typically+english

SUBJECT AREA: Land transport, Railways and locomotives, Steam and internal combustion engines

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Trevithick's father was a tin-mine manager, and Trevithick himself, after limited formal education, developed his immense engineering talent among local mining machinery and steam-engines and found employment as a mining engineer. Tall, strong and high-spirited, he was the eternal optimist.

About 1797 it occurred to him that the separate condenser patent of James Watt could be avoided by employing "strong steam", that is steam at pressures substantially greater than atmospheric, to drive steam-engines: after use, steam could be exhausted to the atmosphere and the condenser eliminated. His first winding engine on this principle came into use in 1799, and subsequently such engines were widely used. To produce high-pressure steam, a stronger boiler was needed than the boilers then in use, in which the pressure vessel was mounted upon masonry above the fire: Trevithick designed the cylindrical boiler, with furnace tube within, from which the Cornish and later the Lancashire boilers evolved.

Simultaneously he realized that high-pressure steam enabled a compact steam-engine/boiler unit to be built: typically, the Trevithick engine comprised a cylindrical boiler with return firetube, and a cylinder recessed into the boiler. No beam intervened between connecting rod and crank. A master patent was taken out.

Such an engine was well suited to driving vehicles. Trevithick built his first steam-carriage in 1801, but after a few days' use it overturned on a rough Cornish road and was damaged beyond repair by fire. Nevertheless, it had been the first self-propelled vehicle successfully to carry passengers. His second steam-carriage was driven about the streets of London in 1803, even more successfully; however, it aroused no commercial interest. Meanwhile the Coalbrookdale Company had started to build a locomotive incorporating a Trevithick engine for its tramroads, though little is known of the outcome; however, Samuel Homfray's ironworks at Penydarren, South Wales, was already building engines to Trevithick's design, and in 1804 Trevithick built one there as a locomotive for the Penydarren Tramroad. In this, and in the London steam-carriage, exhaust steam was turned up the chimney to draw the fire. On 21 February the locomotive hauled five wagons with 10 tons of iron and seventy men for 9 miles (14 km): it was the first successful railway locomotive.

Again, there was no commercial interest, although Trevithick now had nearly fifty stationary engines completed or being built to his design under licence. He experimented with one to power a barge on the Severn and used one to power a dredger on the Thames. He became Engineer to a project to drive a tunnel beneath the Thames at Rotherhithe and was only narrowly defeated, by quicksands. Trevithick then set up, in 1808, a circular tramroad track in London and upon it demonstrated to the admission-fee-paying public the locomotive Catch me who can, built to his design by John Hazledine and J.U. Rastrick.

In 1809, by which date Trevithick had sold all his interest in the steam-engine patent, he and Robert Dickinson, in partnership, obtained a patent for iron tanks to hold liquid cargo in ships, replacing the wooden casks then used, and started to manufacture them. In 1810, however, he was taken seriously ill with typhus for six months and had to return to Cornwall, and early in 1811 the partners were bankrupt; Trevithick was discharged from bankruptcy only in 1814.

In the meantime he continued as a steam engineer and produced a single-acting steam engine in which the cut-off could be varied to work the engine expansively by way of a three-way cock actuated by a cam. Then, in 1813, Trevithick was approached by a representative of a company set up to drain the rich but flooded silver-mines at Cerro de Pasco, Peru, at an altitude of 14,000 ft (4,300 m). Low-pressure steam engines, dependent largely upon atmospheric pressure, would not work at such an altitude, but Trevithick's high-pressure engines would. Nine engines and much other mining plant were built by Hazledine and Rastrick and despatched to Peru in 1814, and Trevithick himself followed two years later. However, the war of independence was taking place in Peru, then a Spanish colony, and no sooner had Trevithick, after immense difficulties, put everything in order at the mines then rebels arrived and broke up the machinery, for they saw the mines as a source of supply for the Spanish forces. It was only after innumerable further adventures, during which he encountered and was assisted financially by Robert Stephenson, that Trevithick eventually arrived home in Cornwall in 1827, penniless.

He petitioned Parliament for a grant in recognition of his improvements to steam-engines and boilers, without success. He was as inventive as ever though: he proposed a hydraulic power transmission system; he was consulted over steam engines for land drainage in Holland; and he suggested a 1,000 ft (305 m) high tower of gilded cast iron to commemorate the Reform Act of 1832. While working on steam propulsion of ships in 1833, he caught pneumonia, from which he died.

