Decision modeling of risks in pharmaceutical supply chains

Moktadir, Md. Abdul; Ali, Syed Mithun; Mangla, Sachin Kumar; Sharmy, Tasnim Ahmed; Luthra, Sunil; Mishra, Nishikant; Garza‐Reyes, Jose Arturo

doi:10.1108/imds-10-2017-0465

Cited by 77 publications

(96 citation statements)

References 88 publications

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“…Therefore, as an accepted alternative of AHP, the FAHP can cover all these issues. This methodology has been vastly investigated in previous studies focusing on different area of knowledge [38]. So, in summary as there are many problems considering different criteria to make the final decision [17,27], MCDM approaches are supportive for concurrent consideration of all these criteria [16,48,54].…”

Section: Methodsmentioning

confidence: 99%

Application of fuzzy analytic hierarchy process (FAHP) in failure investigation of knowledge-based business plans

et al. 2019

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This research develops a fuzzy analytic hierarchy process (FAHP) to investigate and prioritize the failures of knowledgebased business plans. Although the concept of failure investigation has been discussed in previous studies, its integration with knowledge-based business plans is not investigated yet. To fill this gap, this research is completed in three main steps as follows. Firstly, the general failures of business are developed from the previous literature. Secondly, the developed failures are investigated with regard to their availability in knowledge-based business plans. Finally, a FAHP is applied to prioritize the failures of knowledge-based business plans. According to the obtained results, lack of managerial experience, lack of written business plans and ineffective advertising are the most important reason of failing knowledge-based business plans. As the major implications of this study, the developed measures, methodology and obtained results assist managers, researchers and investors to determine the sources of failures in knowledge-based business plans.

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Section: Methodsmentioning

confidence: 99%

Application of fuzzy analytic hierarchy process (FAHP) in failure investigation of knowledge-based business plans

et al. 2019

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“…At the risk analysis stage, data processing is carried out using the FMEA and Fuzzy-FMEA methods. The use of the FMEA method is only to determine the differences that occur before the data is processed using the fuzzy concept [11]. The results of data processing before and after are contained in Table 8.…”

Section: Risk Analysismentioning

confidence: 99%

Proposed Action of Supply Chain Risk Mitigation Air Compressor Type L Unloading ¼ HP Using The Fuzzy – FMEA and Fuzzy – AHP Method in PT. XYZ

Trenggonowati¹,

Bahauddin

Ridwan

et al. 2021

jit

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Established since the 80s, PT. XYZ is an air compressor manufacturing industry located in Tangerang, Indonesia. In the production process, the Air compressor type L Unloading 1/4 HP has a very complex supply chain network which makes the supply chain network activities experience obstacles in the form of risks or potential risks that can occur so that it can disrupt the smooth running of the supply chain activities of the air compressor type L. Unloading 1/4 HP. However, PT. XYZ has not maximally implemented risk mitigation actions in its supply chain. The purpose of this study is to propose a mitigation action against risk events that occur along the supply chain of water compressor type L Unloading 1/4 HP using the FFMEA (Fuzzy Failure Mode and Effect Analysis) and FAHP (Fuzzy Analytical Hierarchy Process) methods. Based on the research, there were 29 identified risk events with 5 priority risk events. And of the 5 priority risk events, each has 2 proposed mitigation actions per priority risk event

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“…Furthermore, as the complexity of the healthcare chain/network increases, the need of supply chain coordination grows. Moreover, the instability is greater as we regress more to upstream steps on the chain, generating situations that can result in the so-called "bullwhip" effect, which is widely referred to in the literature (Forrester, 1997;Frazee & Standridge, 2016;Geary et al, 2006;Moktadir et al, 2018;Sodhi & Tang, 2011;Wee et al, 2016).…”

Section: The Covid-19 Crisis From An Operations Management Perspectivementioning

confidence: 99%

Operations Management at the service of health care management: Example of a proposal for action research to plan and schedule health resources in scenarios derived from the COVID-19 outbreak

