Article citation information:
Sharma, M.K. Cloud
manufacturing: identifications and prioritization of opportunities using AHP. Scientific Journal of Silesian University of
Technology. Series Transport. 2022, 115,
161-173. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2022.115.11.
Mohneesh Kumar SHARMA[1]
CLOUD MANUFACTURING: IDENTIFICATIONS AND PRIORITIZATION OF OPPORTUNITIES
USING AHP
Summary. The origin of
a cost-efficient, service-oriented, customer-centric, manufacturing system
called cloud manufacturing has evolved due to advancements in cyber systems and
the availability of internet facilities worldwide. However, there is a
significant number of opportunities before the adoption of cloud manufacturing.
Through literature survey, expert opinions from academicians and
industrialists, various opportunities, namely, pay-as-use, scalability, cost
efficiency, flexibility, autonomy, low-risk backup and recovery, low startup
cost and location independence associated with the espousal of cloud
manufacturing are identified. Further, the Analytic Hierarchy Process (AHP)
model is applied to find the weights and prioritize these opportunities,
thereby finding the significant key opportunities. Moreover, the consistency
ratio is calculated for the accuracy and consistency of the results. As the
obtained value of consistency ratio is less than .1, it shows that the result
obtained is consistent and accurate. The managerial implication of these
outcomes is that the results would indirectly help entrepreneurs in the
adoption of cloud manufacturing.
Keywords: cloud
manufacturing, opportunities, pay-per-use, scalability, analytic hierarchy
process
1. INTRODUCTION
Recent
advancements in technologies and customized requirements of customers and
manufacturing industries have gone through significant changes leading to a
highly competitive environment globally. Cost, customization, flexibility, reliability,
and quality are the key factors that enterprises must improve to sustain in the
global market. Following the path of networking some of the manufacturing
models were introduced, namely, Agile Manufacturing (AM) [1, 2], Network
Manufacturing (NM) [3, 4], Manufacturing Grid (MG) [5, 6], Virtual
Manufacturing (VM) [7, 8], Additive Manufacturing [9, 10], and Smart
Manufacturing (SM) [11, 12], which have changed the manufacturing process
significantly. The cloud computing model helps in enabling a user secure and
convenient on-demand network access to a shared pool of computing resources
like storage, network, applications services, etc. [13]. Moreover, the
virtualization and service-oriented characteristics of cloud computing make
manufacturing suitable for customization. Gibson et al. (2012) explained the
benefits of service models of cloud computing [14]. When services like SaaS,
PaaS, and IaaS of cloud computing are applied to manufacturing, then the new
manufacturing model obtained is called Cloud Manufacturing [15].
Cloud
manufacturing is preferred over other manufacturing systems due to its cost
efficiency, pay as per requirement and scalability. Ghomi et al. (2019)
provided an overview of cloud manufacturing challenges,
recent advancements, issues related to research, and its future trend [16].
Since
this manufacturing model is growing at a significant pace, its merits should
not be left unknown. Knowledge regarding positive aspects of the manufacturing
model helps in its future development and eases its adoption. Narwane et al. (2019) presented a review on issues
related to manufacturing and its adoption, they also highlighted the
application of the manufacturing model for various industries and sectors [17].
Abubakar et al. (2014) studied the issues related to the adoption of cloud
computing for small and medium-sized enterprises in developing countries of the
Saharan Africa [19]. Zhang et al. (2020) developed a service platform to
increase the competitiveness among small and medium scale industries regarding
cloud manufacturing [21]. Abd et al. (2018)
compared the current adoption of the manufacturing model with the ideal
manufacturing model [22]. Brief studies have been done in past papers on
the merits of cloud manufacturing; however, no study deals with its
prioritization. Hence, this endeavour is made to identify the opportunities of
cloud manufacturing and their prioritization.
