Editorial Board

A Refereed Monthly International Journal of Management

Fuzzy Model for Risks Assessment in a Healthcare Supply Chain

Author

Vinaytosh Mishra Assistant Professor Quantitative Techniques and Operations Management Area FORE School of Management New Delhi, India

Abstract

Diabetes is a major health challenge in South East Asia. The disease management require wide spectrum of medicine including oral medicine, insulin and antibiotics; which makes the supply chain complex. The medicine supply chain faces diverse risk in geographically diverse country like India. The disruption due to these risks can be costly; and hospitals aim to minimise these risks. For effective management of these risks, hospitals need a comprehensive method to quantify all of their significant supply chain risks. We worked with a speciality hospital in diabetes-care to map their supply chain for a few representative medicines. The disruption risks accounted in the study are due to supplier bankruptcy, natural disasters and geopolitical events. The study develops, a fuzzy model for risk assessment for the diabetes clinic. The quantification of value- at- risk of a hospital in its supply chain gave them a clearer picture of where hospital needs to focus risk management efforts.

Keywords: Diabetes drugs, Supply Chain Risk, Supply Chain Disruption, Value-at-Risk

Introduction

A healthcare supply chain is fragmented and complex in developing countries like India. The complexity of healthcare supply chain lies in fact that people’s health requires adequate and accurate medical supply according to the patient’s needs (Beier, 1995). Managing healthcare supply chain for hospital involves managing suppliers and ensuring drugs and equipment delivered in time. In this process physical goods and information about product and services go through number of independent stakeholders, including manufacturers, distributors, wholesalers, retailers of hospital pharmacies. The supply accounts for 25-30 percent of operational costs for hospitals (Roark, 2005). The disruption in supply chain can have adverse effect on cost of medicine for the patients. The hospitals are generally opened in urban and densely populated areas, while the medicine manufacturing companies are located in remote areas (Gangolli et al, 2005). The reason behind this phenomenon is low cost infrastructure and benefit. In developing countries like India, there is long lead-time to transport the medicine to hospital and intermediaries have less robust infrastructure to protect against and recover from natural disaster. The drug supply chain is exposed to increased geopolitical risks from regional conflicts, criminal violence, and corruption. The hospitals should be able to quantify and visualize the risk in their supply chain in order to effectively manage it (Heckmann et al, 2015).The researchers across globe have made significant efforts in the past to quantify the value-at-risk in supply chains due to the effects of natural disasters (Network, R.R., 2011).

2.1 Literature Review

The increase in communication and transportation technologies has helped supply chain eliminate excess inventory and adapt a just-in-time supply chain strategy (Presutti, 2003). However, there is a trade-off between eliminating excess inventory and the cost of disruptive risks (Kleindorfer & Saad, 2005). For effective management of inventory, a hospital need not to minimise cost of inventory but also the value-at-risk. Value –at-risk, a term borrowed from financial engineering, is a useful construct in estimating the economic implications of supply chain risks and in implementing the best strategies for supply chain risk management (McCormack et al, 2008). Supply Chain disruption is caused by various sources, such as geopolitical, financial, environmental, and other accidental events, and rate of increase in disruptions is increasing day-by-day (Myerson, 2016). The risk management company Aon reports increasing level of supply chain disruption due to geopolitical risk in recent years (AON, 2017). There has been a marked increase in both the frequency and economic losses from natural and man-made catastrophes (Stecke & Kumar, 2009). Despite well documented advantage of supply chain management practices in risk and cost reduction, healthcare industry has been slow in embracing these practices (McKone Sweet et al, 2005). There is no study focused on measuring the value-at-risk for healthcare supply chain for a diabetes clinic from risks such as the risk of natural disasters, geopolitical events, and suppliers' financial instability.

Diabetes Care Industry

Diabetes is one of the major chronic diseases in India; as such a large healthcare and treatment industry has built up around it. According to IDF report, there were 69.1 million cases of diabetes in India in 2015. This chronic disease develops over time and develops complications like neuropathy, retinopathy, diabetic foot, nephropathy and cardiac risk (Mishra et al, 2017). The comorbidity involved in the disease makes prescription complex and dynamic (Hodgson& Cohen, 1999). Thus demand for the drugs can’t be forecasted with accuracy hence inventory holding doesn’t mitigate the risk effectively. More over medicines require special stoking infrastructure and they come up with an expiry date. The diabetes care supply chain includes oral drugs, diagnostic items, insulin and equipment necessary for living with diabetes. The hospital pharmacy receives supply from various players of the supply chain (Fig 1). This helps them in mitigating supply chain risk and reducing the cost but this switching between supplier results in less commitment and increased lead time (Richardson, 1993).

