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Integrating risk management in
implementing circular economy
principles in the healthcare sector:
a case study from Indonesia
Kartika Nur Alfina
School of Business and Management, Bandung Institute of Technology,
Bandung, Indonesia and Department of Mechanical and Structural Engineering
and Material Science, University of Stavanger, Stavanger, Norway
R.M. Chandima Ratnayake
Department of Mechanical and Structural Engineering and Material Science,
University of Stavanger, Stavanger, Norway, and
Dermawan Wibisono,Nur Budi Mulyono and Mursyid Basri
School of Business and Management, Bandung Institute of Technology,
Bandung, Indonesia
Abstract
Purpose –The purpose of this study is to explore the integration of risk management and circular economy
(CE) principles within the healthcare sector to promote sustainability and resilience. Specifically, the study
aims to demonstrate how risk management can support the transition to a circular economy in healthcare
supply chains. By integrating risk management practices with CE principles, healthcare organizations can
identify potential risks and opportunities associated with circular initiatives.
Design/methodology/approach –This study adopts a qualitative research approach, using a case study
methodology with semi-structured interviews conducted at primary care facilities to understand the
© Kartika Nur Alfina, R.M. Chandima Ratnayake, Dermawan Wibisono, Nur Budi Mulyono and
Mursyid Basri. Published by Emerald Publishing Limited. This article is published under the Creative
Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create
derivative works of this article (for both commercial and non-commercial purposes), subject to full
attribution to the original publication and authors. The full terms of this licence may be seen at http://
creativecommons.org/licences/by/4.0/legalcode
The authors would like to thank the interview respondents who participated in this study for their
time and significant contributions.
Declarations.
Funding: This study was funded by the Norwegian Program for Capacity Development supported this
research through the Higher Education and Research for Development (NORHED II) Project ID 68085
initiative for the project, “Enhancing Lean Practices in Supply Chains: Digitalization,”under the sub-theme of
“Politics and Economic Governance.”This project is a collaboration involving the University of Stavanger
(Norway), the Bandung Institute of Technology (Indonesia) and the University of Moratuwa (Sri Lanka).
Data availability: Not applicable.
Informed consent Informed consent was obtained from all individual participants included in the study.
Conflict of interests: The corresponding author has received research grants from NORHED II. On
behalf of all authors, the corresponding author states that there is no conflict of interest.
Journal of
Responsible
Production and
Consumption
Received22 March 2024
Revised 21 June 2024
23 July 2024
Accepted5 August 2024
Journal of Responsible Production
and Consumption
Emerald Publishing Limited
2977-0114
DOI 10.1108/JRPC-03-2024-0014
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2977-0114.htm
application of CE principles in practice. The study uses fuzzy logic methods to assess and mitigate risks
associated with strategies promoting CE principles. Additionally, key performance indicators are identified to
evaluate the effectiveness and enhance the resilience of these strategies within healthcare supply chains.
Findings –The study highlights the critical role of robust risk management strategies in facilitating the
transition to a circular economy within healthcare organizations. Primary care facilities, which are
critical to frontline healthcare delivery, are particularly vulnerable to product shortages due to supply
risks. This study focuses on critical protective equipment, specifically latex gloves and assesses
operational risks, including supply, demand and environmental risks, using a fuzzy logic-based model.
Import delays were found to be a moderate risk, typically occurring once a year. The research highlights
critical KPIs for a successful CE transition within healthcare supply chains, such as on-time delivery and
service quality, which are directly related to the risk of supply chain disruption. In addition, the study
highlights the significant impact of other CE strategies on healthcare supply chains, including localized
production and manufacturing, innovation in product development, reverse logistics, closed-loop supply
chains and the adoption of lean principles.
Practical implications –This study provides valuable insights for healthcare organizations to optimize
resource efficiency, reduce waste and promote circularity in their operations. By implementing the proposed
solutions and focusing on the identified KPIs, organizations can develop strategies to achieve sustainability
goals and enhance resilience in healthcare supply chains.
Originality/value –This study contributes to the literature by demonstrating the application of risk
management in facilitating the transition to a circular economy in the healthcare sector. The use of fuzzy logic
methodology offers a novel approach to assessing and mitigating risks associated with critical product failures
in supply chain activities. The study’sfindings provide practical guidance for healthcare organizations seeking
to integrate circular economyprinciples and improve sustainability performance.
Keywords Circular economy, Fuzzy-logic based model, Healthcare supply chain, Performance,
Resilience, Risk assessment
Paper type Research paper
1. Introduction
Building resilience for future sustainable growth is complex and has become a primary concern
for leaders worldwide, becoming the main agenda at the World Economic Forum consortium
(WEF, 2023). Strengthening resilience beyond a survival capacity to enable long-term,
sustainable and inclusive growth is vital. Climate change, the COVID-19 pandemic, the
Ukraine–Russia war and economic uncertainty have driven organizations to acknowledge that
disruption needs to be accounted for within the new normal (Chhimwal et al.,2021). Disruption
occurs in many industries and is inseparable from the healthcare sector. Supply chain
vulnerabilities are inevitably faced across all industries, from pharmaceutical and consumer
goods manufacturers to the electronics and automotive sectors (Bailey et al.,2019). There is no
formula for mitigating all these disruption risks since the lack of historical data precludes the use
of predictive statistical tools for such mitigation. However, some organizations deal with the
prospect and manifestation of quantifiable risk far better than others. Resilience is a key
component for dealing with risk.
Forward-thinking in the supply chain presents a once-in-a-generation opportunity to
future-proof supply chains (Henrich et al.,2022). Here, there are three new priorities
alongside the traditional objectives of cost/capital, quality and service, the first of which is
resilience, which refers to addressing the challenges to overcome the disruptions that have
become a widespread topic of conversation. The second priority is agility, which entails
equipping organizations to meet the rapidly evolving and increasingly volatile consumer
needs. The third priority, sustainability, recognizes the critical role of supply chains in
transitioning to a clean and socially just economy. The future supply chain framework is
shown in Figure 1.
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1.1 Practical implications background
One of the requirements of supply chain resilience is risk management (Singh et al.,2019).
The discipline of risk management has become essential across various sectors: from nuclear
to supply chain to healthcare (Verbano and Venturini, 2011). The COVID-19 pandemic
exposed significant vulnerabilities in supply chains, including as shortages and production
shutdowns, emphasizing the importance of robust supply chain risk management processes
(El Baz and Ruel, 2021). Risk management is a proactive method to identifying, assessing
and mitigating these risks. Practices like supplier diversification, robust inventory
management and effective communication are crucial for building resilience (USAID,
2013). As a matter of fact, the latest version of ISO 9001 and ISO 14001 explicitly require
organization integration risk management in the business practice: in ISO 9001, version
2015, preventive actions were replaced by the concept of “risk-based thinking”, a systematic
risk evaluation (de Oliveira et al., 2017). Despite the recognition of the importance of risk
management in improving supply chain resilience, there remains a notable research gap in
the application of CE principles to healthcare supply chains. Addressing this gap is critical as
the healthcare sector increasingly faces sustainability challenges and regulatory pressures
related to environmental impact and resource efficiency.
Traditional risk management often relies on probability and statistical approaches.
However, many real-world scenarios involve imprecise, subjective or incomplete data.
Fuzzy logic-based risk management models address this gap. Existing literature frequently
highlights the use of fuzzy logic-based approaches to translate human cognitive processes
into a format that computers can process (AlAlawin et al., 2022;Moreno-Cabezali and
Fernandez-Crehuet, 2020;Samarakoon and Ratnayake, 2020;Tanak C oşkun and Yılmaz
Yalçıner, 2021). Fuzzy set theory is widely used in expert systems due to its simplicity and
alignment with human reasoning processes. These systems offer unique advantages by
integrating expertise from different fields, thereby reducing consultation costs, minimizing
variability and ensuring rapid responses (Ratnayake, 2014). In the healthcare sector, fuzzy
logic-based methods have been applied in areas such as alert systems, decision-making and
risk assessment, with the potential to improve the performance of healthcare workers by
Figure 1. Future supply chain framework
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simulating human reasoning processes in complex situations (Al-Dmour et al., 2019;Barach
et al.,2012;Gürsel, 2016). Despite these applications, there remains a research gap
regarding the comprehensive integration of fuzzy logic-based risk management models into
various aspects of healthcare operations, including supply chain activities. Addressing this
gap is critical to realizing the potential benefits of fuzzy logic in improving healthcare
outcomes and operational resilience.
Risk management can also be a powerful tool to support the transition to a circular
economy in healthcare (Gaustad et al., 2018). The CE model has gained considerable
worldwide attention across numerous industries as a better option than the dominant linear
economic model (Genovese et al., 2017). By applying risk management principles to areas
such as implementing robust risk assessment processes for reusable products to identify
potential hazards associated with hazardous substances, healthcare organizations can
promote sustainability and reduce reliance on virgin materials, while proactively addressing
challenges and ensuring a more robust and environmentally friendly supply chain (Bodar
et al.,2018). The CE approach includes waste management activities aligned with the
sustainable development program devised by the UN (2022), which prioritizes public health,
environmental concern, resource value and economic development (Sharma et al., 2021). CE
principles aim to minimize waste and maximize resource use (Ellen MacArthur Foundation,
2013). KPIs help quantify progress toward these goals (Dolatabad et al., 2022). KPIs might
involve tracking metrics like waste reduction rates, product lifespans or the percentage of
recycled materials used. By monitoring these KPIs, organizations can assess the
effectiveness of their circular initiatives and identify areas for improvement (Howard et al.,
2018). However, there exists a research gap in developing specific KPIs tailored to
effectively measure and optimize circular economy initiatives within healthcare supply
chains, highlighting the need for further research and refinement in this area.
