Yellow Belt Analyze Phase Walkthrough Tools Tollgate Questions and Improve Readiness Matrix
A Yellow Belt team can waste weeks fixing the wrong thing if the Analyze phase is rushed. This phase is where the team stops guessing, studies the process, and proves which causes are most likely driving the problem.
For this walkthrough, the project goal is:
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 is clear, measurable, time-bound, and tied to customer impact. The Analyze phase turns it from a good statement into a focused improvement plan.
What the team does in the Analyze phase
The Analyze phase answers one main question:
What is causing eligible technician parts orders to miss same-day shipment?
The team does not jump to solutions yet. Instead, it uses data and process knowledge to narrow the problem. A Yellow Belt team usually does the following:
Reviews the current process and confirms how work actually happens
Breaks the shipment metric into useful categories
Looks for patterns in missed same-day shipments
Identifies possible root causes
Tests which causes have evidence behind them
Selects the few causes worth fixing in the Improve phase
For the warehouse goal, the team needs to understand why 18% of eligible orders do not ship the same day. The answer may involve staffing, order release timing, inventory location, pick errors, late carrier cutoff, training gaps, system delays, or unclear eligibility rules.
The Analyze phase helps the team avoid broad fixes such as “train everyone” or “add more people” before it knows which causes matter most.
Start with the project measure and the right data
The first Analyze step is to confirm the measurement. If the team cannot trust the data, it cannot trust the root cause analysis.
For this project, the main measure is:
`Same-day shipment rate = eligible technician parts orders shipped same day ÷ total eligible technician parts orders`
The team should define each term before analyzing anything.
Term | Working definition |
Eligible technician parts order | An order that qualifies for same-day shipment based on cutoff time, inventory availability, shipping method, order status, and warehouse rules |
Same-day shipment | The order leaves the main parts warehouse on the same calendar day it becomes eligible |
Missed shipment | An eligible order that does not leave the warehouse on the same calendar day |
Baseline | Current same-day shipment rate of 82% |
Target | At least 95% by June 30, 2026 |
The team should also confirm that the data source captures the right timestamps. Common fields include order creation time, release time, pick start time, pick complete time, pack complete time, carrier scan time, and shipment confirmation time.
A simple data check can reveal issues before the team draws conclusions.
Data check | Question to answer |
Completeness | Are any orders missing key timestamps? |
Accuracy | Do system timestamps match warehouse activity? |
Consistency | Does every shift use the same scan steps? |
Definition match | Do reports include only eligible orders? |
Time zone and cutoff | Do dates and cutoff times match warehouse rules? |
If the baseline includes ineligible orders, the 82% rate may be misleading. If the shipment timestamp records label creation instead of carrier handoff, the team may overstate performance.
Map the process before judging the process
Next, the team builds a simple process map. Yellow Belt teams do not need a complex diagram. They need a clear picture of how an order moves from eligible status to shipment.
For the parts warehouse example, the process may look like this:
Technician order enters the system
System checks eligibility and inventory
Order releases to the warehouse queue
Picker receives the order
Picker pulls parts from bin locations
Parts move to packing
Packer verifies contents
Shipping label is printed
Package moves to carrier staging
10. Carrier picks up or scans shipment
The value of the map is not the boxes themselves. The value comes from the questions the map creates.
Where does work wait?
Where do errors occur?
Where does priority become unclear?
Where does the order leave the normal flow?
A basic swimlane map can help if several groups touch the order. For example, one lane for order system, one for picking, one for packing, and one for shipping. The team should mark rework loops, handoffs, and wait points.
In this project, the map might show that orders released after 2:00 p.m. often wait for pickers, even though the carrier cutoff is 5:00 p.m. That does not prove root cause yet, but it gives the team a place to look.
Stratify the data to find patterns
Stratification means splitting the data into meaningful groups. This is one of the most useful Analyze tools because overall averages hide problems.
The team can stratify same-day shipment performance by:
Day of week
Shift
Order release time
Part type
Part storage zone
Picker
Packer
Carrier
Order size
Backorder history
Training status
Exception reason code
For the warehouse example, the team may create a table like this:
Data segment | Same-day shipment rate | What the team should ask |
Orders released before noon | 96% | What works well for early orders? |
Orders released noon to 2:00 p.m. | 88% | Does work begin soon enough? |
Orders released after 2:00 p.m. | 61% | Is the release time too close to cutoff? |
Small orders with 1 to 2 lines | 93% | Is the process stable for simple orders? |
Orders with 5 or more lines | 70% | Do larger orders need a different flow? |
Zone C parts | 66% | Is there a location, inventory, or travel issue? |
These numbers are illustrative. The team should use its actual project data.