Cloud
manufacturing is based on shared manufacturing infrastructure, services, and
resources through a cloud platform. It uses algorithms to make intelligent
decisions and provides the most optimized way for sustainable and robust
manufacturing [23]. The recent advancements in technology have made
manufacturing more flexible, resourceful, efficient, and customized. Given that
cloud manufacturing is still an explored field in developed countries and a new
concept to be adopted by developing countries, this study aims to identify the
benefits of cloud manufacturing for improving the existing model of cloud
manufacturing in developed countries and the initial adoption of cloud
manufacturing model in developing countries. Further, it is to identify the
opportunities of the cloud manufacturing model and prioritize them to provide
prior information on the benefits of this manufacturing model to entrepreneurs
interested in its adoption, especially in developing countries. This
paper mainly discusses the opportunities of cloud manufacturing. Hence,
identification of opportunities is done and through AHP key opportunities are
obtained.
The
brief of this paper is as follows:
1. Identification of
cloud manufacturing opportunities.
2. Application of the
AHP approach for prioritization of opportunities.
3. Analysis, result,
discussion and conclusion.
2.
OPPORTUNITIES
In
this section, the opportunities of cloud manufacturing is discussed.
Significant factors supporting the adoption of cloud manufacturing are
pay-per-use, scalability, cost efficiency, flexibility, autonomy, low-risk
backup and recovery, low startup cost, and location independence. All the
opportunities are discussed in the section below and finally listed in Table 1.
2.1. Pay-Per-Use or Pay-As-You-Go
Service
A
user can request services as and when required and can pay according to the
“pay-per-use” model, where he pays for the time he has availed of
the services, resources, or infrastructure. This scheme is managed by a cloud
platform [23]. Pay-as-you-go facility can be availed by users without having
any direct interaction with the service provider [24]. The pay-as-you-go model
ensures the exchange of services between the manufacturing providers and
consumers [25]. It also promotes the paying scheme named pay-as-you-go to the
customers [26, 27].
2.2. Scalability
Cloud
manufacturing facilitates the user to run a production system on market demand
[23]. It provides the facility of scaling where the user can scale (up or down)
the use of resources according to his needs [24]. Removal, modification, and
addition of resources can be done as required [25]. Cloud manufacturing makes
it easier to scale up or down their production according to customer demand
[28].
2.3. Cost Efficiency
The
support of the Internet, IoT (Internet of things), and Big Data to
manufacturing lowers the entry cost for smaller firms. Moreover, with the
optimized use of resources, this manufacturing system has become the most
cost-efficient manufacturing system in recent times [29]. Cloud manufacturing
leads to an increase in the usage of manufacturing resources through
outsourcing [25]. By adopting cloud manufacturing, the cost of manufacturing
can be reduced [30].
2.4. Flexibility
Cloud
manufacturing can adapt to unpredicted changes in circumstances [23]. Further,
it can also adapt and respond to changing customer demands [25]. Cloud
manufacturing can generate new types of classes of manufacturing processes and
deliver manufactured products to clients [28].
2.5. Autonomy
Every
manufacturing provider and customer is an independent identity and works
independently without having a direct link between them. For cost reduction in operating costs,
considerable autonomy is needed in the administration [31]. Service agents
provide this autonomy [32].
2.6. Low-risk Backup and Recovery
Cloud
manufacturing reduces the risk for small-scale enterprises as all the tasks are
outsourced to other companies through the cloud platform [27]. Data storage by
the provider makes the data safe with a recovery functionality in case of an
emergency. Sharing the benefits among manufacturers lowers the risk factor
[30].
2.7. Low Startup Cost
Being
that there is no infrastructure or machinery, and the user owns the software,
working on the pay-as-use model makes it free from upfront investment for
start-ups. Cloud Manufacturing lowers startup and operating costs [27].
Infrastructure and administration costs are also reduced resulting in lower
upgrading and maintenance costs [28].
2.8. Location Independence
Independence
from locational constraints for the user and the provider takes the freedom of
this system to another level. Work can be performed anywhere and at any time.
Tasks can be done from suitable enterprises located at any place, thus making
this model location independent. Since the system is independent of location,
the customer is not required to be concerned about the location of the
resources he is using [33]. There is a sense of locational independence where
the customer has no control over the location of the service provider [34].
Tab. 1.
