Newsletter March 2020

by | Mar 8, 2020 | Newsletters | 0 comments

In this newsletter we are pleased to announce DataLyzer Qualis 4.0. We also share an article that explains short run SPC. Finally, we introduce our CEO from DataLyzer India: Jayanth Purushottam. We hope you enjoy our newsletter. Please feel free to reach out to us with any thoughts or suggestions. We’d love to hear from you.

– The DataLyzer Team

 What’s new: DataLyzer announces DataLyzer Qualis 4.0

DataLyzer is proud to announce our new generation of quality software: Qualis 4.0.

DataLyzer was the first company to release commercial SPC software for computers running on DOS back in 1979 called DataLyzer II.The next generation for Windows was called DataLyzer 9000. With the arrival of database systems we released the 3th generation DataLyzer Spectrum.

Over the last 10 years we have expanded both our company and our products and can now offer a full suite for the APQP Core Tools. Now we are ready to announce the next generation of our SPC software: Qualis 4.0

Why the name Qualis 4.0?
Qualis is Latin for Quality. But Qualis is also short for a complete Quality Information System.
The extension 4.0 is added because this is the 4th generation of our SPC software flagship, which will fit perfectly in an industry 4.0 roadmap. We also use 4.0 because this refers to the fact that DataLyzer exists 40 years.
Qualis 4.0 is the latest generation of SPC software which will have all Spectrum functionality you are familiar with and much more….

It is developed using the latest technology. You can run Qualis 4.0 in a browser or as an installed application. It seamlessly integrates with Spectrum, because it uses the exact same database structure therefore no conversion is required.

If you want to know more please contact your account manager or email
Or read the brochure here.

Must read: Short run SPC in DataLyzer Qualis 4.0

Short-run statistical process control, often referred to as nominals, target or DNOM charting or Z charting, is perceived as the solution for manufactures with a high mix- low volume environment. In this article we will show it should be used in a lot more environments and we also show how this can be easily realized with DataLyzer Qualis 4.0.
If companies start with SPC, we often see they simply chart the product measurements, which is a missed opportunity. Charting all products on different charts is not helping you to control the process. It is merely an advanced product control tool. For example, if you spray a coating on products and a lot of products have a different target and you use control charts for coating thickness you are not really controlling the process. You will hardly see that variation in the spraying process is increasing, because each run will not have more than a few subgroups and there might be a lot of time between runs.

So if you chart the deviation from target you can really monitor the process. Another advantage that you only need 1 chart instead of sometimes hundreds of charts if you do SPC per product. The introduction of an automated SPC system is exactly the right moment to start these discussions.

How does Short Run SPC work Short run statistical process control allows the user to graph several parts on the same chart instead of creating individual charts for each part. The DNOM chart stores the deviation from target. So the chart always has a target of 0.

Before getting started there are a few simple rules that usually must be followed.First, the variation of each product being measured must be similar.The parts must be produced using a similar process – machine, method, and material.The tolerance must be the same.If the variation is not the same or the tolerance is not the same you could in principle still use DNOM charts but you have 2 problems. If the variation is not the same you need to work with either changing limits or if you fix the limits products with very low variation will not get any alarm signals. Although theoretically not 100% correct this might still be very useful in practice. If the tolerance is not the same then your Ppk reports will not be correct.

In some cases you can go a step further. Instead of taking the deviation from target you can take the relative deviation from target which is (measurement – target) / target. The result will be a percentage. In literature they even go one step further and instead of dividing by the target they divide by the standard deviation for that product. So the formula is (measurement – target) / stddev. The resulting value should be between -3 and 3.

With the last option (Z-charts) you can even combine multiple characteristics in 1 chart. So 50 subgroups could show the results of 50 different measurements on a product. The big disadvantage for Z charts is that you start to loose the operator. The operator measures 24.3 inch with a target of 24.1 and the value in the chart is 1.42 indicating the value is 1.42 * normal standard deviation above target.

Read the entire whitepaper here.

Let’s introduce: CEO of DataLyzer India and co-owner: Jayanth Purushottam

A senior Management leader, with over two decades of experience leading and creating value for technology-led Enterprises. One and a half decades of technology expertise in Unified Communications, Telecom Applications, Quality Management, Industrial Automation, Enterprise Mobility, New-age technologies, Team Mentoring and Leadership Coaching.
Jayanth is a leader with a passion for solving mission-critical business challenges, and enabling high ROI and cost-effective solutions for customers. A determination to be a game-changer for emerging leaders, young technocrats and entrepreneurs. A vision towards transforming lives, a passion and experience for building teams from ground zero, professional ethics and value systems, a constant zeal to make a significant difference.

Currently, successfully implementing and overall driver of real-time SPC, MSA, FMEA and OEE solutions for Automotive industry, Pharmaceutical and Medical Devices, Packaging industry, Food and Beverages, Solar and Hi-tech Industries, across diverse geographies and complex business architectures. He leads a team of more than 20 developers, and is the architect of Qualis 4.0.

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