Q840

As AI enters process improvement initiatives, the role of the Improvement Champion does not disappear — it evolves. While AI can surface patterns, simulate scenarios, and recommend actions, someone still needs to ensure the problem is framed correctly, analysis and solutions are appropriate, improvements are sustainable, and people stay engaged.

Think of a specific improvement initiative in your domain where AI could play a role.
What should an MBB own, challenge, and safeguard in an AI-enabled improvement journey to ensure outcomes are meaningful, ethical, and sustained?

⚠️ 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:

• Clarity in defining the MBB’s evolving role

• Depth of insight into integrating AI with improvement discipline with specific scenario explained (Important)

• Practicality of the responsibilities identified
  

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The Master Black Belt (MBB) has long been the designer of reason and the defender of statistical discipline in the Business Process Outsourcing (BPO) industry. With AI becoming more than a cool tool and being turned into the driver of the process, the role of MBB changes to that of the data analyst to be replaced by an AI Controller or AI Manager.

Scenario: Artificial Intelligence usage - Attrition Prediction and Intervention.

This is due to the fact that in a high volume BPO call center the loss maker of profitability is attrition the silent murderer. Traditionally, an MBB would apply Pareto chart and Logistic Regression to find out the reason why people move away.

The AI Shift: We implement an AI (Machine Learning) system capable of real-time sentiment analysis based on internal chats, badge-in/out habits, and performance changes that forecasts who amongst individual employees faces the risk of leaving a company within 30 days.

What the MBB Must Be In possession of: The Strategy and the Why.

AI is also very good at identifying correlations, yet it is ignorant of business situations. The MBB should possess the Problem Definition and Human-AI collaboration.

Problem Framing: The MBB is not merely predicting attrition, but it is addressing one of the business sore spots (e.g., the cost of reducing the cost of attrition during the first 90 days of the business).

Deliverables: MBB is the owner of the bridge between prediction and intervention. In case the AI raises a red flag on the employee, the MBB predetermines the "Standard Work" that is to be intervened by the Team Lead. The MBB designs the how but the AI identifies the who.

What the MBB has to Question: The Data and the hidden or unknown internal workings of the functions.

The quality of AI model is dependent on the Lean Six Sigma principles on which it is based. The MBB should become the ultimate unbeliever.

Contesting the Logic of the Model: When the AI tells the MBB that one predictor of quitting is high tenure, then the MBB needs to apply the concept of Root Cause Analysis (RCA) to estimate whether that’s a quality insight or data anomaly. They test the details of those complex hidden internal workings of the functions to make sure that there is an explanation of the results.

Information & Data: Data in a BPO can be noisy (e.g. manual logs, different shift patterns). The MBB needs to question the source of data, so that the AI is not taught to follow the incorrect or biased processes of history.

What the MBB Needs to protect: Ethics and Sustainability.

This is where the MBB safeguards the culture as well as the long-term benefits of an organization.

Ethical AI Implementation: The MBB is a guard against Data driven management in its case of attrition. They will make sure that the AI will be utilized to serve and mentor workers rather than install a sense of being watched and be unfairly punitive towards those who are flagged by a machine.

Sustainability of Gains: On the one hand, AI models have the problem that the accuracy of the models decreases over time as a result of behavioural changes in humans. The MBB does the spin of AI into the Control Phase of DMAIC, creating the Control Charts on the performance of the AI itself, so that the solutions stays effective and would hold a year after.

AI is the speed and MBB is the steering of the BPO world. An AI-fueled advancement is not a temporary glamour display by the MBB; the firm ensures that it can impact the strategy, question the algorithms, and protect the ethics over time and its human-related changes.

Domain:

Strategic Sourcing, Procurement & SCM – Asset intensive organization, into Power & Water

Context:

We build & operate multiple plants in 14 countries, different technologies with high procurement complexity, multiple categories, and large, diverse supplier base. Three years back, we roll out an e-sourcing tool, for our procurement process. Main idea was to bring order, consistency in the process, and overall transparency into sourcing and negotiations.