1.2 Research gap and contribution
This study focuses on the resilience of the healthcare industry and its supply chains. In
healthcare, smooth supply chain operations are crucial as disruptions can directly impact
patient care (Bradaschia and Pereira, 2015). This research addresses gaps identified in
previous studies, such as (Guzzo et al., 2020), which emphasize the importance of detailed
case studies, systematic impact analyses, knowledge sharing across industries and a global
perspective to enhance risk management practices during the transition to a circular economy
in the medical device industry and beyond. Similarly, Kazançoğlu et al. (2021) highlight the
need to integrate risk assessment tools within a big data-enabled framework to better identify
and assess risks associated with the circular economy transition. Healthcare organizations
can use risk assessment approaches to identify potential risks and establish effective
mitigation strategies proactively. Currently, there is a notable lack of studies integrating risk
management with circular economy principles in the healthcare sector, revealing a
significant gap in existing research. Additionally, while KPIs are critical for measuring
progress toward a circular economy across various industries, there is a deficiency in
research establishing specific KPIs tailored to the healthcare sector to support supply chain
resilience performance. Furthermore, there is an identified need to translate expert
knowledge into actionable risk mitigation strategies, suggesting the application of fuzzy
logic in risk assessment could enhance sustainable resilience performance within a circular
economy framework for healthcare supply chains. Considering the research gaps identified,
this study proposes several research questions to further explore the integration of risk
management with circular economy principles in the healthcare sector:
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RQ1. How can healthcare organizations leverage human expertise and intelligence to
assess and manage risks associated with the transition to a circular economy within
their supply chains, ensuring the success of circular transition projects?
RQ2. How can fuzzy logic be effectively applied in risk assessment methodologies
within healthcare supply chains to enhance sustainable resilience performance
within a circular economy framework?
RQ3. What are the most relevant and effective strategies and KPIs for measuring
progress towards circular economy goals in healthcare supply chains, and how can
these KPIs be optimized to support sustainable practices?
This research is critical in contributing to improving the resilience performance of the
healthcare supply chain by integrating circular economy principles into risk assessment and
resilience strategies. The research aims to achieve several key objectives. First, it aims to
explore how healthcare organizations can use human expertise and intelligence to assess and
manage risks associated with the transition to a circular economy within their supply chains,
thereby ensuring the success of circular transition projects. Second, the study aims to develop
and validate the application of fuzzy logic in risk assessment methodologies within
healthcare supply chains to improve sustainable resilience performance within a circular
economy framework. This objective focuses on establishing and testing the effectiveness of
fuzzy logic as a tool for nuanced and adaptive risk assessment in the context of healthcare
supply chains transitioning to circular economy practices. Third, the research aims to
identify and optimize the most relevant and effective strategies and KPIs for measuring
progress toward circular economy goals in healthcare supply chains, thereby supporting
sustainable practices. This includes identifying the key strategies and KPIs that are critical
for tracking and promoting circularity in healthcare supply chains and developing ways to
improve these KPIs for better sustainability outcomes. Through these objectives, the study
aims to provide practical and actionable insights to improve risk management, resilience and
sustainability in healthcare supply chains by integrating circular economy principles.
2. Literature review
2.1 Integrating risk management and circular economy in healthcare sector
Sustainable development, as defined by (United Nations, 1987), means meeting the needs of
the present without compromising the ability of future generations to meet their own needs
and encompasses economic, environmental and social dimensions. In healthcare,
sustainability must consider these multifaceted variables due to the inherent complexity of
the system. Studies (Barbero and Pallaro, 2017) advocate analyzing the interactions between
care seekers, providers and their context, while (Mehra and Sharma, 2021)defines
sustainable healthcare as a multidisciplinary field that aims for operational efficiency,
profitability, patient satisfaction and affordability. Considering the climate change debate,
there is an urgent need to address the environmental impact of healthcare. A study from Daú
et al. (2019) highlights greenhouse gas emissions from healthcare, particularly from landfills
and emphasizes the importance of recycling. Furthermore, healthcare contributes more than
5% of global greenhouse gas emissions, equivalent to 514 coal-fired power plants (Karliner
et al., 2019). These findings highlight the need for sustainable practices in healthcare. In this
study, sustainable healthcare is defined as providing healthcare services without
compromising future generations, achieved by minimizing emissions and maximizing
materials at their highest value.
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A key approach to achieving sustainable healthcare is the adoption of circular economy (CE)
principles. Defined by Ellen MacArthur Foundation (2013), CE aims to maintain products,
components and materials at their highest utility and value throughout their lifetime,
distinguishing between technical and biological cycles to ensure appropriate recycling or
composting. Implementing CE in the healthcare sector offers benefits such as resource efficiency,
waste reduction, cost savings, reduced environmental impact and improved supply chain
resilience. As shown in Figure 2, CE strategies incorporate the “R”strategies required to
Figure 2. The 9R framework strategies of CE
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transform a linear economy into a CE and are ordered from R0 to R9 based on their priority level
in the transition from linear economy to CE (Potting et al.,2017). R0 denotes the most similar
state to circularity in CE, while R9 denotes the most similar state to the linear economy (Sitadewi
et al.,2021). Adopting the recycling strategy (R8) indicates that the system is primarily governed
by a linear economy while implementing circular reduction strategies (R
2
) implies moving closer
to the CE model. These practices align with broader sustainability goals and position the
healthcare industry as a responsible and forward-thinking contributor to environmental and
societal well-being.
A recent study by Daú et al. (2019) illustrates the transition of the healthcare supply chain to a
circular economy, highlighting the positive impact of corporate social responsibility (CSR)
programs in healthcare institutions on renewable resources. CSR links the social role of
healthcare facilities with sustainable practices and the adoption of smart technologies. The
importance of life cycle assessment (LCA) as a tool for greening supply chains and healthcare
delivery has been highlighted in the healthcare sector (Voudrias, 2018). Previous studies on waste
management (Benson et al., 2021;Chauhan et al., 2021;van Straten et al., 2021) have adopted
CE approaches to address healthcare waste generation, focusing on materials such as plastic,
stainless steel and medical waste. Waste management is central to the circular economy
framework and is guided by the EU waste hierarchy, which prioritizes prevention, reuse,
recycling, energy recovery and safe disposal (Voudrias, 2018). Globally, approximately 15% of
healthcare waste consists of hazardous materials such as infectious, toxic or radioactive
substances, which require effective management to mitigate risks (Khan et al., 2019;Worl d
Health Organization, 2018). Healthcare waste includes both hazardous and non-hazardous types
generated from medical procedures and diagnostics, with dental practices contributing significant
amounts of infectious clinical waste, amalgam and chemicals (Muhamedagic et al., 2009). Proper
disposal requires safe packaging and labeling for the handling of infectious clinical waste
containing microorganisms or toxins (Martin et al., 2021b). Healthcare professionals, including
dentists, play a critical role in minimizing waste generation, ensuring proper disposal practices
and participating in recycling efforts such as metal recovery (Duane et al., 2019).
The impact of improper healthcare waste management on supply chain risk and resilience
needs to be explored, and research today should focus on developing risk assessment
frameworks to evaluate the operational, environmental and regulatory risks associated with
healthcare waste within supply chains. This includes assessing the potential disruption to
supply chain operations, the environmental impact and compliance with regulatory
standards. Addressing these gaps can significantly improve the resilience and sustainability
of healthcare supply chains while mitigating the risks associated with inappropriate waste
management practices.
In simple terms, risk is defined as probability of failure times the consequence of that
failure (impact of loss) (Schlegel and Trent, 2015). The demand for risk-based thinking is
formally implied by the International Organization for Standardization’s standard for quality
management systems (ISO 9001:2015). Indeed, risk-based thinking is essential to ensuring a
quality management system (International Organization for Standardization, 2015). The
public health supply chain’s sources of disruption and dysfunction can be identified and
reduced through the formal process of risk management (USAID, 2013). Risk management
can raise the likelihood of meeting objectives, reducing costs and improving the overall
efficiency of operations. Risk is classified into four categories: hazard, financial, operational
and strategic (Schlegel and Trent, 2015). The hazard category refers to property damage
caused by fire, as well as personal injury, theft, liability claims or other crimes. The financial
risks relate to price, liquidity, credit, inflation, purchasing power and hedging/basis risk,
while the operational risks pertain to business operations (product development, supply
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chain management, human resources, etc.) and information/business reporting (e.g.
budgeting and planning, accounting information and investment evaluation). Finally, the
strategic risks relate to reputational damage, competition, customer demands, technological
innovation, capital availability and regulatory and political trends.
Supply chain risk management involves the collaboration of all partners in the supply
chain to develop a collaborative risk management process to manage the risks and
uncertainties associated with logistics activities and resource allocation (Tang, 2006). A
recent study on ISO 31000:2009 risk assessment tools and techniques aimed to present the
integration of procedures for supply chain risk management (de Oliveira et al., 2017). As
noted by (Schlegel and Trent, 2015), the four risk pillars of supply chain risk management
are supply, demand, process and environmental risks, which are described in more detail
below:
•Supply risk includes intrinsic risks caused by supplier failure to deliver on time, as
well as quality failure, financial failure, compliance failure, channel complexity and
communication failure.
•Process risk involves disruptions caused by quality issues, stock shortages, late
deliveries, capacity constraints, equipment failures, IT outages, poor overall
execution and the misalignment of strategy and metrics.
•Demand risk includes inherent disruptions caused by distribution issues, competitor
actions, product reputation, brand management, social media/trending, logistics and
customer sentiment.
•Environmental risks include natural disasters, geopolitical and energy risks, port
security, security of logistics and facilities, currency volatility, global economy, war,
pandemic and civil unrest.
