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・ Predictive analytics
・ Predictive behavioral targeting
・ Predictive buying
・ Predictive control of switching power converters
・ Predictive costs
・ Predictive dialer
・ Predictive failure analysis
・ Predictive genomics
・ Predictive homeostasis
・ Predictive Index
・ Predictive inference
・ Predictive informatics
・ Predictive intake modelling
・ Predictive learning
・ Predictive maintenance
Predictive manufacturing system
・ Predictive marker
・ Predictive medicine
・ Predictive Model Markup Language
・ Predictive modelling
・ Predictive policing
・ Predictive power
・ Predictive probability of success
・ Predictive profiling
・ Predictive state representation
・ Predictive testing
・ Predictive text
・ Predictive validity
・ Predictive value of tests
・ Predictor


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Predictive manufacturing system : ウィキペディア英語版
Predictive manufacturing system

The predictive manufacturing system (PMS) is a manufacturing paradigm wherein the system is programmed with certain amount and type of intelligence that allows assets to estimate their own condition, detect the presence of a fault or an anomaly, infer future fault events and even diagnose potential root cause of the problem. The goal of predictive manufacturing system (PMS) is to enable machine and systems with “self-aware” capabilities. The core technology of PMS in order to deliver “self-aware” capabilities is the smart computational agent that will contain advanced analytics to conduct predictive modeling functionalities.
== Background ==

Transparency in an organization allows unraveling and even quantifying sources of uncertainty. It is an objective estimation of the company’s manufacturing capability and readiness. Most manufacturing strategies (such as mass production, lean manufacturing, flexible manufacturing, and reconfigurable manufacturing) haphazardly assume continuous equipment availability and constant maximum performance, which may never occur in a real factory. Manufacturing machines, like any other mechanical engineering assets, are prone to wear and tear that inhibit uninterrupted uptime. In order to achieve transparence within a facility, manufacturing industry is then asked to metamorphose into predictive manufacturing system so that data can be systematically processed into information that can explain the uncertainties and thereby enable users of the manufacturing assets to make more informed and evidence-based decisions.
The foundations of predictive manufacturing have already been laid out with the aggressive adoption of “Internet of Things” (IoT) principles wherein essential structures such as smart sensor networks and smart machines have become more prevalent.
Such scenario enables for seamless aggregation of data from assets (or asset fleet) on to a central location for appropriate processing. With the discovery and development of more advanced analytics, such data can then be transformed into information that can provide clarity or transparency, eventually addressing the uncertainties in the factory.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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