Open Loop Reverse Supply Chain Network Design

Ene, Seval; Öztürk, Nursel

doi:10.1016/j.sbspro.2013.12.596

Cited by 25 publications

(17 citation statements)

References 7 publications

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“…A closed-loop supply chain changes the supply chain configuration as companies make decisions regarding their collection/acquisition centres, inspection/sorting centres, disposal facilities etc. (Ene and Ozturk, 2014). Closed-loop supply chains enable improvements in eco-efficiency in the companies' operational systems by recycling materials, minimising waste, and reusing natural resources (Stock, 1998 Natural resources play an important role in the decision of the location of manufacturing companies and particularly in the face of natural resource scarcity as natural resources can be globally scarce such as platinum or locally scarce such as water (Bell et al, 2012).…”

Section: Supply Chain (Re-) Design Resource Efficiency and Competitimentioning

confidence: 99%

Supply chains under resource pressure

Kalaitzi

Matopoulos

Bourlakis

et al. 2019

IJOPM

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Purpose The purpose of this paper is to investigate the implications of supply chain strategies that manufacturing companies can use to minimise or overcome natural resource scarcity, and ultimately improve resource efficiency and achieve competitive advantage. The relationship between resource efficiency and competitive advantage is also explored. Design/methodology/approach The proposed research model draws on resource dependence theory. Data were collected from 183 logistics, purchasing, sustainability and supply chain managers from various manufacturing companies and analysed by applying the partial least squares structural equation modelling technique. Findings The results indicate that both buffering and bridging strategies improve resource efficiency; however, only bridging strategies seem to lead to firm’s competitive advantage in terms of ownership and accessibility to resources. The relationship between resource efficiency and competitive advantage is not supported. Research limitations/implications Future research could confirm the robustness of these findings by using a larger sample size and taking into account other supply chain members. Practical implications This research provides guidance to managers faced with the growing risk of resource scarcity to achieve a resource efficient supply chain and an advantage over competitors. Originality/value Studies have explored the appropriate strategies for minimising dependencies caused by the scarcity of natural resources in the field of supply chain management; however, there is limited empirical work on investigating the impact of these strategies on resource efficiency and competitive advantage.

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Section: Supply Chain (Re-) Design Resource Efficiency and Competitimentioning

confidence: 99%

Supply chains under resource pressure

Kalaitzi

Matopoulos

Bourlakis

et al. 2019

IJOPM

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“…This study is focused on the collection activity of the reverse supply chain of the WEEE since it represents a critical phase impacting the overall performance [6,41,42,52].…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

The Reverse Supply Chain of the E-Waste Management Processes in a Circular Economy Framework: Evidence from Italy

Isernia¹,

Passaro

Quinto

et al. 2019

Sustainability

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In the last several decades, Waste Electrical and Electronic Equipment (WEEE) reverse supply chain management has increasingly gained more attention due to the development of an environmental awareness, the rapid raise of e-wasted products and the EU regulations. In particular, although the new EU WEEE collection target has not been reached by many EU countries, several studies show that an optimized WEEE wastes management processes could represent a relevant way to achieve economic, environmental and social benefits expected by the adoption of circular economy approaches. According to this, the paper aims to evaluate the extent to which the current Italian organization of the WEEE management system and the related legislation are able to support the achievement of the targets defined by EU with a specific focus on the collection centers (CCs) which play a key role being the initial point of the WEEE reverse logistic cycle. An illustrative analysis based on the transition probability matrix regarding both the e-waste collecting performance and the distribution of collecting centers in the Italian provinces is illustrated. Furthermore, we have analyzed the presence of a correlation between the WEEE collection rate and the presence of the CCs in different provinces in order to better comprehend the role that can play both the investments in CC system and other soft measures in achieving the WEEE collection targets. Results show that the current Italian organization of the WEEE management system and the related legislations are not so effective in supporting the achievement of EU WEEE collection targets at the national level, although some geographical areas and provinces outperform the EU targets.

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“…Gou et al [20] presented a stochastic inventory model for an open-loop reverse supply chain to find the optimal economic delivery batch size for collection points. Ene and Öztürk [21] developed an open-loop reverse supply chain network design with a mixed-integer linear programming model. Özceylan [3] handled the CLSC and OLSC network simultaneously by developing a mixed-integer linear programming model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…By using weighted approaches, it is determined that they have become effective in the distribution in-network and that these suppliers adequately assume the entire supply. (10,13,13,16; 0.9, 0.9)) 0.433 S2 ((13, 19, 19, 24; 1, 1), (16,19,19,21,5; 0.9, 0.9)) 0.595 S3 ((25, 29, 29, 30; 1, 1), (27,29,29,29,5; 0.9, 0.9)) 0.900 S4 ((9, 15, 15, 21;1, 1), (12,15,15,18; 0.9, 0.9)) 0.475 C12 S1 ( (7,13,13,19; 1, 1), (10,13,13,16; 0.9, 0.9)) 0.433 S2 ((25, 29, 29, 30; 1, 1), (27,29,29,29,5; 0.9, 0.9)) 0.900 S3 ((13, 19, 19, 24; 1, 1), (16,19,19,21,5; 0.9, 0.9))) 0…”

Section: Linguistic Variables It2fssmentioning

confidence: 99%

An integrated open-loop supply chain network configuration model with sustainable supplier selection: fuzzy multi-objective approach

Çalık

2020

SN Appl. Sci.

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Supply chain management is an essential part of most companies to reduce costs and increase the profit for a competitive business environment. The success of companies is directly connected to the performance of the supply chain. The sustainability of businesses broadly depends on the purchasing decision, and the process becomes more complex with resource limitations. In addition, relationships with suppliers are considered for optimal production and risk minimization in the supply chain network. This research aims to design and optimize a multi-echelon, multi-period, and multi-objective open-loop supply chain (OLSC) network design. The objectives are deemed to be the total costs, the minimization of environmental costs, and the maximization of the importance level of suppliers. Furthermore, a fuzzy approach based on interval type-2 fuzzy sets is designed to evaluate the supplier performance according to sustainable qualitative criteria. After determination of the weights of criteria and evaluation of suppliers by interval type-2 fuzzy numbers, the final score of suppliers is calculated based on the weights of criteria and evaluation scores. Three fuzzy programming approaches are utilized to solve the developed OLSC model. A numerical example is provided to validate the model and effectiveness of the proposed evaluation procedure. The results demonstrate the feasibility of the proposed evaluation procedure and the achievement of a compromise in the developed model.

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Open Loop Reverse Supply Chain Network Design

Cited by 25 publications

References 7 publications

Supply chains under resource pressure

Supply chains under resource pressure

The Reverse Supply Chain of the E-Waste Management Processes in a Circular Economy Framework: Evidence from Italy

An integrated open-loop supply chain network configuration model with sustainable supplier selection: fuzzy multi-objective approach

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