Both risk and resilience analyses are essential to every organization and are applicable to
various different circumstances (Brusset and Teller, 2017;El Baz and Ruel, 2021;Ivanov
and Dolgui, 2021;Park et al., 2013). In contrast to risk-based thinking, which demands an
analysis of forecasting, monitoring and creating mitigating action, resilience-based thinking
requires:
•continuous attention;
•recognition of incompleteness; and
•a departure from traditional design practices (Fernando and Sigera, 2021;Park et al.,
2013).
More specifically, resilience demands continuous management, embracing incompleteness
and embracing a new form of design thinking. Advanced solutions for industry 4.0 regarding
pre-disruption action, the resilience demands of the stress-testing design of supply chains and
scenario planning were investigated by Henrich et al. (2022) and Ivanov and Dolgui (2021).
The requirements for resilience are commonly divided into pre-disruption, during-disruption
and post-disruption strategies. Each stage entails different needs for resilience to help the
company survive. The requirements for supply chain resilience are summarized in Table 1,
with the healthcare industry selected as the main sector application.
Despite the established framework for risk management, there is a notable research gap in
the context of healthcare supply chains, particularly regarding the integration of risk
management and CE principles. The CE approach, which focuses on minimizing waste and
maximizing resource efficiency, has significant potential to improve the sustainability and
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Table 1. Requirements for supply chain resilience based on previous research
Supply chain
resilience
requirements Description Authors
Flexibility Refers to easily adjusting production levels, raw-material purchases and
transport capacity
Aronsson et al. (2011),Brusset and Teller (2017),Gunasekaran
et al. (2015),Riccardo et al. (2021),Scholten and Schilder
(2015)
Collaboration Coordination with internal management and external actors to create
sustainable optimization flow through the supply chain to meet demand and
ensure on-time, in-full delivery efficiency
Gunasekaran et al. (2015),Karl et al. (2018),Scholten and
Schilder (2015),Singh et al. (2019)
Redundancy The ability to withstand any type of failure at any point in the primary supply
chain using backup resources through holding extra inventory, maintaining
low-capacity utilization, using multiple suppliers, etc
Ivanov and Dolgui (2021),Karl et al. (2018),Riccardo et al.
(2021),Singh et al. (2019)
Visibility The ability to track individual components, Sub-assemblies and finished
products as they move from supplier to manufacturer to consumer
Dubey et al. (2018),Ivanov and Dolgui (2021),Karl et al.
(2018),Singh et al. (2019)
Agility A company's capacity to swiftly modify its strategy, particularly in terms of
procurement, inventory control, and delivery, to accommodate the needs of a
rapidly shifting supply chain
Dubey et al. (2018),Karl et al. (2018),Singh et al. (2019),
Schlegel and Trent (2015)
Adaptability The capacity to modify each supply network to consider the changes,
as well as the ability to adapt a supply chain's design to accommodate
structural changes, disruptions and shifting consumer behavior
Dubey et al. (2018),Karl et al. (2018),Singh et al. (2019)
Culture change The process of encouraging employees to behave and think according to the
values and goals of the company
Singh et al. (2019)
Technology used
for information
sharing
Supply chain technology facilitates the analysis of data, the generation of
insights (e.g. customer requirements, transport and storage constraints and
supplier lead times) and the making of decisions that have a direct or indirect
impact on the overall performance of the supply chain
Brusset and Teller (2017),Schlegel and Trent (2015)
Risk management The process of identifying, assessing and mitigating the risks of an
organization’s supply chain. Implementing supply chain risk management
strategies can help to enhance operation efficiency, reduce costs and improve
customer services
Gunasekaran et al. (2015),Ivanov and Dolgui (2021),Karl
et al. (2018),Scholten and Schilder (2015),Singh et al. (2019)
(continued)
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Table 1. Continued
Supply chain
resilience
requirements Description Authors
Robustness The supply chain's ability to resist change and its proactive expectation of
advancement before it occurs
Dubey et al. (2018),Karl et al. (2018),Singh et al. (2019)
Sustainability Characterized as utilizing resources capable of mitigating current problems
while ignoring resources that should be reserved for future generations
Singh et al. (2019)
Awareness Comprehension of supply chain vulnerabilities and making appropriate
corresponding arrangements; requires the capacity to perceive a potential
disturbance by detecting and translating events using early warning systems
Dubey et al. (2018),Karl et al. (2018),Singh et al. (2019)
Security Building security protects the supply chain against counterfeiting (e.g. cyber-
security and freight security)
Dubey et al. (2018),Karl et al. (2018),Singh et al. (2019)
Supply chain
design
To ensure that a supply chain is resilient, there must be an appropriate
understanding of supply chain network design
Singh et al. (2019)
Public–private
partnerships
Public–private partnerships help the supply chain post-disruption through
interpersonal relations and social capabilities
Singh et al. (2019)
Source: Created by author
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resilience of healthcare supply chains. However, the integration of CE principles into risk
management strategies within healthcare supply chains remains to be further addressed.
Furthermore, there is a need to explore how risk management can ensure the success of
circular supply chain projects in the healthcare sector. Effective risk management is required
to identify and minimize the risks associated with the circular economy transition, including
regulatory compliance and operational disruptions. Understanding these risks and
establishing management measures can make a significant difference to the success of
circular economy transformation projects. Addressing this research gap is critical to
increasing the resilience and sustainability of healthcare supply chains, which will lead to
more efficient and environmentally friendly operations.
2.2 Application of the fuzzy logic-based model in risk assessment
Fuzzy set theory is widely used in expert systems because of its simplicity and its alignment
with human reasoning processes. These systems integrate expertise from different domains,
reducing consultation costs, minimizing variability and ensuring rapid responses. They
address challenges such as the high cost of human expert consultations, the migration of
experts between organizations and the absence of experts during critical assessments. By
developing a robust, incrementally growing knowledge base, expert systems can remain
current and effective. Fuzzy logic enhances these systems by qualitatively representing
expressions such as “very low”or “very high”using symbolic statements that are more
natural and intuitive than mathematical equations (Ratnayake, 2016).
Fuzzy logic is a multivalued logic that allows mathematical uncertainty and vagueness to
be represented while also providing appropriate tools for its treatment (Moreno-Cabezali and
Fernandez-Crehuet, 2020). Fuzzy logic is the mapping of an input space to an output space.
The primary mechanism for accomplishing this is a set of “if–then”statements known as
rules (Mathworks, 2014). This study created a fuzzy logic-based model to estimate the risk
based on the likelihood of failure ranges of mean-time-to-arrival (MTTA) and the severity of
the impact on healthcare services. This model was built using the MATLAB Fuzzy Logic
Toolbox, which was designed to analyze, design and simulate fuzzy logic-based systems.
The fuzzy logic in MATLAB works with fuzzy sets, essentially an extension of a classical
set. If Xindicates the discourse universe, and its constituent parts are indicated by x, then a
fuzzy set Ain Xdenotes a set of ordered pairs (Mathworks, 2014). Equation (1) describes
how a fuzzy set is an extension of a classical set:
Afx;μAx
ðÞ
jx∈Xg(1)
where μA(x) is the membership function (or MF) of in A. The membership function assigns
each element of Xto a membership value between 0 and 1.
The first step in developing a risk assessment system is the selection of a fuzzy inference
system. Mamdani and Sugeno are two inference systems included in the MATLAB Fuzzy
Logic Toolbox, and the former was selected for this study. In fact, Mamdani is more
commonly used since it produces reasonable results with a relatively simple structure and
since the rule base is intuitive and interpretable (Moreno-Cabezali and Fernandez-Crehuet,
2020;Zheng et al., 2022). Mamdani fuzzy inference is a method for constructing a control
system that combines a collection of language control rules from experienced human
operators (Mathworks, 2014). Mamdani systems are ideal for expert system applications
where the rules are derived from human expert knowledge, such as medical diagnostics,
because they have more intuitive and understandable rule bases. The risk assessment system
(see Figure 3) is based on two input variables (likelihood of failures and the severity of the
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impact on the health service) and one output variable (risk measurement in relation to
healthcare supply chain performance).
The membership functions for each linguistic variable are defined in the second step of the
model design. A membership function is a curve that maps a fuzzy variable's value to determine
its membership degree between 0 and 1 (Samarakoon and Ratnayake, 2020). In the so-called
“fuzzification process,”membership functions are used to convert input (crisp values) into
fuzzy values. Linguistic terms commonly used to express fuzzy sets include “Ve r y Low ( V L ),”
“Low (L),”“Moderate (M),”“High (H),”and “Very High (VH),”as shown in Figure 4.
However, despite its potential, it is lacking in comprehensive research exploring the
application of fuzzy logic specifically within the healthcare sector for supply chain risk
assessment. This research aims to fill this gap by advancing the application of fuzzy logic in
healthcare supply chain risk assessment, ultimately promoting sustainable resilience within a
CE framework.
2.3 Non-financial healthcare supply chain resilience performance
The resilience of the supply chain is the ability of an organization to recover from a
significant disruption (Brusset and Teller, 2017), encompassing the capacity to absorb stress
and quickly return to normal performance levels in volatile environments. The ability to
absorb shocks, redesigning the global network, setting new parameters for supply chain
buffers, proactively managing suppliers, responding faster to disruptions and managing the
multi-enterprise supply chain are the six pillars of supply chain resilience (Michelman and
Sheffi, 2007). Risk management is integral to supply chain resilience, as many risks cannot
be predicted or avoided (Christopher and Peck, 2004;Hohenstein, 2015;Scholten and
Schilder, 2015). It helps reduce vulnerabilities through forecasting, monitoring and
mitigating risks.
