ArticlePDF Available

Abstract and Figures

3D printing is estimated to play a significant role in offering tangible and commercial benefits to the supply chains making the manufacturing processes more efficient and productive. The application of 3D printing technologies for printing of food is becoming more complex and flexible putting pressure on the 3D food printing companies. 3DP of food is expected to help in controlling the quality of food products, food waste, and offer increased food variety. Despite the vast potential of 3DP of food the adoption is still in its nascent stage. Therefore, this study attempts to identify the various barriers that affect the adoption of 3DP in food industry in the Indian context. The study identifies and investigate the interdependencies between 3D food printing implementation barriers to developing sustainable food supply chains. The hybrid "interpretive structural modelling (ISM) and decision-making trial and evaluation laboratory (DEMATEL)” methodology was employed to better understand the hierarchical and contextual relationships between barriers to implementing 3D food printing. Thirteen barriers were identified from the literature review and validated by 3D food printing experts. The cost of consumables was identified as a major barrier to implementing 3D food printing in supply chains. We also identified linkage barriers and dependent barriers. Our findings led us to put forward suggestions for overcoming some of the implementation barriers to 3D food printed supply chains identified, helping to advance the development of sustainable 3D food printingbased supply chains.
Content may be subject to copyright.
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=cjol20
International Journal of Logistics Research and
Applications
A Leading Journal of Supply Chain Management
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/cjol20
3D Printing for sustainable food supply chains:
modelling the implementation barriers
Virendra Kumar Verma, Sachin S. Kamble, L. Ganapathy, Amine Belhadi &
Shivam Gupta
To cite this article: Virendra Kumar Verma, Sachin S. Kamble, L. Ganapathy, Amine Belhadi
& Shivam Gupta (2022): 3D Printing for sustainable food supply chains: modelling the
implementation barriers, International Journal of Logistics Research and Applications, DOI:
10.1080/13675567.2022.2037125
To link to this article: https://doi.org/10.1080/13675567.2022.2037125
Published online: 04 Feb 2022.
Submit your article to this journal
View related articles
View Crossmark data
RESEARCH ARTICLE
3D Printing for sustainable food supply chains: modelling the
implementation barriers
Virendra Kumar Verma
a
, Sachin S. Kamble
b
, L. Ganapathy
a
, Amine Belhadi
c
and
Shivam Gupta
d
a
Operations and Supply Chain Management, National Institute of Industrial Engineering (NITIE), Mumbai, India;
b
Operations and Supply Chain Management, EDHEC Business School, Roubaix, France;
c
Cadi Ayyad University,
Marrakech, Morocco;
d
Department of Information Systems, Supply Chain and Decision Making, NEOMA Business
School, Reims, France
ABSTRACT
3D printing is estimated to play a signicant role in oering tangible and
commercial benets to the supply chains. 3D printing application in food
is becoming more complex and exible putting pressure on printing
companies. 3DP of food is expected to help in controlling the quality of
food products, reduce waste, and increase food variety. Despite the vast
potential of 3DP of food, the adoption is still in its nascent stage.
Therefore, this study attempts to identify the various barriers that aect
the adoption of 3DP in the food industry. The study identies and
investigates the interdependencies between 3D food printing
implementation barriers for sustainable food supply chains. The hybrid
interpretive structural modeling (ISM) and decision-making trial and
evaluation laboratory (DEMATEL)methodology was employed to
understand the hierarchical and contextual relationships between the
identied barriers. Thirteen barriers were identied and validated by
food printing experts. The cost of consumables was identied as a
major barrier to implementing 3D food printing in supply chains. We
also identied linkage barriers and dependent barriers. Our ndings put
forward suggestions for overcoming some of the implementation
barriers, helping to advance the development of sustainable 3D food
printing-based supply chains.
ARTICLE HISTORY
Received 12 September 2021
Accepted 28 January 2022
KEYWORDS
3D food printing; food
industry; food supply chain;
ISM; DEMATEL
1. Introduction
3D food printing (3DFP) is an emerging smart technology in the food sector that can transform
many aspects of food supply chains. While various aspects of food supply chains are utilised in
many segments of the food industry, people are increasingly concerned about customised food
design, meal ingredients, and nutrition. 3DFP can be used as a platform to oer customised
designer food with specic nutritional ingredients, especially for air and space travellers, military
personnel in remote locations, pregnant women, athletes, and the elderly.
The advantages and potential of modern technologies like 3D printing (3DP) have been widely
reported and acknowledged in industry, particularly in the manufacturing sector (Kamble, Guna-
sekaran, and Gawankar 2018; Saade, Yahia, and Amor 2020; Lagorio et al. 2021). As a smart tech-
nology, 3DP oers transformative digitalisation of the physical supply chain, making operations
exceptionally agile (Shree et al. 2020; Annosi et al. 2021). Enhanced customisation and visibility
across the entire supply chain can generate economic value for the online business ecosystem
© 2022 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Sachin S. Kamble Sachin.kamble@edhec.edu
INTERNATIONAL JOURNAL OF LOGISTICS: RESEARCH AND APPLICATIONS
https://doi.org/10.1080/13675567.2022.2037125
and shareholders, but adoption of 3DP by the consumer food sector is nascent (Sarkar and Dey
2021; Beltagui, Kunz, and Gold 2020). Analysts have nonetheless projected that the global market
for ready-to-eat 3DP food will see a big jump in the next few years, from $6,422.5 million in 2019 to
$44,520 million by the end of 2026 (Baiano 2021). Studies have suggested that the food industry can
benet from 3DP through shorter and faster food supply chains to increase eciency, productivity,
and cost-eectiveness using autonomous food machines and predictive analysis (Lipton 2017;
Simon 2018).
The 3DFP supply network aims to link up with any location at any time through error-free,
ready-to-eat food delivery (Sun et al. 2015a; Sun et al. 2015c), provide robust and safe delivery
of food services (Godoi, Prakash, and Bhandari 2016; Dankar et al. 2018; Baiano 2021), and obtain
strategic advantages (Jiang 2020). 3DP food service stores can adopt such smart technologies to
improve customer value and shopping eciency (Mohr and Khan 2015; Kothman and Faber 2016).
3DP technology is thus driving new applications in food fabrication (Wang et al. 2020; Kozior
and Bochnia 2020). In the food industry, it oers smart food services that connect physical and
online channels using smart 3D food printers (Sun et al. 2018b). 3DFP provides a platform for
real-time interaction between stakeholders such as suppliers, service providers, and consumers,
with the products and services resulting in enhanced, personalised 3DP food services for consumers
(Sun et al. 2015a; Lipton 2017; Simon 2018). 3DFP service providers are developing innovative digi-
tal solutions that oer new food products and tailored services to satisfy enhanced consumer expec-
tations (Van der Linden 2015; Mohr and Khan 2015), with decision-making processes in the 3DFP
industry becoming increasingly data-driven (Sun et al. 2015a; Chan et al. 2018, Sun et al. 2018b). As
3DP implies new competencies for both operations management and control, decision-makers can
obtain fresh insights into value creation and value proposition from the data generated from 3DP
designs, adopting eective food printing and supply practices to strengthen their relationship with
consumers (Ferreira and Alves 2017; Attaran 2017).
Despite the above-mentioned benets and incredible potential of 3DP in the food sector, the
adoption of 3DFP is still in its infancy (Lipton et al. 2015; Pereira, Barroso, and Gil 2021). While
very few studies have been conducted on 3DP in the food supply chain (Sun et al. 2015a; Dankar
et al. 2018; Ramachandraiah 2021), the prior literature to date has identied food shelf life, ordi-
nances and guidance, post-processing, and ingredient restrictions (Dankar et al. 2018), food cus-
tomisation and geometric complexity (Lipton et al. 2015), mass customisation (Sun et al. 2015a),
and food texture (Sun et al. 2018b) as barriers to successful implementation of 3DP in the food
industry.
These studies were conducted from the perspective of food manufacturing companies and
oered limited insights into food supply chain practitioners. The present study is designed to over-
come these research limitations by not just exploring the implementation barriers, but also estab-
lishing the causal relationships between them, identifying the most important inuencing barriers
using the hybrid interpretive structural modelling (ISM) and decision-making trial and evaluation
laboratory (DEMATEL)technique. The study focuses on the barriers facing the entire 3DFP supply
chain. There is a signicant need to analyze these barriers and to establish a hierarchical relation-
ship between them so as to evaluate their causeeect relationship. Our study thus aims to answer
the following research questions:
RQ1: What are the implementation barriers to 3DFP in developing sustainable supply chains in the Indian
context?
RQ2: How can we model these barriers to investigate the interrelations and establish a hierarchical
relationship?
RQ3: What is the cause-and-eect relationship between the dierent barriers?
Our research contributes to the identication of 3DFP supply chain barriers and their interrelation-
ships. Using a hybrid of the ISM and DEMATEL methods, this study identied, analyzed, and
2V. K. VERMA ET AL.
modelled highly inuential barriers and their causeeect relationships. Modelling these barriers
enabled us to gain a better understanding of their existing interrelations. Finally, the study can
help decision-makers to develop appropriate strategies to reduce 3DFP barriers for developing sus-
tainable food supply chains.
The rest of the paper is organised as follows. A literature review of 3DFP and service providers is
presented in section 2. The research methodology is discussed in section 3. Section 4details the
application of the DEMATEL and ISM methodologies for modelling 3DFP barriers, as well as
the ndings. The results and discussion are presented in section 5. Finally, section 6concludes.
2. Literature review
The researchers reviewed the literature from the Web of Science and Scopus to learn more about the
impact ofthe 3D food printing supply chain. The search used combinations of the keywords 3D print-
ing,’‘food supply chain,’‘3D food printing,and3D printed food, producing 78 papers in total. We
also examined some company reports, news articles, and online magazines to obtain additional infor-
mation on the research area. Table 1 presents a taxonomy of selected research studies conducted in the
past that highlights the dierent issues in the implementation of 3DFP supply chains.
Table 1 leads us to present our discussion in the following two categories:
i Impact of 3DFP on the food industry
ii Implementation barriers to 3DFP in supply chains
2.1. Impact of 3DFP on the food industry
Food manufacturers are sharpening their competitive edge by switching from product providers to
product-service solution providers (Xu and Long 2021). Food supply chains play an important role
in the sustainability performance of food processing companies (Bloemhof et al. 2015). 3DP can
print foods like sweets, meals for the elderly, meat with an authentic taste and texture, school lunches,
ice cream, pasta, pizza, pancakes, chocolate, cakes, etc. (Liu et al. 2017; Godoi et al. 2018;Nachaletal.
2019). 3DFP is set to empower the food industry by oering a customer experience platform with
numerous avours and forms (Sun et al. 2015a). 3DFP oers an opportunity to customise individual
needs and popular food products (Lupton and Turner 2016). It can also transform complementary
ingredients like proteins from organic resources into delicious food commodities. People can use cur-
rent 3D food printers, such as the Foodini 3D food printermade by the Natural Machines company
to browse recipes and print items using an Android phone, PC, or laptop. 3D food printers can be
precisely tailored to individual tastes and nutritional requirements (Gholamipour et al. 2020). Barilla,
for instance, an Italian pasta company, collaborated with TNO, a Dutch research organisation, to cre-
ate a 3D food printer that produces various pasta shapes and designs, so customers can now 3D print
their own computer-aided design (CAD) les for any pasta shape they like both quickly and easily
(Sun et al. 2015a; Derossi et al. 2018;Dankaretal.2018).
The AlgaVia company in San Francisco, California is another example. The company has devel-
oped a gluten-free, non-allergenic protein powder with remarkably ecient properties that is a
great source of nutritional bre (Derossi et al. 2018; Dankar et al. 2018), helping to enrich fruitarian
protein more simply, while also providing a rich avour.
The Foodini printer can also print tasty food with fresh ingredients (Sun et al. 2015c; McHugh
and Bilbao-Sainz 2017). Many other companies have developed 3D food printers, such as byFlow
Focus, Netherlands, Natural Machines Foodini, Spain, Createbot 3D Food Printer, China, Micro-
make Food 3D printer, China, Choc Edge Choc Creator V2.0 Plus, UK, ZBOT Commercial Art
Pancakes printer F5, China, ORD Solutions RoVaPaste, Canada, PancakeBot 2.0, Norway,
Mmuse-Chocolate 3D printer, China, and ZMorph VX, Poland (3D Food Printing report, 2020).
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 3
Table 1. Taxonomy of research studies on 3DFP.
Sl.
No. Authors Country The objective of the study Type of study Remarks
1 Lipton et al.
2010
USA Successful printing of new
materials suitable for baking,
broiling, and frying of turkey
meat and celery multi-material
structures.
Quantitative
study
It is still necessary for a user to
switch materials as required.
2 Lipton et al.
2015
USA Production of a single item in less
time using 3DFP instead of a
multi-step process.
Literature
review study
The slow production speed is a
barrier to implementing 3DFP.
3 Sun et al.
2015a
China Impact of 3DFPon food
processing.
Literature
review study
It is vital to explore printing
materials, platform designs,
printing processes, and their
impacts on food
manufacturers to attain
consistency in food
production.
4 Godoi,
Prakash, and
Bhandari
2016
Australia Design food structures and food
material properties using 3DFP.
Literature
review study
It is necessary to address the
design of new textures,
relationships between material
properties, and factors
impacting the balanced design
of food structures.
5 Lupton and
Turner 2016
Australia Benets and positive aspects of
3DFP for the elderly, and those
living in locations with
inadequate food supply.
Literature
review study
Food nutrition and the safety of
the production process have to
be ensured in terms of possible
contamination of food by
chemicals or bacteria.
6 Liu et al. 2017 China Precise 3DFP in aspects of
material properties, process
parameters, and post-
processing.
Literature
review study
Printing quality and consistency,
process productivity, and the
manufacture of multi-
structure, multi-avour, and
multi-coloured foods are all
challenges in 3D food printing.
7 Yang, Zhang,
and
Bhandari
2017
China Advancements in 3D food
printers and material properties
of food ingredients, process
parameters and printed food
properties.