When the tool was implemented, all the existing supplier base was categorized into a category/commodity structure, which was built by the tool implementation team using UNSPSC standards. On paper, the structure was correct & industry accepted. All the suppliers were classified, commodity categories were created and we went live the system technically.

But after living with it for a while, it became clear that something still wasn’t working the way we expected.

 

What we noticed:

What triggered this initiative wasn’t a system failure, we noticed a pattern, we kept seeing in day to day sourcing process.

In many RFQs we observed:

·       Buyers were inviting more than 20 suppliers

·       Only two or three suppliers would respond

·       Lots of effort of buyers went into the managing suppliers who were never going to quote

·       Negotiations didn’t have the pressure & competitiveness we assumed they had, just less than 5% from first quote to final quote

Theoretically for us, activity level was high & compliant, however in reality, the outcome was showing a different picture.

Our question shifted from “Is tool working” to “Why buyers are working so hard and negotiating so weakly”.

That’s when we decided to drill down and realized this wasn’t a tool issue, it was a process and decision-making issue.

 

From the Lean Six Sigma lens, this was clear waste:

Overprocessing (Too many RFQ invitations)

Waiting (Low response cycles)

Lost Opportunity (Poor negotiation leverage)

 

 Process (What we are now implementing)

We started by slowing things down instead of adding more dashboards.

We mapped the sourcing flow end to end:

·       How categories are defined

·       How suppliers are shortlisted

·       How RFQs are issued

·       What actually happens between the first quote and the final quote

Once we looked at the five years of spend (approx. 2.2 Bln Euro) and item level data (150K Item codes), RFQs (45000+), the gap was obvious.

The category structure made sense theoretically, but it didn’t reflect how money was being spent.

We made a call to rebuild the logic.

This is where AI plays a role:

·       Classify five years of item-level spend from L1 to L5

·       Rework L3 & L4 (commodity & sub-commodity) based on the real buying behaviour

·       Link items to suppliers using actual spend & receiving history

·       Build a complete category, item & supplier close loop in a structure manner

Our goal is very simple:

When a buyer raises an RFQ, system should naturally surface relevant 5-7 suppliers, to help them, not 20 irrelevant ones.

To make this happen, we have implemented:

·       The new supplier category structure in the tool

·       All buyers are being trained on the same

·       Clear RFQ rule in tool on threshold e.g. Max 7 suppliers for RFQs >8000 value, Max 3 suppliers for RFQs <8000 value

·       We are tracking new process leading indicators- RFQ Response Rate, Negotiation Delta (First Quote vs Final Quote), Savings achieved per RFQ

In this case, AI will help us to calculate and present these insights, but it does not own the decisions, which led us to the next part where I will talk on role of MBB.

 

The Role of MBB in this AI-enabled improvement journey:

If I am being honest, the biggest role of MBB here is to keep asking why we are doing this in first place, which means MBB must own the problem framing.

This initiative is not about, improving tool, cleaning supplier data, using AI for classification.

The real problem definition is, "sourcing effort is misaligned with supplier relevance, reducing negotiation effectiveness and savings".

What MBB Ensures:

·       The improvement stays focused on the value creation

·       CTQs are clear (response rate, negotiation depth & savings)

·       AI is applied to improve the process, not just to analyze it.

 

What MBB must challenge:

I fully expect AI to do clean classifications. That’s what it’s good at.

What I don’t take at face value is what comes after that.

 

For example:

Whether the new category logic improves competition

Whether suppliers are being excluded due to any historical bias

Whether response rate improvement translates into the real savings

 

This is Lean Six Sigma thinking:

Correlation is not causation

Patterns must translate into better decisions

Insights must lead to action

AI accelerates analysis.

The MBB ensures the analysis is decision-relevant.

What MBB has to safeguard in this case:

From my own experience, biggest risk here is not AI being wrong, the real risk is buyers quietly going back to old habits.

For them, RFQ rule slowly becomes just guidelines, exceptions become normal, and before management know it, buyers are back again inviting 20 suppliers because they feel safer.

