Q841

DMAIC has been the backbone of structured improvement for decades — helping teams define problems clearly, analyze root causes, and sustain gains. But when AI is introduced — surfacing insights instantly, simulating outcomes, or automating parts of analysis — the sequence, depth, and ownership of DMAIC stages may change.

Think of a specific improvement initiative in your domain where AI could be applied.
How would you adapt or reinterpret DMAIC in that context?
Which stages become stronger with AI, and where must human judgment still dominate?

⚠️ Any answer that is generic or does not connect with a specific improvement initiative or process will not be approved.

🏆 The best answer will be selected on the basis of:

• Depth of understanding of DMAIC principles

• Insight into how AI reshapes (but does not replace) the method

• Practicality of the adapted DMAIC approach

• 
  

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Domain :

Plant based ingredients Manufacturing, high volume production.

Context :

We manufacture Plant based ingredients with key head products and many by-products in high volume while processing high volume of agriculture crop as raw material. The process is of complex in it's nature in separation of components from raw material as main product and By-products.

Condition of the Process :

The Yield of a prime byproduct was 4.5 % against 5.5 %+ this yield is of one of the 5 different products produced out of primary rawmaterial from different workshops. Interesting factor yield of one product is can be lost in to other product stream out of 5 and which is loss on yield of one product and increase in yield of another and this is not a straight number, each product price is different example for product 12000 INR/T where as another 52000 INR/T, 18000 INR/T Etc,, when loss of yield happens to another it impacts the quality of product and revenue. The other major concern was product loss to effluent treatment plant (not recovered as product) and impacting it's performance by increased solid load.

Goal/Improvement initiative: Increase the Total Yield by Increasing and optimising the Yield of every product to it's standard Yield from 4.5% to 5.5 %+ by deploying a DMAIC project.

Why and how this really add value to processwith other benefits to Organisation :

• Improved Yield by 1.0 %

• Increased performance of workshops : OEE by 2 to 3%

• Increased revenue with respect to each product : in total 1.2 Cr/month

• Environment : Reduced Effluent generation by 500 m3/day

• Reduced water consumption : 50 m3/day

• Reduced Over processing and solid waste generation by 1.5 T/Day

• Reduced transportation and motion to and fro to warehouse due to reduced waste generation.

• Increase in standards of work, ease of operation to shop floor people.

• Aim for 5S workplace.

This opportunity was observed in May-2023, As reactive approach We planned to roll out a DMAIC project.

Define Phase :

As part of Define phase Business case, Goal statement (SMART) and problem state, Team members of 8 including process expert and with a time line of 6 months were identified in Live project charter.

Business case :

The Yield of 5.5 % from current yield of 4.5 % would increase Revenue of 1.2 Cr/month, not doing this is a loss of revenue, loss of product to waste, effluent, OEE < by 2-3%, transportation and motion loss in between production and warehouses. This increase in Yield has to be implemented by Dec-2023.

Goal statement :

To Improve the Yield of Ingredient process from 4.5% to 5.5 % by Dec-23

Problem Statement :

Very low Yield of 4.5% in place of 5.5 %+ since 3 months (Mar-23 & May-23) leading to loss of 350 T of product and corresponding impact on revenue of 1.2 Cr/month

Team Members : 8 people including process expert, Production operator, supervisors, process owner, maintenance/instrumentation team member

Project Scope :

SIPOC : Identified all In scope, out scope, Supplier customer of the process were identified with respect to each products and by-products.

Overview of process flow were documented so that each product process were fully considered to identify the loss of yield in each step and opportunity to improve.

Toll gate review was done to ensure alignment with organizational goals and the strategic direction of the ‘business, goal statement existed that defined the results expected to be achieved by the process, Problem statement were clearly defined and measured interns yield %

Measure Phase :

• More detailed process flow map was defined to map the ingredients main product and by product and water/solid extraction streams

• Theoretical versus present operating solids with water distributions data were gathered for each respective process stream

• MSA Gauge R & R was done for main product stream and few by-product stream, Gauge R&R % was <8% and contribution % was < 3 %  and part to part was high for main ingredients and for one of the by-product was gauge R&R was >42 % and contribution was above 10% so  it was rejected, measurement machine was replaced and few were calibrated and retested the Gauge R & R.

• Few parameters Logical validation was done.

• Measured the stability and capability of the process parameters of decanters RPM, Pressure and flow settings , temperature, Dry solids % and on other parameters like water ratio settings, feed flow Vs each stage pressure settings (few parameters of the chart are Xbar S and found to be with out lairs and unstable)

Analyse Phase :

• Conducted the Root cause analysis (Fishbone analysis) to identify the potential causes of poor yield at each stage of the process, identified causes for poor separation of streams and reasons for cross over of solid yield, further continued to find the critical X's using Pareto analysis, scattered diagram and multiple regression analysis to identify the critical X's between the flow, pressure, Solids % in the stream, RPM of the decanter, overflow and underflow parameters.

• Trails conducted to improve the R2(adj) value above 70% and closer to 90%

• DOE was modelised for the by-product stream on a decanter to separate concentrated high protein above 60%, it was 4 factorial design, with interaction plot and contour plot found two parameters were in strong interrelation, Flow and RPM in strong relation with feed solids %.

Improve Phase :

• Based on the analyse phase inferences, the solution was generated on decanter parameters through DOE optimisation data of contour plot and interraction plot.

• optimized value settings were also derived from DOE regression Equation

• Solutions were generated by Brainstorming and solution evaluation done by multivoting and expert and process team, operators validation

• Evaluated for VA and NVA steps in process maps and optimized, identified one of the process step called as humidification was not in working condition, it was taken in line for operation.

Control Phase.

• DOE Analysis and data, Contour plots, interaction plots, optimization settings chart and check lists were displayed as visuals in shop-floor and operator control room.

