Understanding Measuring And Improving Daily Management
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Understanding, Measuring, and Improving Daily Management
Understanding, Measuring, and Improving Daily Management explains the critical parts of a continuous improvement strategy to achieve Operational Excellence and where reactive improvement through effective daily management fits in. In addition, it shows the consequences to your Operational Excellence journey if daily management is not performed well. Reactive improvement develops the capability and discipline within the organization to be able to rapidly recover from an event or incident that stops you from achieving your expected or target performance for the day, shift, or hour and most importantly -- your ability to capture the learning and initiate corrective actions so that the event or incident will not re-occur anywhere across the organization. As such, reactive improvement focuses on improving daily management through your daily review meetings, your information centers supporting the daily review meetings, and your frontline problem-solving root cause analysis capability at all levels. The book introduces the seven elements of reactive improvement that must work in concert for effective daily management and allows the reader to rate their site or department to determine their starting point compared to best practices: 1. Supportive organization structure to support development of your people so they have ownership and accountability for the performance of their area of responsibility; 2. Effective frontline leaders to ensure everyone else in the leadership structure are not working down a level; 3. Appropriate measures with expected targets that are linked to the site’s Key Success Factors for Operations to ensure goal alignment, and are relevant to the area being focused on; 4. Structured daily review meetings to identify opportunities (problems/incidents) and monitor progress of their solution so they don’t happen again; 5. Visual information centers that visually display daily and trending performance along with monitoring of actions to address problems/issues raised; 6. Frontline problem-solving root cause analysis capability across the site; and 7. Rapid sharing of learning capability across shifts, departments, and the organization. The author outlines in detail why each of the seven elements are important to achieving Operational Excellence, and most importantly, how to implement each element supported with many templates and tools.
Understanding, Measuring, and Improving Overall Equipment Effectiveness
Understanding, Measuring, and Improving Overall Equipment Effectiveness: How to Use OEE to Drive Significant Process Improvement explains why the Overall Equipment Effectiveness (OEE) measure was created and how it should be used. Based on 20 years of hands on experience applying OEE at over 150 sites, this step-by-step practical guide provides templates, assessments, a comprehensive loss-analysis framework to identify all possible variables that could affect OEE, and supporting spreadsheets to measure and improve OEE. It outlines the different operational situations in which OEE can foster improvements, and the implications, before providing an easy-to-understand template for creating appropriate definitions for all the losses and a loss model. The author explains how to calculate OEE using examples to improve performance, and then shows, in detail, how to use an OEE Loss Analysis Spreadsheet to understand all losses, set an ideal vision, and then classify losses so improvement can be approached in the most sustaining way.
Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making
This book comprehensively discusses nature-inspired algorithms, deep learning methods, applications of mathematical programming and artificial intelligence techniques. It will further cover important topic such as linking green supply chain management practices with competitiveness, industry 4.0, and social responsibility. This book: Addresses solving practical problems such as supply chain management, take-off, and healthcare analytics using intelligent computing Presents a comparative analysis of machine learning algorithms for power consumption prediction Discusses a machine learning-based multi-objective optimization technique for load balancing in an integrated fog cloud environment Illustrates a data-driven optimization concept for modeling environmental and economic sustainability Explains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goals The text is primarily written for graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, mathematics and statistics, computer science and engineering.