Details

Autor(en) / Beteiligte

• Goswami, Mohit
• Daultani, Yash
• De, Arijit

Titel

Decision modeling and analysis in new product development considering supply chain uncertainties: A multi-functional expert based approach

Ist Teil von

• Expert systems with applications, 2021-03, Vol.166, p.114016, Article 114016

Ort / Verlag

New York: Elsevier Ltd

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• •Bayesian theory is employed for supply chain risk components.•An optimization model is developed to select optimal combination of modules.•An example of construction power tool product line of a global firm. Successful new product development projects and extant research literature advocate for inclusion of inputs pertaining to the supply chain at early stages of product development to proactively identify risk averse product design concepts. To this end, we devise an analytical framework to converge upon product design concept(s) that would be associated with lesser supply chain risks, usually function of both technical and commercialization considerations. The high-level and constituent lower-level supply chain risks are represented by parent and root nodes respectively within the devised Bayesian network driven research framework. Thereafter, a quantitative measure denoted as SCRI (supply chain risk index) is evolved that yields overall composite risk numbers corresponding to respective design concepts at different risk states. Validation and comparison of the devised method with an extant study illustrates the consistency and reliability of the study. It is found that the risk propensity of a particular design concept is inversely related to the probabilistic utility of that particular concept. The case of a construction power tool of a global firm is used to demonstrate the methodology. Our research addresses an important future research pathway as argued by Hosseini et al. (2020) that extant research literature is devoid of decision-making frameworks focused on measurement and analysis the propagation of risks on complex networks.