• MSA results were displayed and explained to production and quality team to have sustenance of performance

• Detailed control plan for each X's and Y on Critical X's list and outputs were tabulated with specification, target, responsibility, quality Spec data to maintain and sustain.

• Assessed the stability and capability of renowned process

• celebrated the success of the project achieving 4.93 % at the end of 6th month and 5.38% by 7th month and continued to sustain the improvement.

• Shared this project as best practice with rest of the plants, especially the DOE results and learnings on Protein production.

AI can reshape (but does not replace) the DAMIC method.

• Financial gains were validated by Finance department.

As we have seen above the process is very complex and many manual controls and need lot of expertize to get in to the insights of the process, If we take the example of DOE done. there were times it was very difficult to bring the feed solids constant considering the high volume process, it was a challenge and it was the case for many other parameters, external influence was more on parameter settings, It could be same in other process other than this also,, Though DMAIC can't be replaced by AI still AI can reshape and assist and simplify and give optimized result.

Like in the example of DOE output the optimised result could further be optimised for parameter shift and drift or small bais from the settings.

AI could also reshape the baseline performance setting by evaluating the data at the stage of measure phase, in defining the problem statement and Goal statement based on historical data at the stage of define.

Yes, DMAIC still holds with AI entering the picture

CSAT for project is at 52% and the target for the process was 55%, though other metrics are in green such as AHT and Accuracy and when relationship of CSAT vs AHT and CSAT vs accuracy, tenure etc. checked with the Correlation tool, no correlation observed which are 0.45 and considering industry standard we should consider greater than or equal to 0.6. Hence to rightly understand the issue with the help of AI support we took the entire dump of DSAT comment in order to understand the exact issues with the help of AI interactive tool keywords and datamining was initiated considering manual study on 2000+ customer Dsat comments to be a time-consuming process and which we would have taken a month and we were able to get study within 3-4 days with its clear bifurcation of categories in to soft skill improvement , agent learning , long hold time, rudeness, no quick resolution , as a next step why why analysis was done on this main categories along with agents and cross functional team.

The solution notes with respect to Coaching, modifying pitch, soft skill training, warning on rudeness was built, monthly dashboard, Audits on long hold calls, the implementation plan followed for a month and there was noticeable improvement in CSAT score from 52% to 57%.

In control phase to ensure the sustainability of the metric, Weekly dashboard on CSAT score continued for top and bottom performer, Random test planning every quarter and DSAT comment study with AI sentiment analysis/interactive tool recommender every qtr.

We followed all stages of DMAIC while utilizing AI tool as for any CTQ metric improvement we should adopt a structured approach.

AI can help us in identifying patterns, trends and analysis however DMAIC still stands strong and helps the improvement idea more structured and converts insights into meaningful solutions

Ex: In Claims processing, the issue is related to turn around time, and the primary reason is missing documents. We might use AI to help us identify transactions with missing documents however when we take this idea forward using the DMAIC methodology it becomes imperative to verify and understand the data as to why the documents are missing (Root cause analysis). Post identifying the root cause simple controls and preventive measures can be applied by the project leader to avoid further misses. This simple example reflects how AI and Humans can work hand in hand.

I would want to propose and improvement idea along with AI as per below

Problem statement: The processing time for Auto claims is taking more time i.e. 72 hrs.

Goal Statement : Reduce the TAT from 72 Hrs. to 24 Hrs.

Define Phase : The define phase is totally dependent on humans. The problem is defined basis the customer complaints received, SLA misses, Escalations, VOC. It is also very important that the project sponsor is aligned with the idea and approves it

Measure Phase : In the measure phase, we can use AI to help pull data to track cycle time, rework if any, SOP gaps and miscommunication that is hands off between the teams. It also helps in measuring the data using statistical analysis. Human intervention is required to understand the cause and effects ex. Pareto analysis

Analyze Phase: AI can help us in scanning large volumes and highlight hidden process bottlenecks, delays, Outliars, Duplicate entries, Missing documents and trends that is time consuming to analyze manually. However human intervention and judgement is still needed to understand compliance and regulatory issues, Policy requirements and real operational issues

Improve Phase: In the improve phase the AI can help us in testing different scenarios and providing inputs on what could be the best possible solution for us to apply. It also helps in predicting impact of the proposed changes and also helps us in providing inputs towards robust workflows. The solutions provided still needs to be validated by the human to check the feasibility of implementing the solutions in conjunction with the budget approval, effort required etc.

Control Phase : AI can help us in monitoring process performance through dashboards, reports and flagging early triggers of delay. It also helps us in providing timely updates and alerts if the controls are not working so that it can be fixed immediately. However, we would still need human intervention on periodic reviews and check on the controls applied to ensure the controls are running smoothly.

Overall in DMAIC - AI helps in improving speed, automation, accuracy and visibility while human judgements are required in areas related to Finance, compliance, underwriting and decisions where risk is involved and impact on the customers

Domain: Solar Cell & Module Manufacturing Sector

( Capacity: 1.3 GW Solar Cell & 1.6 GW Solar Module Production and Supply per annum)

Improvement Initiative: Improvement of Solar Cell Efficiency from 23% to 25% takes around 6 months by default and it effects the distribution of Module Efficiency positively. But instead of 6 months, the improvement needs to be completed by 4 months to improve the revenue by 30% minimum.

DMAIC project can be framed to meet the target as per the initiative. But when AI is introduced — surfacing insights instantly, simulating outcomes, or automating parts of analysis — the sequence, depth, and ownership of DMAIC stages may change.

How would you adapt or reinterpret DMAIC in that context?
Which stages become stronger with AI, and where must human judgment still dominate?

--> DEFINE:

Stronger and clearer DEFINE stages make the project reach its target more precisely and within less timeline.