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See also: Blenkinsop, John

   1) Programs and the Complexity of Human Mental Processes

   In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)

   2) Artificial Intelligence Is an Engineering Discipline

   Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)

   3) A Sceptical View of Artificial Intelligence

   Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....

   When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)

   4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, Eventually

   Just as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)

   5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the Program

   Many problems arise in experiments on machine intelligence because things obvious to any person are not represented in any program. One can pull with a string, but one cannot push with one.... Simple facts like these caused serious problems when Charniak attempted to extend Bobrow's "Student" program to more realistic applications, and they have not been faced up to until now. (Minsky & Papert, 1973, p. 77)

   6) The Meaning of a Symbolic Description

   What do we mean by [a symbolic] "description"? We do not mean to suggest that our descriptions must be made of strings of ordinary language words (although they might be). The simplest kind of description is a structure in which some features of a situation are represented by single ("primitive") symbols, and relations between those features are represented by other symbols-or by other features of the way the description is put together. (Minsky & Papert, 1973, p. 11)

   7) The Principle of Artificial Intelligence

   [AI is] the use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. (Boden, 1977, p. 5)

   8) Artificial Intelligence Recognizes the Need for Knowledge in Its Systems

   The word you look for and hardly ever see in the early AI literature is the word knowledge. They didn't believe you have to know anything, you could always rework it all.... In fact 1967 is the turning point in my mind when there was enough feeling that the old ideas of general principles had to go.... I came up with an argument for what I called the primacy of expertise, and at the time I called the other guys the generalists. (Moses, quoted in McCorduck, 1979, pp. 228-229)

   9) Artificial Intelligence Is Psychology in a Particularly Pure and Abstract Form

   The basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found. And that will guarantee semantic imitation as well, since (given the appropriate formal behavior) the semantics is "taking care of itself" anyway. Thus we also see why, from this perspective, artificial intelligence can be regarded as psychology in a particularly pure and abstract form. The same fundamental structures are under investigation, but in AI, all the relevant parameters are under direct experimental control (in the programming), without any messy physiology or ethics to get in the way. (Haugeland, 1981b, p. 31)

    10) There Are Many Types of Reasoning

   There are many different kinds of reasoning one might imagine:

    Formal reasoning involves the syntactic manipulation of data structures to deduce new ones following prespecified rules of inference. Mathematical logic is the archetypical formal representation. Procedural reasoning uses simulation to answer questions and solve problems. When we use a program to answer What is the sum of 3 and 4? it uses, or "runs," a procedural model of arithmetic. Reasoning by analogy seems to be a very natural mode of thought for humans but, so far, difficult to accomplish in AI programs. The idea is that when you ask the question Can robins fly? the system might reason that "robins are like sparrows, and I know that sparrows can fly, so robins probably can fly."

    Generalization and abstraction are also natural reasoning process for humans that are difficult to pin down well enough to implement in a program. If one knows that Robins have wings, that Sparrows have wings, and that Blue jays have wings, eventually one will believe that All birds have wings. This capability may be at the core of most human learning, but it has not yet become a useful technique in AI.... Meta- level reasoning is demonstrated by the way one answers the question What is Paul Newman's telephone number? You might reason that "if I knew Paul Newman's number, I would know that I knew it, because it is a notable fact." This involves using "knowledge about what you know," in particular, about the extent of your knowledge and about the importance of certain facts. Recent research in psychology and AI indicates that meta-level reasoning may play a central role in human cognitive processing. (Barr & Feigenbaum, 1981, pp. 146-147)

    11) Programs Are Beginning to Do Things That Critics Have Asserted to Be Impossible

   Suffice it to say that programs already exist that can do things-or, at the very least, appear to be beginning to do things-which ill-informed critics have asserted a priori to be impossible. Examples include: perceiving in a holistic as opposed to an atomistic way; using language creatively; translating sensibly from one language to another by way of a language-neutral semantic representation; planning acts in a broad and sketchy fashion, the details being decided only in execution; distinguishing between different species of emotional reaction according to the psychological context of the subject. (Boden, 1981, p. 33)

