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Nowadays, AI play big role in all live experience. In this sense, it could be used to increase accuracy and accelerate steps in lean six sigma. Going through DMAIC steps, it’s easy to establish that in the: - Define phase, AI could be used helping on problem statement establishment with SMART objective in a effective way; screening and analysing stakeholder for better management of their expectation and satisfaction. - Measure phase, AI could help automating data collection accurately elimination human bias and errors - Analyse phase, AI could be very effective in pattern detection and Root Cause Analysis while avoiding human bias and errors. - Improve phase, AI could as well help shaping a robust and power simulation model, and optimising algorithms for process improvement - Control phase, AI could help in very effective way on real time monitoring system setting, continuous learning and process adaption overtime and predicting failure with very accurate maintenance planning. All those advantages show us that AI is very linked to Lean Six Sigma and could help reduce DMAIC time while having accurate and sustainable results

To answer this question, we need to understand that the DMAIC methodology is nothing but mindfulness and data driven solution identification for a problem. Thus, ideally this must have data to understand the problem, analyze it and identify root causes, improve through suggestions and validate once executed. Statistics is very helpful in understanding the problem and root causes. And wait a minute, this can be taught to the machine. Yes? Now a project is typically done by constituting a team of experts, who need to resolve this problem over and above their day-jobs. This manning of staff on a project makes it a time consuming, and expensive problem identification and resolution process. If we understand these key aspects of the DMAIC project, it will be fairly easy to understand that Measure, Analyze, and Control Phase can all be done better with technology, with AI/ML driven algorithms to understand and analyze newer trends and patterns in real time and ensure that we deliver on the overall aspects of cost savings, productivity enhancements, and sustainability benefits of using lesser earth resources; and without manning these projects separately. We will still need Subject matter experts to help with approving the improvement, or execution in sub/super systems etc. That is also because as of now, in learning phase, we need to train AI to ensure our learnings are embedded into the systems. All right, going to use cases now: Case 1 - Predictive maintenance : 1- In aviation industry, we have tons of data driven systems, some of them have hard life for change, others have continuous monitoring and we need to replace parts and systems as per their behaviour. In this case, having a digital twin, and analyzing how system is behaving at all points in time outside the actual monitoring on the aircraft, to understand trends and patterns and predict the possible next steps is crucial. 2- Likewise, think about the same on a train or metro. 3- Think about this on construction site where we have tons of expensive assets helping us day in and day out. If we can understand the deterioration better, we can do predictive maintenance and prevent the possible downtime on account of arranging the maintenance work and loss of productivity. 4- Think of the same in machining environment for producing sheet metal parts, doing milling, CNC machines etc. Case 2 - Inventory management & Control: 5- Across industries, inventory management and control is a negative sum game. No matter how many suppliers are on contract basis, no matter whether we have SAP, or other ERP systems, we fail when we have an unseen change in supply-demand. Each case brings in its own challenges, and teams can't face these challenges with any historical reference. Assume a system that has understanding of various epidemics, financial crashes, seasonality trends, minor changes in supply chain issues etc and how much really we should stock now to prevent a furture stock out; and how much less should we order than what we are currently ordering. ML/AI will have a great role to play here. We already see ML driven Inventory ordering systems in use across industries. Case 3 - Troubleshooting 6- In industries, it is a challenge to understand what part finally fixed the problem. With ML/AI driven solutions, we can have a more realistic understanding of what % of problem is solved by Path A or Path B or Path C if each of these paths can be taken to resolve the same fault. Now extend this across industries - whether it is television, or your washing machine or an automobile, or F1, or train or aircraft. The progressive drive to improve has the next action item of ensuring the data visibility is not on need based, but is continuous. AI/ML support is not a choice, it is already becoming a default. The key is for various teams to imbibe these skill sets together, and understand how to optimize resources better and deliver faster and more efficient outcomes.