Yellow Belt Measure Phase Walkthrough Tools Goal Example Tollgate Questions and Decision Matrix
A Measure phase can fail quietly. The team may collect plenty of numbers, but if those numbers do not match the problem, the Analyze phase turns into guesswork.
The purpose of Measure is simple: understand the current process with trustworthy data. For a Yellow Belt project, that means the team confirms what will be measured, defines how to measure it, checks that the data is reliable, and builds a clear baseline.
This walkthrough uses the following SMART goal as the example throughout:
By June 30, 2026, increase the same-day shipment rate for eligible technician parts orders at the main parts warehouse from 82% to at least 95% by implementing improvements and staffing training to enhance operational efficiency and customer satisfaction.
That goal belongs to the full DMAIC project. In Measure, the team does not jump to training plans or warehouse changes yet. The team proves the 82% baseline, defines the measure, and prepares the facts needed for Analyze.
What the team does in the Measure phase
During Measure, the team translates the project goal into a reliable view of current performance. The work usually includes five major activities.
Confirm the process and the customer requirement
The team agrees on what “same-day shipment” means, which orders count, and which orders do not count. This prevents arguments later.
Map the current process
The team documents how eligible technician parts orders move from request to shipment. This helps identify where data should be collected.
Create a data collection plan
The team decides what data to collect, where it comes from, who records it, and how often.
Validate the measurement system
The team checks whether the data source and definitions produce consistent results.
Build the baseline
The team calculates current performance and displays it in a way that helps the next phase find patterns.
A good Measure phase answers one big question:
Can the team trust the data enough to explain what is happening now?
If the answer is no, the project should not move to Analyze yet.
The main tools used in the Measure phase
A Yellow Belt team does not need every Six Sigma tool. It needs the right tools used well.
Tool | What it helps the team do | Example for the warehouse goal |
SMART goal review | Confirm scope and target | Same-day shipment rate improves from 82% to at least 95% by June 30, 2026 |
CTQ definition | Translate customer need into a measurable requirement | Technician receives eligible parts shipped the same day |
SIPOC | Set high-level process boundaries | Supplier, input, order process, output, technician customer |
Process map | Show actual workflow steps | Order received, picked, packed, staged, shipped |
Operational definition | Make each term measurable | “Same-day” means carrier scan or shipment confirmation by the warehouse cutoff time |
Data collection plan | Control how data is gathered | Pull order records daily from the warehouse system |
Check sheet | Collect simple counts consistently | Eligible orders shipped same day and not shipped same day |
Stratification plan | Break data into meaningful groups | Order time, part type, shift, picker, day of week, carrier |
Measurement system check | Test whether data can be trusted | Compare system timestamps with shipment records |
Pareto chart | Show the largest categories of misses | Late pick, inventory issue, packing delay, carrier cutoff missed |
Run chart | Show performance over time | Daily same-day shipment rate across several weeks |
The tools do not replace thinking. They help the team avoid vague claims such as “shipping is slow” and replace them with facts such as “orders released after 2:00 p.m. miss same-day shipment more often than morning orders.”
Step 1 Confirm the goal and define the CTQ
The goal says the team wants to raise the same-day shipment rate for eligible technician parts orders from 82% to at least 95%.
The key customer requirement is speed. A technician waiting for parts may not be able to complete a repair. In Six Sigma terms, that requirement becomes a Critical to Quality measure, often called a CTQ.
For this project, the CTQ could be:
Eligible technician parts orders ship the same day they are received by the main parts warehouse.
Now the team needs clear definitions.
Term | Working definition |
Eligible technician parts order | A technician parts order received by the main parts warehouse that meets stock, approval, and cutoff rules |
Same-day shipment | Shipment confirmation occurs on the same calendar day as order receipt, no later than the warehouse shipping cutoff |
Main parts warehouse | The central warehouse included in the project scope |
Shipment rate | Eligible same-day shipped orders divided by total eligible orders |
Defect | An eligible order that does not ship the same day |
The basic metric is:
`Same-day shipment rate = eligible orders shipped same day ÷ total eligible orders × 100`
If the warehouse had 1,000 eligible technician parts orders in a baseline period and 820 shipped the same day, the shipment rate would be 82%.
