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dc.contributor.authorMukherjee, S.en
dc.contributor.authorPadhi, S. S.en
dc.date.accessioned2022-10-19T05:25:43Z-
dc.date.available2022-10-19T05:25:43Z-
dc.date.issued2022-
dc.identifier.citationMukherjee S. Sourcing decision under interconnected risks: an application of mean–variance preferences approach / S. Mukherjee, S. S. Padhi // Annals of Operations Research. — 2022. — Vol. 313. — Iss. 2. — P. 1243-1268.en
dc.identifier.otherhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85122412317&doi=10.1007%2fs10479-021-04485-3&partnerID=40&md5=53afb6c7dee1c519f7fac2c75c4e85c1link
dc.identifier.urihttp://elar.urfu.ru/handle/10995/118403-
dc.description.abstractSupply chains are customarily associated with multiple interconnected risks originated from supply side, demand side, or from the unanticipated background uncertainties faced by a firm. Also, effective functioning of supply chain hinges on sourcing decisions of inputs (raw materials). Therefore, there is a striking need to analyse the risk preference of the decision maker while going for optimal sourcing decision under varying degree of interconnected supply chain risks. This study addresses this issue by analysing the comparative static effects under interconnected supply chain risks for a risk averse decision-maker, manufacturing and selling products in a regulated market under perfect competition. The decision-maker faces not only supply-side risk (due to random input material prices) but also interconnected risks arising out of background risk (setup costs risk) and demand-side risk (output prices risk). With preferences defined over the mean and standard deviation of the uncertain final profit, this study illustrates the effects of the changes in the pairwise correlations between the three above mentioned risks on the optimum input choice of the manufacturer. To contextualise this study, an India-based generic drug manufacturer cum seller has been considered as a case in the parametric example of our model. Adaptation of the mean–variance framework helps obtaining all the results in terms of the relative trade-off between risk and return, with simple yet intuitive interpretations. © 2022, The Author(s).en
dc.description.sponsorshipUniversity of Nottinghamen
dc.description.sponsorshipThe authors are grateful to the guest editors (Prof. (Dr) Jean-Luc Prigent, Prof. (Dr) Ephraim Clark, and Prof. (Dr) Giovanni Barone Adesi) and two anonymous referees for their constructive comments on its submitted version of December 2019. The authors are also grateful to Prof. (Dr) Stephen M. Wagner (ETH Zurich) and Prof. (Dr) Udo Broll (TU Dresden) for their inputs on another past version of this research, which had been drafted as a CREDIT WP version [Mukherjee, S. & Padhi, S.S. (2018). Risk Connectivity and Risk Mitigation: An Analytical Framework , CREDIT WP Series No. 18/11, University of Nottingham (UK). Available at: <https://www.nottingham.ac.uk/credit/documents/papers/2018/18-11.pdf >]. Both the submitted version of December 2019 and this present revision are substantially different from the above-mentioned draft. The usual disclaimers are applicable.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.sourceAnnals of Operations Researchen
dc.subjectDECISION ANALYSISen
dc.subjectINTERCONNECTED RISKSen
dc.subjectMEAN–VARIANCE MODELen
dc.subjectSOURCING DECISIONen
dc.subjectSUPPLY CHAIN RISKen
dc.titleSourcing decision under interconnected risks: an application of mean–variance preferences approachen
dc.typeArticleen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeinfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.1007/s10479-021-04485-3-
dc.identifier.scopus85122412317-
local.contributor.employeeMukherjee, S., Department of Banking & Finance, Southampton Business School, University of Southampton, 2/4051, Highfield, Hampshire, Southampton, SO17 1BJ, United Kingdom, Associate External Fellow, CREDIT and GEP, University of Nottingham, Nottingham, NG7 2RD, United Kingdom, Ural Federal University, Yekaterinburg, Russian Federationen
local.contributor.employeePadhi, S.S., Faculty of Quantitative Methods and Operations Management Academic Hill, Indian Institute of Management, Room # 16, Block C, Kerala, Kozhikode, 673570, Indiaen
local.description.firstpage1243-
local.description.lastpage1268-
local.issue2-
local.volume313-
dc.identifier.wos000739812100002-
local.contributor.departmentDepartment of Banking & Finance, Southampton Business School, University of Southampton, 2/4051, Highfield, Hampshire, Southampton, SO17 1BJ, United Kingdomen
local.contributor.departmentAssociate External Fellow, CREDIT and GEP, University of Nottingham, Nottingham, NG7 2RD, United Kingdomen
local.contributor.departmentUral Federal University, Yekaterinburg, Russian Federationen
local.contributor.departmentFaculty of Quantitative Methods and Operations Management Academic Hill, Indian Institute of Management, Room # 16, Block C, Kerala, Kozhikode, 673570, Indiaen
local.identifier.pure30457895-
local.identifier.eid2-s2.0-85122412317-
local.identifier.wosWOS:000739812100002-
local.identifier.pmid2545330-
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