Davoud Yadegari and Soroush Avakh Darestani
The purpose of this study is to provide a model for evaluating, prioritizing and allocating orders to suppliers in the supply chain for mega-projects.
Abstract
Purpose
The purpose of this study is to provide a model for evaluating, prioritizing and allocating orders to suppliers in the supply chain for mega-projects.
Design/methodology/approach
By using an integrated model (based on fuzzy analytic network process), suppliers are selected and the appropriate amounts are allocated to them in mega-projects. Initially, a hierarchical model of the research method was introduced. Then, the results on reliability and validity analysis of research measurement tools were presented. Finally, prioritization and allocation of orders to suppliers, with a case study of Iran Mall project, was carried out using Decision Making Trial and Evaluation Laboratory (DEMATEL) and analytic network process (ANP). T-test was used to evaluate the research hypotheses.
Findings
The findings were examined against conventional numerical analysis techniques. Finally, implication and recommendations for future work were presented.
Originality/value
The originality of this work is about using multi-criteria decision-making techniques for evaluating suppliers in mega-projects.
Details
Keywords
Mahsa Fekri Sari and Soroush Avakh Darestani
The overall equipment effectiveness (OEE) is a powerful metric in production as well as one of the methods in evaluating function for measuring productivity in the production…
Abstract
Purpose
The overall equipment effectiveness (OEE) is a powerful metric in production as well as one of the methods in evaluating function for measuring productivity in the production process. In the existing method, measuring OEE is based on three main elements consisting availability, performance and quality. The purpose of this paper is to evaluate the recognized metrics of production: OEE and overall line effectiveness (OLE) by using smart systems techniques.
Design/methodology/approach
In this paper, to improve the calculative methods and productivity with three methods: measuring OEE using Mamdani fuzzy inference systems (FIS), measuring OEE using Sugeno FIS, and measuring OLE using FIS and artificial neural networks (ANNs) are proposed.
Findings
The proposed methodologies aim to decrease some weaknesses of OEE and OLE methods by exploiting intelligent system techniques, such as FIS and ANNs. In particular, this research will solve the following issues that occur in manual and automatic data gathering. This technique is an effective way of measuring OEE and OLE with regard to different weights of losses as well as difference in the weight of the machines. In addition, it allows the operator’s knowledge to take a part in the measurement using uncertain input and output with implementation of linguistic terms. The presented method is the details and capabilities of those methods in various tested scenarios, and the results have been fully analyzed.
Originality/value
In relation to other methodologies, it allows the operator’s knowledge to take part in the measurement using uncertain input and output with implementation of linguistic terms. The presented method is the details and capabilities of those methods in various tested scenarios, and the results have been fully analyzed.
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Soroush Avakh Darestani, Tahereh Palizban and Rana Imannezhad
Correct and well-planned maintenance based on modern global methods directly affects efficiency, quality, direct production costs, reliability and profitability. The selection of…
Abstract
Purpose
Correct and well-planned maintenance based on modern global methods directly affects efficiency, quality, direct production costs, reliability and profitability. The selection of an optimal policy for maintenance can be a good solution for industrial units. In fact, by managing constraints such as costs, working hours and human workforce causing sudden equipment failure, production and performance can increase.
Design/methodology/approach
Therefore, in this research a model was presented to select the best maintenance strategy at Kaghaz Kar Kasra Co of Iran. In this study, it was tried to integrate the two techniques of goal programming and the technique for order of preference by similarity to ideal solution (TOPSIS) to prioritize maintenance strategies. First, all factors affecting maintenance were identified, and based on the Best Worst Method (BWM) the degree of their importance was determined.
Findings
After the evaluation, only 14 criteria in the 4 dimensions of cost, added value, safety and feasibility were selected. The highest points were given to the criteria of equipment cost and depreciation, equipment and personnel performance, equipment installation time and technical feasibility, respectively. In the next stage, using the TOPSIS method the item of maintenance strategy was ranked, and the 3 strategies of preventive maintenance (PM), predictive maintenance (PDM) and corrective maintenance (CM) were chosen. Modeling was performed utilizing a goal programming approach to select the optimal maintenance strategy for 13 devices. All the technical specifications, cost limits and the device time were extracted. After the model was finished and solved the best item for each device was specified.
