The Future of Additive Manufacturing 
(3D Printing) in Kenya: Scenarios for 
2063 Using Delphi Approach
Eric Ondimu Monayo and Godfrey Mugwimi 
Kenya Institute for Public Policy 
Research and Analysis
KIPPRA Discussion Paper No. 330
2024
 
The future of additive manufacturing (3D Printing) in Kenya
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Published 2024
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KIPPRA acknowledges generous training on futures foresight methodology by 
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Chair at Dedan Kimathi University of Technology (DeKUT), Kenya. The course 
on “Building Strategic Foresight Capabilities” by EDHEC Business School was 
beneficial for building capacity of Young Professionals on futures foresight. 
ii
Abstract
The rapid growth of additive manufacturing (AM) is mainly due to its evolution from 
primarily a prototyping tool to a useful end-product fabrication method. As much as 
the Government of Kenya has demonstrated its willingness to support 3D printing 
as an important technology in the development of the manufacturing sector, only 
11.4 per cent of firms have adopted the technology. The study assessed the future 
of additive manufacturing in Kenya through the formulation of the most probable 
scenarios for 2063 using the Delphi approach. 
A country transitioning to additive manufacturing by 2063 may find itself experiencing 
early transition, lagged transition, or late transition. Early transition to additive 
manufacturing would be influenced by the ability to reduce the cost of production, the 
ability to precisely replicate products, product customization, and enhanced product 
performance through additive manufacturing. The lagged transition would be 
occasioned by limited collaboration among firms on the production of 3D technology 
products and low demand for AM products. The late transition may be attributed 
to the absence of a regulatory framework that facilitates the sharing of industry-
specific 3DP technology and the failure of additive manufacturing to reduce carbon 
emissions.
To achieve early transition, the government needs to support firms to invest in 3D 
printing technology, including reducing the cost of production through the purchase 
of equipment, financial support, and training of employees on AM. Further, 
the government to support industrial institutions to build a skilled workforce 
proficient in 3D printing technologies to enhance capabilities to produce products 
with complex geometries. To avoid a lagged transition, there is a need to enhance 
collaboration through the promotion, establishment, and maintenance of shared 
3D printing facilities that small and medium-sized enterprises (SMEs) can access 
without requiring to undertake large capital investments. Further, institutionalize 
collaborations by anchoring partnerships in the National ICT Policy or the National 
Industrialization Policy. To boost demand for AM products, the government may 
ensure government procurement reflects the preference for 3D printed components 
in public projects. To avoid late transition, the government needs to promote the 
adoption of sustainable practices in 3D printing, which include the use of eco-friendly 
materials, energy-efficient processes, recycling, and waste reduction strategies.
iii
The future of additive manufacturing (3D Printing) in Kenya
Abbreviations and Acronyms
3DP  3D Printing Technology 
4IR  4th Industrial Revolution 
WITS  World Integrated Trade Solution
AM  Additive Manufacturing 
KIRDI  Kenya Industrial Research and Development Institute 
ICT  Information Communication Technology 
iv
Table of Contents
Abstract............................................................................................iii
Abbreviations and Acronyms............................................................iv
List of Tables and Figures 
1. Introduction............................................................................1
2. Status of Additive Manufacturing in Kenya...............................3
2.1 Policy Framework in the Context of Additive Manufacturing.................3
3. Literature Review....................................................................5
3.1 Theoretical Literature ........................................................................5
3.2 Empirical Literature...........................................................................7
4. Methodology...........................................................................9
4.1 The Delphi Method ............................................................................9
4.2 Formulation of Projections of the Future of Additive Manufacturing 
.............................................................................................................9
4.3 Selection of Experts and Execution of Delphi Survey.............................11
4.4  Development of Future Scenarios......................................................12
5. Results and Discussion..........................................................13
5.1 Projections on the Future of Additive Manufacturing ......................13
5.2 Probability of Occurrence of the Projections and their Estimated 
Firm Impact ......................................................................................15
5.3 Probable Future Scenarios of Additive Manufacturing ...................18
6.  Conclusion and Recommendations.......................................22
6.1 Conclusion.........................................................................................22
6.2  Recommendations.............................................................................23
References .......................................................................................25
Appendix 1: DELPHI Questionnaire..................................................29
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The future of additive manufacturing (3D Printing) in Kenya
List of Tables and Figures 
List of Tables
Table 5.1: Projections on the Future of Additive Manufacturing..........................13
Table 5.2: Probability of occurrence and the firm impact of the Delphi projections 
..............................................................................................................15
List of Figures
Figure 2.1: Share of firms using 4IR technologies...................................................4
Figure 2.2: Value of AM machines imported in Kenya as of 2022..........................4
Figure 2.3: Scenario planning process....................................................................6
Figure 4.1: Steps in Delphi Method.........................................................................9
Figure 4.2: Response rate......................................................................................11
Figure 5.1:Scenarios cluster...................................................................................18
vi
1. Introduction
The manufacturing industry is experiencing a new wave of evolution. As the 
advancement of technology has accelerated, so too has the adoption of innovations 
in different fields, and manufacturing is no exception. There are several driving 
forces behind these advancements, which include efficiency, cost-effectiveness, and 
environmental and sociological factors. Additive manufacturing (AM), also known 
as 3D printing technology, is one of the 4IR technologies that are revolutionizing 
the manufacturing sector. This technology constructs three-dimensional objects 
by adding layer by layer from a digital file. In the additive process, an object is 
created by laying down successive layers of material until the object is created 
(Mpofu, Mawere, and Mukosera, 2014). Each of these layers forms a thinly sliced 
cross-section of the object. Additive manufacturing is, therefore, the opposite of 
the traditional machining technique that involves the removal of materials by way 
of cutting out or hollowing out a block of material. It is a departure from the mass 
production line to a one-off customizable production. 
Globally, the awareness of 3D printing is increasing due to its transformative 
impact on how items are manufactured in a variety of fields, including healthcare, 
transportation, the food industry, digital art, textiles and clothing, architecture, 
and construction design (Wu, Wang and Wang, 2016). Additive manufacturing 
was valued at US $26 billion as of 2022 up from US $6 billion in 2016. The rapid 
growth of this technology is mainly due to its evolution from primarily a prototyping 
tool to a useful end product fabrication method (Wohlers, Campbell, Diegel, Huff, 
and Kowen, 2020). Additionally, Gerstle et al. (2014) stated that 50 per cent of 
all globally manufactured goods will be printed using additive manufacturing 
technology by 2060 if the current investment in additive manufacturing continues. 
Other benefits of this technology include the creation of products that require 
highly complex geometries, enabling rapid prototyping, which allows companies 
to quickly iterate and test new designs, and the production of customized and 
personalized products in large quantities within a very short time among other 
benefits.
Despite these outstanding capabilities of AM technology for economic 
development, Africa is currently trailing behind some of the developed countries, 
such as the USA with 51 per cent adoption rate, and Germany with a 44 per cent 
adoption rate. The top ten economies in the Competitive Industrial Performance 
Index, which is a measure of the competitiveness of a country’s manufactured 
goods globally, are by extension the leading in additive manufacturing. Germany, 
for instance, was ranked position one (1) followed by the USA according to the 
UNIDO CIP database. Cotteleer and Joyce (2014) and Wu,  Myant, and Weider 
(2017) contend that future development of additive manufacturing is often 
hampered in several countries, especially those in developing countries, by high 
initial capital requirements for additive manufacturing machines and materials, 
intellectual property/privacy issues, lack of human capital, tiny production runs 
and scalability constraints, production standards and requirements, regulatory 
uncertainty in different countries, as well as a lack of choice of materials.
