Big-data-driven anomaly detection in industry (4.0): An approach and a case study

2016 ◽  
Author(s):  
Ljiljana Stojanovic ◽  
Marko Dinic ◽  
Nenad Stojanovic ◽  
Aleksandar Stojadinovic
Keyword(s):  
Big Data ◽  
Anomaly Detection ◽  
Industry 4.0 ◽  
Data Driven

scholarly journals Challenges for the DOE methodology related to the introduction of Industry 4.0

2020 ◽  
Vol 26 (4) ◽  
pp. 190-194
Author(s):  
Jacek Pietraszek ◽  
Norbert Radek ◽  
Andrii V. Goroshko
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Integration ◽  
Design Of Experiments ◽  
Industry 4.0 ◽  
Data Stream ◽  
Data Driven ◽  
Industrial Data ◽  
Existing Problems

AbstractThe introduction of solutions conventionally called Industry 4.0 to the industry resulted in the need to make many changes in the traditional procedures of industrial data analysis based on the DOE (Design of Experiments) methodology. The increase in the number of controlled and observed factors considered, the intensity of the data stream and the size of the analyzed datasets revealed the shortcomings of the existing procedures. Modifying procedures by adapting Big Data solutions and data-driven methods is becoming an increasingly pressing need. The article presents the current methods of DOE, considers the existing problems caused by the introduction of mass automation and data integration under Industry 4.0, and indicates the most promising areas in which to look for possible problem solutions.

scholarly journals Big Data-Driven Macroeconomic Forecasting Model and Psychological Decision Behavior Analysis for Industry 4.0

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jie Liu
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Behavior Analysis ◽  
Industry 4.0 ◽  
Value Added ◽  
Complete Information ◽  
Data Driven ◽  
Macroeconomic Forecasting ◽  
The Government ◽  
Decision Making Behavior

With the advent of Industry 4.0, economic development has become a rapid information age. The content of macroeconomic forecast is very extensive, and the existence of big data technology can provide the government with multilevel, diversified, and complete information and comprehensively process, integrate, summarize, and classify these pieces of information. This paper forecasts the CPI value in the next 12 months according to the CPI in China in the recent 20 years. Compared with the traditional forecasting methods, the forecasting results have higher accuracy and timeliness. At the same time, the trend of growth rate of industrial value-added is analyzed, and the experiments on MAE and RMSE show that the method proposed in this paper has obvious advantages. It also analyzes the disadvantages of traditional psychological decision-making behavior analysis, introduces the development status and advantages of big data-driven psychological decision-making behavior analysis, and opens up new research ideas for psychological decision-making analysis.

Data-Driven Inventory Management in the Healthcare Supply Chain

2020 ◽  
pp. 1390-1403
Author(s):  
Shuojiang Xu ◽  
Kim Hua Tan
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Inventory Management ◽  
Data Driven ◽  
Forecasting Model ◽  
Market Demand ◽  
Improved Model ◽  
Future Work ◽  
Statistic Approach

From 21st century, enterprises combine supply chain management with big data to improve their products and services level. In China healthcare industry, supply chain decisions are made based on experience, due to the environment complexities, such as changing policies and license delay. A flexible and dynamic big data driven analysis approach for supply chain decisions is urgently required. This report demonstrates a case study on CRT forecasting model of inventory data to predict the market demand based on pervious transaction data. First a basic statistic approach has been applied to represent the superficial patterns and suggest some decisions. After that a CRT model has been built based on the several independent variables. And there is also a comparison between CRT and CHAID models to choose a better one to further build an improved model. Finally some limitations and future work have been proposed.

The dual drivetrain model of digital transformation: role of industrial big-data-based affordance

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi Liu ◽  
Wei Wang ◽  
Zuopeng (Justin) Zhang
Keyword(s):  
Big Data ◽  
Digital Transformation ◽  
Data Driven ◽  
Content Type ◽  
Industrial Big Data ◽  
Technology Affordance ◽  
Effective Use ◽  
Practical Implications

PurposeTo better understand the role of industrial big data in promoting digital transformation, the authors propose a theoretical framework of industrial big-data-based affordance in the form of an illustrative metaphor – what the authors call the “organizational drivetrain.”Design/methodology/approachThis study investigates the effective use of industrial big data in the process of digital transformation based on the technology affordance–actualization theoretical lens. A software platform and services provider with more than 4,000 industrial enterprise clients in China was selected as the case study object for analyzing the digital affordance and actualization driven by industrial big data.FindingsDrawing on a revelatory case study, the authors identify three affordances of industrial big data in the organization, namely developing data-driven customized projects, provisioning equipment-data-driven life cycle services, establishing data-based trust and determining affordance actualization actions driven by technology and market. In addition, the authors reveal the underlying drivetrain mechanisms to advance industrial big data affordance and actualization: stabilizing, enriching and pioneering.Originality/valueThis study builds a drivetrain model on digital transformation by industrial big data affordance actualization. The authors also provide practical implications that can help practitioners to implement digital transformation effectively and extract value from their investment.

scholarly journals Big Data-driven Budgeting and Business Planning

2020 ◽  
Author(s):  
Alessio Faccia
Keyword(s):  
Big Data ◽  
Planning Process ◽  
Business Plan ◽  
Financial Accounting ◽  
Data Driven ◽  
Business Planning ◽  
Standard Process ◽  
Proposed Model ◽  
Almost All

The business planning process can be considered as a strategic phase of any business. Given that the business plan is a management accounting tool, there are countless approaches that can be adopted to prepare it since there is no legal requirement, as opposed to obligations relating to financial accounting. However, in general, every business plan consists of a numerical part (budget) and a narrative part. In this research, the author highlights, on the basis of experiences and commonly used theories, a standard process that can be adaptable to the business plan of any type of activity. The use of big data is highlighted as an essential part of feeding the data of almost all the steps of the budget. The author then manages to determine a generally applicable standard process, indicating all the data necessary to prepare an accurate and reliable business plan. A case study will provide adequate support to the demonstration of the immediate applicability of the proposed model.

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