How can AI contribute to process improvement?

How will AI and automation improve age-old approaches to waste reduction in business?
4 September 2019 | 18 Shares

Toyota pioneered the LLS principles of process improvement. Source: Shutterstock

Process improvement methodology— such as Lean Six Sigma (LSS)— has been crucial to Fortune 500 companies’ rise and control of the business world.

Companies like 3M, General Electric, Caterpillar and Dell have all seen operational improvements having implemented these types of strategies— be it for manufacturing, hiring, marketing or sales.

But in the age of disruptive technology where AI, automation, and soon 5G, are making their way into business applications, how will the technology impact this methodology? 

Process improvement relies on data analysis

Process improvement is an analytical approach that seeks to uncover the amount of “waste” that’s being produced by a process. “Waste” in LSS is defined as “anything other than the minimum amount of equipment, materials, parts, space, and workers, which are absolutely essential to add value to the product,” according to Toyota’s Fujio Cho.

If a company is suffering from poor product sales, sales data is collected and studied to find out the root cause of the problem. Issues related to cash flow would study data from the company’s income streams and investments. Problems with manufacturing would study output rates. 

These amount to huge sets of data that a process improvement practitioner can calculate averages. From these results, the practitioner can then analyze data to deduce what the next course of action would be in order to optimize the process by reducing waste and increasing output results. 

Machines can’t derive real-world solutions, yet

But these huge sets of data are sometimes tedious to calculate. It takes time and requires access to sensitive information that companies are sometimes not willing to share even to their internal staff. What’s more, humans are prone to errors— one slight mistake in the calculation can throw the entire initiative to the wind.

This is where AI can enhance things. By combing through the masses of data points across businesses, and the markets and industries they work in, adjustments can be made across the business and its processes in real-time.

In the capacity of process improvement, AI can monitor and adjust to more variables and data points faster than a human— or team of humans— could. The more AI learns about the business processes and market, meanwhile, the more it could be used to predict changes down the line to make business process improvements proactive, rather than reactive.

AI can also be used to predict the effects of potential changes based on past outcomes and other variables, as well as spotting potential bottlenecks and resource limitations.

AI doesn’t necessarily have to make the decisions— simply providing rich insights based on data points can inform and supplement those made by individuals on the ground. In this way, AI could be deployed to handle smaller, marginal tweaks to process improvement, while major efforts are executed by personnel. The software could also indicate where your business might require more specialist knowledge down the line.

While the concepts of process improvement methodology might be unchanged since their initial conception, pioneered by the likes of Toyota and Motorola, AI is enhancing how these approaches can be applied, the breadth of information that feeds them, and the pace in which they can benefit organizations.