Machine Learning in Small and Medium-Sized Enterprises, Methodology for the Estimation of the Production Time

Abstract

Data mining (DM) and machine learning (ML) are widely used in production planning and scheduling. Their application to production time estimation leads to improved planning and scheduling accuracy, resulting in increased overall efficiency. Small and medium-sized enterprises (SMEs) often have a small amount of data, which results in the limited adoption of DM and ML. Instead, production time estimation is still performed using rough approximations, which are inaccurate and non-reproducible. Therefore, this article proposes an ML methodology for production time estimation. It is adapted to the needs of SMEs and is applied with limited data. The methodology is based on the categorization of four job types (from A to D), the partitioning of data according to the limit theorem of data convergence, and the definition of risk based on metrics of probability and statistics. ML was applied by WEKA Workbench (Waikato Environment for Knowledge Analysis). It is also integrated into the Cross Industry Standard Process for DM. The methodology was implemented on data from a medium-sized company, Schoepstal Maschinenbau GmbH, for job types A and B to estimate machine/job cycle time, manufacturing cycle time, and lead time. Different accuracies were obtained for individual estimation models, confirming the strong dependence of the models on data quality. Suitable models were found for the implementation of the estimation of the manufacturing cycle time and the machine/job cycle time. The modeling of lead time estimation was unsuccessful. This was due to the weak dependence between the learning values and the values of the selected model attributes. The implementation of the methodology for job types C and D is the subject of further research.

Keywords

• data mining
• machine learning
• production time estimation methods
• standard time
• predictive analytics
• small and medium-sized enterprises