocols.

• Cloud Gateway: this gateway manages connec-

tions with print shops and enables real-time com-

munication with the production site equipment.

The Load Balancer acts as the primary trafﬁc

manager, distributing incoming requests across ser-

vices to ensure optimal resource utilisation and high

availability. By working in conjunction with the Dis-

covery Service, which enables dynamic service regis-

tration and discovery, these two services eliminate the

need for hard-coded service locations.

The Order Service manages the complete lifecy-

cle of manufacturing orders from inception to com-

pletion. It handles the creation of new orders as

customers make purchases, maintains real-time order

Figure 2: Service-Oriented Cloud Crafting Platform Architecture (adapted from Ivki

c et al., 2024).

CLOSER 2025 - 15th International Conference on Cloud Computing and Services Science

224

status updates, and stores all order-related informa-

tion. This service ensures that each order is properly

tracked and executed throughout the manufacturing

process.

The Authentication/Authorisation Service is the

security backbone of the platform, managing user reg-

istration processes, handling login requests, and val-

idating security tokens for all platform interactions.

This service ensures that only authorised users have

access to speciﬁc platform features and maintains se-

cure communication channels between different com-

ponents of the system. It implements role-based ac-

cess control to ensure that each stakeholder only has

access to the functionality relevant to their role.

The Printer Service orchestrates all aspects of the

manufacturing process, managing the scheduling and

execution of print jobs, monitoring the real-time sta-

tus of connected 3D printers, and maintaining printer

availability information. This service also manages

the queuing of print jobs, ensuring optimal use of

available printing resources while maintaining qual-

ity control throughout the manufacturing process.

The Billing Service handles all ﬁnancial aspects

of the platform, processing transactions, manag-

ing the platform’s proﬁt-sharing mechanism between

stakeholders, and handling the creation, validation,

and redemption of promotional codes. This service

maintains detailed records of all ﬁnancial transactions

and ensures the distribution of revenue between the

web shop operator, Cloud Crafting Platform operator,

CAD model designer, and 3D printer operator.

A centralised database system underpins these ser-

vices, storing essential information including (1) user

proﬁles and authentication data, (2) order details and

status, (3) printer conﬁgurations and availability, (4)

billing and transaction records, and (5) redeem codes

and usage history. All in all, this architecture ensures

that the Cloud Crafting Platform can efﬁciently han-

dle the complex interactions between customers, web

shops, and MaaS production sites while maintaining

security, scalability, and reliability.

3.3 Testbed Setup

To perform a cost-beneﬁt analysis, the overall use

case from Figure 1 was implemented following the

prototype architecture described in the previous sec-

tion. This allows an end-to-end validation of the

Cloud Crafting Platform functionality from product

purchase to local manufacturing. In addition to that

the testbed can be used to evaluate the production

costs associated with manufacturing a given product

using the MaaS approach.

As shown in Figure 3, the Cloud Crafting Plat-

form was deployed in the Microsoft Azure cloud, im-

plementing all of the core services and components

described in the prototype architecture. This cloud

deployment hosts the Load Balancer, the Discovery

Service, and the four core services (Order, Authen-

tication/Authorization, Printer, and Billing), as well

as the central database. In addition, a web shop was

implemented and deployed on a dedicated virtual ma-

chine to serve as the point of sale. This web shop was

integrated with the Cloud Crafting Platform through

the API Gateway, allowing for seamless transfer of

purchase orders to the cloud environment for process-

ing by the platform’s core services.

The point of manufacture was set up in a labo-

ratory environment, simulating a local SME produc-

tion site, including the following three networked 3D

Figure 3: Testbed Setup for End-to-End Validation of the MaaS Approach.

A Cost-Beneﬁt Analysis of Additive Manufacturing as a Service

225

printers:

• Printer 1: Ultimaker 2+ CONNECT

• Printer 2: Creality K1 MAX

• Printer 3: Prusa MK4

Each 3D printer was connected to a dedicated

Raspberry Pi running the OctoPi operating system

(OS), which provides (1) remote printer control ca-

pabilities, (2) real-time monitoring of print jobs, (3)

secure communication with the Cloud Crafting Plat-

form via the Cloud Gateway, and (4) integration

with the platform’s Printer Service for job manage-

ment. The Raspberry Pi devices act as local con-

trollers, managing printer operations and maintaining

bi-directional communication with the Cloud Crafting

Platform via the Cloud Gateway. This setup enables

automated order processing, printer control, and sta-

tus monitoring, completing the end-to-end manufac-

turing process from online purchase to local produc-

tion. The following ﬁgure shows the local setup of a

3D printer connected to a Raspberry Pi that runs Oc-

toPi OS interacting with the Cloud Crafting Platform:

Figure 4: Testbed Laboratory Setup Simulating a Local

SME Production Site.

