Hill A.V. The Encyclopedia of Operations Management: A Field Manual and Glossary of Operations Management Terms and Concepts
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expert system
–
exponential distribution
The Encyclopedia of Operations Management Page 126
Joe Pine and James Gilmore, founders of the management consulting firm Strategic Horizons, wrote many of
the early articles on this subject (Pine & Gilmore 1998). Pine and Gilmore used the term “
experience economy
”
rather than the term “experience engineering.” They argue that economies go through four phases:
Producers can use each phase to differentiate their products and services. As services become commoditized,
organizations look to the next higher value (experiences) to differentiate their products and services. Although
experiences have always been at the heart of the entertainment business, Pine and Gilmore (1998, 1999) argue
that all organizations
stage an experience
when they engage customers in personal and memorable ways. Many
of the best organizations have learned from the Walt Disney Company and have found that they can differentiate
their services by staging experiences with services as the stage and goods as the props. The goal is to engage
individuals to create memorable events.
Pine and Gilmore (1999) offer five design principles that drive the creation of memorable experiences:
1.
Create a consistent theme that resonates throughout the entire experience.
2.
Reinforce the theme with positive cues (e.g., easy-to-follow signs).
3.
Eliminate negative cues, which are visual or aural messages that contradict the theme (e.g., dirty floors).
4.
Offer memorabilia that commemorate the experience for the user (toys, dolls, etc.).
5.
Engage all five senses (sight, sound, smell, taste, and touch) to heighten the experience and make it more
memorable (e.g., the fragrant smell of a great restaurant).
The website for Pine and Gilmore’s consulting firm, Strategic Horizons, is www.strategichorizons.com.
Carbone (2004), who coined the term “experience engineering,” presents two types of
clues
customers pick
up in their customer experience –
functional clues
and
emotional clues
. These are compared in the table below.
Functional clues Emotional clues
Actual functioning of the good or
service.
Interpreted primarily by the logic
part of the brain.
The minimum requirement to enter
the game.
Example: Did the plumber fix the
leak?
Smells, sounds, sights, tastes, and textures.
The environment in which it is offered.
Two types of emotional clues:
Mechanics – Clues emitted by things (signs, facilities, etc.)
Humanics – Clues emitted by people (gestures, comments,
dress, voice tone)
Examples: Feel of leather upholstery, sound and smell of a steak
on a grill, tone of the service rep.
Carbone argues that emotional clues can work synergistically with functional clues to create customer value.
He further proposes that customer value is equal to the functional benefits plus the emotional benefits less the
financial and non-financial costs. He concludes that organizations should manage the emotional component of
their products and services with the same rigor that they bring to managing product and service functionality.
Carbone’s firm, Experience Engineering, has a website at http://expeng.com.
See facility layout, mass customization, service blueprinting, service management, service quality.
expert system – A computer system that is designed to mimic the decision processes of human experts.
An expert system uses knowledge and reasoning techniques to solve problems that normally require the
abilities of human experts. Expert systems normally have a domain-specific knowledge base combined with an
inference engine that processes knowledge from the knowledge base to respond to a user’s request for advice.
See Artificial Intelligence (AI), knowledge management, turing test.
exponential distribution – A continuous probability distribution often used to model the time between arrivals for
random events, such as a machine breakdown or customer arrival to a system; also known as the negative
exponential distribution.
Parameter:
The exponential distribution has only one parameter (
), which is the mean.
Density and distribution functions:
The density and distribution functions for x > 0 are
/
( ) (1 / )
x
f x e
and
/
( ) 1
x
F x e
.
Agriculture Manufacturing Services Experiences
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exponential smoothing
−
exponential smoothing
Page 127 The Encyclopedia of Operations Management
Statistics:
Range [0, ∞), mean
, variance
2
, mode 0, and coefficient of variation c = 1. An indicator of
an exponentially distributed random variable is a sample coefficient of variation (sample mean/sample standard
deviation) close to one.
Graph:
The graph below is the exponential density function with a mean of 1.
Estimating the parameter:
The parameter (
) can be
estimated as the sample mean.
Excel:
In Excel, the
exponential density and distribution
functions are EXPONDIST(x, β,
FALSE) and EXPONDIST(x, β,
TRUE) and the equivalent Excel
formulas are
(1 / )EXP( / )x
and 1 EXP( / )x
, respectively.
Excel does not have an inverse for
the exponential, but the Excel
function GAMMAINV(p, β, 1) and
the Excel formula ln(RAND())
can be used for the inverse. In
Excel 2010, EXPONDIST has been
replaced by EXPON.DIST.