Literature
review study
The future challenges include
optimization techniques for
the 3DFP process, new food
ingredients, suitable food, and
printer cost.
8 Dankar et al.
2018
Lebanon Study of compatibility between a
wide range of food ingredients
and the nest 3DFPparameters.
Literature
review study
Major obstacles hampering the
3DFP process include food
shelf life, ordinances and
guidelines, ingredient
restrictions, a wider range of
food printing ingredients,
post-processing, and customer
acceptability.
9 Godoi et al.
2018
Belgium,
Australia,
China
3DFP and applications. Literature
review study
The FDA lacks clear guidelines
for describing 3D printed
foods, food safety, and
allergen regulations.
10 Sun et al.
2018b
Singapore Impact of the extrusion-based
food printing technique on
design of food texture.
Literature
review study
Future kitchen appliances should
be able to cook quickly and
operate in a small space.
11 Yang et al.
2018
China Eect of 3DFP on baking dough
in gel formation and physical
properties.
Quantitative
study
More studies can be done to see
how to control the modelling
eect of baking dough after
heating, which is a dicult
task.
12 Zhang, Lou,
and
Schutyser
2018
Netherlands Eect of surface/volume ratio on
probiotic survival in wheat-our
food structures during baking.
Quantitative
study
Future research should focus on
reducing baking intensity to
improve probiotic survival.
(Continued )
4V. K. VERMA ET AL.
A German start-up, Biozoon, has created a 3D food printer that produces a healthy food puree
called Smoothfood which is especially benecial for individuals who have diculty eating solid
food (Sher and Tuto 2015; Kodama et al. 2017).
Table 2 shows various 3D printing technologies, food product applications, suitable 3D printed
foods, and 3D food printing machinery currently available on the market.
Table 3 shows 3D printing sweet manufacturers, available food printing machines, printable
materials, and 3D printing technology.
Table 1. Continued.
Sl.
No. Authors Country The objective of the study Type of study Remarks
13 Dankar 2019 Spain Eects of commercial potato
starch microstructure on
rheology and mechanisms to
improve the usage of potato
puree.
Quantitative
study
Challenges in 3DFP are
restrictions on ingredients,
food safety, ordinances and
guidelines, shelf life, and post-
processing.
14 Hertafeld et al.
2019
USA Improvement of 3D food
processing to provide a new
cooking technique.
Quantitative
study
Future models will incorporate
both form and function to
better t userslives.
15 Voon et al.
2019
Singapore Study 3D food ink in meat, plants,
hydrogels, confectionery, and
dairy.
Literature
review study
The signicant challenges are
the excellent quality of food
inks as ingredients and their
interactions with the printing
process, formulation
optimization, printing
conditions, food safety,
palatability, and public
acceptance.
16 Daner et al.
2020
UK Study of protein suspensions and
the impact of pre-processing
parameters.
Quantitative
study
In the future, calcium/protein
concentrations, cream, or
lactose, will provide more
alternatives for customised
and individualised nutrition via
3D food printing.
17 Keerthana
et al. 2020
India Development of bre-rich snacks
from mushrooms by 3DFPand
conditions for post-processing.
Quantitative
study
Future 3DFP predicts sizeable
economic potential, food
customisation, nutrition, and
appearance.
18 Pulatsu et al.
2020
USA Factors aecting the printability
and post-processing of cookie
dough in 3D food printing.
Quantitative
study
Future research will focus on
3DFP parameters, including
printing pattern, inll
percentage, geometrical
properties, and baking
temperatures.
19 Telesetsky
2020
USA Implementation of food safety
law that covers both organic
and conventional products in
the food supply chain in the
United States.
Literature
review study
Diculties in implementing
organic food safety regulations
and failing to meet national
organic criteria in the United
States.
Table 2. 3D printing technology for food applications (Liu et al. 2017).
3D food printing
technology Food applications Suitable
Extrusion based printing Chocolate, dough, cheese, meat paste, mashed
potatoes
Suitable for low viscosity material
Selective sintering
printing
Sweets, candies, toee Suitable for fat-based and sugar-based
materials
Binder jetting-powder Sugar cube Suitable for low viscosity material
Inkjet printing Cookie, cake, candy, pastries, pizza, biscuits,
cupcakes
Suitable for mass production
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 5
2.2. 3DFP Supply chains
3DFP improve the performance of the food supply chain by localising production based on custo-
mersorders. They can thus customise food according to preferences in nutrition, taste, and texture,
enabling large establishments like hotels, restaurants, and hospitals to benet from customised food
for customers and specic patients (Christopher and Ryals 2014; Jia et al. 2016). Ensuring an eco-
logical balance and reducing food wastage are essential to save us from a food crisis. By using just
the amount of fresh ingredients needed to produce the food, 3D printers can minimise wastage and
help to protect the environment (Sun et al. 2015a; Sun et al. 2015b; Sun et al. 2018a; Dankar et al.
2018), with minimum transportation costs as 3D food can be printed in a home location (Godoi
et al. 2018; Perez et al. 2019).
In the future, the 3DFP supply chain will play a signicant role in many upcoming food and
nutrition grade programmes such as army food, aerospace food, etc. (Godoi et al. 2018; Nachal
et al. 2019). Thus, digital technology platforms can make the food supply chainsoperational pro-
cess more ecient and productive (Kamble et al. 2019; Kamble and Raut 2019). Compared to the
traditional food supply chain model, a digital 3D printing supply chain is faster for the global food
business model (Sher and Tuto 2015).
2.3. Barriers to 3D printed food (3DFPB)
In this section we discuss thirteen potential barriers to 3DFP for sustainable food supply chains
based on the literature review.
2.3.1. Food structure (3DFPB1)
In the 3DFP supply chain, researchers and companies need to develop more functional food struc-
ture ingredients such as avour, colour, size, viscosity, as well as input parameters such as printing
distance, printer speed, and nozzle diameter. Firms need to develop new food structures and ingre-
dients as per user needs (Zhang, Lou, and Schutyser 2018). 3DFP can make complex geometries for
food products and customise nutritional food structures (Liu et al. 2018, Sun et al. 2018b). Pastes,
doughs, and viscous slurries can all be printed using FDM technology (Zhang, Lou, and Schutyser
2018; Yang et al. 2018), with their material and structural properties addressed according to con-
sumer preferences (Godoi, Prakash, and Bhandari 2016; Zhang, Lou, and Schutyser 2018).
2.3.2. Food design (3DFPB2)
Design expertise requires cutting edge technical and design human capital input to work on temp-
erature control, taste design, food texture, and personalised nutritional ingredients control.
Improvements are also needed in food design printability, food grade materials, material viscosity,
thermal properties, and to reduce post-processing time according to customer needs (Godoi, Pra-
kash, and Bhandari 2016;Daner et al. 2020).
Table 3. 3D sweets printing company, machine, materials, and technology (Liu et al. 2017).
Company Machine Printable Materials Technology
CandyFeb Project CandyFeb-6000 Sugar Selective laser sintering
3D Systems ChefJet Chocolate sugar, protein, starch Binder jetting
Choc Edge Choc Creator Chocolate Extrusion based printing
3D Systems CocoJet Chocolate Extrusion based printing
3DCloud QiaoKe Chocolate Extrusion based printing
Porimy 3D Food Printer Soft material, Chocolate, Extrusion based printing
Fouche Chocolates Fouche Chocolates Printer Chocolate Extrusion based printing
6V. K. VERMA ET AL.
2.3.3. Speed of production (3DFPB3)
3DFP requires improved production speed, depending on specic parameters such as nozzle size,
printing speed, travel speed, and layer height. Researchers and businesses need to speed up pro-
duction in order to produce items quickly (Van der Linden 2015; Yang, Zhang, and Bhandari
2017; Lille et al. 2018). At present, the production speed of 3DFP can lead to bottlenecks in the
food printing process for restaurants, hotels, etc., which is a signicant barrier to smooth-running
food production (Godoi, Prakash, and Bhandari 2016; Perez et al. 2019).
2.3.4. Multi-material printing (3DFPB4)
3D food printers must select multi-material food ingredients and objects to print specic food pro-
ducts for consumers (Sun et al. 2015a; Hertafeld et al. 2019). The products require specic multi-
grade materials to create textures consistent with the sterilisation process required. The materials
also need to be compatible with the printer (Lipton et al. 2010; Hertafeld et al. 2019). Currently,
3D printing companies are working to improve multi-material printing options in order to provide
ever more multi-ingredient products for customers (Yang, Zhang, and Bhandari 2017; Sun et al.
2018b; Bandyopadhyay and Heer 2018).
2.3.5. Copyright issues (3DFPB5)
The 3DFP industry has several copyright issues, including a lack of specic rules and guidelines,
limited ingredients, food protection regulations, and post-processing challenges that require certi-
cation of future 3DFP applications. Copyright issues can be resolved by introducing globally appli-
cable FDA regulations for personnel, food shelf life, food safety, and facilities (Sun et al. 2015a;
Godoi et al. 2018). At present, copyright of designs and design les are not protected or secure
for 3D food printing.
2.3.6. Safety and contamination (3DFPB6)
Food supply chain safety and contamination standards are a crucial concern in 3DFP. Every partner
in the supply chain must have adequate internal traceability to share food safety (Mangla et al. 2021)
and contamination data with their partners. Retailers need to ensure that the food materials bought
from suppliers are safe to eat and meet quality standards (Sun et al. 2018b; Tran 2019). At present
the 3D food printing industry lacks non-contact-based methods, exposing the process to possible
food contamination. Safety of food ingredient materials and contamination thus need to be urgently
addressed in 3DFP (Lupton and Turner 2016; Liu et al. 2017; Baiano 2021).
2.3.7. Pre-processing (3DFPB7)
Pre-processing of 3DFP parameters (e.g. CAD le, temperature, texture, pre-processing ingredi-
ents) and ingredients can improve non-printable ingredientsprintability (Daner et al. 2020;
Keerthana et al. 2020). This means that consistency is needed at the pre-processing stage to improve
3DFP output, and enhance printed product output quality such as texture, layers of voxels, and look
(Nachal et al. 2019).
2.3.8. Post-processing (3DFPB8)
Post-processing in 3DFP, such as baking, frying, cleaning processes, and deformation of the printed
food, is a critical phase, ensuring that the 3D printed structure preserves the food shapes required
(Lipton et al. 2010;Pulatsu et al. 2020). Post-processing treatment therefore needs to minimise post-
processing time, cost, and steps in 3D printed food products, with the removal or reduction of
excess food material powder. The process also needs to expand the mechanical strength and prop-
erties of 3D printed food products (Liu et al. 2017; Lille et al. 2018; Voon et al. 2019; Dick, Bhandari,
and Prakash 2019; Voon et al. 2019; Nachal et al. 2019).
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 7
2.3.9. Printer cost (3DFPB9)
The high demand for 3D printed food and products can only be met by putting more printers in
restaurants, hotels, food shops, etc. Printer costs are high, however, leading to uncertainty about
future demand for 3D printed food. The situation is likely to further reduce the machine utilisation
rate, consequently aecting nancial performance (Lipton et al. 2015; Attaran 2017; Voon et al.
2019). Industry and research need to create minimal cost 3D food printers to improve demand
and supply to full consumer needs (Sun et al. 2015a; Yang, Zhang, and Bhandari 2017; Nachal
et al. 2019; Attarin and Attaran 2020).
2.3.10. Cost of consumables (3DFPB10)
3DFP technology and materials are currently expensive. The cost of consumables tends to decline
with a rise in demand. Their cost depends on that of 3D printers, processing, and raw material
ingredients (Dankar et al. 2018;Attarin and Attaran 2020). Industry and research teams are cur-
rently seeking to reduce the price of 3D printed food consumables and to ensure supplies of
high-quality raw materials, which would in turn reduce the cost of consumables (DeVieneni
et al. 2012; Tian, Bryksa, and Yada 2016).
2.3.11. Availability of skilled labour (3DFPB11)
The 3DFP industry need skilled labour and experienced people who can enhance business and
supply chain performance compared to unskilled labour (Pearce et al. 2010; Weller, Kleer, and Piller
2015). Skilled labour can help rms in multiple ways other than one single area, increasing pro-
ductivity and helping to lower labour costs through the ecient use of labour. Company and
employee satisfaction is also liable to improve if the management decides to hire only skilled labour
(Lipton et al. 2015; Kailash et al. 2020; Jideani et al. 2020).
2.3.12. Ordinance and guidelines (3DFPB12)
The quality assurance, validation, and inspection techniques adopted by many 3DFP companies are
crucial to the items they produce. At present, 3DFP has no ordinance or guidelines from the US
Food and Drug Administration (FDA). The FDA needs to clarify its guidelines with respect to
3DFP, thereby creating greater stability, validity, and rapid adoption of 3D food printers worldwide
(Endres and Johnson 2011; Dankar et al. 2018; Dankar 2019; Telesetsky 2020).
2.3.13. Printed food shelf life (3DFPB13)
3D printed food has a limited shelf life. Companies and researchers therefore need to improve the
properties of 3D printed food ingredients. There is a huge opportunity to enhance 3D printed food
shelf life, which will create massive demand for safe and nutrient-dense food products. The food
supply chain will be improved due to the improved shelf life of 3D printed food (Lipton et al.
2015; Godoi, Prakash, and Bhandari 2016; Godoi et al. 2018; Keerthana et al. 2020).
3. Research methodology
In this study, we used combined ISM and DEMATEL techniquesfor decision-making. ISM meth-
odology is used to analyze the relationship between a group of selected factors using a multi-level
hierarchical structure. ISM helps to provide clarity to the complex relationships that exist between
these factors. On the other hand, DEMATEL measures the interactive causal eects between the
selected factors (Kamble, Gunasekaran, and Sharma 2020). The use of combined ISM-DEMATEL
techniques was highly relevant considering our research objectives. Other MCDM techniques such
as AHP, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), VlseKriter-
ijumska Optimizcija I Kaompromisno Resenje (VIKOR), and Preference Ranking Organisation
METHod for Enrichment of Evaluations (PROMETHEE) were considered for the use in this
study. However, most of these techniques are focused on computing ranks and fail to establish
8V. K. VERMA ET AL.
causal relationships between the selected variables; thus, the application of ISM-DEMATEL was
found appropriate. Further, the literature claims that the determination of weight coecients
using the DEMATEL method is superior to the other MCDM techniques (Sharma et al. 2021).
