M: MEASURE PHASE

Measure defect rates and

document the

process in its

current incarnation

M: Measure

The purpose of the measure phase is to fully understand the process and it’s current performance by identifying how to best measure performance. These measurements must be realistic as well as useful, in order to provide help in reducing defects.

This phase includes

Identifying specific performance requirements of relevant critical-to-quality (CTQ) characteristics

Mapping of relevant processes, with all inputs that effect a particular output identified.

Analyze measurement system capability & establish relevant baseline for process capacity

Collect data (ensuring opportunities for error are evaluated)

Verify the problem actually exists

Key Questions for the Measure phase

What is the process? How does it function?

Which outputs effect CTQ the most?

Which inputs effect outputs the most?

Is the ability to measure/detect sufficient?

How is the current process performing?

What is the “best” the process was designed to do?

At this point fishbone diagrams, process mapping and cause and effect analysis are useful tools.

The deliverables from the Measure phase include: Operational Definition Measurement System analysis Data collection plan Baseline performance

The three steps to ensure effective project team :-

a. Finalize Project Y, Performance Standards for Yb. Validate Measurement System for Yc. Measure Current Performance and Gap

 

 

A.Finalize Project Y, Performance Standards for Y

Operational Definition – An Operational definition is a precise of the specific Y to

be measured. This helps in ensuring reliability and consistency during the measurement process. (Defining CTQs pertaining to problem Project 1 & 2)

CTQ Performance Characteristics

S. No Project Leader CTQ Data Type Operational Definition LSL USL Target

1 S

Time to receive payments (average time taken to get the invoices

paid)

Continuous

Start + 60 days Credit Period (Starting from Invoice Date). Stop - 65 days of Invoice (5 days over of Customer Credit Period, A Final reminder should be

sent with Penalty norms along with prior gentle reminders note, ex-Credit Note can be send)

- 60

<60, average time taken to

get the invoice should be reduced

2 B Late Payment (defectives) Discrete

Start + 60 days Credit Period (Starting from Invoice Date). Stop - 75 days of Invoice (15 days over of Customer Credit Period, A Final reminder should be sent with Penalty norms along with prior gentle

reminders note)

- 5%

<20%, goal of reducing

defective % from 20% to

5%

B. Validate Measurement System for Y

Q. Project Leader ‘S’ logically validates the measurement system to capture “Time to receive payments” however Project Leader ‘B’ would like to carry out Attribute Agreement Analysis to assess the measurement system reliability in classifying late payment. After running the trials following outcomes have been observe red:-

Samples: 50 Appraisers: 3Replicates: 2 Total runs: 300

 

Attribute Agreement Analysis (Perform an attribute agreement analysis to evaluate the consistency and accuracy of appraisal data. An attribute agreement analysis evaluates the consistency of ratings across appraisers, trials, and versus a standard or known value)

Outcomes:- 

Appraiser  1 2 3 Repeatability 93.33% 86.67% 90%

Accuracy 93.33% 83.33% 86.67%

Indicates Project Acceptability with Caution. 

Reproducibility 73.33% Team Accuracy 73.33%

Measure current performance and Gaps

Y Measure

Operational Definition

Data Source

Sample Size

Who will collect the data

When will the data be collected

How will the data be collected

X data be collected at the same time

Data Collection Plan

Once Measurement system reliability has been established, team develops a data collection plan of Y measure. It is a plan defining the precise data that will be collected, the amount of data that will be collected, a description of the logistical issues – who, where, when data will be collected – and what will be done with data collected.

Points to Remember

Variation in the measurement system will contribute to the observed variation in a process

Sources of variation in a measurement system are: gage, operator and environment

Study how the operator, gage and system influence measurement error

Historical data is probably infested with measurement error issues. Be careful.

Even if your measurement is OK, audit periodically.

Questions to ask at the beginning

Is the correct data being captured and does it reflect what is happening in the process?

Are the measurement units at a level to reflect the variation in the process?

Where is the data actually recorded?

How is the data being recorded today?

Who records the data today?

Is there a measurement error? If yes, what is the size of the measurement error?

Questions to ask at the beginning

What are the sources of measurement error?

Is the measurement system stable over time?

What is the maximum error (specs) that the customers will accept?

Questions to ask at the end

Have you validated the measurement or just your ability to read the database?

How large is your measurement error (as % of allowable)?

Is one aspect (reproducibility or repeatability) dominant?

How do we improve the measurement system?

System generated data – Questions to ask

What are the inputs into the data, and are there any accuracy issues with the same?

What is the coding in the backend and are there any accuracy issues?

Are there any repeatability and reproducibility issues?

Critical Aspects of a Measurement System

Accuracy–the differences between observed average measurement and a standard

Repeatability–variation when one person repeatedly measures the same unit with the same measuring equipment

Reproducibility–variation when two or more people measure the same unit with the same measuring equipment

Stability–variation obtained when the same person measures the same unit with the same equipment over an extended period of time

Precision & Accuracy

Target Analogy

I. Precise, not accurate

==

True Value = Bull's Eye

Xbar

II. Accurate, not precise

==

True Value

Xbar

III. Precise and accurate

==

True Value

Xbar

CTQ Baseline Performance

CTQ Zlt ZSt

Time to receive payments

1.04 2.54

Late Payment 0.78 2.28

Guidelines For Evaluation

Repeatability: 90% of repeated measures for an operator match

Reproducibility: 90% of the repeated measures across operators match

Accuracy: 90% of the individual measures match the standard