Excel simulation:
In an Excel simulation, exponentially distributed random variates X can be generated
with the inverse transform method using ln(1 RAND())X
. Note that the negative sign in this equation is
correct because the natural log of a random number in the range (0,1] is negative. Note that RAND() is in the
range [0,1), but 1 RAND() is in the range (0,1], which avoids the possibile error of taking the natural log of
zero.
Relationships to other distributions:
The exponential distribution is a special case of the gamma, Erlang,
and Weibull. The exponential distribution is the only continuous distribution that has the memory-less property
(i.e., ( | ) ( )P X t s X t P X s for all t, s > 0).
See coefficient of variation, Erlang distribution, gamma distribution, inverse transform method, Poisson
distribution, probability density function, probability distribution, queuing theory, random number, Weibull
distribution.
exponential smoothing – A time series forecasting method based on a weighted moving average, where the
weights decline geometrically with the age of the data; a graphical procedure that removes much of the random
variation in a time series so that the underlying pattern is easier to see.
Exponential smoothing is a popular time series extrapolation forecasting technique. It can also be used as a
data smoothing technique. The focus here is the use of exponential smoothing for forecasting.
An exponentially smoothed average is a
weighted moving average
that puts more weight on recent demand
data, where the weights decline
geometrically
back in time. (Note: Technically, exponential smoothing should
have been called geometric smoothing
16
.) With simple exponential smoothing, the smoothed average at the end
of period t is the forecast for the next period and all other periods in the future. In other words, the one-period-
ahead forecast is
1t t
F A
and the n-period-ahead forecast is
t n t
F A
. Exponential smoothing can be extended
to include both trend and seasonal patterns.
Simple exponential smoothing
– The most basic exponential smoothing model uses a simple equation to
update the exponentially smoothed average at the end of period t. The exponentially smoothed average at the
end of period t is the exponentially smoothed average at the end of period t – 1 plus some fraction (
) of the
16
Exponential decay is defined for continuous time while geometric decay is only defined for discrete values. Exponential decay
is defined as y(t) = a exp(–βt), where t is a real number, and geometric decay is defined as y
n
= an
–b
, where n is an integer.
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exponential smoothing – exponential smoothing
The Encyclopedia of Operations Management Page 128
forecast error. The constant
(alpha) is called the smoothing constant and is in the range
0 1
. The
updating equation can be written as
1t t t
A
A E
, where the forecast error is E
t
= D
t
– F
t
and D
t
is the actual
demand (sales) in period t. The updating equation can be rewritten algebraically as
1 1 1 1
( ) (1 )
t t t t t t t t
A
A E A D A D A
.
This suggests that the new exponentially smoothed average is a linear combination of the new demand and
the old average. For example, when
0.1
, the new average is 10% of the new demand plus 90% of the old
average. The
1t
A
term in the above equation can be defined in terms of the average at the end of period
2t
,
i.e.,
1 1 2
(1 )
t t t
A
D A
. The equation for
2t
A
can be further expanded to show that
2
1 1 2
(1 ) (1 ) (1 ) (1 )
k
t t t t t t t k
A
D A D D D D
. This means that the
exponentially smoothed average at the end of period t is a weighted average with the weight for demand at lag k
of
(1 )
k
. For example, when
0.1
, the weight for the demand k = 5 periods ago is
5
0.1(1 0.1) 0.059
.
These weights decline geometrically as the time lag k increases, which suggests that exponential smoothing
should have been called geometric smoothing
17
.
Exponential smoothing with trend – When a trend component is included, the one-period-ahead forecast is
1t t t
F A T
, where
t
T
is the exponentially smoothed trend at the end of period t. The n-period-ahead forecast
is then
t n t t
F A nT
. The apparent trend in period t is the change in the exponentially smoothed average from
period t – 1 to period t (i.e.,
1t t
A
A
). The trend, therefore, can be smoothed in the same way as the average
demand was smoothed using the equation
1 1
( ) (1 )
t t t t
T A A T
, where
(beta) is the smoothing
constant for the trend with 0 1
. When a trend component is included, the updating equation for the
exponentially smoothed average should be
1 1
(1 )( )
t t t t
A
D A T
. Exponential smoothing with trend is
called double exponential smoothing because it is smoothing the difference between the smoothed averages.
Dampened trend – Gardner (2005) and others suggest that the trend be “dampened” (reduced) for multiple-
period-ahead forecasts because almost no trends continue forever. The dampened one-period-ahead forecasting
equation is then
1t t t
F
A T
, where 0 1
is the dampening factor. For n-period-head forecasts, the
equation is
1
n
k
t n t i
k
F A T
where 1
. The dampening parameter
can be estimated from historical data.
