Providing a model for evaluate the efficiency of supply chain based on information flow using fuzzy network data envelopment analysis

Subject Areas : Statistics

tahmineh Noohi tehrani ¹ , maryam shoar ² , saber saati mohtadi ³

1 - PhD Student - Department of Information Technology Management,- North Tehran Branch-Islamic Azad University-Tehran-Iran
2 - Assistant Professor, Department of Industrial Management, North Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Associate Professor, Department of Mathematics, Islamic Azad University, North Tehran Branch, Tehran, Iran

Received: 2021-02-16 Accepted : 2021-10-25 Published : 2023-01-21

Keywords: تحلیل پوششی داده‌های شبکه ای فازی, جریان اطلاعات, مدیریت زنجیره تامین, کارایی,

Abstract :

Rapid development toward globalization, a competitive market, significant technological advances, and high customer expectations have encouraged companies to reduce costs and increase their competitive advantage. One of the things that can help companies achieve a competitive advantage is supply chain management. Information acts as an intermediary between all activities and operations within the supply chain. The innovation of this research can be shown in two aspects of application and modeling. In terms of application, with a review of the literature and investigating the application of multi-stage and network data development analysis in the supply chain, it was revealed that so far, the efficiency of financial and physical flows in the literature has been evaluated more and the indicators are mostly related to two financial and physical flows in the supply chain, therefore there is more research opportunity to evaluate the efficiency of information flow within the supply chain. Measuring the efficiency of information flow should be an integral part of supply chain management. Therefore, this research aimed to present a model to evaluate the efficiency of information flow in the supply chain. In terms of mathematical modeling, research innovation is considering the model of the network and reversible relationship in the supply chain. With a review of the literature, the indicators are investigated to evaluate the efficiency of information flow in the supply chain, and the validity of indicators is examined by the fuzzy Delphi method. Then the decision making unit and inputs and outputs of the model are introduced. In this research, the fuzzy network data envelopment analysis is used. In order to implement the models, we used the GAMS software.

References:

[1] Sahebjamnia, N., (2020), Resilient supplier selection and order allocation under uncertainly. Scientia Iranica, 27(1), 411-426.

[2] Dai, Z., Aqlan, F., Zheng, X., & Gao, k., (2018), A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints. Computers & Industrial Engineering, 5(8), 15-33.

[3] Quang, H & et al. (2016). An extensive structural model of supply chain quality management and firm performance. International Journal of Quality & Reliability Management, 33 (4): 1-22.

[4] Kros, S.A., (2014). An exploration of quality management practices ،perceptions and program maturity. International Journal of Operations & Production Management, 34(6), 786 -806.

[5] Tripathy, S., Aich, S., Chakraborty, A. & Lee, G., M.,(2016), Information technology is an enabling factor affecting supply chain performance in Indian SMEs: a structural equation modelling approach. Journal of Modelling in Management, 11(1), 269 - 287.

[6] Pham, H.C., Nguyen, T-T., McDonald,S., Tran-Kieu,N,Q.,(2019), Information Sharing in Logistics Firms: An Exploratory Study of the Vietnamese Logistics Sector, The Asian Journal of Shipping and Logistics 35(2),87-95.

[7] Mitchell, Erin. M, Kovach,Jamison.V, (2016), Improving supply chain information sharing using Design for Six Sigma, European Research on Management  and Business Economics 22,147-154.

[8] هوگس، مایکل.، (1387). اصول و مبانی مدیریت زنجیره تامین، شیخ سجادیه، محسن.، اکبری جوکار، محمدرضا.،تهران، آدینه.

[9] Farajpour, F., Yousefli, A., (2018), Information flow in supply chain: A fuzzy TOPSIS parameters ranking, Uncertain Supply Chain Management 6, 181-194.

 [10] Shaout, A., & Yousif, M. K., (2014), Performance evaluation–Methods and techniques survey. International Journal of Computer and Information Technology, 3(5), 966-979.

[11] Badenhorst-Weiss, J.A., Maurer, C. & Brevis-Landsberg, T., (2013), Developing measures for the evaluation of information flow efficiency in supply chains. Journal of Transport and Supply Chain Management7(1),Art. #88, 13 pages. http://dx.doi.org/  10.4102/jtscm.v7il.88.

 

[12] زارعی محمودآبادی، محمد.، (1397)، مدل‌های نوین تحلیل پوششی داده‌ها (DEA)، انتشارات دانشگاه یزد، ص75-77.

[13] جهانشاهلو، غ.، حسین‌زاده لطفی، ف.، نیکومرام.، ه.، (1387)، تحلیل پوششی داده‌ها و کاربردهای آن، تهران، دانشگاه آزاد اسلامی-واحد علوم و تحقیقات.

[14] مومنی، م.، (1389)، مباحث نوین تحقیق در عملیات، مولف، ص166و175.

[15] Chen, C., Yan, H., (2011), Network DEA model for supply chain performance evaluation, European Journal of Operational Research 213 (1), 147-155.

[16]  سالاری، م.، زندیه، م.، (1395)، ارزیابی کارایی فروشگاه های اینترنتی با استفاده از مدل دو مرحله‌ای تحلیل پوششی داده ها، پژوهش‌های مدیریت در ایران، دوره 20، شماره 3.