2.2 Risk Identification

Supply chain risk is variation in the distribution of possible supply chain outcomes, their likelihood and their subjective values (March & Sapria, 1987). Zsidisin defines supply chain risk that correlates the occurrence of an incident with the inability of a supply chain to deal with that (Zsidisin, 2008). For assess and mitigate the risk decision makers need to quantify the risk. Standard deviation, mean-variance approach, value-at-risk are few measures have been used in quantifying the risk. There is lack of customised measures for quantifying the risk in supply chain hence measures used in financial engineering are applied in supply chain management also (Heckmann et al,2015). The widely used measures to assess supply chain risk like variance is under question because they consider lesser disruption than expected as also risk for the supply chain (Cox LA, 2012).Supply chain risk can be measured in term of disruption in supply chain at manufacturer, carry and forwarding (CNF) agent, distributor, wholesaler, retailer or during transit between any one of these. These risks can be dichotomised in to internal and external factors. The following figure (Fig 2) attempts to categorise risks in these two categories (Zsidisin, 2008). Figure 2: Internal and External Risk Factors to the Supply Chain

This paper deals with the internal and external risk associated with a diabetes care supply chain management. In addition to the commonly identified risks in literature the study also include less obvious risk associated with healthcare supply chain, particularly supply chain pertaining to diabetes care.

2.3 Risk Impact

Ample research has done on assessing the impact of the supply chain disruption. It is critical for the organizations working in area of healthcare to manage the risk proactively otherwise it can cost human life. The events like Gujarat riots of 2002,Jat Reservation Agitation of 2016 or Jharkhand Rail Jam of 2016 can put supply chain stand still. The threat of terrorism and naxalism attacks, is affecting not only the ‘physical’ supply chain, but also the organizational dimension of the company, and it has forced them to completely rethink about whole corporate strategy (Sheffi, 2003). In July 2005, a brutal assault on striking workers in Gurgaon’s plant of Honda Scooter and Motorcycle India (HSMI) disrupted the supply chain for many days. In March 2007, the West Bengal state of India, witnessed ugly tussle between government and agitators over compensation issue. The policy led to an insurgency in the region, followed by the death of 14 people by the Police Forces. This incident forced Tata Motors to shift their manufacturing unit to Gujarat, which resulted in delay of production of much hyped Nano Model of Tata Motors. The drug manufacturing sites in India are prone to heavy snow fall in winters and landslides during rainy seasons. The researchers (Xia & Liu, 2014) have measured the value –at –risk due to natural disaster; they also discuss strategy to create an economically resilient supply chain. There has been very little research in healthcare, which combines the risk from natural disasters, geopolitical events, and supplier’s financial stabilities.

2.4 Risk Models

There are many measurement models for risk modelling but these models are limited to the specific are of the risk. The table below (Table 1) lists the various models and their area of applications.

2.5 Risk Mapping

A risk profile attempts to find out vulnerable are of supply chain where management need to focus its attention (Christopher, 2016). Supply Chain Risk=Probability of disruption*Impact To identify the risk profile a hospital need to undertake an audit of risks pertaining to supply chain (Table 2). Table 2: Risk profile of Healthcare Supply Chain in India

The risk profile is directly and indirectly impacts the strategic decision of the organization. The risk profile of a healthcare organization can force it to increase the number of supplier or change the supplier. Figure 3 summarises the connection between the five sources of risk in healthcare supply chain.

Rather than including all possible risk of supply chain, a healthcare organization need to understand its processes in order to pin point most critical threats. The six steps in developing the risk profile are 1) Prioritising the drivers 2)Identify critical infrastructure 3) Locate vulnerabilities 4)Model scenarios 5) Develop responses and 6) Monitor the risk environment (Mitroff & Alpaslan,2003). The mapping is a useful tool for visualise the supply chain risk. The visual depiction of risk forces us to notice the unexpected risk in the healthcare supply chain (Tukey, 1997). This study proposes a method for calculation for risk vector for the node of clinic under study.

2.6 Risk Mitigation

Unmitigated risk can put not only the value-at-risk in healthcare supply chain but can also cost life. A Risk Rating Matrix approach can be used by risk audit team to mitigate the risk in the network (Fike, 2005). Most of the researchers have used mitigating risks based on quantifying the value of specific methods like Return-On-Asset (Huang & Liu,2014) and visualisation of supply chain risk (Xia & Liu,2014). Another research reports that risk should be measured and mitigated not only at a single node but across whole supply chain (Buscher & Poyato, 2015). The clinic under study is concerned with maintaining very high level of fill rate even if disruption occurs. The clinic is involved in a business where stock out can put life at risk. Presently the company mitigate the risk using over stocking. To add much to their chagrin, the patient’s behaviour is very unpredictable. Based on past experience, the patient response in case of stock out can range anywhere between total lost-sales to total back-order. This paper uses identifying, quantifying, and visualizing diabetes speciality clinic supply chain risks an approach very similar to Xia and Liu (2014).

