Alireza Fallahpour, Morteza Yazdani, Ahmed Mohammed and Kuan Yew Wong
In the last decade, sustainable sourcing decision has gained tremendous attention due to the increasing governmental restrictions and public attentiveness. This decision involves…
Abstract
Purpose
In the last decade, sustainable sourcing decision has gained tremendous attention due to the increasing governmental restrictions and public attentiveness. This decision involves diverse sets of classical and environmental parameters, which are originated from a complex, ambiguous and inconsistent decision-making environment. Arguably, supply chain management is fronting the next industrial revolution, which is named industry 4.0, due to the fast advance of digitalization. Considering the latter's rapid growth, current supplier selection models are, or it will, inefficient to assign the level of priority of each supplier among a set of suppliers, and therefore, more advanced models merging “recipes” of sustainability and industry 4.0 ingenuities are required. Yet, no research work found towards a digitalized, along with sustainability's target, sourcing.
Design/methodology/approach
A new framework for green and digitalized sourcing is developed. Thereafter, a hybrid decision-making approach is developed that utilizes (1) fuzzy preference programming (FPP) to decide the importance of one supplier attribute over another and (2) multi-objective optimization on the basis of ratio analysis (MOORA) to prioritize suppliers based on fuzzy performance rating. The proposed approach is implemented in consultation with the procurement department of a food processing company willing to develop a greener supply chain in the era of industry 4.0.
Findings
The proposed approach is capable to recognize the most important evaluation criteria, explain the ambiguity of experts' expressions and having better discrimination power to assess suppliers on operational efficiency and environmental and digitalization criteria, and henceforth enhances the quality of the sourcing process. Sensitivity analysis is performed to help managers for model approval. Moreover, this work presents the first attempt towards green and digitalized supplier selection. It paves the way towards further development in the modelling and optimization of sourcing in the era of industry 4.0.
Originality/value
Competitive supply chain management needs efficient purchasing and production activities since they represent its core, and this arises the necessity for a strategic adaptation and alignment with the requirement of industry 4.0. The latter implies alterations in the avenue firms operate and shape their activities and processes. In the context of supplier selection, this would involve the way supplier assessed and selected. This work is originally initiated based on a joint collaboration with a food company. A hybrid decision-making approach is proposed to evaluate and select suppliers considering operational efficiency, environmental criteria and digitalization initiatives towards digitalized and green supplier selection (DG-SS). To this end, supply chain management in the era of sustainability and digitalization are discussed.
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Ehsan Shekarian and Alireza Fallahpour
The housing sector is one of the main sources of economic growth in both developing and developed countries. Although many methods for modeling house prices have been proposed…
Abstract
Purpose
The housing sector is one of the main sources of economic growth in both developing and developed countries. Although many methods for modeling house prices have been proposed, each has its own limitations. The present paper aims to propose gene expression programming (GEP) as a new approach for prediction of housing price.
Design/methodology/approach
This study introduces gene expression programming (GEP) as a new approach for predicting housing price. This is the first time that this metaheuristic method is used in the housing literature.
Findings
The housing price model based on the gene expression programming is compared with a least square regression model that is derived from a stepwise process. The results indicate that the GEP‐based model provides superior performance to the traditional regression.
Originality/value
Data used in this study is derived from the Household Income and Expenditure Survey (HIES) in Iran that is conducted by the Statistical Center of Iran (SCI). Housing price model is estimated by administering the questionnaires of this survey in Hamedan Province. To show the applicability of the derived model by GEP technique, it is verified applying parts of the data, namely test data sets that were not included in the modeling process.
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Alireza Moradi, Saber Saati and Mehrzad Navabakhsh
Many researchers and analysts are interested in evaluating the performance of a system with a network structure as a decision-making unit. In this regard, fuzzy network data…
Abstract
Purpose
Many researchers and analysts are interested in evaluating the performance of a system with a network structure as a decision-making unit. In this regard, fuzzy network data envelopment analysis (FNDEA) is a noticeable and worthy method for evaluating the efficiency of a system with fuzzy data. Based on the structure of a fuzzy network system, which consists of at least two serial stages, an intermediate factor has an output nature for the first stage and an input nature for the second stage. Hence, it is inappropriate to allocate the same weight for each stage using this factor. Unfortunately, contrary to real-world conditions, all previous conventional FNDEA studies have considered the same role for intermediate factors to linearize or simplify models. For the first time, this study attempts to determine the upper and lower bounds of the overall efficiencies of a fuzzy two-stage series system and its subprocesses with unequal intermediate product weights.
Design/methodology/approach
The proposed model remains in its original nature as a complex combinatorial problem in the nonlinear programming category of NP-hard problems. A genetic algorithm (GA) is utilized as a metaheuristic algorithm, and a novel hybrid GA-FNDEA algorithm is presented to solve the problem.
Findings
The findings of the study outlined several theoretical contributions and practical implications, including as compensatory property of DEA, determining upper and lower bounds, improving efficiency in nonlinear systems, reducing computational burden, enhancing evolutionary algorithms and retaining real-world conditions.
Originality/value
Contrary to real-world conditions, all previous conventional FNDEA studies have considered the same role for intermediate factors to linearize or simplify models. For the first time, this study attempts to determine the upper and lower bounds of the overall efficiencies of a fuzzy two-stage series system and its subprocesses with unequal intermediate product weights.
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Kuldip Singh Sangwan, Vikrant Bhakar and Abhijeet K. Digalwar
The purpose of this paper is to develop a framework and key performance indicators (KPIs) to assess the sustainability of the manufacturing organizations along the integrated…
Abstract
Purpose
The purpose of this paper is to develop a framework and key performance indicators (KPIs) to assess the sustainability of the manufacturing organizations along the integrated supply chain.
Design/methodology/approach
A systematic literature review of existing peer-reviewed articles has been carried out to understand the strengths and weaknesses of current frameworks. A sustainability assessment framework has been proposed for the manufacturing sector. KPIs to assess sustainability performance of manufacturing organizations are identified. An empirical study is carried out for the cement industry to test the proposed framework and KPIs.
Findings
The existing frameworks on sustainability assessment lacks an integrated assessment consisting product life cycle, resources, critical factors (product, process and policy), KPIs and their interrelationship with sustainability dimensions. In total, 121 KPIs are identified for sustainability assessment of manufacturing organizations. The empirical study of the Indian cement industry identifies 52 KPIs (17 social, 15 economic and 20 environmental), which are classified into 13 factors using exploratory factor analysis.
Research limitations/implications
The proposed framework is tested for the cement sector. More studies are required to validate and refine the framework to make it generalized for the manufacturing sector.
Originality/value
This study has developed for the first time a close interrelation among life cycle engineering, resources, critical factors, KPIs and sustainability dimensions.