Exponential smoothing with seasonality – Exponential smoothing can be extended to handle seasonality by
using a multiplicative seasonal factor. The one-period-ahead forecast is the underlying average times the
seasonal factor (i.e.,
1t t
F A R
, where R is the multiplicative seasonal factor). The seasonal factors are
generally in the range (0.3, 3.0), indicating that the lowest demand period is about 30% of the average and the
highest demand period is about 300% of the average.
The one-period-ahead forecast is defined as
1 1t t t m
F A R
, where A
t
is the underlying deseasonalized
average and the multiplicative seasonal factor (
1t m
R
) inflates or deflates this average to adjust for seasonality.
The seasonal factor for the forecast in period t+1 has the subscript
1t m
to indicate that it was last updated m
periods ago, where m is the number of periods in a season. For example, m = 12 for monthly forecasts. The
seasonal factor for a forecast made in January 2012 for February 2012 uses the seasonal factor that was last
updated in February 2011. The n-period-ahead forecast with seasonality is then
t n t t n m
F A R
, where
t n
F
is
the forecast n periods ahead and
t n m
R
is the exponentially smoothed multiplicative seasonal factor for period
t + n.
17
The weights going back in time follow a geometric progression with initial value α and a common ratio of (1 − α). The sum of
the weights from lag 1 to infinity is 1.
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eXtensible Markup Language − extrusion
Page 129 The Encyclopedia of Operations Management
The updating equation for the deseasonalized smoothed average is
1
/ (1 )
t t t m t
A
D R A
. Multiplying
by
t n m
R
inflates or deflates for seasonality, so dividing by
t n m
R
deseasonalizes the demand. Therefore, the
term
/
t t m
D R
is the deseasonalized demand in period t. Seasonal factors can be smoothed using the equation
/ (1 )
t t t t m
R
D A R
, where
/
t t
D A
is the apparent deseasonalized demand in period t and
(gamma) is
the smoothing constant for seasonality with 0 1
.
Exponential smoothing with trend and seasonality – Exponential smoothing with trend and seasonality is
known as the Winters’ Model (Winters 1960), the Holt-Winters’ Model, and triple exponential smoothing. The
forecast equation is
( )
t n t t t n m
F A nT R
, where
t n
F
is the forecast n periods ahead, T
t
is the exponentially
smoothed trend at the end of period t, and
t n m
R
is the exponentially smoothed multiplicative seasonal factor for
period t + n. The equations for the Winters’ Model must be implemented in this order:
Update smoothed deseasonalized average
1
/ (1 )( )
t t t m t t
A
D R A T
Update smoothed trend
1 1
( ) (1 )
t t t t
T A A T
Update smoothed seasonal factor
/ (1 )
t t t t m
R
D A R
Make n-period-ahead forecast
( )
t n t t t m n
F A nT R
Forecasting lumpy demand – Exponential smoothing does a poor job of forecasting when the demand is
“lumpy” (i.e., has many zeros between demands). A good rule of thumb is that an item with a coefficient of
variation of demand greater than 1 has “lumpy” demand and therefore should not be forecasted with exponential
smoothing methods. Croston (1972) suggests a method for forecasting the time between “lumps” and the size of
the lumps, but few firms have used this approach. The best approach for lumpy demand is often to increase the
size of the time buckets to avoid zero demand periods.
See Box-Jenkins forecasting, centered moving average, coefficient of variation, Croston’s Method,
dampened trend, demand, demand filter, forecast error metrics, forecasting, geometric progression, lumpy
demand, Mean Absolute Percent Error (MAPE), moving average, Relative Absolute Error (RAE), reorder point,
seasonality, time bucket, time series forecasting, tracking signal, trend, weighted average.
eXtensible Markup Language – See XML.
external failure cost – See cost of quality.
external setup – Activities done to prepare a machine to run a new job while the machine is still running the
current job; also called an off-line or running setup.
Whereas external setups are done while a process is running, internal setups are done while a machine is idle.
Therefore, moving the setup time from internal to external reduces downtime for the process and reduces cycle
time for work in process.
See internal setup, lean sigma, setup, setup time reduction methods.
extranet – The use of Internet/Intranet technology to serve an extended enterprise, including defined sets of
customers or suppliers or other partners.
An extranet is typically behind a firewall, just as an intranet usually is, and closed to the public (a “closed
user group”), but is open to the selected partners, unlike a pure intranet. More loosely, the term may apply to
mixtures of open and closed networks.
