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Embracing the Future: How Manufacturing Improvement Approaches Will Evolve in the AI and Digital Age

5 minutes read · 3rd June 2024

Author: Dan Orford, Principal, Chartwell

Embracing the Future: How Manufacturing Improvement Approaches Will Evolve in the AI and Digital Age

For the past seven decades, numerous improvement methodologies have driven significant advancements in manufacturing operational performance. As we look 15 years into the future, we must consider how these methodologies will evolve in the context of advanced AI and digitalization. Will AI dominate the landscape? Will traditional methodologies become obsolete? Will companies still require an OpEx function? 

The individuals who master the integration of new technologies, understanding their strengths and limitations, will spearhead tangible improvements. To envision this future, we must first outline the current improvement processes and examine how they might transform.

Defining the improvement process

To answer these questions, we need to firstly define the different stages of the improvement process. 

Improvement process

Detailed Process Understanding:

A fundamental step in any improvement effort is a thorough understanding of each stage of the manufacturing process, including the underlying physics and chemistry. This knowledge is crucial for identifying potential areas for enhancement. 

Identifying and Prioritizing Opportunities: 

The second step requires converting information gathered from time spent on the line, process data, and conversations with the operations team into opportunities by applying improvement methodologies. 

Testing Phase: 

The testing phase ensures that any proposed changes are safe, maintain high product quality, and are an improvement. This is usually through a mixture of lab and plant trials. 

Behavioral change and belief in improvement:  

Convincing people that improvement is always possible and fostering a culture of continuous improvement are essential. This step involves overcoming long-standing beliefs and habits. 

Sustaining change: 

Ensuring that improvements are sustained over time is critical. Without this, the entire exercise of improvement is pointless. 

The three buckets of AI and Digitalization

We next need to look at the different types of technologies that we expect to see over the next 15 years. Different industries will have different technologies making the largest impact. At Chartwell, we work in a wide variety of industries, and here we  will primarily consider the chemical industry. However, we expect a lot of overlap with the pharmaceutical industry. 

These AI and digitalization advancements can be categorized into three buckets. 

Bucket 1: Improved decision making for leaders 

Enhanced visualization, data capture and processing capabilities allow leaders to make better-informed decisions. For example, AI can predict market demands, optimize supply chains, and improve planning. 

Bucket 2: Digital capital expenditure 

Investing in digital and AI technologies can yield a strong return on investment in a similar way to traditional CapEx. For example, predictive AI could be used to predict quality failures early in the testing cycle, increasing testing capacity and saving costs.  This is very similar to upgrading the testing machine. 

Bucket 3: Improvement tools 

These tools enhance continuous improvement efforts. As a professional improver, the most exciting advancements to me are those that enable more effective and sustained operational improvements. It is the technologies in this bucket that will have the largest impact on manufacturing improvement approaches. 

Technological capabilities for the future manufacturer 

If we look ahead, what could chemical plants look like? We see five AI and digitalization capabilities that we think are achievable for manufacturers to include in their facilities in the next 15 years: 

Data capture, processing and visualization 

There is no shortage of data being collected in chemical plants, however the challenge is processing and visualizing this effectively. AI could be part of the solution. We may also see better data sharing across sites, allowing learnings to be shared across locations. This has the potential to generate more opportunities and achieve new performance levels across the network. 

Multivariate analysis 

The use of increasingly more advanced analytical techniques, including multivariate analysis and machine learning, can support engineers to solve problems more quickly by identifying patterns and relationships between variables. 

Digital Twins 

While Digital Twins may seem like one of the largest buzz words at the moment, they offer huge opportunities. The ability to run trials and value opportunities in the digital space will allow faster progression through the testing phase of implementation. 

Defect identification 

This offers the capability to leverage improved data that analyzes each individual failure. While this applies more to fast moving consumer goods and pharmaceutical manufacturing, chemicals manufacturers can also benefit. This will add problem solving. 

AI chatbots 

Chatbots aren’t just there to act as a replacement for customer service agents. In manufacturing, AI chatbots can be used to aid in problem solving and provide an easier way of accessing information stored in manuals or even support with the process understanding and key variable identification.  

The biggest challenge: Changing people’s behavior 

If we look at where these technologies will impact the improvement process detailed earlier, we see a lot of them will help process understanding, opportunity identification and prioritization, and the testing phase.  

However, these are not the hardest stages of the improvement process. The hardest stage is changing people’s behavior. Doing something different is uncomfortable and comes with risks. Therefore, people are naturally reluctant to do so.  

Despite technological advancements, the most challenging aspect of the improvement journey remains changing people’s behavior. Integrating AI and digital tools effectively requires not just technological adaptation but also a cultural shift within organizations. 


To stay relevant in the AI and digital age, current manufacturing improvement approaches must evolve. Leveraging AI and digital advancements will be crucial for staying competitive. Those who can effectively integrate the right technologies will be able to implement more complex improvements faster. 

However, the core challenge will persist—changing people’s behavior and fostering a culture of continuous improvement will remain paramount. 



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