Buy or Make? – Which gives the best ROI for Manufacturing Analytics system

In the electronics manufacturing industry, Original Equipment Manufacturers (OEM) and Contract Manufacturers (CM) are always trying to optimize their manufacturing processes to improve efficiency, quality and reduce wastage. With the introduction of Industrial 4.0 enablers, the digital transformation of the smart factory presents many possible areas of opportunity to invest in. It would be obvious to consider transforming the digital backbone of a typical factory.

For many decades past, the digital backbone of factories are the Manufacturing Execution System (MES) solutions. MES has been invaluable in being able to monitor the process status of the products being manufactured as it moves through the line, providing visibility, tracking, tracing, and general high-level control. The data source that is acquired from these processes is summary data that has been designed to provide just what is needed yet make the payloads light and efficient, which as an example consists of serial numbers, stage in production, time stamps, process pass or fails, etc. Of course, database technology was kept simple and straightforward, typically using traditional Relational Database Management System (RDMS) data warehouses, rather than the current trend of complex big data no-SQL systems. All these are a result of addressing rather simpler use cases where the most challenging ones are at most sampled complex statistical computation for quality control best practices and standards.

In the electronic boards manufacturing industry specifically, quality gates such as In-Circuit Test (ICT) and Functional Testing (FT) systems* are ubiquitously used to ensure only good parts get shipped out. However, these test systems had very high-level summary data that were visible in the MES systems. Some of these summarized data would include metrics such as First Pass Yield (FPY), Final Yield (FY) Statistical Process Control (SPC), downtime, test time, test volume, and so on.

Whenever there is a low FPY incident or if there was a need to improve existing FPY of a product, an in-depth offline root cause analysis are usually undertaken by engineers. The deep analysis may point the root cause at some process upstream which was caught well at test. The common practice will be to extract the data logs manually from the test systems. Then the data will need to be cleaned and transformed to something that can be used in an offline tool such as excel or other similar tools.

This offline analysis solution is a slow and painful method. In addition, repeating the extraction, cleansing and transformation of the test data logs is time-consuming and prone to human error. To make things worse, importing large amounts of test data into any tool in one attempt usually takes a long time. The feeling of waiting hours for the import process to finish and then seeing an error prompt is all too familiar for many.

With the above-mentioned concern from the existing solutions in most factories, there is a need to close the gap for the current analytical solutions specifically in In-Circuit-Testers and Functional Test systems if the manufacturers intend to digitally transform improving yields and quality.

In order to close the gap, manufacturers need to consider big data analytics solutions that are scalable, easy to deploy, and fast. PathWave Manufacturing Analytics (PMA) is Keysight's industry-leading Industry 4.0 analytical solution that enables electronics manufacturers to herald their factory into the next smart digital transformed factory, by providing real-time advanced data analytics on the massive raw test data logs. It handles source-to-target with Keysight's innovative big data stack that uses the best big data components such as KX's kdb+. What makes it truly differentiated is that PMA combines years of test and measurement knowledge in manufacturing test and emerging data science to cater to electronic manufacturing use cases, especially in ICT and FT systems.

By adopting the big data analytical platform, actionable insights can be generated from seemingly wasted massive amount of raw test data that are being produced every second. Some of these advanced analytical insights include probe degradation predictions, false failures trends and anomaly detection to name a few.

Now we have established the need to adopt big data advanced analytics platform in the smart factory, then the first question is whether we have to build an internal solution or explore matching solutions from solution providers. Not surprisingly, it is quite often the case where there is this DIY mindset planted in the mind of all engineers that they would like to try out and analyse the machine data themselves. However, it is the case of easier said than done. Is it worth developing your own manufacturing analytics platform? Will we get a good return on investment (ROI)?

Here are some of the questions that you might ask yourself to make the right decision.

How much time and money at what risks are you willing to spend on developing your own manufacturing analytics platform?