See corporate portal, e-business, intranet.
extrinsic forecasting model – See forecasting.
extrusion – (1) A manufacturing process that makes items by forcing materials through a die. (2) An item made by
an extrusion process.
Materials that can be extruded include plastic, metal, ceramics, foods, and other materials. Many types of
products are created through an extrusion process, including tubes, pipes, noodles, and baby food. Most
materials that pass through extruders come out in a long uniform solid or hollow tubular shape. Extrusions often
require additional machining processes, such as cutting. The three types of extrusion processes are hot (850-
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fabrication – facility layout
The Encyclopedia of Operations Management Page 130
4000F, 454-2204C), warm (800-1800F, 427-982C), and cold at room temperature (70-75F, 21-24C). Cracking is
the most common problem with extrusion and can be caused by improper temperature, speed, or friction.
See manufacturing processes.
F
fabrication – A manufacturing process that transforms materials to make parts to go into an assembly.
Fabrication includes manufacturing processes (such as molding, machining, forming, or joining) and not
assembly. Small businesses that specialize in metal fabrication are called fab shops. A semiconductor
fabrication facility is called a wafer fab and makes integrated circuits (silicon chips).
See assembly line, manufacturing processes, respond to order (RTO).
facility layout – The physical organization of processes in a facility.
The layout of a factory should minimize the total cost of moving materials between workcenters. Some
processes must be located next to each other, while others cannot be located next to each other due to heat,
sound, or vibration. Other issues include cycle time, waste, space efficiency, communications, safety, security,
quality, maintenance, flexibility, customer waiting time, aesthetics for workers, and aesthetics for customers.
The layout problem is not unique to manufacturing. Service businesses, such as hospitals and banks, have
the same issues. Retailers use planograms to help layout retail stores. Similar layout problems are common in
other design contexts, such as laying out an integrated circuit. Experience engineering issues are important
when the customer contact is high.
The three basic types of facility layouts include the process layout (functional layout), the product layout,
and the fixed-position (project) layout. Each of these is discussed briefly below.
Process layout (functional layout) – A layout that groups similar activities together in departments or
workcenters according to the process or function that they perform. For example, all drills might be located
together in the drill workcenter. The process layout is generally used in operations that are required to serve a
wide variety of customer needs, so the equipment must serve many purposes and the workforce needs to be
highly skilled. Although process layouts offer high flexibility, they are relatively inefficient because of long
queues, long cycle times, and high materials handling costs. The best example of a process layout is a job shop.
The major concerns in a process layout are cycle times, utilization, order promising, routing, and scheduling.
Traditional industrial engineering approaches to improving a process layout include process analysis,
graphical methods, computer simulation, and computer optimization. CRAFT (Computerized Relative
Allocation of Facilities Technique) is a heuristic approach developed by Buffa, Armour, and Vollmann (1964)
that uses a heuristic (non-optimal) approach to solving a quadratic assignment formulation of the facility layout
problem.
Product layout – A product layout arranges activities in the sequence of steps required to manufacture or assemble a
product. Product layouts are suitable for mass production or repetitive operations in which demand is relatively
steady and volume is relatively high. Product layouts tend to be relatively efficient, but not very flexible. The
best example of a product layout is an assembly line. The major concern in a product layout is balancing the
line. In designing an assembly line, many tasks (elements) need to be assigned to workers, but these assignments
are constrained by the target cycle time for the product and precedence relationships between the tasks (i.e., some
tasks need to be done before others). The line balancing problem is to assign tasks to workstations to minimize
the number of workstations required while satisfying the cycle time and precedence constraints. Many
operations researchers have developed sophisticated mathematical computer models to solve this problem
18
. The
line balancing problem becomes less important when the organization can use cross-trained workers who can
move between stations as needed to maximize flow. Cellular manufacturing is a powerful approach for
converting some equipment in a process layout into a product layout for families of parts. Mixed model
assembly allows some firms to justify having product layouts that are not dedicated to a single product. See the
cellular manufacturing and mixed model assembly entries.
18
The webpage www.wiwi.uni-jena.de/Entscheidung/alb provides a video and an overview of the research on this problem.
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facility location − factorial
Page 131 The Encyclopedia of Operations Management
Fixed-position layout (project-layout) – A fixed-position layout is used in projects where the workers,
equipment, and materials go to the production site because the product is too large, fragile, or heavy to move.
