|
Items in cart [1] |
Questions? Call 888-624-8373 |
| Copyright © 2009. National Academy of Sciences. All rights reserved. Terms of Use and Privacy Statement |
Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 27
IV. PERFORMANCE INDICES FOR ASSESSING INVENTORY
MANAGEMENT
4.1 INVENTORY MANAGEMENT PERFORMANCE OBJECTIVES
Inventors management theory defines two cor~icting objectives of inventor management:
minimize the amount of inventory and maximize (or maintain) the availability of inventory items. These
objectives are cor~icting since increasing inventory can have the effect of increasing availability and
reducing inventory can lead to a reduction in availability. The primary task of inventory management is
to effectively balance these two objectives so that inventory is available to sufficiently support the
demand for inventory items, while at the same time controlling the dollars tied up in inventory.
Inventory management performance indicators usually measure the performance of one of the
two primary objectives. For example, "total inventory dollars" measures the amount of inventory,
while the "percent of demand" that is filled measures the availability (or service level) provided by the
inventory. A third category of performance indicators measures the level of effort and cost required to
manage inventory. Minimizing the cost of managing inventory is also an objective of inventory
management. Examples of performance indicators for this category include the personnel cost per
inventory dollar of personnel assigned to inventory functions and the percent inventory carrying cost.
The fourth category of performance indicators measures the accuracy of inventory records, such as the
percent of items whose perpetual balance is incorrect.
Section 4.2 of this chapter defines the inventory management performance indicators that are
commonly used by the public transit industry, based on our survey results. In addition, Section 4.2
defines other useful performance indicators that can be derived Dom the survey data.
Section 4.3 of this chapter examines the effects of agency characteristics (such as the
population served, operating budget, and number of annual passenger miles) and fleet characteristics
(such as the number of foreign manufactured vehicles, the average age, etc.~. These characteristics can
be found in Sections ~ and ~ of the survey, respectively.
Section 4.4 presents the values of the performance indicators, based on the survey responses.
These values can be used for benchmarking by individual transit properties.
Section 4.5 compares the values of the performance indicators for the public transit industry
with the values nonnally found in other industries.
27
OCR for page 28
4.2 PUBLIC TRANSIT INVENTORY
INDICATORS
MANAGEMENT
4.2.1 Use of Inventory Management Performance Indicators -- Survey Response
PERFORMANCE
Our survey shows that most public transit properties use a small number of indicators to
monitor inventory performance. Moreover, many properties, particularly those with less than 50
vehicles, do not formally monitor inventory performance. These properties merely set minimum and
maximum levels for inventory items to control replenishment and address parts shortages as they occur.
The table below shows the inventor perfonnance indicators named in the survey and the percent of
survey respondents in each size category that use the indicators. The size of the properties is based on
the number of vehicles as follows:
Small Properties
Medium Properties
Large Properties
Vely Urge Properties
50 or fewer vehicles (3 ~ survey respondents)
51 - 300 vehicles (33 survey respondents)
301 - 2000 vehicles (15 survey respondents)
over 2000 vehicles (7 survey respondents)
__
. . . . . . . - ·. . . . · . . . - · . ;. . . . · . . .
......... . . Resonance lnolmton - . . bma 1 . MmIum: ~e: ~ ; very . . :Iotal ~ .
: . , .. . . .; ....... ...... .... .. . . . . ... ........ .. . ;. . . . ~ .; . ..
.,., ........ ,., . ,.,, . . . , . . , .:: ; . . . . .
Inventory Amount Indicators 19% 48% 47% 57 /o 41%
Toad Inventory Dollars ~19% T 39% ~27% T 57% ~31%
DoDars per Vehicle | 0°/O | 9% | 20% r 0% | 10%
Inventory Turnover | 0% | 9% ~7% | 29% | 7%
Availability/ServiceIndicators | 10% | 45% 1 47% | 86% 1 36%
% rhxnand Feed 1 0% ~3% 1 13% 1 57% 1 8%
Number of Stockouts 1 0% | 21% | 27% | 0% 1 13%
Number of Back Orders 1 60/0 T 210/0 ~27% ~860/0 ~22%
TirnetoF~Backorders | 3% | 0% | ~ 0% | 0% | 1%
Vehicles Out of Service | 0% | 6% | 7% | 14% | 5%
_
Inventory AccuracyIndicators ~32% ~73% T 60% ~86% r 57%
Dollar Vananoc ~10% 1 42% 1 27% 1 29% 1 27%
Item Vanance 1 23% 1 54% 1 47% 1 71% 1 43%
Management Cost Indicators | 0% | 9% | 7% | 0% | 5%
% Inventory Carrying Cost ~0% ~9% | 7% | 0% | 5%
. . . . . ..
28
OCR for page 29
As the above table shows, only 41% of the survey respondents use inventory amount indicators, 36%
use availability or seance level indicators, 57% use inventory accuracy indicators, and only 5% use
indicators for the cost of managing inventory. Many of the respondents stated that inventory
performance is tracked less formally, using annual persome1 perfonnance reviews and complaints or
comments Dom inventory "customers".
Although many of the survey respondents do not regularly track the above indicators, most
were able to provide values in response to the survey questions. The respondents also provided
information to calculate and examine additional indicators, such as the percent of obsolete inventory
and the number of inventory personnel per vehicle. Therefore, the survey provides sufficient
information to analyze and benchmark inventory performance indicators, even though some indicators
may not currently be in widespread use in the public transit industry.