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The future of additive manufacturing (3D Printing) in Kenya
Under Vision 2030, Kenya aspires to be a middle-income, rapidly industrializing 
country and globally competitive by 2030. To achieve this, the government 
through the National ICT Policy has demonstrated its willingness to support 3D 
printing technology in the development of the manufacturing sector. To make 
the technology more accessible to the public, the government has encouraged 
all tertiary and secondary schools to acquire 3D printers and at the same time 
committed to providing grants to all innovation hubs and constructed labs to boost 
additive manufacturing capabilities. Further, the government has committed to 
ensure that intellectual property rights are granted to protect data objects capable 
of being manufactured as physical objects using 3D printing (National ICT Policy, 
2019). 
Among the labs constructed by the Government of Kenya to boost additive 
manufacturing capabilities is the University of Nairobi’s Fab Lab. The lab was the 
first to introduce 3D printers in Kenya and since then, the technology has grown 
to the various sectors of the economy. For instance,  Kijenzi Medtech Company 
uses 3D printing technology to provide medical solutions to Kenya’s rural clinics. 
Similarly, Ultra Red Technologies Company uses 3D printing to print customized 
canopies for wildlife exploration vehicles which is an untapped territory in the 
wildlife sector in Africa among other companies. Despite the steps taken to 
integrate additive manufacturing in various sectors of the economy, the adoption 
rate is still very low, (11.4%) according to the World Bank. Further, Kenya’s ranking 
in the Competitive Industrial Performance Index is not impressive (position 108 
out of 150 countries) as per the UNIDO CIP database. 
This study, therefore, intends to assess experts’ opinions on the future of this 
technology in Kenya and develop the most probable scenarios for 2063. In 
addition, the study will enrich the understanding of additive manufacturing 
among various stakeholders, that is, academia, industry, researchers, and the 
government in general given that the concept has not been widely researched in 
Kenya. Specifically, the study aims to develop projections of the future of additive 
manufacturing; determine the probability of occurrence and the impact of the 
projections on manufacturing firms; and develop probable future scenarios.
2
2. Status of Additive Manufacturing in Kenya 
This section presents the current policy framework, statistics on the number 
of firms that have adopted additive manufacturing, and the value of imports of 
machines for additive manufacturing. This will provide a clear picture of the rate 
of adoption of this technology.  
2.1 Policy Framework in the Context of Additive Manufacturing
2.1.1 National ICT Policy
This policy aims to harness the potential of the digital economy by establishing 
favourable conditions for all citizens and stakeholders. The decision to revise 
the 2006 policy was prompted by rapid advancements in the ICT sector, global 
trends, and evolving public demands. The Fourth Industrial Revolution, marked 
by automation and extensive data exchange, is profoundly influencing the 
environments at macro and micro levels, and increasing global ICT consumption. 
Hence, this policy initiative aims to capitalize on these transformations and 
trends to position Kenya as a more prosperous participant in the global economy. 
The ICT Policy outlines the government’s proactive stance on various aspects of 
Kenya’s evolving ICT sector landscape. 
The policy recognizes the technological trends in terms of mass personalization and 
personalized manufacturing. Personalized manufacturing represents a significant 
and growing advancement within the ICT and manufacturing sectors. To enhance 
the accessibility of this technology to the general public, the government plans to 
encourage all secondary and tertiary schools to adopt 3D printing capabilities. 
Additionally, innovation hubs and maker labs will receive grants to acquire 
additive manufacturing capabilities. Intellectual property protection will be 
extended to physibles, which are data objects capable of being manufactured into 
physical items using additive manufacturing methods. This policy aims to actively 
promote the emergence of new enterprises centred around the development of 
physibles.
2.1.2 Firms Using 4IR Technologies in Kenya 
The World Bank estimates that about 11.4 per cent of the firms in Kenya have 
adopted additive manufacturing (Figure 2.1). This signifies that the manufacturing 
sector is slowly embracing additive manufacturing, though on a small scale.
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The future of additive manufacturing (3D Printing) in Kenya
Figure 2.1: Share of firms using 4IR technologies
Source: World Bank (2022)
From Figure 2.1, cloud computing is the most adopted technology at 47.3 per cent, 
followed by precision agriculture at 22.4, per cent, additive manufacturing at 11.4 
per cent, and robotic technology at 3.4 per cent. Only 1.8 per cent of the firms have 
adopted big data analytics. 
2.1.3 Value of Additive Manufacturing Machines Imported in Kenya 
In terms of imports, only US$ 30,000 worth of machines for additive manufacturing 
were imported as of 2022, which further signifies a low rate of adoption of this 
technology in Kenya (Figure 2.2).
Figure 2.2: Value of AM machines imported in Kenya as of 2022
Source: World Integrated Trade Solution (2022)
The low adoption rate as shown by the percentage of firms using AM technology 
and the low value of import for the additive manufacturing machines demonstrate 
that Kenya still lags in the adoption of this technology. 
4
3. Literature Review
3.1 Theoretical Literature 
The theories anchoring this study were linked to the objectives and discussed in 
detail below. 
3.1.1 Theories for rigorous forecasting
Theories for rigorous forecasting encompass two main facets: domain-specific 
theories and general theories related to the mental way of solving problems in 
a quick way, reasoning biases, and the attributes of good forecasters. Domain-
specific theories involve various theories relating to phenomena like economic 
behaviour and political dynamics that help in projecting the future of these 
phenomena. Forecasting also involves exploring hypothetical scenarios through 
’what-if’ questions, where diverse events and their probable outcomes are 
systematically evaluated based on theoretical assumptions.
The general theories involve discussions on mental ways of solving problems in 
a quick way, rational biases, and the traits of successful judgmental forecasters 
that are drawn from sources such as systems and operations research (Winkler 
and Moser, 2016). The primary aim of these theoretical discussions is to strive 
for rational and unbiased forecasts. This body of work tends to focus more on 
developing methodologies for empirical research, like the Delphi method, rather 
than extensive theorization (Hasson and Keeney, 2011). 
3.1.2 Theories for effectively representing futures
These theories aimed at effectively conceptualizing future scenarios. They 
encompass scenario planning, explorative futures studies, and possibilistic 
approaches. Emphasis has been placed on supporting the theoretical foundations 
of scenario planning. The theoretical frameworks informing scenario planning are 
largely derived from organizational studies and decision analysis. Wilkinson (2009) 
underscores the need to distinguish scenarios clearly from forecasts, consider the 
aspect of effectiveness, and incorporate new theoretical advancements. 
Scenario Planning was developed by Kahn in 1967. He introduced a technique 
titled ’future-now thinking’. This approach intended to combine detailed analysis 
with imagination and come up with the reports in a way people would write them 
in the future. The name ’scenario’ was adopted when Hollywood determined the 
original name outdated. The theory is based on the premise of a participative 
approach to strategy that entails diverse thinking and conversations. Diverse 
thinking and conversations are used to change how the external environment 
is perceived. It seeks to anticipate some future scenarios rather than predict. 
Scenario planning involves strategic thinking, strategic decision making, and 
strategic planning (Figure 2.3).
5
The future of additive manufacturing (3D Printing) in Kenya
Figure 2.3: Scenario planning process
 Foresight Strategic thinking 
approaches and generating options Options 
methods What might happen? 