    12) The Synthesis of Man and Machine

   Can the synthesis of Man and Machine ever be stable, or will the purely organic component become such a hindrance that it has to be discarded? If this eventually happens-and I have... good reasons for thinking that it must-we have nothing to regret and certainly nothing to fear. (Clarke, 1984, p. 243)

    13) The Thesis of Good Old-Fashioned Artificial Intelligence (GOFAI)

   The thesis of GOFAI... is not that the processes underlying intelligence can be described symbolically... but that they are symbolic. (Haugeland, 1985, p. 113)

    14) Artificial Intelligence Provides a Useful Approach to Psychological and Psychiatric Theory Formation

   It is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognise when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognised to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using "classical" mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach-a different mathematics-is needed, and that AI provides just this approach. (Hand, quoted in Hand, 1985, pp. 6-7)

    15) Four Kinds of Artificial Intelligence

   We might distinguish among four kinds of AI.

   ♦ Nonpsychological AI

   Research of this kind involves building and programming computers to perform tasks which, to paraphrase Marvin Minsky, would require intelligence if they were done by us. Researchers in nonpsychological AI make no claims whatsoever about the psychological realism of their programs or the devices they build, that is, about whether or not computers perform tasks as humans do.

   ♦ Weak Psychological AI

   Research here is guided by the view that the computer is a useful tool in the study of mind. In particular, we can write computer programs or build devices that simulate alleged psychological processes in humans and then test our predictions about how the alleged processes work. We can weave these programs and devices together with other programs and devices that simulate different alleged mental processes and thereby test the degree to which the AI system as a whole simulates human mentality. According to weak psychological AI, working with computer models is a way of refining and testing hypotheses about processes that are allegedly realized in human minds.

   ♦ Strong Psychological AI

... According to this view, our minds are computers and therefore can be duplicated by other computers. Sherry Turkle writes that the "real ambition is of mythic proportions, making a general purpose intelligence, a mind." (Turkle, 1984, p. 240) The authors of a major text announce that "the ultimate goal of AI research is to build a person or, more humbly, an animal." (Charniak & McDermott, 1985, p. 7)

   ♦ Suprapsychological AI

   Research in this field, like strong psychological AI, takes seriously the functionalist view that mentality can be realized in many different types of physical devices. Suprapsychological AI, however, accuses strong psychological AI of being chauvinisticof being only interested in human intelligence! Suprapsychological AI claims to be interested in all the conceivable ways intelligence can be realized. (Flanagan, 1991, pp. 241-242)

    16) Determination of Relevance of Rules in Particular Contexts

   Even if the [rules] were stored in a context-free form the computer still couldn't use them. To do that the computer requires rules enabling it to draw on just those [ rules] which are relevant in each particular context. Determination of relevance will have to be based on further facts and rules, but the question will again arise as to which facts and rules are relevant for making each particular determination. One could always invoke further facts and rules to answer this question, but of course these must be only the relevant ones. And so it goes. It seems that AI workers will never be able to get started here unless they can settle the problem of relevance beforehand by cataloguing types of context and listing just those facts which are relevant in each. (Dreyfus & Dreyfus, 1986, p. 80)

    17) Form and Content Are Not Fundamentally Different

   Perhaps the single most important idea to artificial intelligence is that there is no fundamental difference between form and content, that meaning can be captured in a set of symbols such as a semantic net. (G. Johnson, 1986, p. 250)

    18) The Assumption That the Mind Is a Formal System

   Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped into the other (the computer). (G. Johnson, 1986, p. 250)

    19) A Statement of the Primary and Secondary Purposes of Artificial Intelligence

   The primary goal of Artificial Intelligence is to make machines smarter.

   The secondary goals of Artificial Intelligence are to understand what intelligence is (the Nobel laureate purpose) and to make machines more useful (the entrepreneurial purpose). (Winston, 1987, p. 1)

    20) Mathematical Logic Provides the Basis for Theory in AI

   The theoretical ideas of older branches of engineering are captured in the language of mathematics. We contend that mathematical logic provides the basis for theory in AI. Although many computer scientists already count logic as fundamental to computer science in general, we put forward an even stronger form of the logic-is-important argument....