Opportunities in espousal cloud manufacturing
|
S.No. |
Opportunities |
References |
|
O_1 |
Pay-per-use or
Instant service |
[23-27] |
|
O_2 |
Scalability |
[23,24,25,28] |
|
O_3 |
Cost
efficiency |
[25,29,30] |
|
O_4 |
Flexibility |
[23,25,28] |
|
O_5 |
Autonomy |
[31,32] |
|
O_6 |
Low-risk backup
and recovery |
[27,30] |
|
O_7 |
Low
startup cost |
[27,28] |
|
O_8 |
Location
independence |
[33,34] |
3. RESEARCH
METHODOLOGY
In this article, the
Analytic Hierarchy Process (AHP), one of the popular MCDM techniques, is used
to prioritize identified opportunities in the adoption of cloud manufacturing.
A consistency ratio to identify whether the results obtained are robust and
consistent is obtained for the validation. For the stepwise application of this
method, the flowchart is displayed in Figure 1. AHP can be applied to many
sectors in the industry for selection making, decision making, and
prioritization [35]. Hu et al. (2019, 2020) used it in the selection of
manufacturers in cloud manufacturing [36, 37]. Sevinc et al. (2018) used to solve
issues SMEs face transitioning to Industry 4.0. Mian et al. (2020) applied
SWOT-AHP to quantify and rank the opportunities and challenges for
sustainability education in Industry 4.0. While Metin Da˘gdeviren (2008) used
the AHP–PROMETHEE integrated approach for decision making in equipment
selection [38-40]. Prioritization of challenges to Industry 4.0 for the supply
chain is obtained with the help of AHP [41]. The cloud manufacturing concept is quite
new so finding experts in this field is difficult; however, a total of 30
experts from the industry and academics (10 industrialists, 10 mechanical
engineering academicians, 6 computer science academicians, and 4 industrial
engineering academicians) were used for this study. After discussion, a total
of 8 opportunities were finalized and detailed questionnaires (Appendix-1) were
sent to the experts for filling. After collecting the responses, an average was
obtained to get the final average pairwise matrix.
Steps
to apply the AHP approach are as follows:
Step 1:
Develop a structural hierarchy
Step 2: Develop
an average pairwise comparison matrix
Experts
were asked to fill the survey questionnaires where they have to do a pairwise
comparison of attribute i with attribute j on a scale of 1, 3,5,7,9. Then all
questionnaire matrices are collected and the average matrix Aij is obtained.
a11
…….. a1j…………. a1n
Aij= ai1……….
aij………… a1j
an1………
anj……… .ann
Step 3:
Develop normalized decision matrix
cij= aij /
where
i=1,2,3,4……. n and j=1,2,3,4…………. n
Step 4:
Develop weighted normalized decision matrix
wi =
Step 5: Calculate eigenvector and row matrix
E =Nth rootvalue
/Nth rootvalue
(3)
Row matrix=
Step 6: Calculate the
maximum eigenvalue, λmax.
λmax =Rowmatrix / E
(5)
Step 7: Obtain the Consistency Index (CI)
and Consistency Ratio (CR).
CI= (λmax - n)/ (n-1)
(6)
CR=CI/RI
(7)
Literature review on cloud manufacturing Identification of opportunities of cloud
manufacturing Taking experts (Industry and Academic)
opinion before finalizing the significant opportunities Inviting experts to fill questionnaires Matrices Apply the AHP
Approach Result and
Discussion Conclusion
Fig. 1. Research methodology
flowchart
Tab. 2.
Random Index
|
n |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
|
RI |
0 |
0 |
0.85 |
0.9 |
1.12 |
1.24 |
1.32 |
1.41 |
1.45 |
1.51 |
Where n and RI denote the order
of matrix and Randomly Generated Consistency Index, respectively.
4. RESULTS AND DISCUSSION
This section
discusses the prioritization of opportunities and the consistency of results
obtained. The consistency ratio is
obtained for the validation of the result. After averaging all the individual
matrices obtained from the experts, a final average pairwise comparison matrix
is obtained followed by the procedure of the AHP approach given in section 3.
For all calculation purposes, the matrices are linked in Microsoft Excel to
obtain error-free and accurate results.
After the average
pairwise comparison matrix is obtained for opportunities as shown in Table 3,
normalization is done to get the normalized decision matrix (Table 4).
