By Kris Puthucode, CEO and Principal Consultant, Software Quality Center LLC
With the rapid advancement of Industry 4.0 (and Smart Factory 4.0), it has been quite some time that leading consulting and research organizations are focusing on understanding the impact of today’s global economy, couple with the IoT and Cyber Physical systems., (CPS) on Quality management.
Of specific interest, is many organizations that have started using some, if not all, of the technologies and harvesting the data, are reporting that they see anywhere between 20-50% reduction in the total cost of Quality. We explore key aspects that you, as the Quality Leader in your organization should worry about to fully be ready to build and sustain a “Quality System 4.0” or QMS 4.0 as many are calling it.
1. Data Availability – in the current “Smart Factory” scenario, one of the key things is availability of real time data., a lot more than our usual manual or semi-automated processes of Industry 2.0 or 3.0 have been producing. All this is powered by the availability and close-knit operations of CPS. So, should we be learning about big data and analytics as a Quality professional? The answer lies in which industry you really belong to. If you lead Quality in a large production house, such as Automotive., Manufacturing, Construction or like-wise, impact of availability of data is to be seen as boon to the Quality professional. Data can protect and prevent things from going wrong by the right analysis. Even if you are in a small organization, such as a small IT Service provider., you are certainly impacted by data.
2. Data Analysis – Use the right type of Data analysis will lead to prevention – Using Predictive analysis has never been so important as now. Predictive analysis includes the use of statistical and quantitative methods for analyzing trends, formulating baseline performance., and establishing models to predict one more parameter of critical business concern. With Industry 4.0., powered by AI and Machine Learning (ML), analyses methods are likely to be lot more reliable., due to availability of data that is powered from CPS and non-manual data. But the key for the Quality professional, is to learn and understand data analytics.
3. “Leading Indicators” vs. Lagging Indicators – A very critical aspect of Industry 4.0 data analysis, is that data analysis will now be providing a lot more “leading indicators”, as opposed to “lagging indicators”. A simple example is to visualize the most typical metrics collected and analyzed in the industry today tends to answering questions such as “how much of project is done”, or “how much resources or $$ have we spent” but very limited analysis to answer questions that are predictive in nature., such as “what if a major risk occurs” and “will we be able to complete the project given the remaining time, $$ and resources”?
4. Embed “daily” Risk Management – Including operational risk management as a critical aspect of every day quality activities will gain importance., as the learning process with AI and Machine Learning being much more fast, capable of making decisions quicker. The Quality professional has to be ready for more proactive and rapid approach to risk management.
5. Active involvement in Planning – So with all this, the Quality team has to be actively involved in planning the overall Quality management activities right from the initial stages., but not merely an “enforcer” or being the “Process Police” from time to time. It is now that you need to transform to be a “Business Assurance” professional as opposed to a “Quality Assurance” professional for your organization.
With these 5 transformational ideas., you can truly ensure your organizations investments in Quality will come to fruition and enable ROI sooner than and contrary to traditional Quality initiatives – Seize the opportunity and being the transformation, personally and within your Organization. Lead the change., don’t wait for it to arrive! Industry 4.0 is already here!