   AI deals mainly with the problem of representing and using declarative (as opposed to procedural) knowledge. Declarative knowledge is the kind that is expressed as sentences, and AI needs a language in which to state these sentences. Because the languages in which this knowledge usually is originally captured (natural languages such as English) are not suitable for computer representations, some other language with the appropriate properties must be used. It turns out, we think, that the appropriate properties include at least those that have been uppermost in the minds of logicians in their development of logical languages such as the predicate calculus. Thus, we think that any language for expressing knowledge in AI systems must be at least as expressive as the first-order predicate calculus. (Genesereth & Nilsson, 1987, p. viii)

    21) Perceptual Structures Can Be Represented as Lists of Elementary Propositions

   In artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)

    22) Definitions of Artificial Intelligence

   Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)

    23) The Computer Can Not Be a Model of the Mind

   Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)

    24) Computers Will Not Always Be Inferior to Human Brains

   The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)

 

аутентификация
Проверка принадлежности субъекту доступа предъявленного им идентификатора, подтверждение подлинности [4].
[Государственная техническая комиссия при Президенте Российской Федерации.
Сборник руководящих документов по защите от несанкционированного доступа. М., 1998]
[ОСТ 45.127-99]

аутентификация
Процесс проверки подлинности человека (персоны), устройства или процесса. Обычно выполняется посредством использования одного или нескольких аутентификационных факторов, таких как:
- то, что вы знаете (например, пароль и парольная фраза)
- то, что у вас есть (например, ключи или смарт-карты)
- то, что вы есть (например, биометрические параметры)
[Стандарт безопасности данных индустрии платежных карт (DSS) и стандарт безопасности данных платежных приложений (PA-DSS)]

EN

authentication
Process of verifying identity of an individual, device, or process. Authentication typically occurs through the use of one or more authentication factors such as:
- Something you know, such as a password or passphrase
- Something you have, such as a token device or smart card
- Something you are, such as a biometric
[ https://www.pcisecuritystandards.org/security_standards/glossary.php]

Тематики

• защита информации

EN

• authentication

 

аутентификация (подлинности субъекта доступа)
Действия по проверке подлинности субъекта доступа в информационной системе [1].
[Р 50.1.056-2005 ]

Тематики

• защита информации

EN

• authentication

 

аутентификация (установление подлинности)
Мера безопасности, состоящая в проверке подлинности идентификатора (преимущественно с помощью криптографических методов). Проверка личности пользователя, как правило, осуществляется с помощью пароля, PIN-кода или цифрового сертификата.
[ http://www.dtln.ru/slovar-terminov]

Тематики

• информационные технологии в целом

EN

• authentication

 

обнаружение подлинности, достоверности
—
[ http://www.security-bridge.com/shkola_bezopasnosti/anglorusskij_slovar/?p=3&search=A]

Тематики

• системы охраны и безопасности объектов

EN

• authentication

 

проверка прав доступа
проверка пароля при входе в защищенную систему
подтверждение права на доступ к информации
аутентификация
предъявление полномочий
—
[Л.Г.Суменко. Англо-русский словарь по информационным технологиям. М.: ГП ЦНИИС, 2003.]

Тематики

• информационные технологии в целом

Синонимы

• проверка пароля при входе в защищенную систему
• подтверждение права на доступ к информации
• аутентификация
• предъявление полномочий

EN

• authentication

 

удостоверение подлинности
—
[Упрощение процедур торговли: англо-русский глоссарий терминов (пересмотренное второе издание) НЬЮ-ЙОРК, ЖЕНЕВА, МОСКВА 2011 год]

EN

authentication

[Trade Facilitation Terms: An English - Russian Glossary (revised second edition) NEW YORK, GENEVA, MOSCOW 2047]

Тематики

• торговля

EN

• authentication

3.1 аутентификация (authentication): Обеспечение однозначного соответствия заявленного идентификатора объекту.

[ИСО/МЭК 10181-2]

Источник: ГОСТ Р ИСО/МЭК ТО 13335-4-2007: Информационная технология. Методы и средства обеспечения безопасности. Часть 4. Выбор защитных мер

аутентификация (authentication): Акт проверки заявленной личности субъекта (ИСО/МЭК 2382-8).

