Operations Management Research
https://doi.org/10.1007/s12063-023-00390-z
Enabling artificial intelligence for sustainable food grain supply
chains: an agri 5.0 and circular economy perspective
Sumanta Das1 · Akhilesh Barve2 · Naresh Chandra Sahu3 · Kamalakanta Muduli4
Received: 8 November 2022 / Revised: 2 May 2023 / Accepted: 2 June 2023
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023
Abstract
The majority of the food grain supply chain (FGSC) is run in a linear fashion, requiring substantial inputs that produce
mostly inedible by-products, environmental damage and wastage. Moreover, population increase, declining food resources,
shifting weather patterns, and dwindling supplies pose serious problems to the FGSC. Effective usage and consumption
of resources to harmonize ecological, economic, and social elements is the need of the hour from the Agri 5.0 and circular
economy (CE) perspective. Fortunately, modern technological developments like artificial intelligence (AI) might represent a
paradigm change in this context. However, enablers for AI adoption haven't been studied sufficiently despite AI's popularity.
Hence, the fundamental objective of this research is to identify and examine key enablers that facilitate rapid AI adoption in
FGSC, empowering Agri 5.0 and CE in India. The primary facilitators for AI adoption have been explored via a literature
review and expert interviews followed by a questionnaire survey. The fuzzy decision-making trial and evaluation laboratory
(F-DEMATEL) approach was then used to create a causal model of the identified enablers. The F-DEMATEL method helps
resolve the uncertainty of researching enabler interactions. Research findings suggest that “Legal and regulatory interventions from the government (E7)” and “Green IoT-driven total automation (E5)” have a significant influence in integrating
AI in FGSC. The results have major ramifications for policymakers. The results may be used to justify future investments
and will also aid decision-makers in India in advancing AI initiatives.
Keywords AI-adoption · Agri 5.0 · Circular Economy (CE) · Food Grain Supply Chain (FGSC) · Fuzzy-DEMATEL
1 Introduction
* Kamalakanta Muduli
kamalakanta.muduli@pnguot.ac.pg
Sumanta Das
sd38@iitbbs.ac.in
Akhilesh Barve
akhileshbarve@yahoo.com
Naresh Chandra Sahu
naresh@iitbbs.ac.in
1
School of Mechanical Sciences, IIT Bhubaneswar,
Bhubaneswar, Odisha, India
2
Department of Mechanical Engineering, MANIT Bhopal,
Bhopal, Madhya Pradesh, India
3
School of Humanities, Social Sciences & Management, IIT
Bhubaneswar, Bhubaneswar, Odisha, India
4
Department of Mechanical Engineering, Papua New Guinea
University of Technology, Lea, Papua New Guinea
The management, protection, and availability of agrifood supplies are key concerns for countries all over the
globe (Das et al. 2021; Mangla et al. 2018). The advent
of globalization and the resulting increase in competitiveness has pushed businesses toward the digital world,
where technologies enabled by Industry 5.0 are playing an
increasingly important role (Akundi et al. 2022; Garske
et al. 2021; Liu et al. 2021). AI is one such technology; it
includes all programming codes, systems, computational
methods, and automated systems that exhibit intelligent
behavior (Barros et al. 2020; Vincent et al. 2019). In a
nutshell, AI is the study and development of computer systems that mimic human intellect to generate new forms of
information and resolve issues (Fraga-Lamas et al. 2021;
Naz et al. 2021). By 2030, AI may add up to $15.7 trillion
to the world's economy, making it essential for the development of any given country’s economic growth (PWC
2018). AI-driven technologies have found widespread use
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S. Das et al.
in a variety of fields, including medicine (Haleem et al.
2019a, b), engineering (Pan and Zhang 2021), business
(Rana et al. 2022), etc. According to Belhadi et al., (2021),
supply chain management can be one of the potential areas
which may benefit most from AI integration. Despite a
recent flurry of studies, the full capabilities of AI in SCs
have yet to be investigated (Garske et al. 2021).
Given the complicated and ambiguous interactions of
SC, AI can help develop a synchronized SC that shares
information and resources cooperatively (Belhadi et al.
2021). According to Taylor and Fearne (2006), using big
data is necessary to provide a unified method for forecasting demand activity in FSC. Utilizing and capitalizing on
such massive datasets to make informed decisions is one
area where AI is expected to be very useful. Better data
management with the use of AI might also be beneficial
for SC members (Bhat and Huang 2021; Talari et al. 2022).
Also, AI may be used to save expenses, give businesses
an edge, and boost SC efficiency (Haleem et al. 2019a,
b; Holmes et al. 2022; PWC 2018). While the preceding
studies have highlighted the relevance of AI in SCs, they
lack domain-specific insights that management may utilize
when considering AI deployment in particular organizations. This is because organizational variables affect AI
integration in SCs.
There are four phases of FGSCs: inputs, harvesting,
processing, and distribution. An AI-integrated FGSC
would allow real-time surveillance of material flows,
would boost responsiveness and efficiency, make it easier
to spot dangers, and help in strategy development. For
instance, the FGSC uses both digital and physical connectivity, dealing with large concentrations, low profitability, limited asset allocation, and tight schedules; this is
where AI technologies can assist in optimizing the systems
and enable coordination among stakeholders to meet the
requirement of the stocks (Vincent et al. 2019). FGSC is
crucial for the Indian economy because of the many issues
triggered by climate change and biodiversity loss, such
as pollution, broken nutrient cycles, lack of groundwater, starvation, economic hardship, and resource warfare
(Alberti et al. 2021; Bhat and Huang 2021; Khandelwal
and Chavhan 2019). FGSC entails constant quality modifications until customers receive the food grains. Hence,
large post-harvest losses (PHL) have a detrimental effect
on food security (Das et al. 2023a, b; Chandrasekaran and
Ranganathan 2017; Gunasekera et al. 2017). Ensuring the
safety and quality of food grains along the SC becomes
a problem (Ling and Wahab 2020; Rathore et al. 2020).
Cutting-edge technology like AI may increase SC visibility
and traceability, while addressing food quality and safety
(Das et al. 2021; Faisal and Talib 2016; Haleem et al. 2019a,
b). Figure 1 shows the current problems in FGSC and how
AI can eliminate these problems to improve SC efficiency.
13
As a natural component, agriculture is integral to the
circular economy's emphasis on reusing and recycling
resources; its proximity to human consumption patterns
makes it ideally suited to facilitate the coordinated humannatural relationships and ensuing sustainable human development (Fraga-Lamas et al. 2021). Agri 5.0 deals with the
smooth collaboration between humans and robots whereas
CE deals with the recycling and optimization of resources.
The adoption of AI with CE and Agri 5.0 ideas and methodologies is necessary to address environmental degradation,
ecological harm, and resource depletion in today's traditional
farming practices (Liu et al. 2021).
Although modern farming has greatly increased yield, it
has also come at a high cost in the form of excessive use of
energy and other resources; destruction of the natural agricultural ecosystem has been witnessed (Cadden et al. 2021;
Dora et al. 2021; Vincent et al. 2019). In this context, environmentalism and sustainable development are two examples of social concerns that AI is expected to address. This
could be done, for example, by increasing the efficiency with
which resources and energy are used, streamlining logistics
throughout the entire SC, or by better integrating activities
and ecological use from the Agri 5.0 and CE perspective
(Akundi et al. 2022; Belhadi et al. 2021; Liu et al. 2021).
AI has enormous potential in the food industry. Previous
work has mostly concentrated on the use and advantages of
AI in FSC and has not sufficiently investigated the crucial
feature of AI-based data exchange for boosting SC efficiency
(Manning et al. 2022; Monteiro and Barata 2021; Vincent
et al. 2019). There are limited articles on AI uptake and
utilization in developing areas like India. According to existing research on AI in FGSC, key success criteria that might
influence and promote AI adoption from the much-needed
Agri 5.0 and CE perspectives have not been explored. Many
organizations still don't know how to account for the impact
of AI adoption on their overall company strategy (Dora et al.
