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Record W4402859562 · doi:10.1002/9781394242641.ch4

Improving Manufacturing

2024· other· en· W4402859562 on OpenAlexaff
Arpita Nayak, Atmika Patnaik, Ipseeta Satpathy, Vishal Jain, B. C. M. Patnaik, Majidul Islam

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsManufacturing engineeringBusinessEngineering

Abstract

fetched live from OpenAlex

Firms must rely on innovation to achieve long-term corporate success in today's continuously changing industrial environment. Growth necessitates knowledge management and a solid basis. To compete, industrial groups must have an unquenchable urge to produce. After all, improving per-product quality and cost efficiency is just half the story. To stay in business, a corporation must always create innovative methods to accomplish things. Industrial businesses must have strategic knowledge management strategies to navigate this new road. It entails learning, sharing, and using new knowledge all at the same time. This is a critical foundation for fostering an innovative culture at these organizations. McElroy's knowledge life cycle, for example, gives a view into the interconnected phases of knowledge management and discovery. The life cycle is divided into two parts. First, we must obtain new information from within and beyond; second, we must disseminate it along with internal distribution following the occurrence. Throughout the consumption phase, expertise is employed to improve processes and goods. The highest amount of creativity is attained at the inventories stage, when you develop new knowledge either by effortlessly mixing or by making something entirely new. To foster such an environment of creativity, daring, and risk taking, information must flow seamlessly from collection to usage. As a result, good knowledge management provides the foundation for the development of contemporary industrial structures. This is the gathering of new data, distributing it to such individuals, and applying it to create value. As a business culture, creating a workforce centered on teamwork, learning, and innovation is required to achieve this. A collaborative culture, a learning culture, and an inventive culture, on the other hand, promote cooperation and mutual understanding, learning and training, innovation and development, and product creation. These components work together to provide continuous success and progress, allowing businesses to remain relevant in a changing industry. The purpose of this research is to offer an overview of how knowledge production (knowledge creation, capture, sharing, and application) helps manufacturing organizations acquire new information, discuss best practices, successfully use knowledge, and incorporate new knowledge to promote innovation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.029
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.187
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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