تخمین تابع هزینه- فعالیت در هزینه‌یابی بر مبنای فعالیت با استفاده از الگوی ترکیبی شبکه‌های عصبی- تحلیل پوششی داده‌های چندلایه در بانک مسکن

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه حسابداری،‌دانشکده علوم اقتصادی و اجتماعی ،‌دانشگاه الزهرا،‌تهران ،‌ایران

2 دانشیار، گروه حسابداری، دانشکده اقتصاد و مدیریت دانشگاه الزهرا، دانشگاه الزهرا، تهران

3 دانشیار، گروه مدیریت IT، دانشکده اقتصاد و مدیریت دانشگاه الزهرا، دانشگاه الزهرا

چکیده

هزینه یابی بر مبنای فعالیت از زمان معرفی شدن تا کنون توجهات زیادی را به خود جلب کرده است. لیکن عملا مشکلات اجرایی در پیاده سازی این نظام هزینه یابی وجود دارد که باعث می شود علیرغم برتری محاسباتی هزینه یابی بر مبنای فعالیت نسبت به هزینه یابی سنتی، سازمان ها و شرکت ها همچنان علاقمند به استفاده از این روش هزینه یابی نباشند. در پژوهش حاضر مشکلات اجرایی که عملا در پیاده‌سازی هزینه‌یابی بر مبنای فعالیت وجود دارد، بررسی گردیده و برای حل مسئله تخمین رابطه هزینه- فعالیت (CER) و همچنین کاهش هزینه‌های انجام زمان‌سنجی در سازمان‌ها از رویکرد شبکه‌های عصبی مصنوعی استفاده شده است. جامعه آماری تحقیق کلیه شعب بانک مسکن می‌باشد که با استفاده از روش تحلیل پوششی داده‌های چند لایه (CI- DEA) و بر اساس مشابهت عملکرد در سال 1395 خوشه‌بندی گردیده و 450 شعبه به عنوان نمونه انتخاب گردید و برای آموزش و آزمون مدل شبکه‌های عصبی استفاده شده است. ویژگی متمایزکننده این الگو نسبت به سایر الگوها در نظر گرفتن رابطه بین هزینه- فعالیت بصورت غیرخطی است. معماری خاص شبکه پیشنهادی باعث می‌شود تا علاوه بر پیش‌بینی هزینه فعالیت، مقدار سهم محرک منبعی (زمان) که به عنوان محرک تسهیم هزینه به فعالیت در مدل اجرایی مرسوم، استفاده می‌شود نیز از مدل قابل استخراج باشد. نتایج RMSE و MAE مدل معرفی شده نشان داد که مدل ارائه شده قابلیت تخمین رابطه هزینه- فعالیت را دارا می‌باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Estimation of cost-activity function in activity-based costing using combination of neural networks-Multilayer data envelope analysis in Maskan Bank

نویسندگان [English]

  • Samaneh Sadeghi Askari 1
  • Gholamreza Soleimany Amiri 2
  • Ameneh Khadivar 3
1 accounting department,economic and behavioral science,alzahra university
2 Department of Accounting, Alzahra University, Economic & Management Faculty, Tehran, Iran
3 Department of IT Management, Alzahra University, Economic & Management Faculty, Tehran, Iran
چکیده [English]

Activity-based costing since it's introduction has attracted so much attention. There are, however, practical problems in implementing this costing system, which, in spite of the computational superiority of activity-based costing compared to traditional costing, organizations and companies are still not interested in using this costing method. In the present study, implementation problems that are practically related to implementation of activity-based costing have been investigated and artificial neural networks have been used to solve the problem of estimating the cost-activity relationship (CER) as well as reducing the costs of doing timing in organizations. The statistical population of the research is all branches of Maskan Bank which has been clustered using CI-DEA Data Envelopment Analysis (CI-DEA) and based on performance similarity in 1395. 450 branches were selected as samples and used to train and test the model of neural networks. The distinctive feature of this pattern is to consider non-linear relationship between cost-activity and other patterns. The proposed architecture of network makes it possible, in addition to the cost-of-activity forecast, to be extrapolated from the model, the amount of cost-driven input (time) used as a cost-sharing actuator to the activity in the conventional executive model. The results of the RMSE and MAE models showed that the proposed model has the capability to estimate the cost-activity relationship.

کلیدواژه‌ها [English]

  • activity-based costing
  • cost-activity function
  • multilevel data envelope analysis
  • Neural Networks

 

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