2021). A comprehensive evaluation of such variables for AI
deployment in FGSC may be crucial in using this technology
to enhance an organization's reputation, profitability, and
competitive edge. Therefore, this article is primarily motivated by the very low rate at which businesses embrace AI.
In this context, we intend to propose a meaningful theoretical and empirical framework employing many fundamental variables from existing literature and a dedicated
questionnaire survey for AI adoption in FGSC in a growing
market like India from the Agri 5.0 and CE perspective; this
can improve economic growth by using the natural ecosystem's material recycling process. Hence the research aims to
provide answers to the following research questions:
RQ 1: What are the enablers that can help facilitate
the adoption of AI in the context of Agri 5.0 and the
CE in FGSC?
Enabling artificial intelligence for sustainable food grain supply chains: an agri 5.0 and…
Fig. 1 Benefits of AI integrated FGSC
RQ 2: How are these enablers interdependent or connected to each other?
RQ 3: How can understanding the relationships between
these enablers be useful for decision-makers seeking to
integrate AI from an Agri 5.0 and CE perspective?
This paper contributes to the following areas of knowledge:
Firstly, it expands the AI adoption literature by reviewing AI enablers. Secondly, the study adopted an F-DEMATEL approach
to interpret the interrelationships among the enablers. F-DEMATEL has the advantage of classifying identified variables into
cause and effect groups; this provides insights into prioritizing
the enablers when decisions are being made. This methodology
has been extensively used in different fields such as food supply
chain management (Das et al. 2021), the manufacturing industry
(Parmar and Desai 2020) etc. Finally, the findings of the research
will be helpful to policymakers, solution providers, and supply
chain experts in assisting them to concentrate on the most crucial
elements that impact AI adoption in the FGSC.
The remainder of the paper is as follows. Section 2
shows the literature review. Section 3 depicts the research
gaps, followed by data collection in Section 4. The
research methodology is highlighted in Section 5. Results
and discussion are highlighted in Section 6. Section 7
shows the sensitivity analysis for validation of the data
analysis. Finally, Section 8 discusses the research implications and directions for further research.
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S. Das et al.
2 Literature review
2.1 Role of AI in FSC
In recent years, technological advancements have enabled
a massive rise in the production of food grains. Still, concerns over food production and supply are rising dramatically across the globe (FAO 2021; Mithun Ali et al. 2019).
Problems include a shortage of storage space and poor communication among members of the supply chain; these are
at the heart of inefficient FGSCs and are responsible for the
majority of losses (Chandrasekaran and Ranganathan 2017;
Gunasekera et al. 2017). An estimated 8–10% of worldwide
greenhouse gas emissions are related to wasted food (UNEP
2021). In emerging markets like India, post-harvest losses
account for 40% of food loss due to inadequate storage facilities; this has serious ecological, social, and economic consequences (Chauhan et al. 2020; Mangla et al. 2018; Sharon
et al. 2014). At the same time, traceability issues have also
been reported by many researchers, causing diversion of
food grains and circumventing the planned beneficiaries.
An estimation of 42% of food grains was diverted during
2011–12 (Drèze and Khera 2015). It is very important to
have a proper tracking and traceability system. It is vital
to check whether the entitled quantity of grains is reaching
the beneficiaries; if not. the efficiency of the SC must be
improved (Das et al. 2021). Due to demand fluctuations, perishability, periodicity, and temperature sensitivities, FGSC
provides a particularly attractive environment for study (Das
et al. 2023a, b; Kamble et al. 2020).
AI has the capacity and is more effective in taking into
account the specific difficulties encountered by FGSC. AI
gives superior technological methods for increasing agricultural output of produce, distribution, reducing food waste,
and maintaining a safe food supply (Dora et al. 2021; Jain
et al. 2021). For example, traditional approaches to crop
health monitoring are time-consuming and arduous. AI is
a useful tool for checking soil for nutrients and detecting
any problems with the crop. Deep learning is used to assess
agricultural crop health characteristics. AI-enabled apps help
analyze soil quality, pest problems, and diseases (Bhat and
Huang 2021; Syeda et al. 2021). To aid farmers and land
owners, researchers have highlighted the need for an AIdriven system that helps in decision-making in terms of site
selection with the use of big data science (Vincent et al.
2019). These resources help farmers anticipate pest and disease outbreaks and facilitate more informed decisions about
how to best manage their crops. Every day, farms generate a
mountain of data at ground level. AI has made it possible for
farmers to examine data in real time, allowing them to make
more informed choices based on factors like weather, temperature, water consumption, and soil properties. Utilizing
13
the readily available data, farmers can now cultivate crops
that are both healthy and resource-efficient due to advances
in AI technology (Belhadi et al. 2021; Bhat and Huang
2021). Hence, technological advancements play a pivotal
role in sharing data, especially in creating data-sharing and
computational infrastructures. AI may boost the productivity
of FSCs by facilitating improved integration among channel
partners (Bhat and Huang 2021; Maddikunta et al. 2022). It
may aid in accelerating the intricate procedures of an FGSC,
making it more trustworthy as a result. Table 1 gives a short
description of previous literature on AI in the field of SCs.
The identified enablers are shown in Table 2.
3 Research gaps and highlights
By completing an analysis of previously published research,
we were able to identify a great number of topics that need
more investigation. Several studies have looked into the
adoption of the latest technological models in multiple areas
(Bestelmeyer et al. 2020; Cadden et al. 2021; Dora et al.
2021; Durrant et al. 2021). Although there has been a growth
in research into cutting-edge technologies such as the Internet of Things (IoT), big data analytics (BDA), drones etc.,
the role of key enablers in the adoption of AI in FSC has
received little attention (Barros et al. 2020; Dora et al. 2021).
According to the available research, AI has a great deal of
untapped potential for driving transformation inside FSCs.
In contrast, an adequate integration system is needed since
Indian FGSC is complex, unorganized, quasi, and includes a
wide range of intermediaries (Nyamah et al. 2017; Rathore
et al. 2020). Technologies like AI might play a significant
role in this context.
However, the adoption of AI in FGSC is crucial as an
advanced technological transformation has significant ecological footprints. Therefore, AI adoption needs to be energy
efficient and reduce the greenhouse gas effect (Fraga-Lamas
et al. 2021). However, there has been no study on the impact
of key enablers for AI adoption in FGSC from the Agri 5.0
and CE perspective. There has only been investigation into
AI integration in Indian FSC from the CE perspective. This
study has not considered some of the significant factors like
‘Customer satisfaction in demand volatility’, ‘Economic
incentives from the government and private parties’, and
‘Green IoT-driven total automation’; these are all significant aspects of Agri 5.0 and CE (Fraga-Lamas et al. 2021;
Garske et al. 2021; Vincent et al. 2019). This study has only
prioritized the AI adoption enablers; it has not explored
any interrelationships between the enablers. Therefore, an
industry-specific study is needed to understand the case better and to provide more logical insights (Barros et al. 2020;
Cadden et al. 2021).