Figure 3. Structure of the developed fuzzy logic-based model
Figure 4. Membership function based on the input and output variables
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The resilience of the healthcare supply chain refers to the ability to respond to disasters
and breakdowns while continuing to provide a full range of services to patients. Despite its
limited framework, a recent study by Pascarella et al. (2021) highlights critical variables
associated with healthcare supply chain resilience. Performance measurement, as defined by
Neely et al. (1997), quantifies the effectiveness and efficiency of actions, with effectiveness
being the degree to which customer expectations are met. A study on performance
measurement system (PMS) implementation in eye hospital organizations (Tibyan et al.,
2019) emphasizes the need for a suitable project management framework to manage
complex performance measures effectively. Healthcare organizations are exploring
transformational approaches to scale up, improve cost efficiency and innovate business
models (Berlin et al., 2019). A recent study indicates that the performance objectives of a
circular business model are centered on the triple bottom line, focusing on social,
environmental and economic outcomes (Vegter et al., 2020).
Organizations use KPIs to manage such processes and activities, be they local or global
(Karl et al., 2018). In general, KPIs are quantifiable (metric) aspects that reflect important
factors that organizations must monitor and manage to succeed. For certain, KPIs capable of
depicting an organization's current scenario and its supply chain should be established for
this purpose, assisting in the monitoring and evaluation of all processes (Neely et al., 1997).
As an example, “supplier delivery efficiency”is regarded as a risk-monitoring KPI since it
allows for monitoring and observing a drop in supplier performance and, as a result, a
possible disruption in the flow of goods (Gunasekaran et al., 2004). Analyzing the data
pertaining to these KPIs can help managers reduce the risk of supply disruptions (Chan,
2003).
However, previous research on KPIs has limitations in the complex healthcare
environment. To address these limitations and improve resilience, healthcare organizations
can consider using composite KPIs that include both timeliness and quality control metrics.
In addition, a shift to risk-based analysis is needed, as traditional financial KPIs may not fully
capture the complexity of healthcare supply chain risk and resilience. This study describes
several KPIs that relate to the non-financial performance of a healthcare supply chain, which
ultimately contributes to its ability to adapt and recover from disruptions:
•Capacity utilization quantifies the intensity with which a resource is used to produce
a good or service. Constrained processes, direct labor availability and critical
components/material availability are all factors to consider (Min, 2014).
•Stock/inventory level indicators refer to the importance of monitoring stock levels
from suppliers and customers to avoid or reduce the bullwhip effect (Chan, 2003).
Tracking stock levels is critical during disruptions to ensure that the available stock
can cover any urgent orders. To allow upstream and downstream visibility, supply
chain partners must be able to share information on organizational assets (e.g.
available stock) (Karl et al., 2018).
•Quality of delivered goods/services refers to quality control. Any failures from one
source can be identified, and actions can be then taken for reallocation to another
source (Chan, 2003;Karl et al., 2018).
•Order lead time is the summation of the order processing and healthcare service
delivery times. Maintaining the lead time will help enhance consumer satisfaction
(Min, 2014).
•Order fulfillment rate is the percentage of healthcare orders satisfied by available
healthcare providers or medical supplies and medicines at hand; a percentage of
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prescription drug orders delivered on time and in full without quality failures or
missing required documentation (Min, 2014).
•Delivery lead time refers to the on-time delivery of medical supplies and
pharmaceuticals, a measure of fulfilling patient demand by the designated deadline
(Min, 2014;Supply Chain Council, 2012).
•Forecast accuracy is calculated in terms of products for markets/distribution
channels in unit measurement. Demand forecasts calculate actual demand into
forecast accuracy ratios. Forecast accuracy is crucial to preventing waste of any
required services, drugs or medical supplies, with a higher accuracy reducing any
generated waste (Min, 2014).
•Service flexibility refers to how quickly a hospital’s capacity –including in terms of
available beds, medical doctors and nurses –can be adjusted to meet the changes in
patient demand (Min, 2014).
•Response time refers to patient waiting times, a summation of patient call response
time, emergency vehicle deployment time and hospital admission time (Min, 2014).
•Purchase requisition is the practice of issuing a purchase order for a number of
products required in the short to mid-term. Supplier lead times are taken into account
for such orders (Supply Chain Council, 2012).
•Supplier delivery efficiency is a measure of the supplier’s reliability in delivering
materials. Failures on the supply side may simultaneously result in a failure in
service delivery performance (Supply Chain Council, 2012).
•Supplier rejection rate refers to the percentage of products from the supplier
classified as “poor quality”or “out of standard.”Collaboration with suppliers can
reduce the number of issues and improve operational results (Karl et al., 2018).
•Supplier selection (with ISO 14000 certification) refers to the number of suppliers
that entirely meet the environmental agreement criteria or the percentage of
suppliers that have a validated Environmental Management System or ISO 14000
certification (Supply Chain Council, 2012).
•Consumer satisfaction relates to the perception of the extent to which products or
services supplied by a company meet or surpass customer expectations (Chan, 2003;
Karl et al., 2018).
•Damage return rate is the percentage of products from suppliers classified as
“damaged”and returned to the distributors or directly to the suppliers (Karl et al.,
2018).
3. Methodology
3.1 Research methodology for integration of risk management in the implementation of
circular economy in healthcare
This study advocates for integrating risk management practices with the implementation
of circular economy (CE) principles within the healthcare sector. The international
standard for quality management systems, ISO 9001:2015 (International Organization for
Standardization, 2015), mandates proactive risk identification and management, crucial
for circular transition projects in healthcare. Incorporating a robust risk management
framework addresses potential challenges, ensuring a smooth transition to a circular
healthcare system and maximizing the benefits of sustainable practices. Effective risk
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management enhances supply chain resilience by anticipating and mitigating disruptions,
ensuring resource flow and minimizing patient care impacts.
The study begins by establishing its context, focusing on risk management, circular
economy and their implementation in the healthcare sector, including supply chain and
service provider perspectives. The first stage involves a literature review to identify recent
relevant studies. The second stage involves data collection through qualitative research
methods, including case studies and semi-structured interviews. Data analysis follows the
basic steps of risk management according to ISO 31000 for supply chain risk management,
including risk identification, analysis, evaluation and mitigation (de Oliveira et al., 2017).
Finally, the results link CE principles with risk mitigation, offering improvement suggestions
and managerial implications for future sustainable healthcare. The research methodology
guidelines used in this study are illustrated in figure below (see Figure 5).
3.2 Qualitative research method
3.2.1 Justification the need for a qualitative study. A qualitative approach, as outlined by
Grose et al. (2016), was used to explore knowledge and attitudes. In this study, a qualitative
approach was used to explore expert justifications for adopting a circular economy
Figure 5. Research methodology flowchart
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framework within the healthcare supply chain. This included exploring risk justifications
aimed at mitigating the impact of critical product shortages, an aspect that could be more
extensively explored in the literature. The research method incorporates a qualitative
method, integrating a case study and semi-structured interviews. Case study-based research
presents one form of social science research. Meanwhile, as opposed to a straightforward
question-and-answer format, a semi-structured interview design with open-ended questions
allows for a comprehensive discussion with the interviewee(s) (Creswell and Poth, 2017;
Neuman, 2002). Here, the interviews were conducted with practitioners from primary care
facilities, including doctors, operational staff and logistic department managers, using a
semi-structured interview format. The single case study was carried out in a primary care
setting. It was discovered that CE awareness in the healthcare sector is still developing, and
the experts interviewed had limited knowledge of the concept. We can justify our choice of a
single case study since the data collection resources were limited. The permissibility of such
justification was advanced by (Yin, 2016). The health service was standardized and regulated
by the government as a required primary care facility service, meaning the interview results
will provide a generalized description of primary healthcare services.
3.2.2 Indonesia healthcare system. The facility under consideration is located in
Indonesia, Asia's second most populous country and the world's fourth largest. Indonesia's
population is characterized by great diversity in a number of dimensions, including
demographics, economics, social structures, politics and culture. With a population of 273.8
million, Indonesia has recently experienced a significant increase in infections, posing a
potential threat to an already fragile post-pandemic health system (OECD, 2020). As a lower
middle-income country, Indonesia comprises over 15,000 islands, 34 provinces and 416
districts, with 56% of the population living in urban areas (Adawiyah et al., 2022). Despite
its rapidly growing middle-income status, Indonesia faces distinct challenges related to
health systems and the goal of achieving universal health coverage (Agustina et al., 2019).
Indonesia's National Health Development Program is based on the concept of primary
healthcare, with community health centers as the basic health facility, complemented by
hospitals and other community-based health facilities. Between 1960 and 2001, Indonesia's
centralized health system made significant progress, with the medical infrastructure growing
from virtually no primary healthcare facility to 20,900 centers. The Ministry of Health
(MoH) oversees national health policy and manages program related to human resources,
education, training and health services (iPharmaCenter, 2023). The healthcare system in
Indonesia (see Figure 6) is divided into three levels:
•Primary healthcare: This level provides basic health services, including preventive
care, health education and treatment of minor illnesses and injuries. In general,
primary healthcare is provided through community health centers and private clinics.
•Secondary healthcare: This level provides more specific services, including surgery,
obstetrics and emergency care. District hospitals and private hospitals are the usual
providers of secondary healthcare.
•Tertiary healthcare: This level provides highly specified services such as organ
transplants and cancer treatment. Referral hospitals and specialized private hospitals
are the main providers of tertiary care.
3.2.2.1 Justification unit of analysis. This study focuses on healthcare service providers,
specifically primary care facilities, which are the most critical part of healthcare services. In
Indonesia, primary care facilities, known as Puskesmas, serve as the first point of contact for
health services under the jurisdiction of district health offices. Puskesmas provide initial
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health consultations, assessments, medications, health information, promotion advice and
referrals to secondary and tertiary services. Despite their importance, access to primary care
in remote, underdeveloped and border areas is challenged by poor road conditions
(Soewondo et al., 2019). Providing primary care in these rural areas involves complex
logistical issues such as transport disruptions and navigating difficult terrain, resulting in
longer average arrival times for medical supplies. Therefore, managing risks effectively at
this level is imperative, justifying the selection of primary care facilities as the focus of this
research.