Источник: ГОСТ Р ИСО/ТС 18308-2008: Информатизация здоровья. Требования к архитектуре электронного учета здоровья

2.5 аутентификация (authentication): Процесс достоверной идентификации субъектов информационной безопасности посредством надежной связи между идентификатором и его удостоверением.

Примечание - См. также аутентификацию источника данных и аутентификацию равноправного объекта.

Источник: ГОСТ Р ИСО/ТС 22600-2-2009: Информатизация здоровья. Управление полномочиями и контроль доступа. Часть 2. Формальные модели

3.7 аутентификация (authentication): Предоставление гарантии заявленной идентичности объекта. [ИСО/МЭК 10181-1:1196] [4], [ИСО/МЭК ТО 13335-4:2000] [5]

Источник: ГОСТ Р ИСО/ТО 13569-2007: Финансовые услуги. Рекомендации по информационной безопасности

3.7 аутентификация (authentication): Предоставление гарантии заявленной идентичности объекта.

[ИСО/МЭК 10181-1:1196] [4], [ИСО/МЭК ТО 13335-4:2000] [5]

Источник: ГОСТ Р ИСО ТО 13569-2007: Финансовые услуги. Рекомендации по информационной безопасности

 

логистическая цепочка
1. Процесс создания товарного «продукта», рассматриваемый по всему его производственному и логистическому циклу, то есть от материально-технического снабжения, через производственный процесс и складирование готовой продукции до системы дистрибуции и розничных продаж.
2. «Прослеживание» товара до поставщиков комплектующих и материалов для его производства, с одной стороны, и точек розничной продажи, с другой.
3. Методология управления, основанная на рассмотрении и оптимизации всего процесса создания товарного продукта - формализация процесса управления мультинациональными производственными компаниями.
[ http://www.lexikon.ru/dict/uprav/index.html]

Тематики

• бухгалтерский учет

EN

• supply chain

 

цепочка поставок
(ITIL Service Strategy)
Деятельности в цепочке добавления ценности, выполняемые подрядчиками. В цепочку поставок обычно вовлечено множество подрядчиков, каждый из которых добавляет ценность в продукт или услугу.
См. тж. интегрированная партнерская сеть.
[Словарь терминов ITIL версия 1.0, 29 июля 2011 г.]

EN

supply chain
(ITIL Service Strategy)
The activities in a value chain carried out by suppliers. A supply chain typically involves multiple suppliers, each adding value to the product or service.
See also value network.
[Словарь терминов ITIL версия 1.0, 29 июля 2011 г.]

Тематики

• информационные технологии в целом

EN

• supply chain

 

цепь поставок
торговая цепочка
система поставок
[Упрощение процедур торговли: англо-русский глоссарий терминов (пересмотренное второе издание) НЬЮ-ЙОРК, ЖЕНЕВА, МОСКВА 2011 год]

EN

supply chain

[Trade Facilitation Terms: An English - Russian Glossary (revised second edition) NEW YORK, GENEVA, MOSCOW 2802]

Тематики

• торговля

Синонимы

• система поставок
• торговая цепочка

EN

• supply chain

3.9 цепь поставок (supply chain): Взаимосвязанный набор ресурсов и процессов, начинающийся с получения сырья и простирающийся через доставку продукции или услуг конечному пользователю посредством транспортных систем.

Примечание - Цепь поставок может включать в себя продавцов, промышленные предприятия, логистические центры, внутренние центры распределения, дистрибьюторов, оптовых продавцов и других юридических лиц, ведущих к конечному пользователю.

Источник: ГОСТ Р 53661-2009: Система менеджмента безопасности цепи поставок. Руководство по внедрению оригинал документа

3.25 цепь поставок (supply chain): Взаимосвязанный набор ресурсов и процессов, который начинается с оформления контракта на поставку, продолжается процессом получения сырья, производством, обработкой и заканчивается передачей товаров и относящихся к ним услуг конечному пользователю.

Примечание - Цепь поставок может включать в себя продавцов, промышленные предприятия, логистические центры, внутренние центры распределения, дистрибьюторов, оптовых продавцов и других юридических лиц, участвующих в производстве, обработке и доставке товаров и относящихся к ним услуг.

