Key Points

• Total Quality Management is integral for an organization to run as effectively as possible.
• Utilizing AI within TQM has several benefits, including enhanced data analysis.
• Automation will lead to a TQM process less prone to human error.

It won’t surprise anyone to learn that smart decision-making relies heavily on the accuracy of all available data. What also won’t be surprising is that traditional Total Quality Management systems have heavily relied on manual processes to bring about change, which isn’t likely to continue. 

The rise of AI and automation has ensured that we are in a new era of Total Quality Management across all kinds of industries. For better or worse, AI and automation are here to stay, and companies can either embrace these changes or get left behind. 

What Is Total Quality Management? 

In simple terms, Total Quality Management (TQM) is an organization-wide effort to reduce or eliminate a company’s errors in its processes. TQM also seeks long-term success for a business by explicitly focusing on customers’ needs and expectations. 

More than 100 years old, this process will seek to understand culture, collective and individual expertise, organizational dynamics, and how internal and external relationships come together. TQM also applies to supply chain management and ensuring that employees are up to speed on their training, as TQM seeks to hold everyone responsible for any failures that might arise as part of a project. 

Total Quality Management Essentials

To better understand Total Quality Management, you must realize that as many as eight separate factors are essential to the organization, according to ASQ, utilizing this method for success. 

Customer Focus

The main goal of a TQM process is for an organization to meet and exceed a customer’s expectations. However, the first step is understanding the customer’s needs so a company can provide value-added services or products. 

Employee Involvement

Employees at all levels in an organization should be active participants in quality management efforts, which include training, empowerment, and establishing a culture of accountability. 

Process Approach

Understanding TQM means an organization emphasizes managing and improving its processes for the best possible results. This means focusing on efficiency and effectiveness at all levels. 

Integrated System 

The hope is that any organization using TQM is doing so with an eye on working toward the same common objectives. This would mean that cross-functional teams are all aligned collaboratively. 

Strategic and Systematic Approach

For total Quality Management to be successful, it must be aligned with an organization’s main goals, both short-term and long-term. 

Continual Improvement

Any time an organization enables TQM, it should seem to be looking to make significant improvements in not only existing processes but also the products and services being delivered to customers. 

Fact-Based Decision Making

Any organization should make decisions based on the most accurate data available and using objective data analysis. In other words, decisions should not be made based on assumptions but facts. 

Communications 

It goes without saying that communication is the lifeblood of Total Quality Management and enables it to promote a culture of delivering quality work everywhere inside an organization. 

AI Will Transform Total Quality Management

Over the last few years, AI has undoubtedly changed the landscape for many industries, in many ways for the better. This leads directly to the question of how AI will transform Total Quality Management and if it will be for the better. 

The good news is that AI does introduce an entirely new level of opportunity around data analytics, predictive modeling, and machine learning. These will allow organizations to react faster and, in many ways, become more proactive about locating and fixing potential quality control issues. 

Automating Repetitive Tasks 

Among the most adaptable ways AI will make a difference with TQM is how well AI is showing it can automate remedial and manual tasks. As part of TQM, organizations have to manually inspect products, check compliance orders, and review processes, much of which are done by human hands and eyes. 

The challenge is that humans can and will make mistakes, and some things may go unnoticed all too often. The good news is that AI can not only shorten the amount of time processes are reviewed but also reduce the number of mistakes, or at least the risk of mistakes. 

Real-Time Monitoring

Another potential benefit of adding AI to TQM is a significant boost in real-time monitoring. This is especially true for the quality of products being delivered to customers and the internal process performance in an organization. 

The use of AI will help analyze data from everywhere in real time, including production lines, which can be fed directly into the systems of those managing this department. From this real-time data, the leaders of this organization can create not just actionable insights but properly defined recommendations on any processes that need to change. 

The hope is that AI will identify any product defects over time, identify which stage of a process they occur, and then provide corrective recommendations. 

Enhanced Data Analysis

You’ve heard the phrase “data analysis” quite a few times, but AI’s power will help make this all the more effective for TQM. Artificial intelligence can dig through vast amounts of data far better than any human can and in far less time. The benefit here is not just the time savings as you’ll also get near-instant insights that can be acted on and any areas for improvement. 

When you think about industries like finance or healthcare, where large amounts of data are necessary and accuracy is the difference between millions of dollars and life or death, these organizations will hugely benefit from the fast and accurate data analysis that AI can provide. 

Customer Centricity

Another benefit of integrating AI with TQM is that it will allow companies to receive far better insights from customer feedback. Digging into customer feedback is easier because AI can use things like natural language processing. 

Using this customer feedback, organizations will have a much easier time identifying customer pain points and trends by being able to dig through customer reviews, social media listening, or any other data source. By using AI, companies can react much faster to customer feedback, which customers will take as a positive and create longer, more loyal relationships with a business. 

Cost Reduction 

The benefits of adding AI to any Total Quality Management process are plentiful, but reducing costs is often overlooked. Introducing AI into your organization will not just make things more accurate or efficient, but it will also reduce costs. 

Unfortunately, human errors, however unintentional, can lead to increased costs as processes have to be revamped or new products rolled out. Instead, with AI, you will find that reducing human interaction in some processes will minimize errors and reduce costs associated with those errors. 

Automation Will Benefit TQM

Alongside AI, automation will also significantly affect how Total Quality Management works moving forward. While it’s too early to tell if AI or Automation will be more beneficial or integral to TQM in the future, there is a definite hope that both will be equally valuable. 

Increased Efficiency

There is also little question about whether integrating automation into TQM will help streamline processes. Like AI, automating manual tasks will provide employees with free time that can be better used to respond to customer feedback and work on more strategic business initiatives. This means increased productivity at all levels, from top to bottom, which will be great for everyone. 

Enhanced Accuracy 

Once again, process automation will help reduce errors in the same way AI will, as automated systems will outperform human review almost every time. The biggest benefit here will be consistent quality and accuracy now and into the future. Better yet, automated systems can detect issues in real time and provide and/or recommend the action that should be used to correct any problems. 

Better Data Collection 

This also overlaps with AI, so there’s a mix and balance between both, but automation will play a role in how data is collected and analyzed. The best use of automation will be taking in large amounts of data, just as the finance and healthcare industries do, and providing near-instant analysis into the quality processes that are currently taking place in an organization. 

By collecting and analyzing all of this data, businesses will be able to identify trends and make data-driven decisions. This will ensure that processes are being improved in real time, which is good news for the company and its customers. 

Other Useful Tools and Concepts

Anything that can improve processes and efficiency is welcomed in Total Quality Management. As this is written, AI and automation are most likely already being integrated into thousands of companies worldwide. 

This isn’t to say that integration will be seamless and that AI is perfect, but the reality is that anything that can help make companies more efficient and customers happier seems like a win-win for customers in every type of business. 

Conclusion 

While training will be required to get employees up to speed on AI and automation processes, the reward is obvious. Anytime employees can get more done in a day and do so in a way that reduces errors and enables them to be more strategic in their thinking rather than spending time reviewing data, it will be better for all parties.