Primary care facilities provide preventive, promotive and curative care at the sub-
district level, focusing on community and individual health. They deliver seven
essential services: health promotion, communicable disease control, outpatient care,
maternal and child health with family planning, community nutrition, dental care and
pharmacy support. The global expansion of the medical and dental sectors and the
increased use of disposable products have significantly increased medical and dental
waste. Managing dental waste is complex, given the diverse materials and instruments
used, including cotton, plastic, latex, glass and other materials, many contaminated with
body fluids (Antoniadou et al., 2021;Grose et al., 2016;Muhamedagic et al., 2009).
Despite a growing focus on infection control and quality in dental practices, the
environmental impact, particularly in primary dental care, is poorly understood and
rarely studied. This study focuses on dental care due to the significant risks of infectious
disease contamination and the substantial waste generated by disposable and single-use
products during oral treatments.
3.3 Case study
To explore the challenges and opportunities related to the implementation of CE in the
healthcare sector, semi-structured interviews were conducted with practitioners from
primary care facilities, including doctors, operational staff and logistics managers. These
interviews focused on understanding expert justifications for adopting CE principles, with a
particular emphasis on mitigating risks associated with critical product shortages. The
interviews reinforced the importance of proactive risk management, aligning with
established best practices that recommend risk assessments and business continuity planning
Figure 6. Indonesia’s healthcare systems hierarchical level
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for building supply chain resilience. This aligns with the need to identify potential
disruptions and anticipate challenges to ensure a baseline level of supply, especially for
critical products, during disruptions.
3.3.1 Prioritization, critical product selection, mean time to arrival. To effectively
improve the resilience of their supply chains, healthcare organizations must first identify
potential sources of disruption and anticipate the challenges that may arise during a crisis.
Prioritizing supply chain redundancy for high-value customers enables organizations to
maintain baseline levels of supply during crises, while enhancing the customer experience.
This approach not only ensures a premium level of service by reducing lead times in regular
business operations (Christopher and Peck, 2004),butisparticularlyrelevantinhealthcare,
where a “premium customer experience”denotes an elevated standard of care provided to
individuals with private health insurance, supplemental coverage or those willing to pay out-
of-pocket for certain healthcare services (Soewondo et al.,2019). This concept involves
providing exceptional care, personalized attention and additional services to meet patients’
specific needs and preferences. Organizations can achieve greater reliability, improved
customer experience and effective redundancy by prioritizing regional expansions and
establishing back-up or buffer medication stocks specifically for high-value customers. The
goal of prioritization is to identify critical dependencies for serving premium customers by
evaluating each segment of regional operations based on specific factors, such as minimizing
customer and revenue impact in the event of a disruption. As a result, the creation of a buffer
stock of critical products is essential during a crisis.
3.3.1.1 Justification of critical product: gloves. Several critical products in dentistry
serve specific purposes in infection control, patient care and dental procedures,
including gloves, saliva ejectors, dental amalgam and masks (Martin et al., 2021a).
Gloves, along with masks and gowns, are essential components of personal protective
equipment (PPE) and play a key role in improving infection control and safety (Kumar,
2015). Unlike masks and gowns, gloves provide a direct and specific barrier to potential
contaminants, offering a comprehensive approach to protecting both dental
professionals and patients. Common glove materials include latex, nitrile and vinyl,
with latex gloves being the most widely used due to their high tactile sensitivity,
flexibility, comfort and effective barrier properties against bacteria and viruses. Saliva
ejectors are essential for infection control, contributing to a clean and dry oral
environment and facilitating efficient dental procedures (Martin et al., 2021b). These
flexible tubes with disposable tips are used to remove saliva, blood and other fluids,
maintaining a dry field and enhancing procedural efficiency. Dental amalgam is a
mixture of metals including mercury, silver, tin and copper used to fill cavities
(Muhamedagic et al., 2009). Although effective, dental amalgam raises environmental
and health concerns due to its mercury content, leading to a shift toward alternatives like
composite resins for their aesthetic and environmental benefits. Each of these products
has an important role to play in ensuring the safety of patients, preventing infection and
providing effective dental care. According to the World Health Organization (WHO,
2022), standard precautions for reducing the transmission of pathogens in healthcare
settings, including dental services, focus on hand hygiene, PPE and respiratory hygiene.
PPE in dental care includes masks, gloves, gowns and headgear, essential for infection
control. Among these, gloves are the most frequently used and disposable, contributing
significantly to waste generation due to their high volume. Saliva ejectors are typically
disposable due to challenges in sterilization, while materials like dental amalgam, lead
and silver, though used in larger quantities, are less frequently disposed of, posing
environmental disposal challenges.
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This study focuses primarily on latex gloves due to their extensive use in dental
procedures. On average, 400 pairs of latex gloves are used monthly in dental care,
emphasizing the importance of proper disposal to manage potentially infectious or hazardous
waste. The mean-time-to-arrival (MTTA) for these supplies, including customs clearance
and transportation, is approximately 30 days based on historical logistics data, underscoring
the logistical challenges in supply chain management for dental facilities.
3.4 Selection of potential critical risks
Potential risks associated with supply chain performance for resilience were selected using a
combination of essential supply chain risks management, as developed by Schlegel and Trent
(2015), and risk assessment pertaining to CE adoption (Dulia et al., 2021;Ethirajan et al.,
2021). A breakdown of the categories of risk management type and the operational risks in
supply chain risk management is shown in Figure 7.
Tab le 2 presents a detailed description of the risks, including the hazard, financial,
operational and strategic risks. The risk categories only apply to operational risks involving
supply, demand, process and environmental factors.
Figure 7. Breakdown of the potential critical supply chain risks in resilience performance
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Table 2. Risks pertaining to the CE in the healthcare supply chain
Type of risks Risk categories Potential risks Description
Hazard risks Cargo thefts Cargo theft is a challenging security issue
during shipment and transportation in the
supply chain, having a severe impact on
operational and financial status; the
compliance risk of an organization may
affect the future work of CE industries
Safety measures Violating the safety measures in
production operations affects overall
process effectiveness
Financial risks Investment risk Lack of upfront cost invested in CE,
insufficient source of funds and
potentially reduced profits affect the
organization’sfinancial status, leading to
layoffs and lockdowns, which may affect
other organizations in the circular network
Product price The higher price of environmentally
friendly materials and the high price
differences between recycled products
and virgin products potentially escalate
production costs
Controlled cash-flow In the CE and closed-loop supply chain, a
poor financial flow destroys profitability
and good supply chain coordination
Operational risks Supply risks Logistics risk Improper location selection of depots,
sorting waste stations and containers.
Inefficient collection routes
Capacity risk Improper selection of size, type and
capacity of the transport fleet
Material delay A CE reutilizes waste materials since
resources between industries cause the
delay (variation in the distribution time)
of one company resource, which will
affect the possible future outcomes and
operational processes of other companies
Import delay Potential issues from customs paperwork,
port strikes and labor issues
Material quality Lack of quality raw material procurement
minimizes the value of green production
and operational performance
Supplier performance Supplier performance is considered an
important area in CE, but due to multiple
risks, unreliable supplier performance and
financial losses also occur
Product performance Product performance regulates timeline
production, product quality and
profitability in the CE for any changes in
performance that lead to economic and
manufacturing risk
(continued)
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Table 2. Continued
Type of risks Risk categories Potential risks Description
Product service life A CE also should focus on quality
products. Low-quality products are
assumed to require more repairs,
renovations and upgrades; poor service
creates life and quality risks
Demand risks Customer satisfaction Successful organizations are primarily
based on customer satisfaction, but
customer satisfaction is based on the value
of cost spent over a quality product. If the
quality value decreases, the risk value
increases
Forecast error Seasonal problems, lead times, poor
information, poor systems, poor
communication, poor skills
Product quality In a CE, the quality of core products
determines the cost of remanufacturing
and remanufactured product quality
Market risks Lack of an appropriate mechanism for
take-back, the obstacles service providers
face to retain ownership of a sold product
in legal terms
Process risks Organizational risks Poor leadership and management toward
CE in the supply chain and a lack of
appropriate organizational structure to
implement a CE in the healthcare sector
CE framework risk Lacking successful business models and
frameworks for implementing a CE;
difficulties in making product
disassembly operation easier and safer
during the reverse chain; and
biodegradable resources in circular
supplies
Workers' coordination Deviation in the progressed work will
lower the expected value of specific
supply-chain performances and other
company performances
Environment risks Natural disaster Natural disasters cause internal/external
supply chain risks, including material
shortages and process and delivery delays
Currency risk Inflation and currency exchange rates
Social and cultural risks Lack of consumer knowledge about
reused/recycle components and
unsustainable cultural behavior toward
CE
Government policy Lack of industry incentives for greener
activities and an appropriate vision, i.e.
goals, objectives, targets and indicators
regarding CE adoption
(continued)
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3.5 Development and application of a fuzzy logic-based model to calculate the risk
The two input variables (likelihood of failures and severity of the impact on the health
service) and the output variable were investigated in this study (risk measurement in
relation to healthcare supply chain performance). The linguistic terms for the
variables, risk, occurrence and severity, as well as their corresponding triangular and
trapezoidal fuzzy numbers, are defined in Ta ble 3 –Table 5 below. The impact of the
disruption from the severity and occurrence failure is considered in the risk category
classification, which is divided into very high, high, moderate and low risk. The
linguistic number is based on existing regulations and expert knowledge, as shown in
Tab le 3.
The table above demonstrates how the trapezoidal fuzzy number is applied in extreme
conditions (lowest and highest). Triangular fuzzy numbers apply to the mid-range of chances
ranging from slight to moderate to very high. The fuzzy number is the same as the input
variable (i.e. failure and severity). The risk occurrence linguistic number is derived from the
failure likelihood based on MTTA ranges. Based on a specific period, the general
interpretation of risk occurrence is divided into rare, unlikely, possible, likely and almost
certain (Table 4 ).