The point is to move from “same-day shipment is 82%” to a sharper finding such as:
Eligible orders released after 2:00 p.m. and containing parts from Zone C account for a large share of missed same-day shipments.
That statement gives the Improve phase a target.
Use a Pareto chart to focus on the biggest causes
A Pareto chart ranks categories from largest to smallest. It helps the team focus on the few issue types that create most of the misses.
The team can create reason codes for missed same-day shipment, such as:
Miss reason | Count of missed orders |
Late order release | 104 |
Picker unavailable | 76 |
Part not in expected location | 58 |
Packing rework | 31 |
Carrier cutoff missed | 27 |
Label or system error | 18 |
Other | 15 |
A Pareto chart would show whether a few categories dominate. If late release, picker availability, and location issues make up most missed shipments, the team should not spend equal time on every possible cause.
A good Yellow Belt Analyze Phase Walkthrough keeps the team anchored in evidence. The loudest complaint is not always the biggest driver.
Build a cause and effect diagram
After finding patterns, the team lists possible causes with a fishbone diagram. For this project, useful branches might include:
Fishbone branch | Possible causes |
People | New staff not trained on priority orders, uneven skill by shift, unclear escalation steps |
Process | No separate path for same-day orders, late release queue not reviewed, handoffs unclear |
Systems | Order release delay, scanner downtime, label errors, poor visibility to cutoff risk |
Materials | Parts stored in hard-to-reach zones, inventory mismatch, damaged packaging supplies |
Equipment | Printer downtime, cart shortages, scanner battery issues |
Environment | Long travel paths, crowded staging area, carrier pickup congestion |
The fishbone is a brainstorming tool, not proof. The team should mark which causes have data support and which need more checking.
Use 5 Whys to test likely root causes
The 5 Whys method helps the team move past symptoms. It works best when the team starts with a specific problem.
Example problem:
Orders released after 2:00 p.m. often miss same-day shipment.
Why question | Example answer |
Why do late-release orders miss same-day shipment? | Pickers do not start them soon enough before carrier cutoff. |
Why do pickers not start them soon enough? | The queue does not clearly flag which late orders are still eligible for same-day shipment. |
Why does the queue not flag them clearly? | Same-day priority status appears only in order details, not in the main pick list. |
Why has the team relied on order details? | The current process assumes staff manually check priority status. |
Why is manual checking inconsistent? | Training varies by shift, and there is no standard work step for late-day priority review. |
A possible root cause from this chain is:
Late-day eligible orders lack clear priority visibility in the pick queue, and staff do not follow a standard review step across shifts.
That root cause connects directly to the project goal and suggests specific improvements, but the team should still confirm it with data or observation.
Confirm root causes with simple evidence
Yellow Belt teams can use practical tests before recommending fixes. The goal is not advanced statistics. The goal is enough evidence to make a sound decision.
Useful confirmation methods include:
Tool | How to use it in this project |
Check sheet | Track missed orders by reason during a sample period |
Time study | Measure time from release to pick start by release window |
Scatter plot | Compare order release time with shipment success |
Run chart | Track same-day shipment rate by day or week |
Bar chart | Compare performance by shift, zone, carrier, or order size |
Gemba walk | Observe the real work at the warehouse floor |
Standard work review | Compare actual steps with documented steps |
Training matrix | Check whether staff assigned to same-day orders are trained |
For example, a time study could show that orders released after 2:00 p.m. wait an average of 90 minutes before pick start, while earlier orders wait 20 minutes. A check sheet could show that many delayed orders came from the same storage zone. A training matrix could show that only some staff know the late-day priority review process.
The team should summarize findings in plain language.
Suspected cause | Evidence found | Root cause status |
Late order release causes misses | High miss rate after 2:00 p.m. | Supported |
Pick queue does not show priority clearly | Staff must open order details to see priority | Supported |
Zone C parts cause delays | Zone C orders have lower same-day rate and longer pick time | Supported |
Carrier pickup time causes most misses | Few missed orders were packed before cutoff | Not supported |
Packing rework causes most misses | Low count compared with late release and picking delay | Lower priority |
This table gives the team a clear bridge into Improve. It shows what to fix, what not to fix, and why.
Analyze phase deliverables for the warehouse project
By the end of Analyze, the team should have a short set of deliverables. These do not need to be long. They need to be clear.
