supply chain management
Homa Abedi Dehkordi; Ghasem Tohidi; Shabnam Razavyan; Mohammad Ali Keramati
Abstract
Cement production in Iran takes place across various geographical locations, each characterized by distinct weather conditions. The technology employed in cement production varies depending on the availability of raw materials, fuel sources, and essential resources like water. Consequently, diverse inputs ...
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Cement production in Iran takes place across various geographical locations, each characterized by distinct weather conditions. The technology employed in cement production varies depending on the availability of raw materials, fuel sources, and essential resources like water. Consequently, diverse inputs and outputs assume significance in each production technology, resulting in non-homogeneity among cement factories. Despite these differences, all these facilities are engaged in cement production, warranting a comparative analysis of their efficiency. This study examines the operational processes of five different cement production technologies—dry, semi-dry, humid, semi-humid, and wet slurry—across four companies comprising a total of nine factories. The study evaluates their efficiency between 2017 and 2020 using network data envelopment analysis under non-homogeneous conditions across three modeling stages. An important aspect of this study is its focus on the entire supply chain, from raw materials to the final product. Although the raw materials employed vary among different cement production technologies, the end product remains largely consistent.IntroductionIn certain real-world scenarios, even with similar production technologies, the assumption of homogeneous decision-making units may not hold true. Practical applications often involve supply chain structures that differ significantly from others. For instance, some supply chains may, at certain stages, eject intermediate products to meet specific needs, a phenomenon not universal to all supply chains, resulting in non-homogeneous chains. The cement industry, including Iran, constitutes one of the pivotal economic sectors. Therefore, mitigating shortcomings, including resource and material waste reduction, can have a substantial impact on this industry and consequently on the broader economy. Due to varying climatic conditions, cement production employs diverse technologies, primarily categorized as dry or wet processes. This study investigates the operational processes of five different cement production methods—dry, semi-dry, humid, semi-humid, and wet slurry—across four companies with a total of nine factories. Their performance between 2017 and 2020 is evaluated using network DEA under non-homogeneous conditions, encompassing three modeling stages.Materials and MethodsIn novel approaches, DEA is utilized to assess the performance of network decision-making units. The models typically assume homogeneity among decision-making units, which may not always align with real-world conditions. Practical situations often violate assumptions of unit homogeneity and uniformity in input and output parameters. Consequently, it is imperative to present and employ methods and models capable of accommodating non-homogeneous units. This study employs a scientific library research approach and practical purposive data collection to gather relevant information. This information informs specific adjustments to operational processes. Consequently, the development of a robust system for evaluating supply chain performance becomes essential. The study utilizes common models to evaluate efficiency under non-homogeneous conditions. Classification of operational processes and related data, followed by modeling using Lingo software, is employed in this research.Discussion and Result:This article consists of two parts. Initially, it introduces the fundamental performance evaluation model and subsequently delves into the three-stage model of data envelopment analysis (DEA) within the supply chain context. In the second part, the production processes of Portland cement are examined, covering dry, semi-dry, humid, semi-humid, and wet slurry processes. The proposed approach assesses the performance of four cement production companies over a four-year period. Efficiency calculations for nine factories are conducted in three stages:The first stage consists of three steps as follows:First step: Input and output parameters used across the entire production process are categorized based on the different production methods.Second step: Processes utilizing similar production steps, as determined in the first stage, are grouped into four categories.Third step: Efficiency assessments for factories sharing similar production stages from the previous step are conducted, resulting in the identification of nine categories.Second stage: The efficiency of each category, characterized by a common feature from the previous step, is calculated.Third stage: To determine the overall efficiency of each factory, the efficiencies of individual processes are multiplied.ConclusionsThe results indicate that the fourth cement production company exhibits the highest efficiency, while the first company has the lowest efficiency. Notably, the lowest efficiency for the years 2017 to 2020 was recorded by the first company in 2020, while the fourth company achieved the highest efficiency in the same year. Among the factories, the lowest efficiency was observed in 2017 for the first company's five semi-dry factories, the fourth company's four semi-humid factories in 2018, the fourth company's nine wet slurry factories in 2018, the third company's seven semi-humid factories in 2020, and the fourth company's four semi-humid factories in 2020, which recorded the highest efficiency. Further examination and identification of suitable solutions to enhance efficiency in cases with lower efficiency levels can follow this study.
modeling and simulation
Salman Abbasi Siar; Mohammad Ali Keramati; MohammadReza Motadel
Abstract
Because of the dissemination of impulse buying behavior in consumers its academic studies have increased over the last decade. Because in large stores, sales have to be increased, the behavior of consumers in impulse buying to be taken into account by the researchers and managers of the stores. The purpose ...
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Because of the dissemination of impulse buying behavior in consumers its academic studies have increased over the last decade. Because in large stores, sales have to be increased, the behavior of consumers in impulse buying to be taken into account by the researchers and managers of the stores. The purpose of this paper is agent–based simulation impulse buying behavior consumer (customers) considering the discount, the optimal time of customer presence in stores from the point of view the store managers and learning from previous buying. The population and the statistical sample of the present study include 15 academic professors who are expert in impulse buying and marketing topics. The present study is executive in terms of purpose. It is mathematical in terms of data type and modeling method. This model examines the existing reality of consumer buying behavior to develop impulse buying models in the agent-based simulation environment by Netlogo software. After reviewing the theoretical foundations and research background 5 dimensions and seventeen indicators have been identified by fuzzy screening method. The results showed that the factors considered in this study describe the impulse buying behavior of consumers as an economic analysis based on consumer relations and customer-product relationship. This achievement by simulating customer behavior at the time of purchase strives to provide valuable information for managers shareholders and store decision makers.