Risk assessment is already a part of government regulation. However, predicting
the risk measurement associated with MTTA failure in supply chain activities has yet
to be implemented. The severity linguistic number is determined by considering the
impact of the patient's injury on the healthcare system (Table 5). The impact is
classified as “not harmful,”“slightly dangerous,”“moderately dangerous,”
“dangerous,”or “extremely dangerous.”The risk measure in the matrix is used to
customize the category. All potential failures in the healthcare supply chain are
assessed using the risk matrix.
Table 2. Continued
Type of risks Risk categories Potential risks Description
Strategic risks Technological risk Lack of technology transfers from the
inventor to a secondary user, the quality
degradation of recycled products and
insufficient information for tracking
materials or resources in CE
implementation
Effectivity Loss of effectivity damages the supply
chain flow, affecting the entire discrete
business activities: inbound/outbound
logistics, processes, service, disposal and
recycling
Vision statements A lack of effective business monitoring
and future analysis for CE processes
strongly influences operational,
innovation and market strategies
Marketing strategies The failure of marketing strategies
increases the supply chain's complexity
and affects a product's circularity
Source: Created by author
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Table 3. Linguistic terms for risk
Risk
description General interpretation Fuzzy no
Very high Causes extreme disruption and customer death, which requires immediate
review and action
15 20 25 30.6
High High disruption and serious injury, which require a detailed review and
urgent treatment
10 15 20
Moderate Moderate disruption and moderate injuries, which require significant
rework
51015
Low Low disruption and minor injuries, which require minor action −5.67 −0.038 5 10
Source: Created by author
Table 4. Linguistic terms for risk occurrence
Likelihood description General interpretation Fuzzy no.
Rare Once every 5 years or more [−2.25 −0.25 1 3]
Unlikely Once every 3 years [1 3 5]
Possible Once every 1–2 years [3 5 7]
Likely Once a year or often [5 7 9]
Almost certain Every week/month [7 9 10 12.2]
Source: Created by author
Table 5. Linguistic terms for severity
Severity description General interpretation Fuzzy no.
Not harmful The error does not cause injury and has no
impact on the system
[−2.25 −0.25 1 3]
Slightly dangerous The error does not cause injury, and the
customer is not aware of the problem, but it
has the potential to cause minor injury or
might have some effect on the system
[1 3 5]
Moderate dangerous The error causes very minor or no injury but
is perceived as annoying by the customer
and/or causes minor system problems that
can be resolved with minor modifications
[3 5 7]
Dangerous Faults that can cause minor to moderate
injury with high levels of customer
dissatisfaction and/or cause system crashes
that require major repairs or significant
rework
[5 7 9]
Extremely dangerous Errors that can cause serious/permanent
injury or death to customers or serious
disruptions to the system that can stop the
service with a preceding indication
[7 9 10 12.2]
Source: Created by author
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Use the MATLAB Fuzzy Logic Toolbox to simulate the fuzzy number. The first step
involves defining a membership function that takes the fuzzy number for the risk occurrence
and severity variables listed in the previous table as the input. As a result, the occurrence and
severity can be presented in graph form, as shown in Figure 8.
The second step involves developing a membership function that outputs the fuzzy
number for each of the risk variables listed in the previous table. As such, the risk can again
be presented in graph form, as shown in Figure 9.
3.5.1 Establishment of if–then rules. The if-then rules that form the basis of the fuzzy
inference process are created in the next step. When the values of the input variables
(likelihood of risk, risk occurrence and severity) are expressed using different linguistic
terms, these rules show the value of the output variable. These rules are simple to grasp
(Moreno-Cabezali and Fernandez-Crehuet, 2020;Samarakoon and Ratnayake, 2020). As
shown in Table 6, a total of 25 rules were established in this study to design the proposed
model.
3.5.2 Fuzzy inference process. The fuzzy inference process is the final step. This step
entails using fuzzy logic to create a mapping from the two input variables to the output
variable. This procedure is divided into the following five steps (Zheng et al., 2022):
(1) the fuzziness of input variables;
(2) for the antecedent, use the fuzzy operator (AND or OR). The fuzzy operator
“AND”was used in this study;
(3) the implication from the preceding to the following;
(4) combination of consequences across rules; and
(5) defuzzification.
The aggregate output fuzzy set is used as the input for the defuzzification process, and the
output is a single number (Mathworks, 2014). A fuzzy set's aggregate encompasses a range
of output values and should be defuzzified to produce a single output value from the set.
Here, the relevance of each risk analysis was calculated using a fuzzy logic-based model
developed specifically for this study and implemented in the MATLAB Fuzzy Logic
Toolbox.
4. Results and discussion
4.1 Risk assessment using the circular economy approach
To address the research question of how healthcare organizations can leverage human
expertise and intelligence to assess and manage risks associated with a CE transition in their
supply chains, this study investigates the application of Failure Modes, Effects and
Criticality Analysis (FMECA). The focus is on minimizing disruptions in the supply of
critical products, such as latex gloves, that could negatively impact patient care (details on
FMECA in Appendix). The critical product, latex gloves, was evaluated in terms of the
implications of failure in healthcare supply chain activities that affect health services. The
form was altered to fully reflect the research objectives. As a result, the supply chain risk
category, the impacts, the KPIs that relate to the potential risk, the mitigation of failure
modes (related to sustainable development with the CE approach), and suggestions for
improvement in circular capabilities could be organized.
The “low risk”category of risk measurement includes various supply chain risks,
including demand, supply, technological, environmental and process risks. The “demand
risks”stem from the possibility of failure due to forecast error and rising demand
(uncertainty demand) and are thus related to KPIs such as forecast accuracy, capacity
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Figure 8. Membership function for input variables (MATLAB result)
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utilization and warehouse stock level. The “supply risks”include potential product failure,
allergenic substances and transportation disruption, with damage return rate, supplier
delivery efficiency and selection the KPIs that are likely to be impacted. The “process risk”
refers to a potential failure caused by human error, the “environment risk”relates to political
risk and the “technological risk”relates to system failure. Order fulfillment rate, purchase
requisition orders, service flexibility and stock level are the KPIs related to the occurrence
and severity of the risk. Hazard risk and supply risk were included in the risk measurement as
a moderate risk category. The “hazard risk”is the possibility of gloves being ripped by the
failure of sharp instruments. A potential failure from import delay activity is a “supply risk”
that is rated as moderate. The import delay is a major concern because it will result in a
lengthy out-of-stock period as well as a bullwhip effect on the supplier or distributor. The
quality of the delivered service, capacity utilization, order and delivery lead time are the
KPIs that relate to the hazard and supply factors for moderate risk.
4.1.1 Fuzzy logic-based model results. To answer the research question of how fuzzy
logic can be effectively applied in risk assessment methodologies within healthcare
supply chains to promote sustainable resilience within a CE framework, this section
Figure 9. Membership function for output (MATLAB result)
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explores the role of defuzzification in the MATLAB Fuzzy Logic Toolbox. After the risk
assessment is completed, the occurrence and severity values are fed into the simulation
model. It's essential for the risk assessment and the fuzzy logic model to be aligned to
ensure accurate interpretation of these values. Defuzzification then transforms the fuzzy
outputs of risk occurrence and severity (represented by membership functions) into a
single, precise risk score. This final risk score is automatically generated based on the
relationship between the membership function calculations and the previously defined if-
then rules. As shown in Figure 10, the low-risk level result is derived from the occurrence
and severity inputs of 3 and 3. This value is simulated from a risk assessment for
transportation disruption failure that includes supply risk, with a risk rank result of 3.83
indicating a low risk. Defuzzification simulates the input from another supply risk caused
by import delay issues. The risk occurrence input variable is 7, and the severity input
variable is 3 (see Figure 11). The result would be a risk rank of 10, indicating a moderate
risk level. The fuzzy logic-based model could be used for any future risk assessment
arising from another PPE product that may be in short supply due to uncertain demand.
Other critical products in the healthcare sector that should have separate assessments for
each potential failure include drugs, vaccines and medical equipment. Risk management
may assist supply chain resilience in proactive ways. As a result, it is critical to have
documentation of the risk model for critical products.
Table 6. Fuzzy rule base
Rule no. Rule description
1 If (occurrence is rare) and (severity is not harmful) then (risk is low)
2 If (occurrence is rare) and (severity is slightly dangerous) then (risk is low)
3 If (occurrence is rare) and (severity is moderate dangerous) then (risk is low)
4 If (occurrence is rare) and (severity is dangerous) then (risk is moderate)
5 If (occurrence is rare) and (severity is very dangerous) then (risk is moderate)
6 If (occurrence is unlikely) and (severity is not harmful) then (risk is low)
7 If (occurrence is unlikely) and (severity is slightly dangerous) then (risk is low)
8 If (occurrence is unlikely) and (severity is moderate dangerous) then (risk is low)
9 If (occurrence is unlikely) and (severity is dangerous) then (risk is moderate)
10 If (occurrence is unlikely) and (severity is very dangerous) then (risk is moderate)
11 If (occurrence is possible) and (severity is not harmful) then (risk is low)
12 If (occurrence is possible) and (severity is slightly dangerous) then (risk is moderate)
13 If (occurrence is possible) and (severity is moderate dangerous) then (risk is moderate)
14 If (occurrence is possible) and (severity is dangerous) then (risk is high)
15 If (occurrence is possible) and (severity is very dangerous) then (risk is high)
16 If (occurrence is likely) and (severity is not harmful) then (risk is moderate)
17 If (occurrence is likely) and (severity is slightly dangerous) then (risk is moderate)
18 If (occurrence is likely) and (severity is moderate dangerous) then (risk is high)
19 If (occurrence is likely) and (severity is dangerous) then (risk is high)
20 If (occurrence is likely) and (severity is very dangerous) then (risk is very high)
21 If (occurrence is almost certain) and (severity is not harmful) then (risk is moderate)
22 If (occurrence is almost certain) and (severity is slightly dangerous) then (risk is moderate)
23 If (occurrence is almost certain) and (severity is moderate dangerous) then (risk is high)
24 If (occurrence is almost certain) and (severity is dangerous) then (risk is very high)
25 If (occurrence is almost certain) and (severity is very dangerous) then (risk is very high)
Source: Created by author
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On considering the supply performance and indicators, healthcare service providers or
any organization operating within healthcare supply chains can maintain and monitor the
potential KPIs that may affect operations if the potential risks occur, especially with the high
or very high mode of risk, using the risk mapping and fuzzy logic-based model. The KPIs
can also be improved, especially if we include a link to the 2030 Sustainable Development
Goals (UN, 2022). The CE approach is a good option for making continuous improvements
in terms of sustainability, with an emphasis on the economy, the environment and society
(World Health Organization, 2018). Starting with highlighting the critical indicators (KPIs)
that have a significant impact, such as supplier selection with ISO 14000 certification, it can
provide a sustainable medical supply chain and increase the circularity capabilities in the
health sector.
