ارزیابی و کشف تقلب در فرایند زنجیره تامین مبتن ی بر رویکرد تلفیق ی ANN-Big data
محورهای موضوعی : مدیریت
الناز علیخانی زنجانی
1
(1 دانشجوی دکتری مدیریت صنعتی_ مالی، واحد ابهر، دانشگاه آزاد اسلامی، ابهر، ایران.)
فرید عسگری
2
(2نویسنده مسئول، استادیار گروه اقتصاد مالی، واحد ابهر، دانشگاه آزاد اسلامی، ابهر، ایران.
آدرس پست الکترونیکی: fi.asgarii@gmail.com)
امیر نجفی
3
(دانشیار گروه مدیریت، واحد زنجان، دانشگاه آزاد اسلامی، زنجان، ایران.)
بابک حاجی کریمی
4
(استادیار گروه مدیریت، واحد زنجان، دانشگاه آزاد اسلامی، زنجان، ایران.)
کلید واژه: زنجیره تامین, کلمات کلیدی: کشف و ارزیا بی تقلب, ANN, Big data,
چکیده مقاله :
چکیدهدر ده ههای اخیر، رقابت جهت ارائه ارزش برتر به مشتریان، از رقابت میان بنگاهها به سمت رقابت میان زنجیرههای تامین سوقپیدا کرده است. طراحی مناسب زنجیره تامین با توجه به ابعاد اقتصادی، اجتماعی و زیستمحیطی در سطوح استراتژیک،تاکتیکی و عملیاتی ضامن بقا و توسعه پایدار بنگاهههای فعال در هر بخش از زنجیره تامین است. یکی از مسائل مهم در زنجیرهتامین، وجود تقلب و مخاطرات مرتبط با آنها در سرتاسر زنجیره است. همچنین با توجه به حجم پژوه شهای علمی و تجربیصورت گرفته در سالهای اخیر، مساله کشف و ارزیابی تقلب با استفاده از روشهای محاسباتی به تنهایی یک موضوع بااهمیت برا ی پژوهش م یباشد . از مه مترین چالشهای کشف و ارزیابی تقلب میتوان به د ر دسترس نبود ن مجموعههای داد هایواقعی، وجود مجموعههای داد های نامتقارن، عظیم بودن اندازه مجموعههای داد های، رفتار پویای متقلب و پراکندگی رخدادهایتقلبآمیز اشاره نمود که نتیجه این دو عدم اطمینان و ابهام در تصمیمسازی است. لذا هدف این پژوهش، ارائه مدلی جهتکشف و ارزیابی تقلب در فرایند زنجیره تامین مبتنی بر رو یکرد تلفیقی ANN-Big data میباشد. این پژوهش از نظر هدف،یک پژوهش توسعهای و کاربردی است. نخست به دلیل ا یجاد ی ک مدل جامع، با در نظر گرفتن منابع مختلف اخذ تصمیم درخصوص قانون ی یا تقلبآمیز بودن تراکنشهای کارت الکترونیک و الزامات تح لیل بزرگ داده، سعی ب ه توسعه دانشی نظری د راین حوزه دارد، این پژوهش را م یتوان از لحا ظ ماهی ت ی ک پژوهش تحلیل ی ریاضی در نظر گرفت، بر همین اساس اینپژوهش از نظر رویکرد، ی ک پژوهش کمی است . فرآیند استاندارد میا ن صنعتی داد هکاوی ی ا به اختصار CRISP DM )چپمنو همکاران 2000 (، ب هعنوان روششناسی تحلیل مورد استفاده قرار کرفته است. از مدلهای خوشهبندی و الگوریت مهای K-means ، شبکه عصبی Kohone ، کشف ناهنجاری مبتنی بر خوشهبندی، مدل مخفی مارکوف، روش پردازش موازینگاشتکاهش و مدل ه مجوشی دمپستر شیفر فازی استفاده میشود. تجزیه و تحلیل با استفاده از نرمافزارهای MATLAB و R انجامشده است. مدل پیشنهادی توانسته است از نظر خروجی و زمان اجرا ، نسبت به مدلهای دیگر عملکرد برتری را به نمایشگذارد.کلمات کلیدی: کشف و ارزیا بی تقلب ، زنجیره تامین ، ANN, Big data
Abstract In recent decades, competition to provide superior value to customers has shifted from competition between companies to competition between supply chains. Appropriate design of the supply chain with regard to the economic, social and environmental dimensions at the strategic, tactical and operational levels guarantees the survival and sustainable development of enterprises active in each part of the supply chain. One of the important issues in the supply chain is the existence of fraud and the risks associated with it throughout the chain. Also, according to the amount of scientific and experimental research conducted in recent years, the problem of detecting and evaluating fraud using computational methods alone It is an important topic for research. The most important challenges of fraud detection and evaluation are the unavailability of real data sets, the existence of asymmetric data sets, the large size of data sets, the dynamic behavior of fraudsters and the dispersion of fraudulent events, which are the result of these two Uncertainty and ambiguity in decision making. Therefore, the aim of this research is to provide a model to detect and evaluate fraud in the supply chain process based on the ANN-Big data integrated approach. In terms of its purpose, this research is a developmental and applied research. First, due to the creation of a comprehensive model, taking into account the various sources of decision-making regarding the legitimacy or fraud of electronic card transactions, and the requirements of big data analysis, it tries to develop theoretical knowledge in this field, this research can be considered Considering the nature of a mathematical analytical research, on the basis of this research, in terms of approach, it is a quantitative research. The inter-industry standard process of data mining or CRISP DM (Chapman et al. 2000) has been used as the analysis methodology. Clustering models and K-means algorithms, Kohonen neural network, anomaly detection based on clustering, hidden Markov model, parallel processing method of reduction mapping and fuzzy Dempster cipher fusion model are used. The analysis was done using MATLAB and R software. The proposed model has been able to show superior performance compared to other models in terms of output and execution time.
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