Strategic decision making; 
making choices 
Where will we go? Decisions 
Strategic planning 
Taking Action Action 
What will we do? 
Strategic thinking involves the exploration of signals and driving forces of the 
envisioned changes and is about generating options. Strategic decision making 
involves assessing options, examining choices, and setting a destination or 
direction. Strategic planning is about implementation and taking appropriate 
action.
3.1.1 3.1.3 Theories for making sense of anticipatory processes
These methods differ from previous approaches by focusing not on exploring 
potential or desired futures but on analyzing the processes and mechanisms 
concerning future concepts. The theory surrounding anticipatory processes 
seeks to understand how individuals and organizations anticipate, prepare, and 
respond to future developments. This theoretical framework is multidisciplinary 
and applied in psychology, sociology, economics, and futurology. 
From a psychological perspective, anticipatory processes involve cognitive 
mechanisms such as imagination, prediction, and planning. Researchers explore 
how individuals construct mental representations of future scenarios, assess 
probabilities, and make decisions based on these assessments. Emotions also play 
a crucial role, in influencing how individuals perceive and respond to anticipated 
events.
6
Scenario 
Planning 
Literature review
Sociologists examine anticipatory processes within the context of social systems 
and institutions. They investigate how collective expectations, norms, and 
values shape anticipatory behaviour at both individual and societal levels. Social 
dynamics, such as collective anxiety or optimism about the future, can influence 
group behaviour and decision making.
Economists study anticipatory processes concerning economic phenomena such 
as investment, consumption, and production. For instance, Rational Expectations 
Theory indicates that individuals and firms form expectations about future 
economic variables based on all available information. These expectations guide 
current economic decisions and behaviour, hence influencing market outcomes.
Futurologists or foresight practitioners focus on developing methods and 
frameworks for systematically anticipating future trends and scenarios. This 
includes scenario planning, trend analysis, and speculative fiction. They explore 
how various drivers of change, such as technological innovation, social movements, 
and geopolitical shifts, interact to shape future trajectories.
Discussions around anticipatory processes also involve ethical and moral 
considerations. For instance, questions may arise regarding the responsibility of 
individuals, organizations, or governments to anticipate and mitigate potential 
risks or adverse consequences of future developments. Ethical foresight involves 
considering the long-term impacts of present decisions on future generations and 
the environment.
Theory for making sense of anticipatory processes, therefore, provides valuable 
insights into how individuals, groups, and societies navigate uncertainty and 
prepare for the future. By understanding the underlying mechanisms and 
dynamics of anticipation, researchers can develop more informed strategies for 
decision making, planning, and policy formulation.
3.2 Empirical Literature
According to Kulkarni, Kumar, Chate, and Dandannavar (2021), the advantages 
of additive manufacturing are reduction in inventory cost, lowering wastage in 
production, and customization of products. However, factors such as the cost 
of machinery, and a higher level of cost in integrating metal components have a 
negative impact on the adoption of this technology in small and medium-sized 
industries. Martens (2018) also concurs that as a result of design freedom, the use 
of additive manufacturing in combination with its environmental efficiency, the 
implications for positive social change, which include possibilities for increasing 
local employment, improving the environment, and enhancing healthcare for 
the prosperity of local and global citizens by providing potential solutions that 
managers could use to deploy the technology. In addition, Candi and Beltagui 
(2019) also indicate that adopting 3DP for innovation brings greater benefits to 
firms that orchestrate the functions involved in its implementation and use. 
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The future of additive manufacturing (3D Printing) in Kenya
Ahsan and Rahman (2019) agree with Kulkarni, Kumar, Chate, and Dandannavar 
(2021) that the critical implementation challenges in additive manufacturing are 
predominantly technology (production) and cost related. The major challenges 
are related to the quality of products in terms of surface finish, standard, strength, 
and colour. Furthermore, the requirement of new skills in designing the products 
and the cost of raw materials are major challenges. On the other hand, Zhao, Yang, 
Shu, and Liu (2021) assert that sustainability orientation has a stronger positive 
effect on the acquisition of 3DP technologies in the USA firms than in Indian ones, 
but it has a stronger positive effect on the application of 3DP technologies in India 
than in the USA. 
8
4. Methodology
4.1 The Delphi Method 
In line with previous studies (Jiang, Kleer and Piller, 2017), this study adopted 
the Delphi method to predict the future of additive manufacturing in Kenya. 
The method was developed by the RAND Corporation to generate scenarios for 
strategic planning. It is an interactive method that relies on the expert’s opinions 
to identify technical developments and trends. The objective is to obtain complex 
group opinions and develop a consensus on future developments among experts 
(Linstone and Turoff, 2002). Application of this method in this study involved the 
steps shown in Figure 4.1.
Fi gure 4.1: Steps in Delphi Method
Formulation of Delphi Projections   
Selection of experts 
Execution of Delphi Survey 
Development of future scenarios 
Source: Authors’ compilation
4.2 Formulation of Projections of the Future of Additive 
Manufacturing
To address the first objective of this study, a systematic literature review was 
conducted to identify relevant literature on additive manufacturing.  The search 
strategy was guided by the following central question: what are the political, 
economic, social, technological, environmental, and legal drivers of additive 
manufacturing/3D printing technology adoption? The search was done from 
trusted online sources which included Springer, Elsevier Science Direct, Wiley 
Online Library, Oxford Journals, Emerald Insight, Sage, and JSTOR. The search 
was conducted by identification of the key terms in the central research question. 
The inclusion criteria was considered including the article discussing any of the 
9
The future of additive manufacturing (3D Printing) in Kenya
six factors stated in the central research question whereas the exclusion criteria 
considered was; that the article does not discuss any of the six factors stated in the 
central research question. 
The articles were searched using specific keywords such as political factors 
affecting the adoption of additive manufacturing, economic factors affecting 
the adoption of additive manufacturing, social factors affecting the adoption of 
additive manufacturing, technological factors affecting the adoption of additive 
manufacturing, environmental factors affecting the adoption of additive 
manufacturing and legal factors affecting the adoption of additive manufacturing. 
A quality assessment of the articles was conducted to provide more detail about 
the inclusion and exclusion criteria. The quality assessment involved a detailed 
review of the articles with great emphasis on the objectives, the methodology used, 
and the findings. The following questions guided the quality assessment criteria:
i. Were the articles published between 2004 and 2024?
ii. Do the articles provide a clear description (political, economic, social, 
technological, environmental, and legal drivers) of additive manufac-
turing adoption?
The selected articles were utilized in collecting data that helped in answering the 
first objective of the study. A data extraction form containing the title, objectives, 
methodology, findings, and recommendations was used to collect data from the 
articles. The final stage involved data synthesis to determine the consistency and 
inconsistency of results in the different articles. 
In the systematic literature review, 58 articles were found after conducting an 
online search in the following databases: Springer, Elsevier Science Direct, 
Wiley Online Library, Oxford Journals, Emerald Insight, Sage, and JSTOR. The 
identified articles were then subjected to the inclusion and exclusion criteria and 
out of the 58 articles identified, only 20 were left. The selected 20 articles also 
fulfilled the quality assessment criteria. From the selected 20 articles, 13 drivers of 
additive manufacturing were identified after carefully reviewing them.