This type of layout is also called a project layout because the work is usually organized around projects. The
equipment is often left on-site because it is too expensive to move frequently. Due to the nature of the work, the
workers in a fixed position layout are usually highly skilled. The most familiar examples of a fixed position
layout are the construction of building or house. However, a fixed position layout is also used in shipbuilding
and machine repair. The major concerns with a fixed-position layout are meeting the project requirements,
within budget, and within schedule. See the project management entry.
The Theory of Constraints literature suggests that the focus for all layouts should be on the bottleneck
process. See the Theory of Constraints (TOC) entry.
See 5S, assembly line, cellular manufacturing, continuous flow, CRAFT, cross-training, experience
engineering, flowshop, focused factory, job order costing, job shop, lean thinking, line balancing, mixed model
assembly, planogram, plant-within-a-plant, process design, process map, product-process matrix, project
management, spaghetti chart, Theory of Constraints (TOC), workcenter.
facility location – The physical site for a building.
The facility location problem is to find the best locations for the organization’s facilities (warehouses, stores,
factories, offices). The facility location problem is often defined in terms of minimizing the sum of the incoming
and outgoing transportation costs. In a retail context, the problem is often defined in terms of maximizing
revenue. In the service context, the problem is defined in terms of meeting some service criterion, such as
customer travel time or response time for customer needs.
Facility location theory suggests that the problem can be broken into finite and infinite set location models.
The finite set location models evaluate a limited number of locations and determine which one is best. The
infinite set location models find the best x-y coordinates (or latitudes and longitudes) for a site (or sites) that
minimize some mathematical objective function. The center-of-gravity and numeric-analytic location models are
infinite set location models. The gravity model for competitive retail store location and the Kepner-Tregoe
Model are finite set location models.
Some location models assume that vehicles can travel directly across any geography, while others assume
that vehicles are constrained to existing transportation networks. Some models assume that cost is proportional
to the distance or time traveled, whereas others include all relevant costs, including tariffs, duties, and tolls.
See center-of-gravity model for facility location, gravity model for competitive retail store location, great
circle distance, greenfield, Kepner-Tregoe Model, numeric-analytic location model, process design, supply chain
management, tariff, warehouse.
factor analysis – A multivariate statistical method used to reduce the number of variables in a dataset without
losing much information about the correlation structure between the variables.
Factor analysis originated in psychometrics and is used in behavioral sciences, social sciences, marketing,
operations management, and other applied sciences that deal with large datasets. Factor analysis describes the
variability of a number of observed variables in terms of a fewer number of unobserved variables called factors,
where the observed variables are linear combinations of the factors plus error terms. To use an extreme example,
a study measures people’s height in both inches and centimeters. These two variables have a correlation of 100%
and the analysis can be simplified by combining the variables into one “factor” without losing any information.
Factor analysis is related to Principal Component Analysis (PCA). Because PCA performs a variance-
maximizing rotation of the variable space, it takes into account all variability in the variables. In contrast, factor
analysis estimates how much of the variability is due to common factors. The two methods become essentially
equivalent if the error terms in the factor analysis model can be assumed to all have the same variance.
Cluster analysis and factor analysis are both data reduction methods. Given a dataset with rows that are
cases (e.g., respondents to a survey) and columns that are variables (e.g., questions on a survey), cluster analysis
groups cases into clusters and factor analysis groups variables into factors. Using the survey example, cluster
analysis groups similar respondents and factor analysis groups similar variables.
See cluster analysis, Principal Component Analysis (PCA).
factorial – A mathematical function denoted as n! and defined as the product of all positive integers less than or
equal to n; in other words, n! = n · (n – 1) · (n – 2) · · · 2 · 1.
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Fagan Defect-Free Process – Failure Mode and Effects Analysis (FMEA)
The Encyclopedia of Operations Management Page 132
The factorial function is important in many fields of mathematics and statistics. For example, a sequence of
items is called a permutation and a set of n items can be ordered in n! different ways (permutations). The
exclamation symbol (!) is called the “factorial” operator. Note that 0! = 1 is a special case. For example, 5! =
5·4·3·2·1 = 120. The factorial function is not defined for negative numbers. The definition of the factorial
function can be extended to non-integer arguments using the gamma function, where !
( 1)n n . The gamma
function entry covers this subject in more detail.
Most computers cannot accurately compute values over 170! In Excel, n! = FACT(n). Excel reports an
integer overflow error for FACT(n) when n ≥ 171. For large n, n! can be computed (approximately) in Excel
with EXP(GAMMALN(n + 1)) and the ratio of two large factorials can be computed approximately as !/ !m n
EXP(GAMMALN(m + 1) – GAMMALN(n + 1)).
See combinations, gamma function.
