Integrating risk management in implementing circular economy principles in the healthcare sector: a case study from 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 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’s findings provide practical guidance for healthcare organizations seeking to integrate circular economy principles and improve sustainability performance.

Citation

Alfina, K.N., Ratnayake, R.M.C., Wibisono, D., Mulyono, N.B. and Basri, M. (2024), "Integrating risk management in implementing circular economy principles in the healthcare sector: a case study from Indonesia", Journal of Responsible Production and Consumption, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JRPC-03-2024-0014

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.

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 Coşkun 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 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:

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.

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 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 (R²) 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; World 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 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 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 X indicates the discourse universe, and its constituent parts are indicated by x, then a fuzzy set A in X denotes a set of ordered pairs (Mathworks, 2014). Equation (1) describes how a fuzzy set is an extension of a classical set:

(1) A{x,μA(x)|x∈X}

where μA(x) is the membership function (or MF) of in A. The membership function assigns each element of X to 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 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 “Very Low (VL),” “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.

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 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 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 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 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 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), but is particularly relevant in healthcare, 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.

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.

Table 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.

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 Table 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 Table 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.

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 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 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.

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 implementation 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 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 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 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 friendly materials 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 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 (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.

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 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.

Figures

Figure 1.

Future supply chain framework

Figure 2.

The 9R framework strategies of CE

Figure 3.

Structure of the developed fuzzy logic-based model

Figure 4.

Membership function based on the input and output variables

Figure 5.

Research methodology flowchart

Figure 6.

Indonesia’s healthcare systems hierarchical level

Figure 7.

Breakdown of the potential critical supply chain risks in resilience performance

Figure 8.

Membership function for input variables (MATLAB result)

Figure 9.

Membership function for output (MATLAB result)

Figure 10.

Illustrative risk assessment result (low level-MATLAB result)

Figure 11.

Illustrative risk assessment result (moderate level-MATLAB result)

Figure 12.

Three-dimensional surface result (MATLAB result)

Figure 13.

Framework for non-financial KPIs resilience requirements through CE implementation

Figure A1.

Modification of the FMECA sheet

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)
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)

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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’s financial 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
		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
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

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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	5 10 15
Low	Low disruption and minor injuries, which require minor action	−5.67 − 0.038 5 10

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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]

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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]

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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)

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