quality management
Ali Ebrahimi Kordlar; Hossein Safari; Helyeh Sadat Aghamiri; Fatemeh Sharifi Tabar; Mohsen Moradi Moghaddam
Abstract
In today’s competitive environment, organizations need innovative capabilities and strategies for competitive advantage, with organizational capabilities playing a key role in success. Excellence models like EFQM help identify improvement areas and enhance performance. Since the organizational ...
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In today’s competitive environment, organizations need innovative capabilities and strategies for competitive advantage, with organizational capabilities playing a key role in success. Excellence models like EFQM help identify improvement areas and enhance performance. Since the organizational capabilities of the Mobile Communications of Iran (MCI) have not been assessed using the latest EFQM model, this study aims to identify key capabilities and develop a mathematical optimization model. Using a descriptive survey with an applied purpose, the research targeted academic experts, excellence practitioners, and MCI specialists. First, a systematic literature review identified and categorized critical capabilities. Then, expert judgment and a fuzzy inference system modeled causal links between capabilities and the EFQM framework. Mathematical equations quantified each capability, forming an integrated model. A genetic algorithm was used to optimize parameters and determine the best capability combination. The study concludes with practical implementation recommendations and suggestions for future research.IntroductionIn today’s globalized and competitive environment, organizations face increasing competition and a dynamic external landscape. To survive and lead, organizations must differentiate themselves by creating a competitive advantage through innovation. This requires management excellence models that help organizations adapt to these changing conditions. The competitive environment, characterized by geographical dispersion and organizational innovation, demands unique capabilities known as dynamic capabilities, which help organizations create, expand, and maintain their core resources. The 2020 edition of the EFQM model, based on design thinking, has evolved from an assessment tool into a vital framework for addressing the changes and disruptions organizations face daily. Its strategic focus, combined with operational performance and a results-oriented approach, makes it an ideal framework for examining the alignment of an organization's ambitions. The aim of this article is to develop a mathematical model of organizational capabilities within the EFQM 2020 excellence model, helping organizations evaluate and improve their current performance.Materials and MethodsThis research is applied in nature with a comparative approach. It follows a quantitative methodology and is based on library research. The research strategy is survey-based, and from a goal perspective, it falls under the descriptive category. Data collection is conducted through interviews and questionnaires. Organizational capabilities are first extracted from scientific sources, then matched with the sub-criteria of the model by reviewing guidelines. Based on the findings, if-then rules and a fuzzy inference system are designed using MATLAB software.Meta-heuristic methods are used to solve complex optimization problems where classical optimization and heuristic methods are ineffective. Among these, the genetic algorithm is commonly used as a function optimizer. In this model, due to the complex, non-linear, and fuzzy relationships in the fuzzy inference system within the objective function, it can be compared to a neural network. The genetic algorithm is then applied to solve the model.ResultsThe desired capabilities for the fuzzy inference system are determined by specifying the capabilities of each criterion and sub-criterion of the EFQM model. A fuzzy inference system is defined for each of the 23 sub-criteria, and at the criterion level, the systems of the sub-criteria are combined. Sensing, learning, integration, coordination, and reconfiguration routines are used to measure the capabilities of the EFQM excellence model.This research focuses on MCI. By comparing the current values with the target and the scores obtained from the genetic algorithm, it is found that, within the budget limits, the desired goal can be achieved for 38 capabilities. For capabilities such as sensing, abduction, business model development, reporting, environmental management, networking, modeling, and social responsibility, the values fall within the target range. However, for three capabilities—organizational governance development, transformation management, and improvement—the target values fall outside the selected range. These differences are minor and can likely be ignored. The transformation management capability score (25.6) is close to the minimum value of 26, indicating that improvement is not feasible within the current budget for this sub-criterion. Increasing the budget could raise the score. The organizational governance development score differs by almost 4 points, which may be due to the fuzziness in scoring and inaccuracies in the budget values assigned to each sub-criterion.ConclusionOrganizational excellence models are generally frameworks that organizations use to develop a culture of excellence, and each model attempts to provide a set of management principles that are generally employed by organizations in their geographical areas of influence. Organizational resources and capabilities are the key success factors for the organization. In this research, using the fuzzy inference system, the combination of organizational capabilities in the sub-criteria of the EFQM 2020 excellence model was designed, and the mathematical model was developed using linear programming. Finally, a genetic meta-heuristic algorithm was used to solve the model. Each sub-criterion is a fuzzy inference system composed of the organizational capabilities related to it. A set of organizational capabilities makes up each of the sub-criteria of the excellence model, and we have a point limit for each capability. The budget limit defined in this model consists of the total budget dedicated to each organizational capability constituting the relevant sub-criterion. A case study was used to check the validity of the model and its practical application in an internal organization. In this research, the studied organization is MCI.
supply chain management
Mina Kazemi Miyangaskari; Mohammad Reza Mehrregan; Hossein Safari; samira keivanpour; Mahmoud Dehghan Nayer
Abstract
In today's competitive business landscape, the efficient management of supply chains has become a cornerstone of success for economic enterprises. Supplier selection, as the initial link in the supply chain, holds significant sway over various critical factors, such as product quality, return rates, ...