Fagan Defect-Free Process – A formal process for reviewing products that encourages continuous improvement.
Fagan (2001) created a method of reviews and inspections for “early detection” of problems in software
development while working at IBM. Fagan argues that the inspection process has two goals: (1) find and fix all
defects and (2) find and fix all defects in the process that created the defects. The process specifies specific roles
for a team of four people: Moderator, Reader, Author, and Tester.
See agile software development, early detection, New Product Development (NPD), scrum.
fail-safe – See error proofing.
Failure Mode and Effects Analysis (FMEA) – A process that identifies the possible causes of failures (failure
modes), scores them to create a risk priority number, and then mitigates risk starting with the most important
failure mode.
Failure Mode and Effects Analysis (FMEA) was invented by NASA early in the U.S. Apollo space program.
NASA created the tool to alleviate the stress between two conflicting mottos: “Failure is not an option” and
“Perfect is the enemy of good.” The first meant successfully completing the mission and returning the crew.
The second meant that failure of at least some components was unavoidable.
FMEA is a simple process that identifies the possible causes of failures (failure modes), scores them on three
dimensions (severity, occurrence, and detection) to create a risk priority number, and then mitigates risk starting
with the most important failure mode. The first step in FMEA is to identify all potential failure modes where a
failure might occur. Once these failure modes have been identified, FMEA then requires that each one be scored
on three dimensions: severity, occurrence, and detection. All three dimensions are scored on a 1 to 10 scale,
where 1 is low and 10 is high. These three scores are then multiplied to produce a Risk Priority Number
(RPN). The failure models can then be prioritized based on the RPNs and risk mitigation efforts can then be
designed for the more important failure modes.
Many organizations have found FMEA to be a powerful tool for helping them prioritize risk mitigation
efforts. At 3M and other firms, FMEA is a required tool for all lean sigma projects.
Whereas Root Cause Analysis (RCA) identifies contributors to an adverse event after the fact, FMEA is
intended to be a proactive (before the fact) tool. Ideally, FMEA anticipates all adverse events before they occur.
The scoring part of an FMEA requires the subjective evaluation of three dimensions for each failure mode,
where each dimension is scored on a 1 to 10 scale:
Severity – Impact of the failure. If failure occurred, what is the significance of the harm of this failure in terms
of cost, time, quality, customer satisfaction, etc.?
Occurrence – Frequency of occurrence. What is the probability that this failure will occur? (This is sometimes
called the probability of occurrence.)
Detection – Ability to detect the problem and avoid the impact. Can the failure be detected early enough that it
does not have a severe impact? (Important note: A 10 on detection means that it is hard to detect.)
Risk Priority Number (RPN) = (Severity) x (Occurrence) x (Detection)
It is easy to create an Excel workbook for FMEA. The following is a typical format. These actions usually
target the likelihood of occurrence, but should also seek to make detection easier and reduce severity. After
creating the workbook, the user can sort the rows by the RPN to prioritize the “actions to reduce risk.”
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family − Final Assembly Schedule (FAS)
Page 133 The Encyclopedia of Operations Management
Failure Model and Effects Analysis (FMEA) example
Process
step
Failure
mode
Failure
causes
Failure
effects
Severity
(1-10)
Occurrence
(1-10)
Detection
(1-10)
Risk
priority
number
(RPN)
Project/
actions to
reduce risk
Surgery
preparation
Wrong side
surgery
Chart is incorrect
Patient agony.
Law suits.
Brand damage.
9 2 3 45
Have second
MD check.
Surgery
close up
Surgical tool
left in
patient
Lack of attention
to tools
remaining in
patient
Patient agony.
Law suits.
Brand damage.
6 3 2 36
Have RN
count tools
before close
up.
Source: Professor Arthur V. Hill
See the Design Failure Mode and Effects Analysis (DFMEA) entry for information about how FMEA can be
applied to design.
See Business Continuity Management (BCM), causal map, critical path, Design Failure Mode and Effects
Analysis (DFMEA), error proofing, fault tree analysis, Hazard Analysis & Critical Point Control (HACCP),
impact wheel, lean sigma, operations performance metrics, Pareto Chart, Pareto’s Law, prevention, risk, risk
assessment, risk mitigation, robust, Root Cause Analysis (RCA), work simplification.
family – See product family.
FAS – See Final Assembly Schedule (FAS).
Fast Moving Consumer Goods (FMCG) – Any product sold in high volumes to end customers.