The next step involved the formulation of projections, that is, statements about 
the possible future of additive manufacturing from the identified drivers. A set of 
14 projections were formulated and to accommodate multiple perspectives, the 
PESTEL analysis framework was applied as shown in Table 1 (Jiang, Kleer, and 
Piller, 2017). The estimated time frame for the occurrence of the projections was 
aligned with the African Agenda 2063, which talks of Science and Technology, 
Innovation-driven manufacturing/industrialization, and value addition. In 
addition to Kenya being a member of the African Union, Kenya’s Vision 2030 is only 
six (6) years away from actualization, and currently, no policy on manufacturing 
spans beyond 2030. 
10
Methodology
4.3 Selection of Experts and Execution of Delphi Survey
The panel experts were identified depending on their relevance, interest in 
the study, and capabilities. As such, experts were obtained from three fields 
namely academia comprising lecturers from the faculty of engineering, industry 
comprising manufacturing firms using additive manufacturing technology, and 
policy research comprising research professionals from research and policy 
institutions. The presence of a heterogeneous panel in this study was intended 
to obtain more accurate estimates as more diverse views serve to reduce the 
polarization of responses (Hussler, Muller, and Rondé, 2011). According to Jiang, 
Kleer, and Piller (2017), experts may be identified through database search, 
networking approach, and professional social networks. The survey targeted 35 
experts from the three selected fields, which is in line with other Delphi studies 
(Gordon and Helmer-Hirschberg, 1964).
An online questionnaire was developed and sent to 35 experts. The experts were 
asked to estimate the probability of occurrence of the 14 projections and their firm 
impact. The probability of occurrence was measured in percentages where 25 per 
cent = Very Low, 50 per cent= Low, 75 per cent= High, and 100 per cent= Very 
High.  Firm impact was measured on a Likert scale (ranging from 1=’very low’ to 
5=’very high’ impact). 31 experts out of the targeted 35 experts participated in 
the survey, which translates to 88.57 per cent response rate. The highest number 
of respondents were policy researchers (38.7%), followed by experts in academia 
(35.5%) and the remaining 28.5% were industry experts (Figure 4.2).
Figure 4.2: Response rate
11
The future of additive manufacturing (3D Printing) in Kenya
4.4  Development of Future Scenarios
Delphi results were analyzed where interquartile range, means of probability 
of occurrence, and means of firm impact were obtained to determine whether a 
consensus was reached for every projection and to select the projections that would 
form the most probable future scenario. While the interquartile range was used 
to determine whether consensus was reached for each projection, the probability 
of occurrence and the firm impact was used to determine the projections with 
the highest level of certainty that would be used to determine the most probable 
future scenario. 
In line with previous Delphi studies, an interquartile range (IQR) of two (2) or 
less indicates that consensus was reached among experts while an interquartile 
range of above two (2) indicates that consensus was not reached. Aside from 
the interquartile range, projections with firm impact equal to or higher than 
3.0 imply that the projections were relevant to the purpose of the study (Jiang, 
Kleer, and Piller, 2017). Further to that, projections with an expected probability 
of occurrence above 50 per cent indicate that consensus was reached among the 
experts, which is in line with other Delphi studies (Von der Gracht and Darkow, 
2010; Jiang, Kleer, and Piller, 2017). 
12
5. Results and Discussion
This section presents the results of the study. Section 5.1 presents findings on 
objective one, Section 5.2 presents findings on objective two, and Section 5.3 
presents findings on objective three.
5.1 Projections on the Future of Additive Manufacturing
The first objective of the study was to develop projections for the future of additive 
manufacturing. Table 5.1 presents findings obtained after carefully reviewing 20 
research papers to obtain drivers of additive manufacturing, which informed the 
development of the Delphi projections for 2063. 
Table 5.1: Projections on the Future of Additive Manufacturing
Framework Drivers of Sources Delphi 
additive projections 
manufacturing
Political Opportunities D’Oca, Ferrante, Ferrer, A significant 
for local and Pernetti, Gralka, number of local and 
international Sebastian and op’t Veld international firms 
collaborations (2018) will collaborate in 
the production of AM 
products
A significant number 
of Micro, small, and 
medium enterprises 
will share industry-
specific 3DP 
technology to achieve 
higher machine 
utilization, learning 
effects, and quality 
assessments
Government Li, Hojati, Wu, Piasente, Kenya will be the 
investment in 3DP Ashrafi, Duarte and leading country in 
projects Radlinska (2020) Eastern Africa in 
3DP manufactured 
products
Economic Reduced costs of Esposito, Casagrande, The cost of 
production Menna, Asprone and production will 
Auricchio (2021) be significantly 
Guimaraes et al., 2021; reduced due to 
Hossain et al., 2020;  the use of additive 
Kulkarni, Kumar, Chate manufacturing 
and Dandannavar technology
(2021)
13
The future of additive manufacturing (3D Printing) in Kenya
Competitive Muylle (2019) Competitive 
advantage advantage will shift 
from manufacturing 
and supply chain 
capabilities to 
customer access and 
designer networks
Production time Muylle (2019) Production time 
will significantly 
reduce due to the 
use of additive 
manufacturing
Demand for AM Shahrubudin The demand for 
products and Ramlan AM products will be 
(2019);Martens, significantly higher 
Fan and compared to products 
Dwyer(2020);Kulkarni, manufactured 
Kumar,Chate and through traditional 
Dandannavar (2021) methods
Ability to produce Niaki, Torabi and Manufacturers will 
complex designs Nonino (2019); Ahsan be able to produce 
and Rahman (2019) products that require 
complex geometries 
that could not be 
produced through 
traditional methods
Social Shift in consumer Subramani (2004) Consumers will use 
behaviour and design databases 
demand to purchase or 
download freely 
accessible product 
designs for additive 
manufacturing 
printing purposes
Technological Precise replication Gibson et al. (2010) Additive 
of products manufacturing will 
be able to precisely 
replicate products
Customizing Kurfess and Cass It will be possible to 
products (2014) produce customized 
for specific products as per 
applications consumer needs 
through additive 
manufacturing
Performance Gausemeier et al. (2011) Product performance 
increases by will be enhanced 
improving product through improved 
function and product function and 
reducing weight reduced weight
14
Results and discussion
Environmental Creating safer Buchanan and Gardner The carbon emissions 
factors environments that (2019); Ning et al. from production 
reduce health and (2021) will be significantly 
safety risks reduced by additive 
manufacturing
Legal factors Regulatory and Shahrubudin and An important 
legal issues Ramlan (2019); regulatory measure 
Kamble,Belhadi, Gupta, will be the regulation 
Islam, Verma and of additive 
Solima (2023) manufacturing file-
sharing platforms
5.2 Probability of Occurrence of the Projections and their Estimated 
Firm Impact 
The second objective of this study was to determine the probability of occurrence 
and the firm impact of the Projections. Table 5.2 shows the results of the Delphi 
survey indicating the interquartile range, probability of occurrence, standard 
deviation, and the firm impact for each projection. In particular, the results 
depict consensus development through Delphi. The study participants were able 
to reach a consensus in all 14 projections after the first round. The fact that all 
14 projections fulfilled the interquartile range criterion shows that the topic of 
additive manufacturing is finally gaining awareness among different stakeholders. 
Aside from consensus among stakeholders, all 14 projections had a firm impact 
equal to or higher than 3.0, which implies that the projections were relevant to 
the purpose of the study (Jiang, Kleer, and Piller, 2017). Further to that, all the 
projections had an expected probability of occurrence above 50 per cent, which 
is in line with other Delphi studies (Jiang, Kleer, and Piller, 2017; Ogden et al., 
2005). 