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In today's competitive business landscape, the efficient management of supply chains has become a cornerstone of success for economic enterprises. Supplier selection, as the initial link in the supply chain, holds significant sway over various critical factors, such as product quality, return rates, and production costs. However, the real world is rife with uncertainties, making the application of a fuzzy approach highly advisable. This study's primary objective is to develop a model for supplier selection and order quantity determination for perishable protein products in a retail setting under uncertain conditions. Initially, a comprehensive fuzzy multi-objective model is designed for Kourosh Protein, a company in the closed-loop supply chain, aiming to minimize costs, waste, and maximize profit, customer satisfaction, quality, and profit margin in the face of uncertainty. Subsequently, this full-fledged fuzzy multi-objective model is transformed into a deterministic single-objective model using the Sharma and Agarwal method (2018), yielding optimal order quantities from each supplier. The model's practical implementation in an Iranian retail store for protein products, such as sausages, bologna, hamburgers, etc., demonstrates its potential to reduce costs and boost profits.IntroductionThe global population's rapid expansion and shifts in lifestyle have significantly elevated the food sector's importance in the global economy, specifically in Sustainable Food Supply Chain Management (SFSCM). SFSCM plays a pivotal role in balancing economic, social, and environmental criteria to optimize supply chain performance. Within the complex food supply chain, suppliers wield considerable influence due to their impact on product attributes, safety, quality, and perishability. Supplier selection, a critical facet of SFSCM, substantially affects a company's strategic and operational performance, product pricing, and quality. In this context, this research introduces a fully fuzzy multi-objective model (FFMOP) to enhance the sustainable supply chain performance of a retail company's protein products. Given the inherent uncertainties associated with supplier selection, the proposed model incorporates an extensive array of variables to simulate real-world scenarios. This innovative approach aims to address identified gaps in existing literature, providing a more robust and realistic tool for bolstering supply chain sustainability.Materials and MethodsThis study constructs a full fuzzy multi-objective model with the objective of determining optimal order quantities within the food supply chain while integrating sustainability criteria. The analyzed supply chain network encompasses multiple suppliers, a single retailer, and end consumers, characterized by multi-product and multi-level interactions. The model seeks to optimize profit, customer satisfaction, brand acceptance, quality, profit margin, and minimize waste production while determining the optimal order volume for each product from each supplier. Reviewing the existing literature reveals various approaches to tackle Full Fuzzy Multi-Objective Problems. This research employs the methodology proposed by Sharma & Aggarwal in 2018 to solve the FFMOP model. After defuzzification, the final model is solved using GAMS software to determine the optimal values of decision variables.ResultsThis research utilizes a case study of an Iranian retail company with eight main suppliers providing 15 protein food products. However, the focus is primarily on four key products: sausages, bologna, hamburgers, and pizza cheese, which are examined. Data for the study was collected from historical company records and interviews with experts from June 2021 to 2022. Model parameters are defined using trapezoidal fuzzy numbers. A comparison of optimal order quantities with the company's actual orders and sales reveals that the proposed model for order allocation leads to reduced ordering, maintenance, and procurement costs for the company. Additionally, the model mitigates waste resulting from unsold products.ConclusionSupplier selection stands as a pivotal process in an effective supply chain, exerting substantial influence on a company's strategic outcomes and performance metrics. This study employs a full fuzzy multi-objective model to identify the most suitable supplier and determine optimal orders within a sustainable food supply chain context. To better mimic real-world conditions, variables and parameters are treated as trapezoidal fuzzy numbers. A comparison of the model's outputs with actual sales data indicates that this methodology aligns more accurately with sales figures. Consequently, applying this model has the potential to reduce waste production and economic consequences. The study's achievement lies in selecting a supplier through a methodology that simultaneously considers sustainability criteria within a fully fuzzy environment while determining optimal order quantities from various suppliers. Moreover, the model's flexibility allows for its application across diverse industries, including dairy and dried fruit, for procuring and selling an array of products from potential suppliers.