[17] نیلچی،م.، فدائی نژاد، م. ا.، رضوی حاجی آقا، س.ح.، بدری، ا.، (1396) ، ارائه مدل تحلیل پوششی داده‌های چند بخشی جدید برای ارزیابی کارایی شعب بانک ها، فصلنامه علمی-پژوهشی مطالعات مدیریت صنعتی- سال پانزدهم، شماره46، ص73 تا96.

[18] سلیمانی دامنه، ر.، (1398)، توسعه یک مدل تحلیل پوششی داده‌های شبکه‌ای (NDEA) مضربی جهت بررسی ساختار درونی واحدهای تصمیم‌گیرنده، فصلنامه مدیریت صنعتی دانشکده علوم انسانی، دانشگاه آزاد اسلامی واحد سنندج- سال چهاردهم، شماره 49، ص 53-72.

[19] واعظی، ا.، نجفی، س. ا.، حاجی مولانا، س.م.، حسین‌زاده لطفی، ف.، احدزاده نمین،م.، (1399)، اندازه‌گیری کارایی یک شبکه سه مرحله‌ای با استفاده از رویکرد تحلیل پوششی داده‌ها با در نظر گرفتن مرز دوگانه، پژوهش‌های نوین در ریاضی، سال ششم، شماره بیست و چهارم، 141-158.

[20] پیکانی، پ.، قیدر خلجانی، ج.، (1399)، ارزیابی عملکرد زنجیره ارزش پروژه‌های تحقیق و توسعه برای سیستم‌ها و محصولات پیچیده: رویکرد تحلیل پوششی داده‌های سه مرحله‌ای فازی، پژوهش‌های نوین در ریاضی، سال ششم، شماره بیست و پنجم، 41-58.

[21] قلیها، ع.م.، حسین‌زاده لطفی، ف.، شهریاری، م. ر.، واعظ قاسمی، م.، (1399)، ارایه یک مدل بر اساس متغیرهای کمکی برای محاسبه کارایی و اثربخشی ایستگاه‌های مترو شهر تهران در تحلیل پوششی داده‌ها با ورودی و خروجی‌های وابسته، پژوهش‌های نوین در ریاضی، سال ششم، شماره بیست و هفتم، 5-16.

[22] Shafiee, M., Hosseinzadeh Lotfi, F., Saleh, H.,(2014), Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach, Applied Mathematical Modelling 38, 5092-5112.

 

[23] Tavana, M., Kaviani, M.A., Di caprio, D., Rahpeyma, B., (2016), A two-stage data envelopment analysis model for measuring performance in three-level supply chains, Measurement 78,322-333.

 

[24] Huang, C-W., (2018), Assessing the performance of tourism supply chains by using the hybrid network data envelopment analysis model, Tourism Management 65,303-316.

 

[25] Cannella, S.,(2014), Order-Up-To policies in Information Exchange supply chains, Applied Mathematical Modelling 38, 5553-5561.

 

[26] Dominguez, R., Cannella, S., M.Framinan, J., (2014), On bullwhip-limiting strategies in divergent supply chain networks, Computers & Industrial Engineering 73, 85-95.

 

[27] Fu, D., M.lonescu, C., Aghezzaf, EL-H., De Keyser, R., (2014), Decentralized and centralized model predictive control to reduce the bullwhip effect in supply chain management, Computers & Industrial Engineering 73, 21-31.

[28] Costantino, F., Di Gravio, G., Shaban, A., Tronci, M., (2015), The impact of information sharing on ordering policies to improve supply chain performances, Computers & Industrial Engineering 82, 127-142.

 

[29] Hosoda, T., M.Disney, S., Gavirneni, S., (2015), The impact of information shring, random yield, correlation, and lead times in closed loop supply chains, European Journal of Operational Research 246, 827-836.

 

[30] Li, T., Zhang, H.,( 2015), information sharing in a supply chain with a make-to-stock manufacturer, Omega 50, 115-125.

 

[31] Wang, X., M.Disney, S.,(2016), The bullwhip effect: progress, trends and directions, European Journal of Operational Research 250, 691-701.

 

[32] Kaipia, R., Holmstrom, J., Smaros, J., Rajala, R., (2017), Information sharing for sales and operations planning: Contextualized solutions and mechanisms, Journal of Operations Management 52, 15-29.

 

[33] Kembro, J., Naslund, D., Olhager, J., (2017), Information sharing across multiple supply chain tiers: A Delphi study on antecedents, International Journal of Production Economics 193, 77-86.

 

[34] M.Ali, M., Zied Babai, M., E.Boylan, J., A.Syntetos, A., (2017), Supply chain forcasting when information is not shared,  European Journal of Operational Research 260, 984-994.

 

[35] Quigley, J., Walls, L., Demirel, G., L. MacCarthy, B., Parsa, M., (2018), Supplier quality improvement: The value of information under uncertainly, European Journal of Operational Research 264, 932-947.

 

[36] Chileshe, N., Senerath Jayasinghe, R., Rameezdeen, R., (2019), Information flow-centric approach for reverse logistics supply chains, Automation in construction 106, 102858.

 

[37] Cheng, C-H. & Lin, Y., (2002), Evaluating the best main battle tank using fuzzy   decision theory with linguistic criteria evaluation, European. Journal of Operational Research, 142: 174-186.