Pregunta 21 ¿Cómo califica al manejo de quejas y sugerencias de las salas de cine a las que acude?

There is a need for decision makers to adopt some risk mitigation strategies in order to reduce any adverse impacts. The risk mitigation procedure represents the method of dealing with unexpected hazardous events. The literature in SCRM has provided extensive researches in assisting decision making for analysing and mitigating various types of supply chain risks like Multiple Criteria/attribute Decision Making, Bayesian Theory, System Dynamics (SD), Data Envelopment Analysis and Structural Equation Modelling (SEM), etc. However, there are some drawbacks in each of these method. For example, by employing Bayesian theory, a large amount of data is required in order to generate stable results; Data Envelopment Analysis focuses on measuring organizational performance in respect of the inputs; in order to apply Artificial Neural Networks, Genetic Algorithms, and Simulation-based Methods, high computer language design skills and extensive quantitative data are usually required.

Among the mentioned MCDM methods, TOPSIS is a practical and advantageous technique for ranking and choosing the best alternatives. TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) is based on the suggestion that the selected option should be closer to the positive ideal exposition and far from the negative ideal exposition when addressing complex issues. The most preferred alternatives should have the shortest distance from the positive ideal solution and the longest distance from the negative ideal solution (Hwang and Yoon 1981). As indicated by Abidin et al., (2016), the method sensibly represent the cogent option with consideration of both the best and the worst-case scenario of the alternatives in a simultaneous way, which is highlighted by a scalar value. The capability for TOPSIS to be effective in dealing with various weight estimation systems makes it to be a scalable method for risk mitigation strategies evaluation. However, the limitation of TOPSIS is in its inability to handle the vagueness and imprecision inherent in the cognitive process of

140

mapping the perceptions of decision makers which also leads to its combination with fuzzy set theory (Krohling and Campanharo, 2011). Thus, it is affirmed by Sodhi and TV (2012) that Fuzzy TOPSIS is effective in systematic and objective evaluation of multiple criteria’s alternatives. The technique can be used in evaluating different alternatives at the same time and against the identified criteria. Just as the TOPSIS method is conducted, an optimal value is arrived at in Fuzzy TOPSIS by identifying and selecting an alternative that is closer to the Fuzzy Positive Ideal Solution (FPIS) and far from the Fuzzy Negative Ideal Solution (FNIS). The FPIS and FNIS are best and worst performance values respectively. Fuzzy TOPSIS has successfully been applied to solve different types of MCDA issues, such as supply chain risk management strategies evaluation and mitigation (Nazam et al., 2015; Chatterjee and Kar 2016; Wang et al., 2017), supply chain risk modelling (Samvedi et al., 2013; Wang and Hao 2016), supplier selection and evaluation (Chen et al., 2006; Sevkli et al., 2008; Zouggari and Benyoucef, 2012), evaluation of the banks’ performance (Seçme et al., 2009), location selection for the ITU Faculty of Management (Suder and Kahraman, 2015), logistics provider selection and evaluation (Kannan et al., 2009, Selçuk 2009) and rank the solutions of knowledge management adoption in SC (Patil and kant, 2014).

As presented in Figure 4.4, the following steps of Fuzzy TOPSIS are given:

 Conduct the empirical studies for identified the implemented risk management strategies.

 Choose the appropriate linguistic ratings values for alternatives with respect to criteria.

 Aggregate the weight of criteria to get the aggregated fuzzy weight of criterion.

 Construct the fuzzy decision matrix and the normalized fuzzy decision matrix.

 Construct the weighted normalized fuzzy decision matrix.

 Determine the fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS) and calculate the distance of each alternative from FPIS and FNSI, respectively.

 Calculate the closeness coefficient of each alternative.

 According to the closeness coefficient, the ranking order of all alternatives can be determined.

141 4.4 CONCLUSION

This chapter develops a conceptual framework in the healthcare supply chain sector as a research platform. It takes considerations of risk drivers, risk sources, decision makers, risk management process, SC strategies as well as performance outcomes. The proposed framework can be built as a guidance to address the industrial needs for practical decision support methodology. Based on the framework, an integrated healthcare supply chain risk management model is proposed to support effective risk factors identification, assessment as well as sensible decision-making on the adoption of supply chain risk mitigation strategies. With the increasing emphasis on risk management across different industries, many approaches including both quantitative and quanlitative methods have been suggested in the literature. However, neglecting the call to integrate modern risk management models has a negative impact on the whole procedure as most of the risks behind the failures experienced in the supply chain of healthcare organizations are complex in nature. There are a lot of complex systems and procedures involved in facilitating the supply chain and the application of the suggested risk management framework offers a dynamic approach to dealing with the causes of such turbulences. It enables us to take explicit account of multiple types of risk in the analysis systematically and to compare and prioritise current alternative mitigation strategies based on the experts’ professional experience and knowledge both from academic and industrial fields. The proposed model has the capacity of reflecting the internal hierarchical nature of a healthcare organization’s extensive systems, and this allows a deeper analysis of complicated systems as they are linked to a supply chain. Integrating these risk management methods allows the facilitation of trade-off analysis, particularly among different subsystems and the general system. Finally, the method adds realism to the overall risk management process by identifying any form of disruptions in a supply chain and determining useful measures that can be numerable for the efficiency of a healthcare organization. The application of the proposed integrated risk management model is followed in the next chapters to identify risk factors, assessment and reduce the associated risks in the healthcare supply chain.

142