4 COST-BENEFIT ANALYSIS

To evaluate the economic viability of the Cloud Craft-

ing Platform, we conducted a comprehensive cost-

beneﬁt analysis using a designed ring as a test prod-

uct. This analysis evaluates the operational costs

across three key dimensions: web shop hosting, cloud

platform operation, and actual production costs. Our

analysis focused on a speciﬁcally designed ring that

was uploaded to the deployed Cloud Crafting Plat-

form and made available for purchase through the in-

tegrated web shop. The following ﬁgure shows a de-

signed test product (ring) for the cost-beneﬁt analysis:

The cost-beneﬁt analysis included three primary

cost categories that are essential for evaluating the

economic viability of the Cloud Crafting Platform.

Figure 5: Designed Ring as a Test Product for the Cost-

Beneﬁt Analysis.

The Web Shop Operational Costs include all ex-

penses associated with maintaining an online retail

presence through Shopify. This includes the monthly

subscription fees for hosting the e-commerce plat-

form, transaction processing fees for each sale, and

the technical integration costs required to connect the

web shop with the Cloud Crafting Platform. Addi-

tional costs include domain registration, Secure Sock-

ets Layer (SSL) certiﬁcates, and any premium fea-

tures or plug-ins required to run the online storefront.

The Microsoft Azure Cloud Infrastructure Costs

are a signiﬁcant component of the operational ex-

penses. These costs include compute resources for

running the microservices architecture, storage capac-

ity for maintaining the CAD model repository, and

bandwidth consumption for data transfer between dif-

ferent platform components. The analysis includes

Azure service fees across multiple components, in-

cluding the API Gateway, the Load Balancer, the

Discovery Service, and the four core services (Order,

Authentication/Authorization, Printer, and Billing).

Additionally, the costs include database hosting, mon-

itoring tools, and security services required to main-

tain a robust and reliable cloud platform.

The Production Costs represent the actual manu-

facturing expenses incurred at the local SME level.

This category includes three critical components:

First, the temporal costs measure the complete pro-

duction cycle time, from initial setup and printer

preparation to the ﬁnal post-processing steps. Second,

material consumption costs include both the primary

printing material and any support structures required

during the manufacturing process. Thirdly, energy

consumption costs are evaluated using Shelly Plug de-

vices, which measure the power consumption of both

the 3D printers and their controlling Raspberry Pi.

These measurements provide a comprehensive view

of the resource requirements and associated costs for

local, on-demand manufacturing.

To evaluate the economic viability of the Cloud

Crafting Platform, we conducted a systematic cost-

beneﬁt analysis through repeated test runs of the en-

tire manufacturing process. Using our deployed web

shop, we initiated three simultaneous purchase orders

for identical rings, which were processed through the

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226

Cloud Crafting Platform and distributed to our three

different 3D printers (Ultimaker 2+ CONNECT, Cre-

ality K1 MAX, and Prusa MK4). This parallel pro-

duction setup allowed us to directly compare the man-

ufacturing performance and associated costs between

different printer models. To ensure statistical signif-

icance and to account for potential variations in the

manufacturing process, we performed 50 complete

test runs, resulting in each printer producing 50 iden-

tical rings (n=50). This comprehensive approach al-

lowed us to collect data on production times, material

usage, and energy consumption for different printer

models while operating under identical conditions.

4.1 Evaluation

The material cost to produce a single ring varied be-

tween the three 3D printers based on their respective

ﬁlament prices and usage. The Ultimaker 2+ CON-

NECT (Printer 1), which uses 2.9 grams (g) of ﬁla-

ment at a cost of C 42.99 per 750g, had a material cost

of C 0.17 per ring. The Creality K1 MAX (Printer

2), which uses 2.835g of ﬁlament at a cost of C 23.14

per kilogram (kg), had a material cost of C 0.07 per

ring. Similarly, the Prusa MK4 (Printer 3), which

uses 2.85g of ﬁlament at a cost of C 29.99 per kg, had

a material cost of C 0.09 per ring.

While all three printers used comparable amounts

of ﬁlament (between 2.835g and 2.9g), the Creality

K1 MAX (Printer 2) proved to be the most cost-

effective in terms of material usage, followed by

the Prusa MK4 (Printer 3), while the Ultimaker 2+

CONNECT (Printer 1) had the highest material cost

per ring, primarily due to its more expensive ﬁlament.