Table 5.2: Probability of occurrence and the firm impact of the Delphi 
projections
Projections for Interquartile Probability Std. Firm im-
2063 range of deviation pact 
occurrence (%)
(%) 
1. Collaboration 2 72.50 19.08 3.82
in the 
production of 
3D printing 
technology 
products
15
The future of additive manufacturing (3D Printing) in Kenya
2. Sharing 2 63.33 15.64 3.35
industry-
specific 3DP 
technology to 
achieve higher 
machine 
utilization, 
learning 
effects, 
and quality 
assessments 
3. Kenya will be 1 65.83 22.98 3.26
the leading 
country in East 
Africain 3DP 
manufactured 
products
4. 3DP will 1 77.50 20.14 3.84
reduce the cost 
of production 
5. Competitive 1 73.33 20.90 3.87
advantage 
will shift from 
manufacturing 
and supply 
chain 
capabilities 
to customer 
access and 
designer 
networks
6. Production 1 80.00 17.92 4.06
time will 
significantly 
reduce due 
to the use 
of additive 
manufacturing.
16
Results and discussion
7. The demand 1 70.00 19.46 3.74
for AM 
products will 
be high 
8. 3DP will be 1 81.67 19.57 4.00
useful in the 
production 
of products 
that require 
complex 
geometries 
9. Use of design 1 69.17 20.83 3.61
databases to 
purchase or 
download 
freely 
accessible 
product 
designs for AM
10. 3DP will 1 80.83 19.33 4.13
enable precise 
replication of 
AM products 
11. 3DP will 1 85.83 16.88 4.23
enable 
customization 
of products as 
per consumer 
needs 
12. 3DP adoption 1 78.33 15.93 3.94
will enhance 
product 
performance 
13. 3DP use will 1 70.83 24.78 3.55
reduce carbon 
emissions 
14. Regulation of 1 70.00 23.64 3.55
AM file sharing 
Source: Authors’ computation
17
The future of additive manufacturing (3D Printing) in Kenya
5.3 Probable Future Scenarios of Additive Manufacturing 
The third objective of this study was to develop probable futures of additive 
manufacturing. Results on average estimates for the probability of occurrence 
and firm impact were plotted in Figure 5.1 for all 14 projections to develop the 
scenarios cluster.
Figure 5.1:Scenarios cluster
 
Scenario 2: Lagged Transition  Scenario 1: Early Transition 
 
Scenario 4: Future Exploration zone 
 
Scenario 3: Late Transition 
 
 Key:   Represents the Projections 
 
Source: Authors Compilation
5.3.1 Scenario 1: Early transition
The scenario is characterized by six projections (projections 4,6,8,10,11 and 12) 
with a high probability of occurrence (above 75%) and high firm impact (above 
3.7). These projections have a high influence on firms’ decisions to adopt additive 
manufacturing in the future. As such, if these projections are realized, they will 
serve as an impetus for the manufacturing firm to adopt additive manufacturing 
by 2063. Projection 4, for instance, asserts that additive manufacturing is expected 
to significantly reduce the cost of production through a faster and cheaper design 
process. Fast prototyping is one of the main uses of additive manufacturing and 
prototypes that are currently expensive to develop will likely be produced cheaply 
in the future as research on 3D printing materials intensifies. With reduced costs, 
even small and medium enterprises will be able to pursue product development 
in the future. Secondly, projection 6 predicts that firms will significantly reduce 
the time taken to manufacture products because the technology enables quick 
prototyping. Instead of waiting for weeks or months for prototypes to be machined 
or molded, engineers and designers will be capable of producing prototypes in 
18
Results and discussion
a matter of hours or days. This will speed up the product development cycle 
significantly. 
Third, this scenario is also characterized by projection 8, which predicts that 3DP 
will be useful in producing products requiring complex geometries. Traditional 
manufacturing methods often struggle with complex geometries, leading to time-
consuming processes like machining or assembly. Advancements in materials 
compatible with 3D printing will likely lead to improved properties such as 
strength, flexibility, and heat resistance. These materials will produce even more 
complex and functional parts across a wider range of applications. As printing 
speed increases, the time required to produce complex geometries will decrease, 
making 3D printing more competitive with traditional manufacturing methods 
for high-volume production. In addition, this scenario is also characterized by 
projection 10 which asserts that 3DP will enable precise replication of products. 
This development will be driven by advancements in printing techniques such as 
selective laser sintering (SLS), stereolithography (SLA), or binder jetting, which 
enable finer details and smoother surfaces, resulting in higher-quality prints. The 
advancement in 3D printing materials will allow the production of more precise 
and functional parts with greater consistency. 
Another characteristic of this scenario is projection 11, which asserts that 3DP 
will enable the customization of products as per consumer needs. Additive 
manufacturing will open exciting possibilities for product customization to meet 
individual consumer needs and preferences. This technology can create unique 
designs for individual consumers. As such, it will be possible to tailor products 
based on specific requirements such as size and functionality. It will be possible 
for companies to insert customers’ names and initials and other personalized 
elements directly on the product, hence creating more meaningful and engaging 
experiences for the consumer. Traditional manufacturing methods often rely on 
the mass production of standardized products. In contrast, 3D printing will enable 
mass customization. This means that companies will efficiently produce large 
quantities of customized products, each tailored to meet the unique preferences 
of individual consumers.
Lastly, this scenario is also characterized by projection 12, which asserts that 
product performance will be enhanced through improved product function and 
reduced weight. Continued research and development in materials science will 
lead to the manufacturing of new lightweight materials with superior mechanical 
properties. These materials will be specifically tailored for 3D printing processes, 
enabling the production of parts with enhanced performance characteristics 
such as strength, stiffness, and durability. Future 3D printers will be capable 
of printing with multiple materials simultaneously or sequentially, allowing for 
the fabrication of hybrid structures with optimized properties. By combining 
materials with different properties, engineers can create parts that are lightweight 
yet strong, flexible yet rigid, and thermally conductive yet electrically insulating.
19
The future of additive manufacturing (3D Printing) in Kenya
5.3.2  Scenario 2: Lagged transition 
This scenario is composed of projections with high firm impact (above 3.7) but 
a low probability of occurrence (below 75%). Projections 1,5 and 7 fall under 
this category. The low probability of occurrence will lead to a lagged transition 
into additive manufacturing by 2063. These projections will have a moderate 
influence on a firm’s decision to adopt additive manufacturing in the future. 
According to Projection 1, a considerable number of local and international 
firms will collaborate in the production of additive manufactured products. The 
collaboration could be in sharing resources and expertise to lower costs associated 
with research, development, and production of additive manufactured products. 
Collaboration is also expected to lead to faster innovation by combining different 
perspectives, skills, and technologies, leading to more advanced and market-
ready products. Partnering with other firms is also expected to open access to new 
markets and customer bases that might not have been accessed independently. 
Collaborations could also spread the financial and operational risks associated 
with new manufacturing technologies, making it less daunting for individual firms 
to invest in 3D printing. In addition, working together can help establish industry 
standards and best practices, which can facilitate wider adoption and integration 
of 3D printed products in various sectors. Lastly, joint efforts can help firms 
navigate complex regulatory environments more effectively by pooling resources 
and knowledge to meet compliance requirements. 