So, big part of my role being the project manager of this initiative is protecting a few basics:

·       Adoption of new logic and is used in all RFQs, day to day sourcing

·       Ensuring the governance is real and not just optional

·       Transparency, so buyers understand the why for supplier’s suggestion in tool

·       Ethical use, where AI recommendations are explainable and challengeable without ignoring them completely

Without this balance, we will end up with sophisticated model, same old habit, or even worse, buyers stops trusting the system or process.

 

Why this really matters for organization:

If I get this right:

·       Reduced wasted sourcing efforts from buyers

·       Improved RFQ response rate, naturally

·       Increased negotiation effectiveness and more focused

·       Converting AI insights into real & repeatable savings

If done poorly, it will become another good analytics project for us with no lasting impact.

Bottom Line

In my view, AI is not something an MBB competes with in this journey.

AI is exceptional at handling big data and spotting the patterns at a scale which no team even can, however MBB can turn that into sustainable improvements.

What I believe AI cannot do is to decide what is worth fixing, what is fair, so MBB ensures AI is used in the right place, for the right problem and in an ethical way people can trust.

Disclaimer: Exact numbers are not shared due to external communication guidelines of company.

Domain: Aerospace subcontract precision machining shop

(€85M turnover, 420 people, Make to Print for Airbus, Boeing, Safran – High Mix/Low Vol structure parts, AS 9100 D, Zero Defect pressure

Improvement initiative: Reducing first-article inspection (FAI) cycle time from 12-18 days to under 7 days on new programs

(This is an chronic bottleneck, as any new component or significant change-over involves FAI, which postpones the release of production and cash receipt. We initiated an AI - driven project in mid 2025 to work on autonomous measurement path, collision simulation and risk based inspections.)

What The Master Black Belt (MBB) Must Own, Challenge and safeguard In this Ai-Enabled Journey

"The MBB is no longer ‘the one who runs the analysis’ — this is now done 70-80% by the machine, faster and deeper. Instead, 'the MBB is 'the guardian of DMAIC, judgment, and sustainability.'"

1. Own the problem definition and scope (Define phase – non-negotiable)

The AI will happily optimize whatever you give it to. But if the problem is formulated incorrectly (‘speed up the inspection’), it will recommend skipping verification or sampling or reduced inspection, which is essentially ‘suicide’ in aerospace sector.

MBB shall own:

a.      Well-formed CTQ (Critical To Quality) tree related to customer / airworthiness requirements.

b.      Boundary conditions (what cannot be touched: full FAI compliance, no risk to zero-defect escape).

c.      Success metric that encompasses both speed and safety APIs FAI lead time < 7 days AND escape rate = 0

2. Challenge the output and assumptions of AIs critically (Measure & Analyze phases)

AI detected a pattern ; 62% of FAI delays are caused by the manual programming of the probe path. It recommendation: Generative AI paths.

MBB Challenged:

·       "Show me validation data on 50+ different geometries -- not just the training set." (Observed weak on deep pockets.)"

·       “What if the batch hardness of the materials drifts by 5%?” (AI did not have sensitivity analysis; we forced it to.)

·       “Is this recommendation compliant with AS9102 FAI requirements?” (Not fully – required manual override layer.)

3. Safeguard people engagement and change management (Improve & Control phases)

Operators and inspectors worried about job security or accountability (“My path was not good, says the AI”).

MBB owned:

·       Co-creation workshops: inspectors assisted with distinguishing between "acceptable" and "optimal"

·       Feedback loop: every AI suggestion is given a 1click "accept / fine tune / reject" with reason → so we can retrain the model.

·       Sustainability Dashboard: monitoring adoption rate, override reasons and monthly actual versus predicted time.

·       Recognition: Inspectors who provide good-quality feedback are given credit for their work. This credit is not restricted to cost savings.