The selection-based and combined approaches are described in the following section.
3.1. Interpretive structural modelling (ISM)
ISM was developed by Wareld (1974) for the interactive process. It is a powerful tool for working
with groups of experts, where organised discussion helps the latter to arrive at a consensus. ISM
allows various connected aspects to be directly and indirectly shaped and connected into a compre-
hensive systematic model (Sage 1977). The main aim of ISM use is to break down the core knowl-
edge of subject expertise in complex problems into minor subsystems to create a multi-level
structural model (Rana et al. 2019; Piya, Shamsuzzoha, and Khadem 2020; Trivedi, Jakhar, and
Sinha 2021). ISM emergent visions help to study the inter-relations and forms of dependency
among the variables employed to demonstrate the impression (Faisal 2010). Initially, researchers
used ISM to address diculties in the evidence situation (See Table 4).
ISM methodology has been widely used for modelling structural variables, although it has a few
limitations. The key weakness of ISM is the unfairness shown by some people who rate the relation-
ships between the variables. Individual understanding and knowledge of a technology or sector can
aect the relations between the variables (Dubey and Gunasekaran 2016). Dubey and Gunasekaran
(2016) list the following steps for implementing the ISM methodology. Related methodologies have
been utilised in previous studies (Gupta, Singh, and Suri 2018; Kamble, Gunasekaran, and Sharma
2020).
i Identify the variables that aect or have an indirect or direct impact on the structure. Studies
that conduct exploratory research can nd these variables in the current literature. Nonethe-
less, a group of experts must validate the variables identied.
ii Contextual relationships should be determined by experts between all the selected variables.
iii Develop the SSIM using V, A, O, and Xto create contextual relations between the measured
variables as follows: If ihelps to determine j, then put Vin cell (i,j). If jhelps determine
i, then put Ain the cell (i,j). If iand jcan determine one another, then put Xin cell (i,j).
If iand jare not related, put Oin the cell (i,j).
iv Create an initial reachability matrix (IRM) with binary elements, using 1for Vor Xand
0for Aand O.
v Check for the existence of transitivity in the IRM. Allow the transitivity rules if variable P
inuences variable Q, and Q inuences variable R, then P eectively inuences the R vari-
able. Calculate the nal reachability matrix (FRM) using transitivity.
vi Obtain a directed graph (DG) from the subdivided stages achieved in the FRM.
vii Remove the transitivity links from the directed graph and rebuild the initial reachability
matrixs contextual linkages.
Table 4. ISM-MICMAC literature.
Sl. No. Application of ISM-MICMAC References
1 Dairy supply chain vulnerability Karwasra (2021)
2 Factors inuencing additive manufacturing Palaniappan, Vinodh, and Ranganathan (2020)
3 Digital technology implementation: smart manufacturing Ghobakhloo (2020).
4 Indian logistics providers Gupta, Singh, and Suri (2018)
5 Blockchain enabled traceability in agriculture SC Kamble, Gunasekaran, and Sharma (2020)
6 IoT embedded sustainable supply chain Manavalan and Jayakrishna(2019)
7 Additive manufacturing Sonar, Khanzode, and Akarte (2020)
8 Dual cycling at ports Kamble, Gunasekaran, and Raut (2019)
9 Industry 4.0 technologies Kamble, Gunasekaran, and Sharma (2018)
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 9
viii Turn the directed graph into an interpretive structural model, swapping the variable links
with the declarations.
3.2. ISM-DEMATEL
The contextual relationships between the 3DFPBs are represented in ISM by binary values (0 = no
relationships, 1 = relationships exist). However, not all relationships adopt the same procedure,
which is a key weakness of the ISM approach. The relationship could be low, moderate, strong,
and very strong (Rajput and Singh 2019). Here, we utilised the DEMATEL approach to address
the constraints of ISM and to identify additional perceptions in the inter-relations of 3DFPBs.
The DEMATEL outcomes are depicted by a causal diagram using a digraph, illustrating the contex-
tual relations and the impact value between the variables (Shen et al. 2014; Gandhi et al. 2016;
Rajput and Singh 2019). The DEMATEL approach has been utilised in numerous research papers
(see Table 5).
The DEMATEL methodology was developed between 19721979 by the Science and Human
Aairs Programme of the Geneva Battelle Memorial Institute for the analysis of composite and
interconnected issues by determining the cause-and-eect relations between the evaluation criteria
and creating inter-relationships between reasons (Wang, Cao, and Zhou 2018; Yang, Lan, and
Tseng 2019; Sharma et al. 2021). An impact relationships map (a graphical illustration of interde-
pendencies with the nominated aspects) drives the DEMATEL techniques productivity (Sharma
et al. 2018; Banik et al. 2021; Chauhan, Jakhar, and Chauhan 2021).
3.2.1. Development of the direct inuence matrix (A)
The diagonal elements of the initial reachable matrix found in ISM are set at zero during development
A. Simultaneously, experts recommend that the relationships be changed to 0, 1, 2, 3, 4 (0 = no inu-
ence,1 = low inuence,2 = medium inuence,3=highinuence,4 = very high inuence). Fac-
tor I aects factor j to the extent shown by X
ij
in A. An independent n x n non-negative matrix for
each expert is created as (X
k
=[x
k
ij
]), where k denotes the number of experts consulted (1 kH) and
n is the number of factors. The average of these matrices (A = [a
ij
]), is depicted as:
A=1/HH
k=1xkij
3.2.2. Calculation of the normalised initial direct-relation matrix
An initial direct-relation matrix (D) is created from the average direct inuence matrix (A), as
shown below:
D=AxS;
where, S = 1/ max
1in
n
j=1
aij.
The range of values of each element in matrix D varies from 0 to 1.
Table 5. ISM-DEMATEL applications.
Sl. No. ISM-DEMATEL applications References
1 Identifying industry 4.0 IoT enablers Rajput and Singh (2019)
2 IoT based system in food retailing Kamble, Gunasekaran, and Sharma (2020)
3 Enhance the safety and security of food Sharma et al. (2018)
4 System failure analysis Shen et al. (2014)
5 Coal mine production safety Wang, Cao, and Zhou (2018)
10 V. K. VERMA ET AL.
3.2.3. Total relations matrix development
Develops the total relationship matrix (T), which uses T = D (I-D)
1
, where I = identity matrix to
determine the sum of columns and rows in the total relation matrix to calculate the degree of
inuence.
3.2.4. Degree of inuence calculation
The sum of any i
th
row (r
i
) in the total relation matrix reects both the indirect and direct impacts
provided by any given factor on the rest of the factors. Likewise, the total of columns in any j
th
col-
umn (d
j
) illustrates both the direct and indirect impacts of that element on other factors. As a result,
the total of r
i
+d
j
highlights the importance of factor I in the overall system, while the r
i
-d
j
dierence
represents the net eect that factor i has on the overall structure. When (r
i
-d
j
) is positive, factor i is
interpreted as a net cause, and once the dierence (r
i
-d
j
) is negative, component i is interpreted as a
receiver or result.
3.2.5. Digraph plot
In DEMATELsnal stage, the (r
i
+d
j
) and (r
i
-d
j
) values obtained earlier to plot the digraph are used.
Threshold values are used to lter out the most signicant eects.
Figure 1 depicts the methodological framework adopted for this study.
Figure 1. Methodology framework for research.
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 11
4. Application of the ISM-DEMATEL for 3D printing food barriers
This section describes the ISM and DEMATEL approaches adopted to establish the relationships
between 3DFP implementation barriers in the food supply chain.
4.1. ISM Methodology
4.1.1. Identifying 3DFP barriers for the food supply chain
Our literature analysis identied the 3DFP barriers for the food supply chain, which was later vali-
dated by an expert group. The group of twelve experts was made up of the following individuals:
a. Four director-level senior executives from the 3DFP rms.
b. Four senior managers from 3DFP rms.
c. Two middle managers from the 3DFP rms.
d. Two academics whose research focus is on operations and supply chain processes in food
supply chains.
The experts selected in the study are involved in 3DP of food, having a techno-managerial back-
ground and rich experience in the food industry. Seven of the twelve experts were graduates in food
processing and technology from reputed institutions in India. The average experience ranged
between 1235 years, with an average of 17 years making them highly qualied to participate in
this study. The literature supports the application of ISM and DEMATEL approaches for small
sample sizes in the range of 1015 participants (Sage 1977; Wang, Cao, and Zhou 2018; Sharma
et al. 2021; Kamble et al. 2019). The twelve experts also fullled the group size needed to perform
exploratory research studies as recommended by Robbins, Stuart-Kotze, and Coulter (2000) and
Murry and Hammons (1995). A total of sixteen 3DFP barriers (3DFPB) were identied in the lit-
erature and reviewed by a group of experts for validation. Finally, thirteen 3DFPBs were validated
and selected for the study. Section 2discusses the operational process used to identify these barriers
as well as their descriptions.
4.1.2. ISM hierarchical level-based contextual relationships
Analyzing the 3DFPBs food supply chain in India, we further investigated the nding that one
3DFPB leads to another 3DFPB, which signies a contextual relationship structure.
4.1.3. SSIM development
By developing the contextual relations between the thirteen selected 3DFPBs, the structural self-
interaction matrix was created. The same team of experts who validated the 3DFPBs helped to
develop the SSIM. As mentioned earlier, the following four symbols (V, A, X, and O) were used
to describe the contextual relations:
V3DFPBi will lead to achievement of 3DFPBj
A3DFPBj will lead to achievement of 3DFPBi
X3DFPBi and 3DFPBj are correlated and support one another in their respective development
O3DFPBi and 3DFPBj do not have a relationship, and are independent
As shown in Table 6, the SSIM is as follows:
.The food structure (3DFPB1) barriers lead to the attainment of pre-processing (3DFPB7). As a
result, the letter Vis employed to represent the relationship between food structure (3DFPB1)
and speed of production (3DFPB3).
12 V. K. VERMA ET AL.
Table 6. SSIM Matrix.
3DFPBs 3DFPB13 3DFPB12 3DFPB11 3DFPB10 3DFPB9 3DFPB8 3DFPB7 3DFPB6 3DFPB5 3DFPB4 3DFPB3 3DFPB2 3DFPB1
3DFPB1 O O A A A V V O O A V V
3DFPB2 O O A A A V V O O A V
3DFPB3 O O A O A V V O O A
3DFPB4 V O A A A V V O O
3DFPB5 O X O O O O O X
3DFPB6 V X A O A A A
3DFPB7 V O A A O O
3DFPB8 V O A A O
3DFPB9 O A A O
3DFPB10 V V O
3DFPB11 O A
3DFPB12 X
3DFPB13
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 13
.The printer cost barrier (3DFPB9) is achieved with the support of multi-material printing
(3DFPB4). Thus, Ais used to denote the relationship between the availability of skilled labour
(3DFPB11) and pre-processing (3DFPB7).
.The barriers, safety and contamination (3DFPB6), and ordinance and guidelines (3DFPB12)
support the achievement of each other. Xis used to denote the relations between copyright
issues (3DFPB5) and safety and contamination (3DFPB6).
.When none of the 3DFPBs are related, the letter Ois applied. In line with our ndings, the letter
Ois employed in food structure (3DFPB1), ordinance, and guidelines (3DFPB12).
4.1.4. IRM development
The initial reachability matrix is obtained by converting the V, A, X, and O terms in the structural
self-interaction matrix into binary factors (0 and 1). Table 7 shows the initial reachability matrix,
which revealed the following pattern:
.If the SSIM input for a specic cell (i, j) is V,the cell (i, j) will be substituted for 1in the IRM,
and the cell (j, i) should be entered as a 0.
.If the SSIM entry for a specic cell (i, j) is A,then (i, j) should be substituted by 0,and the
associated cell (j, i) should be inserted with a 1.
.If the entry for a cell (i, j) in the structural self-interaction matrix is X,then the cell (i, j) is sub-
stituted for 1in the IRM, and the next cell (j, i) is substituted for 1.
.If the entry for a given cell (i, j) in the structural self-interaction matrix is O,then both cells (i, j)
and (j, i) are subtracted from the IRM by 0.
4.1.5. FRM development
This step includes the elimination of transitivity from the initial reachability matrix to establish the
level partitions. A MATLAB code was developed to eliminate transitivity. The nal reachability
matrix obtained is shown in Table 8. The partition levels obtained in the nal reachability matrix
are utilised to draw a directed graph for the iterations (See Table 9).
4.2. Analysis of ISM-MICMAC
A data set of 3DFPBs was classied into four quadrants based on the FRM values for driving and
dependence power that appear in Table 9. Explanations for the 3DFPBs in each quadrant (Figure 2)
are set out below.
.Autonomous barriers: This quadrants 3DFPBs are related to weak dependence and driving
power. An autonomous barrier, also categorised as 3DFPBs, is removed from the system and
does not inuence the 3DFP supply chain. The ndings show that all the 3DFPBs identied
in our research are important and play a considerable role in overcoming barriers to 3DFPB
implementation in the food supply chain.
.Dependent barriers: this quadrants 3DFPBs correspond to weak drive and strong dependence on
power. The dependent barriers are shown in the ISM hierarchical modelsupperhalf.Two
3DFPBs, namely speed of production (3DFPB3) and copyright issues (3DFPB5), were found as
dependent barriers in this study. According to the research, these two 3DFPBs are the most critical
factors the industry needs to consider when implementing 3DFP in the supply chain.