Another characteristic of this scenario is projection 5 which asserts that competitive 
advantage will shift from manufacturing and supply chain capabilities to customer 
access and designer networks.  3D printing allows for highly customized and 
personalized products, which can be tailored to individual customer needs and 
preferences. Firms that excel in understanding and accessing customer needs 
can leverage 3D printing to offer unique, made-to-order products, providing a 
significant competitive edge. In addition, this technology supports localized and 
decentralized production, reducing the reliance on large-scale manufacturing 
facilities and complex supply chains. It is also important to note that competitive 
advantage will increasingly come from the ability to collaborate with a wide 
network of designers who can create innovative and appealing products. 
Companies that build and maintain robust designer networks will continuously 
introduce fresh, creative designs, keeping them ahead in the market. Lastly, 
the ability to quickly design, prototype, and produce new products using this 
technology shortens the time to market. Firms with strong designer networks can 
rapidly innovate and introduce new products faster than competitors relying on 
traditional manufacturing methods.
This scenario is also characterized by projection 7, which predicts that the demand 
for additive-manufactured products will be significantly higher compared to 
products manufactured through traditional methods. This technology allows for 
high levels of customization and personalization. Consumers can have products 
tailored to their specific needs, preferences, and measurements, which is 
difficult and costly to achieve with traditional manufacturing methods. Secondly, 
technology excels in producing complex geometries and intricate designs that are 
20
Results and discussion
challenging or impossible to achieve with traditional manufacturing methods. 
For applications requiring highly customized or unique shapes, 3D printing offers 
superior capabilities.
5.3.3  Scenario 3: Late transition
This scenario is characterized by projections with a low probability of occurrence 
(below 75%) and low firm impact (less than 3.7). Projections 2,3,9, 13, and 14 fall in 
this category and according to the experts, these projections have a low influence 
on the firms’ decision to transition to additive manufacturing technology by 2063. 
Projection 2 for instance predicts that a significant number of micro, small, and 
medium enterprises will share industry-specific 3DP technology to achieve higher 
machine utilization, learning effects, and quality assessments.  This can lead to 
collective learning effects where each participant benefits from the insights and 
experiences of others, accelerating the pace of innovation within the industry. 
Sharing industry-specific technology can collectively assess and improve the 
quality of products, which ultimately benefits all participants by enhancing the 
reputation and reliability of 3DP products in the market. Collaborative efforts to 
share technology can also facilitate market expansion. This benefits all participants 
by increasing demand and creating new business opportunities.
Secondly, this scenario is also characterized by projection 3 which predicts that 
Kenya will be the leading country in Eastern Africa in 3DP manufactured products. 
This implies that a significant number of firms in various sectors will adopt this 
technology and there will be high consumer preference for additive manufactured 
products to drive production.  Another characteristic of this scenario is projection 
9, which states that consumers will use design databases to purchase or download 
freely accessible product designs for additive manufacturing printing purposes. 
There are numerous online marketplaces specifically dedicated to 3D printable 
designs. Consumers can browse these platforms to find a wide range of designs for 
various products, ranging from household items to decorative objects to functional 
prototypes. Examples of such platforms include Thingiverse, MyMiniFactory, and 
Cults. On top of that, many designers and organizations contribute to open-source 
repositories where they freely share their 3D printable designs. These repositories 
often host a vast collection of designs covering diverse categories. Consumers can 
access these repositories to download designs for personal use or to contribute 
their designs to the community. In some instances, manufacturers and brands 
offer downloadable 3D printable designs of their products on their websites. This 
allows consumers to customize or replicate products according to their preferences 
or specific needs. For example, a furniture company might provide downloadable 
designs for its furniture pieces, enabling consumers to customize dimensions or 
features before printing.
Another characteristic of this scenario is projection 13, which predicts that the 
technology will significantly reduce carbon emissions from production. The use of 
sustainable and recyclable materials in 3D printing is on the rise. Biodegradable 
polymers, recycled plastics, and other eco-friendly materials can be used in 3D 
21
The future of additive manufacturing (3D Printing) in Kenya
printing, reducing the reliance on non-renewable resources and minimizing the 
environmental impact of production.  The last feature of this scenario is projection 
14, which states that an important regulatory measure will be the regulation 
of additive manufacturing file-sharing platforms.  Platforms will be required 
to comply with local and international laws and regulations regarding product 
safety, consumer protection, and other relevant areas through regular audits and 
reporting to regulatory bodies. In addition, they will also be required to implement 
robust cybersecurity measures to protect them from hacking, data breaches, 
and other malicious activities that could compromise user data or file integrity. 
Another important consideration would be the establishment of guidelines and 
moderation policies to prevent the sharing of files that could be used for illegal or 
unethical purposes, such as creating counterfeit goods or offensive items.
5.3.4  Scenario 4: Future exploration zone
Projections characterizing this scenario have a high probability of occurrence 
(above 75%) but a low firm impact (less than 3.7). However, none of the 14 
projections fell under this category. This may be explained by several factors 
including limitations of the study in terms of the number of projections, and 
biases among others. 
22
6.  Conclusion and Recommendations 
6.1 Conclusion
This study aimed to determine the future of additive manufacturing in Kenya 
using the Delphi approach. Through an extensive analysis, a set of 14 projections 
was developed from the identified drivers of additive manufacturing. Analysis 
of the probability of occurrence and firm impact of the projections revealed that 
the country’s early transition to additive manufacturing by 2063 will be highly 
influenced by reduced cost of production and time; the ability of the technology to 
produce products with complex geometries; precise replication of AM products; 
the ability of the technology to customize products as per consumer needs; and 
enhanced product performance.
Additionally, the lagged transition into additive manufacturing may be caused by 
a lack of industry c collaborations in the production of AM products; a lack of 
shift in competitive advantage from manufacturing and supply chain capabilities 
to customer access and designer networks; and low demand for AM products.
Finally, the county may experience a late transition due to its inability to position 
itself as the leader in AM-manufactured products in East Africa; the inability of 
the technology to reduce carbon emissions; and the lack of regulation on AM file 
sharing.
6.2  Recommendations
Scenario 1: The country’s early transition 
Some of the interventions that will promote early transition into additive 
manufacturing include:
Cost of production
The government may consider providing direct financial support to firms to 
enable them to invest in 3D printing technology, including purchasing equipment 
and training employees. In addition, the government may consider providing 
tax breaks or credits for investments in 3D printing technology and related 
innovations.
Capability of firms to produce products with complex geometries
The government may consider increasing funding for research and development 
in 3D printing technologies to build a skilled workforce proficient in 3D 
23
The future of additive manufacturing (3D Printing) in Kenya
printing technologies. More funding may also be provided for innovation hubs 
and labs focused on 3D printing to foster knowledge sharing and technological 
advancement. In addition, the government may promote skills development 
through educational institutions by integrating 3D printing into engineering, 
design, and manufacturing curriculums. Currently, very few institutions of learning 
are offering 3D printing lessons despite the National ICT Policy advocating for all 
secondary and tertiary institutions to acquire 3D capabilities.
Scenario 2: The country lagging in transition
To ensure a fast transition into the use of additive manufacturing by 2063, there 
is a need for:
Increased collaboration among firms to produce 3D-printed products
This can be achieved through the promotion, establishment, and maintenance 
of shared 3D printing facilities that small and medium-sized enterprises (SMEs) 
can access without the need for large capital investments. Firms may also create 
more technology parks or industrial clusters that bring together 3D printing 
firms, suppliers, and related industries to foster collaboration and innovation. 
