Taxonomy and review of non-deterministic analytical methods for supplier selection

Karsak, E. Ertuğrul; Dursun, Mehtap

doi:10.1080/0951192x.2014.1003410

Cited by 38 publications

(21 citation statements)

References 170 publications

(138 reference statements)

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“…Environmental criteria for supplier selection (based on Humphreys et al (2006), Tsoulfas and Pappis (2008), Bai and Sarkis (2010), Genovese et al (2013), Govindan, Khodaverdi, and Jafarian (2013), Zimmer, Fröhling, and Schultmann (2016), Fallahpour et al (2017), Ghadimi, Dargi, and Heavey (2017) 2016 Figure 1. Classification of decision-making methods in supplier selection and evaluation, adapted from Ho, Xu, and Dey (2010), Chai, Liu, and Ngai (2013), Zimmer, Fröhling, and Schultmann (2016) and Karsak and Dursun (2016)…”

Section: Discussionmentioning

confidence: 99%

A fuzzy decision tool to evaluate the sustainable performance of suppliers in an agrifood value chain

Liu¹,

Eckert²,

Bertoluci³

et al. 2019

Computers & Industrial Engineering

106

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Section: Discussionmentioning

confidence: 99%

A fuzzy decision tool to evaluate the sustainable performance of suppliers in an agrifood value chain

Liu¹,

Eckert²,

Bertoluci³

et al. 2019

Computers & Industrial Engineering

106

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“…Despite this development towards environmental decision making, the specific research on CO 2 as one of diverse green selection criteria is still scarce (Govindan et al 2015;Karsak and Dursun 2016;Nielsen et al 2014;Zimmer et al 2016). Moreover, investigations on economic effects resulting from green supply chain management activities are absent (Ahi and Searcy 2015;Igarashi et al 2013).…”

Section: Green Supplier Selectionmentioning

confidence: 99%

Integrating site-specific environmental impact assessment in supplier selection: exemplary application to steel procurement

et al. 2020

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In times of fast-growing stakeholder interest in sustainability, the ecological and social perspective of industrial companies and its products is gaining increasing importance. In particular, the emission of greenhouse gases (GHG) in the automotive industry has come to the forefront of public and governmental attention. The transport sector accounts for 27% of all European GHG emissions and constitutes the largest emitter of CO 2 e (CO 2 equivalents) among all energy demanding technologies. Due to increasingly efficient combustion engines and technology innovation towards e-mobility, the emissions from car manufacturing gain in importance. So far little focus has been laid upon the emissions created throughout the production process in automotive supply chains from a purchasing perspective. The purchasing of raw material from environmentally efficient suppliers can constitute a possibility to significantly reduce CO 2 e emissions in automotive supply chains and thus contribute to the two degrees global warming goal. Supplier selection decisions, which cover approximately 75% of the value adding process of a car, are today mainly cost and quality-driven. In order to integrate CO 2 e as decision criterion for supplier selections, site-specific and comparable data on CO 2 e emissions from the upstream supply chain is necessary, but currently lacking. To estimate CO 2 e emissions of steel suppliers' production sites, a model has been developed to estimate manufacturing processes on a site-specific level without the necessity of confidential primary data. The model is applied on 22 integrated steel mills in EU-15. The results, which can be transferred and used for various products and industries, e.g. the construction industry, demonstrate the partially large disparities of manufacturing efficiency regarding CO 2 e emissions among steel manufacturers due to different levels of process integration and internal process know-how. A range between 1879 and 2990 (kg CO 2 e/t crude steel) has been revealed. Finally, the estimated data on CO 2 e performance of suppliers is applied in a case study of supplier selection of a German automobile manufacturer in order to simulate environmental as well as economic effects. A. Schiessl et al.

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“…The literature on supplier selection and order allocation is numerous; several studies [4][5][6] have reviewed the body of literature. Other studies [7,8] have reviewed the effect of uncertainty on supplier selection and order allocation.…”

Section: Review Of Literaturementioning

confidence: 99%

Supplier portfolio selection based on the monitoring of supply risk indicators

Mokhtar

Bahri

Moayer

et al. 2019

Simulation Modelling Practice and Theory

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This paper introduces a dynamic optimisation model for a manufacturer's optimal selection of a portfolio of suppliers. The model uses a set of indicators that measure risks imposed by suppliers on the manufacturing supply chain. These indicators measure financial stability, production stability, product quality and cost of suppliers. The model uses a combined simulation-optimisation framework to select suppliers and allocate orders to them based on real-time monitoring of supply risk indicators. This model uses a multi-period order allocation approach based on the viewpoint of a manufacturer in a manufacturing supply chain system. A system dynamics model simulates the interrelations and feedbacks among parties in the supply chain, i.e. suppliers, the manufacturer, and the manufacturing product market. It models the effect of supply risk indicators on a manufacturer's profit over a planning horizon. The result of the simulation is fed to a portfolio optimisation model to determine an optimal supplier order allocation based on the manufacturer's propensity for risk. The model informs the manufacturer to rebalance its supply portfolio in response to early changes in supply risk indicators over a planning horizon. The results show that supplier portfolio selection based on this framework provides higher expected profit and less risks to the manufacturer over the planning horizon. For instance, in our numerical example, the high-level risk averse decision maker made a profit of 5.4% and a risk of 1% less than those of low-level risk averse decision maker at the end of the planning horizon.

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Taxonomy and review of non-deterministic analytical methods for supplier selection

Cited by 38 publications

References 170 publications

A fuzzy decision tool to evaluate the sustainable performance of suppliers in an agrifood value chain

A fuzzy decision tool to evaluate the sustainable performance of suppliers in an agrifood value chain

Integrating site-specific environmental impact assessment in supplier selection: exemplary application to steel procurement

Supplier portfolio selection based on the monitoring of supply risk indicators

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