1. AI can help to extract the previous Pareto Charts or analyze the SPC charts faster.

2. It Can help to finalize the CTQ drill down report and find the hidden CTQs (like Improvement of Sheet resistance of the Wafer, Improve the Cell defects during Diffusion / Annealing process. ) quickly.

3. AI Can help to simulate the ROI of the project before the finalization of Project Charter.

4. It can simulate financial impact scenarios in Real time.

   But still human Judgment is must to:

   1. Align all the team members, stakeholders with the target and discuss about their ownership.

   2. Differentiate and Prioritization of CTQ which truly matters for customer not just which is statistically viable.

   3. To fine tune the AI responses with the help of subject matter experts. ( It can be assumed to have AI as a member of Six Bono Hats Discussions)

--> MEASURE:

AI has brought a huge breakthrough in case of Measure stage. It can help to:

1. 100% AI driven MSA for measurement devices like EL and AOI systems for defect inspections and Sun Simulators.

2. AI can automatically detect the outliers in the SPC.

3. It can validate the MSA outputs or the detect the faulty human operators by learning the bias during inspections.

But human intelligence (HI) still have an upper hand over its artificial counterpart at the time of

1. Selection of 'fit to use' data.

2. Involvement of Regulatory and certification alignments like IEC, BIS, TÜV, UL etc.

--> Analysis:

AI definitely have more speed and accuracy to analyze data, which helps to:

1. Simulate DOE across thousands of parameters in the machines like Thermal Tools in Solar Cell Parameters.

2. Explore the Multi-dimensional cause effects which human intelligence(HI) can't imagine.

3. identifies the non-linear interactions which conventional six sigma tools can visualize.

but to succeed with the above approach HI needs to be used for:

1. check the causality at the shop floor.

2. engineering feasibility for AI recommendations.

3. ethical and safety implication of Parameter change.

--> Improve:

From the Analysis results AI can easily predict the solutions, optimize the parameters to get to the target easily. Other than that it can also:

1. Simulate multiple pilot runs with different type of scenarios.

2. plan preventive maintenance schedules

3. predict secondary effects.(Increasing diffusion temperature flow rate vs. Cell thermal crack generation)

4. perform multi objective optimization.(Cell Yield, Cell Efficiency, Cost, Warranty Risk)

But HI still dominates in :

1. FMEA ownership and sign off.

2. Change management.

-->Control:

AI plays a strong role while controlling comes into play, it can

1. predict control charts that act before outliers come to the chart.

2. Control the sigma levels continuously.

3. automatically generate corrective action by studying previous actions.

4. Smart audits that target high risk stations proactively.

5. prevents drift instead of reacting to it.

Without HI the circle of Control is not complete. The necessity of HI is there when,

1. Governance,

2. Exception handling,

3. Supplier Escalation are needed.

Human Intervention is also needed while there is a process change and process owners need to authorize it.

Hence all the segments of a DMAIC projects get stronger with the association of AI, but if the project is needed to reach its goal without any unsafe implication or catastrophic results (like 5-10 MW of cells with 80% PID problems while the efficiency is very high, i.e. costly cells) a Human intervention is recommended in all steps.

Each steps of DMAIC can be used Fastly with AI algorithms, but a HI is required to ask the right question to AI at right stage, otherwise it can chain react to a false and a very divergent result.

I work in Investment Data Operations, where multiple teams collect 46,000 data points across \20,000 fund companies using diverse external sources. Despite reported quality levels of 98% across Accuracy, Timeliness, and Completeness, clients raised frequent escalations related to missing data, low coverage, and incorrect values, indicating a significant gap between reported quality and experienced quality.

 

Define:
With AI support, we reframed Define as problem validation, not problem articulation.

• Dashboards helped identify patterned complaint clusters by data point, fund type, and client segment.

• This revealed that the “98% quality” baseline was statistically invalid and masking localized failures.

• Human judgment was important in reframing the problem statement, aligning sponsors, and defining SMART goals that balanced client impact and operational feasibility.

Measure:

Measure was the most transformed phase.

• Manual sampling (30–100 records per fund) was replaced by automated audits connected directly to final production databases.

• True completeness and accuracy measurement at the data-point level

• Revised baselines were established using statistically valid methods instead of uniform targets.

Analyze:

 lack of VSMs and shallow RCAs.

• AI-enabled lineage mapping traced each escalation to the exact source, transformation, and handoff.

• Usage analytics identified high-impact data points disproportionately affecting strategic clients.

• Validations are still manual for few to avoid false alarms created by AI

 Improve:

• Automated audit mechanisms

• Power BI monitoring dashboards with lineage-based impact views

• SOP knowledge hub using an LLM to validate process adherence and version accuracy

• AI helped simulate;

  • Impact of focused improvements on high-usage data points

  • Expected reduction in escalations by client segment

Control:

Established governance

• AI-driven monitoring flagged anomalies, drift, and emerging risk patterns

• Governance models, escalation thresholds, and ownership remained human-defined

• Control plans evolved dynamically instead of being “set and forget”

Final solution selection remained a human decision, balancing cost, risk, and change adoption.

Yes, DMAIC still holds a bigger picture.

Domain: E-commerce

Executive Summary:   In 95 Countries, we made TAX (AWS, ESS, and Retail) payments as below:

Year 2022 – USD 47,962 million

Year 2023 – USD 55,392 million – 115% YoY increase when compared with FY 2023

Year 2024 – USD 61,232, million – 111% YoY increase when compared with FY 2024

The above listed payments were processed through 136 unique banks in 95 countries. Out of 136, 43 (32%) were direct payments (without intermediary bank dependency - but involved multi-currency payments) and 93 (68%) unique banks must process the payments to tax department with the involvement of intermediary banks.

Out of 93 banks it was further identified that 39 banks had to make the payment through one intermediary bank and 54 banks had to make the payment through two intermediary bank accounts (local banks due to country\multi-currency dependencies).