Thereafter, further steps are followed regarding the AHP approach to obtain a
weighted normalized decision matrix (Table 5) for weights of opportunities.
Finally, ranking is done based on weights obtained (Table 6).
For the final result
obtained, Cost-efficient (O_3) has the highest positive value; therefore, it is
the most critical opportunity in this category. Pay-per-use (O_1), Scalability
(O_2), and Flexibility (0_4) secured the 2nd, 3rd, and 4th places,
respectively, in the category as per their weights obtained. Low-risk Backup
and Recovery (O_6) and Low Startup Cost (O_7) are ranked 5th and 6th; however,
since the differences in weights are very less so they can be assumed at the
same level if required. Location Independence (O_8) factor obtained the lowest
weight and is ranked 8th among all. All the above rankings are shown in Table
6.
Table 7 is used to
calculate the consistency ratio. The obtained value of λmax (Table 7) and
random index (Table 2) is finally used to calculate the value of Consistency
Ratio (CR). The obtained value of
CR is .07, which is less than .10, implying that the result is accurate and
consistent.
Tab. 3.
Average pairwise
comparison matrix
|
|
O_1 |
O_2 |
O_3 |
O_4 |
O_5 |
O_6 |
O_7 |
O_8 |
|
O_1 |
1.67 |
0.29 |
3.00 |
6.33 |
5.00 |
5.00 |
6.33 |
|
|
O_2 |
0.78 |
1.00 |
0.29 |
3.00 |
5.00 |
3.00 |
3.00 |
6.33 |
|
O_3 |
3.67 |
3.67 |
1.00 |
3.67 |
5.67 |
5.00 |
5.00 |
6.33 |
|
O_4 |
0.33 |
0.33 |
0.33 |
1.00 |
3.00 |
3.00 |
3.00 |
5.67 |
|
O_5 |
0.16 |
0.20 |
0.20 |
0.33 |
1.00 |
0.29 |
0.29 |
3.00 |
|
O_6 |
0.20 |
0.33 |
0.20 |
0.33 |
3.67 |
1.00 |
1.67 |
3.00 |
|
O_7 |
0.20 |
0.33 |
0.20 |
0.33 |
3.67 |
0.78 |
1.00 |
3.00 |
|
O_8 |
0.16 |
0.15 |
0.15 |
0.20 |
0.33 |
0.33 |
0.33 |
1.00 |
Tab. 4.
Normalized decision matrix
|
|
O_1 |
O_2 |
O_3 |
O_4 |
O_5 |
O_6 |
O_7 |
O_8 |
|
O_1 |
0.154 |
0.217 |
0.108 |
0.253 |
0.221 |
0.272 |
0.259 |
0.183 |
|
O_2 |
0.119 |
0.130 |
0.108 |
0.253 |
0.174 |
0.163 |
0.156 |
0.183 |
|
O_3 |
0.564 |
0.478 |
0.377 |
0.309 |
0.198 |
0.272 |
0.259 |
0.183 |
|
O_4 |
0.051 |
0.043 |
0.125 |
0.084 |
0.105 |
0.163 |
0.156 |
0.163 |
|
O_5 |
0.025 |
0.026 |
0.075 |
0.028 |
0.035 |
0.016 |
0.015 |
0.087 |
|
O_6 |
0.031 |
0.043 |
0.075 |
0.028 |
0.128 |
0.054 |
0.086 |
0.087 |
|
O_7 |
0.031 |
0.043 |
0.075 |
0.028 |
0.128 |
0.042 |
0.052 |
0.087 |
|
O_8 |
0.025 |
0.019 |
0.055 |
0.017 |
0.012 |
0.018 |
0.017 |
0.029 |
Tab. 5.
