Process Improvement and KPOVs and Data
Overview
Lean Sigma is different to many traditional Process Improvement initiatives in its reliance on data to make decisions. During Define the major metrics, otherwise known as the Key Process Output Variables (KPOVs) or Big Ys, to measure performance of the process are identified and a ballpark historical value determined.
Thenceforth, the equation Y = f(X1,X2,..., Xn) used to identify and narrow down the Xs that drive the Y is completely data driven."Term originates from Dr. Steve Zinkgraf's early work at Allied Signal."
Prior to undertaking that road, it is important to have a basic understanding of data and measures and how to deal with them.
There are different ways to categorize data and measures:
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Continuous versus Attribute
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Results versus Predictors
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Efficient versus Effective
The type of data is important because it affects how measures are defined, how to go about collecting data, and which tools to use in analyzing the data, an example is shown below
Selecting the statistical tool based on the data type.
Continuous Measures
Continuous measures are those that can be measured on an infinitely divisible scale, such as weight, temperature, height, time, speed, and decibels. They are often called Variable measures.
Continuous data is greatly preferred over Attribute type data because it allows the use of more powerful statistical tests and can facilitate decisions being made with much less data (a sample size of 30 works well).
Continuous data also allows measurement of performance (i.e., how good is it?) versus conformance (i.e., is it just good or bad?). If the data used in the project is Attribute type, then Belts are strongly encouraged to identify a related Continuous type measure and use that instead.
Attribute Measures
Any measures that aren't Continuous are known as Attribute or Discrete measures. Despite the lower statistical power, they are generally easier and faster to use than Continuous data and are effective for measuring intangible factors such as Customer Satisfaction or Perception.
There are several different types of Attribute data along a spectrum as follows:
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Binary has just two categories to classify the data for example, pass/fail, win/lose, or good/bad
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Count is a simple count of entities for example, the number of defects or the number of defective items
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Percentage or Proportion is the count expressed as a percentage or proportion of the total for example, the percentage of items scrapped or the proportion of items damaged
As the number of categories increase in Attribute data, the more like Continuous data it becomes.
The key failings of Attribute data are the need for a large number of observations to get valid information from any tests performed and also that the data can hide important discrimination.
Results
The Lean Sigma equation Y = f(X1,X2,..., Xn) shows the relationship between the Result (the Y) and the Predictors (the Xs).
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Results are measures of the process outcomes and are often called "output" factors or KPOVs. Results might be short-term (on-time delivery) or longer-term (Customer satisfaction), but they are typically lagging indicators.
Also, because Results are the Ys and are driven by multiple Xs in the process, they are reactive rather than proactive variables.
Results tend to be much easier to collect than Predictors and are usually already being measured in the process. Measurement of a Result is typically done against a specification and becomes a Process Capability measure.
Predictors
Predictors are the "upstream" factors that, if measured, can forecast events "downstream" in the process; for example, an increase in raw-material order lead times might predict an increase in late deliveries.
Predictors are the Xs in the equation Y = f(X1,X2,..., Xn) and are often called "input" factors or KPIVs.
Predictors are the leading indicators of the process and generally are more difficult to identify and collect. It is unlikely that this data is available historically, so the Team has to set up a measurement system to collect it.
Due to the proactive nature of Predictors, they form the basis of the strongest process control measures.
Effectiveness
There are two categories of metrics used to describe the Big Ys in the process, namely effectiveness and efficiency. Effectiveness is an external measure of the process related to the VOC. An effective process is one that produces the desired results, from the Customer's perspective. To understand effectiveness, the questions to ask relate to
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How closely are Customers' needs and requirements met?
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What defects have they received?
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How satisfied and loyal have they become?
Effectiveness measures include
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Delivery performance.
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Quality performance, such as problems consumers report with their new product during the first three months of ownership, defects per unit (DPU), or customer percent defective
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Customer Satisfaction.
Efficiency
Efficiency is the second category of metrics used to describe the Big Ys. Efficiency is an internal measure of the process and relates to the level of resources used in the process to achieve the desired result.
To understand efficiency, questions to ask relate to how the process performs in the eyes of the business:
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What is the process cycle time?
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What is the process yield?