Waiting time was identified as one of the top reasons for payment delays directly impacting the penalties and interests, followed by banks or tax portal system maintenance or server issues (system latency issues), human errors (incorrect processing), and other technical dependencies (lack of full information about payments).

For FY 2022, a total of $2.35 million was paid towards penalties - $1.4 million, $479K as interests, $239K as bank fees (including direct and intermediary banks), and forex conversion fees as $191K.

During 2023, a total of $2.71 million was paid towards penalties - $1.6 million, $553K as interests, $276K as bank fees (including direct and intermediary banks), and forex conversion fees as $221K. An increase in trend of 115% when compared with FY 2022.

In FY 2024, a total of $3.01 million was paid for penalties - $1.8 million, $612K as interests, $306K as bank fees (including direct and intermediary banks), Forex conversion fees as $244K. An increase in trend of 111% when compared with FY 2023.

It was a very difficult stage to set the bridge from the external parties like banks, intermediary banks (financial institutions), tax department (Government authorities) and Amazon payments\treasury and tech teams.

Factors like geographical and time zone differences, language dependencies, people movement\availability, communication channels (telephone\FAX\Email\in person meetings), Forex (currency conversion) involvements, cross tie up between banks and intermediary banks and tax departments, banks\tax departments (mainly in African countries) follow old methods (not migrated to advanced technology), some banks and tax portals were in the stage of digital transformation caused delays while streamlining the process.

Waiting time (to reflect\apply the payment against the filings submitted in the respective tax portals) triggered auto penalties, interests and the daily changes in FX fees in the tax portals.

Apart from these barriers I and my MBB did examine to understand the country-specific laws related to bank policies and FX conversion policies and need to ensure they all adhere and set-fit during the transition phase.

A team of 90 with clear roles and responsibilities were involved right from defining the problem, measuring (quantifying) the financial exposure (funds outflow), analyzing the problem root causes with the facts provided, improvised or re-built the payment process with banks, reduced the dependencies on intermediary banks and implemented controls at each stage. Post implementing DMAIC techniques between the internal and external teams the leakage was controlled.

First, we involved payments and treasury team to discuss the challenges and possible solutions with the banks (external) and reduced 78% dependencies on intermediary banks. This saved the cost of $559K (68% decrease) in bank wire fees. Once the dependency is eliminated, the payments processing time is reduced from 5 to 2 working days. This helped to reduce the 91% payment penalties, and 95% late interest charges. We involved top leadership at VP level to deal with tax departments officials and changed the filing currency mode (country currency) and in parallel we made the payment currency set up in Amazon systems in sync with the country specific currency which saved 55% of Forex fees. DMAIC’s core principles are problem solving through systematic methods based on data driven solutions, followed by decision making and through continuous improvements and of course AI will be a powerful tool that will make each step more accurate, effective and insightful.

Let’s talk about an AHT improvement project in any industry.

Define Phase

• AI can quickly process historical data to highlight key drivers, outliers, and bottlenecks, helping to build a stronger business case.

• Human judgment is necessary to select appropriate team members and to validate whether the AI-generated data aligns with business needs.

Measure Phase

• AI can extract and generate AHT data sets (monthly, quarterly, team data, etc.) much faster than humans. AI can also assist in creating process diagrams.

• Humans are needed to ensure and validate that the data provided by AI is correct and aligns with operational standards.

Analyze

• AI can recommend and produce relevant analyses, such as correlations, Pareto charts, box plots, etc., with less effort.

• Human involvement is essential to validate AI findings, interpret root causes, and determine their impact.

Improve

• Teams can implement AI-powered solutions such as chatbots, voice bots, or automate manual processing tasks. AI can also assist with macro programming.

• Humans play a critical role during user acceptance testing (UAT), ensuring that current outputs and processes remain compliant.

Control

• Teams can leverage AI to create real-time performance dashboards and automated reports.

• Human oversight is still needed to monitor results and investigate the root causes of new patterns.

In summary: Humans should utilize AI to improve certain tasks and support solution implementation.

Talking about DMAIC methodology in the era of AI definitely the execution part got affected by AI but DMAIC will continue to be the backbone of the continuous improvement.

With introduction of AI DMAIC project's speed of execution has improved tremendously.

In the Scientific Publishing industry majorly we want to improve the production turnaround time where these scientific articles are getting published once they are accepted and in those cases if I need to analyse the DMAIC methodology now and before invention of AI then I could clearly see the difference right from the define till control phase that we now started using AI extensively the problem identification journey of data have become very easy to define the problem and best part is from continuous improvement side we are the responsible people for most of the activities in DMAIC framework and we are guiding and mentoring the project leads so before AI and with AI now I could see that AI have made her life very easy when it becomes mentiring and guiding the project, right from the define phase of the project, project leads are also using the AI to draft the project charter to analyze the data and make the data inferences .In measure phase also AI is helping user to collect the data effectively rather than depending on the MIS to collect the data at the end of month. Right now AI is helping us to generate the data at very micro level and on real time basis. AI also helping us in building the assumptions running some equations and simulations to support these assumption to identify the root causes. Same it goes for analyse and improve phases.

And recently most of the solutions right now we are generating through the DMAIC project are related to the AI or some quick automation fixes were also AI is helping. Same goes to the control phase, we can we can set automation based governance over there in less time without so much dependency with which DMAIC methodology and the project would be executed faster and more effectively with reduced dependencies on the IT and tech teams. This has helped to reduce the delays in solution executions as well.

Therefore,DMAIC will be the backbone of the improvement journey and AI will act as helping tool to execute the projects faster.

The Business Process Outsourcing (BPO) industry, where revenue is time, and data are created by the milliseconds, the integration of AI and DMAIC is not only an upgrade, but a lifeboat.