Enabling artificial intelligence for sustainable food grain supply chains: an agri 5.0 and…
Table 1 Previous literature on AI in SC
Authors
Field of the study
Research type
Methodology
(Manning et al. 2022)
Generic FSC
Qualitative
Discussion
Work done
The study highlighted the importance of
creating a common language for the
deployment of technology throughout
the SC while considering the ethical
implications of AI in the food industry
(Kollia et al. 2021)
Belgium
Quantitative
Deep learning
The study adopted a deep learning
algorithm that can recognize and
validate expiration dates on food
package photographs collected during
manufacture
(Durrant et al. 2021)
Agri food sector
Qualitative
Discussion
Highlighted that using semantic web,
distributed ledger, machine learning,
and information security will allow
future transformational agri-food
data-sharing infrastructures
(Jain et al. 2021)
Indian food processing sector Quantitative
Statistical analysis
The study highlighted that operational
efficiency can be increased through
the adoption of AI in the SC for SMEs
(Liu et al. 2021)
Agriculture sector
Qualitative
Discussion
The study highlighted different challenges and enabling factors that are
needed for Industry 4.0 transformation
in agriculture
(Garske et al. 2021)
Europe
Qualitative
Discussion
The study highlighted that a trustworthy legal framework for safety, data
privacy, access, and security is vital
for the adoption of AI
(Cadden et al. 2021)
UK
Quantitative
Statistical analysis
The study highlighted the cultural enablers in the integration of AI into SC
of the manufacturing sector
(Dora et al. 2021)
Indian FSC
Quantitative
Fuzzy SWARA​
The study analyzed the critical success
factors for AI adoption in Indian FSC
(Bhat and Huang 2021) Agriculture
Qualitative
Discussion
The study focused on different challenges in big data and AI adoption
in agriculture, and also highlighted
the importance of this cutting-edge
technology in farming
(Barros et al. 2020)
Agriculture and CE
Literature review Discussion
The study highlighted different trends
and practices in agriculture from the
CE perspective and concluded that
agricultural waste must be minimized
or recycled
(Bestelmeyer et al. 2020) Agri food sector
Qualitative
Discussion
Highlighted that integrating AI into
agricultural research on a larger scale
can improve the efficiency of the SC;
AI will need long-term, wide-ranging
agricultural observational data
This study integrates smart sensors with
(Vincent et al. 2019)
Agriculture sector
Quantitative
Neural network and
AI systems like neural networks and
Multi-Layer Perception
MLP to evaluate agricultural land
(MLP)
compatibility for farming
Hence, identifying and analyzing the key enablers for
AI adoption from both the Agri 5.0 and CE perspective is
what makes this study unique, adding to the growing body
of empirical literature on this topic. This study not only prioritizes the enablers for AI adoption but can classify these
enablers into cause and effect groups. Hence, the study provides the causal relationships among identified factors; this
can create a vision and provide insights for industry managers and government to draft more informed policy and
improve decision making.
13
S. Das et al.
Table 2 Identified AI enablers for sustainable FGSC
Enablers
How it influences the adoption of AI
References of the evidence in prior studies
Development of computing infrastructure
• Transfer of data across borders and legal
authority
• Reduced cost
• Increased data storage capacity
• IT connectivity
• Smooth flow of information
• Stability of internet and low latency
• Ability to reach consensus
• Better accountability and trust
• Efficient networking
• Smooth flow of information
• Strategic collaboration and inter-firm
networks
• More people-centric approach
• Develop building trust and reliability
• Providing resources
• Helps formulate the policy India needs right
now
• Easily accessibility of Agri 5.0 products
• More people-centric approach to solving
problems
• Develop building trust and reliability
• Understanding the sustainability initiative's
importance and benefits
• More popularity of the AI product
• Efficient networking
• Real-time accurate data
• Better resource optimization
• Efficient transition from a linear economy to
a circular economy
• Create an opportunity for the pilot project
• Triggers sustainability
• Create opportunities for bilateral collaboration
• Better policy formulation
• Financial support
• Data privacy and comprehensive security
system
• Setting standards for technology and infrastructures
• Mitigating bias and building trust
• Transparency
• Reliability and accountability
• Protection and reinforcement of positive
human values
• Right to equality
• Grievance redressal
• Formulate enforcement mechanisms to
adopt AI
• Accurate data sharing
• Better trust and transparency
• Better accountability
• Effective analysis of the data
• Well established eco-system
• Smart resource management
• Cleaning the dataset for further analysis
• Data standardization
(Kollia et al. 2021; Manning et al. 2022;
Monteiro and Barata 2021)
Coordination and trust among organizations
Economic incentives from the government
and private parties
Customer satisfaction in demand volatility
Green IoT-driven total automation
Promoting private partnerships and bilateral collaboration
Legal and regulatory interventions from
the government
Information sharing and communication
among SC stakeholders
Build a data integration team
13
(Bestelmeyer et al. 2020; Durrant et al. 2021)
(UNEP 2021; Wong et al. 2020)
Expert Review
Expert Review
(Dora et al. 2021; Fraga-Lamas et al. 2021)
(Akundi et al. 2022; Jain et al. 2021)
(Alberti et al. 2021; Devi et al. 2021; Hari et al.
2018)
(Bestelmeyer et al. 2020; Liu et al. 2021;
Manning et al. 2022)
Enabling artificial intelligence for sustainable food grain supply chains: an agri 5.0 and…
Table 2 (continued)
Enablers
How it influences the adoption of AI
Job security post AI adoption
• Increased interest to learn ML and deep
learning algorithms
• Enhanced popularity of AI
• Faster diffusion of AI in SC
Collaborative research for responsible AI
• Looks for the potential areas of AI implementation
• Better sustainability initiatives
• Better human-centric solutions
• Smart resource management
• Knowledge transfer and organizational
maturity
Top management’s involvement, support,
• Better accountability and trust
and commitment
• Provide resources
• Smooth flow of information
• Transparency
• Data security
Data-driven organizational culture
• Improve diffusion of AI
• Smooth flow of information
• Looks for potential areas of AI implementation
• Better accountability and trust
• More reliable outcome
Rescaling internal talent; workforce hiring • Improve knowledge and skills of the
employees
• Capability of integrating functionality, data,
and processes
• Improve diffusion of AI
• Better popularity of AI products
Implementing prototype projects for check- • High reliability
ing viability
• Better responsibility
• Provide a better solution
• Smart resource optimization
Digital literacy and awareness building
• The popularity of AI products
• Better applicability of AI in sustainability
initiatives
• Enhanced accountability
• More human-centric solutions
• New educational and talent initiatives
References of the evidence in prior studies
(Cadden et al. 2021; Garske et al. 2021; Liu
et al. 2021)
(Dora et al. 2021; Fraga-Lamas et al. 2021)
(Belhadi et al. 2021; Pan and Zhang 2021;
Taylor and Fearne 2006)
(PWC 2018; Rana et al. 2022)
(Bhat and Huang 2021; Dora et al. 2021;
Khanzode et al. 2021)
Expert Review
(Bhat and Huang 2021; Mangla et al. 2018)
4 Case analysis and data collection
4.1 Demographic profile and data collection
The case study technique was employed in this research to
gain a thorough understanding of enablers for the adoption
of AI in the Indian FGSC from the CE and Agri 5.0 perspective. AI-enhanced farming solutions might represent a watershed moment in the history of the FSC. Hence, this study
attempts to collect the advice of industry professionals and
conduct a comprehensive literature evaluation to identify the
most important factors facilitating the widespread application of AI in the field of FGSC. The most important enablers
and the strength of their causal effects have been uncovered
via quantitative analysis of identified enablers.
After reviewing the available research, 18 AI enablers in
Indian FGSC were found. Through the use of the Delphi
method, a standardized questionnaire was developed for
all 18 enablers and distributed to panel experts to receive
their feedback. A 5-point Likert scale was used in the survey to assess their input. 77 professionals from the food SC
industry and academic institutions were interviewed using
a questionnaire. 72 of the 77 experts responded with 67 of
them completing the task. The demographic statistics of the
analysts are shown in Table 3.