.Linkage barriers: this quadrants 3DFPBs have high dependence power and driving power,
associated with instability. Food structure (3DFPB1), design of food (3DFPB2), multi-material
printing (3DFPB4), safety and contamination (3DFPB6), pre-processing (3DFPB7), post-proces-
sing (3DFPB8), printer cost (3DFPB9), availability of skilled labour (3DFPB11), ordinance and
14 V. K. VERMA ET AL.
Table 7. Initial Reachability Matrix (IRM).
3DFPBs 3DFPB1 3DFPB2 3DFPB3 3DFPB4 3DFPB5 3DFPB6 3DFPB7 3DFPB8 3DFPB9 3DFPB10 3DFPB11 3DFPB12 3DFPB13
3DFPB1 111000110 0000
3DFPB2 011000110 0000
3DFPB3 001000110 0000
3DFPB4 111100110 0001
3DFPB5 000011000 0010
3DFPB6 000011000 0011
3DFPB7 000001100 0001
3DFPB8 000001010 0001
3DFPB9 111101001 0000
3DFPB10 110100110 1011
3DFPB11 111101111 0100
3DFPB12 000011001 0111
3DFPB13 000000000 0011
3DFPB1: Food structure, 3DFPB2: Design of food, 3DFPB3: Speed of production, 3DFPB4: Multi-material printing, 3DFPB5: Copyright issues, 3DFPB6: Safety and contamination, 3DFPB7: Pre-proces-
sing, 3DFPB8: Post-processing, 3DFPB9: Printer cost, 3DFPB10: Cost of consumables, 3DFPB11: Availability of the skilled labour, 3DFPB12: Ordinance and guidelines, 3DFPB13: Printed food shelf life.
Table 8. Final Reachability Matrix (FRM).
3DFPBs 3DFPB1 3DFPB2 3DFPB3 3DFPB4 3DFPB5 3DFPB6 3DFPB7 3DFPB8 3DFPB9 3DFPB10 3DFPB11 3DFPB12 3DFPB13 Driving Power
3DFPB1 1 1 1 0 0 1 1 1 0 0 0 1 1 8
3DFPB2 0 1 1 0 0 1 1 1 0 0 0 1 1 7
3DFPB3 0 0 1 0 0 1 1 1 0 0 0 1 1 6
3DFPB4 1 1 1 1 1 1 1 1 1 0 1 1 1 12
3DFPB5 0 0 0 0 1 1 0 0 1 0 1 1 1 6
3DFPB6 1 1 1 1 1 1 1 1 1 0 1 1 1 12
3DFPB7 1 1 1 1 1 1 1 1 1 0 1 1 1 12
3DFPB8 1 1 1 1 1 1 1 1 1 0 1 1 1 12
3DFPB9 1 1 1 1 1 1 1 1 1 0 1 1 1 12
3DFPB10 1 1 1 1 1 1 1 1 1 1 1 1 1 13
3DFPB11 1 1 1 1 1 1 1 1 1 0 1 1 1 12
3DFPB12 1 1 1 1 1 1 1 1 1 0 1 1 1 12
3DFPB13 1 1 1 1 1 1 1 1 1 0 1 1 1 12
Dependency power 10 11 12 9 10 13 12 12 10 1 10 13 13 136
3DFPB1: Food structure, 3DFPB2: Design of food, 3DFPB3: Speed of production, 3DFPB4: Multi-material printing, 3DFPB5: Copyright issues, 3DFPB6: Safety and contamination, 3DFPB7: Pre-proces-
sing, 3DFPB8: Post-processing, 3DFPB9: Printer cost, 3DFPB10: Cost of consumables, 3DFPB11: Availability of the skilled labour, 3DFPB12: Ordinance and guidelines, 3DFPB13: Printed food shelf life.
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 15
guidelines (3DFPB12), and printed food shelf life (3DFPB13) are ten 3DFPBs identied in the
study as having signicant correlation between two extensive types, viz., driving and dependence
barriers.
.Driving barriers: this quadrants 3DFPBs correspond to strong driving power and weak depen-
dence power. These 3DFPBs are represented in the lower part of the ISM hierarchical model in
the study. The cost of consumables (3DFPB10) was identied as a major driver of barriers. The
results suggest that 3DFPBs are the main barriers to the potential starting place in the 3DFBP
supply chain.
Based on input from the FRM, the ISM hierarchical model is developed in Figure 3.
4.3. DEMATEL methodology
4.3.1. Average direct inuence matrix
The DEMATEL technique was applied to gain further insights into causal dependencies and their
eect on one another. The same experts who worked on ISM were tasked with locating DEMATEL
Table 9. Iterations of Level Partitions.
Iteration No. Reachability set Antecedent Set Intersection set Level
3DFPB1 1,2,3,6,7,8,12,13 1,4,6,7,8,9,10,11,12,13 1,6,7,8,12,13 3
3DFPB2 2,3,6,7,8,12,13 1,2,6,7,8,9,10,11,12,13 2,6,7,8,12,13 2
3DFPB3 3,6,7,8,12,13 1,2,3,4,6,7,8,9,10,11,12,13 3,6,7,8,12,13 1
3DFPB4 1,2,3,4,5,6,7,8,9,11,12,13 4,6,7,8,9,10,11,12,13 4,6,7,8,9,11,12,13 4
3DFPB5 5,6,9,11,12,13 4,5,6,7,8,9,10,11,12,13 5,6,9,11,12,13 1
3DFPB6 1,2,3,4,5,6,7,8,9,11,12,13 1,2,3,4,5,6,7,8,9,10,11,12,13 1,2,3,4,5,6,7,8,9,11,12,13 1
3DFPB7 1,2,3,4,5,6,7,8,9,11,12,13 1,2,3,4,6,7,8,9,10,11,12,13 1,2,3,4,6,7,8,9,10,11,12,13 2
3DFPB8 1,2,3,4,5,6,7,8,9,11,12,13 1,2,3,4,6,7,8,9,10,11,12,13 1,2,3,4,6,7,8,9,10,11,12,13 2
3DFPB9 1,2,3,4,5,6,7,8,9,11,12,13 4,5,6,7,8,9,10,11,12,13 4,5,6,7,8,9,11,12,13 4
3DFPB10 1,2,3,4,5,6,7,8,9,10,11,12,13 10 10 5
3DFPB11 1,2,3,4,5,6,7,8,9,11,12,13 4,5,6,7,8,9,10,11,12,13 4,5,6,7,8,9,11,12,13 4
3DFPB12 1,2,3,4,5,6,7,8,9,11,12,13 1,2,3,4,5,6,7,8,9,10,11,12,13 1,2,3,4,5,6,7,8,9,11,12,13 1
3DFPB13 1,2,3,4,5,6,7,8,9,11,12,13 1,2,3,4,5,6,7,8,9,10,11,12,13 1,2,3,4,5,6,7,8,9,11,12,14 1
Figure 2. MICMAC analysis.
16 V. K. VERMA ET AL.
inputs. In a direct impact matrix (A), the relationships between the 3DFPBs were identied using
integer scores ranging from 0 to 4 (0 = no inuence,1=low inuence,2=medium inuence,3
=high inuence,4=very high inuence). Table 10 shows the average direct inuence matrix.
4.3.2. Normalised initial direct-relation matrix
The average direct inuence matrix is normalised to develop an initial direct-relation matrix (D), as
shown in Table 11.
4.3.3. Total relation matrix
In this step, the total relation matrix (T) was developed using the expression T = D (I-D)
1
, where I
is the identity matrix. We also calculated the sum of rows and the sum of columns of the total
relation matrix (See Table 12).
4.3.4. Degree of inuence
The r
i
+d
j
total represents the relevance of 3DFPB i in the overall structure, while the r
i
-d
j
dierence
signies the net-eectthat 3DFPB i delivers to the structure. The 3DFPB i is interpreted as a net-
causefor positive values of (r
i
-d
j)
and a receiver or an outcome for negative values of (r
i
-d
j
). The net
impact prominence cause values for the 3DFPBs are presented in Table 13.
4.3.5. Digraph plot
Table 13 provides the (ri + dj) and (ri-dj) values for plotting the digraph. Only the inuences with a
value greater than 0.1 are depicted in the above diagram. The threshold value of 0.1 was calculated
using the average of all the elements in matrix T (Kamble et al. 2019).
Figure 3. Obtained ISM model.
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 17
Table 10. Average direct inuence matrix.
3DFPBs 3DFPB1 3DFPB2 3DFPB3 3DFPB4 3DFPB5 3DFPB6 3DFPB7 3DFPB8 3DFPB9 3DFPB10 3DFPB11 3DFPB12 3DFPB13
3DFPB1 0.000 3.400 0.600 3.600 3.200 3.600 3.600 3.200 3.600 3.600 3.200 0.600 3.600
3DFPB2 0.600 0.000 3.800 3.600 0.000 0.600 0.000 0.000 3.600 3.400 3.200 0.600 3.600
3DFPB3 0.000 0.000 0.000 0.600 3.200 3.200 3.000 3.000 3.400 3.000 3.000 3.200 3.200
3DFPB4 0.600 0.000 0.000 0.000 3.000 3.000 3.200 3.000 3.600 3.600 3.000 3.000 0.000
3DFPB5 0.600 0.400 0.000 0.000 0.000 3.000 3.000 0.000 0.000 0.000 0.000 3.000 2.800
3DFPB6 0.600 0.200 0.000 0.000 1.800 0.000 0.600 0.000 3.200 0.000 3.200 0.000 3.800
3DFPB7 1.800 0.400 0.000 3.000 0.000 0.000 0.000 3.200 2.400 2.200 1.800 0.000 2.200
3DFPB8 0.600 0.400 0.000 0.600 0.000 0.000 0.600 0.000 1.800 1.800 3.200 0.000 2.400
3DFPB9 0.000 0.000 0.000 1.400 0.000 0.000 0.000 0.000 0.000 3.200 1.800 0.000 1.800
3DFPB10 1.800 2.200 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.600 1.400 3.200
3DFPB11 0.600 1.800 0.000 0.000 0.000 3.000 0.000 3.200 0.000 0.000 0.000 0.000 1.800
3DFPB12 0.400 0.000 0.000 0.000 3.600 3.000 1.800 0.000 0.000 0.000 1.600 0.000 2.200
3DFPB13 2.200 0.000 0.000 0.000 0.000 0.000 0.000 1.600 0.000 0.000 1.800 2.200 0.000
3DFPB1: Food structure, 3DFPB2: Design of food, 3DFPB3: Speed of production, 3DFPB4: Multi-material printing, 3DFPB5: Copyright issues, 3DFPB6: Safety and contamination, 3DFPB7: Pre-proces-
sing, 3DFPB8: Post-processing, 3DFPB9: Printer cost, 3DFPB10: Cost of consumables, 3DFPB11: Availability of the skilled labour, 3DFPB12: Ordinance and guidelines, 3DFPB13: Printed food shelf life.
Table 11. Normalised initial direct relation matrix.
Barriers 3DFPB1 3DFPB2 3DFPB3 3DFPB4 3DFPB5 3DFPB6 3DFPB7 3DFPB8 3DFPB9 3DFPB10 3DFPB11 3DFPB12 3DFPB13
3DFPB1 0.000 0.095 0.017 0.101 0.089 0.101 0.101 0.089 0.101 0.101 0.089 0.017 0.101
3DFPB2 0.017 0.000 0.106 0.101 0.000 0.017 0.000 0.000 0.101 0.095 0.089 0.017 0.101
3DFPB3 0.000 0.000 0.000 0.017 0.089 0.089 0.084 0.084 0.095 0.084 0.084 0.089 0.089
3DFPB4 0.017 0.000 0.000 0.000 0.084 0.084 0.089 0.084 0.101 0.101 0.084 0.084 0.000
3DFPB5 0.017 0.011 0.000 0.000 0.000 0.084 0.084 0.000 0.000 0.000 0.000 0.084 0.078
3DFPB6 0.017 0.006 0.000 0.000 0.050 0.000 0.017 0.000 0.089 0.000 0.089 0.000 0.106
3DFPB7 0.050 0.011 0.000 0.084 0.000 0.000 0.000 0.089 0.067 0.061 0.050 0.000 0.061
3DFPB8 0.017 0.011 0.000 0.017 0.000 0.000 0.017 0.000 0.050 0.050 0.089 0.000 0.067
3DFPB9 0.000 0.000 0.000 0.039 0.000 0.000 0.000 0.000 0.000 0.089 0.050 0.000 0.050
3DFPB10 0.050 0.061 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.045 0.039 0.089
3DFPB11 0.017 0.050 0.000 0.000 0.000 0.084 0.000 0.089 0.000 0.000 0.000 0.000 0.050
3DFPB12 0.011 0.000 0.000 0.000 0.101 0.084 0.050 0.000 0.000 0.000 0.045 0.000 0.061
3DFPB13 0.061 0.000 0.000 0.000 0.000 0.000 0.000 0.045 0.000 0.000 0.050 0.061 0.000
3DFPB1: Food structure, 3DFPB2: Design of food, 3DFPB3: Speed of production, 3DFPB4: Multi-material printing, 3DFPB5: Copyright issues, 3DFPB6: Safety and contamination, 3DFPB7: Pre-proces-
sing, 3DFPB8: Post-processing, 3DFPB9: Printer cost, 3DFPB10: Cost of consumables, 3DFPB11: Availability of the skilled labour, 3DFPB12: Ordinance and guidelines, 3DFPB13: Printed food shelf life.
18 V. K. VERMA ET AL.
Table 12. Total relation matrix.