Originality/value
1. Developing a goal programming model and decision-making dashboard. 2. Identifying the criteria and factors affecting the selection of the maintenance strategy for paper production Industry
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Yasamin Tavakoli Haji Abadi and Soroush Avakh Darestani
The food industry is directly related to the health of humans and society and also that little attention has been paid to the assessment of sustainable supply chain risk…
Abstract
Purpose
The food industry is directly related to the health of humans and society and also that little attention has been paid to the assessment of sustainable supply chain risk management in this area, this will be qualified as an important research area. This study aims to develop a framework for assessing the sustainable supply chain risk management in the realm of the food industry (confectionery and chocolate) with a case study of three generic companies denotes as A1–A3. The proposed risk management was evaluated in three aforementioned manufacturing companies, and these three companies were ranked by the Fuzzy-Weighted Aggregated Sum Product Assessment (F-WASPAS) method in EXCEL.
Design/methodology/approach
The evaluation was carried out using integrated multi-criteria decision-making methods Best-Worst method (BWM)-WASPAS. Via an extensive literature review in the area of sustainable supply chain, sustainable food supply chain and risks in this, 9 risk criteria and 59 sub-criteria of risk were identified. Using expert opinion in the food industry, 8 risk criteria and 39 risk sub-criteria were identified for final evaluation. The final weight of the main and sub-criteria was obtained using the F-BWM method via LINGO software. Risk management in the sustainable supply chain has the role of identifying, analyzing and providing solutions to control risks.
Findings
The following criteria in each group gained more weight: loss of credibility and brand, dangerous and unhealthy working environment, unproductive use of energy, human error, supplier quality, quality risk, product perishability and security. Among the criteria, the economic risks have the highest weight and among the alternatives, A3 has obtained first ranking.
Originality/value
Modeling of risk for the food supply chain is the unique contribution of this work.
Details
Keywords
Mahdi Zarepour, Niloufar Hojat Shemami and Soroush Avakh Darestani
In today’s world, one of the most important factors of the country’s economic development is improving the productivity of manufacturing industries. Identifying factors affecting…
Abstract
Purpose
In today’s world, one of the most important factors of the country’s economic development is improving the productivity of manufacturing industries. Identifying factors affecting the productivity of manufacturing industries and prioritizing them is effective in promoting productivity and can promise to achieve organizational and national productivity. The purpose of this research is to identify the effective factors in improving the productivity of manufacturing industries.
Design/methodology/approach
The present research method is a descriptive-survey and the data collection tool is a questionnaire. In the first step, according to the studies conducted by reviewing the research literature using a comparative method, library studies and asking opinions from experts, potential factors affecting the productivity of industries were identified and analyzed. Then the factors were divided into four main categories, and the selected factors were determined by using a questionnaire and combining the opinions of experts. Then, the importance of the selected factors was determined using the Fuzzy SWARA decision-making method, and the final ranking of the selected industries of the province was done using the MOORA method.
Findings
The results of this research showed that the factors “profit margin,” “The ratio of sales on current assets” and “The ratio of exports to sales,” respectively, have the highest importance, among the pharmaceutical and household appliances industries of the province that are present in the stock exchange, Caspian Tamin Company has the highest productivity with a productivity score of 0.437.
Originality/value
Looking at the background of the research, no comprehensive research has been conducted to identify indicators that affect productivity in the manufacturing industry, and only a few studies have evaluated productivity indicators for small, specialized industries. Therefore, in the current research, considering the uncertainty in experts' opinions, a hybrid model is presented to identify and comprehensively evaluate the productivity indicators of manufacturing industries using the decision-making method of Fuzzy SWARA and MOORA, which is unique in its turn.