13
S. Das et al.
Table 3 Demographic statistics of the respondents
Characteristics Profile
No of
respondents
Percentage
Sector/profile
09
07
11
11.68
9.09
14.28
12
15.58
14
14
10
17
37
18.18
18.18
12.98
22.07
48.05
23
30
32
15
51
26
29.87
38.96
41.55
19.48
66.23
33.76
Experience in
years
Qualifications
Gender
Executive Director
General Manager
Supply Chain Manager
Assistant General
Manager
Divisional Manager
General Manager
Academician
Less than 5 years
Between 5 and
10 years
More than 10 years
Graduates
Postgraduates
PhD
Male
Female
15 experts were selected from a pool of 67 experts
through stratified sampling (Taherdoost 2016). In this
case, the population of experts was first divided into three
groups based on their levels of experience (see Table 3); five
experts were then selected from each group to collect data
for F-DEMATEL. Finally, only 5 experts were chosen randomly from the final 15 experts for the sensitivity analysis.
4.2 Delphi study
Table 4 displays the results of a Microsoft Excel spreadsheet
analysis of the replies. The characteristics of central tendency (mean, standard deviation, and median) were used to
finalize the enablers. According to Kumar et al. 2022, to be
included in the Delphi research, the mean and median scores
must be greater than or equal to 4. As a result, the same cutoff point is used for inclusion of enablers in this research.
5 Research methodology
5.1 Fuzzy DEMATEL
The study adopted the fuzzy DEMATEL technique. This is an
efficient method to deal with a complicated and fuzzy environment where subjective and imprecise assumptions can influence any decisions made (Das et al. 2021; Yadav et al. 2020).
The experts communicate their opinions on linguistic expression in matters linked to complex structures. The guideline
to linguistic communication is a typical way for experts to
stimulate their judgments; fuzzy figures may convey linguistic
principles in actuality. Fuzzy set theory has long been used by
scholars to express and handle uncertainty in decision-making
(Jeng 2015; Yadav and Barve 2018; Zhou et al. 2011). Experts’
Table 4 Selection of enablers for the adoption of AI in Indian FGSC
SN No
Enablers
Mean
Median
Standard
deviation
Accept/ Reject
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Development of computing infrastructure
Coordination and trust among organizations
Economic incentives from the government and private parties
Customer satisfaction in demand volatility
Green IoT-driven total automation
Promoting private partnerships and bilateral collaboration
Legal and regulatory interventions from the government
Information sharing and communication among SC stakeholders
Build a data integration team
Job security post AI adoption
Collaborative research for responsible AI
Top management’s involvement, support, and commitment
Data-driven organizational culture
Rescaling internal talent; workforce hiring
Implementing prototype projects for checking viability
Digital literacy and awareness building
Open communication
Democratic methods of governance
4.494
4.390
4.519
4.429
4.481
4.338
4.455
4.260
4.468
4.351
4.377
4.455
4.312
4.416
4.403
4.312
3.182
3.299
5
5
5
5
4
4
5
4
5
4
4
5
4
4
4
4
3
3
0.641
0.764
0.661
0.696
0.503
0.837
0.680
0.657
0.640
0.556
0.708
0.597
0.799
0.593
0.654
0.799
1.060
1.136
Accept
Accept
Accept
Accept
Accept
Accept
Accept
Accept
Accept
Accept
Accept
Accept
Accept
Accept
Accept
Accept
Reject
Reject
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Enabling artificial intelligence for sustainable food grain supply chains: an agri 5.0 and…
Find out the enablers for AI adoption in FGSC
Standardization of fuzzy numbers
Develop an initial direct relation matrix using
expert’s assessment
Calculate low (ls) and high (hs) normalized values
Calculate the total normalized crisp values
Design the fuzzy linguistic variables
Find the crisp score of the kth expert’s assessment
Develop fuzzy initial direct relation matrix
Calculate integrated score by averaging the crisp scores of
all experts
Find the normalized fuzzy initial direct relation
matrix
Develop total relation matrix
Calculate the prominence and net effect of every
criterion
Set a threshold value to obtain the diagraph
Develop the causal diagram
Fig. 2 Flow chart of the research
uncertain decisions may be expressed by triangular fuzzy
numbers (TFN); these are widely used for such situations
(Das et al. 2021). Figure 2 shows the flow chart of the
research methodology.
Let  be the TFN, denoted as (e, f, g), where e ≤ f ≤ g and
e, f, and g are real numbers (see Fig. 3, and Eq. (1)). The
membership functions of  in-universe X can be defined as
follows:
⎧0,
x<e
⎪
⎪x − e∕f − e, e ≤ x ≤ f
𝜇Â (x) = ⎨
⎪g − x∕g − f f ≤ x ≤ g
⎪0,
x>g
⎩
(1)
The step-wise F-DEMATEL method is as follows (Das
et al. 2021).
μà (x)
1.0
0
x
Fig. 3 The membership function for the TFN
13
S. Das et al.
5.1.1 Step 1: set up the initial direct relation matrix (IDRM)
The expert linguistic judgment via paired comparison of[the]
variables is derived as an initial direct relation matrix X = xij
using Table 5 based on the influence score. A
[ non-negative
]
k
matrix for each expert can be defined as X = xijk , where k is
the number of experts with 1 ≤ k ≤ K, and n is the number of
performance variables.
( )
TRM = tij = D(I − D)−1
(4)
5.1.6 Step 7: evaluate the direct and indirect effects
The TRM results may be explored in Ri and Cj in Table 6 by
using Eqs. (5–6).
]
)� ( ) [∑n
(
tij
Ri = R1 , ..., Ri , ..., Rn = Ri n×1
(5)
j=1
5.1.2 Step 3: formation of fuzzy initial direct relation
matrix (F‑IDRM)
n×1
]
[∑n
)� ( )
( )�
(
tij
Cj = C1 , ..., Ci , ..., Cn = Cj n×1 = Cj n =
i=1
The IDRM is converted to the F-IDRM matrix using the linguistic scale described in Table 5 based on the corresponding
fuzzy scores. Let ̃
xijk = (ekij , fijk , gkij ) represent the fuzzy judgments of expert k (k = 1, 2, 3,…, K) on the degree to which
criterion i influences the criterion j.
5.1.3 Step 4: compute the overall direct matrix (ODM)
The ODM is calculated by combining all the experts chosen
for the study using Eq. (2).
Xk =
∑k
i=0
(2)
(xk )∕k
5.1.4 Step 5: compute the normalized direct matrix (NDM)
NDM can be derived by Eq. (3), i.e., NDM = [dij ] for all
0 ≤ dij ≤ 1.
NDM =
max
l≤i≤n
1
∑n
j=1
aij
A
(3)
1×n
(6)
where Ri indicates the direct and indirect effect of the enabler i on other factors. Likewise, Cj represents the direct and
indirect effects of enabler j by other factors. A positive value
of Ri demonstrates the net influence of the parameters on
the system, while a negative value reveals the system's net
impact on the parameters.
5.1.7 Step 8: setting up a threshold value for the diagraph
Usually, it is up to the pool of decision-makers whether they
want to set a threshold value of the TRM or not. However,
a threshold value must be established to sort out the most
influential variables among the large number of variables
chosen for the analysis (Das et al. 2021). The threshold value
must be obtained by adding one standard deviation (SD) to
the mean of the TRM. Here, the SD and mean are 0.072 and
0.176. Thus, the threshold value is 0.249. Table 7 highlights
all the values equal to or greater than the threshold value of
the TRM.
5.1.8 Step 9: develop the cause‑effect relationship digraph
5.1.5 Step 6: compute the total relation matrix (TRM)
TRM depicts the comprehensive connection between each
pair of variables, with the component tij designating the indirect influence that factor i has on factor j. I is the identity
matrix of n × n (see Eq. (4)).
Scatter plotting the data set ( Ri+Cj , Ri-Cj ) produces a causal
digraph. Figure 4 shows 16 key enablers for AI adoption in
FGSC. Cause group variables are above the X-axis, while
effect group variables are below.