In addition, a framework for collaborations between government research 
institutions, universities, and private firms to advance 3D printing technologies 
and applications may be developed and anchored in the National ICT Policy or the 
National Industrialization Policy. 
Promote demand for AM products
To hasten the transition into the adoption of additive manufacturing, there is a 
need to create demand for additive-manufactured products through the use of 
government procurement to create demand for 3D printed products by setting 
targets or preferences for 3D-printed components in public projects. Firms 
may also launch marketing and educational campaigns to inform businesses 
and consumers about the benefits and potential of 3D printed products, such as 
customization, cost-efficiency, and sustainability. In addition, media may assist 
in showcasing case studies and success stories of companies and industries that 
have successfully adopted 3D printing, highlighting the tangible benefits they 
experienced.
Scenario 3: The country experiencing late transition
For a smooth and fast transition into the adoption of additive manufacturing in 
Kenya by 2063, the government could consider:
24
Conclusion and recommendations
Positioning Kenya as a destination hub for additive manufacturing
This could be achieved by providing support to firms in accessing international 
markets by promoting 3D printed products in foreign markets and providing 
export assistance. Similarly, the government may work with industry bodies 
to develop and implement standards for 3D printing materials, processes, and 
products to ensure quality and safety.
Reduction of carbon emission
Efforts to reduce carbon emissions by firms may include the adoption of sustainable 
practices in 3D printing, which include the use of eco-friendly materials, energy-
efficient processes, recycling, and waste reduction strategies. The government may 
also provide incentives for firms to adopt sustainable 3D printing technologies 
and practices, as well as promote awareness and education on the environmental 
benefits of 3D printing. 
Framework for AM file sharing
Firms need to invest in robust digital infrastructure to support the high data 
demands of 3D printing, including high-speed internet and secure data storage 
solutions. In addition, the government may develop a comprehensive framework 
or regulations to govern AM file sharing. 
25
The future of additive manufacturing (3D Printing) in Kenya
References 
Ahsan, K., and Rahman, S. (2019), Implementation challenges of three-
dimensional printing (3DP) in medical device manufacturing supply 
chains. In Symposium on Logistics (p.2).
Besklubova, S., Skibniewski, M. J., and Zhang, X. (2021), “Factors affecting 3D 
printing technology adaptation in construction”. Journal of Construction 
Engineering and Management, 147 (5): 04021026.
Boddenberg, F., Schwarz, J. O., and Schmies, P. (2024),” Scenario planning in 
the digital transformation of the fashion industry: Starting point for the 
adjustment of business processes from a colour management perspective”. 
Journal of Futures Studies, 28 (3): 49-63.
Bogue, Robert. (2013), 3D Printing: The dawn of a new era in manufacturing?
Candi, M., and Beltagui, A. (2019),” Effective use of 3D printing in the innovation 
process”. Technovation, 80: 63-73.
Cirera, X. (2015), Catching Up to the Technological Frontier? Understanding 
Firm-level Innovation and Growth in Kenya.
Cirera, X., Comin, D., Cruz, M., Lee, K. M., Martinez, P., and Martins-Neto, A. 
(2022), Firm-level Adoption of Technologies in Kenya.
Cotteleer, M., and Joyce, J. (2014), ”3D opportunity: Additive manufacturing 
paths to performance, innovation, and growth”. Deloitte Review, 14(1): 
3-19.
Coughlin, P. E., and Ikiara, G. K. (Eds.) (1988), Industrialization in Kenya: In 
search of a strategy. East African Publishers.
D’Oca, S., Ferrante, A., Ferrer, C., Pernetti, R., Gralka, A., Sebastian, R., and op ‘t 
Veld, P. (2018), “Technical, financial, and social barriers and challenges in 
deep building renovation: Integration of lessons learned from the H2020 
cluster projects”. Buildings, 8 (12): 174.
Dodziuk, H. (2016), “Applications of 3D printing in healthcare”. Kardiochirurgia 
i Torakochirurgia Polska/Polish Journal of Thoracic and Cardiovascular 
Surgery, 13(3): 283-293.
Dzogbewu, T. C., Fianko, S. K., Amoah, N., Jnr, S. A., and de Beer, D. (2022), 
“Additive manufacturing in South Africa: Critical success factors”. Heliyon, 
8 (11).
Esposito, L., Casagrande, L., Menna, C., Asprone, D., and Auricchio, F. (2021). 
“Early-age creep behaviour of 3D printable mortars: Experimental 
characterisation and analytical modelling”. Materials and Structures, 54: 
1-16.
26
References
Gerstle, T. L., Ibrahim, A. M., Kim, P. S., Lee, B. T., and Lin, S. J. (2014), “A plastic 
surgery application in evolution: Three-dimensional printing”. Plastic and 
reconstructive surgery, 133(2): 446-451.
Cotteleer, M., and Joyce, J. (2014), “3D opportunity: Additive manufacturing 
paths to performance, innovation, and growth”. Deloitte Review, 14(1): 
3-19.
Wu, B., Myant, C., and Weider, S. (2017), The value of additive manufacturing: 
Future opportunities. Imperial College London, Briefing Paper, (2).
Hasson, F., and Keeney, S. (2011), “Enhancing rigour in the Delphi technique 
research”. Technological forecasting and social change, 78(9): 1695-1704.
Hussler, C., Muller, P., and Rondé, P. (2011), “Is diversity in Delphi panelist 
groups useful? Evidence from a French forecasting exercise on the future 
of nuclear energy”. Technological Forecasting and Social Change, 78(9): 
1642-1653.
Kamble, S., Belhadi, A., Gupta, S., Islam, N., Verma, V. K., and Solima, L. (2023), 
Analyzing the barriers to building a 3-D printing enabled local medical 
supply chain ecosystem. IEEE Transactions on Engineering Management.
Klenam, D. E. P., Bamisaye, O. S., Williams, I. E., van der Merwe, J. W., and 
Bodunrin, M. O. (2022), “Global perspective and African outlook on 
additive manufacturing research− an overview”. Manufacturing Review, 9: 
35.
Kulkarni, P., Kumar, A., Chate, G., and Dandannavar, P. (2021), “Elements of 
additive manufacturing technology adoption in small and medium-sized 
companies”. Innovation and Management Review, 18 (4): 400-416.
Li, Z., Hojati, M., Wu, Z., Piasente, J., Ashrafi, N., Duarte, J. P., and Radlińska, 
A. (2020), “Fresh and hardened properties of extrusion-based 3D-printed 
cementitious materials: A review”. Sustainability, 12 (14): 5628.
Lipton, J. I., Cutler, M., Nigl, F., Cohen, D., and Lipson, H. (2015), “Additive 
manufacturing for the food industry”. Trends in food science and 
technology, 43(1): 114-123.
Marak, Z. R., Tiwari, A., and Tiwari, S. (2019), „Adoption of 3D printing 
technology: An innovation diffusion theory perspective”. International 
Journal of Innovation, 7 (1): 87-103.
Martens, R. (2018), Strategies for Adopting Additive Manufacturing Technology 
into Business Models (Doctoral dissertation, Walden University).
Martens, R., Fan, S. K., and Dwyer, R. J. (2020), “Successful approaches for 
implementing additive manufacturing. World Journal of Entrepreneurship, 
Management and Sustainable Development, 16 (2): 131-148.
27
The future of additive manufacturing (3D Printing) in Kenya
Miladinov, S. (2018), Adoption of 3D printing technology and its impact on 
business.
