BayMiner QVM is a new quality analysis method developed for manufacturers of complex products.
BayMiner QVM is intended for manufacturers with mid size volumes in the hundreds or thousands per year. BayMiner identifies co-occurrences of several problem factors, which you cannot easily do with conventional calculation methods such as spreadsheets and classic statistics. BayMiner enables you to find the root causes to quality problems. Opposite to conventional tools BayMiner QVM groups the problem cases and visualizes them and their difference to other product groups with an easy-to-understand profile presentation.
Your situation?
Your organisation occasionally delivers equipment too late or of poor quality but you do not know what are the root causes.
Are these symptoms familiar?
- You receive explanations that you have heard several times.
- Data is missing or inconsistent.
- You do not have means to compare deliveries considering simultaneously up to tens of factors.
Solution overview
Start the QVM development focusing on finding the most useful variables. Using standard BayMiner identify those variables that include the necessary information. Use historical data about the deliveries. Analyse this data to reveal which processes may be the weakest in your supply chain. Create a process to join data from various legacy databases.
The challenges:
- Delivery delays come as surprises.
- Classic statistics do not identify the root causes to quality problems.
The solution the customer looks for should:
- Identify the non self-evident causes.
- Utilize the company’s collective competence.
Technical advantages:
- BayMiner QVM does not require changes in IT-systems.
- Sparse data is adequate to reach a useful result.
Benefits:
- Delivery delays will probably decrease with 75%.
- Combined competence steers category management.
Exclusive advantage provided by BayMiner QVM for the customer
You can avoid the delivery delays by identifying several hidden factors that jointly occurring cause the delay. In this situation the classic statistics fail as each delivery includes so many variations that there are hardly two similar deliveries.