6 Results and discussion
Table 5 Linguistic assessment scale and corresponding TFNs
Linguistic Variable
Influence Corresponding
Score
Triangular Fuzzy
Numbers (TFNs)
No Influence (NO)
Very Low Influence (VL)
Low Influence (L)
High Influence (H)
Very High Influence (VH)
0
1
2
3
4
13
(0,0,0.25)
(0,0.25,0.5)
(0.25,0.5,0.75)
(0.5,0.75,1)
(0.75,1,1)
A causal diagram (Fig. 4) was developed after confirmation from the assessment group and the determination of
the TRM. The causal diagram simplifies the case's complexity, allowing for more in-depth analysis and better
judgments. The present study's findings are broken down
into three categories: an overall ranking of enablers,
categorization of enablers into cause and effect groups,
and explanations of the major enablers for managerial
decision-making.
Enabling artificial intelligence for sustainable food grain supply chains: an agri 5.0 and…
Table 6 Cause and Effect group enablers for AI adoption in FGSC
EN1
EN2
EN3
EN4
EN5
EN6
EN7
EN8
EN9
EN10
EN11
EN12
EN13
EN14
EN15
EN16
Enablers
Ri
Cj
Ri + Cj
Rank
Ri—Cj
Cause/Effect
Development of computing infrastructure
Coordination and trust among organizations
Economic incentives from the government and private parties
Customer satisfaction in demand volatility
Green IoT-driven total automation
Promoting private partnerships and bilateral collaboration
Legal and regulatory interventions from the government
Information sharing and communication among SC stakeholders
Build a data integration team
Job security post AI adoption
Collaborative research for responsible AI
Top management’s involvement, support, and commitment
Data-driven organizational culture
Rescaling internal talent; workforce hiring
Implementing prototype projects for checking the viability
Digital literacy and awareness building
3.0677
3.467
3.2668
2.1409
3.4069
3.7404
4.1294
3.4088
1.7997
1.0334
2.6939
4.1154
2.7621
1.9559
1.8958
2.2476
2.0843
3.0283
2.155
3.175
3.1338
2.1949
2.9863
2.8227
3.5851
3.4763
3.1937
1.078
2.3996
3.7783
3.0072
3.0032
5.152
6.495
5.422
5.316
6.541
5.935
7.116
6.232
5.385
4.510
5.888
5.194
5.162
5.734
4.903
5.281
14
3
8
10
2
5
1
4
9
16
6
12
13
7
15
11
0.984
0.439
1.112
-1.034
0.273
1.546
1.143
0.586
-1.785
-2.443
-0.500
3.038
0.362
-1.823
-1.112
-0.786
Cause
Cause
Cause
Effect
Cause
Cause
Cause
Cause
Effect
Effect
Effect
Cause
Cause
Effect
Effect
Effect
6.1 Ranking of the enablers based on (Ri+Cj ) score
The identified enablers for AI adoption from the CE and
Industry 5.0 perspective in FGSC are ranked based on the
( Ri+Cj ) scores (see Table 6). For the current research, ‘Legal
and regulatory interventions from the government (EN7)’
is the most significant enabler with the highest ( Ri+Cj )
score of 7.116, while the least important is ‘Job certainty
after AI adoption’ (EN10), with ( Ri+Cj ) score of 4.510. AI
generates hazards that existing laws and regulations cannot
address, hence new legislation is required. If AI technology
manufacturers explain how AI judgments are made, the
existing method can operate well in most circumstances.
Many researchers have highlighted the need for proper regulatory interventions from the government (Manning et al.
2022; Monteiro and Barata 2021). Next, ‘Green IoT-driven
total automation (EN5)’ has the second highest ( Ri+Cj ) score
of 6.541. IoT-driven agriculture can drastically boost the SC
industry by using the resources optimally. This can help to
access real-time data and better forecasting. Similar reports
have been noted (Devi et al. 2021; Hari et al. 2018). Next,
‘Coordination and trust among the organizations (EN2)’
Table 7 Total relation matrix (TRM)
EN1
EN2
EN3
EN4
EN5
EN6
EN7
EN8
EN9
EN10
EN11
EN12
EN13
EN14
EN15
EN16
EN1
EN2
EN3
EN4
EN5
EN6
EN7
EN8
EN9
EN10
EN11
EN12
EN13
EN14
EN15
EN16
0.104
0.122
0.182
0.108
0.183
0.199
0.205
0.124
0.135
0.038
0.130
0.214
0.131
0.066
0.068
0.077
0.208
0.176
0.214
0.112
0.240
0.258
0.278
0.246
0.092
0.054
0.191
0.262
0.206
0.151
0.162
0.180
0.146
0.128
0.116
0.126
0.188
0.202
0.219
0.176
0.118
0.074
0.101
0.218
0.119
0.070
0.070
0.085
0.199
0.221
0.242
0.113
0.247
0.265
0.288
0.202
0.165
0.106
0.212
0.288
0.217
0.104
0.119
0.185
0.228
0.250
0.239
0.115
0.176
0.250
0.285
0.251
0.096
0.056
0.192
0.285
0.210
0.154
0.164
0.183
0.163
0.193
0.185
0.112
0.126
0.134
0.221
0.195
0.089
0.041
0.168
0.221
0.104
0.089
0.070
0.083
0.190
0.244
0.199
0.158
0.205
0.255
0.205
0.224
0.141
0.103
0.188
0.274
0.192
0.102
0.145
0.162
0.213
0.232
0.206
0.105
0.229
0.212
0.264
0.162
0.087
0.051
0.179
0.264
0.199
0.157
0.091
0.173
0.253
0.279
0.247
0.130
0.272
0.292
0.316
0.278
0.105
0.062
0.232
0.316
0.232
0.187
0.182
0.202
0.246
0.269
0.261
0.197
0.267
0.288
0.309
0.218
0.126
0.063
0.176
0.292
0.227
0.179
0.178
0.183
0.233
0.254
0.209
0.163
0.233
0.268
0.289
0.239
0.150
0.059
0.143
0.289
0.215
0.172
0.155
0.123
0.058
0.119
0.060
0.038
0.080
0.086
0.145
0.134
0.032
0.021
0.053
0.073
0.055
0.039
0.038
0.048
0.190
0.203
0.200
0.151
0.203
0.217
0.220
0.188
0.076
0.043
0.127
0.235
0.109
0.074
0.074
0.090
0.243
0.289
0.259
0.203
0.283
0.305
0.331
0.285
0.167
0.137
0.225
0.329
0.195
0.125
0.190
0.214
0.202
0.244
0.215
0.135
0.238
0.255
0.277
0.244
0.109
0.072
0.171
0.276
0.156
0.168
0.095
0.150
0.191
0.244
0.233
0.175
0.239
0.257
0.279
0.244
0.113
0.054
0.207
0.278
0.194
0.118
0.096
0.111
The bold values represent values equal to or greater than the threshold value of the TRM
13
S. Das et al.
Fig. 4 Causal diagram of the
enablers
has the third highest ( Ri+Cj ) score of 6.495. Management
often has too little understanding of what AI needs. Modern
skills and technology are unquestionably required but it's
also critical to match an industry's culture, organizational
structure, and working practices to promote widespread AI
adoption. However, old attitudes and methods of working
are incompatible with those required for AI at the majority of firms that weren't born digital. Hence, coordination
and trust must be instilled to ensure better transparency and
decision-making; this will facilitate widespread AI adoption in FGSC. Similar insights have been explored by other
researchers (Akundi et al. 2022; Dora et al. 2021; Rajput and
Singh 2019). Figure 5 highlights the rank of all enablers.
6.2 Discussion on cause group enablers
Based on their ( Ri - Cj ) scores, the enablers have been categorized into two groups—the cause group and the effect
group (Table 6). Out of 16 enablers, nine enablers with
Fig. 5 Prioritized enablers
based on ­(Ri + ­Cj) value
positive ( Ri - Cj ) scores are found to be in the cause group
(see Fig. 6), while the other seven enablers with negative
( Ri - Cj ) scores are found to be in the effect group. To what
extent the AI adoption in Indian FGSC is successful depends
on the performance of the enablers specified in the cause
group. Therefore, this is a more important area than the variables influencing the impact group (Das et al. 2021; Kumar
et al. 2022).
