Real time, data-driven decision making is a key way of achieving six sigma process control. In manufacturing environments, the equipment has multiple sensors, which generate data at high speeds.
ShopFloor online data analysis allows for the structuring of data and the subsequent application of batch and/or machine learning algorithms. This allows you to make data-driven decisions.
Quality is influenced by everything that happens during each manufacturing step (operation).
Each raw material, each consumable, the condition of the equipment, process variables/attributes, residence times and changes in the manufacturing environment can all lead to variability in product quality.
Traditional SPC (statistical process control) charts help with quality control by identifying control instances.
However, because of its single dimensional nature, it is difficult to identify and understand the causes of variables going out of specification and the relationships between them.
Along with classic SPC charting capabilities, the LZ Lifescience ShopFloor Online MES System can also provide multi variate data visualizations.
This allows for a greater understanding of the relationships between key process variables in a 3D chart.
Multi Variate process charts in 3D with a cross-batch or work order view helps to detect deviations or defects in manufacturing.
It also detects potential issues and the possible cause for the deviations or defects.
Multi variate charting focuses on correlation between these variables.
This is why it becomes easier to identify variability issues and resolve them.
Developing multivariate models that account for each potential cause of variability, and by applying process analytics, lifescience manufacturers can then establish a foundation for a real time manufacturing
quality system in their facilities.
Batch pattern change event charts help to monitor processes closely and identify any abnormal variations. These abnormal variations can lead to an out of control situation.
Getting visibility of cross-batch changes, raw material part numbers, each consumable or disposable used, the status of the equipment, process variables/attributes against limits, equipment residence times, operation durations, equipment wait times and changes in the manufacturing environment, highlight the sources of variability.
It is then possible to identify the areas to investigate as change events are flagged by ShopFloor Online.
Outlier detection becomes easy in our Multi Variate 3D process chart. Drill down for the cause of the outlier variables with the help of these multi variable relationships in the variability models.
At every stage of the manufacturing process machine learning techniques play a very important part in correct decision making.
With the help of self-learning algorithms, prediction continuously improves as the data sample set increases.
Maintenance of equipment and assets in manufacturing is a key factor in quality assurance.
Faults in equipment and assets can cause unnecessary downtime in addition to quality issues related to inventory and materials. Tools that have a finite lifetime can also lead to defective products.
Our smart application (using machine learning) can constantly monitor how these machines and tools are performing.
It can also provide a precise picture of their operating condition against the product quality.
This means that maintenance or calibration can be manually or automatically scheduled to avoid these downtimes.
Many manufactured biotech products need a forecast of success rate to perform better decision making and help prioritize responses and schedule changes.
Success probabilities depend on many factors including material, people, equipment status and other external conditions.
LZ Lifescience provides solutions which perform Multi Variate analysis to predict the success probability in real time for Batches, individual manufacturing operations and equipment or machines.
These results can be viewed in mobile friendly applications providing this critical information directly to the key decision makers.
Self-learning algorithms can report predefined sources of error and also help in detecting unknown source of error.
For real time planning (scheduling) of asset, resource and material availability, the ability to predict the estimated time to completion (ETC) for batches, operations and equipment or machines is very useful.
Using our machine learning techniques, we can accurately predict the ETC for these batches or key pieces of equipment. This allows for better planning and it improves batch execution times.
See the material requirements against stock levels in real time, as the schedule automatically changes the estimated start times for the next batches.
This is based on the estimated time to completion of the current batch.
Our predictive raw material/consumable pattern change monitoring allows operations to pinpoint the source of quality variability between batches or work orders.
It can also highlight differences in part numbers when suppliers make a change to their products.
Having visibility over these events as they occur in real time helps manufacturers to see what has changed.
For biotech companies, the huge increase in single-use disposable materials has increased the bill of materials.
Changes to bags, tubing, connections or filter part numbers between batches also needs to be highlighted to both production and quality.
Contact us to find out how your business could benefit from our software.