Building your own new big data advanced analytics platform requires a sizeable team of experienced specialists, which a mix of computer science, data science, full-stack developers, UX designers, big data engineers, testers, and product owners to work closely with test engineers that have the domain knowledge of the use cases. Aside from the time spent on design and development, the time spent on the hiring process or upskilling existing employees had to be taken into account as well. And after developing and deploying the first iteration of the solution which may take anywhere from 6 to 12 months, the developer teams will be re-leveraged into other projects or even let go if under contract. However, a smaller team is still needed to be retained for continuous maintenance, enhancements, improvements, and support. Whenever there is a need for major enhancements, there will be a challenge to find additional resources to support the job.

In contrast, by choosing a suitable solution from solution providers, probably a week or two of setting up and maybe a few hours of training and all your KPIs and analysis will be yours to work with. What is important here is that it is a choice! It is not a given that you have to build something just because perhaps you can. Valuable time can be better utilized to focus on your core business operations and improve your organization's efficiency.

Even better is that specialized solution providers such as Keysight can provide the after-sales service and support for PMA through onboarding, customer success consultancy, new feature enhancements, bug fixes, and high availability.

What is your plan for connecting all the different types of electronics test systems on your production floor?

The big challenge here is that it is common to have many flavors of test systems in the factory. Every test system usually has its own format of data outputs and test results depending on the individual engineer developing the test plan or script. The granularity of the test data can be ranging from the common pass/fail data, to test data with specific Bin/Error code. More often than not, we have observed in many cases where the test data logs are missing some types of data that would be useful for big data advanced analytics! This will potentially create a permanent gap between what data are available versus the desired outcome and insights that will never be solved unless changes are made to the original test plans. Of course, that is if the original test engineer is still around or at least has the test plan well documented before the initial final release.

This is precisely why it is very important to understand the raw 'datascape' of the test systems out on the floor and whether there are any potential gaps or chasms between what is available and what your desired analytical outcomes and insights are. We have seen many cases where the attitude of 'bring whatever we have into the data lake first, and then we discover what we can do' has proved to be massive failures and money sinks. Rather, 'begin with the end in mind' as they say and you will probably get your actionable insights.

How do you ensure that you are on par with your competitors in terms of production efficiency and quality?

It is not possible to answer that question unless one is able to get into their competitors' firewall. Creating your own customized analytical platform that caters only to your needs may sound good. However, it might be a double-ended sword as it will be an inside-out product where you may be blinded by the industrial movement.

By choosing off-the-shelf manufacturing, and analytical solutions such as PMA, you will get a sense of what the industry is measured and what other insights are being generated, for instance, anomaly alert scoring, and test coverage monitoring to name a few. By understanding, developing, and deploying industrywide use cases with a lot of industry inputs and validation, you can be assured you will be getting the best insights that any analytic solution can offer. Bigger data means bigger truths.
Moreover, by providing continuous support to our customers and constant two-way engagements, we can be perceptive to new use cases and needs of the industrial movement and thus continuously improve our product and a true agile fashion.

To Recap

In today's highly competitive and complicated electronics manufacturing industry, time to market, data transparency, product quality, data driven insights and scalability are topping the chart. For most companies, analytics alone are no longer adequate to effectively monitor their efficiency and quality.

It takes expertise to build a platform that gives long-term benefits to the users.

Companies should consider both the advantages and disadvantages of either buying or making an effective analytical platform. They may face the risks of cyber security risks, cultural changes, a variety of the big data formats, as well as extra expenses in staffing, maintenance and server hardware.

Before embarking on the era of big data analytics, especially in the field of electronic manufacturing, the stakeholders must carefully weigh all possible factors in their particular case.

Investing in the right tool to extract the maximum benefits and return on investment from your manufacturing line.

Feel free to contact our sales team to explore the benefits of PathWave Manufacturing Analytics (PMA) or visit the links below to understand more.

* ICT testing procedures are capable of performing several comprehensive checks on the circuits on the PCB while the FT performs functional tests to ensure the quality of the final product.