4. Own the ethics & risk gate (throughout)

MBB must be the one saying “no” when speed tempts shortcuts or skipping steps:

a.      No Auto-Release for AI-Generated Inspection Plans without Human Validation for Critical Features.

b.      Mandatory traceability: In aerospace traceability is everything, every path traced with program version, confidence score and final humans review.

c.      Escalation protocol if AI confidence < 80% on safety-critical dimensions

Practical outcome after 9 months

a.      FAI cycle time reduced to 6.2 days on average

b.      Escape rate unchanged (0)

c.      Inspector satisfaction up (they now focus on judgment calls, not repetitive programming)

d.      AI Adoption rate of above 85% since people influenced the development of AI, rather than having it forced on them.

Bottom line from the FAI Work center

MBB is no longer the analysis hero – the AI is,

MBB is now the discipline role model: even if pressure comes lines will not be crossed, corners will not be cut. Framing the problem is critical aspect if not done correctly we will be running after symptoms while actual problem is still killing us.

So AI solves the right thing, challenging outputs or one wrong unquestioned assumption makes the process unstable. Garbage doesn’t become gospel and safeguarding people & compliance, so the improvement lasts beyond the pilot hype.

Without that, you get fast garbage.

With it, you get fast, safe and sustainability improvement.

My MBB and I played a major role in transforming the ‘Payment’s tool’ and improving the ‘TAX’ payments process. During 2022-2024 - 17,646 (5882 payments per year) tax payments were processed through ‘CRTR’ (Creature) tool. For each payment execution, the processing time taken by preparer is estimated at 15 minutes (including excel latencies). Each Analyst\Preparer must fill around 28 mandated parameters in an excel template from three different sources (1). Payee Central tool and (2). Chart of Accounts from Frames site and (3). Manual inputs.

Payee Central tool is managed by Vendor Maintenance team, and they store 13 details of each Tax department i.e. department name, department registration number, filing currency, payment currency, payment method, payment instructions, tax department’s full bank account details (including Swift code), intermediary bank, bank charges, FX charges, International Bank Account number (IBAN number). The second input is updating the 7-segment (Company-Location-Cost Center-Account-Product-Channel-Project) payment GL string details from Frames site managed by Central Accounting team. The third component ownership is with tax preparer (Compliance team) has 8 inputs i.e. update the invoice number (no specific invoice format), invoice amount, entity tax registration number, payment description, line description, invoice date, payment due date, payment reference number (a unique alpha numeric number generated post submitting the filings in the tax portal).

Post updating these details and payments initiated, the CRTR tool will validate the parameters (in the back end) and the estimated validation time is 15-20 minutes for each invoice. The CRTR tool throw errors to the preparer if any. There is no visibility from which component the error triggered from. Preparer must download the template again, validate each parameter and re-submit in the tool post deleting the prior uploaded payment template. Even if all the parameters were matching it was very difficult for the preparer to identify what exactly was missing. An extra ‘space’ would also be treated as error and not exactly match. The estimated time to re-work and correct the errors is 5 to 7 minutes and the re-estimated validation time remains constant at 15-20 minutes for each invoice (validates from first component till last component instead of validating the revised parameter). If there aren’t any errors - the CRTR tool push the payment to ‘approve’ queue by generating a CRTR request number.

The preparer must notify the CRTR request number to the payment approvers through email (no standard format followed) to approve the payment. Once approved, the payment approver must notify the preparer through email (a complete manual process). The waiting time at this stage (of course with no structured email) is between 3-5 minutes depending on the availability of the preparer and approvers.

My MBB and I worked backwards and collected each payment unique rejection reasons from the above listed 28 mandated parameters, identified additional tools and teams’ dependencies. Post brainstorm sessions My MBB and I designed a new AI tool - TAX Obligation Manager’ (TOM). The tool is designed in 5 components (visible in a single window) capturing information from Master data and auto populating into (1) Entity registration details information, (2) vendor (tax department) information from Payee central tool, (3). 7-Segment payment GL strings from Frames site, (4). Excluding invoice number, invoice amount and payment reference number columns (which are unique each month), invoice parameters will be auto populated. To avoid duplicating invoice numbers the new tool is configured with combination of Company\entity name + tax registration numbers. + tax filing period (unique each filing period). Standard line and payment descriptions are auto updated. Invoice date will be auto populated based on payment initiation date and the payment due date will be auto captured with a buffer of 5 days based on tax department payment due date captured from master data file. The new tool was also designed to raise reminder flag to preparers\reviewers and approvers based on filing due dates and high dollar payments (above 2 million as threshold).

