Case Study
Company
Industry
Application
Benefit
Ford
Motor Vehicles
Engine Line Planning
Rapid Simulation
Development
WITNESS Supports Ford’s Six Sigma Initiative
Six Sigma is a structured, statistically-orientated approach to process improvement, concentrating on the customer and variation reduction. Ford is the first automaker to adopt Six Sigma as
its approach to quality improvement—and the first company to use Six Sigma as a tool for
directly improving customer satisfaction. Ford has invested significantly in the Six Sigma
Breakthrough Methodology to improve its long-term competitiveness. The automaker’s aim is
to drive down waste and improve process performance to increase customer satisfaction. Ford
is also one of the first companies to adopt WITNESS simulation to support Six Sigma in developing effective manufacturing facilities. WITNESS has proven beneficial in the analysis and
improvement phases of Six Sigma, providing a test-bed to measure the relative effects of alternatives without requiring expensive real-time experiments.
Background to Six Sigma
in Ford
Ford started using Six Sigma about
three years ago. The program has
been deployed throughout the
organization using a hierarchy of
“master belts,” “black belts,” and
“green belts” with defined roles,
responsibilities, methodologies
and targets for each. Personnel
are trained in Six Sigma and other
key leadership skills. They then
focus on delivering projects over
a two-year period.
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The black belts are at the heart
and soul of the Six Sigma quality
initiative. Their main function is
to lead quality projects, remain
focused on the delivery of the
improvements and develop controls to ensure improvements are
sustained. Black belts typically
complete four to six projects per
year with substantial improvements
in quality, delivery, cost reduction
and customer satisfaction.
Black belts also train, coach and
mentor green belts. The master
black belt will act as a coach or
mentor to the black belts and is
expert in the use of the toolkit.
Green belts are trained in Six
Sigma and employ the breakthrough methodology in completing
job-focused projects, supporting
black belt projects and replicating
improvements into their normal
work area, while maintaining
their regular work role and
responsibilities.
Within Ford, black belts are aligned
to specific products or programs
with responsibility for leading the
improvement projects. They bring
a consistent approach and a focus
on robust analysis using a proven
set of tools and techniques to
ensure objectivity and sustainable
recommendations.
Six Sigma Methodology
The fundamental objective of
the Six Sigma methodology is the
implementation of a structured
measurement-based strategy that
focuses on process improvement
and variation reduction, through
the application of improvement
projects. This is accomplished
using two Six Sigma sub-methodologies: DMAIC and DMADV.
The Six Sigma DMAIC process
(Define, Measure, Analyze,
Improve, Control) is an improvement system for existing processes
or products falling below specification and looking for incremental
improvement. The Six Sigma
DMADV process (Define,
Measure, Analyze, Design, Verify),
is an improvement system used
to develop new processes or
products at Six Sigma quality
continued
Case Study
levels. Ford employs a variation
on these approaches DCOV
(Define, Characterize, Optimize,
Verify), to realize advantage in its
product development. The latest
vehicle line up proves this, including the Mondeo, Focus and Fiesta,
all of which have benefited from
the use of these methodologies.
test-bed to measure the relative
effects of improvement alternatives without undertaking expensive real-time experimentation.
The optimum alternative can be
selected and implemented with
confidence in the real world, minimizing the risks to the process
and the customer.
One of the crucial elements of a
Six Sigma project is the selection
of project metrics. Project metrics
should reflect the voice of the
customer. Metrics should be
measurable, meaningful and
provide a common language
among diverse team members.
WITNESS provides a natural tool
to support the Analyze and
Improve phases of DMAIC, while
providing less critical aid in other
phases as well.
To support Six Sigma, WITNESS
contains a number of specifically
designed features including:
• direct links with MINITAB
• tailored optimization capabilities
matching the process improvement methodology
• automatic calculation of Sigma
ratings for chosen processes
• a range of modeling tools and
reports tailored to the needs of
the Six Sigma specialist.
WITNESS and Six Sigma
WITNESS offers a number of
benefits to Ford’s Six Sigma
program. If the organization does
not understand the “science” of
its processes, they cannot control,
modify or improve them. It is first
necessary to study a process in
order to know which variables
affect it. Once this is understood,
the variables affecting the process
can be manipulated in a controlled
fashion to optimize performance.
In addition to studying the real
process, building a simulation
model can help with understanding. The model also provides a
Examples Within Ford
Dagenham Engine Plant
Machining Line
On one Ford Six Sigma project,
specialists investigated a machining line experiencing capacity
concerns that were impacting on
downstream activities. Data was
collected in MINITAB specifically
to support the project and the
planned analysis.
The project team understood that
there were many changes it could
make to improve the machining
line. Its task was to select a small
number of changes that would
work together to create maximum impact on debottle-necking.
A small number of changes are
required because of the potential
disruption and risk inherent in
disturbing too many aspects of
the production system at once.
A version of WITNESS known as
FIRST (Fast Interactive Replacement
Simulation Tool) was used. This is
a Ford proprietary tool and was
specially developed in partnership
with John Ladbrook of Ford and
designed for quickly modeling
machining lines. FIRST automatically creates a simulation model
of the facility based upon data
input through Excel. This approach
provided quick and easy access to
WITNESS simulation without
requiring the Six Sigma specialists
to have model-building skills.
Having populated FIRST with
the data that had been collected,
the model provided the analytical
capability to determine the key
changes, which, if implemented
successfully, would provide a
significant step improvement.
Having experimented with the
factors within FIRST, two major
opportunities were identified.
These improvement opportunities
reaped a 10% increase in capacity
once implemented. Black belt,
Debbie Crouch said, “The WITNESS
model provided the ‘what-if’ capability to access the improvement
options, so that improvements
could be selected having accurately
predicted the outcomes.”
continued
Case Study
Chiller Capacity
Chillers provide chilled water to
cool machines, support coolant
systems and provide environmental cooling. Correctly predicting
cooling requirements is vital as
too much capacity has a significant cost penalty while too little
capacity can have quality and
environmental penalties. Chiller
capacity has historically been
purchased based upon the sum
of machine supplier stated
requirements and an estimate
of system uptime.
A predictive model was required
to relate the requirements of individual production machines and
their operating states to average
and peak load chiller requirements. A model of this kind
would deliver capital expenditure
and operational benefits to the
Ford plants. As part of this Six
Sigma project, Lanner was asked
to provide expert modeling of
process chiller capacity.
A WITNESS simulation model
was developed in partnership
with Ford using Lanner’s SIMBA
(Simulation Based Application)
ActiveX control embedded within
an Excel spreadsheet. The model
is used by entering key machine
data and operating requirements
into the Excel spreadsheet. The
outputs show minute-by-minute
cooling parameters, power
consumption and control requirements. These outputs, which are
provided in graphical and tabular
formats, have been validated
with live process data collated
during the Six Sigma program
and have been shown to be
statistically robust.
future projects and to allow
them to evaluate and compare
individual machine demand. With
this model, engineers are able to
quickly and accurately predict
cooling capacity requirements.
This analytical power will result
in significant capital expenditure
savings and provide valuable
information during machine
specification discussions and
negotiations.
This case study has been
written with the support of
Ford Six Sigma black belts
Dave Shelley CEng MIEE and
Debbie Crouch BEng.
The model was constructed in
this way to provide Ford engineers with an easy-to-use tool
for assessing chiller capacity on
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