Weighted normalized decision matrix
|
|
O_1 |
O_2 |
O_3 |
O_4 |
O_5 |
O_6 |
O_7 |
O_8 |
Weighted
sum value |
Weight |
|
O_1 |
0.154 |
0.217 |
0.108 |
0.253 |
0.221 |
0.272 |
0.259 |
0.183 |
1.667 |
0.2084 |
|
O_2 |
0.119 |
0.130 |
0.108 |
0.253 |
0.174 |
0.163 |
0.156 |
0.183 |
1.287 |
0.1609 |
|
O_3 |
0.564 |
0.478 |
0.377 |
0.309 |
0.198 |
0.272 |
0.259 |
0.183 |
2.640 |
0.3300 |
|
O_4 |
0.051 |
0.043 |
0.125 |
0.084 |
0.105 |
0.163 |
0.156 |
0.163 |
0.889 |
0.1112 |
|
O_5 |
0.025 |
0.026 |
0.075 |
0.028 |
0.035 |
0.016 |
0.015 |
0.087 |
0.306 |
0.0383 |
|
O_6 |
0.031 |
0.043 |
0.075 |
0.028 |
0.128 |
0.054 |
0.086 |
0.087 |
0.532 |
0.0665 |
|
O_7 |
0.031 |
0.043 |
0.075 |
0.028 |
0.128 |
0.042 |
0.052 |
0.087 |
0.486 |
0.0607 |
|
O_8 |
0.025 |
0.019 |
0.055 |
0.017 |
0.012 |
0.018 |
0.017 |
0.029 |
0.192 |
0.0240 |
Tab. 6.
Ranking matrix
|
|
Weight |
Rank |
|
O_1 |
0.2084 |
2 |
|
O_2 |
0.1609 |
3 |
|
O_3 |
0.3300 |
1 |
|
O_4 |
0.1112 |
4 |
|
O_5 |
0.0383 |
7 |
|
O_6 |
0.0665 |
5 |
|
O_7 |
0.0607 |
6 |
|
O_8 |
0.0240 |
8 |
Tab. 7.
Calculation of
consistency
|
|
O_1 |
O_2 |
O_3 |
O_4 |
O_5 |
O_6 |
O_7 |
O_8 |
Weighted
value (WV) |
Weight
(W) |
R=WV/W |
|
O_1 |
1.00 |
1.67 |
0.29 |
3.00 |
6.33 |
5.00 |
5.00 |
6.33 |
1.92 |
0.2084 |
9.231725 |
|
O_2 |
0.78 |
1.00 |
0.29 |
3.00 |
5.00 |
3.00 |
3.00 |
6.33 |
1.46 |
0.1609 |
9.096636 |
|
O_3 |
3.67 |
3.67 |
1.00 |
3.67 |
5.67 |
5.00 |
5.00 |
6.33 |
3.10 |
0.3300 |
9.391174 |
|
O_4 |
0.33 |
0.33 |
0.33 |
1.00 |
3.00 |
3.00 |
3.00 |
5.67 |
0.97 |
0.1112 |
8.754888 |
|
O_5 |
0.16 |
0.20 |
0.20 |
0.33 |
1.00 |
0.29 |
0.29 |
3.00 |
0.31 |
0.0383 |
8.092786 |
|
O_6 |
0.20 |
0.33 |
0.20 |
0.33 |
3.67 |
1.00 |
1.67 |
3.00 |
0.57 |
0.0665 |
8.555815 |
|
O_7 |
0.20 |
0.33 |
0.20 |
0.33 |
3.67 |
0.78 |
1.00 |
3.00 |
0.51 |
0.0607 |
8.468272 |
|
O_8 |
0.16 |
0.15 |
0.15 |
0.20 |
0.33 |
0.33 |
0.33 |
1.00 |
0.21 |
0.0240 |
8.595446 |
λmax=8.77 RI=1.41
CI= (8.77-8)/7 = .11 CR=.11/1.41= .07
In the values obtained above, λmax is
the eigenvalue of the final matrix obtained from Table 7. RI is a random index
obtained from Table 2 as the number of factors is 8, thus corresponding to the
number 8 value, 1.41, is taken for calculation. Consistency index (CI) is
obtained by applying equation 6. Finally, CR is the ratio of consistency index
to random index, which signifies how much the observed values and the
calculated values are related, and by applying equation 7 and it came out to
.07, which means the observed value is very close to the calculated value, so
the result obtained is accurate.