Out of 28 mandatory parameters, 25 parameters were automated\auto populated with embedded control checks, and the remaining 3 parameters i.e. invoice number, invoice amount and payment reference number columns (which are unique each month) are to be updated manually by preparer.

Post designing, implementing and improving the new tool payment processing time has come from 15 to 2 minutes. In addition, we also made enhancements to the new tool to trigger auto notifications from preparer to reviewer with a standard payment instruction format along with payment approval hyperlinks.

During ‘Improvement’ journey phase in 2025, MBB and I played a very crucial to identify the current process gaps, acted as a bridge between the technical department and non-technical teams (compliance in my case), guide the transformation from manual process to fully automated solutions. Through additional dive deep and by working backwards I am confident that MBB’s will help to reinforce production deployment mechanism through planned and tactical actions. The above study is a classic example of it.

As AI enters Process improvement initiatives , Let me share from my industry AI enabled Time motion study to reduce conveyor line downtime.

From operational leadership perspective , MBB plays a critical role in ensuring AI delivers authentic and sustained improvements and ensure it strengthens improvement journey .

As a MBB must own following things :

• Clarity of “Working time vs. non-working time”: Ensure AI differentiates true conveyor line working time from avoidable idle, motion , and diagnosis delays.

• Breakdown of work task : Convert AI insights into standardized work task for operators and maintenance staff to ensure seamless operations

• Expected response times: Define target times for fault response, repair, and restart—incorporated into daily management check sheet .

As a MBB must challenge following things :

• Fallacious averages data : AI insights that hide variation across shifts, different models, or team with varied skill set.

• Tool-driven recommendations: Recommendations that optimize data patterns but ignore actual physical movements and access constraints.

• Operators Skill set : Faster operator motion that does not reduce overall line stop time.

As a MBB must safeguard following things :

1. Safety : No reduction in maintenance time that compromises safe access or lockout–tagout practices.

2. Sustainability : avoid unrealistic cycle-time expectations for operators/team.

3. Process ownership: Ensure improvements remain incorporated in standard work, not dependent on AI dashboards.

As a MBB our job to ensure to leverage AI insights and convert them into smooth process flow and reliable machine availability and results in conveyor uptime to boost productivity ( PQSCDM approach )

P- Productivity boost

Q- Sustain Quality ( Consistent product quality )

S- Safety

C- Cost savings

D- On time delivery

M - Improved operators Morale

Domain: Construction Chemical Industry

Problem: Raw material, Production and Dispatch planning activity in an industry with very low forecast accuracy

The present situation involves the production planner getting the product requirement from SAP or any ERP that has been posted by sales. The delivery day count starts once the order has been placed by sales and this is the key KPI for the plant teams on how soon the material can be delivered. Also, there are over 2000 different product mixes which have to be carefully planned to meet all customer demands and in between there are also ad hoc orders based on customer priority and weather conditions.

The production planner has to check his capacity, look at the changeover matrix, check the space available at the factory, plan for the manpower and much more.

The production planner then checks, if the we already have stock of these material with us. If yes, the plan is shared with the dispatch planner who is responsible for working out the logistics details which includes but is not limited to logistics planning, freight value negotiations, selection of transporter based on area to where the material has to be delivered etc., The dispatch planner has the main role of optimising cost for both the company and the customer. They have to look at full truck load as well as part truck load and see the impact on cost. They have to take a call on lead time also. The question arises what if i wait for another order from the same customer to same location to fulfill a full truck load which is considerably cheaper? Will this increase my lead time? is this okay with the customer? The role is a basic day to day transactional role but with n number of permutations and combinations.

