The industrial statistical analysis is known to help many companies to make more accurate decisions and at the same time, to reduce the risk of losses, because the analysis provides reliable information and data.
Why it matters?
"We mine data, we analyze it, we decide our company's direction."
As an important part of management system, industrial statistics helps the company to continuously improve quality and increase productivity and efficiency by using data and statistical means. It is known that every system in the industries, optimized or not, are never perfect. Thus, it is imperative for the company to quantify the usefulness, manageability and reliability of the system; whether it directs the company better or not.
In this industrial statistics field, we provide information and indicators on economic activity of a company, particularly in the areas of mining, manufacturing and utilities, to describe the state and growth of the company.
A hybrid RF-LSTM based on CEEMDAN for improving the accuracy of building energy consumption prediction (Karijadi and Chou, 2022)
Deep learning-based automatic optical inspection system empowered by online multivariate autocorrelated process control (Wang and Asrini, 2022)
Model for deep learning-based skill transfer in an assembly process (Wang et al., 2022)
Application of Ensemble Empirical Mode Decomposition based Support Vector Regression Model for Wind Power Prediction (Karijadi and Mulyana, 2020)
Autocorrelated Multivariate Contro Chart in Cooking Oil Industry (Pudjihardjo, Mulyana, Asrini, 2019)
Electricity Load Prediction using Fuzzy c-means Clustering EMD based Support Vector Regression for University Building (Karijadi et al., 2019)
Improvement of Building Electricity Load Prediction Accuracy Using Hybrid k-Shape Clustering EMD Based Support Vector Regression (Karijadi et al., 2019)