DMAIC is the skeletal model of the improvements, whereas AI fulfills the role of the nervous system and faster to change from what happened to what is going to happen. In a BPO environment-where there is an operation of high-volume customer transactions, claims, or financial services- AI changes the approach of cleanup after the fact to optimization beforehand.

Reinterpretation of DMAIC to the AI-Enhanced BPO industry:

Define (D): Problems Statements to Opportunity Mining.

Historically, it takes weeks to draw reports to provide a problem definition by BPOs. Before a human even realizes a decline in KPIs, AI (in particular, Process Mining) can detect a bottleneck.

The AI adoption: AI can be used to specify the Ideal Path as opposed to defining a specific problem (e.g., AHT is too high). It brings out real time variances.

BPO Case Study: An AI system identifies that one of its telecom clients has 40 percent of customer calls following the third day of the month are due to a customer not paying their bills. The Define step has now been immediately subdivided down into a certain technical trigger which is time-limited.

Measure (M): Data streams Continuously, real-time.

Measurement has now become not a final overview state, but a running feed and real-time status.

The AI Adoption: Logging mistakes through manual means are removed with the aid of automated data collection. AI will be able to quantify the soft measurements (also known as Sentiment Score or Agent Empathy) on a large scale because that was something a human previously considered too subjective.

BPO Instance: Speech analytics scores 100 percent of calls recorded on compliance and sentiment, which is much higher as compared to the previous system where a Quality Analyst manually scored only 2 percent of calls.

Analyse (A): The hidden or unknown information challenge.

AI is able to process thousands of variables that a human mind cannot process within a reasonable amount of time.

The artificially intelligent transition: We are no longer on simple Pareto charts but on Predictive Analytics. AI will be able to say that the 5 seconds delay in a particular screen of a CRM system successfully reduced the CSAT by 15 percent.

BPO descriptive example: A Machine Learning model is used to examine attrition among agents and reveals that agent attrition is caused by "commute time" not by a single factor of salaries which was human intuition.

Improve (I): Rapid Prototyping and Simulation.

In the old world, Improve was putting into practice the changes in one team and it took weeks to see the results.

The AI adoption: The AI Shift lets BPO leaders recreate change in a process within a virtual environment. A simulation model helps you to test a new call routing logic and see how it performs prior to implementing it on 5,000 agents.

BPO Case Study: The implementation of GenAI-driven Agent-Assist bots offering real-time suggestions. The Improvement is not a classroom training, 2 week, but incorporated in the workspace of the agent when working.

Control (C): Auto- Repair Processes or Self- Restoration.

Control is turned to Always-On instead of attained through a monthly check-in.

The AI adoption: AI-generated notifications are activated when a process is out of the supposedly standard work. It is no longer about a simple box to check but it is about automated intervention.

BPO example: Once an agent attains a bad compliance score during a live call, the AI automatically sends an alert to the dashboard of the supervisor to coach the agent right away, or assigns a particular course of micro-learning that the agent can follow on his next break.

Stages wise - Where Human Decision Prevails vs. AI Power.

Stage : Define

AI Strength: Thus it is reported to identify patterns and hidden leaks.

Human dominance: Empathy Strategy-Ensuring that the project is inline with the long term brand values of the client.

Stage : Measure

AI Strength: accuracy, volume and real time tracking.

Human dominance: Context-Knowing when data is contaminated by exogenous anomalies (e.g. a global outage).

Stage: Analyze

Artificial Intelligences Power: Multivariate correlation; speed.

Human supremacy: Ethical Emerging Why there is a correlation and making sure that there is no prejudice in the logic of the AI.

Stage: Improve

AI Strength Automation and generative solutions.

Human dominance: Change Management-Managing the human element--upskilling and employee morale.

Stage: Control

AI Strength: Monitoring and reporting is automated.

Human dominance: Accountability-Finally, the risk and final decision to pivot must be an ownership of a human.

Conclusion: DMAIC is Faster Not Dead.

AI does not put DMAIC out of business; it just takes the dots out of it. Within a BPO, it implies that the Six Sigma Black Belt and Master Black Belt will spend less time in Excel and more time on the strategic decisions and impacting the stakeholders. The process is more hectic with the repeat rate of the cycle being higher, the analyze step is extensive and the control step is more stringent.

It isn't that AI will defy DMAIC it is that humans will rely on the AI to analyze and not filter this information with a filter that is called Common Sense.

In my process, We are adapting DMAIC to support the introduction of in house AI for recurring and repetitive tasks, while analysts retain ownership of making final judgemental calls, in depth research and deal interpretation.

Previously, the problem statement was Defined around cycle time and quality issues. With AI, now Define phase is more focused on 1. Analysts spending excessive time of recurring & repetitive tasks and refinement of the Narratives draft 2. Inconsistencies and information repetition errors 3. Reduced time of analysts on higher value research and insights. AI in this phase will strengthen clarity on which activites are AI eligible (Usage accurate Language, structure of the draft, removal of repeated information from draft) and which activites will remain under anaylsts control ( research from govt websites, county websites, narrating property nuances and deal story teeling)

AI will enable in keeping the Measure phase more objective. In our process, We measure beyound TAT and it will include reduction in drafting time, reduction in language related errors and repeated information, consistency of tone and structure across drafts, Audit feedbacks before and after AI introduction.

Analysts will continue to have authority of accepting AI improvements and also to check if it aligns with MBs expectation of Narratives draft

In Analyze, AI can help identifying patterns such as 1) Common repetition issues (within draft) 2) Frequently used unclear and/or overused phrasing 3)Structural inconsistencies

Analysts will continue to ascertain the rootcause, deal specific risks and do SWAT analysis of property, understanding nuances of property that AI cannot

While in the Improve Phase, AI will be used or tested as main processesor for repeated and recurring tasks including language. Improvements will include 1) Standardized prompts for drafting Narratives as per research material and instructions provided about the deal 2) AI driven/checked language clarity, grammar, redundancy removal 3) Faster draft creation, allowing analysts to focus on research and accuracy

Analysts will have complete ownership of writing prompts, deciding if AI output is acceptable, determinings for tasks AI cannot be used.