Finally, the risk profile generated using the membership function and the rule base in
MATLAB to evaluate supply chain risk management in the healthcare sector is depicted in
Figure 12. The surface viewer helped generate low, moderate, high and very high-risk factors
in the case study involving primary care facilities. The importation of equipment was a
significant issue at the start of the pandemic when there was an unprecedented global
increase in demand for medical protection equipment, including gloves, masks and surgical
gowns (Cascante-Sequeira et al., 2020). The indicators that relate to the import issue are “on-
time delivery”and “stock level”, and by monitoring these two indicators simultaneously, a
good resilience performance can be achieved while making sustainable efforts to diversify
suppliers that also promote the green movement. Thus, implementing circular economy
strategies in the healthcare supply chain will be crucial to mitigate the risk of critical
shortages of essential products.
4.1.2 Identify key performance indicators for supply chain resilience in the implementa-
tion of circular economy in healthcare The utilization of a fuzzy logic-based method in
conjunction with risk assessment facilitated the prediction of risk levels ranging from low to
Figure 10. Illustrative risk assessment result (low level-MATLAB result)
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very high. The final item on the agenda is the presentation of the Key Performance Indicators
(KPIs) associated with supply chain resilience requirements. Resilience is currently of great
importance and is a key focus of the global agenda. Thirteen elements define the
requirements for resilience, with potential variations in different industries. It has been
concluded that these 13 elements are sufficient to build and improve resilience, particularly
in the healthcare sector. Resilience, defined as the ability to recover after a setback (Brusset
and Teller, 2017), requires consideration of both the pre- and post-disruption phases.
Consequently, requirements can be categorized into pre-disruption, during disruption and
Figure 11. Illustrative risk assessment result (moderate level-MATLAB result)
Figure 12. Three-dimensional surface result (MATLAB result)
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post-disruption elements, which are closely linked to different indicators (KPIs). Strategic
insights from circular economy principles can inform stockpiling strategies for critical
medical supplies. Through careful inventory management and the adoption of circular
approaches, healthcare providers can more effectively manage product shortages. In
addition, circular economy principles, particularly collaborative networks, encourage
collaboration between healthcare providers, pharmaceutical companies and suppliers. This
collaborative sharing of resources, expertise and information can increase the resilience of
the healthcare supply chain during shortages. The unique findings of this study, which have
not been found in previous research, will help healthcare managers to maintain correlated
indicators and manage risk in the future. The results of the risk assessment, which links
indicators with mitigation suggestions through the circular economy approach, contribute to
building resilience in the performance of the healthcare supply chain. Non-financial KPIs for
the resilience performance of the healthcare supply chain through CE implementation are
illustrated in figure below (see Figure 13).
The proposed model clearly describes the relationship between resilience requirements
and their corresponding indicators. This model is a valuable tool for stakeholders and
managers in the healthcare supply chain, facilitating the identification of key indicators that
can improve resilience performance and contribute to sustainable development. By initiating
simulations to assess the impact of disruptions ranging from minor to major, such as a
pandemic, the results can be scrutinized to optimize performance and strengthen competitive
advantage through sustainability initiatives. Studies such as (Bradaschia and Pereira, 2015;
Spieske et al.,2022;Zamiela et al., 2022) have delved into strategies to strengthen supply
chain resilience during a pandemic. The findings confirm the applicability of resource
dependence theory in explaining organizational responses to pandemic disruptions.
To answer the research question of identifying the most relevant KPIs for measuring
progress toward CE goals in healthcare supply chains, and how to optimize these KPIs for
sustainable practices, this study analyses the effectiveness of various risk mitigation
Figure 13. Framework for non-financial KPIs resilience requirements through CE implementation
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strategies. The study finds that implementing bridging measures within the healthcare supply
base, such as providing procurement support to suppliers or leveraging ongoing buyer-
supplier relationships, is more effective in securing medical supplies than relying solely on
buffering measures. The combination of bridging and buffering, such as extended upstream
procurement or resource sharing between hospitals, can provide superior risk mitigation,
particularly where the capacity of the existing supplier base may prove inadequate. In pursuit
of sustainable goals, KPIs are aligned with the principles of the green movement, promoting
green procurement, establishing circular systems for glove recovery and recycling and
producing easily recyclable the critical product such as gloves while maintaining stringent
performance standards.In addition, the integration of environmentally friendlymaterials into
specific processes or treatments contributes to the overall sustainability effort. Finally, risk
management is now understood as a twofold endeavor aimed at meeting resilience
performance standards and achieving CE objectives.
Continuing with the third research question, the previous discussion highlighted the
effectiveness of aligning KPIs to meet resilience performance standards and achieve CE
goals. These findings provide the basis for advancing CE strategies within healthcare supply
chains. As we move toward a more sustainable healthcare system, the following sections
explore specific CE strategies that can be implemented to promote circularity within the
healthcare sector. These strategies aim not only to improve risk mitigation, but also to
promote environmental responsibility and resource conservation.
4.1.3 Diversification, localized production and manufacturing. The literature defines
the Circular Supply Chain (CSC) concept as “the configuration and coordination of supply
chains to close, narrow, slow down, intensify, and dematerialize resource loops”
(Geissdoerfer et al.,2018). This entails extending the traditional supply chain perspective to
a broader “supply chain network”or “industrial ecosystems,”fostering collaboration and
reconfiguration of supply chain networks to share low entropy wastes for potential use as
inputs in the production processes of other supply chains, as indicated by Herczeg et al.
(2018). The adoption of a circular supply chain may entail using different sources of
materials, reducing reliance on a single supplier, thereby mitigating risks associated with
supply chain disruptions or shortages from specific regions or suppliers (Gaustad et al.,
2018). Localized Production and Manufacturing are highlighted as key strategies in circular
supply chains, emphasizing the need for structural flexibility and reduced geographic
barriers, with Small and Medium-sized Enterprises (SMEs) and innovators within regional/
local loops playing crucial roles in implementation and helping mitigate product shortage
risks (De Angelis et al., 2018).
In the context of the healthcare sector, implementing regional manufacturing as a strategy
involves establishing localized manufacturing hubs for medical supplies and equipment,
thereby reducing dependence on a centralized supply chain. This approach shortens supply
chains, enhancing their resilience and responsiveness to fluctuations in demand.
Additionally, exploring local sourcing and production options helps reduce dependency on a
single source or region, enhancing supply chain resilience and mitigating risks associated
with geopolitical or logistical disruptions, thereby preventing critical product shortages.
Finally, emphasizing Sustainable Procurement is crucial in the scale and scope of a circular
supply chain. Procurement policies in both the private and public sectors of service
organizations serve as a significant lever for the transition, particularly when they surpass
minimum legal requirements to include CE principles.
4.1.4 Product development and innovation. In recent years, European Union (EU)
policymakers have increasingly urged society to move toward a circular economy, which
aims to eliminate waste through deliberate and design-led practices, where one industry's
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waste becomes another's raw material and vice versa (Antoniadou et al., 2021). The concept
of a circular economy, as defined by the Ellen MacArthur Foundation (EMF), envisions an
industrial economy that is restorative and regenerative, prioritizing the maintenance of
products, components and materials at their highest utility and value (Ellen MacArthur
Foundation, 2013). This approach distinguishes between technical and biological cycles and
is based on three principles:
(1) preserving and enhancing natural capital,
(2) optimizing resource yields, and
(3) promoting system efficiency (Ellen MacArthur Foundation, 2013).
Health service providers, including primary care facilities, have first-hand experience of the
demands, preferences and challenges of health care delivery. Their active involvement in the
new product development (NPD) process is critical, as they define needs, suggest areas for
improvement, and provide feedback on product prototypes. This collaboration ensures that
new solutions are designed to meet the specific needs of healthcare facilities and workflows.
The researchers emphasize that implementing circular economy principles throughout the
healthcare supply chain means incorporating CE principles throughout the product lifecycle,
from design to end-of-life management.
The advancement of CE strategies in the healthcare sector hinges on innovative product
development by design and technological core improvements, guided by the 9R framework
(Potting et al.,2017). This approach emphasizes refuse, reduce and re-think principles to
enhance product sustainability and circularity. Integrating supply chain planning with CE
involves developing reusable, recyclable or remanufactured products that reduce hazardous
waste generation and use biodegradable materials (de Vries and Huijsman, 2011).
Collaboration among suppliers, manufacturers and healthcare facilities is crucial to
implement these strategies, aligning profitability with sustainability goals (Ripanti and
Tjahjono, 2019). Key strategies include durable design to extend product lifespan, modular
design for component flexibility and reusable product design to reduce reliance on
disposables, supported by product-as-a-service models promoting long-term use and
maintenance over ownership.
