At ETQ, we recognize that the future of quality will increasingly rely on artificial Intelligence (AI) and more particularly, machine learning (ML), as a way to augment the insights and capabilities of quality teams.
This is a topic Morgan Palmer, ETQ’s CTO, explored recently in an article for Quality Magazine.
As he mentioned, ML tools are finding the needle in the haystack when it comes to sifting through loads of quality data and helping to unlock hidden data patterns that could be impossible for the human mind or eye to absorb. Through these data patterns, quality managers will be able to predict what will happen in the future, with a cone of confidence.
A recent S&P Global report found that 95 percent of businesses consider AI to be important to their digital transformation goals, yet integrating it into quality management processes is still nascent. It will take time for widespread adoption since it requires a systemic change in how companies operate, as well as skilled personnel to implement and optimize it.
Deploying machine learning can be a complex digital transformation for any company, and requires careful planning. Morgan’s article shared five best practices that can help companies ensure a smooth transition to AI-driven quality management:
- Identify the problem. Before deploying machine learning, identify what it is you are really hoping to accomplish. Is it to predict the likelihood of product defects with great, statistical accuracy, or to identify manufacturing floor behaviors that are creating corporate risk? Once you identify your goals you can work backwards to the solution.
- Gather the data. Good data – and lots of it – is required for smarter AI. Companies need to take a data audit, identifying where data, including structured and unstructured data, resides across the company, and making sure it’s cleaned and classified for use as training data for the algorithm.
- Democratize the data. Data should be gathered across the enterprise and the supply chain and centrally shared to inform insights across functions.
- Start with baby steps. While technology is advancing, AI-driven quality is a very new and complex concept and it pays to tread lightly. When quality leaders become comfortable with how ML works at a basic level, they can pursue more advanced applications.
- Work with data scientists. ML will always require the expertise of data scientists for proper management and continuous data maintenance. Quality professionals should collaborate with data science experts for effective deployment.
Augmenting human-centric intelligence with machine learning can bring quality to new heights in enterprises, but it requires strategic planning, lots of data and data science expertise. It’s a journey that will take time, but the new levels of quality insights gained will be well worth the wait.