That confirms the starting point in the SMART goal, if the data is valid.
Step 2 Build a SIPOC to set boundaries
A SIPOC keeps the team from making the project too broad. It shows the high-level flow without getting lost in detail.
SIPOC element | Warehouse example |
Suppliers | Technicians, field service system, inventory system, warehouse staff, carriers |
Inputs | Parts order, part number, quantity, stock availability, order timestamp, shipping address |
Process | Receive order, review eligibility, pick part, pack order, stage shipment, hand off to carrier |
Outputs | Shipped parts order, shipment confirmation, tracking number |
Customers | Field technicians, service schedulers, end customers waiting for repair |
The SIPOC also helps the team see what is out of scope. For example, supplier lead time for out-of-stock parts may matter to the business, but it may not belong in this project if the goal only covers eligible in-stock technician orders.
Step 3 Map the current process
Next, the team creates a current-state process map. The key is to map what actually happens, not what the written procedure says should happen.
A simple version may look like this:
Technician submits parts order
Warehouse system receives order
Order is checked for eligibility
Order is released to picking queue
Picker locates the part
Picker confirms quantity
Order moves to packing
Pack station prints label
Order is staged for carrier pickup
10. Carrier scan or shipment confirmation occurs
At each step, the team asks:
Where can time be lost?
Where is the timestamp created?
Where can the order status be wrong?
Who touches the order?
What system records the step?
What counts as complete?
The map also identifies likely data points. For this goal, the team may need:
Order received date and time
Eligibility status
Pick start and completion time
Pack completion time
Shipment confirmation time
Carrier pickup time
Order type
Part category
Shift
Day of week
Reason code for missed same-day shipment
The map should not become a debate about solutions. If people start saying, “We need more staff on second shift,” capture that as a possible input for later, then return to measuring the current process.
Step 4 Create the operational definition
The operational definition is one of the most important Measure phase outputs. Without it, two people may calculate the same-day shipment rate differently.
A strong operational definition for the main metric might read:
An eligible technician parts order counts as shipped same day when the order is received by the main parts warehouse, passes the eligibility rules, and has a shipment confirmation timestamp dated the same calendar day on or before the warehouse cutoff time. Orders that are canceled, out of stock at receipt, missing approval, or outside the project scope are excluded from the denominator.
The team should also define the defect:
An eligible technician parts order is defective for this project when it does not receive shipment confirmation on the same calendar day by the cutoff time.
This avoids confusion around common cases:
Situation | Count it as eligible? | Same-day shipped? |
Order received at 10:00 a.m., shipped at 3:00 p.m. | Yes | Yes |
Order received at 4:55 p.m. after cutoff rule, released next day | No, if cutoff exclusion applies | Not counted |
In-stock order received at noon, packed but not carrier scanned until next day | Yes | No, unless shipment confirmation rule says otherwise |
Order canceled by technician | No | Not counted |
Order missing required approval | No, if approval is required for eligibility | Not counted |
This is where many teams find that the original 82% baseline is not as solid as it looked. That is not failure. That is exactly why Measure exists.
Step 5 Build the data collection plan
The data collection plan tells the team how to gather the facts in a repeatable way.
Data field | Why it matters | Source | Collection frequency | Owner |
Order ID | Unique record | Warehouse system | Daily | Yellow Belt or process owner |
Order received timestamp | Starts the clock | Order system | Daily | Data analyst or assigned team member |
Eligibility status | Defines denominator | Warehouse rules or system field | Daily | Process owner |
Shipment confirmation timestamp | Confirms same-day shipment | Shipping system | Daily | Shipping lead |
Miss reason code | Helps Analyze phase | Check sheet or system field | Daily | Warehouse lead |
Shift | Supports stratification | Labor schedule | Weekly | Supervisor |
Part category | Shows product patterns | Inventory system | Weekly | Inventory lead |
Carrier | Shows handoff patterns | Shipping records | Weekly | Shipping lead |
The plan should also define the baseline period. For example, the team may use the most recent four to eight weeks of stable operation, assuming no unusual holiday shutdowns, system outages, or major policy changes distorted the data.