With, the Control phase will move away from regular flow and now we are focusing more on goverance of

1) Prompt libraries and updating library when required 2)Mandatory review of AI assisted drafts 3)Random audits of 10% drafts of each analysts each week by audit team to ensure Narrative quality and prevent/stop over dependence on AI.

In conclusion, AI can add to DMAIC but cannot replace it.

Domain: Aerospace, MRO (Maintenance, Repair & Overhaul) - Engine shop for narrow body turbofan engines

(€220M turnover facility, covering CFM56, V2500, and LEAP Shop Visits of various airlines and lessors)

Specific Improvement Initiative: Reducing engine module Turn Around Time (TAT)  from 45 – 60 days to <35 days during performance restoration visits in a span of 6 Months

(This effectively represents another high-value chokepoint because releasing underlying store engines into service reduces costly leases and makes airline dispatch more dependable. We recently initiated an AI-driven project in late 2025 that seeks to incorporate predictions of module status using borescope, oil analysis and test cell data, then auto generates work scopes and part forecasts.)

How we adapted DMAIC with AI — stage by stage

Define

This will still remain 100% human owned with no shortcuts.

Simply, Artificial Intelligence cannot describe what "good" means for airworthiness parts.

For few weeks we worked with airline customers, lessors and regulatory representatives to map CTQ’s: TAT < 35 days, zero escape on critical elements (HPT, HPC), > 12% cost reduction, and full traceability for EASA/FAA.

AI also helped in the visualization of the current vs. target Pareto of delays. Boundaries (no compromise in Safety and/or Compliance) remained the responsibility of the MBB and the project sponsor.

Measure

"AI becomes super strong here — it accelerates data collection and baseline accuracy massively."

Rather than manually sampling 200 historic Shop Visits, AI ingested 1800+ data points, including borescope images, oil debris, vibration trend, test cell parameters, etc., and created a clean baseline in a few days:

·       Avg breakdown TAT: TAT breakdown – disassembly 8d, Inspection 12d, Repair 28d, Assembly/test 12d.

·        Variation drivers (HPT blade rework 42% of variance)

The judgment of humans is still predominant, i.e., validating data quality, excluding outliers resulting from non-standard visits, and checking if there is no survivorship bias present in the data set itself.

Analyze

This stage roles flip: AI takes the lead on root-cause discovery, humans will challenge and refine it. AI ran pattern recognition across thousands of features

·       "Predicted that 68% of HPT delays are caused by unexpected coating wear (not visible with a standard borescope). "

·       Simulated ‘what if’ scenarios (add ultrasonic inspection on blades → TAT -4 days, cost + €8k) etc.

MBB owns:

·       Forcing AI to explain (SHAP values & counterfactuals).

·       Rejecting Correlations which Violate Physics/Engineering Judgment.

·       Prioritizing causes with the team (fishbone + AI insights).

Improve

AI excels in solution generation and testing, whereas piloting decisions fall under human expertise.

AI produced 12 different workscope variants, ranked according to their TAT/cost/risk.

We tested top 3 on 8 engines:

·       Added Predictive coating inspection → Reduced Surprise Rework

·       Auto parts pre-kitting based on AI prediction → Assemble wait time

Human judgment prevails: deciding  which approved variant will go  ‘live’, managing change with technicians and getting sign-off from regulators.

Control

Human + AI Hybrid With Human ownership of Sustainability.

AI Monitors and tracks real-time adherence (daily TAT tracker for deviances).

MBB owns:

·       Control plan update (with new SOPs on the use of AI).

·       Monitoring adoptions: Technicians' override of AI predictions (<15%).

·       Monthly review: review of escapes/misses to retrain the model.

·       Celebrating success with the shop floor (sharing savings made visible)

Which of the phases will be made stronger with the use of AI

·       Measure: 10× faster, more granular baseline

·       Analyze: reveals patterns not yet perceived by humans

·       Improve: generates/test thousands of scenarios in hours

Where human judgment still needs to dominate?

1.      Define: framing value and non-negotiables such as safety,

2.      Analyze: rejecting physically impossible correlations.

3.      Improve: Piloting real engines, i.e., one can’t simulate customers trust.

4.      Control: Sustaining Culture & Accountability

Practical result after 7 months

·       TAT average 33.8 days

·       Cost per visit down by 14%

·       No Escapes

·       Technician satisfaction up (less firefighting, more predictable work)

Bottom line from the engine shop

DMAIC may not be replaced, but it’s turbocharged!

The AI takes care of the hard work on data and simulation, but MBB makes sure the method remains disciplined . right problem definition, right boundaries and right decision.

Without humans "owning" Define and "challenging" Analyze, AI is a matter of "optimizing the wrong things faster,

With humans owning, you get sustainable, compliant, high value improvement that regulators and customers actually trust.

Industry

Power & Water Production- Asset intensive organization

Process & Context

I am going to share a project that already exists on Master data management in Supply Chain Management for all the plants we operate in 14 countries and under different technologies as CCGT, CSP, Wind, Solar & SWRO.

 

This is the first step of our procurement process, all procurement is done via a unique material code.

Our material master has grown to 378000 different items. The huge size of our database was not created in one day or because of a one reason. Let me explain, our Master data creation used to be decentralized. We acquired plants also, integrated different systems, ERPs, migrated data many times. In all cases, our company’s priority was to keep operations running, not the standardization.

 

The effects of that legacy are now clearly visible to us:

We have a lot of things in our inventory. Total approx. value is around 200 million euros.