Efficiency metrics include
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Process Lead Time
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Work content, such as assembly time per unit
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Process cost, such as labor and materials cost per unit
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Resources required
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Cost of poor quality (COPQ), such as Defects per Million Opportunities (DPMO), First Pass Yield, Scrap, Rework, Re-inspect, and Re-audit
Each process and sub-process should have at least two Efficiency requirements established:
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A ratio of input-to-output value, such as cost per entity processed, process yield
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A measurement of Cycle Time, such as hours to process an entity
Operational Definitions
After a metric has been identified, the work isn't complete. To be a useful metric, a clear, precise description of the metric has to exist. This is critical so that everyone can evaluate and count the same way and there is a common understanding of and agreement on the results.
In Transactional processes in particular, Operational Definitions are often the only real way to maintain control and, if poorly defined, are commonly the biggest cause of Measurement System issues.
To describe a measure fully, it is useful to create an Operational Definition for it including
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Who measures (by role, not person's name)
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What they measure (entities, measurement units)
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When they measure (timing and frequency)
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Where they measure (physical location in the process to make measure)
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How they measure (technique, steps involved)
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Why they measure (what is the measure used for)
Roadmap
The roadmap to identifying, defining, and capturing data is as follows:
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Step 1.
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Identify the measures that translate what is happening in the
process into meaningful data. Determine if each measure is a KPOV or KPIV, if it
is a measure of Effectiveness or Efficiency, and if it is Continuous or
Attribute.
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Step 2.
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Create a clear Operational Definition of the characteristic
to be measured (who, what, when, where, how, and why).
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Step 3.
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Test the Operational Definition with process Stakeholders
(all those that affect and are affected by the process) to ensure consistency in
understanding. Revise the Operational Definition if necessary. Any metric chosen
should be both repeatable and reproducible Active data capture is the norm in the majority of Lean Sigma projects, so the Team has to create a Data Collection and
Sampling plan. |
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Step 4.
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Identify stratification. Before collecting the data, it is important to take time to consider how the Team wants to analyze the data after it is collected. For example, determine which Xs are to be investigated with respect to their effects on the Ys, and so on. To facilitate this, the Xs need to be built into the data collection plan and essentially are means to stratify the data. They might include things such as time, date, person, shift, operation number, Customer, buyer, machine, subassembly #, defect type, defect location, component and defect impact and criticality, and so on. Be aware
though that the number of sub-categories has a big impact on the amount of data
required from which to make statistical inferences. |
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Step 5.
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Create a sampling plan.Data collected is only a sample from the process; it never represents every entity to be processed (known as the population). Even if 100% of the data points are collected for the process during a certain period of time, it is still only a sample of the whole population. In fact, this issue drives the use of statistics in the roadmap. Statistics are necessary because there is only a sample taken from the population. The Belt then uses the properties of the sample to draw inferences (predictions, guesses) as to the properties of the population. Clearly there is
some guesswork (statistics) involved, but this is a faster, less costly way to
gain insight into a process or large population. Sample Quality is a measure of how well the sample represents the population and meets the needs of the sampler. Lean Sigma statistical methods generally assume random sampling in which every entity or member of the full population has an equal chance of being selected in the sample. Whichever sampling approach is taken, the Team must strive to
Standard Sampling Strategies.
Unfortunately, this is not a one-to-one relationship. In fact, mathematically it
increases in proportion to For Continuous data meaningful Sample Sizes are
For Attribute Data meaningful Sample Sizes are
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Step 6.
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Based on the Sampling Plan, create a data collection form and
tracking system. This comprises the who, what, where, when, and how the data is
captured. Some common methods include
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Step 7.
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Create procedures for completing the data collection forms. Any instructions should be visual and understandable by all. It is useful to include pictures of the form, describe what goes in each box and include
examples of completed forms. Choose the data collectors carefully and train them
on the procedures using the instructions to ensure consistent data
collection. |
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Step 8.
Step 9. |
Test the data collection method. Do a short dry run of the data capture (a few data points) to identify problems and make adjustments. After completion, assess the accuracy, repeatability, and reproducibility of the data collection system
Collect the Data as per the Data Collection Procedure. The Team must follow the Sampling Plan consistently and record any changes in operating conditions not part of the normal or initial operating conditions. Any events out of the ordinary should be immediately written into a logbook. The
Belt for the project should check that data collection procedures are followed
at all times. When the desired sample size is reached, stop data
collection. |
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Step 10.
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Enter the collected data promptly into a database, such as a
spreadsheet or statistical software. Make a backup copy of the electronic file.
Keep all the paper copies to be archived as part of the project report.
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>
and so doubling sample size doesn't double accuracy. For
example, when the sample size increases from 1 to 100, inaccuracy decreases by
only 1/10th, and, therefore, it is important to keep sample sizes
relatively small.
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