Ghanshyam Kumawat

Control Phase is the final stage of the DMAIC used in Six Sigma. Control Phase ensures that the improvements made during the Improve Phase are sustained over time. It helps maintain the gains achieved during the project and ensure long-term process stability and capability. Key Activities included are development of control plans, implement monitoring systems, standardize processes by update SOPs, training materials, and documentation, train the teams establish response plans if performance deviates and verify sustainability. Most common tools used are control charts, control plan, SOPs, mistake proofing, etc.

AI can be trained to learn from continuous improvement initiatives, and when combined with Lean Six Sigma framework, it can bring huge transformational impact. Continuous Improvement gives AI context and AI gives continuous improvement speed and scale. Together, they build agile and intelligent operations. AI models equipped with ML can analyze vast volumes of process data from multiple iterations of improvement projects to identify failure modes in value streams. It can also identify hidden patterns in defects, waste or quality. By embedding DMAIC Logic into AI workflows, AI models can detect outliers and abnormal trends in real-time, it can suggest next-best action or root causes, simulate and evaluate impact of corrective actions, create documentations, trigger alerts or notifications, etc. Moreover, by feeding results of continues improvement feedback into the model, it can continuously refines its predictions and smarter prioritization over the time. With this approach, organizations can achieve operational Excellence at scale. They can automate improvement prioritization and decision-making which will allow teams to move from reactive to predictive. Cycle time for improvement can be reduced. With intuitive AI agents, non- six sigma people can also engage in problem-solving, making continuous improvement part of daily operations.

When an AI solves the wrong problem, it still gives the right answer to a question no one asked. It will still be running smoothly, giving accurate answers, and showing good performance metrics. however all that effort would be wasted as it would not be addressing what actually matters to the user or organisation. People might think the problem is solved, only to realize later that they are not anywhere close to real solution. This can be avoided if we dont rush into building without having a clear understanding of the root cause of the problem. Practices like researching about users, VoC, and problem framing are so critical.

In any organization that’s already focused on business excellence, AI governance should act as a catalyst to ensure that AI initiatives not only deliver value but deliver it ethically and sustainably. It should be a strategic enabler that aligns AI initiatives with the organization's vision, core values and performance goals. Organisations would prioritize AI initiatives that enhances process, customer satisfaction, and promotes continuous improvement. To achieve a successful AI governance, business excellence should define clear RACI for AI lifecycle roles like data owners, developers, business users, compliance leads, and champions. Also define performance and risk metrics by expanding current set of KPIs. By doing this, BE team can include relevant KPIs to measure and monitor AI success such as Model accuracy, latency, automation success rate, customer satisfaction, and scalability. This can be combined with real-time analytics and dashboards to continuously monitor and flag anomalies. BE team should also enable feedback loops from frontline users to refine models and policies.

MBBs or Master Black Belts drive excellence using Lean Six Sigma framework and methods. MBBs are experts in implementing data and LSS frameworks to identify and improve wastage or inefficiencies in a process, driving change and transformation, and ensure the improvement is sustainable. AI Teams are experts in Gen AI capabilities, automation tools, predictive modelling using big data and advance analytics. They identify tools and capabilities to implement for a given use case. MBBs and AI teams can do wonders when they work together. One can identify problems, use data to validate the RCA and solutions, and for the same problem AI team can implement appropriate solutions to make it faster, leaner, bolder and scalable. For example, in SCM quality from organizations' suppliers is very critical to monitor and govern as poor quality leads to delay in production and increase in cost. Here MBB can apply root cause analysis (like Fishbone or 5 Whys) to trace defects originating from a specific supplier or process. And AI Team can create a predictive model that identify and flags suppliers which might cause problem wrt poor quality using historical performance data.

Process improvement is required when the process is well defined, documented and working well. However there could be instances e.g. in Supplier onboarding process chances of manual data entry error or error in validation of supplier document could occur. in such situations, process improvement can be kicked off using lean six sigma tools or using OCR to capture and validate the content of a document. Process reimagining with AI in situations where process is very old, and require frequent adjustments to suit to current products/deliverables. AI capability can be used to transform the current outdated process by utlizing available innovations/digital capabilities like smart workflows with robots/ML to handle manual regularly performed tasks or smart iOCR technology to validate the authenticity of supplier provided document, validate the content using 3rd party tools, taking action like rejecting or accepting a supplier onboarding and making outcome/insights available to stakeholders as smart customizable analytics.