The enabler “Top management involvement, support, and
commitment (EN12)” has the highest impact power among
all the enablers in the cause group with (­ Ri—Cj) score of
3.038. This means that the support of upper management is
crucial for securing the resources (facility, capital, IT, and
human resource) needed to put various strategies into action
and generate greater rewards. Support from top management
is frequently cited as a critical enabler and a necessity for
technology adoption. The enabler “Promoting private partnerships and bilateral collaboration (EN6)” has the second
highest impact power among the other enablers in the cause
Prioritized enablers based on (Ri + Cj) value
8.000
7.000
Ri + Cj
6.000
5.000
4.000
3.000
2.000
1.000
0.000
EN7 EN5 EN2 EN8 EN6 EN11 EN14 EN3 EN9 EN4 EN16 EN12 EN13 EN1 EN15 EN10
Ri + Cj 7.116 6.541 6.495 6.232 5.935 5.888 5.734 5.422 5.385 5.316 5.281 5.194 5.162 5.152 4.903 4.510
13
Enabling artificial intelligence for sustainable food grain supply chains: an agri 5.0 and…
Fig. 6 Prioritized cause group
enablers
Ri - Cj
Cause group enablers
3.500
3.000
2.500
2.000
1.500
1.000
0.500
0.000
3.038
1.546
EN12
EN6
1.143
EN7
1.112
EN3
0.984
EN1
0.586
0.439
0.362
0.273
EN8
EN2
EN13
EN5
Enablers
group with ­(Ri—Cj) score of 1.546. This enabler dispatches
direct impact to other enablers such as EN2, EN4, EN5,
EN7, EN9, EN10, EN11, EN14, EN15, and EN16. Hence,
to maximize the value of present and prospects, it will be
necessary to work together to maximize the effect of investments and activities now underway at all levels. India has a
solid research foundation across all of its priority sectors, but
the country has to defragment its research infrastructure to
establish AI policies that favour the development of useful,
trustworthy, and resilient AI.
“Legal and regulatory intervention from the government
(EN7)” has the third highest impact power among the other
enablers in the cause group with ­(Ri—Cj) score of 1.143.
However, the highest Ri score (4.129) indicates that it is
one of the most important cause enablers with a significant
impact on others. It is evident from the literature that data
governance, data protection, connectivity, and data privacy
are all crucial to the future of agriculture's AI adoption. As
the use of AI and other data-centric processes continues
to grow, so are the number of laws governing them. These
regulations are obligatory for businesses, particularly those
in a heavily regulated sector like agriculture. Similar insights
have been highlighted by other researchers that legal safeguards may need to be updated to account for the unique
challenges posed by algorithmic decision-making (Hangl
et al. 2022; Manning et al. 2022). Developing a set of guiding principles and norms is essential to ensure that all parties
involved in the SC have a shared view of what constitutes
appropriate behavior.
The enabler “Long-term investment and economic incentives from the government and private parties (EN3)” has
the fourth highest ­(Ri—Cj) score of 1.112. The use of robots
has benefited the agriculture sector in various ways, including increased production yield, but implementing AI from
the Agri 5.0 and CE perspective comes with a huge cost
involved. Hence, a long-term strategic investment plan
is necessary to facilitate the adoption process in FGSC.
This study’s results can help to highlight the importance
of long-term commercial investment and incentives while
encouraging initial investment for AI adoption at the farm
level (Cadden et al. 2021; Garske et al. 2021).
“Development of computing infrastructure (EN1)” has
the fifth highest (­ Ri—Cj) score of 0.984 among the cause
group enablers. It is evident from previous research that
currently, India lacks the computational infrastructure
which can eliminate many critical challenges of data storage, data accessibility, data privacy, cloud computing, and
data analysis, etc. Development of computing infrastructure
can be beneficial in collecting information in many forms
(audio, video, picture, text, and digital maps); data may then
be gathered and stored centrally on the cloud for further
analysis and to improve the decision-making process (FragaLamas et al. 2021; Maddikunta et al. 2022).
The other significant cause group enablers are “Information sharing between the stakeholders (EN8)”, “Coordination and trust among organizations (EN2)”, “Data-driven
organizational culture (EN13)”, and “IoT-driven total automation (EN5)”. The ­(Ri—Cj) scores of EN8, EN2, EN13,
and EN5 are 0.586, 0.439, 0.362, and 0.273 respectively.
These enablers are less impacted by other enablers as seen
by the lower value of ­(Ri—Cj); on the other hand, a comparatively high ­(Ri + ­Cj) score indicates the significant
capability of these enablers to facilitate the process of AI
adoption in FGSC (Manning et al. 2021; Ritchie & Brindley
2007). For example, the success of AI implementation is
highly dependent on information sharing between members
involved in the SC. Hence, coordination and trust among
stakeholders are vital to ensure credible and quality data.
Similarly, a data-driven culture guarantees that everyone has
a data-first approach and uses data in every decision. By
introducing enhanced systems and new technology, more
teams may access previously segregated or hidden information, investigate patterns, and execute changes in response
to emerging opportunities. Other researchers (Cadden et al.
2021; Dora et al. 2021) have also found that data-driven
organizational culture plays an important role in the process
of AI adoption in FSC.
13
S. Das et al.
6.3 Discussion on effect group enablers
The SC for food grains operates in very turbulent and unpredictable conditions; this does not imply that the factors in
the effect group are not important to the cause group. Each
factor is necessary, but since all key enablers cannot be concentrated at once, it is often important to separate them into
cause and effect group factors. These effect group enablers
are categorized based on the negative (­ Ri—Cj) scores. The
ranking of the effect group enablers is shown in Fig. 7.
The factor “Collaborative research for responsible AI
(EN11)” has a ­(Ri—Cj) score of -0.5 but the fifth highest
­(Ri + ­Cj) score of 5.888. These values suggest that while this
enabler is greatly impacted by other enablers, it also has a
significant impact on others. Although EN11 is assigned in
the effect group, collaborative research work will help find
solutions to real-world complex problems in FGSC and can
provide a roadmap for successful implementation. Several
researchers have highlighted the importance of research
for exploring the scope, developing policies, creating data
repositories etc.for the adoption of AI in SC (Hangl et al.
2022; Manning et al. 2022).
The factor “Digital literacy and awareness building
(EN16)” has a strong interconnection ­(Ri + ­Cj = 5.281) and
significant impact on other enablers ­(Ri—Cj = -0.786) and
thus plays an important role for AI adoption in FGSC. This
result is in line with both Hangl et al. 2022 and Junaid et al.
2021. These studies emphasized that digital literacy and
awareness building among farmers will create an opportunity and demand for AI and Agri 5.0-based products in SC.
The enabler “Customer satisfaction in demand volatility
(EN4)” has a (Ri + Cj) score of 5.316 and (­ Ri—Cj) score
of -1.034, indicating that EN4 has a significant impact on
other enablers but is also significantly impacted by other
enablers. Thus, to cope with the market's ambiguity and
volatility and to meet the demands of the food supply,
businesses must embrace creative technologies that show
a high degree of agility as a consequence of the market's
wild fluctuations. The enabler “Implementing prototype
Fig. 7 Prioritized effect group
enablers
projects for checking viability (EN15)” has the third lowest relation value ­(Ri—Cj = -1.112) and does not significantly interconnect to other enablers ­( R i + ­C j = 4.903).
Several respondents highlighted the importance of a pilot
project to explore the scope, develop policies, create data
repositories etc.for the adoption of AI in SC.