We have around 15,000 materials that the Material Requirements Planning system or MRP is actively planning for.

We have around 78,000 items physical in stock across the fleet.

We have active transactions in last 4 to 5 years on unique 125,000 material codes.

 

We started seeing a trend, almost similar spares were existing under multiple material codes across the fleet, our stock was spread across multiple look-alike stock items.

We also missed opportunities to manage common spare/stock for the fleet and incomplete, inconsistent master data in SAP due to legacy templates.

We clearly understood, this was not just a data cleansing issue, and it was significantly impacting our fleet inventory levels, spareparts optimization, and our procurement planning efficiency for 50+plants.

 

 My current ongoing initiative

I am working on master data enhancement initiative, using AI to support the heavy analysis work. Here, AI is helping us to completely check the full masterdata, indicating the duplicates, descriptions comparison, technical details check, matching the part numbers with our usage/consumption trend, and finally tracing the link back to higher level equipment in plant, all using the SAP data.

We have setup 23 key parameters as criteria for AI to check, and you can imagine, without AI, this analysis would have taken many years.
Now, we can truly focus on the human efforts on the identified cases that really needs attention.

AI is enabling the work and people still makes the decision.

 

Ongoing Results & Early Wins

Practically, we have already compiled our spares across Wind turbine plants, CSP Plants & CCGT plants. In many cases, we have found more than 3 material codes under the same functional spare, and keeping the parallel stock without clear visibility on consumption.

We have started seeing fewer duplicates in MRP items, better visibility on fleet level stocks, elimination of duplicate codes creation at first step of new MD creation.

Our estimation is (based on the validated items sofar), we will achieve 7-9% cost avoidance, which will reflect around 14-18 Million Euro on inventory base.

DMAIC Role In This Case

With AI being used for find patterns very fast, Define step has become even more significant. The real question for us was never how many duplicate codes exist, but which item codes may actually create the risks or value.

We clearly had to be focused from start that this project is not about deleting the records; it is about “Optimizing the spares without putting our asset reliability at risk”.

With this clarity, Measure become better, we moved from sampling to analyzing the full 378000 item database, linking the duplication, gaps to actual inventory value and our consumption across the fleet and technologies.

In Analyze, we used AI to group the similar materials, but this alone was not enough.

Engineering, maintenance & Supply Chain Teams still had to assess the OEM constraints, safety requirements, LTSA, functional perspective & limitations.

AI helped to speed things up for us, but the main judgement remained essential.

In Improve, we are taking suggestions as options not answers, teams reviewed them cross-functionally, we piloted the changes at selection of plants and even assessed risk before scaling to our fleet size of 50+.

For Control, now AI flags the near-duplications during the new master data creation, and also underlining the deviations from standards set. However, the Governance of whole remains with us, that is human-led, We approve the exceptions, decide when such cases make sense, and we are feeding the learnings back into the rules.

Conclusion

While AI has significantly improved the speed of execution for this initiative, however it hasn’t changed who owns the critical thinking.

In my view, Measure & Analyze are pretty much stronger with AI; Define, Improve, and Control still depends on human judgement.

My role is to ensure DMAIC doesn’t turn into automatic answers, but continues to support decisions that faster, better and sustainable for the organization.

AI is creating the opportunity, and disciplined improvement makes it real success.

I have been a part of two different industries in last 1 year abd have witnessed very interesting discussion around this. the first one was a product based one and the current being a ITIL industry. The previous company which was product based had more flexibility in terms of adopting and experimenting but my current ITIL org is far more sensitive when it comes to deliverables as the impact often goes to the end customer. My product based org was actually taking the AI things slowly without changing much but here we are more focussed on shorter projects. with AI actually, DMIAC will become far more feasible. For this year, we have kept that as one of our ways to run atleast one improvement project in a quarter and the proposal is under final review.DMAIC in itself isn't a tool but a guiding principle and methodology. With AI, its actually is a good thing for our scenario where we need speed more than accuracy. it'll go hand in hand with the DMAIC methodology. every step which used to take days and weeks of analysis will take minutes or hours now, the takeaways will be easier to uncover.With ITIL framework,I can already think of a project which can run DMAIC with integration of AI. In our service desk environment,a common improvement initiative is reducing incident resolution time while maintaining service quality.This is a basic thing which we are dealing with everyday. Traditionally DMAIC would take lots of discussion, workshops, and manual analysis. With AI,the define stage itself can start with AI application, just using data dump itself is enough to identify the scope and target, this activity usually takes days of work. The same would continue into the next stage, baselining of current incident counts, the current process capability can be easily calculated using data dumps from dashboards and service now. Possibly the best combination of AI and traditional approach comes in the next stage of Analyse and improve.AI can quickly analyze large volumes of incident data, SLA breaches,user sentiment and ticket data to highlight patterns that normally takes weeks to understand, it can reveal that delays are less about analyst performance and more about poor categorization,repeated reassignments or gaps in knowledge articles. This strengthens the improvement effort by providing a clearer, data backed view of the problem. Also here our judgment remains critical. AI may show correlations,but it lacks the background or domain knowledge that a SME can possess, it cannot fully understand business priorities,customer frustration or cultural realities within .Decisions such as which pain points to address first,whether automation is appropriate or how much change a team can absorb will still need our expertise.Even during solution selection out of the multiple options presented by AI, ultimately it'll come down to us to pick the right one knowing the management priorities, future demand etc.

Also the Control stage would need a lot of manual validation and human intervention. Although AI can help in the statistical part faster which we really need in my scenario.

To really summarise, AI will help will actually help our case to initiate DMAIC due to speed, accuracy etc but we'll continue to be accountable for the case specific judgement and outcome. The next few months is going to be more exciting for sure.

Context: Construction Chemical Industry

Project: Increase the OEE from 50% across 5 factories in India to 65%.