Mpofu, T. P., Mawere, C., and Mukosera, M. (2014), The impact and application 
of 3D printing technology.
Muylle, L. (2019), Technology readiness and adoption of 3D printing in the 
construction industry. Ghent University, Ghent.
Ng, J. S., and Hashimoto, M. (2021), “3D-PAD: Paper-based analytical devices 
with integrated three-dimensional features”. Biosensors, 11 (3): 84.
Niaki, M. K., Torabi, S. A., and Nonino, F. (2019), “Why manufacturers adopt 
additive manufacturing technologies: The role of sustainability”. Journal of 
cleaner production, 222: 381-392.
Nollet, L. J. E. (2017), Factors influencing the adoption decision of metal additive 
manufacturing technologies (Master’s thesis, University of Twente).
Sardesai, S., Stute, M., and Kamphues, J. (2021), A methodology for future scenario 
planning. Next Generation Supply Chains: A Roadmap for Research and 
Innovation, 35-59.
Shahrubudin, N., and Ramlan, R. (2019), “An overview of critical success factors 
for implementing 3D printing technology in manufacturing firms”. Journal 
of Applied Engineering Science, 17 (3): 379-385.
Shahrubudin, N., and Ramlan, R. (2019), “An overview of critical success factors 
for implementing 3D printing technology in manufacturing firms”. Journal 
of Applied Engineering Science, 17 (3): 379-385.
Shahrubudin, N., Lee, T. C., and Ramlan, R. J. P. M. (2019), “An overview on 3D 
printing technology: Technological, materials, and applications”. Procedia 
Manufacturing, 35, 1286-1296. 
Shahrubudin.  N., Koshy, P., Alipal, J., Kadir,M.H.A., Lee, T.C. (2020), Challenges 
of 3D printing technology for manufacturing biomedical products: A case 
study of Malaysian manufacturing firms. National Library of Medicine.
Spahiu, T., Canaj, E., and Shehi, E. (2020), “3D printing for clothing production”. 
Journal of Engineered Fibers and Fabrics, 15: 1558925020948216.
Spicek, N., Besklubova, S., and Skibniewski, M. J. (2023), “Benchmarking critical 
success factors in construction projects utilizing 3D printing technology”. 
International Journal of Applied Science and Engineering, 20 (4): 1-18.
Turoff, M., and Linstone, H. A. (2002). The Delphi method techniques and 
applications.
Ukobitz, D. V. (2020),” Organizational adoption of 3D printing technology: A 
semi systematic literature review”. Journal of Manufacturing Technology 
Management, 32 (9): 48-74.
28
References
Winkler, J., and Moser, R. (2016),” Biases in future-oriented Delphi studies: A 
cognitive perspective”. Technological forecasting and social change, 105: 
63-76.
Wohlers, T., Campbell, R. I., Diegel, O., Huff, R., and Kowen, J. (2020), Wohlers 
Report 2020 3D Printing and Additive Manufacturing State of the Industry.
Wu, P., Wang, J., and Wang, X. (2016), "A critical review of the use of 3D printing 
in the construction industry". Automation in Construction, 68: 21-31.
Wu, B., Myant, C., and Weider, S. (2017), The value of additive manufacturing: 
Future opportunities. Imperial College London, Briefing Paper, (2).
Zhao, M., Yang, J., Shu, C., and Liu, J. (2021), “Sustainability orientation, the 
adoption of 3D printing technologies, and new product performance: A 
cross-institutional study of American and Indian firms”. Technovation, 
101: 102197.
29
The future of additive manufacturing (3D Printing) in Kenya
Appendix 1: DELPHI Questionnaire
S/ Projections for 2063 Probability Impact on 
NO. of manufacturing firms 
occurrence 
1. In 2063, a significant 25 [ ] Very low [ ] 
number of local and 50 [ ] Low [ ] 
international companies 75 [ ] Moderate [ ] 
will collaborate in 100 [ ] High [ ]   
the production of 3D 
technology products. Very high [ ]
2. In 2063, a significant 25 [ ] Very low [ ] 
number of micro, small, 50 [ ] Low [ ] 
and medium enterprises 75 [ ] Moderate [ ] 
will share industry-specific 100 [ ] High [ ]   
3DP technology to achieve Very high [ ]
higher machine utilization, 
learning effects, and quality 
assessments.
3. In 2063, Kenya will be the 25 [ ] Very low [ ] 
leading country in Eastern 50 [ ] Low [ ] 
Africa in 3DP manufactured 75 [ ] Moderate [ ] 
products. 100 [ ] High [ ]   
Very high [ ]
4. In 2063, the cost of 25 [ ] Very low [ ] 
production will be 50 [ ] Low [ ] 
significantly reduced due 75 [ ] Moderate [ ] 
to the use of additive 100 [ ] High [ ]   
manufacturing technology Very high [ ]
5. In 2063, competitive 25 [ ] Very low [ ] 
advantage will shift 50 [ ] Low [ ] 
from manufacturing and 75 [ ] Moderate [ ] 
supply chain capabilities 100 [ ] High [ ]   
to customer access and Very high [ ]
designer networks.
5. In 2063, production time 25 [ ] Very low [ ] 
will significantly reduce 50 [ ] Low [ ] 
due to the use of additive 75 [ ] Moderate [ ] 
manufacturing. 100 [ ] High [ ]   
Very high [ ]
30
Appendix
6. In 2063, the demand 25 [ ] Very low [ ] 
for AM products will 50 [ ] Low [ ] 
be significantly higher 75 [ ] Moderate [ ] 
compared to products 100 [ ] High [ ]   
manufactured through Very high [ ]
traditional methods.
7. In 2063, manufacturers will 25 [ ] Very low [ ] 
be able to produce products 50 [ ] Low [ ] 
that require complex 75 [ ] Moderate [ ] 
geometries that could 100 [ ] High [ ]   
not be produced through Very high [ ]
traditional methods.
8. In 2063, consumers will 25 [ ] Very low [ ] 
use design databases to 50 [ ] Low [ ] 
purchase or download freely 75 [ ] Moderate [ ] 
accessible product designs 100 [ ] High [ ]   
for additive manufacturing Very high [ ]
printing purposes.
9. In 2063, additive 25 [ ] Very low [ ] 
manufacturing will be 50 [ ] Low [ ] 
able to precisely replicate 75 [ ] Moderate [ ] 
products. 100 [ ] High [ ]   
Very high [ ]
10. In 2063, it will be possible 25 [ ] Very low [ ] 
to produce customized 50 [ ] Low [ ] 
products as per consumer 75 [ ] Moderate [ ] 
needs through additive 100 [ ] High [ ]   
manufacturing. Very high [ ]
11. In 2063, product 25 [ ] Very low [ ] 
performance will be 50 [ ] Low [ ] 
enhanced through 75 [ ] Moderate [ ] 
improved product function 100 [ ] High [ ]   
and reduced weight. Very high [ ]Very high [ ]
12. In 2063, the carbon 25 [ ] Very low [ ] 
emissions from 50 [ ] Low [ ] 
manufacturing will be 75 [ ] Moderate [ ] 
significantly reduced by 100 [ ] High [ ]   
additive manufacturing. Very high [ ]
13. In 2063, an important 25 [ ] Very low [ ] 
regulatory measure will be 50 [ ] Low [ ] 
the regulation of additive 75 [ ] Moderate [ ] 
manufacturing file-sharing 100 [ ] High [ ]   
platforms. Very high [ ]
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