Defining Industrial Analytics
Industrial Analytics (IA) defines the collection, analysis (R Programming) and usage of data (Big Data) produced in industrial operations and throughout the entire product lifecycle. It contains techniques of data collection and statistical and dynamic modelling (Vensim). The value will be occurring by improvements in connectivity (IoT) and improved methods for analysing, modelling and understanding data (Machine Learning). Increased revenue and customer satisfaction are the benefits
Industrial Analytics has become important to the process and production industry and uncovers new market opportunities. It could also lead to process advantages and cost reductions within the production. The required Industrial Analysis is different from other areas and requires knowledge beyond statics, operations or information technology.
Data analysis and its correct interpretation is the key to successful transformation into the era of Industrial Analytics. The processing and interpretation of data it’s essential. Misinterpretations lead to the frustration and aversion of the employees of this new technology. The first internal projects should give the responsible as well as the employees confidence in this technology and its approach. Older employees must also be involved in the structural change as well. Especially the recent successes and breakthroughs in artificial intelligence (AI) are due to the cooperation between practitioners and theoreticians as well as the quality of the underlying data. The aim of this website is to illustrate this topic with practical examples for industry and universities.