If the warehouse has strong historical data, the team may use it. If the historical data is incomplete or inconsistent, the team may need to collect fresh data for a short period.
Step 6 Check the measurement system
Before calculating performance, the team checks whether the data can be trusted.
For this type of project, the measurement system is usually a mix of system timestamps and human classifications. The team should test both.
Good checks include:
Compare a sample of order records against shipment confirmations
Verify that timestamp fields use the same time zone
Confirm that the warehouse cutoff rule is applied the same way each day
Review excluded orders to make sure they truly fall outside eligibility
Test whether staff choose the same miss reason code for the same scenario
Check for missing timestamps or duplicate order records
For reason codes, the team can run a simple agreement check. Give several team members the same set of missed shipment examples. Ask each person to assign a reason code. If the answers vary widely, the definitions need work before Analyze.
Step 7 Establish the baseline
Once the definitions and data checks are complete, the team calculates current performance.
The baseline should include more than one number. The 82% rate is useful, but it is only the start.
A strong baseline package may include:
Total eligible orders reviewed
Count of same-day shipped orders
Count of missed same-day orders
Same-day shipment rate
Daily or weekly run chart
Performance by shift, order time, part category, carrier, and day of week
Top miss reasons in a Pareto chart
Notes about data limits
A sample baseline summary could look like this:
Baseline measure | Example result |
Eligible technician parts orders | 2,000 |
Same-day shipped orders | 1,640 |
Missed same-day shipments | 360 |
Same-day shipment rate | 82% |
Target same-day shipment rate | At least 95% |
Gap to target | 13 percentage points |
The gap is important. To reach 95%, the process can miss no more than 5 out of every 100 eligible orders. At the current 82%, it misses 18 out of every 100.
That means the team needs to reduce missed same-day shipments by about 72% relative to the current miss rate, from 18 misses per 100 orders to 5 misses per 100 orders.
Step 8 Stratify the data for Analyze
Stratification means slicing the data into useful groups. The goal is not to explain the cause yet. The goal is to prepare clear views for Analyze.
Useful cuts for the warehouse example may include:
Stratification factor | Question it helps answer |
Order received time | Are late-day orders more likely to miss same-day shipment? |
Shift | Does performance vary by staffing pattern? |
Day of week | Are Mondays or Fridays worse? |
Part category | Are bulky, fragile, or high-value parts delayed more often? |
Carrier | Does one pickup schedule create more misses? |
Pick zone | Are certain warehouse areas slower? |
Eligibility reason | Are exclusion rules being applied correctly? |
Staffing level | Do low-coverage periods align with missed shipments? |
This step connects directly to the SMART goal’s mention of staffing training. The team should not assume training is the answer. It should measure whether staffing levels, new-hire status, skill coverage, or training gaps appear to line up with missed shipments.
If the data shows no pattern tied to training, the team should be willing to follow the evidence somewhere else.
Measure Phase tollgate questions
The Measure tollgate checks whether the team has enough reliable data to move forward. These questions help the sponsor, Yellow Belt, and process owner make that decision.
Project and scope questions
Does the team still agree with the SMART goal?
Is the process scope clear?
Are eligible technician parts orders clearly defined?
Are exclusions documented and reasonable?
Does the metric connect to customer satisfaction and warehouse performance?
Process understanding questions
Has the team created a SIPOC?
Has the team mapped the current process?
Did the team observe the actual work, not only the written procedure?
Are the process start and stop points clear?
Are key handoffs visible on the process map?
Data definition questions
Is there an operational definition for same-day shipment?
Is there a clear formula for the shipment rate?
Is the defect definition clear?
Are cutoff times and time zones defined?