3DFPBs 3DFPB1 3DFPB2 3DFPB3 3DFPB4 3DFPB5 3DFPB6 3DFPB7 3DFPB8 3DFPB9 3DFPB10 3DFPB11 3DFPB12 3DFPB13 Row Total (r
i
)
3DFPB1 0.043 0.123 0.031 0.138 0.121 0.152 0.138 0.145 0.164 0.163 0.180 0.063 0.200 1.661
3DFPB2 0.043 0.022 0.109 0.118 0.032 0.061 0.031 0.046 0.139 0.139 0.149 0.055 0.161 1.106
3DFPB3 0.032 0.022 0.003 0.039 0.114 0.128 0.110 0.120 0.130 0.118 0.145 0.118 0.164 1.244
3DFPB4 0.044 0.024 0.003 0.024 0.107 0.120 0.114 0.116 0.134 0.135 0.140 0.106 0.075 1.144
3DFPB5 0.034 0.019 0.003 0.015 0.019 0.101 0.097 0.021 0.024 0.016 0.034 0.095 0.114 0.593
3DFPB6 0.032 0.017 0.002 0.011 0.057 0.020 0.027 0.023 0.100 0.018 0.113 0.016 0.134 0.571
3DFPB7 0.070 0.031 0.004 0.102 0.019 0.028 0.022 0.120 0.098 0.098 0.098 0.023 0.106 0.817
3DFPB8 0.031 0.025 0.003 0.027 0.007 0.017 0.024 0.022 0.063 0.067 0.113 0.013 0.094 0.506
3DFPB9 0.012 0.011 0.001 0.043 0.007 0.012 0.007 0.015 0.009 0.098 0.068 0.013 0.069 0.365
3DFPB10 0.064 0.073 0.009 0.016 0.014 0.022 0.013 0.021 0.020 0.019 0.074 0.053 0.119 0.516
3DFPB11 0.028 0.058 0.007 0.012 0.010 0.094 0.009 0.101 0.025 0.018 0.034 0.010 0.083 0.487
3DFPB12 0.027 0.010 0.001 0.010 0.111 0.104 0.066 0.020 0.020 0.012 0.071 0.017 0.097 0.565
3DFPB13 0.069 0.012 0.002 0.011 0.015 0.021 0.014 0.061 0.015 0.015 0.072 0.067 0.027 0.402
Column Total (d
j
)0.527 0.446 0.179 0.566 0.633 0.881 0.674 0.830 0.941 0.916 1.291 0.648 1.443 9.977
3DFPB1: Food structure, 3DFPB2: Design of food, 3DFPB3: Speed of production, 3DFPB4: Multi-material printing, 3DFPB5: Copyright issues, 3DFPB6: Safety and contamination, 3DFPB7: Pre-proces-
sing, 3DFPB8: Post-processing, 3DFPB9: Printer cost, 3DFPB10: Cost of consumables, 3DFPB11: Availability of the skilled labour, 3DFPB12: Ordinance and guidelines, 3DFPB13: Printed food shelf life.
Table 13. Prominence cause values.
3DFPBsd
j
score r
i
score (r
i
+d
j
) score (r
i
-d
j
) Score Type
3DFPB1 0.527 1.661 2.188 1.133 Cause
3DFPB2 0.446 1.106 1.552 0.660 Cause
3DFPB3 0.179 1.244 1.423 1.065 Cause
3DFPB4 0.566 1.144 1.709 0.578 Cause
3DFPB5 0.633 0.593 1.226 0.041 Eect
3DFPB6 0.881 0.571 1.452 0.311 Eect
3DFPB7 0.674 0.817 1.491 0.143 Cause
3DFPB8 0.830 0.506 1.336 0.324 Eect
3DFPB9 0.941 0.365 1.305 0.576 Eect
3DFPB10 0.916 0.516 1.432 0.399 Eect
3DFPB11 1.291 0.487 1.779 0.804 Eect
3DFPB12 0.648 0.565 1.214 0.083 Eect
3DFPB13 1.443 0.402 1.845 1.041 Eect
3DFPB1: Food structure, 3DFPB2: Design of food, 3DFPB3: Speed of production, 3DFPB4: Multi-material printing, 3DFPB5: Copyright issues, 3DFPB6: Safety and contamination, 3DFPB7: Pre-proces-
sing, 3DFPB8: Post-processing, 3DFPB9: Printer cost, 3DFPB10: Cost of consumables, 3DFPB11: Availability of the skilled labour, 3DFPB12: Ordinance and guidelines, 3DFPB13: Printed food shelf life.
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 19
5. Results and discussion
5.1. ISM results
The ISM model was designed to obtain the levels of hierarchy needed to understand interdependen-
cies between the barriers identied in sustainable 3DFP supply chains in the Indian context. This
aim is to help practitioners to plan suitable strategies for the eective implementation of sustainable
3DFP supply chains. The results of the ISM model are remarkable as they reveal a ve-level hier-
archy to illustrate the relationship between the constructs identied. Level one consists of speed of
production (3DFPB3), copyright issues (3DFPB5), safety and contamination (3DFPB6), ordinance
and guidelines (3DFPB12), and printed food shelf life (3DFPB13). Level two includes food design
(3DFPB2), pre-processing (3DFPB7), and post-processing (3DFPB8). Level three involves food
structure (3DFPB1). Level four encompasses multi-material printing (3DFPB4), printer cost
(3DFPB9), and availability of skilled labour (3DFPB11). Level ve concerns the cost of consumables
(3DFPB10). It was evident from the ISM model that the cost of consumables (3DFPB10) was the
most signicant barrier to 3DFP supply chains in the Indian context, as they form the basis of
the ISM hierarchy and require more attention by 3DFP supply chain professionals.
5.2. DEMATEL results
DEMATEL can trace the intensity of relations between the constructs identied, which is not possible
with the ISM hierarchy model as the latter only indicates whether interrelationships exist. The r + d
and r-d values represent the cause-and-eect group factors respectively. When it comes to under-
standing the critical 3DFP supply chains, we need to strongly focus on causal factors with higher r
+ d values. This implies that the cause group factors are independent of one another, and that the
eect group factors are easily inuenced by the cause group factors (Hori and Shimizu 1999).
The carefully identied barriers were arranged as follows, depending on the r + d values and the
outcomes provided in Table 13 and Figure 4:
3DFPB1 > 3DFPB13 > 3DFPB11 > 3DFPB4 > 3DFPB2 > 3DFPB7 > 3DFPB6 > 3DFPB10 >
3DFPB3 > 3DFPB8 > 3DFPB9 > 3DFPB5 > 3DFPB12.
The food structure (3DFPB1) of 3DFP commodities was observed to be a signicant barrier (r + d
=2.188).Table 13 shows that food structure (3DFPB1), food design (3DFPB2), speed of production
(3DFPB3), multi-material printing (3DFPB4), and pre-processing (3DFPB7) are cause group con-
structs based on (r-d) values. Eect group constructs include copyright issues (3DFPB5), safety and
contamination (3DFPB6), post-processing (3DFPB8), printer cost (3DFPB9), cost of consumables
(3DFPB10), availability of skilled labour (3DFPB11), ordinance and guidelines (3DFPB12), and
printed food shelf life (3DFPB13). The direct and indirect inuences of all constructs are shown in
Table 12. However, we used a threshold value to show signicant inuence, which is an average of
the elements in the total relation matrixas described by Kamble, Gunasekaran, and Raut (2019).
In our case, it was 0.1. Figure 4 depicts the most signicant relationship, which is greater than the
threshold value.
Table 13 shows that the 3DFPB1 (Food structure) inuences almost all the barriers, but is not
itself inuenced by any barrier. Moreover, 3DFPB10 (Cost of consumables) and 3DFPB11 (Avail-
ability of skilled labour) aect 3DFPB13 (Printed food shelf life) and are inuenced by all the other
barriers. Our ndings identied food structure, food design, speed of production, multi-material
printing, and pre-processing as the net cause of barriers, nding support in previous studies
(Zhang, Lou, and Schutyser 2018; Nachal et al. 2019; Liu et al. 2017; Sun et al. 2015a; Dankar
et al. 2018).
The analysis of signicant barriers supports the claims made in previous research (Dankar et al.
2018). Our ndings also support the claim that food supply chain mediatorsprominent barriers
impact food structure and speed of production. The design of food and multi-material printing
20 V. K. VERMA ET AL.
can help the supply chain to beat the challenges of pre-processing with the help of available skilled
labour. Food structure was found to be the primary dependent driver of the impact of 3DFP on the
supply chain. Our ndings revealed that cost of consumables and food structure are the key driving
factors for 3DFP in the supply chain, which also nds support in the literature (Lipton et al. 2015;
Godoi, Prakash, and Bhandari 2016).
Figure 4. Prominence cause diagram.
Figure 5. Network relationship map [3DFPB1 = B1, 3DFPB2 = B2, 3DFPB3 = B3, 3DFPB4 = B4, 3DFPB5 = B5, 3DFPB6 = B6,
3DFPB7 = B7, 3DFPB8 = B8, 3DFPB9 = B9, 3DFPB10 = B10, 3DFPB11 = B11, 3DFPB12 = B12, and 3DFPB13 = B13].
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 21
A network relationship map (Figure 5) is a visual representation of the connections between dis-
tinct barriers, helping to identify and analyze logical connections between various elements in a
given situation.
5.3. Implications
While implementing 3DFP, our study ndings provide vital suggestions for practitioners and
implementation consultants in food supply chains. The ISM concept of dependent and driving bar-
riers can be useful for practitioners in addressing 3DFP supply chain issues, with the driving bar-
riers informing the implementation impact of the 3DFP supply chain. By classifying the 3DFPBs
into cause-and-eect groups, the DEMATEL approach reveals the prominent causal relationship
between the 3DFP supply chain barriers, allowing professionals to focus their attention on the cau-
sal eects of 3DFPBs on the food supply chain, and helping them to step up the deployment of
3DFP supply chains. The ndings suggest that professionals should focus on identied causes
(3DFPB1, 3DFPB2, 3DFPB3, 3DFPB4, and 3DFPB7) rather than receivers (3DFPB5, 3DFPB6,
3DFPB8, 3DFPB9, 3DFPB10, 3DFPB11, 3DFPB12, and 3DFPB13). The value of (r + d)and (r-
d)indicates that speed of production (3DFPB3), food structure (3DFPB1), and food design
(3DFPB2) are the main barriers in 3DFP supply chains. Overcoming these barriers can help
3DFP supply chains to achieve multi-material printing (3DFPB4) and pre-processing (3DFPB7),
potentially optimising the 3DFP environment, including copyright issues (3DFPB5), safety and
contamination (3DFPB6), post-processing (3DFPB8), printer costs (3DFPB9), cost of consumables
(3DFPB10), availability of skilled labour (3DFPB11), ordinance and guidelines (3DFPB12), and
printed food shelf life (3DFPB13).
The study ndings highlight the signicant benets that 3DFP oers the 3DFP supply chain, and
identies specialists as crucial keys to overcoming the barriers (Skartsaris and Piatti 2019).
The ndings also suggest that specialists are highly motivated to develop a food structure that
can improve the shelf life of 3D printed food. We identied the importance of tracking and validat-
ing the food supply chain to understand the origin and need for an eective food structure (Sun
et al. 2018b; Godoi, Prakash, and Bhandari 2016; Zhang, Lou, and Schutyser 2018). Professionals
need to ensure that their 3DP food supply chain can achieve food structure with a reliable data
source by storing genuine-time data of 3DFP fabrication, printer food processing, the food supply
chain, and sales by a 3DFP storage supply chain platform. The aim of professionals should be to
build strong partnerships with prominent 3DFP businesses and develop systems to create food
structures for their customers. The ndings show that to build an eective food structure, 3DFP
needs to have a decentralised, protected, and sharable platform. Specialists are needed to implement
the 3DFP supply chain to boost food structure (Zhang, Lou, and Schutyser 2018), food design
(Nachal et al. 2019), speed of production (Liu et al. 2017), multi-material printing (Sun et al.
2015a), and pre-processing (Dankar et al. 2018).
This paper helps shareholders to quantify, monitor, and manage risks in 3DFP supply chain
sustainability beyond the barriers. The 3DFP supply chain oers its stakeholders signicant indu-
cements to secure the extensive and reliable information needed to build an eective food
structure.
6. Conclusions, future research directions, and limitations
Our study identied, analyzed, modelled, and evaluated barriers to implementing 3DFP in supply
chains in the Indian context. In total, thirteen barriers were identied from the literature and vali-
dated by experts from the food industry and academia. Furthermore, a hybrid ISM and DEMATEL
approach was used to model the barriers. ISM provided interrelations to establish a hierarchical
relationship, whereas DEMATEL investigated the causeeect relationship between barriers.
Each barriers driving and dependence power was analyzed by MICMAC analysis to establish a
22 V. K. VERMA ET AL.
four-quadrant clustered structure. The cost of consumables was identied as a major barrier in the
implementation of 3DFP supply chains, while speed of production and copyright issues were found
to be the prominent dependent barriers in the present study. In linkage barriers, the food structure,
availability of skilled labour and printed food shelf life were found to be signicant obstacles.
According to the study ndings, these 3DFPBs are the most signicant barriers that the food indus-
try needs to take into consideration when implementing 3DFP in the sustainable food supply chain.
6.1. Future research and limitations
This study identied thirteen barriers to 3DFP supply chains. All the barriers were found through a
literature review and veried by 3DFP industry specialists and academics. As all the experts came
from India, some 3DFPBs may have been missed that could be signicant from a perspective exter-
nal to India. We therefore suggest conducting related research on barriers aecting 3DFP technol-
ogy in other nations and investigating newly identied barriers that could inuence the 3DFP
supply chain. Outcomes should be compared with the present study ndings and used to highlight
the interdependencies between barriers. Given that 3DFP practices in developed countries are tech-
nologically advanced, similar research in other developing and developed countries would be extre-
mely interesting.
The ISM-DEMATEL approach utilised subjective decisions from academicians, experts, and
structure integrators to establish inter-relationships between the 3DFP barriers identied. Despite
the great care taken by the authors, the selected expertsindividual biases could have aected the
study ndings. Future work could examine the causeeect relationship found in this research,
using the empirical study models established in the survey approach. We suggest adopting other
multicriteria decision-making tools and structural equation modelling (SEM) for this purpose.
Data availability statement
Due to the sensitive nature of the research, participants in this study did not want their data to be publicly shared, so
supporting data is not available.
Disclosure statement
No potential conict of interest was reported by the author(s).