For each printer, the material cost per ring can be ex-

pressed using the following equations:

material, printer

ring

spool

∗ p

spool

(1)

where:

ring

is the mass of ﬁlament used per ring

spool

is the mass of ﬁlament per spool

spool

is the price per spool

material, printer

is material cost printer for each ring

The following equations calculate the material

costs for each printer:

material, printer1

2.9g

750g

∗ C 42.99 = C 0.17

material, printer2

2.835g

1000g

∗ C 23.14 = C 0.07

material, printer3

2.85g

1000g

∗ C 29.99 = C 0.09

To provide a detailed insight into energy con-

sumption patterns, the power consumption measure-

ments were divided into three distinct phases. The

”Pre-Print” phase includes the initial warm-up period

during which the printer heats both the nozzle and the

build plate to the required temperatures before print-

ing can begin. The ”Print” phase is the actual pro-

duction period during which the printer actively 3D

prints the ring. Finally, the ”Post-Print” phase cap-

tures the energy consumed during the cool-down pe-

riod when the printer returns to its standby state at the

end of the production process. This three-phase mea-

surement approach provides a comprehensive under-

standing of energy consumption patterns throughout

the entire manufacturing cycle and allows for more

accurate cost calculations of the additive manufactur-

ing process. The following equation calculates the

power consumption costs for each printer:

E, printer

printer

)

1000

∗ T

(2)

where:

printer

represents the power consumption of the

3D printer in Watt hours (Wh)

represents the power consumption of the

Raspberry Pi in Wh

1000

converts Wh to kWh

represents the energy rate in Euro (C)/kWh

(we used 0.30 C/kWh for the experiment)

E, printer

represents the total energy cost in C

for each printer setup

Similar to the energy consumption measurements,

the printing process time is also divided into the

three different operational phases (Pre-Prin, Print, and

Post-Print) to enable more accurate cost calculations.

These time measurements can be converted into mon-

etary costs by taking into account the power consump-

tion during each phase. The total time-based energy

cost can be calculated using the following equation:

time

pre

∗ P

pre

∗ P

post

∗ P

post

)

1000

∗ T

(3)

where:

pre

, t

post

represents the duration

of each phase in hours

pre

, P

post

represent the total power

consumption in watts (W)

during each respective phase

1000

converts Wh to kWh

represents the energy rate

in Euro (C)/kWh (we used

0.30 C/kWh for the experiment)

A Cost-Beneﬁt Analysis of Additive Manufacturing as a Service

227

Table 2: Summary of Production Costs per 3D-Printer to manufacture the Test Product (Ring).

Phase Time Power (W) Material (C) Total ( C)

Printer 1:

Ultimaker

2+ CONNECT

Pre-Print 00:03:18 12.28 0

0.197Print 00:35:05 77.93 0.17

Post-Print 00:05:52 0.52 0

Printer 2:

Creality

K1 MAX

Pre-Print 00:04:14 14.18 0

0.081Print 00:09:06 22.06 0.07

Post-Print 00:00:10 0.1 0

Printer 3:

Prusa MK4

Pre-Print 00:04:47 15.73 0

0.105Print 00:10:40 34.29 0.09

Post-Print 00:00:46 1.02 0

Equation (3) calculates the energy cost based

on time and power consumption, converting the 3D

printing time (production) into energy costs using the

power consumption rate and energy price. The fol-

lowing equation calculates the total production cost

of a single ring by adding up all the individual cost

components:

production

= C

material

time

(4)

Table 2 summarises the total production cost per

ring for each printer, including the production time,

power consumption and material usage in the pre-

print, print and post-print phases. To calculate the

operational costs of running a Shopify-based web

shop there are several components to considered.

Shopify’s basic plan, which provides essential e-

commerce functionality, costs C 29.00 per month.

This includes hosting, SSL certiﬁcation, and basic e-

commerce features. In addition, Shopify charges a

transaction fee of 2% per sale for using external pay-

ment providers. When distributing these ﬁxed costs

over an average monthly production volume of 100

rings, the Web Shop Operational Costs are C 0.29.

The Cloud Crafting Platform, deployed on Mi-

crosoft Azure, incurs costs based on resource con-

sumption and service usage. Using the Azure pric-

ing calculator, the monthly cost to run the com-

plete platform architecture includes: API Gateway (

0.421 per million calls), Load Balancer (C 0.0225

per hour), and the core services running on Azure

App Service ( C 0.149 per hour), resulting in approx-

imately C 175 per month for the entire infrastructure.