Can different team members apply the definitions the same way?
Data quality questions
Has the team checked the measurement system?
Are system timestamps reliable?
Are missing or duplicate records understood?
Are reason codes consistent enough to use?
Is the sample size large enough to represent normal work?
Baseline questions
Has the team confirmed or corrected the 82% baseline?
Does the baseline cover a fair time period?
Are unusual events identified?
Is variation shown over time?
Has the data been stratified for Analyze?
Readiness questions
Can the team explain how the data was collected?
Can the process owner defend the baseline?
Are data limits documented?
Are open issues small enough that Analyze can proceed?
Does the sponsor agree that the team should move forward?
Decision matrix for moving to Analyze
Use a decision matrix when the team feels close to the end of Measure but still has a few open questions. The matrix below uses a simple 0 to 2 scoring method.
0 means not ready
1 means partly ready
2 means ready
The team should score each criterion honestly. A low score does not punish the team. It protects the project from weak analysis.
Readiness criterion | 0 score | 1 score | 2 score | Evidence to review |
SMART goal and scope | Goal or scope is unclear | Minor scope questions remain | Goal and scope are clear | Charter, SIPOC |
CTQ and metric definition | Metric is debated | Metric mostly defined | Metric is clear and accepted | CTQ tree, formula |
Operational definitions | Key terms are vague | Some terms need cleanup | Terms are specific and testable | Definition sheet |
Process map | No current-state map | Map exists but has gaps | Map reflects actual work | Walkthrough notes |
Data collection plan | No plan or owner | Plan exists but is incomplete | Plan covers fields, source, timing, and owner | Data plan |
Measurement system | Data reliability unknown | Some checks completed | Data checks support use in Analyze | Audit results |
Baseline calculation | Baseline missing or doubtful | Baseline calculated with limits | Baseline is clear and defensible | Baseline summary |
Stratification | No useful cuts prepared | Some cuts prepared | Key cuts are ready for Analyze | Run charts, Pareto charts |
Data issues | Major unresolved issues | Minor issues documented | Issues are resolved or controlled | Data log |
Sponsor and process owner agreement | No agreement | Conditional agreement | Clear agreement to proceed | Tollgate notes |
Add the scores. The maximum is 20.
Total score | Decision | What to do next |
0 to 12 | Not ready | Fix major Measure gaps before Analyze |
13 to 16 | Almost ready | Close the highest-risk gaps, then hold a short review |
17 to 20 | Ready | Move to Analyze with documented data limits |
A team should also apply one override rule:
If the metric definition, measurement system, or baseline is not trustworthy, do not move to Analyze, even if the total score looks acceptable.
Those three items are the foundation of the next phase.
What good Measure phase output looks like
By the end of Measure, the team should have a clean project file that includes:
The SMART goal
SIPOC
Current-state process map
CTQ and metric definition
Operational definitions
Data collection plan
Measurement system check
Baseline calculation
Run chart or trend view
Pareto chart or preliminary category view
Stratification plan
Tollgate answers
Decision matrix score
For the warehouse project, a strong Measure conclusion might sound like this:
The team confirmed that eligible technician parts orders currently ship same day at 82% during the baseline period. The same-day shipment metric is defined as shipment confirmation by the warehouse cutoff on the same calendar day as order receipt. The measurement system is reliable enough for Analyze after correcting several missing reason codes. Early stratification shows variation by order received time, shift, carrier, and pick zone. The team is ready to investigate root causes.
That statement is powerful because it avoids guessing. It does not claim that staffing training will solve the problem. It says the team knows the current condition and has the right data to study causes.
Final takeaway
The Measure phase is where a Yellow Belt team earns the right to analyze. For the same-day shipment goal, that means proving what counts as eligible, defining same-day shipment, checking the data, and confirming the 82% baseline before chasing fixes.
When the team can answer the tollgate questions and score ready on the decision matrix, Analyze becomes far more focused. The team can stop debating the numbers and start finding the reasons eligible technician parts orders miss same-day shipment.
Comments