How AI Can Turbocharge Your TQM Program

Forbes

01-05-2025

Bankim Chandra is Director & CEO of Dotsquares. Always committed to innovative solutions and mentoring the next generation in the industry.
Over the years, business owners and management professionals have been faced with an ever-increasing number of business process models, including Lean, Six Sigma, Kaizen and total quality management (TQM), to name a few. All of this is further complicated by artificial intelligence (AI).
The big question is: How will we use AI in these legacy models? What benefits can it offer, and how will those benefits manifest for end customers?
Let's take a look at TQM and how it might benefit from AI. TQM is, as the name implies, focused on facilitating quality improvement. The International Organization for Standardization describes it as "a management approach of an organization centered on quality, based on the participation of all its members and aiming at long term success through customer satisfaction and benefits to all members of the organization and society."
It is essentially a management approach that aims for long-term success through customer satisfaction. Key elements of this approach focus on strategy, data and effective communication to optimize processes, products, services and the culture of an organization. TQM also easily integrates with other approaches, such as Six Sigma, lean manufacturing and global ISO (9000 series) standards.
Is it the data element of TQM that can be furthered by the adoption of AI? As we know, generative AI can sift through huge quantities of data quickly to derive insights from those datasets. For example, sifting through multiple communication channels, including social media, can help you discern what the customer actually wants.
There are many ways in which AI could be a real game changer for TQM. AI can analyze any historic legacy data to accurately predict any potential quality issues before they occur. In manufacturing, cameras and sensors can identify defects much more efficiently than a human eye. As machine learning progresses, these quality management practices could become more accurate and efficient, spotting anomalies quickly and suggesting preventative measures.
AI can also be tasked to look for the insights in customer feedback. By analyzing the data from reviews, surveys, social media and other sources, AI can quickly ascertain any trends and highlight potential areas for improvement. Similar processes can be used to identify and mitigate risks in the supply chain.
But AI can also help in other areas of the business, such as identifying process improvements. By looking at legacy and current data, it can identify roadblocks in business processes and suggest improvements. This could lead to potential AI-designed 'experiments' in, for example, business process data flows.
Two important elements need to be considered here. The quality of the data being is imperative, as is the choice of AI model the algorithm it applies. Combined, these can give businesses a great competitive advantage, providing key insights and allowing informed decisions to be made in real time.
In software development, there is already a plethora of AI automated testing tools being used to both write and check code. Coding assistants such as GitHub Copilot and Google AI Studio are already prevalent in many organizations, allowing coders to concentrate on more intricate tasks.
There are many AI tools available, and of course, choosing the right one for your industry and organization is going to be critical.
Before you make your choice, you need a clear view of the challenges and goals that your implementation might face and achieve. Review current market offerings and their relevant case studies. Once you've narrowed down those offerings, evaluate your choices firsthand by trying them out. Pay close attention to the reputation ascribed by peers to the tools you review; that will tell you a lot about its market position and whether it will become one of the major players in its particular niche.
Additionally, take into account the speed by which organizations are able to derive real value from the tool. Assess the migration potential for any legacy data that you'll need to import into the tool. Furthermore, assess the tool's security infrastructure and how safe that data is going to actually be. See how much support the vendor offers to those who use their tool, too.
Any TQM program now has a wealth of resources available to them. With these tools, you can pull even more value from your data, which can help you build better relationships with customers, drive your bottom line and more.
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