3. Research Objective

The objective of this research is to develop an approach to quantify the combination of conventional and unconventional risks in diabetes clinic’s supply chain. The research further tries to calculate the value at risk for the diabetes clinic using a case study and discusses the measures to mitigate it.

4. Research Methodology

Diabetes is group of metabolic disease and results in comorbidity as the disease advances (ADA, 2010). A speciality diabetes clinic manages large number of stock keeping units (SKU). This study selected anti- diabetic, anti-hypertensive, insulin and lipid lowering drugs for the study as they are most critical for the diabetes care supply chain. The drug supply chain in India involves more than hundred different risk categories. The diabetes clinic under study helped us in selecting most significant source of supply chain disruption from their perspective: natural disasters, geopolitical events, and supplier financial instability. After deciding upon the type of disruption the study selects criteria for quantifying those risks. The attribute for the risk assessment in supply chain include geopolitical risk, natural disaster risk and risk due to financial bankruptcy of the supplier. A focus group of eight members, containing hospital management and managers of pharmaceutical supply chain was created to select criteria for the three attribute suggested by the literature. The focus group was asked to select three most important criteria for each of the attributes. The disruption at each location is assumed to be independent of each other.

4.1 Fuzzy Model for Risk Assessment

Assumen nodes of supply chain connecting the manufacturer to the diabetes clinic to be assessed and ranked make up a set:

X={x_(1 ,) x_(1 ,)……..,x_(n ) }(1)

Suppose there are l attributes at the second stage and the k-th attribute is related to m-criteria at the first stage. For k-th attribute, the features of its m criteriafor n node at first stage may be expressed as a feature matrix of the criteria.

Where is the feature of the i-th criteria of the j-th node for the k-th attributes at the first stage, and i=1,2,….m; j=1,2,….,n; k=1,2,….,l The process of risk assessment and ranking for the nodes in the supply chain follow properties of fuzziness and relativity. The risk at any node is a fuzzy concept and can be expressed as membership grade, while risk at any node is assumed to be independent of the risk on other node. The risks in supply chain propagate downstream with a multiplier, which may have a multiplicative or damping effect. But to figure out the most critical node we need to assess the independent risk of each node.

The purpose of risk assessment of among the nodes is to determine the relative membership grade of each node such that

Based on equation 17 and 19 and the principal of maximum membership grade, not only the order of the risk of potential source but also the order of the risk of disruption at n supply chain node can be calculated and ranked comprehensively

4.2 Calculation of Weight

The fuzzy ranking method require the weights of criteria and attributes and at first and second stage respectively. The weight of the feature and attributes are calculated using Rank Reciprocal Weights (Sullivan,1989). If is rank position of the attribute i ( With 1 being highest rank) and l attributes ,then the weight for each attribute may be calculated as:

5. Case Study

Based on input of experts in focus group ten nodes were selected for the risk analysis. The name of the firms and drugs was masked to comply with the non-disclosure agreement with the partnerorganization. The experts were asked to rate the ten nodes on nine criteria on scale of 1 to 5(where 5 is maximum risk exposure). The risk score for a node is average of the score given by the eight experts. The details of the nodes selected on basis of expert opinion is mentioned in Table 3 below:

The Table 4 list the value of the weights and risk score for the attributes and criteria for the supply chain under discussion.

The risk assessment and ranking of risk begins at first stage. The order of the ten nodes is assessed and ranked for the riskiness. Based on three criteria of the geopolitical risk the membership grade matrix can be calculated using equation (4).

From equation (20) N4, N3, N5 and N6 are the riskiest out of all order in the given order. The value-at-risk is highest for the riskiest node and need measures for management of the risk.

Conclusion

The insulin supply chain feature twice in top four risky node list. Hence we need to take measures to mitigate the risk in insulin supply chain on priority basis. The drugs from all three categories feature in the top fours of risk nodes. Hence we can infer that the risk at a node is not dependent on category of the drug. The diabetes self- management equipment supply chain has moderate risk exposer. The diabetes clinic under study can mitigate the risk by either searching for substitute for the risky nodes or stock the inventory before the disruption occurs. The diabetes clinic under study can forecast the disruption and can take the preventive measures for the risky nodes.

Acknowledgement

The Infrastructural support provided by FORE School of Management, New Delhi is greatly appreciated.

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