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Lean Thinking aims to eliminate non-value added activities in the system. The application of Lean Thinking allows firms, enterprises and organizations to manage quality, decrease variation, reduce costs by eliminating waste. The real time data/ information are important to understand the value of the organization. In order to maintain flow, information should be accurate, up to date and available in the shortest possible time. To reveal the continuous improvement opportunities data must be processed effectively and efficiently. In this point the importance of Business Intelligence (BI) Technologies arises. The integration of Lean Thinking and BI complement each other by BI's providing useful information to lean decision making process improvements. In addition to IT integration with Lean Thinking, the utilized IT software needs to be designed in a way that supplies right information, on the right time and on the right way. The chapter organized in four subsections which are dealt with respectively, BI with Lean Thinking, Lean Thinking with BI, Lean BI, and Adaptation of Lean Tools in BI.

Interest in multiobjective permutation flow shop scheduling (PFSS) has increased in the last decade to ensure effective resource utilization. This study presents a modified self-adaptive local search (MSALS) algorithm for the biobjective permutation flow shop scheduling problem where both makespan and total flow time objectives are minimized.Compared to existing sophisticated heuristic algorithms, MSALS is quite simple to apply to different biobjective PFSS instances without requiring effort or time for parameter tuning. Computational experiments showed that MSALS is either superior to current heuristics for Pareto sets or is incomparable due to other performance indicators of multiobjective problems.PFSS problems are NP-hard problems even for a single objective and that is why there are numerous stateof-the-art metaheuristic algorithms for the approximate solutions of the problem. Referring interested readers to the comprehensive reviews of [3] and [4], Section 2 covers the studies that presented metaheuristic algorithms for biobjective PFSS problems. The metaheuristic algorithms given in Section 2 are all managed by a set of parameters, which has a significant impact on the solution quality and/or computational time. Searching for an * Correspondence: cigdem.uslu@marmara.edu.tr This work is licensed under a Creative Commons Attribution 4.0 International License.