With AI in the picture, the way DMAIC is seen changes dramatically. AI helps in faster identification of problem, faster measurement system analysis, Analyse the result and also provide improvement plans. It can also provide with control plans-renewed Poka Yoke methods to ensure the problem doesn't persist. Although AI can do all these, it still lacks the ability to understand the limitations of data, understand the meaning of metrics and understand the real time issues being faced.

Going with the mentioned project, the goal is to increase the OEE of 5 factories across india from 50% to 65% in order to avoid capex investment for expansion projects for the the next 5 years and to meet the increasing demand.

Define: AI can analyse the factory capacity vs present demand to tell us that we lack in capacity. But an MBB or an improvement specialist is needed to define the project strategy. AI tells us the gap but an MBB will tell us where we need to act on.

Measure: An AI will read and analyse the data faster. Much faster than a human can. Incase we have IOT sensors in the plant, the AI can actually provide the best analysis. An MBB's role is to understand the data first. For the AI, Garbage in = Garbage Out. So we basically need someone who can maintain the quality of data. Set correct system which actually gives the right data to AI. If the data is manual, then someone has to check the data before feeding into AI.

Analyse: This is where the MBB plays a major role. Now we have massive amount of Data. AI can analyse and give an output in seconds. But it cannot tell us where to act. What an MBB would do is

• Financial Loss Analysis-Analyse from results as to what are the losses, how practical are the losses and can we actually work on them? What is the First pass rate? Does is show too high? or is it too low that there is lot or re-work

• Time Loss Analysis-Where are we lagging? are our cycle times higher than other counters? Are our cycle times different across different factories for same product? Is there a standardisation or not? Is the mixing time too high? is the QC time too high?

• Value Stream Mapping-AI cannot decide what processes are needed and what can be eliminated. You need an MBB to map the process and actually look at what is the value.

• Creation of a Defect Pareto-Problems have been identified and now you need to see the feasibility of eliminating these problems. For example, Quality check time is a non value added but essential activity. But can we actually eliminate it?

What is the role of AI?

• AI basically takes away the headache of choosing which graph or plots to be used. Is it a P-chart, NP-Chart etc., Still it has to verified by the MBB.

• Is the data normal or not? its done within seconds by AI provided the data is authentic which is checked by BB

• All the data available can be evaluated, models can be run and simulated. AI reduces the analysis time by a great extent.

Improve: You have analysed the data. You have the entire report ready. The role of an MBB is to identify what are actionable and what are not. For example: lets say one factory has a license limitation for its capacity. Although your capacity is very high, you cannot legally achieve it. So the question arises-Do i implement the improvement measures in this factory or not? instead of focusing on OEE, Do i focus on improving the productivity? Or do i work on increasing the license capacity? These questions can only be answered by an BB and the management.

Control: The AI can give you methodologies to implement the control mechanism but we need an MBB to actually build and implement the system. For example, AI gives the suggestion of implementing a Visual digital board on the shop floor. But for a construction chemical company is it actually possible to do this? is it cost optimum? is it safety compatible looking at the dust in the shop floor? An BB can take a call to rather have a manual Daily Work Management board which is cost effective and also dust proof.

After all this let's assume the project has been implemented and we have successfully increased the OEE. But one big question only an MBB can answer is Although we avoided the capex for the next 5 years, did we actually sacrifice the flexibility of fulfilling customer demand to increase utilisation of our plants? will we be able to fulfil ad-hoc demands with present new systems implemented?

An MBB or a BB would be able to answer this question in accordance with the management before the project even began.

🏆 Best Answer: Adil Khan

Outstanding depth and clarity. A sharply defined MRO TAT initiative with explicit AI vs human ownership across DMAIC, strong safety/compliance boundaries, and measurable results.

✅ Approved: Ankit Kulkarni

Strong master-data initiative. Clear DMAIC framing with AI accelerating Measure & Analyze, while humans retained engineering, risk, and governance ownership.

✅ Approved: Dipali Yadav

Very strong initiative. Thoughtful reinterpretation of Define and Measure using AI, with clear human judgment retained for prioritization and solution selection.

✅ Approved: Suman Acharjee

Specific and well-structured solar efficiency case. Good articulation of where AI strengthens DMAIC and where certification, CTQs, and FMEA remain human-led.

✅ Approved: Bharath CN

Detailed yield improvement initiative with solid DMAIC rigor. AI linkage is lighter but still relevant and contextually sound.

✅ Approved: Abhinandan Kunder

Clear OEE improvement initiative. Practical distinction between AI-driven analysis speed and human-led feasibility and trade-off decisions.

✅ Approved: Smitha Muralidharan

Focused, real initiative on AI-enabled drafting. Strong Control and governance thinking with appropriate human safeguards.

✅ Approved: Rabiya Bronekar

Clear DSAT-mining initiative with measurable impact. Acceptable use of AI within DMAIC, though articulation could be sharp

🟡 Approved (Conditional): Taby Sheikh

Strong conceptual reinterpretation of DMAIC with AI, but next time I expect one clearly defined improvement initiative instead of multiple illustrative examples.

🟡 Approved (Conditional): Vijay Gonsalves

Correct direction and a relevant claims example. Next time, I expect tighter scoping with one concrete initiative.

🟡 Approved (Conditional): Vijay Yivaturi

High effort and rich context, but too broad. Next time, focus on one initiative.

🟡 Approved (Conditional): Domz D

Framework is valid but generic. Next time, I expect your actual process and initiative, not a generalized walkthrough.

🟡 Approved (Conditional): Aditya Bhavsar

Good intent, but generic. Next time, provide a specific initiative using AI.

🟡 Approved (Conditional): Aloke Biswas

Directionally right for ITIL/service desk, but still high-level. Next time, I expect one sharply defined initiative with concrete metrics and AI usage.