FMCG companies are firms that manufacture, distribute, or sell packaged consumer goods, food, hygiene
products, grocery items, cleaning supplies, paper products, toiletries, soft drinks, diapers, toys, pharmaceuticals,
and consumer electronics. These products are generally sold at low prices. FMCG seems to be synonymous
with consumer packaged goods. The opposite of FMCG (and consumer packaged goods) is durable goods.
Books and CDs are not FMCGs because consumers usually only buy them once.
See category captain, category management, consumer packaged goods, durable goods, Efficient Consumer
Response (ECR).
fast tracking – See critical path.
fault tree analysis – A graphical management tool for describing the cause and effect relationships that result in
major failures; a causal map usually used to identify and solve the causes for a specific actual historical problem.
Fault tree analysis is a causal map drawn from the top down. The actual historical fault or major failure
being analyzed is identified as the “top event.” All possible causes of the top event are identified in a tree. The
main distinquishing feature of a fault tree compared to other types of causal maps is the use of “OR” nodes for
independent causes and “AND” nodes for multiple causes that must exist concurrently for a failure to occur.
See causal map, error proofing, Failure Mode and Effects Analysis (FMEA), risk assessment, risk
mitigation, Root Cause Analysis (RCA), root cause tree.
faxban – A pull signal for a kanban system that is sent via fax.
Faxban uses fax communication rather than physical kanban cards to send a pull signal. Faxban can reduce
the delay between the time that pull is needed and the time that the pull signal is sent. An e-kanban is similar,
except that the signal is sent via e-mail, EDI, or through the Web.
See kanban.
FED-up model – See service quality.
field service – Repair and preventive maintenance activities performed by service technicians at the customer site.
Field service technicians (techs) often travel from their homes to customer sites to perform repairs. Techs
usually carry inventory in their vehicles and often replace this inventory from a central source of supply on a use-
one-order-one basis. Tech performance is often measured by average response time and customer satisfaction.
Service calls can be under a warranty agreement or can be for time and materials. Hill (1992) presented a number
of models for planning field service territories and tech truck stock inventory.
See response time, reverse logistics, Service Level Agreement (SLA), service parts.
FIFO – See First-In-First-Out (FIFO).
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fill rate – See service level.
Final Assembly Schedule (FAS) – A schedule for the respond to order (RTO) customer interface of a
manufacturing process.
For assemble to order (ATO), make to order (MTO), and RTO products, the Master Production Schedule
(MPS) is a materials plan for longer-leadtime materials, subassemblies, and components that are kept in
inventory until customer orders arrive. The MPS, therefore, is a statement of a longer-term plan for longer-
leadtime inventoried materials based on a demand forecast. MRP uses this MPS to create a detailed materials
plan that schedules the manufacturing and purchasing activities needed to support the materials plan for these
master-scheduled items.
In contrast, the Final Assembly Schedule (FAS) is a short-term (e.g., 1-2 week) materials plan based on
actual customer orders. The final assembly process might include assembly or other finishing operations, such as
adding accessories, labeling, and packing. The push-pull boundary separates items that are in the MPS and the
FAS. The Master Production Schedule (MPS) entry provides more information on this topic.
See assemble to order (ATO), bill of material (BOM), make to order (MTO), Master Production Schedule
(MPS), push-pull boundary.
financial performance metrics – Economic measures of success.
All managers need to have a good understanding of financial performance metrics to make good decisions
regarding capital investments, such as new plants and equipment, and also for process improvement projects. In
virtually all public organizations and in most privately owned organizations, the financial performance metrics
are the main goal. However, the operations performance metrics are often the “drivers” of the financial metrics.
The cross-references below include most of the financial metrics that managers need to know.
See asset turnover, balance sheet, Balanced Scorecard, break-even analysis, Compounded Annual Growth
Rate (CAGR), cost of goods sold, DuPont Analysis, EBITDA, Economic Value Added (EVA), goodwill, income
statement, Internal Rate of Return (IRR), Net Present Value (NPV), operations performance metrics, payback
period, performance management system, Return on Assets (ROA), Return on Capital Employed (ROCE), Return
on Investment (ROI), Return on Net Assets (RONA), sunk cost, total cost of ownership, Y-tree.
finished goods inventory – The inventory units (or dollars) that are “finished” (completed) and ready for shipment
or sale to a customer; sometimes abbreviated FG or FGI.
Other types of inventory include raw materials, Work-in-Process (WIP), Maintenance-Repair-Operations
(MRO), and pipeline (in-transit) inventory.
See Maintenance-Repair-Operations (MRO), Work-in-Process (WIP) inventory.
finite loading – See finite scheduling.
finite population queuing model – See queuing theory.
finite scheduling – Creating a sequence of activities with associated times so that no resource (person, machine,
tool, etc.) is assigned more work time than the time available.
