AI-enabled Enterprise Information Systems for Manufacturing

Zdravković, Milan; Panetto, Hervé; Weichhart, Georg

doi:10.1080/17517575.2021.1941275

Cited by 45 publications

(22 citation statements)

References 183 publications

(151 reference statements)

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“…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). This effort encourages small-scale, supply networks of regional socioeconomic actors to use community sources based on local area needs, resulting in ecological advantages.…”

Section: Production Processmentioning

confidence: 99%

“…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. The problems established by changing customer requirements and policy changes, and the demand for new areas of expansion and improved profitability, establish a sense for rethinking the customer items production process (Zdravković, 2022). Those entails exploring ways of the current processes could be changed into more synchronized, regenerative, and regional ways of consumption and production.…”

Section: Introductionmentioning

confidence: 99%

“…The study focuses on the function and possibility of BD to affect DM processes within retail items segments. Data analytics in the field of buyer items segments is related to strengthened capabilities to produce, record, and utilize information in various ways, not merely to strengthen manufacturing, but also to better incorporate logistics, production, and demand (Zdravković, 2022).…”

Section: Introductionmentioning

confidence: 99%

See 2 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: Production Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Sazu¹,

Jahan²

2022

J Process Man & New Technol

View full text Add to dashboard Cite

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“…In the era of Industry 4.0, the importance of lot-size one increases, which places high demands on workers' cognitive abilities in production and maintenance (Ansari et al 2018). In particular, flexibility, adaptability and problem-solving abilities are required (Zdravković et al 2021). The realization of comparable cognitive skills in an AI-based agent system holds immense potential for developing industrial assistance systems.…”

Section: Functional Capabilities For Ai-enhanced Assistance Systemsmentioning

confidence: 99%

A Modular Federated Learning Architecture for Integration of AI-enhanced Assistance in Industrial Maintenance - A novel architecture for enhancing industrial maintenance management systems in the automotive and semiconductor industry.

Kohl¹,

Ansari²,

Sihn³

2021

Competence Development and Learning Assistance Systems for the Data-Driven Future

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Artificial Intelligence (AI) plays an increasingly important role for the implementation and failure-free operation of Cyber-Physical Production Systems (CPPS). Recent market studies show that investment in AI-enhanced maintenance is increasing as one of the most important use cases of Industry 4.0. AI systems enable the improvement of various Key Performance Indicators (KPI), ultimately leading to a reduction in costs and optimizing plant management in smart factories. At the same time, manufacturing enterprises in diverse sectors have very high expectations from any kind of AI solution comparing to conventional solutions. Today manufacturing enterprises use only a quarter of their data and therefore leave an enormous, untapped potential. The use of Text Mining (TM) realizes the untapped value of existing unstructured or semi-structured textual data. This paper presents a transferable and scalable architecture for a cognitive maintenance system of a human-centered assistance system that enables holistic sensing of the environment by using physical and virtual sensors. By focusing on generalizability, scalability, adaptability, reliability, and user acceptance, a novel architecture for cognitive maintenance system is proposed. The so called ARCHIE, Architecture for a Cognitive Maintenance System, addresses common challenges in the application of AI systems in the industrial environment. Human-centered cognitive systems aim to automate manufacturing processes and assist workers in their cognitive tasks. This can be achieved by using the untapped potential of combining unstructured and structured data in order to extract hidden knowledge. ARCHIE aims at realizing an AI-enhanced approach for a human-centered assistance system. ARCHIE incorporates physical and virtual sensors that capture machine states, parameters, human knowledge, and skills to optimize relevant KPIs. This includes a reduction in documentation time, Mean Time Between Failures (MTBF) and Mean Failure Detection Time (MFDT), as well as an increase in uptime, leading ultimately to an improved Overall Equipment Efficiency (OEE). These improvements are enabled by the combined use of AI in the form of TM, Federated Learning and Knowledge Graphs. In the presented use-case from the automotive industry, a reduction in MFDT below 60min by 97.3% and an increase in OEE by 5.3% was achieved. In the Semiconductor industry, the partial application of ARCHIE allows the querying of competence distributions based on a given maintenance task, enabling automated allocation of maintenance technicians and trend analyses. Generalizability, scalability, adaptability, reliability, and user acceptance were also evaluated in the use cases presented.

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“…The results showed that this method is more efficient than the traditional shipping economic evaluation model. Zdravkovic et al [ 19 ] integrated industrial Internet technology, distributed systems, cloud computing, and deep learning technology to study the application of artificial intelligence technology in enterprises. This method can realize the independent decision-making function of corporate finance.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Classification Method of Multisource Enterprise Financial Data Based on SAS Model

2022

Computational Intelligence and Neuroscience

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An enterprise is often faced with a large amount of financial information and data information. It is inefficient to rely solely on manual work, and the accuracy is difficult to guarantee. For the multisource data of corporate finance, it is more difficult for financial personnel to accurately analyze the connections between the data. For the multisource financial data of enterprise, this is also a time-consuming and laborious task for financial personnel. At the same time, it is difficult to find the correlation between multiple sources of data and then formulate financial data that guides the development of the enterprise. With the advancement of intelligent algorithms, an intelligent classification algorithm similar to the SAS model has emerged, which can realize the intelligent classification of enterprise financial multisource data and accurately predict the future development trend, which is extremely beneficial to the development and performance of the enterprise. This article mainly combines the financial intelligence classification model SAS with clustering and decision tree methods to classify the financial multisource information and uses the neural network method to carry out the future development trend of corporate finance. The research results show that the maximum error of enterprise financial classification after using the intelligent classification method is only 3.71% and that the forecast error of the future development trend of enterprise finance is only 1.77%. This is an acceptable error range, and this intelligent classification method is also greatly improving the efficiency of corporate financial management.

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AI-enabled Enterprise Information Systems for Manufacturing

Cited by 45 publications

References 183 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?

A Modular Federated Learning Architecture for Integration of AI-enhanced Assistance in Industrial Maintenance - A novel architecture for enhancing industrial maintenance management systems in the automotive and semiconductor industry.

An Intelligent Classification Method of Multisource Enterprise Financial Data Based on SAS Model

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