One core element of Total Quality Management (TQM) is constant monitoring and process improvements performed to achieve an excellent level of quality. This fosters improvements as the company, its customers, and the ever-changing environment evolve over time.
In other words, one should treat a quality management system as a living and breathing being that learns from mistakes and continues to improve to ultimately produce better results.
The primary implementation of artificial intelligence includes using machine learning to enhance or replace the actions and decisions of human beings.
This is typically done by analyzing a vast amount of data, grouping it into different areas and categories, and using it to find connections and insights that can help improve the overall model.
With this in mind, the core element of machine learning mimics TQM via constant improvements using insights gleaned from data.
So, how can we use artificial intelligence to achieve TQM?
The center of TQM is customer-focused.
When you really think about it, who actually measures the quality of your product or service if not the customers you’re selling to?
To do this, we must work to better understand our customers more quickly so we can respond to ever-changing conditions and any expectations they might have.
Tracking our customer communication efforts can help us gather a bevy of accumulated data we can then use to gain insight into what they want and which types of issues they face with respect to our product(s) or service(s).
AI will pinpoint both positive and negative trends, allowing us to better focus on our customers and their needs much more quickly than in the past.
An additional TQM element is employee involvement and communication.
How can we know if our employees are engaged and better understand their roles and responsibilities within the organization? Using artificial intelligence, we can analyze employee performance based on how they complete actions, via processes they participate in, or in training they pass or fail. Relevant suggestions for additional trainings or other actions we can execute to bring employees on board can help us excel in these areas as well.
Quality excellence springs from processes we successfully execute and complete from start to finish on a consistent basis.
Some of these require a short amount of time to complete, while others take longer.
In wrapping up more and more processes, we actually create a large pool of data that can afford us many insights into how these help achieve our goals and how to fine-tune them, if necessary. If, for example, you’re performing a CAPA of some kind and implement an action plan that actually succeeds, you want to ensure you’re learning from this fruitful experience. On the other hand, if this action plan failed and/or did not achieve the right objective, you don’t want to repeat the same mistake(s) again. When we begin to accumulate a large amount of data, that’s precisely when artificial intelligence steps in to improve processes at their core and achieve better results.
Integrated systems aspects are another core TQM component. To achieve this, you’ll require the visibility of the entire organization as this affects quality. In aligning diverse systems within the organization, this can help you keep track of your processes, analyze past iterations, engage in real-time monitoring, and better prepare for the future. Artificial intelligence feeds us the insights we need from the entire organization so that we can connect additional quality system components in order to improve its integrated nature.
A strategic and systematic approach to quality requires us to understand that quality is our business and we do not have a right to exist without it. So, in order to really look at quality as our everyday business, we must integrate it within our strategy and make fact-based decisions using the insights we receive.
Prior to the machine-learning age, utilizing big data required expensive BI consultancy services that a machine can now offer on a much better, much faster basis: ensuring fact-based decisions made against real-time data.
In conclusion, artificial intelligence can help us constantly work toward higher-quality systems by helping us know “what” to improve. Yet, knowing how to improve is not enough. It’s quite complex to change a quality process—or any process in general—especially in a highly regulated environment. Digital quality systems typically make these procedures even more difficult, requiring technical work usually performed by experienced (and expensive) consultants that are inherently error-prone due to human involvement; it is slow and requires constant risk assessments, verifications, and validations to ensure nothing breaks amidst process “improvements.” This risk usually prevents companies from engaging in precisely what TQM is all about.
If we can simply consider “what we would like to improve” without thinking about “how to achieve this,” wouldn’t that be easier?
Providing end-to-end quality powered by AI, Simploud does exactly this.