Next, “Build a data integration team (EN9)” has been
identified as one of the key enablers for AI adoption with
a ­( Ri—C j) score of -1.785 and ­( R i + ­C j) score of 5.385.
Thus, this enabler is not strongly interconnected to others
but is strongly influenced by other enablers. The result is
in line with other research (Cadden et al. 2021; Dora et al.
2021; PWC 2018) that has highlighted how data integration will continue to be crucial in ensuring that AI can
meet these obstacles and provide useful results. Not only
is the dimensionality important, but also the amount of
data. For machine learning algorithms to have a greater
probability of arriving at the correct results, they need a
thorough comprehension of all data properties.
The enabler “Rescaling internal talent (EN14)” significantly contributes to the adoption of AI ­(Ri + ­Cj = 5.734)
and has the second lowest relational value ­(R i —
Cj = -1.823). This research outcome is in line with previous studies (Barros et al. 2020; Dora et al. 2021).
“Job security post-AI adoption (EN10)” is impacted by
other enablers ­(Ri—Cj = -2.443) with the lowest (­ Ri + ­Cj)
score of 4.510. This indicates that EN10 doesn’t have
much influence on the adoption of AI in FGSC but it can
be achievable by approaching other enablers (Cadden et al.
2021; Garske et al. 2021). Indicator checks for both the
enablers in the cause group and the effect group are shown
in Tables 8 and 9, respectively.
6.4 Correlation between the enablers
The TRM may be used to examine the interplay between
enablers. A threshold value of 0.249 is applied to each member of the TRM to exclude any associations with insignificant effects. We further categorize the important impacts
Effect group enablers
3.000
Ri - Cj
2.500
2.443
1.823
2.000
1.785
1.500
1.112
1.000
1.034
0.786
0.500
0.000
EN10
EN14
EN9
EN15
Enablers
13
EN4
EN16
0.500
EN11
Enabling artificial intelligence for sustainable food grain supply chains: an agri 5.0 and…
Table 8 Indicator check for cause group enablers
Cause group enablers – Causal enablers
SN no Indicator check
Description
1
The highest degree of influence power R
2
Highest ­(Ri—Cj) score in the group
3
Lowest ­(Ri—Cj) score
4
Highest ­(Ri + ­Cj) score
EN7 – Legal and regulatory intervention from the government has the highest impact power
R score of 4.129; this indicates that it is one of the most important cause enablers with a
significant impact on others
EN12 – Top management involvement, support, and commitment have the highest (­ Ri—Cj)
value of 3.038; this indicates that EN12 is the least impacted enabler by other enablers
EN5 – Data-driven organizational culture has the lowest (­ Ri—Cj) value of 0.273; this indicates that although EN5 is a causal factor, it is highly impacted by other enablers
EN7 – Legal and regulatory intervention from the government has the highest ­(Ri + ­Cj) value
of 7.116; this indicates that management should pay attention to this enabler because of its
potential capacity of enabling AI implementation in FGSC
between the various enablers into three equal-sized value
categories. The minimum–maximum difference is divided
by 3 to compute the following interaction. Table 10 shows
that there are modest (green; values from 0.249 to 0.276),
moderate (yellow; values from 0.277 to 0.304), and substantial (red; values from 0.305 to 0.331) impacts between the
enablers. It can be seen from the heatmap that “Legal and
regulatory interventions from the government (EN7)” has
a substantial effect on the following factors, EN9, EN10,
and EN14, and has a moderate effect on EN2, EN4, EN5,
EN11, EN15, and EN16. Similarly, EN12 has a significant
effect on EN9 and EN14 and a moderate effect on EN4,
EN5, EN10, EN11, and EN16. It is also evident that EN9
is mostly impacted by EN7 and EN12, and moderately
impacted by EN2, EN6, and EN8. Consequently, EN14 is
mostly impacted by EN6, EN7, and EN12 in addition to
the moderate effect from EN2, EN5, and EN8. Similarly,
insights can be drawn from the other enablers.
6.5 Sensitivity analysis
The link between cause and effect group variables or bias in
the expert data was examined using a sensitivity analysis (SA).
For the experts listed in Table 3, a mix of different weights
was allocated (see Table 11). We assigned the weights to the
experts based on their credibility with weights varied between
0 and 1. Three experts had more than 10 years of experience in
the relevant area among the randomly chosen five experts. So,
we assigned a comparatively higher weighting to those experts
(SCE1, SCE2, SCE3) than the others. The ranking order has
also been contrasted with the average matrix of the experts
(Scenario 4). Figure 8 replicates the same ranking for the
cause and effect group components as presented in Table 12's
sensitivity analysis result, suggesting a negligibly small order
difference. As a consequence, the findings are reliable and not
susceptible to weight changes.
7 Research implications
The current research makes original contributions in the
form of theoretical and practical suggestions; both are discussed in the following sub-sections.
7.1 Theoretical implications
While some researchers have studied the factors that encourage the use of new technologies like AI in the SC (Queiroz
Table 9 Indicator check for effect group enablers
Effect group enablers – Impacted enablers
SN no Indicator check
1
2
3
4
Description
The highest degree of influence power R EN11 – Collaborative research for responsible AI has the highest impact power R score of
2.694 indicating that EN11 is the most effective enabler to the administration in terms of
turning AI adoption into practice
Highest ­(Ri—Cj) score in the group
EN11 – Digital literacy and awareness building has the highest ­(Ri—Cj) value of 0.5; this
indicates that EN11 is most impacted by the rest of the enablers
Lowest ­(Ri—Cj) score
EN10 – Employment stability post-AI adoption has the lowest (­ Ri—Cj) value of -2.443; this
indicates that EN11 is least impacted by the rest of the enablers
Highest ­(Ri + ­Cj) score
EN11 – Digital literacy and awareness building has the highest ­(Ri + ­Cj) value of 5.888,
indicating that EN11 is highly significant compared to other enablers
13
S. Das et al.
Table 10 Correlation heatmap of the enablers
Table 11 Weights assigned for
experts for SA
Supply chain
expert 1
(SCE1)
Scenario 1
Scenario 2
Scenario 3
Scenario 4
Scenario 5
Scenario 6
Fig. 8 Sensitivity analysis
13
Supply chain
expert 2
(SCE2)
Considering the average matrix only
0.20
0.20
0.35
0.30
0.15
0.15
0.20
0.40
0.25
0.35
Supply chain
expert 3
(SCE3)
Supply chain
expert 4
(SCE4)
Supply
chain
expert 5
(SCE5)
0.20
0.20
0.35
0.30
0.30
0.2
0.10
0.25
0.05
0.05
0.20
0.05
0.10
0.05
0.05
14
3
9
11
2
5
1
4
8
16
6
12
13
7
15
10
5.087
6.401
5.328
5.227
6.490
5.867
7.002
6.172
5.329
4.450
5.835
5.144
5.112
5.673
4.801
5.2032
0.984
0.491
1.057
-1.028
0.242
1.554
1.159
0.597
-1.793
-2.489
-0.507
3.058
0.371
-1.807
-1.082
-0.8081
5.158
6.492
5.397
5.310
6.611
5.966
7.116
6.281
5.421
4.510
5.937
5.223
5.188
5.777
4.875
5.3301
0.960
0.475
1.054
-1.008
0.261
1.519
1.164
0.576
-1.776
-2.445
-0.491
3.012
0.365
-1.814
-1.072
-0.7797
Ri-Cj
Ri + Cj
Ri-Cj
Ri + Cj
Rank
Scenario 2
Scenario 1
Table 12 SA results
14
3
9
10
2
5
1
4
8
16
6
12
13
7
15
11
Rank
5.091
6.410
5.337
5.244
6.499
5.875
7.014
6.180
5.337
4.456
5.844
5.149
5.115
5.688
4.828
5.2238
Ri + Cj
0.973
0.471
1.060
-1.009
0.259
1.524
1.148
0.580
-1.774
-2.446
-0.494
3.014
0.366
-1.811
-1.081
-0.7814
Ri-Cj
Scenario 3
14
3
9
10
2
5
1
4
8
16
6
12
13
7
15
11
Rank
5.191
6.543
5.435
5.348
6.680
6.025
7.167
6.340
5.470
4.539
5.991
5.269
5.233
5.825
4.890
5.39
Ri + Cj
0.994
0.504
1.054
-1.037
0.231
1.576
1.166
0.605
-1.807
-2.520
-0.511
3.085
0.374
-1.811
-1.088
-0.8148
Ri-Cj
Scenario 4
14
3
9
11
2
5
1
4
8
16
6
12
13
7
15
10
Rank
5.090
6.403
5.328
5.226
6.495
5.869
7.005
6.176
5.333
4.451
5.839
5.147
5.115
5.676
4.798
5.2054
Ri + Cj
0.957
0.478
1.051
-1.009
0.260
1.519
1.169
0.576
-1.777
-2.448
-0.491
3.014
0.365
-1.814
-1.068
-0.7825
Ri-Cj
Scenario 5
14
3
9
10
2
5
1
4
8
16
6
12
13
7
15
11
Rank
5.092
6.411
5.336
5.236
6.500
5.877
7.011
6.181
5.337
4.455
5.844
5.151
5.119
5.680
4.807
5.2137
Ri + Cj
0.964
0.475
1.056
-1.010
0.260
1.523
1.163
0.577
-1.778
-2.450
-0.491
3.016
0.366
-1.816
-1.076
-0.7787
Ri-Cj
Scenario 6
14
3
9
10
2
5
1
4
8
16
6
12
13
7
15
11
Rank
Enabling artificial intelligence for sustainable food grain supply chains: an agri 5.0 and…