Источник: ГОСТ Р 53662-2009: Система менеджмента безопасности цепи поставок. Наилучшие методы обеспечения безопасности цепи поставок. Оценки и планы оригинал документа

3.9 цепь поставок (supply chain): Взаимосвязанный набор ресурсов и процессов, начинающийся с получения сырья и простирающийся через доставку продукции или услуг конечному пользователю посредством транспортных систем.

Примечание - Цепь поставок может включать в себя продавцов, промышленные предприятия, логистические центры, внутренние центры распределения, дистрибьюторов, оптовых продавцов и других юридических лиц, ведущих к конечному пользователю.

Источник: ГОСТ Р 53663-2009: Система менеджмента безопасности цепи поставок. Требования оригинал документа

3.18 цепочка поставок (supply chain): Сущности и процессы, в том числе внешние процессы организации, которые задействованы при поставке товаров и услуг, необходимых для предоставления продуктов и услуг клиентам.

Источник: ГОСТ Р 53633.2-2009: Информационные технологии. Сеть управления электросвязью. Расширенная схема деятельности организации связи (eТОМ). Декомпозиция и описания процессов. Процессы уровня 2 eTOM. Основная деятельность. Управление и эксплуатация ресурсов оригинал документа

2.21 цепочка поставок (supply chain): Сущности и процессы, в том числе внешние процессы организации, которые задействованы при поставке товаров и услуг, необходимых для предоставления продуктов и услуг клиентам.

Источник: ГОСТ Р 53633.0-2009: Информационные технологии. Сеть управления электросвязью. Расширенная схема деятельности организации связи (eТОМ). Общая структура бизнес-процессов оригинал документа

3.18 цепочка поставок (supply chain): Сущности и процессы, в том числе внешние процессы организации, которые задействованы при поставке товаров и услуг, необходимых для предоставления продуктов и услуг клиентам.

Источник: ГОСТ Р 53633.3-2009: Информационная технология. Сеть управления электросвязью. Расширенная схема деятельности организации связи (eТОМ). Декомпозиция и описания процессов. Процессы уровня 2 eTOM. Основная деятельность. Управление взаимоотношениями с клиентами оригинал документа

3.18 цепочка поставок (supply chain): Сущности и процессы, в том числе внешние процессы организации, которые задействованы при поставке товаров и услуг, необходимых для предоставления продуктов и услуг клиентам.

Источник: ГОСТ Р 53633.6-2012: Информационные технологии. Сеть управления электросвязью. Расширенная схема деятельности организации связи (eTOM). Декомпозиция и описания процессов. Процессы уровня 2 eTOM. Стратегия, инфраструктура и продукт Разработка и управление услугами оригинал документа

3.18 цепочка поставок (supply chain): Сущности и процессы, в том числе внешние процессы организации, которые задействованы при поставке товаров и услуг, необходимых для предоставления продуктов и услуг клиентам.

Источник: ГОСТ Р 53633.8-2012: Информационные технологии. Сеть управления электросвязью. Расширенная схема деятельности организации связи (eTOM). Декомпозиция и описания процессов. Процессы уровня 2 eTOM. Стратегия, инфраструктура и продукт. Разработка и управление цепочками поставок оригинал документа

3.18 цепочка поставок (supply chain): Сущности и процессы, в том числе внешние процессы организации, которые задействованы при поставке товаров и услуг, необходимых для предоставления продуктов и услуг клиентам.

Источник: ГОСТ Р 53633.5-2012: Информационные технологии. Сеть управления электросвязью. Расширенная схема деятельности организации связи (eTOM). Декомпозиция и описания процессов. Процессы уровня 2 eTOM. Стратегия, инфраструктура и продукт. Управление маркетингом и предложением продукта оригинал документа

6.8 цепочка поставки (supply chain): Участники, связанные между собой восходящими и нисходящими связями в рамках процессов (6.4) и деятельности, создающие ценности в виде продукции (6.2) для пользователя.

Примечание 1 - На практике выражение «взаимосвязанная цепочка» распространяется на участников от поставщиков до участников конечной переработки продукции при ее утилизации и/или удалении.

Примечание 2 - На практике часто используют выражения «производственная цепочка» и «цепочка создания ценности».

[ИСО/ТО 14062:2002]

Источник: ГОСТ Р ИСО 14050-2009: Менеджмент окружающей среды. Словарь оригинал документа