Statistical Operations Data Analysis reveals Savings Potential
The operations data recording systems of modern production facilities contain a treasure trove of data that is too seldom utilized. In order to systematically evaluate this data, we have developed Statistical Operations Data Analysis (SODA). Based on results obtained, we prepare measures to reduce operating costs and increase productivity in your company.
Statistical Operations Data Analysis – Data treasure instead of data cemetery
Using the data models from Statistical Operations Data Analysis (SODA), the reasons for variations including their statistical significance can be determined and the essential cost drivers identified. Thus, effective targeted improvements can be implemented and productivity improvements sustained.
Statistical Operations Data Analysis tailored
The TARGUS-developed Statistical Operations Data Analysis is an effective method to reveal savings potential, especially for complex production processes. Using our tailored data model, we can identify relationships which were not earlier apparent. Additionally, we can test in advance, the impact on organization and procedural changes using Statistical Operations Data Analysis. Unsuspected cause-effect principles lead to individual solutions for your company.
Typical Results from Statistical Operations Data Analysis
- Reduction in consumption
- Improvement of the organization
- Strengthen productivity
- Transparency of the disturbance variables
Statistical Operations Data Analysis in Action
At an aluminum rolled products manufacturer, isolated quality problems occurred which were visible under the microscope. Here, it was a matter of physical changes during the production process. The reasons for the quality defect were unknown and could lie in a pre-production stage at a supplier or in an internal production step. To identify the cause of the defect we applied Statistical Operations Data Analysis. We systematically examined internal, as well as external factors. The roll data from the last two years was retrieved and expanded with quality data from the suppliers.
Through the evaluation of linear and multivariable correlations of the influencing variables and the quality problems using Statistical Operational Data Analysis, we were able to isolate the causes. We determined a connection of several input parameters such as speed and temperature. Based on these factors, various optimization measures to significantly reduce the quality problems were implemented and sustainable product quality was achieved.