طراحی مدل ارزیابی عملکرد زنجیره تامین خدمات محصول در صنایع لوازم خانگی با استفاده از تحلیل عاملی و شبکه‌های عصبی فازی با مطالعۀ موردی شرکت‌های لوازم خانگی در کشور ایران

در این مقاله مدلی مفهومی برای ارزیابی عملکرد زنجیره تامین خدمات در صنایع لوازم خانگی ارائه شده است. نوع زنجیره تامین خدمت– محصول و به‌کارگیری شبکه‌های عصبی– فازی برای ارزیابی عملکرد این نوع زنجیره تامین خدمات لحاظ شده است. هدف از پژوهش حاضر، توسعۀ مدلی جامع برای ارزیابی عملکرد با تاکید بر سنجه های عملکرد مدل های زنجیره تامین خدمات به‌جای زنجیره تامین تولید در صنایع لوازم خانگی است. روش شناسی این پژوهش ازنظر اجرا، توصیفی اکتشافی و با رویکرد پیمایشی و تحلیل داده ها به‌روش کمی و با استفاده از تحلیل عاملی اکتشافی و تاییدی است. نمونه ای شامل 58 شرکت مطرح لوازم خانگی و نرم‌افزارهای smartpls، spss و matlab برای تحلیل داده ها استفاده شده است. درنهایت 10 سازۀ اصلی و 29 معیار عملکرد از نتایج این پژوهش برای ارزیابی عملکرد این نوع زنجیره تامین خدمات به دست آمده است. هم‌چنین عملکرد چندین شرکت لوازم خانگی با استفاده از این مدل و به‌کارگیری شبکه های عصبی– فازی، ارزیابی شده است.

Designing a productservice supply chain performance evaluation model in the home appliance industry using factor analysis and fuzzy neural networks Case study: home appliance companies in Iran

The aim of this study is to propose a comprehensive performance evaluation model with emphasis on service performance metrics in the serviceproduct supply chain rather than the production supply chain in the home appliance industry and using neuralfuzzy networks for performance evaluation. The present study is typically a descriptiveexploratory research with survey approach in which, data analysis has been conducted using quantitative method and exploratory and confirmatory factor analysis. For the purpose of this study, a sample of 58 home appliance companies has been selected and SmartPLS, SPSS and Matlab software have been used for data analysis. Findings indicated 10 main constructs and 29 performance criteria obtained for evaluating the performance of service supply chain and fuzzy neural networks of several home appliance companies.Introduction: Based on predictions, services are a key component of the growth of the global economy in future (Arnold et al. 2011). Acording to Jane and Kumar (2012), services play a critical role in a supply chain. Also, according to Wang et al. (2015), a "product" or "service" must exist in each supply chain which is produced by the upstream sectors and delivered to downstream. Recently due to increasing customer expectations, companies’ competition has been replaced by the supply chains competition and as a result, competition has been increased in the simultaneous supply of products and services. This has led to challenges in integrating companies and in coordinating the materials, information and financial flow that were previously overlooked. Accordingly, a new managerial philosophy has been developed known as ProductService Supply Chain (PSSC) (Stanley & Wisner, 2002). This study seeks to develop a performance evaluation model for the productservice supply chain in the home appliance industry, which is finally solved using Adaptive NeuroFuzzy Inference System (ANFIS). Design/Approach: In this paper, performance evaluation constructs and criteria of service supply chain are identified by reviewing the literature and exploratory and confirmatory factor analysis and then, the performance evaluation of service supply chains in Iran’s home appliance industry has been performed using these constructs, criteria and ANFIS.Findings and Discussion: Based on the findings, ten main extracted constructs can be suggested for the performance evaluation of the supply chain. They include "Operational Performance (OP)", "Strategic Performance (SP)", "Financial Performance (FP)", "Performance of Information and Communication Technology (PICT)", “Return Performance” (REP), “Risk Performance (RIP)”, “Logistic Performance (LP)”, “Market Performance (MP)”, “Internal Structure Performance (PIS)” and “Growth and Innovation Performance (PGI)”, among which, the Strategic Performance (SP) and Return Performance (REP) are the most important and the least important constructs, respectively.ConclusionsBased on the findings, the following practical recommendations are suggested to the companies:Enhancing the demand forecasts performance and utilizing more appropriate methods and software to improve forecasts in demand and order management areas.Improving the return management status by increased attention and more investment in return management processes.Effective investment in service development management to enhance the R&D services performance.Utilizing risk management approaches and methods to identify and take preventive actions on the risks in the companies’ service supply chain. ReferencesArnold, J.M., Javorcik, B.S., & Mattoo, A. (2011). “Does services liberalization benefit manufacturing firms? evidence from the Czech Republic”. Journal of International Economics, 85(1), 136146.Azar, A., Gholamzadeh, R., & Ghanavati, M. (2012). PathStructural Modeling in Management: SmartPLS Application, Tehran: Publishing Knowledge Look.Rezaei Moghadam S., Yousefi, O.,  Karbasisan, M. and Khayambashi, B. (2018). “Integrated productiondistribution planning in a reverse supply chain via multiobjective mathematical modeling; case study in a hightech industry”. Production and Operations Management, 9(2), 5776.Zhou, H., & Benton, W. C. (2007). “Supply chain practice and information sharing”. Journal of Operations Management, 25(6), 13481365.