ORCID
Shivam Gupta http://orcid.org/0000-0002-2714-4958
References
Annosi, M. C., F. Brunetta, F. Bimbo, and M. Kostoula. 2021.Digitalization Within Food Supply Chains to Prevent
Food Waste. Drivers, Barriers and Collaboration Practices.Industrial Marketing Management 93: 208220.
Attaran, M. 2017.The Rise of 3-D Printing: The Advantages of Additive Manufacturing Over Traditional
Manufacturing.Business Horizons 60 (5): 677688.
Attarin, S., and M. Attaran. 2020.Food Printing: Evolving Technologies, Challenges, Opportunities, and Best
Adoption Strategies.Journal of International Technology and Information Management 29 (1): 2555. https://
scholarworks.lib.csusb.edu/jitim/vol29/iss1/2.
Baiano, A. 2021.3D Printed Foods: A Comprehensive Review on Technologies, Nutritional Value, Safety, Consumer
Attitude, Regulatory Framework, and Economic and Sustainability Issues.Food Reviews International 37 (8): 1
31. doi:10.1080/87559129.2020.1762091.
Bandyopadhyay, A., and B. Heer. 2018.Additive Manufacturing of Multi-Material Structures.Materials Science
and Engineering: R: Reports 129: 116.
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 23
Banik, A., H. M. M. Taqi, S. M. Ali, S. Ahmed, M. Garshasbi, and G. Kabir. 2021.Critical Success Factors for
Implementing Green Supply Chain Management in the Electronics Industry: An Emerging Economy Case.
International Journal of Logistics Research and Applications 24 (1): 128. doi:10.1080/13675567.2020.1839029.
Beltagui, A., N. Kunz, and S. Gold. 2020.The Role of 3D Printing and Open Design on Adoption of Socially
Sustainable Supply Chain Innovation.International Journal of Production Economics 221: 107462.
Bloemhof, J. M., J. G. van der Vorst, M. Bastl, and H. Allaoui. 2015.Sustainability Assessment of Food Chain
Logistics.International Journal of Logistics Research and Applications 18 (2): 101117.
Chan, H. K., J. Grin, J. J. Lim, F. Zeng, and A. S. Chiu. 2018.The Impact of 3D Printing Technology on the Supply
Chain: Manufacturing and Legal Perspectives.International Journal of Production Economics 205: 156162.
Chauhan, A., S. K. Jakhar, and C. Chauhan. 2021.The Interplay of Circular Economy with Industry 4.0 Enabled
Smart City Drivers of Healthcare Waste Disposal.Journal of Cleaner Production 279: 123854.
Christopher, M., and L. J. Ryals. 2014.The Supply Chain Becomes the Demand Chain.Journal of Business Logistics
35 (1): 2935.
Daner, K., S. Vadodaria, L. Ong, S. Nobel, S. Gras, I. Norton, and T. Mill. 2020.Design and Characterization of
Casein-Whey Protein Suspensions via the pH-Temperature-Route for Application in Extrusion-Based 3D-
Printing.Food Hydrocolloids 112: 105850.
Dankar, I. T. 2019. Study and characterization of microstructural and physio-chemical properties of potato products
for 3D food printing (Doctoral thesis). http://hdl.handle.net/2117/127502.
Dankar, I., A. Haddarah, F. E. Omar, F. Sepulcre, and M. Pujola. 2018.3D Printing Technology: The new era for
Food Customization and Elaboration.Trends in Food Science & Technology 75: 231242.
Derossi, A., R. Caporizzi, D. Azzollini, and C. Severini. 2018.Application of 3D Printing for Customized Food. A
Case on the Development of a Fruit-Based Snack for Children.Journal of Food Engineering 220: 6575.
DeVieneni, A., C. A. Velez, D. Benjamin, and J. Hollenbeck. 2012.Cost-eective additive manufacturing in space:
HELIOS technology challenge guide.https://ntrs.nasa.gov/citations/20130001751.
Dick, A., B. Bhandari, and S. Prakash. 2019.3D Printing of Meat.Meat Science 153: 3544.
Dubey, R., and A. Gunasekaran. 2016.The Sustainable Humanitarian Supply Chain Design: Agility, Adaptability
and Alignment.International Journal of Logistics Research and Applications 19 (1): 6282.
Endres, A. B., and N. R. Johnson. 2011.United States Food Law Update: The FDA Food Safety Modernization Act,
Obesity and Deceptive Labeling Enforcement.Journal of Food Law & Policy 7: 135. https://scholarworks.uark.
edu/jp/vol7/iss1/7.
Faisal, M. N. 2010.Analysing the Barriers to Corporate Social Responsibility in Supply Chains: An Interpretive
Structural Modelling Approach.International Journal of Logistics Research and Applications 13 (3): 179195.
Ferreira, I. A., and J. L. Alves. 2017.Low-cost 3D Food Printing.Ciência & Tecnologia dos Materiais 29 (1): e265e269.
Gandhi, S., S. K. Mangla, P. Kumar, and D. Kumar. 2016.A Combined Approach Using AHP and DEMATEL for
Evaluating Success Factors in Implementation of Green Supply Chain Management in Indian Manufacturing
Industries.International Journal of Logistics Research and Applications 19 (6): 537561.
Ghobakhloo, M. 2020.Determinants of Information and Digital Technology Implementation for Smart
Manufacturing.International Journal of Production Research 58 (8): 23842405.
Gholamipour-Shirazi, A., M. A. Kamlow, I. T. Norton, and T. Mills. 2020.How to Formulate for Structure and
Texture via Medium of Additive Manufacturing-A Review.Foods (basel, Switzerland) 9 (4): 497.
Godoi, F. C., B. R. Bhandari, S. Prakash, and M. Zhang. 2018.Fundamentals of 3D Food Printing and Applications.
London: Academic Press. https://www.sciencedirect.com/science/book/9780128145647.
Godoi, F. C., S. Prakash, and B. R. Bhandari. 2016.3D Printing Technologies Applied for Food Design: Status and
Prospects.Journal of Food Engineering 179: 4454.
Gupta, A., R. Singh, and P. Suri. 2018.Analysis of Challenges Faced by Indian Logistics Service Providers.
Operations and Supply Chain Management: An International Journal 11 (4): 214225.
Hertafeld, E., C. Zhang, Z. Jin, A. Jakub, K. Russell, Y. Lakehal, and H. Lipson. 2019.Multi-material Three-
Dimensional Food Printing with Simultaneous Infrared Cooking.3D Printing and Additive Manufacturing 6
(1): 1319.
Hori, S., and Y. Shimizu. 1999.Designing Methods of Human Interface for Supervisory Control Systems.Control
Engineering Practice 7 (11): 14131419.
Jia, F., X. Wang, N. Mustafee, and L. Hao. 2016.Investigating the Feasibility of Supply Chain-Centric Business
Models in 3D Chocolate Printing: A Simulation Study.Technological Forecasting and Social Change 102: 202213.
Jiang, J. 2020.A Novel Fabrication Strategy for Additive Manufacturing Processes.Journal of Cleaner Production
272: 122916.
Jideani, A. I., A. P. Mutshinyani, N. P. Maluleke, Z. P. Mafukata, M. V. Sithole, M. U. Lidovho, E. K. Ramatsetse, and
M. M. Matshisevhe. 2020.Impact of Industrial Revolutions on Food Machinery - An Overview.Journal of Food
Research 9 (5): 4252.
Kailash, M., L. Kavitha, S. V. Madan, and C. Ravichandran. 2020.3D Food Printing: A Technology for Fabricating
Customized Artistic Food.Emerging Technologies in Food Science 1: 273287. doi:10.1007/978-981-15-2556-8_
25.
24 V. K. VERMA ET AL.
Kamble, S. S., A. Gunasekaran, and S. A. Gawankar. 2018.Sustainable Industry 4.0 Framework: A Systematic
Literature Review Identifying the Current Trends and Future Perspectives.Process Safety and Environmental
Protection 117: 408425.
Kamble, S. S., A. Gunasekaran, H. Parekh, and S. Joshi. 2019.Modeling the Internet of Things Adoption Barriers in
Food Retail Supply Chains.Journal of Retailing and Consumer Services 48: 154168.
Kamble, S. S., A. Gunasekaran, and R. D. Raut. 2019.Analysing the Implementation Barriers of Dual Cycling in Port
Container Terminal Using Interpretive Structural Modeling- Indian Context.International Journal of Logistics
Research and Applications 22 (2): 119137.
Kamble, S. S., A. Gunasekaran, and R. Sharma. 2018.Analysis of the Driving and Dependence Power of Barriers to
Adopt Industry 4.0 in Indian Manufacturing Industry.Computers in Industry 101: 107119.
Kamble, S. S., A. Gunasekaran, and R. Sharma. 2020.Modeling the Blockchain Enabled Traceability in Agriculture
Supply Chain.International Journal of Information Management 52: 101967.
Kamble, S. S., and R. D. Raut. 2019.Evaluating the Factors Considered for Procurement of raw Material in Food
Supply Chain Using Delphi-AHP Methodology - a Case Study of Potato Chips Processing Company in India.
International Journal of Productivity and Quality Management 26 (2): 176189.
Karwasra, K., G. Soni, S. K. Mangla, and Y. Kazancoglu. 2021.Assessing Dairy Supply Chain Vulnerability During
the Covid-19 Pandemic.International Journal of Logistics Research and Applications 24 (1): 119. doi:10.1080/
13675567.2021.1910221.
Keerthana, K., T. Anukiruthika, J. A. Moses, and C. Anandharamakrishnan. 2020.Development of Fiber-Enriched
3D Printed Snacks from Alternative Foods: A Study on Button Mushroom.Journal of Food Engineering 287:
110116.
Kodama, M., Y. Takita, H. Tamate, A. Saito, J. Gong, M. Makino, A. Khosla, M. Kawakami, and H. Furukawa. 2017.
Novel Soft Meals Developed by 3D printing.Future Foods 1: 161181. doi:10.5772/intechopen.70652.
Kothman, I., and N. Faber. 2016.How 3D Printing Technology Changes the Rules of the Game.Journal of
Manufacturing Technology Management 27 (7): 932943.
Kozior, T., and J. Bochnia. 2020.The Inuence of Printing Orientation on Surface Texture Parameters in Powder
bed Fusion Technology with 316L Steel.Micromachines 11 (7): 639.
Lagorio, A., G. Zenezini, G. Mangano, and R. Pinto. 2021.A Systematic Literature Review of Innovative
Technologies Adopted in Logistics Management.International Journal of Logistics Research and Applications
24 (1): 124. doi:10.1080/13675567.2020.1850661.
Lille, M., A. Nurmela, E. Nordlund, S. Metsa-Kortelainen, and N. Sozer. 2018.Applicability of Protein and Fiber-
Rich Food Materials in Extrusion-Based 3D Printing.Journal of Food Engineering 220: 2027.
Lipton, J. I. 2017.Printable Food: The Technology and its Application in Human Health.Current Opinion in
Biotechnology 44: 198201.
Lipton, J., D. Arnold, F. Nigl, N. Lopez, D. L. Cohen, N. Noren, and H. Lipson. 2010.Multi-material Food Printing
with Complex Internal Structure Suitable for Conventional Post-Processing.Solid Freeform Fabrication
Symposium 68: 809815. doi:10.26153/tsw/15245.
Lipton, J. I., M. Cutler, F. Nigl, D. Cohen, and H. Lipson. 2015.Additive Manufacturing for the Food Industry.
Trends in Food Science & Technology 43 (1): 114123.
Liu, Z., B. Bhandari, S. Prakash, and M. Zhang. 2018.Creation of Internal Structure of Mashed Potato Construct by
3D Printing and its Textural Properties.Food Research International 111: 534543.
Liu, Z., M. Zhang, B. Bhandari, and Y. Wang. 2017.3D Printing: Printing Precision and Application in Food Sector.
Trends in Food Science & Technology 69: 8394.
Lupton, D., and B. Turner. 2016.Both Fascinating and Disturbing: Consumer Responses to 3D Food Printing and
Implications for Food Activism.In Digital Food Activism, edited by Tanja Schneider, Karin Eli, Catherine Dolan,
and Stanley Ulijaszek, 117. London: Routledge. https://ssrn.com/abstract=2799191.
Mangla, S. K., A. Bhattacharya, A. K. Yadav, Y. K. Sharma, A. Ishizaka, S. Luthra, and R. Chakraborty. 2021.A
Framework to Assess the Challenges to Food Safety Initiatives in an Emerging Economy.Journal of Cleaner
Production 284: 124709.
McHugh, T., and C. Bilbao-Sainz. 2017.3D Food Printing: A new Dimension in Food Production Processes.Food
Technology 71 (4): 123125. https://www.ars.usda.gov/research/publications/publication/?seqNo115=346517.
Mohr, S., and O. Khan. 2015.3D Printing and Supply Chains of the Future.Innovations and Strategies for Logistics
and Supply Chains: Technologies, Business Models and Risk Management.Proceedings of the hamburg
International conference of Logistics (HICL), 20: 147-174. Berlin: epubli GmbH. http://hdl.handle.net/10419/
209254.
Murry Jr, J.W. and Hammons, J.O. 1995.Delphi: A Versatile Methodology for Conducting Qualitative Research.
The Review of Higher Education 18 (4): 423436.
Nachal, N., J. A. Moses, P. Karthik, and C. Anandharamakrishnan. 2019.Applications of 3D Printing in Food
Processing.Food Engineering Reviews 11 (3): 123141.
Palaniappan, A., S. Vinodh, and R. Ranganathan. 2020.Analysis of Factors Inuencing AM Application in Food
Sector Using ISM.Journal of Modelling in Management 15 (3): 919932.
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 25
Pearce, J., C. Blair, K. Laciak, R. Andrews, A. Nosrat, and I. Zelenika-Zovko. 2010.3-D Printing of Open Source
Appropriate Technologies for Self-Directed Sustainable Development.Journal of Sustainable Development 3
(4): 1729. doi:10.5539/jsd.v3n4p17.