Marín-García

García-Sabater

Ruiz³

et al. 2020

JIEM

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Purpose: With this work, we intend to promote research on the application of Operations Management tools in order to assist with decision-making in health crisis situations. During the first six weeks of the COVID-19 crisis in Spain, we have contacted a large number of hospital and health department managers in the Valencian Community and other regions of Spain. The result is that very few, at least when contact was made and at the time of writing this article, had consulted staff members in the Operations Management area for advice on this situation, and they are quite reluctant to do so. This is in spite of the fact that some medical sources also consider this crisis to be one of resources, not merely a medical crisis. Our opinion is that Operations Management can make a useful and valuable contribution to anticipate and improve the management of scarce resources, even in times of crisis. If those responsible for public health or heads of hospitals do not see this usefulness, then there is a huge gap between research and practice in Operations Management and what is transmitted to the healthcare sector. Our aim is to help reduce this gap.Design/methodology/approach: In the first part of the article, we will justify that this crisis, besides being a medical crisis, is also a crisis in terms of resources. We will briefly review some of the Operations Management tools that we believe are appropriate for managing this crisis. We will focus on those that we will illustrate in the second part of the article as a practical example of the action research protocol (only the action planning phase), in which we are currently involved.Findings: Due to the peculiarities of the COVID-19 epidemic, it is reasonable to think that the evolution and duration of each outbreak, and the way it is dealt with, will be different in each hospital and/or each health department. Anticipating the number of patients who will require care and forecasting the consumption of resources over time is complex, due to the long and variable incubation times and the uncontrolled evolution of the treatments needed by each patient. Furthermore, since the evolution of new patient admissions is not homogeneous, the forecasting of requirements is difficult. Providing real-time support and detecting with a certain degree of anticipation the potential levels of demand for health care will enable hospitals to define what stages of the contingency plan to apply and how to (re)assign the available resources in the most efficient way possible.Research limitations/implications: Hardly any current data are available, not only publicly, but also through public health ministries and managers; those that are available are not very reliable and come from studies with non-representative samples. For this reason, all forecasts must be taken with a note of caution, and in particular, designing review mechanisms every time a new set of updated reliable data becomes available (something that occurs on a weekly basis).Originality/value: In this work, we will show an example of a proposal for the practical application of Operations Management tools. Our proposal may be useful to hospital managers and public administrators in the healthcare sector (regional ministries or their equivalents). Each health department, hospital or hospital group will generate the aggregate data that facilitate the calculation of needs as input parameters for the model (available and occupied beds, daily admissions, mean lengths of stay in each healing phase and the percent of patients who evolve from one stage to another in the progression of the disease/healing process) and will be able to analyze how the system responds and evaluate different strategies or mitigating actions. These tools have been shown to be useful and reliable in manufacturing and service contexts that show a strong seasonal demand (both occasional and recurring) requiring a great deal of resources. We believe these situations are analogous to those faced by hospitals and the healthcare system in an epidemic like COVID-19. With them, we could facilitate the aggregate planning of resources for hospital administrators in order to provide them operational support during the COVID-19 contingency plan. However, the array of useful tools for this crisis is not limited to those that we will illustrate. Our intent is to encourage the Operations Management academic community to demonstrate how they contribute to the service of society in this COVID-19 epidemic, providing knowledge that can be extrapolated to other similar situations in the future that we may be unfortunate enough to experience.

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Decision modeling of risks in pharmaceutical supply chains

Cited by 77 publications

References 88 publications

Application of fuzzy analytic hierarchy process (FAHP) in failure investigation of knowledge-based business plans

Application of fuzzy analytic hierarchy process (FAHP) in failure investigation of knowledge-based business plans

Proposed Action of Supply Chain Risk Mitigation Air Compressor Type L Unloading ¼ HP Using The Fuzzy – FMEA and Fuzzy – AHP Method in PT. XYZ

Operations Management at the service of health care management: Example of a proposal for action research to plan and schedule health resources in scenarios derived from the COVID-19 outbreak

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