As
costs remain the most important criteria for almost all the manufacturing
systems, therefore, from the result obtained in section 4, Cost-efficient (O_3)
emerged as the most critical opportunity in this category. While Pay-per-use
(O_1) became the next most important opportunity as paying heavy amounts at one
time for small or medium scale industries is critical; therefore, the facility
to pay as per requirement attracts them. Scalability (O_2), the facility
of scaling where the user can scale (up or down) the use of resources according
to his needs secured the 3rd place. Flexibility (0_4), the ability to adapt and
respond to the changing customer demands secured the 4th place. Lastly,
the Location Independence (O_8) factor is the least important criterion on the list.
5. CONCLUSION
To
provide easy decision-making for entrepreneurs for the adoption of cloud
manufacturing, opportunities are identified and prioritization of these
parameters is obtained. Identification of these opportunities was obtained by
experts’ opinions and a survey was conducted to get raw data from experts
from different fields, namely, industrial, computer, and mechanical and also
from professions such as the academics and the industry to obtain the ranking
using the MDCM method. Identified opportunities in the context of cloud
manufacturing are, namely pay-as-use, scalability, cost efficiency,
flexibility, autonomy, low-risk backup and recovery, low startup cost, and
location independence. With the application of AHP on data obtained from the
survey, weights are calculated and ranking is obtained. Results show that cost
efficiency is the biggest opportunity in the adoption of cloud manufacturing.
Furthermore, the calculated consistency ratio for opportunities is .07, which
is less than .10, indicating accuracy and consistency of results. Cloud manufacturing process originated in China and
spread to countries like Japan, the U.S, Canada, Germany, France Russia, and
some countries in Latin America. This study provides prior information
regarding the significant factors of the process to entrepreneurs of developing
countries, especially for small-scale industries for easy and smooth adoption
of the cloud manufacturing process. Conclusively, process cost efficiency,
scalability, and pay-as-use qualities would surely attract new industrialists
for its setup in developing countries.
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APPENDIX-1
Survey
questionnaire (to be filled by experts)
You are supposed
to compare two opportunities at a time (that is, in pairs). The scores of
comparisons are 1, 3, 5, 7, 9. Scores are assigned as:
|
Equal importance |
1 |
|
Moderate importance |
3 |
|
Strong importance |
5 |
|
Very strong importance |
7 |
|
Extreme importance |
9 |
For
example, if you are comparing
opportunity (O_1) of a row with opportunity (O_2) of the column and assigned
value 5, it means that opportunity O_1 is of strong importance than
opportunity O_2.
NOTE:
No need to fill cells with value 1.
Opportunities
in the espousal cloud manufacturing
|
|
Opportunities in column i |
|||||||||
|
Opportunities in row i |
|
0_1 |
0_2 |
0_3 |
0_4 |
0_5 |
0_6 |
0_7 |
0_8 |
0_9 |
|
0_1 |
1 |
|
|
|
|
|
|
|
|
|
|
0_2 |
|
1 |
|
|
|
|
|
|
|
|
|
0_3 |
|
|
1 |
|
|
|
|
|
|
|
|
0_4 |
|
|
|
1 |
|
|
|
|
|
|
|
0_5 |
|
|
|
|
1 |
|
|
|
|
|
|
0_6 |
|
|
|
|
|
1 |
|
|
|
|
|
0_7 |
|
|
|
|
|
|
1 |
|
|
|
|
0_8 |
|
|
|
|
|
|
|
1 |
|
|
|
0_9 |
|
|
|
|
|
|
|
|
1 |
|
Table
1
|
No |
Opportunities |
|
O_1 |
Pay-per-use or
Instant service |
|
O_2 |
Scalability |
|
O_3 |
Cost efficiency |
|
O_4 |
Flexibility |
|
O_5 |
Autonomy |
|
O_6 |
Low-risk backup and
recovery |
|
O_7 |
Low startup cost |
|
O_8 |
Location independence |
Received 15.02.2022; accepted in
revised form 30.03.2022
Scientific Journal of Silesian University of Technology. Series
Transport is licensed under a Creative Commons Attribution 4.0
International License
[1] Department of
Mechanical Engineering, Delhi Technological University, New Delhi 110042,
India. Email: simplymohneesh@gmail.com. ORCID:
https://orcid.org/0000-0003-3347-9700