4.1.5 Reverse logistics and closed-loop supply chain. A key strategy for preventing
critical product shortages is the implementation of reverse logistics systems (Bernon et al.,
2018;Julianelli et al., 2020). These systems facilitate the recovery of products at the end of
their lifecycle through processes such as remanufacturing, refurbishing or extracting
valuable components for reuse. By reducing the need for new production, these measures
help to mitigate supply chain risks. In the healthcare sector, a proactive strategy is to initiate
pharmaceutical take-back programs. Implementing pharmaceutical take-back programs
ensures the safe disposal and recycling of expired or unused medicines, thereby reducing
environmental impact and minimizing the need for continued production during shortages.
Establishing take-back programs to recover and recycle products at the end of their life cycle
is critical. This approach helps to extract value from used products and materials, ultimately
reducing the overall demand for new resources. In addition, establishing closed-loop systems
ensures continuous recycling and reintroduction of materials into the supply chain,
minimizing the need to mine new resources and maintaining a stable supply of materials (De
Angelis et al.,2018;Pisitsankkhakarn and Vassanadumrongdee, 2020;Ripanti and Tjahjono,
2019).
4.1.6 Adopt lean principles. Lean thinking, synonymous with increased
competitiveness, transforms waste into customer value through continuous improvement
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(Platchek and Kim, 2012). In healthcare, Lean Supply Chain Management (LSCM) drives
cost reductions and service quality improvements by optimizing processes and minimizing
resource consumption (Khorasani et al., 2020). Despite challenges like non-value-added
activities, lean principles effectively reduce waste and enhance resource efficiency.
Integrating lean principles into a circular healthcare supply chain can further reduce medical
waste through efficient inventory management and promote proper disposal and recycling of
materials, aligning with sustainability goals. This approach not only improves patient care
delivery but also minimizes environmental impact. Through a focus on waste elimination
and value maximization, lean principles contribute to a more efficient, patient-centered and
sustainable healthcare system.
4.2 Managerial implications
Applying risk management to improve resilience performance is critical across industries,
especially with the growing emphasis on sustainability. Integrating CE strategies can bring
significant benefits to both industries and society at large. In the healthcare sector, the focus
of risk management is on addressing concerns related to patient safety, medical errors,
regulatory compliance, infectious diseases and the overall resilience of the healthcare
system. This includes ensuring the delivery of quality patient care, maintaining patient
confidentiality and complying with healthcare regulations. Key considerations in healthcare
risk management include patient outcomes, regulatory compliance and the incorporation of
rapidly advancing medical technologies. The healthcare supply chain faces risks that can
affect the availability and accessibility of essential medical products and services.
Effectively managing these risks requires collaboration among stakeholders, strategic
planning and the implementation of resilient and adaptive supply chain practices, while
aligning with CE principles.
The healthcare sector's significant waste generation and resource dependence necessitate
a transition to CE model. While this shift offers environmental and economic benefits, it may
also present new challenges, such as disruptions to established supply chains, integration of
new technologies and potential cost implications. Risk management emerges as a powerful
tool to navigate this transition smoothly. Healthcare organizations can effectively address
potential challenges and maximize the benefits of sustainable practices by proactively
integrating risk management into the transition to a CE model. This integrated approach
offers several advantages:
•Anticipating and mitigating risks, helps identify potential challenges upfront,
allowing organizations to develop mitigation strategies and contingency plans. This
proactive approach minimizes disruptions and ensures a smoother transition.
•Ensuring compliance, risk management helps ensure that CE initiatives comply with
environmental and safety regulations, avoiding potential delays or setbacks.
•Maintaining quality, a robust risk management framework safeguards the quality of
care during the transition. This includes mitigating risks associated with potential
disruptions in product or material supply.
•Optimizing costs, by identifying and addressing potential cost pitfalls associated
with CE implementation, risk management helps organizations optimize resource
allocation and achieve cost efficiency throughout the transition.
•Gaining stakeholder support, by demonstrating a proactive approach to potential
challenges, risk management fosters trust and transparency, garnering support from
stakeholders such as staff, patients and regulatory bodies.
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Despite the significant benefits of risk management in the healthcare sector's transition to a
circular economy, some potential drawbacks/disadvantages need to be considered. First,
developing and implementing a comprehensive risk management plan requires time, effort
and expertise. These requirements can translate into significant costs, particularly for smaller
healthcare organizations with limited resources. Second, the relatively new nature of circular
economy in healthcare presents a challenge. As this approach is still evolving, certain risks
may be unforeseen or difficult to predict, potentially limiting the effectiveness of a pre-
established risk management plan.
Fuzzy logic offers a nuanced approach to risk management by incorporating degrees of
risk rather than strict categories, which is ideal for situations with imprecise or incomplete
data, such as circular economy transitions in healthcare. A fuzzy logic model involves
defining relevant risk factors (e.g. sourcing of recycled materials, product quality variation,
user acceptance), assigning fuzzy membership functions to these factors (e.g. the difficulty of
sourcing recycled materials might have a high membership if reliable suppliers are scarce)
and applying fuzzy rules to assess the overall risk. The benefits of the fuzzy logic approach
include its ability to better reflect reality by considering the uncertainties inherent in a
circular economy transition, thus providing a more realistic assessment of risk. It informs
decision-making by providing a nuanced risk profile that highlights areas requiring more
focus or resources to mitigate risk. In addition, the fuzzy model is adaptable and can be
updated as new information or experience emerges during the transition process. However,
developing and implementing a fuzzy logic model requires expertise and significant data
collection. Determining the appropriate fuzzy membership functions and rules can also be
subjective. Despite these challenges, the benefits of more comprehensive risk assessment
may be greater than these limitations.
Similar implications extend beyond healthcare to other industries, such as oil and gas.
Risk management in the oil and gas industry has focused primarily on managing the complex
technical and operational risks associated with the exploration, production, transportation
and processing of hydrocarbons. This includes mitigating risks related to environmental
hazards, equipment failure, geopolitical uncertainties and commodity price fluctuations. The
most prominent risk management method used in this sector is Structural Health Monitoring
(SHM), especially for offshore structures such as oil platforms and wind turbines. SHM
involves the use of various technologies to continuously assess structural integrity, detect
potential problems and provide timely information for maintenance or corrective action
(Ejlersen, 2022). To improve sustainability, the oil and gas industry should also promote CE
principles in conjunction with risk management. This study provides potential insight and
can be applied not only to the healthcare and oil and gas sectors, but also to diverse industries
such as manufacturing, construction and marine.
5. Conclusions and future developments
The study highlights the critical importance of applying risk management to improve
resilience performance and align with sustainability goals across industries. The integration
of CE strategies is highlighted as critical to preventing critical product shortages and
sustainable development. The use of a fuzzy logic-based methodology alongside risk
assessment offers a novel approach to predicting risk levels. KPIs associated with supply
chain resilience are highlighted, providing unique insights for healthcare managers in
maintaining correlated indicators and effective risk management. The study extends its
implications beyond healthcare to various industries, emphasizing the promotion of CE
principles in risk management. The convergence of risk management and CE objectives is
highlighted, emphasizing a dual effort to meet resilience standards and achieve CE
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objectives. The introduction of circular supply chains in the healthcare sector underlines the
importance of collaboration and reconfiguration to mitigate risk. CE strategies such as
localized production, sustainable procurement, reverse logistics systems and the use of lean
principles are advocated for building sustainable and resilient supply chains. This study
serves as a valuable tool for stakeholders and managers, providing insights into key
indicators to improve resilience and contribute to sustainable development. The holistic
integration of CE principles and risk management methodologies provides a comprehensive
approach to addressing challenges and optimizing performance.
The primary care level, the most critical stage in the frontline of healthcare service
providers, is vulnerable to product shortages due to supply risks. The average time to arrival
for primary care facilities is 30 days, with only one delivery per month in some cases. The
critical product adopted in this study was a form of protection equipment, specifically latex
gloves. The risk assessment of this product was primarily focused on operational risks,
which include supply risk, demand risk and environmental risk. Import delay failures that
result in long-term shortages were found to have a moderate risk level, with the most likely
event occurring once a year. The fuzzy logic-based model simulates a moderate rank level of
10 with a variable risk occurrence set to 7 and the severity set to 3. The model's output is
relevant to the risk matrix, which is used to represent the likelihood of MTTA failure and the
severity of the impact on the healthcare service.
The results of this research have significant implications for managerial aspects of the
circular economy transition in the healthcare sector. By implementing robust risk
management strategies, healthcare organizations will be able to identify and mitigate
potential risks that could inhibit the transition to a circular economy, ensuring the
uninterrupted delivery of healthcare services. Using a fuzzy logic-based risk translation
model simplifies the complexity of healthcare supply chains, encompassing suppliers,
manufacturers and logistics. Collaboration among stakeholders is crucial for minimizing
risks and facilitating a smooth transition. This involves selecting biodegradable materials,
identifying KPIs that enhance supply chain resilience and promoting awareness about
maintaining product value. Effective risk management is therefore critical for the success of
initiatives aimed at achieving a circular economy.
Future research can further explore the role of digitalization in automating risk
management for CE transitions. Artificial intelligence could improve supply chain analysis
for more reliable risk identification. In addition, research on supply chain resilience,
incorporating barrier management and promoting extended producer responsibility (EPR) in
processing, could contribute to a robust framework for sustainable healthcare performance.
These areas, and their potential application to other sectors, highlight the general application
of risk management to the successful adoption of the circular economy.
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Appendix
Corresponding author
Kartika Nur Alfina can be contacted at: kartika_nur@sbm-itb.ac.id
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Figure A1. Modification of the FMECA sheet
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