The opposite of finite scheduling is infinite loading, which ignores capacity constraints when creating a
schedule. Hill and Sum (1993) developed the following terms for finite scheduling: A due-date-feasible
schedule satisfies all due date requirements for all tasks (orders or operations). A start-date-feasible schedule
does not have any tasks (orders or operations) scheduled before the current time. A capacity-feasible schedule
does not require any resource to work more time than is available in any period.
Most finite scheduling systems begin with the current due date and therefore will always create start-date
feasible schedules. However, if the capacity is insufficient, these systems will not be able to create due-date
feasible schedules.
In contrast, MRP systems plan backward from the due date and therefore always create due-date feasible
schedules. However, MRP systems are infinite loading systems and ignore capacity when creating a schedule,
which means they create schedules that are often not capacity feasible. MRP systems will “schedule” some
orders in the “past-due” time bucket, which means that the orders should have been started before the current
time. When MRP systems create orders in the “past-due” bucket, the schedule is not start-date feasible, but is
still “due-date” feasible.
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Page 135 The Encyclopedia of Operations Management
Although many ERP systems and project management tools have finite scheduling capabilities, few firms use
these tools. Most firms use infinite loading for both ERP and project management and then resolve resource
contention issues after the plan (schedule) has been created. Finite scheduling was one of the main needs that
motivated the development of Advanced Planning and Scheduling (APS) systems.
See Advanced Planning and Scheduling (APS), backward loading, closed-loop MRP, forward scheduling,
infinite loading, load, load leveling, Materials Requirements Planning (MRP), project management, time bucket.
firm order – In the manufacturing context, a customer order that has no uncertainty with respect to product
specifications, quantity, or due date; also called a customer order or firm customer order.
In contrast a “soft order” is a forecast or a planned order sometimes provided to suppliers via EDI.
See Electronic Data Interchange (EDI), Master Production Schedule (MPS), Materials Requirements
Planning (MRP).
firm planned order – A manufacturing order that is frozen in quantity and time and is not affected by the MRP
planning system.
Firm planned orders are created manually by planners and only planners can change the date or quantity for a
firm planned order. When an MRP system regenerates materials plans for all items, firm planned orders are not
changed. Firm planned orders give planners the ability to adjust schedules to handle material and capacity
problems. The order quantities in a master production schedule are often called firm planned orders. A firm
planned order is similar to, but not identical to, a firm customer order.
See buyer/planner, manufacturing order, Master Production Schedule (MPS), time fence.
first article inspection – The evaluation of the initial item in an order to confirm that it meets all specifications.
Approval to run additional parts is contingent on this first item meeting all specifications.
See inspection.
first mover advantage – The benefit sometimes gained by the first significant company to enter a new market.
Amazon is a good example of a firm that gained competitive advantage by being the first significant firm to
enter the online book market. Although other firms sold books on the Internet before Amazon, it was the first
firm to do so with appropriate systems and capitalization. Now that Amazon has established itself as the largest
Internet book retailer, it has the economies of scale to offer low transaction costs (through good information
systems and order fulfillment operations) and the economies of scope (and the network effect) to offer superior
value to publishers and authors. The same arguments can be made for eBay in the on-line auction market.
Of course, the first firm in a market is not always able to establish a long-term competitive advantage. Dell
Computer currently has the largest market share for personal computers, but many other firms, such as IBM,
Apple, and Compaq, entered this market long before Dell.
See market share, operations strategy.
first pass yield – See yield.
first pick ratio – Percentage of items successfully retrieved from the initial warehouse storage location
recommended on the pick list.
This is a performance measure for inventory accuracy and warehouse efficiency.
See operations performance metrics, picking.
First-In-First-Out (FIFO) – Using the arrival date as the priority for processing or as an accounting rule.
First-In-First-Out (FIFO) has several similar meanings:
Service priority – The customer who arrived first is serviced first. This is a common practice in walk-in
clinics and restaurants.
Production scheduling – The customer order that was received first is processed first. Most lean systems
use the FIFO rule. Although FIFO is the “fairest” rule, other dispatching rules often have better shop
performance in terms of the average time in system.
Stock rotation – The method of picking goods from inventory that have been in inventory for the longest
time.
Stock valuation – The method of valuing stocks that assumes that the oldest stock is consumed first and thus
issues are valued at the oldest price.
See dispatching rules, inventory valuation, Last-In-First-Out (LIFO), queuing theory, warehouse.