EconPapers    
Economics at your fingertips  
 

Inclusive risk modeling for manufacturing firms: a Bayesian network approach

Yash Daultani (), Mohit Goswami, Omkarprasad S. Vaidya and Sushil Kumar
Additional contact information
Yash Daultani: Atal Bihari Vajpayee Indian Institute of Information Technology and Management
Mohit Goswami: Indian Institute of Management
Omkarprasad S. Vaidya: Indian Institute of Management
Sushil Kumar: Indian Institute of Management

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 8, No 1, 2789-2803

Abstract: Abstract This paper focuses on modelling the enterprise level risks from the perspective of an original equipment manufacturer. We intend to converge on an overall risk measure that is representative of the cumulative effect of risks emanating from considerations pertaining to respective functional divisions within the enterprise. Further, due to multitude of interplays between the core objectives of various functional divisions, modeling the cumulative risk pertaining to any project within a firm presents significant challenges. This paper proposes a systematic risk assessment methodology considering various enterprise specific risk characteristics (primarily technical, commercial, and operational in nature) related to multiple functional divisions of an enterprise. Specifically, we consider six different functional divisions i.e. planning, sourcing, operations, marketing, logistics and service. A Bayesian network model is then evolved by mapping the risk parameters related to various functional divisions and their interdependencies. Further, each of these risk parameters are represented in terms of parent and root nodes. In order to determine the probabilities of existing nodes in a Bayesian network, a methodical approach is developed that focuses on obtaining the conditional probabilities of the nodes with multiple parents. Thereafter, an enterprise level value chain risk measure is proposed that evaluates the feasible risk states in terms of an aggregate risk number. Employing an example of a typical automotive company, the methodology is illustrated.

Keywords: Inclusive manufacturing; Value chain; Enterprise risks; Bayesian network (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-017-1374-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:30:y:2019:i:8:d:10.1007_s10845-017-1374-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-017-1374-7

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:joinma:v:30:y:2019:i:8:d:10.1007_s10845-017-1374-7