There have been more than a few ventures that have used various methods of data analysis and intelligent algorithms to help provide solutions to a broad set of industries. From claiming to provide predictive capabilities on sales numbers, customer identification and health performance of patients to forecasting performance of industries.
Over the last few months, we have been in conversation with multiple precision manufacturing industries. Medical device manufacturing where the processes and performance need to be in tight control. Pharmaceutical industries. Electronics component manufacturers where yields need to be high to be competitive and when integrated into a larger device, failure is a killer. The operations manager is at the heart of this industry - so we wanted to find out how current predictive tools help.
These tools are able to track process parameters over time and show trends on how the processes change. With sophisticated visualization techniques, these tools allow the operations manager to perhaps get better insight into the processes and the impact of deviations (even within the allowed process operations range) in the process on yield or product performance. Having said that, the role of the operations manager still remains the same.
They still need to track raw materials acceptance and raw materials inventory. If raw materials failed inspection, they still need to work with the receiving officer to plan new orders. The receiving officer still needs to review the raw materials documents and approve / reject the material and then send it to the appropriate location.
The operation manager (or an assistant) stills needs to know which operators worked on what processes, review documents that processes were run appropriately, within specifications and the resulting component tested to within spec. If there was a failure, an expert or a group of expert had to identify from the data possible reasons of failure and identify which other groups of products may be at risk owing to having shared process conditions or raw material lots.
When the yields do not meet expectations, the operations manager (or an assistant) has to identify what steps needed to be taken. And when the component was successfully made, they needed to assess all documentation to ensure that it met raw material and process standards and sign the correct release papers.
As a result, every operations manager shared with us that 50-70% of their time was spent on manually managing these activities. While the current software tools helped them get the data together (and that was not mean task) and visualize the data appropriately, they wish they had time to identify, plan and implement continuous improvement projects for higher yields, faster processing or lower Cost of operations.
Wynum was motivated to address exactly these pain points. Now successfully implemented in 6 different manufacturing operations, it was built to be easy to implement and easy to scale while ensuring that
It could work with any current ERP system. It was ERP agnostic
It could learn to do receiving of materials by assessing documentation, decide the next step based on the assessment and take action
Identify failures in process or testing and isolate other lots that may be liable to fail
Learn to identify possible future shortfalls in inventory of raw material and sub-component and send instructions to the right person
Assess documentation and put together product release forms
Respond to field failures
It includes visualization of data, like other similar tools. However, it is also able to make decisions and follow up with instructions to correct personnel, thereby reducing the "busy work" and bureaucratic processes time of the operations manager and the manufacturing team. It is built using dynamic AI & Machine learning algorithms - it is not meant to take business decisions. Rather, it learns the business decisions and parameters of those decisions that the operations manager or other personnel take, and replicates those decisions based on scenario assessment.
With improved receiving capabilities, release capabilities, better inventory management and improved failure detection, Wynum's clients have seen operations cost improvement of 10-40%, yield improvement of 3-12%, and operational error reduction of 10-30%. In addition, the operations manager and their teams have had between 10-30% less paper work and bureaucracy to deal with, helping focus on process improvement projects.