شناسایی و اولویت‌بندی چالش‌های پیاده‌سازی سیستم‌های خدمات محصول هوشمند به‌روش بهترین بدترین راف فازی

هدف: هدف از این پژوهش، بررسی و شناسایی چالش‌های پیاده‌سازی سیستم‌های خدمات محصول هوشمند و ارزیابی و اولویت‌بندی این چالش‌ها، به‌عنوان یکی از اقدام‌های اساسی اولیه برای اجرای این سیستم است.روش: در این مقاله ابتدا چالش‌های پیاده‌سازی سیستم‌های خدمات محصول هوشمند، از طریق مرور ادبیات، بررسی اسناد، مصاحبه با صاحب‌نظران و روش دلفی فازی شناسایی شد. در ادامه با استفاده از روش بهترین بدترین راف فازی، چالش‌های شناسایی‌شده اولویت‌بندی شدند.یافته‌ها: بیست چالش پیاده‌سازی سیستم‌های خدمات محصول هوشمند، در هفت گروه اصلی طبقه‌بندی و به‌روش بهترین بدترین راف فازی ارزیابی شدند. بر اساس نتایج، چالش‌های مالی و فرایندی، مهم‌ترین گروه شناخته شد. روش بهترین بدترین راف فازی با بررسی هم‌زمان عدم قطعیت درون‌فردی و بین‌فردی باعث می‌شود که ارزیابی به‌دست‌آمده، دقت و عینیت بیشتری را در مقایسه با روش بهترین بدترین مبتنی بر منطق فازی یا  مبتنی بر تئوری راف ارائه دهد.نتیجه‌گیری: با استفاده از نتایج این پژوهش شرکت‌های تولیدی و خدماتی، می‌توانند چالش‌های پیش‌ِرو در مسیر پیاده‌سازی سیستم‌های خدمات محصول هوشمند را دقیق‌تر ارزیابی کنند و به‌نحوی مناسب،‏ منابع محدود سازمانی را برای رفع چالش‌های شناسایی‌شده اختصاص دهند.

identifying and prioritizing challenges of implementing smart product-service systems using the best-worst rough-fuzzy method

objective: smart product-service systems (spss) as a new paradigm was presented recently, which created considerable changes in the industry and production, especially in the production of smart home appliances. despite many achievements in implementing spss, this system faces many challenges in the development and implementation processes. this study aims to identify and analyze the challenges of implementing an spss and evaluate and prioritize them as one of the most basic initial steps in implementing the system.methods: in this paper, first, the challenges of implementing spss are identified through literature review, document review, interviews with experts, and the fuzzy delphi method. then the best-worst rough-fuzzy method is used to prioritize the identified challenges.results: twenty challenges of implementing spss were classified into seven main groups consisting of staff, information security, technology, finance and process, design, infrastructure, and government/market, and evaluated by the best-worst rough-fuzzy method. the main challenges are financial and process, technology, employees, design, market and government, infrastructure and information security, respectively. in the same way, the weight and rank of all twenty challenges classified in these groups were determined. since evaluating and prioritizing the challenges of implementing the spss is a multi-criteria decision-making process and includes uncertainties that may lead to incorrect evaluation results, in this research, the concepts of rough set theory and fuzzy logic, which are efficient in such conditions, are used as the prioritization method. since the best-worst rough-fuzzy method simultaneously integrates both intra-individual and interpersonal uncertainty and takes advantage of the features of the fuzzy set in managing individual verbal ambiguities and the benefits of the rough set theory in examining the diversity of group preferences, the model has a good performance for finding optimal and fuzzy rough weights of challenges and makes the obtained evaluation more accurate and objective than the best-worst method based on fuzzy logic or rough theory.conclusion: since spss is rapidly expanding as a new field in manufacturing and industry, and the development of smart home appliances is one of the waves of technology in the future and will provide new and exciting opportunities for businesses, home appliance manufacturing companies are bound to compete to get their share of these opportunities. industrial enterprises, especially in the home appliance industry, can be more profitable, and improve their market share in this industry by turning to the integration of products and smart services based on information and communication technology and joining the production companies that use spss. using the results of this study, manufacturing and service companies can more accurately assess the challenges ahead in implementing spss and appropriately allocate organizational resources to manage the challenges.