ارزیابی کارایی ساختار داخلی واحدهای تصمیم گیرنده در گذشته ، حال و آینده با استفاده از تحلیل پوششی داده‌های شبکه‌ای پویا و شبکه عصبی مصنوعی
محورهای موضوعی : مدیریت صنعتیجواد نیک نفس 1 , محمد علی کرامتی 2 , جلال حقیقت منفرد 3
1 - دانشگاه آزاد اسلامی واحد تهران مرکزی
2 - دانشگاه آزاد اسلامی واحد تهران مرکزی
3 - دانشگاه آزاد اسلامی واحد تهران مرکزی
کلید واژه: تحلیل پوششی داده‌ای شبکه‌ای, تحلیل پوششی داده‌ای شبکه‌ای پویا, شبکه عصبی مصنوعی, ارزیابی کارایی, بانک,
چکیده مقاله :
مدل‌های تحلیل پوششی داده‌ای شبکه‌ای و مدل‌های تحلیل پوششی داده‌ای شبکه‌ای پویا نمی‌توانند کارایی آینده ساختار داخلی واحدهای تصمیم‌گیرنده را در آینده ارزیابی نمایند. به عبارت دیگر همه مدل‌های شبکه‌ای و شبکه‌ای پویا عملکرد گذشته واحدها و ساختار درونی آن‌ها را ارزیابی کرده و کارایی و ناکارایی آن‌ها را سنجش می‌کنند و در نهایت بر اساس آن ارزیابی، رتبه‌بندی می‌نمایند. در این مقاله برآنیم که کارایی بخش‌های جمع‌آوری سپرده و وام‌دهی در شعب بانک را در آیندهارزیابی کنیم. تا بتوانیم ناکارایی در ساختار درونی یک واحد را قبل از وقوع مطلع شده و از آن جلوگیری نماییم. این رویکرد می‌تواند نقش مدیران را از ناظر و ارزیاب به برنامه‌ریز تغییر دهد. ابتدا با استفاده از ادبیات موضوع و نظر خبرگان ساختار درونی شعب بانک و متغیرهای شبکه در آن مشخص شد. سپس مقادیر متغیرها با استفاده از شبکه عصبی مصنوعی برای دو دوره آتی پیش‌بینی‌شده است. و در نهایت یک مدل شبکه‌ای پویا با استفاده از مقادیر دوره‌های گذشته و مقادیر پیش‌بینی‌شده فرموله شده و با استفاده از آن کارایی شعب و ساختار داخلی آن در گذشته، حال و آینده ارزیابی شده است.
Network data envelopment analysis models and dynamic network data envelopment analysis models cannot evaluated the future performance of the internal structure of decision-making units .In other words, all NDEA and DNDEA models evaluate the past performance of their DMUs and their internal structure, and measure their efficiency and inefficiency, and ultimately rank them based on that assessment .In this paper, we are going to evaluation the future efficiency of deposit and lending sections in bank branches. In order to notified inefficiencies in the internal structure of a unit before the occurrence, we will prevent it.This approach can change the role of managers from the evaluator to the planner .First, using the literature of the subject and opinion of the experts, the structure of the bank branches and the network variables were determined .Then, the values of variables are forecasted using the artificial neural network for the next two periods.Finally, a DNDEA model is formulated using the values of past periods and predicted values.Using its efficiency, its branches and its internal structure have been evaluated in the past, present and future.
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