Among generic technology management activities, rapid technology identification and selection stand as the significant determinants of technology adoption success in the digital transformation era. Especially for manufacturing SMEs in developing countries, rapid digital technologies are critical since they struggle to protect their competitiveness in global value chains threatened by digitalization. Previous studies introduce various multi-criteria decision-making model-based approaches to identify and select appropriate manufacturing technologies. However, these approaches were relatively rigid and required an advanced understanding of the technology for criteria and alternative settings and evaluation. Decision-makers need more flexible and scalable contextual frameworks for technology selection in digitalization. Since digital technologies offer both benefits and challenges, the decision-making models should reflect this dialectic nature of Industry 4.0 adoption and contextually optimize their decisions by combining multiple quantitative methods for technology identification and selection. Besides, case studies on digital technology selection are rare in manufacturing SMEs from developing country context in the literature. In this context, this study proposes a technology selection framework that utilizes the three dimensions (industry 4.0 technologies, benefits, and challenges) and combines AHP with a QFD-inspired intervention matrix and an optimization model by Mixed Integer Programming (MIP). The proposed model is validated with a case study from the automotive supplier industry in Turkey with the data provided from interviews and a Delphi survey with 11 experts from the digitalization value chain of the selected industry. Case study results revealed that the highest benefits of industry 4.0 lie in process/quality efficiency improvement and reduced inventory. At the same time, data analytics and sensor technologies occurred as the most critical tools. Significant challenges of digital technology adoption are insufficient expert know-how and budget constraints.