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Arezou Asgharnezhad and Soroush Avakh Darestani
To outsource part of their work, organizations are looking for suppliers who also have green criteria with other criteria. Selecting suppliers begins with the definition of…
Abstract
Purpose
To outsource part of their work, organizations are looking for suppliers who also have green criteria with other criteria. Selecting suppliers begins with the definition of potential suppliers and then selects the best among them. This study aims to present a two-part approach for selecting suppliers consisting of suppliers’ prioritization.
Design/methodology/approach
In the first part, the criteria that influence on selecting the suppliers have been identified and extracted using the literature review and experts’ opinion which consists of 19 criteria. Then, these criteria were evaluated by the content validity ratio index and using experts’ opinions, and finally, 16 criteria were selected for selecting green suppliers in a polyethylene’s products producer company in Iran. In the next step, suppliers are selected in a green supply chain using multi-criteria decision-making methods such as Dempster–Shafer theory and grey relationship analysis, which is a strategic decision.
Findings
This study attempts to improve the level of reliance on the whole uncertain degree by combining Dempster–Shafer theory and grey relational analysis (GRA), which makes the grey analysis method more robust and its results more reliable. The findings show that Supplier 4 is ranked as first within six suppliers.
Originality/value
Using GRA and Dempster–Shafer theory for green supplier selection problem in polyethylene industry is the novelty of this work.
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Soroush Avakh Darestani, Azam Moradi Tadi, Somayeh Taheri and Maryam Raeiszadeh
Shewhart's control charts are the most important statistical process control tools that play a role in inspecting and producing quality control. The purpose of this paper is to…
Abstract
Purpose
Shewhart's control charts are the most important statistical process control tools that play a role in inspecting and producing quality control. The purpose of this paper is to investigate the attributes of fuzzy U control chart.
Design/methodology/approach
If the data were uncertain, they were converted into trapezoidal fuzzy number and the fuzzy upper and lower control limits were trapezoidal fuzzy number calculated using fuzzy mode approach. The result was grouped into four categories (in control, out of control, rather in control, rather out of control). Finally, a case study was presented and the method coding was done in MATLAB software using design U control chart; then, the results were verified.
Findings
The definition of fuzzy numbers for each type of defect sensitivity and the unit can be classified into four groups: in-control and out-of-control, rather in-control and rather out-of-control which represent the actual quality of the products. It can be concluded that fuzzy control chart is more sensitive on recognition out of control patterns.
Originality/value
This paper studies the use of control charts, specifically the attributes of a fuzzy U control chart, for monitoring defects in the format of a case study.
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Soroush Avakh Darestani and Mina Nasiri
In this context, process capability indices (PCI) reveal the process zones base on specification limits (SLs). Most of the research on control charts assumed certain data…
Abstract
Purpose
In this context, process capability indices (PCI) reveal the process zones base on specification limits (SLs). Most of the research on control charts assumed certain data. However, to measure quality characteristic, practitioners sometimes face with uncertain and linguistic variables. Fuzzy theory is one of the most applicable tools which academia has employed to deal with uncertainty. The paper aims to discuss these issues.
Design/methodology/approach
In this investigation, first, fuzzy and S control chart has been developed and second, the fuzzy formulation of the PCIs such as C pm ,C pmu ,C pml , C pmk , P p , P pl , P pu , P pk are constructed when SLs and measurements are at both triangular fuzzy numbers (TFNs) and trapezoidal fuzzy numbers (TrFNs) stages.
Findings
The results show that using fuzzy make more flexibility and sense on recognition of out-of-control warnings.
Research limitations/implications
For further research, the PCIs for non-normal data can be conducted based on TFN and TrFN.
Practical implications
The application case is related to a piston company in Konya’s industry area.
Originality/value
In the previous researches, for calculating C p , C pk , C pm and C pmk indices, the base approach was calculate standard deviation for a short term variation. For calculating these indices, the variation between subgroups are being ignored. Therefore, P p and P pk indices solved this fault by mentioning long term and short term variations. Therefore these two indices calculate the actual process capability.