Deliverable | What good looks like |
Validated problem statement | The team can explain the 82% baseline and the gap to 95% |
Current state process map | The map shows the real order flow, handoffs, waits, and rework |
Data stratification | The team knows which order types, times, zones, or shifts drive the gap |
Pareto analysis | The team has ranked the largest categories of missed shipments |
Root cause analysis | The fishbone and 5 Whys identify likely causes |
Root cause confirmation | Data, observation, or both support the main causes |
Improve focus list | The team has a short list of causes to address next |
For the example goal, the final Analyze summary might read:
The largest drivers of missed same-day shipment are late-day order release visibility, inconsistent priority review by shift, and longer pick time for Zone C parts. These three areas account for most missed eligible orders in the sample reviewed. Carrier cutoff and packing rework occur, but they do not explain most of the gap from 82% to 95%.
That summary is specific enough to guide the next phase.
Analyze phase tollgate questions
The tollgate review checks whether the team has done enough analysis to move forward. These questions help a Champion, sponsor, or project lead test readiness.
Problem and goal questions
Is the SMART goal still valid and clearly tied to customer satisfaction?
Did the team confirm the baseline same-day shipment rate of 82%?
Does everyone use the same definition of eligible technician parts orders?
Is the target of at least 95% by June 30, 2026 still realistic based on what the team learned?
Data questions
Did the team verify the data source and key timestamps?
Are missing or inaccurate records understood?
Did the team separate eligible and ineligible orders correctly?
Did the team analyze enough data to see real patterns?
Can the team explain how the same-day shipment rate is calculated?
Process questions
Did the team map the current process as it actually works?
Did warehouse staff validate the process map?
Did the map show handoffs, wait points, rework, and decision points?
Did the team observe the work instead of relying only on reports?
Root cause questions
Did the team use tools such as Pareto charts, fishbone diagrams, 5 Whys, and stratification?
Are the top suspected causes backed by data or observation?
Did the team separate symptoms from root causes?
Did the team rule out causes that are not major drivers?
Can the team state the main root causes in clear language?
Improve readiness questions
Does the team know which causes to address first?
Are the causes within the project scope?
Do the likely improvements connect to staffing training, process changes, or workflow changes?
Does the team have enough evidence to avoid guessing?
Are stakeholders aligned on what the Improve phase should test?
Improve readiness decision matrix
Use this matrix to decide whether the team is ready to leave Analyze and enter Improve. Score each item from 1 to 3.
Score | Meaning |
1 | Not ready |
2 | Partly ready |
3 | Ready |
Readiness criterion | 1 | 2 | 3 | Score |
Problem and goal clarity | Goal or metric is unclear | Goal is mostly clear, but some terms need work | Goal, metric, baseline, and target are clear | |
Data reliability | Data has major gaps or definition issues | Data is usable with known limits | Data is checked and trusted | |
Process understanding | Current flow is assumed | Process map exists but needs validation | Process map is validated by observation and staff input | |
Pattern analysis | Team reviewed only the overall average | Some stratification was completed | Key patterns by time, shift, zone, order type, or other segments are clear | |
Root cause evidence | Causes are mostly opinions | Some causes have support | Main root causes are confirmed with data or observation | |
Scope fit | Causes fall outside project control | Some causes fit the scope | Top causes match the project scope and goal | |
Improve focus | Team has many broad ideas | Team has a rough list of possible fixes | Team has a short list of causes ready for solution testing | |
Stakeholder alignment | Sponsor or process owner is not aligned | Alignment is partial | Sponsor and process owners agree to move forward |
How to use the score
Add the scores for all eight criteria.
Total score | Decision |
8 to 13 | Stay in Analyze. The team is still guessing or missing key evidence. |
14 to 19 | Close gaps before Improve. The team may need one more data cut or validation step. |
20 to 24 | Move to Improve. The team has enough focus and evidence to test solutions. |
A team should also check for any automatic stop signs. Even with a decent score, do not move forward if:
The baseline cannot be trusted
The main root causes are not supported by evidence
The proposed Improve work does not match the project scope
The process owner disagrees with the findings
The team cannot explain why the selected causes matter
What ready for Improve looks like
A team is ready for the Improve phase when it can tell a simple evidence-based story:
The same-day shipment rate is 82%, and the target is at least 95%. The team confirmed the metric and mapped the warehouse order flow. Data shows that misses occur most often for late-day releases, specific parts zones, and inconsistent priority handling. Root cause tools point to poor priority visibility, uneven training, and longer pick time in certain locations. The team has evidence for these causes and can now test improvements that address them.
For the warehouse project, Improve ideas might include a visual priority flag for late-day eligible orders, standard work for the 2:00 p.m. review, focused training by shift, revised pick paths for slow zones, or a daily same-day shipment risk check.
The Analyze phase does not need to produce perfect certainty. It needs to produce enough confidence to stop guessing and start testing the right fixes.
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