Design pragmatic method to low-carbon economy visualisation in enterprise systems based on big data

Guan, Zhigui; Zhao, Yuanjun; Wang, Xingdong

doi:10.1080/17517575.2021.1898049

Cited by 7 publications

(23 citation statements)

References 67 publications

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“…DM is usually compared to ideas such as shared manufacturing (Wang et al, 2021). Shared production refers to involvement in the development of the main product, while co-production belongs to a principle, which contains the concept that could just be produced with and based on the consumer at the progression of use.…”

Section: Dispersed Productionmentioning

confidence: 99%

“…In the field of manufacturing, a few investigations have been conducted about the collaboration and business aspect of dispersed production methods (Guan et al, 2021). That comprises reproductions of scenarios, cloud-based and production methods inspired by progress in data and CT. An alternative stream investigates dispersed producing in relation to alternate business methods and possibilities for responsive and socially produced production and consumption (Zdravković, 2022).…”

Section: Production Processmentioning

confidence: 99%

“…Choices regarding production plant, together with capacity, position, and size, have significant effects on a business's ambitious abilities. Common approaches to evaluate plant decisions are derived from mathematical techniques or assessment criteria (Wang et al, 2021). In many instances, price reduction and maximizing earnings are set to highest precedence (Moyne & Iskandar, 2017).…”

Section: Plants' Approach and Prioritizationmentioning

confidence: 99%

“…Thus, the leverage of scaling is essential factors for the plant's approach, in which expense benefits could be worked based on paper volume. This is because of the trend that the price per product of paper in standard largescale production generally reduces within the scope, as the fixed prices are sent out to even more products produced (Guan et al, 2021).…”

Section: Plants' Approach and Prioritizationmentioning

confidence: 99%

“…The production side progress shifts inherently affect the field in customer behavior. Considering per year profits of roughly USD 3.8 trillion worldwide, the retail good market hinges tremendously on large-scale production by global companies & worldwide dispersed supply chains (Guan et al, 2021). The change developed a gap between buyer and manufacturer, restricting possibilities for personalization, the upscaling of the development, and small-scale business of data-driven items designed in the regional market.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Impact of big data analytics on distributed manufacturing: Does big data help?

Sazu¹,

Jahan²

2022

J Process Man & New Technol

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Big data (BD) analytics has brought progressive improvement in the business environment. It provides businesses with optimized production, personalization and improvement in the way production is dispersed. Nevertheless, conflicts arise in the use of these methods in certain industries, like retail items, which usually basis on large-scale production and prolonged supply chain. The study develops a theoretical structure to investigate if big data coupled with different production solutions can provide for a dispersed production system. Through investigation of twenty-one buyer products business instances applying secondary and main data, the study investigated changing production processes, the inherent catalyst, the function of analytics, and its effect on distributed production. The study discovers several uses of distributed manufacturing principles to evaluate the current production processes worked for larger customer product solutions by using analytics and industry analysis. The evaluation's suggested structure mentioned in this research has a deeper impact on planning, comprehension relationships, among factors of data analytics and distributed production.

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Section: Dispersed Productionmentioning

confidence: 99%

Section: Production Processmentioning

confidence: 99%

Section: Plants' Approach and Prioritizationmentioning

confidence: 99%

Section: Plants' Approach and Prioritizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Impact of big data analytics on distributed manufacturing: Does big data help?

Sazu¹,

Jahan²

2022

J Process Man & New Technol

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Quality management in industry 4.0 environment: a morphological analysis and research agenda

Prashar

2022

IJQRM

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PurposeDigitalised and interconnected production systems in the era of fourth industrial revolution or Industry 4.0 (I4.0) have triggered the need of reshaping the traditional Quality Management (QM) models and roles of quality professionals. The purpose of this paper is to review the body of literature on QM in the I4.0 environment (also termed as “Quality 4.0”) and identify future research possibilities.Design/methodology/approachThe morphological analysis (MA) framework is used to identify research gaps in a systematic manner. We present the MA framework based on a literature review of 51 papers on the Australian Business Deans Council (ABDC) list identified using systematic review protocol.FindingsThe MA framework, built on six relevant dimensions, namely, production and operations function, scope of QM activities, enabling technologies, latent capabilities of digitalisation, performance metrics and QM principles, and 41 associated variants, reveals 367 research gaps as an upper bound of opportunities for future research.Originality/valueThis paper is possibly the first to use MA for reviewing the QM literature, presenting important contributions. The MA framework acts as a ready reckoner on the QM in I4.0 literature. It documents research gaps through the construction of a cross-consistency matrix.

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Design of Enterprise Economic Management Intelligent Terminal for Big Data Application

Fang

Ouyang

2022

Mathematical Problems in Engineering

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With the rapid development of world economic informatization, the research and application of intelligent terminals are more and more extensive. Through the comprehensive application of big data in modern enterprise economic management, enterprises can more accurately grasp all kinds of information and mine the deep value of data information. In the era of big data, to realize the stable growth of the enterprise economy, relevant personnel need to gradually explore the enterprise economic management mode, take the enterprise financial management as the core development project, speed up the exchange of internal and external data, improve the brand awareness of the enterprise, and let the enterprise occupy a leading position in the market. Traditional enterprise economic management methods cannot run through big data combined with intelligent terminals. Therefore, this article solves the problem of enterprise economic management under the background of big data through the design of intelligent terminals. The design of an intelligent terminal for enterprise economic management under the background of big data can provide complete information for management positions. It is not only a new attempt for enterprises to explore economic management mode but also a major innovation based on an enterprise scale.

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Design pragmatic method to low-carbon economy visualisation in enterprise systems based on big data

Cited by 7 publications

References 67 publications

Impact of big data analytics on distributed manufacturing: Does big data help?

Impact of big data analytics on distributed manufacturing: Does big data help?

Quality management in industry 4.0 environment: a morphological analysis and research agenda

Design of Enterprise Economic Management Intelligent Terminal for Big Data Application

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