If the product is not available, the production planner reaches to raw material planner. They check the availability of Raw material and packing goods. They check what is the present stock? what is the safety stock? What is the re-order level? What is the lead time level? For example; The system data shows lead time is 2 days for a particular material. But because of the proximity with vendor location, practically the Raw material planner orders on call basis. This saves on inventory management. The Raw material planner has to have a close coordination with the central purchase team to plan accordingly. Due to the forecast accuracy being only around 20%, this becomes extremely challenging to analyse and plan.

This is a pure state where one has to prioritize Flexibility vs Productivity.

All these challenges leads to the system being more dependent on personal experiences and people rather than Data alone.

We can feed years together of data along with curated experience collections from subject matter experts into AI. But where the MBB actually comes into place is in:

1. Defining the actual problem-Is it forecast accuracy? is it the nature of business?

2. Checking the accuracy of the data- "Garbage in is equal to Garbage Out". Checking the data quality, ensuring frequency of updates, having a connected system.

3. Identifying the key performance indicators to be considered.

4. Defining the trade offs-Do i lose my productivity or do i focus on being flexible?

5. Defining the legal bounds for the AI-Interpretation of the Legal system becomes challenging and it also varies from location to location.

Keeping all these into account, the role of an MBB improvement lead doesn't end but it rather starts. An AI MBB lead moves from a doer to a strategist

1. Understand what is the base state the system was actually designed for and try to achieve it.

2. Understand the data. Look at the source and its consistency. Ensure quality of the data.

3. Next, define the frameworks within which the AI has to operate. This includes feeding the capacity data, changeover times, product compatibility.

4. Identify the legal bounds.

5. Ensuring the system is being followed after the system implementation and hasn't reverted back to base state.

The AI can only analyse the data provided and give us results, but It is the MBB lead who owns the process and ensures the right implementation of AI.

Evaluation Result – AI-Enabled Improvement & the Evolving MBB Role

🏆 Best Answer: Adil Khan
Outstanding clarity and depth. A concrete improvement initiative (FAI cycle-time reduction) with a crisp articulation of what the MBB must own (problem framing & CTQs), challenge (AI assumptions, compliance gaps), and safeguard (people, ethics, sustainability). Strong DMAIC anchoring, real risk gates, and measurable outcomes make this exemplary.

✅ Approved: Ankit Kulkarni
Very strong, real-world sourcing initiative. Clearly shows the MBB as the problem framer and habit-breaker, ensuring AI insights translate into negotiation effectiveness and savings. Excellent balance of AI acceleration with Lean discipline and governance.

✅ Approved: Taby Sheikh
Relevant BPO scenario (attrition prediction) with good insight into ethics, data skepticism, and sustainment. The role of MBB as challenger and ethical guardian is well-articulated. Slightly abstract in places, but still grounded and meaningful.

✅ Approved: Vijay Yivaturi
Detailed, end-to-end improvement journey in tax payments. Strong illustration of the MBB as a bridge between domain, tech, and governance, ensuring automation translates into durable performance gains.

✅ Approved: Arun Bhatia
Clear manufacturing use case (time-motion study for conveyor downtime). Practical delineation of what the MBB must own, challenge, and safeguard—especially around safety and standard work. Solid and grounded.

✅ Approved: Abhinandan Kunder
Complex planning scenario handled well. Clearly positions the MBB as a strategist defining trade-offs, data boundaries, and legal limits for AI. Good insight into where AI ends and improvement leadership begins.

⚠️ Not Approved: Sahil Anand
Conceptually sound but too generic. Lacks a specific improvement initiative and concrete illustration of MBB responsibilities in action.

⚠️ Not Approved: Rabiya Bronekar
High-level statements without a specific process or scenario. Does not meet the requirement for depth and applied insight.

⚠️ Not Approved: Bharath CN
Good intent and coverage of DMAIC + AI, but overly broad and tool-focused. Missing a single, clearly explained improvement initiative tying responsibilities together.

⚠️ Not Approved: Vijay Gonsalves
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