It is widely accepted that for the successful implementation of lean manufacturing, the senior management commitment is of great importance. However, the lean journey is usually a long one, and eventually management commitment creeps. Furthermore, the involvement of employees in daily improvements is also critical for the success of implementation. Lean leadership can be considered as a way of sustaining and improving the employee performance in lean production systems. In the present study, a thorough literature review is presented focusing in reviewing the principles of lean leadership and the practices that can lead in improving the employee performance. Furthermore, the characteristics and qualities of lean leader are discussed Paper originally presented at Manufacturing Systems 4.0 – Proceedings of the 50th CIRP Conference on Manufacturing Systems, held 3 – 5 May, 2017, Taichung City, Taiwan

The article aims to identify the scientific challenges and point out future research directions on Cost Engineering. The research areas covered in this article include Design Cost; Manufacturing Cost; Operating Cost; Life Cycle Cost; Risk and Uncertainty management and Affordability Engineering. Collected information at the Academic Forum on Cost Engineering held at Cranfield University in 2008 and further literature review findings are presented. The forum set the scope of the Cost Engineering research, a brainstorming was held on the forum and literatures were further reviewed to understand the current and future practices in cost engineering. The main benefits of the article include coverage of the current research on cost engineering from different perspectives and the future research areas on Cost Engineering.

With the rapid development of Industrial 4.0, the modern manufacturing system has been experiencing profoundly digital transformation. The development of new technologies helps to improve the efficiency of production and the quality of products. However, for the increasingly complex production systems, operational decision making encounters more challenges in terms of having sustainable manufacturing to satisfy customers and markets’ rapidly changing demands. Nowadays, rule-based heuristic approaches are widely used for scheduling management in production systems, which, however, significantly depends on the expert domain knowledge. In this way, the efficiency of decision making could not be guaranteed nor meet the dynamic scheduling requirement in the job-shop manufacturing environment. In this study, we propose using deep reinforcement learning (DRL) methods to tackle the dynamic scheduling problem in the job-shop manufacturing system with unexpected machine failure. The proximal policy optimization (PPO) algorithm was used in the DRL framework to accelerate the learning process and improve performance. The proposed method was testified within a real-world dynamic production environment, and it performs better compared with the state-of-the-art methods.