In the Analyze step, the objective is to use the data from the Measure step to begin to
determine the cause-and-effect relationships in the process and to understand the different
sources of variability. That is, in the Analyze step the objective is to determine the potential
causes of the defects, quality problems, customer issues, cycle time and throughput problems,
or waste and inefficiency that motivated the project. It is important to separate the sources of
variability into common causes and assignable causes. Removing a common cause of variability
usually means changing the process, while removing an assignable cause usually involves
eliminating a specific problem. A common cause of variability might be inadequate training of
personnel processing insurance claims, while an assignable cause might be a tool failure on a
machine.
There are many statistical tools that are potentially useful in the Analyze step. Among these are
graphical data displays, control charts, hypothesis testing and confidence interval estimation,
regression analysis, designed experiments, and failure modes and effects analysis. Discreteevent
computer simulation is another powerful tool useful in the Analyze step. It is particularly
useful in service and transactional businesses, although its use is not confined to those typesof operations. For example, there have been many successful applications of discrete-event
simulation in studying scheduling problems in factories to improve cycle time and throughput
performance. In a discrete-event simulation model, a computer model is employed to simulate
a process in an organization. For example, a computer model could simulate what happens
when a home mortgage loan application enters a bank. Each loan application is a discrete event.
The arrival rates, processing times, and even the routing of the applications through the bank’s
process are random variables. The specific realizations of these random variables influence the
backlogs, applications that accumulate at the different processing steps. Other random variables
can be defined to model the effect of incomplete applications, erroneous information and other
types of errors and defects, and delays in obtaining information from outside sources, such as
credit histories. By running the simulation model for many loans, reliable estimates of cycle
time, throughput, and other quantities of interest can be obtained.
The Analyze tools are used with historical data or data that was collected in the Measure step.
These data are often very useful in providing clues about potential causes of the problems that the
process is experiencing. Sometimes these clues can lead to breakthroughs and actually identify
specific improvements. In most cases, however, the purpose of the Analyze step is to explore and
understand tentative relationships between and among process variables and to develop insight
about potential process improvements. A list of specific opportunities and root causes that are
targeted for action in the Improve step should be developed. Improvement strategies will be
further developed and actually tested in the Improve step.
In preparing for the Analyze tollgate review, the team should consider the following issues
and potential questions:
In the Analyze step, the objective is to use the data from the Measure step to begin to
determine the cause-and-effect relationships in the process and to understand the different
sources of variability. That is, in the Analyze step the objective is to determine the potential
causes of the defects, quality problems, customer issues, cycle time and throughput problems,
or waste and inefficiency that motivated the project. It is important to separate the sources of
variability into common causes and assignable causes. Removing a common cause of variability
usually means changing the process, while removing an assignable cause usually involves
eliminating a specific problem. A common cause of variability might be inadequate training of
personnel processing insurance claims, while an assignable cause might be a tool failure on a
machine.
There are many statistical tools that are potentially useful in the Analyze step. Among these are
graphical data displays, control charts, hypothesis testing and confidence interval estimation,
regression analysis, designed experiments, and failure modes and effects analysis. Discreteevent
computer simulation is another powerful tool useful in the Analyze step. It is particularly
useful in service and transactional businesses, although its use is not confined to those typesof operations. For example, there have been many successful applications of discrete-event
simulation in studying scheduling problems in factories to improve cycle time and throughput
performance. In a discrete-event simulation model, a computer model is employed to simulate
a process in an organization. For example, a computer model could simulate what happens
when a home mortgage loan application enters a bank. Each loan application is a discrete event.
The arrival rates, processing times, and even the routing of the applications through the bank’s
process are random variables. The specific realizations of these random variables influence the
backlogs, applications that accumulate at the different processing steps. Other random variables
can be defined to model the effect of incomplete applications, erroneous information and other
types of errors and defects, and delays in obtaining information from outside sources, such as
credit histories. By running the simulation model for many loans, reliable estimates of cycle
time, throughput, and other quantities of interest can be obtained.
The Analyze tools are used with historical data or data that was collected in the Measure step.
These data are often very useful in providing clues about potential causes of the problems that the
process is experiencing. Sometimes these clues can lead to breakthroughs and actually identify
specific improvements. In most cases, however, the purpose of the Analyze step is to explore and
understand tentative relationships between and among process variables and to develop insight
about potential process improvements. A list of specific opportunities and root causes that are
targeted for action in the Improve step should be developed. Improvement strategies will be
further developed and actually tested in the Improve step.
In preparing for the Analyze tollgate review, the team should consider the following issues
and potential questions:
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