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by Jukka Kostiainen, Value Architect at Nortal

From continuous analysis to continuous improvement - unlock the value of your industrial data with AI investments

Your organization may have already taken significant steps by digitizing its silent and structured data, establishing a solid data platform, and crafting a strategic framework. Yet, the journey from data analysis to actionable insights may still feel like a labyrinth of manual effort, relying heavily on scarce resources like data scientists and subject matter experts. But fear not – this is where the latest AI innovations come in, offering a path from data analysis to continuous improvement, revolutionizing how you operate while unlocking a realm of possibilities, from predictive maintenance to streamlined decision-making. With AI, industrial organizations can seamlessly bridge the gap between structured and unstructured data, liberating experts from manual analysis and propelling them toward success. 

Every executive in resource-intensive industries, like manufacturing or forest, knows that efficient data utilization – both structured and unstructured – makes or breaks businesses today. As my colleague Ergin Tuganay wrote in his earlier article, there is often a gap between an organization’s systematic and structured data and the unstructured “silent” data. 

The challenge is to digitally collect, store, analyze, understand, and act upon the daily data your operations generate. This data can be anything from well-structured records from your ERP system to side marks written by your maintenance specialists when they fixed a broken valve ten years ago, but both are just as invaluable.  

Your organization might have had its silent and structured data digitized and, in the early days, already built a robust data platform and strategy. If you are lucky, you have enjoyed the benefits of getting informative insights from data and having that data available to those who need it regardless of time and place. However, analysis is still a tedious task. It requires your scarce data scientists and subject matter experts to crunch bits and pieces of information to build those documents or Power BI reports that fit their purpose and are understandable for the users at all levels of the organization.  

This is where the latest AI innovations come in. With AI, you can free your valuable experts from continuous data analysis to constant improvement in quality control, predictive maintenance, workplace safety, sustainable manufacturing practices, and more.   

AI – an opportunityfor continuous improvement

Rooted in the Japanese philosophy of “Kaizen,” continuous improvement emphasizes minor, incremental enhancements over time. AI can significantly simplify and streamline the PDCA (Plan-Do-Check-Act) cycle that guides iterative enhancements. Therefore, it provides a step change, especially in the root cause analysis and building an action plan.  

Through methodologies like Pareto Analysis, Six Sigma, and AI-boosted Root Cause Analysis, organizations can prioritize efforts, aim for perfection, and predict or even prevent deviations with remarkable precision and efficiency. This convergence of AI and continuous improvement accelerates problem-solving and empowers organizations to stay ahead in a competitive marketplace, ushering in a new era of innovation and excellence. But where to start?  

#1 Prioritize with Pareto Analysis

The 80/20 rule, named after economist Vilfredo Pareto, states that approximately 80% of effects stem from 20% of causes. In the context of continuous improvement, Pareto analysis helps prioritize efforts. Organizations can achieve significant gains by focusing on the 20% of factors that drive most inefficiencies or defects. This could, for example, mean analyzing structured classified information like downtime by reason, a section of a production line, or the most maintenance-intensive machinery by equipment type. 

#2 Aim at perfection with Six Sigma

Six Sigma, in turn, aims to minimize defects and variations by achieving near-perfect processes. A good example would be the setup time when making product changes. As there may be noticeable variations between individual setups and even teams, understanding the underlying reasons helps reduce variation, prevent worst cases, and improve the average.

#3 Perform AI-boosted Root Cause Analysis to predict and prevent

To identify the root causes of deviations, AI tools can help find patterns or recommend solutions and related corrective and preventive actions based on your past analyses and unstructured data sources like diaries. Have we had similar cases earlier? What is related to similar equipment in our global RCA (Root Cause Analysis) library? AI’s Large Language Models (LLM) even let us use data in RCA reports written in languages we cannot understand. The time spent on analysis is cut from weeks or days to minutes or even seconds. 

With proper root cause analysis and actions based on it, organizations can predict and prevent similar incidents from happening again. In addition to the analysis phase, LLM also streamlines the planning of recommended corrective and preventive actions, work instructions, and Standard Operating Procedure documentation. 

 

Harness Microsoft’s AI billions to continuously improve your production efficiency

The latest AI tools can streamline your decision-making, generate predictions that boost your performance and competitiveness, and optimize the planning of follow-up actions. Generative AI can help you analyze and extract insights from unstructured data, such as images, videos, audio, and sporadic diary texts. Combined with telemetry and event data, LLM can find correlations that have been impossible to detect using traditional statistical tools or previous AI methods like deep learning.  

Microsoft’s AI solutions, like Microsoft Copilot, assist in ideating novel solutions based on your organization’s digital data throughout the process. Finally, generative AI and LLMs allow your experts to interact with this data in natural language and through AI-generated illustrations without taking time for lengthy training on the correct commands.  

The synergy of continuous improvement, Pareto analysis, Six Sigma, and AI-boosted root cause analyses propels organizations toward excellence. Microsoft is at the forefront of AI development, with billions invested in OpenAI and its technologies. For an industrial organization already using Microsoft solutions, it is just smart business to grab the emerging opportunity for continuous improvement.  

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