شناسایی و مدل سازی ریاضی قابلیت های سازمانی با رویکرد فازی و بهینه سازی ژنتیک در مدل تعالی سازمانی- مورد مطالعه: شرکت ارتباطات سیار ایران (همراه اول (MCI)) (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
در دنیای رقابتی امروز، سازمان ها برای حفظ جایگاه خود باید از رقبا متمایز شوند که این تمایز از طریق مزیت رقابتی، ناشی از قابلیت ها یا استراتژی های نوآورانه، به دست می آید. قابلیت های سازمانی نقش مهمی در موفقیت کسب وکار دارند. همچنین، استفاده از مدل های تعالی سازمانی مانند EFQM نیز به شناسایی فرصت ها و بهبود عملکرد کمک کرده و در سازمان ها به ارزیابی انسجام داخلی شان می پردازد. با توجه به اینکه وضعیت توانمندی های سازمانی در شرکت همراه اول (شرکت ارتباطات سیار ایران) بر اساس نسخه جدید مدل EFQM مورد بررسی قرار نگرفته است، این تحقیق به دنبال شناسایی قابلیت های سازمانی مدل جدید و طراحی یک مدل ریاضی بهینه سازی بر اساس آن است. روش تحقیق این پژوهش از نظر هدف کاربردی و از نظر جمع آوری داده ها توصیفی پیمایشی است. جامعه آماری شامل متخصصان دانشگاهی، فعالان حوزه تعالی سازمانی و کارشناسان شرکت همراه اول (MCI) است. در این تحقیق ابتدا با بهره گیری از مرور ادبیات سیستماتیک، مهم ترین قابلیت های سازمانی از منابع معتبر استخراج و دسته بندی گردید. در گام دوم، بر مبنای دیدگاه خبرگان و با استفاده از سیستم استنتاج فازی، روابط علیِ میان قابلیت ها و مدل تعالی سازمانی مدل سازی شدند. سپس برای هر قابلیت، معادلات ریاضی به منظور تعریف تابع کمّی سازی استخراج شد و کلیت مدل ریاضی قابلیت ها، تدوین گردید. در نهایت، با به کارگیری الگوریتم ژنتیک، پارامترهای مدل ریاضی بهینه سازی شدند تا بهترین ترکیب قابلیت ها در راستای تعالی سازمانی تعیین شود. در پایان، پیشنهاداتی برای کاربرد عملی چارچوب در شرکت ها و مسیرهای توسعه آتی پژوهش ارائه شده است.Identification and Mathematical Modeling of Organizational Capabilities in the Organizational Excellence Model Using a Fuzzy Inference System and Genetic Algorithm Optimization- Case Study: Mobile Communications of Iran (MCI)
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. Introduction In 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 Methods This 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. Results The 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. Conclusion Organizational 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.
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