13,156 research outputs found
Managing Well Integrity using Reliability Based Models
Imperial Users onl
Systems Engineering Leading Indicators Guide, Version 1.0
The Systems Engineering Leading Indicators guide set reflects the initial subset of possible indicators that were considered to be the highest priority for evaluating effectiveness before the fact. A leading indicator is a measure for evaluating the effectiveness of a how a specific activity is applied on a program in a manner that provides information about impacts that are likely to affect the system performance objectives. A leading indicator may be an individual measure, or collection of measures, that are predictive of future system performance before the performance is realized. Leading indicators aid leadership in delivering value to customers and end users, while assisting in taking interventions and actions to avoid rework and wasted effort.
The Systems Engineering Leading Indicators Guide was initiated as a result of the June 2004 Air Force/LAI Workshop on Systems Engineering for Robustness, this guide supports systems engineering revitalization. Over several years, a group of industry, government, and academic stakeholders worked to define and validate a set of thirteen indicators for evaluating the effectiveness of systems engineering on a program. Released as version 1.0 in June 2007 the leading indicators provide predictive information to make informed decisions and where necessary, take preventative or corrective action during the program in a proactive manner. While the leading indicators appear similar to existing measures and often use the same base information, the difference lies in how the information is gathered, evaluated, interpreted and used to provide a forward looking perspective
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Knowledge management in project-base organizations: the interplay of time orientations and knowledge interventions
The common perception is that all types of work and work organizations appear to involve knowledge: knowledge intensive work, knowledge workers, knowledge products, customerrelated knowledge and knowledge intensive organizations. Therefore, as organizations increasingly organize their activities in the form of projects, effective ways of knowledge management are needed to deliver successful and timely outcomes. However, little research has been done in the area that integrates time orientations into the process of knowledge management. Using the approach of grounded theory, this paper investigates the interplay between time orientations and knowledge interventions through interviews with international project managers drawn from different types of project-based organizations in Sweden and Italy. The perceptions and experiences of the managers are used to construct a model of time orientation and knowledge interventions in project-based organizations. Time orientations are shown to play a critical role in the success or failure of projects. The model integrates time orientations into the project life cycle and illustrates how effectively knowledge interventions can be used to achieve project milestones and meet overall deadlines
Systems Engineering Leading Indicators Guide, Version 2.0
The Systems Engineering Leading Indicators Guide editorial team is pleased to announce the release of Version 2.0. Version 2.0 supersedes Version 1.0, which was released in July 2007 and was the result of a project initiated by the Lean Advancement Initiative (LAI) at MIT in cooperation with:
the International Council on Systems Engineering (INCOSE),
Practical Software and Systems Measurement (PSM), and
the Systems Engineering Advancement Research Initiative (SEAri) at MIT.
A leading indicator is a measure for evaluating the effectiveness of how a specific project activity is likely to affect system performance objectives. A leading indicator may be an individual measure or a collection of measures and associated analysis that is predictive of future systems engineering performance. Systems engineering performance itself could be an indicator of future project execution and system performance. Leading indicators aid leadership in delivering value to customers and end users and help identify interventions and actions to avoid rework and wasted effort.
Conventional measures provide status and historical information. Leading indicators use an approach that draws on trend information to allow for predictive analysis. By analyzing trends, predictions can be forecast on the outcomes of certain activities. Trends are analyzed for insight into both the entity being measured and potential impacts to other entities. This provides leaders with the data they need to make informed decisions and where necessary, take preventative or corrective action during the program in a proactive manner.
Version 2.0 guide adds five new leading indicators to the previous 13 for a new total of 18 indicators. The guide addresses feedback from users of the previous version of the guide, as well as lessons learned from implementation and industry workshops. The document format has been improved for usability, and several new appendices provide application information and techniques for determining correlations of indicators. Tailoring of the guide for effective use is encouraged.
Additional collaborating organizations involved in Version 2.0 include the Naval Air Systems Command (NAVAIR), US Department of Defense Systems Engineering Research Center (SERC), and National Defense Industrial Association (NDIA) Systems Engineering Division (SED). Many leading measurement and systems engineering experts from government, industry, and academia volunteered their time to work on this initiative
Condition Reporting and Resolution
This procedure facilitates a safety conscious work environment by providing a mechanism for employees to make management aware of existing and potential conditions. This procedure establishes the responsibilities and process to be used to ensure that conditions related to, but not limited to, the environment, safety, health, waste isolation, operations, security, or quality of items and services associated with Office of Civilian Radioactive Waste Management (OCRWM) work activities are promptly identified, controlled, evaluated, and corrected as soon as practical. This procedure describes the process flow, controls, interfaces, and requirements for condition identification and resolution. This includes adverse conditions as well as opportunities for improvement and suggestions
Systems Engineering Leading Indicators Guide - Beta Release
This document is the beta release of the Systems Engineering Leading Indicators Guide. This project was
initiated by the Lean Aerospace Initiative (LAI) Consortium in cooperation with the International
Council on Systems Engineering (INCOSE). Leading measurement and systems engineering experts
from government, industry, and academia volunteered their time to work on this initiative.
Government and industry organizations are encouraged to tailor the information in this document
for their purposes, and may incorporate this material into internal guidance documents. Please cite the
original source and release level (currently beta) for traceability and baseline control purposes
Assessment of the NASA Flight Assurance Review Program
The NASA flight assurance review program to develop minimum standard guidelines for flight assurance reviews was assessed. Documents from NASA centers and NASA headquarters to determine current design review practices and procedures were evaluated. Six reviews were identified for the recommended minimum. The practices and procedures used at the different centers to incorporate the most effective ones into the minimum standard review guidelines were analyzed and guidelines for procedures, personnel and responsibilies, review items/data checklist, and feedback and closeout were defined. The six recommended reviews and the minimum standards guidelines developed for flight assurance reviews are presented. Observations and conclusions for further improving the NASA review and quality assurance process are outlined
Integrated Baseline Review (IBR) Handbook
The purpose of this handbook is intended to be a how-to guide to prepare for, conduct, and close-out an Integrated Baseline Review (IBR). It discusses the steps that should be considered, describes roles and responsibilities, tips for tailoring the IBR based on risk, cost, and need for management insight, and provides lessons learned from past IBRs. Appendices contain example documentation typically used in connection with an IBR. Note that these appendices are examples only, and should be tailored to meet the needs of individual projects and contracts. Following the guidance in this handbook will help customers and suppliers preparing for an IBR understand the expectations of the IBR, and ensure that the IBR meets the requirements for both in-house and contract efforts
Fair Labor Association 2007 Annual Report
Assesses the progress made by companies in the move towards sustainable corporate responsibility in their labor standards. Breaks up data by company
Machine learning and its applications in reliability analysis systems
In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA
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