13
S. Das et al.
and Wamba 2019; Pillai and Sivathanu 2020), only a very
few have examined these factors with regard to the FSC. We
believe that it is better to analyze the adoption process of AI
keeping the area-specific constraints and potential into consideration. Hence, identifying and analyzing the enablers for
AI adoption in FGSC from the Agri 5.0 and CE perspective
is novel. The study has listed 16 significant enablers, with
those factors further divided into cause and effect groups.
Thus, this research provides a solid foundation for advancing
the overall understanding of the key enablers of AI adoption
in FGSC in India. Insights from this study may speed up the
process of AI adoption in FGSC.
Secondly, this research makes a significant contribution
to current academic literature by elucidating the interplay
and interdependence of several factors that facilitate the
adoption of AI. Using the fuzzy DEMATEL method, we
have found several relationships between previously unrecognized enablers for AI adoption in FGSC.
7.2 Managerial implications
The research identifies a range of ramifications for both the
government and business organizations. Thus, it provides
an operational and flexible decision-making model for the
management of AI adoption in FGSC.
• Based on the findings of this research, a prioritized list of
enabling factors is provided. Policymakers can use this
list to develop strategies for the successful implementation of AI in FGSC.
• The classification of cause and effect group enablers
helps managers and policymakers in determining the
key elements on which to focus to advance AI adoption.
Managers may now readily determine the aspects that
need more focus than others. The cause group variables
should thus be their main concern since they have a significant influence on the effect group elements.
• According to the results of the study, “Legal and regulatory interventions from the government (EN7)” is the
most important key enabler among all. It is difficult to
regulate AI, as different people have different opinions
on how much and what kinds of regulation will work
best for any particular applications it will be used for.
Hence, it is essential to pinpoint the precise function that
legislation may have while framing regulatory and legal
techniques. Furthermore, given the speed at which this
nascent technology is developing, a continuing assessment of AI ethical concepts and norms will be required.
• According to the study, “Green IoT-driven full automation
(EN5)” is the second most significant enabler for responsible
AI adoption. Management must focus on the quality of the
equipment used in India. It must be sturdy enough to survive
13
field conditions and be self-sustainable to perform consistently without human interference. This requires a system that
can handle a lot of data and work across devices. Agriculture AI devices must be networked and compatible across
platforms for hassle-free data collection; this will encourage
further distribution of AI technology in SC.
• “Top management’s involvement and support (EN12)”,
“Development of computing infrastructure (EN1)”, “Coordination and trust among stakeholders (EN2)”, and “Long
term investment and economic incentives from the government and private parties (EN3)” have been identified
as some of the significant enablers. Management must
work with openness and provide support to promote bilateral collaboration and coordination among SC members.
The government must focus on creating demand through
an awareness program and provide the necessary financial
investment for AI implementation in FGSC.
• Expertise in AI and the accompanying field is scarce.
AI demands highly knowledgeable and experienced
personnel. Although companies are recruiting data scientists and analysts to use AI-based solutions, demand
for trained specialists exceeds supply. Therefore, management must provide training and resources to rescale
the workforce for adoption of AI. Further, the sensitivity
analysis helps detect experts' consistency.
8 Conclusion
Even though India's agricultural industry is in a transitional phase and is more reliant on technological integration for improved operations, it nevertheless confronts
several difficulties throughout the value chain. Technology
offers remedies to these problems by introducing disruptive interferences. Integrating indigenous and traditional
farming knowledge with game-changing smart farming
methods, such as the use of AI, is necessary for the success
of the overall system. Adopting AI technology will allow
for greater output with more efficient use of resources via
improvements in areas such as predictive analysis, crop
health management, quality, traceability etc.
In this context, this study aimed to identify the key enablers for the adoption of AI in Indian FGSC through an
extensive literature review and consultation with industry
experts from Agri 5.0 and CE viewpoints. The study has
drawn up sixteen key enablers for AI adoption and prioritized them based on ­(Ri + ­Cj) scores. Among all the enablers
“Legal and regulatory interventions from the government
(EN7)”, and “Green IoT driven total automation (EN5)” are
the top two significant enablers for successful implementation of AI in FGSC. Hence, decision-makers must concentrate on these enablers while drafting policies or strategic
plans for AI implementation.
Enabling artificial intelligence for sustainable food grain supply chains: an agri 5.0 and…
However, the (R + C) value cannot be used to determine
which enablers should be the focus since certain enablers
may not have an impact on other enablers and hence may
not be an overall success in terms of the outcome. Therefore, using the ­( R i—C j) value, it is essential to categorize the enablers into cause and effect categories. Hence,
F-DEMATEL is used to address the question "What is
the relevance of various components in a comprehensive
decision framework?", as mentioned in the Solution Methodology section.
Currently, agriculture is going through a catalytic shift.
The industry is absorbing and using cutting-edge technology
to improve operational efficiency and consequently increase
production. Technological tools work to bring data science
and analytics to each element of the agricultural value chain,
ultimately optimizing the effectiveness of its distribution.
This article also recognizes some limitations that have
emerged. The research relies on subjective expert views. The
study was also limited to Indian industrial organizations.
Expert views were used to analyze one case organization's
enablers. We appreciate that results may vary according to
country, industry size, domain etc. Future studies may use
ISM, AHP, fuzzy MICMAC etc.
All the data has been provided in manuscript.
Authors' contributions Sumanta Das, Ideas, Writing– Original draft
preparation, Conceptualization, Formal analysis Dr. Akhilesh Barve,
Formal Analysis, Visualization Supervision, Project Administration
Dr. Naresh Chandra Sahu, Review editing, Formal Analysis Dr
Kamalakanta Muduli, Critical review, Data Curation, Validation,
Commentary and Revision.
Data availability All the data has been provided in manuscript.
Declarations
Ethics approval All authors follow the ethics in the research and provide consent to participate in the research.
Consent to participate All authors provide consent for publication.
Conflicts of interest/Competing interests The authors declare that they
have no known competing financial interests or personal relationships
that could have appeared to influence the work reported in this paper.
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