Purpose The purpose of this paper is to identify the obstacles and challenges of talent management as well as its success factors in Iranian automotive industries. Design/methodology/approach This research is a kind of discoverer research done by qualitative approach. The methodology to data collection was interview and research sample was 15 manager in automotive industries. Data analysis was carried out by the coding method, and concepts, minor and major contexts were extracted and lastly the conceptual framework was formed. Findings Based on the findings of the research, framework of obstacles and challenges in talent management was classified into four categories that are structural challenges and barriers, environmental challenges and barriers, behavioral challenges and barriers and lastly managerial challenges and barriers. In addition, the framework of talent management success factors were categorized into three main sections that are structural success factors, environmental success factors and finally managerial success factors. Originality/value Problem finding of talent management in automotive industry and identifying obstacles, challenges and success factors in talent management with qualitative approach through interviews with experts from the Iranian industries is the research value.

Nowadays, the increase in data acquisition and availability and complexity around optimization make it imperative to jointly use artificial intelligence (AI) and optimization for devising data-driven and intelligent decision support systems (DSS). A DSS can be successful if large amounts of interactive data proceed fast and robustly and extract useful information and knowledge to help decision-making. In this context, the data-driven approach has gained prominence due to its provision of insights for decision-making and easy implementation. The data-driven approach can discover various database patterns without relying on prior knowledge while also handling flexible objectives and multiple scenarios. This chapter reviews recent advances in data-driven optimization, highlighting the promise of data-driven optimization that integrates mathematical programming and machine learning (ML) for decision-making under uncertainty and identifies potential research opportunities. This chapter provides guidelines and implications for researchers, managers, and practitioners in operations research who want to advance their decision-making capabilities under uncertainty concerning data-driven optimization. Then, a comprehensive review and classification of the relevant publications on the data-driven stochastic program, data-driven robust optimization, and data-driven chance-constrained are presented. This chapter also identifies fertile avenues for future research that focus on deep-data-driven optimization, deep data-driven models, as well as online learning-based data-driven optimization. Perspectives on reinforcement learning (RL)-based data-driven optimization and deep RL for solving NP-hard problems are discussed. We investigate the application of data-driven optimization in different case studies to demonstrate improvements in operational performance over conventional optimization methodology. Finally, some managerial implications and some future directions are provided.