Pereira, T., S. Barroso, and M. M. Gil. 2021.Food Texture Design by 3D Printing: A Review.Foods (Basel,
Switzerland) 10 (2): 320.
Perez, B., H. Nykvist, A. F. Brogger, M. B. Larsen, and M. F. Falkeborg. 2019.Impact of Macronutrients Printability
and 3D-Printer Parameters on 3D-Food Printing: A Review.Food Chemistry 287: 249257.
Piya, S., A. Shamsuzzoha, and M. Khadem. 2020.An Approach for Analysing Supply Chain Complexity Drivers
Through Interpretive Structural Modelling.International Journal of Logistics Research and Applications 23 (4):
311336.
Pulatsu, E., J. W. Su, J. Lin, and M. Lin. 2020.Factors Aecting 3D Printing and Post-Processing Capacity of Cookie
Dough.Innovative Food Science & Emerging Technologies 61: 102316.
Rajput, S., and S. P. Singh. 2019.Identifying Industry 4.0 IoT Enablers by Integrated PCA-ISM-DEMATEL
Approach.Management Decision 57 (8): 17841817.
Ramachandraiah, K. 2021.Potential Development of Sustainable 3D-Printed Meat Analogues: A Review.
Sustainability 13 (2): 938.
Rana, N. P., S. Luthra, S. K. Mangla, R. Islam, S. Roderick, and Y. K. Dwivedi. 2019.Barriers to the Development of
Smart Cities in Indian Context.Information Systems Frontiers 21 (3): 503525.
Robbins, S. P., R. Stuart-Kotze, and M. Coulter. 2000.Management. Scarborough. Ont.: Prentice Hall Canada.
Saade, M. R. M., A. Yahia, and B. Amor. 2020.How has LCA Been Applied to 3D Printing? A Systematic Literature
Review and Recommendations for Future Studies.Journal of Cleaner Production 244: 118803.
Sage, A. P. 1977.Methodology for Large-Scale Systems. New York: McGraw-Hill. 165-203.
Sarkar, S., and J. K. Dey. 2021.3D Food Printing: An Emerging Technology in Food Production Processes.
Biotica Research Today 3 (6): 550552. https://www.bioticainternational.com/ojs/index.php/biorestoday/article/
view/996.
Sharma, M., S. Kamble, V. Mani, R. Sehrawat, A. Belhadi, and V. Sharma. 2021.Industry 4.0 Adoption for
Sustainability in Multi-Tier Manufacturing Supply Chain in Emerging Economies.Journal of Cleaner
Production 281: 125013.
Sharma, Y. K., S. K. Mangla, P. P. Patil, A. K. Yadav, S. K. Jakhar, and S. Luthra. 2018.Ranking the IT Based
Technologies to Enhance the Safety and Security of Food Using AHP Approach.Mathematics Applied in
Information Systems 2: 108122.
Shen, G. X., S. G. Sun, Y. Z. Zhang, Z. Q. Wang, B. K. Chen, and C. Ma. 2014.System Failure Analysis Based on
DEMATEL-ISM and FMECA.Journal of Central South University 21 (12): 45184525.
Sher, D., and X. Tuto. 2015.Review of 3D Food Printing.Temes de Disseny 31: 104117. https://raco.cat/index.php/
Temes/article/view/299596.
Shree, M. V., V. Dhinakaran, V. Rajkumar, P. B. Ram, M. D. Vijayakumar, and T. Sathish. 2020.Eect of 3D
Printing on Supply Chain Management.Materials Today: Proceedings 21: 958963.
Simon, J., M. Trojanova, J. Zbihlej, and J. Sarosi. 2018.Mass Customization Model in Food Industry Using Industry
4.0 Standard with Fuzzy-Based Multi-Criteria Decision Making Methodology.Advances in Mechanical
Engineering 10 (3): 168781401876677.
Skartsaris, I., and C. Piatti. 2019.Altering Production Patterns in the Food Industry: 3D Food Printing.In Food
Tech Transitions, edited by C. Piatti, S. Grae-Hönninger, and F. Khajehei, 97110. Cham: Springer. doi:10.
1007/978-3-030-21059-5_6.
Sonar, H., V. Khanzode, and M. Akarte. 2020.Investigating Additive Manufacturing Implementation Factors Using
Integrated ISM-MICMAC Approach.Rapid Prototyping Journal 26 (10): 18371851.
Sun, J., Z. Peng, L. Yan, J. Y. H. Fuh, and G. S. Hong. 2015a.3D Food PrintingAn Innovative Way of Mass
Customization in Food Fabrication.International Journal of Bioprinting 1 (1): 2738.
Sun, J., Z. Peng, W. Zhou, J. Y. Fuh, G. S. Hong, and A. Chiu. 2015b.A Review on 3D Printing for Customized Food
Fabrication.Procedia Manufacturing 1: 308319.
Sun, J., W. Zhou, D. Huang, J. Y. Fuh, and G. S. Hong. 2015c.An Overview of 3D Printing Technologies for Food
Fabrication.Food and Bioprocess Technology 8 (8): 16051615.
Sun, J., W. Zhou, D. Huang, and L. Yan. 2018a.3D Food Printing: Perspectives.In Polymers for Food Applications,
edited by T. Gutiérrez, 725755. Cham: Springer. doi:10.1007/978-3-319-94625-2_26.
Sun, J., W. Zhou, L. Yan, D. Huang, and L. Y. Lin. 2018b.Extrusion-based Food Printing for Digitalized Food Design
and Nutrition Control.Journal of Food Engineering 220: 111.
Telesetsky, A. 2020.Food Safety Law in the United States: Risk Management in the Organic Food Supply Chain.In
Regulatory Issues in Organic Food Safety in the Asia Pacic, edited by B. Goh and R. Price, 217228. Singapore:
Springer. doi:10.1007/978-981-15-3580-2_13.
Tian, J., B. C. Bryksa, and R. Y. Yada. 2016.Feeding the World Into the Future Food and Nutrition Security: The
Role of Food Science and Technology.Frontiers in Life Science 9 (3): 155166.
26 V. K. VERMA ET AL.
Tran, J. L. 2019.Safety and Labelling of 3D Printed Food.In Fundamentals of 3D Food Printing and
Applications, edited by F. Godoi, B. Bhandari, S. Prakash, and M. Zhang, 355371. London: Academic Press.
doi:10.1016/B978-0-12-814564-7.00012-2.
Trivedi, A., S. K. Jakhar, and D. Sinha. 2021.Analyzing Barriers to Inland Waterways as a Sustainable
Transportation Mode in India: A Dematel-ISM Based Approach.Journal of Cleaner Production 295: 126301.
Van der Linden, D. 2015.3D Food printing Creating Shapes and Textures.https://www.arbeid.tno.nl/media/5517/
3d_food_printing_march_2015.pdf.
Voon, S. L., J. An, G. Wong, Y. Zhang, and C. K. Chua. 2019.3D Food Printing: A Categorised Review of Inks and
Their Development.Virtual and Physical Prototyping 14 (3): 203218.
Wang, L., Q. Cao, and L. Zhou. 2018.Research on the Inuencing Factors in Coal Mine Production Safety Based on
the Combination of DEMATEL and ISM.Safety Science 103: 5161.
Wang, X., J. Zhou, W. Yang, J. Pang, W. Zhang, G. Chen, X. Dong, et al. 2020.Warpage Optimization and Inuence
Factors Analysis of 3D Printing Personalized JJY Tablets.Drug Development and Industrial Pharmacy 46 (3):
388394.
Wareld, J. N. 1974.Developing Subsystem Matrices in Structural Modeling.IEEE Transactions on Systems, Man,
and Cybernetics SMC-4 (1): 7480. doi:10.1109/TSMC.1974.5408523.
Weller, C., R. Kleer, and F. T. Piller. 2015.Economic Implications of 3D Printing: Market Structure Models in Light
of Additive Manufacturing Revisited.International Journal of Production Economics 164: 4356.
Xu, D., and Y. Long. 2021.The Role of Supply Chain Integration in the Transformation of Food Manufacturers: A
Case Study from China.International Journal of Logistics Research and Applications 24 (2): 198212.
Yang, C., S. Lan, and M. L. Tseng. 2019.Coordinated Development Path of Metropolitan Logistics and Economy in
Belt and Road Using DEMATELBayesian Analysis.International Journal of Logistics Research and Applications
22 (1): 124.
Yang, F., M. Zhang, and B. Bhandari. 2017.Recent Development in 3D Food Printing.Critical Reviews in Food
Science and Nutrition 57 (14): 31453153.
Yang, F., M. Zhang, S. Prakash, and Y. Liu. 2018.Physical Properties of 3D Printed Baking Dough as Aected by
Dierent Compositions.Innovative Food Science & Emerging Technologies 49: 202210.
Zhang, L., Y. Lou, and M. A. Schutyser. 2018.3D Printing of Cereal-Based Food Structures Containing Probiotics.
Food Structure 18: 14-22.
INTERNATIONAL JOURNAL OF LOGISTICS RESEARCH AND APPLICATIONS 27
... Importance of growing operational competencies has made AM a strategic tool for firms to consider different market opportunities and increase their reach and variety to customers (Attaran, 2017;Sood et al., 2022;Sushil, 2017). It enables firms to match the demand with supply as compared to a traditional way of sourcing, making and distribution (Attaran, 2017;Verma et al., 2022). ...
... Therefore, companies are increasingly adopting soft (information systems) and hard (emerging technologies like additive manufacturing) elements to support their objective of green and sustainable growth (Yang et al., 2019). On the one hand, GIS facilitate the efficient management of information and on the other hand AM encourages the minimum wastage in production process and further adds the value to overall supply chain (Durach et al., 2017;Verma et al., 2022;Yang et al., 2019). This defines the converging relationship of GIS and AM to achieve better operational and market performance. ...
... GIS find its applications right from strategic, tactical to operational level in an organization (Zarco et al., 2020). Procurement and outsourcing operations can select suppliers based on environmental indicators, and this information can be utilized to update the sensitive materials frequently Verma et al., 2022). Even sales and marketing functions can track the daily sales of environmentally sensitive products. ...
Article
Full-text available
Given the importance of environmental objectives, companies are continuously trying to achieve them through different means and technologies are not an exception. Companies are increasingly using information systems (soft side) coupled with manufacturing (hard side) technologies to achieve market and operational performance. Therefore, this study investigates the role of green information systems (GIS) and additive manufacturing (AM) in the market and operational performance achievement of an organization. This study explores if the role of GIS and AM is influenced by firm size and number of employees in the organization with the lens of organizational information processing theory. Through survey instrument, data from 211 respondents is collected and analysis is performed using structural equation modeling. Findings indicate that GIS is critical to overall performance as compared to AM. In addition, top management facilitate extension of business activities significantly as compared to internal operations. Control orientation works best for new technologies like GIS and AM. The study offers an array of scope for theoretical and practical implications to utilize GIS and emerging technologies like AM to achieve greater market and operational performance. Further, the study offers implications for AM and GIS professionals and researchers. The study contributes in integrating manufacturing and information systems to facilitate faster technological and information processing capabilities.
... The DEMATEL technique is summarized as follows (Kaur et al., 2018;Verma et al., 2022). ...
Article
Full-text available
Purpose The purpose of this study is to identify, analyse and model the post-processing barriers of 3D-printed medical models (3DPMM) printed by fused deposition modelling to overcome these barriers for improved operational efficiency in the Indian context. Design/methodology/approach The methodology used interpretive structural modelling (ISM), cross-impact matrix multiplication applied to classification (MICMAC) analysis and decision-making trial and evaluation laboratory (DEMATEL) to understand the hierarchical and contextual relations among the barriers of the post-processing. Findings A total of 11 post-processing barriers were identified in this study using ISM, literature review and experts’ input. The MICMAC analysis identified support material removal, surface finishing, cleaning, inspection and issues with quality consistency as significant driving barriers for post-processing. MICMAC also identified linkage barriers as well as dependent barriers. The ISM digraph model was developed using a final reachability matrix, which would help practitioners specifically tackle post-processing barriers. Further, the DEMATEL method allows practitioners to emphasize the causal effects of post-processing barriers and guides them in overcoming these barriers. Research limitations/implications There may have been a few post-processing barriers that were overlooked by the Indian experts, which might have been important for other country’s perspective. Practical implications The presented ISM model and DEMATEL provide directions for operation managers in planning operational strategies for overcoming post-processing issues in the medical 3D-printing industry. Also, managers may formulate operational strategies based on the driving and dependence power of post-processing barriers as well as the causal effects relationships of the barriers. Originality/value This study contributes to identifying, analyzing and modelling the post-processing barriers of 3DPMM through a combined ISM and DEMATEL methodology, which has not yet been reviewed. This study also contributes to decision makers developing suitable strategies to overcome the post-processing barriers for improved operational efficiency.
Article
In recent years, the rapid increase in the global population, the challenges associated with climate change, and the emergence of new pandemics have all become major threats to food security worldwide. Consequently, innovative solutions are urgently needed to address the current challenges and enhance food sustainability. Green technologies have gained significant attention for many food applications, while the technologies of the fourth industrial revolution (Industry 4.0) are reshaping different production and consumption sectors, such as food and agriculture. In this review, a general overview of green and Industry 4.0 technologies from a food perspective will be provided. Connections between green food technologies (e.g., green preservation, processing, extraction, and analysis) and Industry 4.0 enablers (e.g., artificial intelligence, big data, smart sensors, robotics, blockchain, and the Internet of Things) and the Sustainable Development Goals (SDGs) will be identified and explained. Green and Industry 4.0 technologies are both rapidly becoming a valuable part of meeting the SDGs. These technologies demonstrate high potential to foster ecological and digital transitions of food systems, delivering societal, economic, and environmental outcomes. A range of green technologies has already provided innovative solutions for major food system transformations, while the application of digital technologies and other Industry 4.0 technological innovations is still limited in the food sector. It is therefore expected that more green and digital solutions will be adopted in the coming years, harnessing their full potential to achieve a healthier, smarter, more sustainable and more resilient food future.