Distributed over an average monthly production vol-

ume of 100 rings, the Microsoft Azure Cloud Infras-

tructure Costs are C 1.75 per ring. The following

equation can be used to calculate the total production

costs per ring including the Web Shop Operational

Costs, Microsoft Azure Cloud Infrastructure Costs

and Production Costs:

total

= C

webshop

cloud

production

(5)

where:

To sum it up, the total cost per ring, combining

web shop costs (C 0.29), cloud costs (C 1.75), and

production costs (depending on the printer) are:

webshop

is the Web Shop Operational Costs

cloud

is the Microsoft Azure Cloud

Infrastructure Costs

production

is the Production Costs per ring

total, printer1

= 0.29 + 1.75 + 0.197 = C 2.237

total, printer2

= 0.29 + 1.75 + 0.081 = C 2.121

total, printer3

= 0.29 + 1.75 + 0.105 = C 2.145

4.2 Discussion

The cost-beneﬁt analysis demonstrates the economic

viability of the Cloud Crafting Platform and the MaaS

approach to additive manufacturing. The total cost

per ring, including web shop costs ( C 0.29), cloud

platform costs ( C 1.75) and production costs (be-

tween C 0.081 and C 0.197), is between C 2.121 and

C 2.237, depending on the printer model used. With

a reasonable market price of C10-15 per ring, this

results in a signiﬁcant proﬁt margin of around 400-

600%, making it attractive to all stakeholders.

Scaling up production to 100 rings per month, the

Total Cost of Ownership (TCO) would be between C

212.10 and C 223.70. With a potential revenue of

C 1,000 (assuming a selling price of C 10 per ring),

this generates a monthly proﬁt of approximately C

776.30 to C787.90. This proﬁt can be shared between

the four service-providing stakeholders: the web shop

operator, the cloud platform operator, the CAD model

designer, and the 3D printer operator.

Using a weighted distribution model based on

infrastructure investment, operational responsibility,

and ongoing commitment, each stakeholder could re-

ceive the following proﬁt share per month:

• Cloud Crafting Platform Operator (40%): re-

ceives the highest share due to platform mainte-

nance and core service delivery.

Total share per month: C 310.52-315.16

• 3D Printer Operator (30%): receives the second

highest share for providing equipment and exper-

tise including managing physical production and

quality control.

Total share per month: C 232.89-236.37

CLOSER 2025 - 15th International Conference on Cloud Computing and Services Science

228

• Web Shop Operator (20%): receives a share for

managing the customer interface, sales, customer

support and marketing.

Total share per month: C 155.26-157.58

• CAD Model Designer (10%): receives a share

for creating the initial product designs (one-off

contribution per product).

Total share per month: C 77.63-78.79

The Creality K1 MAX (Printer 2) demonstrates

the most cost-effective production metrics, with the

lowest combined material and energy costs (C 0.081

per ring) and the fastest production time (13:30 min-

utes), making it particularly suitable for high-volume

production scenarios. The MaaS approach not only

proves economically viable, but also offers signiﬁcant

resource efﬁciency and scalability beneﬁts. The abil-

ity to produce on-demand eliminates inventory costs

and reduces waste, while the distributed manufactur-

ing model enables local production, reducing trans-

portation costs and environmental impact. In addi-

tion, the proﬁt-sharing model creates a sustainable

ecosystem that incentivises all stakeholders to partic-

ipate in and contribute to the success of the Cloud

Crafting Platform.

5 CONCLUSIONS

This paper presents a comprehensive analysis of a

Cloud Crafting Platform that enables MaaS through

additive manufacturing technologies. Our research

demonstrates both the technical feasibility and eco-

nomic viability of connecting web shops to local

3D printing facilities through a cloud-based platform.

The prototype implementation, with a SOA architec-

ture deployed on Microsoft Azure, successfully in-

tegrates all the components required for end-to-end

manufacturing services: from online product pur-

chase to local additive production. The testbed setup

included three different 3D printer models and pro-

vided valuable insights into the operational character-

istics and cost structures of on-demand production.

The cost-beneﬁt analysis shows a compelling

business case for all stakeholders. With a total pro-

duction cost of approximately C 2.24 per ring and

a retail price of C 10, the platform generates sufﬁ-

cient margins to sustain a proﬁtable ecosystem. The

weighted proﬁt-sharing model (40% cloud crafting

platform operator, 30% 3D printer operator, 20% web

shop operator, and 10% CAD designer) ensures fair

compensation based on investment levels and opera-

tional responsibilities.

In future work we plan to explore the integra-

tion of additional manufacturing technologies beyond

3D printing, enhanced quality control mechanisms,

and optimisation of the proﬁt-sharing model based

on real-world implementation data. The successful

demonstration of this platform contributes to the on-

going evolution of distributed manufacturing systems

and provides a blueprint for the practical implemen-

tation of MaaS.

ACKNOWLEDGEMENTS

Research leading to these results has received funding

from the Digital Innovation Hub S

ud (DIH - S

ud) for

the innovation project RePro3D funded by DIH S

GmbH 2023 - 2024.

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