Purpose 3D food printing technology is an emerging smart technology, which because of its inbuilt capabilities, has the potential to support a sustainable supply chain and environmental quality management. This new technology needs a supportive ecosystem, and thus, this paper identifies and models the enablers for adopting 3D printing technology toward a sustainable food supply chain. Design/methodology/approach The enablers were identified through an extensive literature review and verified by domain experts. The identified enablers were modelled through the hybrid total interpretive structural modelling approach (TISM) and the decision-making trial and evaluation laboratory (DEMATEL) approach. Findings It emerged that stakeholders need technical know-how about the 3D printing technology, well supported by a legal framework for clear intellectual property rights ownership. Also, the industry players must have focused and clear strategic planning, considering the need for sustainable supply chains. Moreover, required product innovation as per customer needs may enhance the stakeholders' readiness to adopt this technology. Practical implications The framework proposed in this research provides managers with a hierarchy and categorization of adoption enablers which will help them adopt 3D food printing technology and improve environmental quality. Originality/value This research offers a framework for modelling the enablers for 3D food printing to develop a sustainable food supply chain using the TISM and DEMATEL techniques.
Article
Full-text available
Purpose This study aims to identify 3D-printed medical model (3DPMM) supply chain barriers that affect the supply chain of 3DPMM in the Indian context and investigate the interdependencies between the barriers to establish hierarchical relations between them to improve the supply chain. Design/methodology/approach The methodology used interpretive structural modeling (ISM) and a decision-making trial and evaluation laboratory (DEMATEL) to identify the hierarchical and contextual relations among the barriers to the 3DPMM supply chain. Findings A total of 15 3DPMM supply chain barriers were identified in this study. The analysis identified limited materials options, slow production speed, manual post-processing, high-skilled data analyst, design and customization expert and simulation accuracy as the significant driving barriers for the medical models supply chain for hospitals. In addition, the authors identified linkage and dependent barriers. The present study findings would help to improve the 3DPMM supply chain. Research limitations/implications There were no experts from other nations, so this study might have missed a few 3DPMM supply chain barriers that would have been significant from another nation’s perspective. Practical implications ISM would help practitioners minimize 3DPMM supply chain barriers, while DEMATEL allows practitioners to emphasize the causal effects of 3DPMM supply chain barriers. Originality/value This study minimizes the 3DPMM supply chain barriers for medical applications through a hybrid ISM and DEMATEL methodology that has not been investigated in the literature.
Article
Full-text available
The growing consumer awareness of climate change and the resulting food sustainability issues have led to an increasing adoption of several emerging food trends. Some of these trends have been strengthened by the emergence of the fourth industrial revolution (or Industry 4.0), and its innovations and technologies that have fundamentally reshaped and transformed current strategies and prospects for food production and consumption patterns. In this review a general overview of the industrial revolutions through a food perspective will be provided. Then, the current knowledge base regarding consumer acceptance of eight traditional animal-proteins alternatives (e.g., plant-based foods and insects) and more recent trends (e.g., cell-cultured meat and 3D-printed foods) will be updated. A special focus will be given to the impact of digital technologies and other food Industry 4.0 innovations on the shift toward greener, healthier, and more sustainable diets. Emerging food trends have promising potential to promote nutritious and sustainable alternatives to animal-based products. This literature narrative review showed that plant-based foods are the largest portion of alternative proteins but intensive research is being done with other sources (notably the insects and cell-cultured animal products). Recent technological advances are likely to have significant roles in enhancing sensory and nutritional properties, improving consumer perception of these emerging foods. Thus, consumer acceptance and consumption of new foods are predicted to continue growing, although more effort should be made to make these food products more convenient, nutritious, and affordable, and to market them to consumers positively emphasizing their safety and benefits.
Article
Full-text available
Frequent occurrence of disruptions in the supply chain due to the Covid-19 pandemic has increased the supply chain vulnerability (SCU), which affects the performance and revenue generation of firms. If the commodity we are dealing with in a supply chain is of a perishable nature, then the situation becomes more complicated, as such products need restrictive storage and transportation facilities. The dairy supply chain is one such perishable product supply chain. This paper, thus, proposes a method to identify key drivers of SCU in a dairy SC followed by establishing a model using interpretive structural modeling (ISM) and a graph theory approach (GTA) to calculate the SCU Index. It is important to quantify the SCU for identifying major factors affecting it and then developing techniques to mitigate it. In order to quantify the SCU, first, the ISM model is used to identify the interrelation between drivers, and then an adjacency matrix is formed by using the interdependence, thereby adding inheritance of each driver. A variable permanent matrix is formed to calculate the SCU Index for the SC. This proposed approach will help managers in mitigating the adverse effects of COVID-19 on the dairy supply chain.
Article
Full-text available
An important factor in consumers’ acceptability, beyond visual appearance and taste, is food texture. The elderly and people with dysphagia are more likely to present malnourishment due to visually and texturally unappealing food. Three-dimensional Printing is an additive manufacturing technology that can aid the food industry in developing novel and more complex food products and has the potential to produce tailored foods for specific needs. As a technology that builds food products layer by layer, 3D Printing can present a new methodology to design realistic food textures by the precise placement of texturing elements in the food, printing of multi-material products, and design of complex internal structures. This paper intends to review the existing work on 3D food printing and discuss the recent developments concerning food texture design. Advantages and limitations of 3D Printing in the food industry, the material-based printability and model-based texture, and the future trends in 3D Printing, including numerical simulations, incorporation of cooking technology to the printing, and 4D modifications are discussed. Key challenges for the mainstream adoption of 3D Printing are also elaborated on.
Article
Full-text available
To mitigate the threat of climate change driven by livestock meat production, a multifaceted approach that incorporates dietary changes, innovative product development, advances in technologies, and reductions in food wastes/losses is proposed. The emerging technology of 3D printing (3DP) has been recognized for its unprecedented capacity to fabricate food products with intricate structures and reduced material cost and energy. For sustainable 3DP of meat substitutes, the possible materials discussed are derived from in vitro cell culture, meat byproducts/waste, insects, and plants. These material-based approaches are analyzed from their potential environmental effects, technological viability, and consumer acceptance standpoints. Although skeletal muscles and skin are bioprinted for medical applications, they could be utilized as meat without the additional printing of vascular networks. The impediments to bioprinting of meat are lack of food-safe substrates/materials, cost-effectiveness, and scalability. The sustainability of bioprinting could be enhanced by the utilization of generic/universal components or scaffolds and optimization of cell sourcing and fabrication logistics. Despite the availability of several plants and their byproducts and some start-up ventures attempting to fabricate food products, 3D printing of meat analogues remains a challenge. From various insects, powders, proteins (soluble/insoluble), lipids, and fibers are produced, which—in different combinations and at optimal concentrations—can potentially result in superior meat substitutes. Valuable materials derived from meat byproducts/wastes using low energy methods could reduce waste production and offset some greenhouse gas (GHG) emissions. Apart from printer innovations (speed, precision, and productivity), rational structure of supply chain and optimization of material flow and logistic costs can improve the sustainability of 3D printing. Irrespective of the materials used, perception-related challenges exist for 3D-printed food products. Consumer acceptance could be a significant challenge that could hinder the success of 3D-printed meat analogs.
Article
Full-text available
The purpose of this paper is to identify various factors influencing Additive Manufacturing (AM) implementation from operational performance in the Indian manufacturing sector and to establish the hierarchical relationship among them. The methodology includes three phases, namely: identification of factors through Systematic Literature Review (SLR), interviews with experts to capture industry perspective of AM implementation factors and to develop the hierarchical model and classify them by deriving the interrelationship between the factors using Interpretive Structural Modelling (ISM), followed with the fuzzy-MICMAC (Matrice d’Impacts Croisés Multiplication Appliqués à un Classement approach) analysis. This research has identified 14 key factors that influence the successful AM implementation in the Indian manufacturing sector. Based on the analysis, top management commitment is an essential factor with high driving power, which exaggerates other factors. Factors namely manufacturing flexibility, operational excellence, and firm competitiveness, are placed at the top level of the model which indicates that they have less driving power and organizations need to focus on those factors after implementing the bottom level factors. The proposed ISM model sets the directions for business managers in planning the operational strategies for addressing AM implementation issues in the Indian manufacturing sector. Also, competitive strategies may be framed by organizations based on the driving and dependence power of AM implementation factors.
Article
Finding sustainable measures for maritime transport has long been a significant issue owing to a rising global population and the internationalization of supply chains. In Indian contexts, it has become imperative to shift from highly congested and polluting road and rail transport to a more sustainable Inland Waterway Transport (IWT). Inland waterway transportation has numerous limitations and challenges in its implementation. This study aims to study the barriers to the implementation of IWTs in India by establishing and analyzing the complex interrelationships between them. Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) methods are employed to analyze the cause-effect relationship among the barriers and to further identify the key influential barriers from an identified set of factors. The results reveal that governance issues, policy bias, high cost requirements, and lack of river-interlinking are the most critical inhibitors for IWT development in India and the policymakers should focus on eliminating these ‘key’ inhibitors to ensure effective implementation of IWT in India. The findings of this study provide useful insights from a strategic and policy-making perspective.
Article
Food supply chains increasingly rely on big-data management solutions to foster collaboration across the food supply chain and improve business performance. However, little is known about collaboration practices that actors on the digital food supply chain adopt to solve problems such as food waste, or about the drivers and barriers related to the digital transformation of the food supply chain. Most of food waste studies rely on quantitative analysis, which cannot reveal relevant details about the tensions and dynamics of collaboration. We conducted a qualitative study drawing on eighteen in-depth interviews - of managers of large multinational and local organizations covering different and relevant roles on the digital food supply chain - to investigate how organizational and food supply chain processes are affected by the digitalization of the operations along the food supply chain. By triangulating emerging findings with literature on supply chain management we discuss different views about collaborative practices for food waste prevention in the food supply chain and provide insights on how supply chain design and firms' operations have been re-conceptualized with the usage of digital technologies and on the institutional forces both limiting (barriers) and fostering (drivers) the diffusion of the digital food supply chain.
Article
Many innovative technologies have been successfully adopted in logistics and supply chain management processes to increase efficiency, reduce costs or enhance communication. In recent years, considerable attention from both practitioners and academics has been focused on evaluating the impacts of innovative technologies adoption. However, the current body of literature on technology adoption, implementation and evaluation in logistics is quite fragmented; thus, an updated and structured overview of the scientific literature in this field might be useful. To this end, this work presents a systematic literature review (SLR) that aims to increase the understanding of the trend toward new technologies in logistics and identify the main research trends and gaps. The principal research trends that emerged from the SLR involve the technologies, their evolution over time and their relationships with the research methodologies. The main literature gaps concern integration and communication, technology-adoption processes and differences between inbound and outbound logistics.
Article
Achieving sustainable supply chain performance in a multi-tier manufacturing supply chain to curtail the non-environmental-friendly practices is one of the most significant challenges. Industry 4.0 advancements in technology not only fosters sustainable supply chain initiatives that can maximize economic gains but also reduce environmental impacts and contribute to social development. The present study perused a systematic way to deal with this problem by evaluation of drivers and barriers for implementation of Industry 4.0 in multi-tier manufacturing supply chains. Based on practitioner's opinion, a total of 37 drivers and 21 barriers were identified under five dimensions viz., technological, organizational , economic, environmental, and social, and their interrelationships were also established in a multi-tier supply chain. The results show that the sustainable aspects, that includes the environmental and social factors, were the highest-ranked drivers and identified as the causal variables. At the same time, organizational and environmental dimensions were identified as the highly ranked barriers and causal factors. The study highlights drivers and barriers to industry 4.0 adoption with the sustainability context in multi-tier manufacturing supply chain. The implications identify the need for developing ethical code and standards between the stakeholders in multi-tier manufacturing supply chain. Theoretical and practical implications are provided for the managers and policymakers
Article
Emerging economies, e.g. India, China and Brazil etc., face challenges to adopt food safety (FS) practices in their food supply chains. Considering food industry’s operations and processes, this study identifies 25 challenges to the FS initiatives involving the opinions of practitioners from six major Indian food producers and academic experts. The challenges are grouped into five categories, viz. organisational, government and policy, global, knowledge and financial. We identify the best and worst challenges to the FS initiatives along with causality among them using combined Best Worst Method (BWM) and ‘Decision Making Trial and Evaluation Laboratory’ (DEMATEL) approaches. BWM prioritises these challenges, while DEMATEL identifies causal relationship maps for the prioritised challenges. The BWM results demonstrate that the government and policy related challenges are the key challenges followed by the organisational, global, knowledge and financial related challenges. The DEMATEL results exhibit the organisational, government and policy, and global related challenges as the cause group challenges. The knowledge and financial related challenges represent the effect group challenges. Mitigation of these challenges inherently necessitates stakeholders’ involvement in the food supply chains. We identify constructs for food safety initiatives policy in the emerging economies to raise public awareness while encouraging greater collaboration and efficiency in food supply chains to help achieve the second Sustainable Development Goal (SDG) for securing food for everyone. The results of the study offer guidance and deeper insights to supply chain managers about synergy requirements between the government policymakers and key players of the industry in the emerging economies.
Article
This study examines critical success factors (CSFs) for the implementation of green supply chain management (GSCM) for the electronics industry of an emerging economy. Based on a literature review, a total of 22 CSFs for GSCM implementation were selected. Sixteen of the 22 CSFs were finalised using Pareto analysis, based on the feedback of 30 experts from three renowned consumer electronics manufacturing firms in Bangladesh. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was then utilised to capture the interactive relationships among the CSFs. Findings reveal that the CSFs for GSCM implementation are top management commitment, government regulations and standards, pollution prevention and hazardous waste management, and environment management certification (ISO 14000). Findings also show that top management commitment is paramount to GSCM implementation, followed by pollution prevention and hazardous waste management. This study helps industrial managers make strategic and tactical decisions to implement GSCM practices in the electronics industry. ARTICLE HISTORY