4.2.2 Definition and Calculation of Performance Indicators
In some cases, the survey respondents defined or calculated perfom~ance indicators differently.
addition, there are some indicators that were not explicitly solicited in the survey, but can be
calculated Dom other survey data. This section presents standard definitions and calculation methods
for the inventory management perfonnance indicators and describes the inventory management
attributes that each measures.
Inventory Amount Indicators
Total Inventory Dollars "Total inventory dollars" is the total cost to the transit agency of ad
items held in inventor at a given point in time. It is calculated by
multiplying the number of units for each item times the item's unit cost,
and summing across ad items. This indicator measures the size of
inventory in terms of the dollars that the transit agency has tied-up in
inventory assets. It is best used to monitor changes In the size of
inventory (increases and/or decreases) by exaniLriing the value at
different points in time.
Inventory DolIa" per Inventory dollars per vehicle is the average amount of inventory dollars
Vehicle on-hand at a point in time to support a vehicle in the transit agency's
fleet. It is calculated by dividing the "total inventory dollars" by the
number of vehicles using items Dom the inventor. This indicator
measures the size of inventor, in dollars, that the transit agency holds
to support a vehicle. It eliminates the effect of fluctuations in Beet size
when monitoring inventory levels across time. It is also useful when
comparing the relative size of inventory across Beets with different
numbers of vehicles, different makes and models, different modes, etc.
Inventory Turnover Inventory turnover is the number of times the "total inventory dollars"
is used by inventory customers in a given period of time. For example,
29
OCR for page 30
annual turnover is calculated by dividing the total dollar value of the
items used Mom inventory dunna the year by the average total dollar
value or items held in inventor during the yew. We average tote
dollar value of items held In inventor for the year can be calculated by
taking the average of"total doDar inventor' levels measured at
different times during the year (for example, at the end of each month,
or the beginning and ending levels for the years. Turnover can be
calculated for any time period.
For example, monthly turnover is
calculated by dividing total monthly dokar usage bv the average tow
doLar inventory for the month.
---cat- - ~- -~- -- -
Inventory turnover indicates inventor size, In dollars, relative to the
amount of inventory that is used dunng a given time period. For
example, an annual inventory turnover of 2.5 means that the transit
agency uses two and a half times the amount of dolDars it holds In
inventory. In other words. inventory is "turned overt' 2.5 times dunna
the year. Since an objective of Inventory management Is to maze
Inventor levels, the higher the inventory turnover, the more efficiently
the inventory level is managed relative to the demand for inventory
items (usage). As an indicator, inventory turnover attempts to
compensate for the size of demand when mon~tonng inventory levels,
and is widely used to compare inventory performance across time and
between different organizations.
Note: The survey respondents provided inventory turnover values
based on a variety of time periods and average "total inventor
dollars". To ensure consistency, the inventory turnover was calculated
for each agency using the 1993 total inventory dollars and the average
monthly usage times 12.
Months on Hand "Months on hand" is the number of months that a transit agencies
inventory will last if no additional items are added to inventor.
Months on hand is the inverse of monthly inventory tumover. It is
another way to measure the size of inventory relative to the demand for
inventory over a specific tane period. It is calculated by dividing the
average "total inventory dollars', for a month by the total dollars used
- from inventor during the month. The fewer months that a transit
agency must keep on hand to support the demand for inventory items,
the better the perfonnance relative to minimizing inventory levels. This
indicator can also be calculated for different time periods, such as "days
on hand" or "years on hand".
Months on hand is a figurative rather than a literal indicator in that it is
a measure of how long an agency's inventory dollars grill last. This
measure assumes that the items on hand are exactly the items that will
30
OCR for page 31
be used during the time period. Like turnover, it attempts to
compensate for the effects of demand levels on the size of inventory.
Note: Sconce months on hand and inventory turnover are
mathematically the inverse of each other, only inventor turnover is
benchmarked and examined in this report. Any inferences regarding
turnover are also valid for months on hand, and benchmark values for
months on hand can be determined using the inverse of the benchmark
values for monthly turnover (or annual turnover divided by 12~.
AvailabilitY/Service Indicators
% Demand Filled (Fill The percent of demand fired, or the inventory fib rate, is the percent of
Rate) items requested from inventor,, that are provided from inventory at the
~ . _ ,, ~ ~
time of the request. It is calculated by dividing the total number of
items requested from inventor into the total number of items issued
from inventory at the time of request during a given time period. This
indicator measures the level of availability of inventory items. It also
defines the probability that an item will be available from inventory
when it is needed. The fill rate is used to monitor how well the items
held in inventory match the items that are needed over a given period
of time. It is also used to compare inventory management
performance, regarding availability, between organ cations.
Number of Stockouts The number of stockouts is the number of unanticipated times that
active inventory items reach a zero balance on hand during a specified
time period. This indicator measures the exposure of inventory to
potential unfilled requests. Only unanticipated stockouts are counted
because, at times, some items are carried at zero balance on a planned
basis (such as seasonal items or items that are ordered only on request).
Unanticipated stockouts are counted regardless of whether there is an
outstanding request for the item. The fill rate measures the ultimate
availability of inventory matenal, but the number of stockouts indicates
the degree to which fate is tempted.
% of Items Stocked The "percent of items stocked out" measures the percent of the total
Out number of inventory items (i.e. part numbers ) that reach a zero balance
during a Even Period of time. The total number of inventors nart
- ~ r~~
numbers are called "stockkeeping units" or SKUs. This indicator is
calculated by dividing the number of unanticipated stockouts during a
given period of time by the total number of SKUs held in inventory.
This indicator provides a measure of exposure to unfilled requests that
is relative to the size of the inventory, in SKUs. It provides a measure
that can be compared across time regardless of the number of items
31
OCR for page 32
added or removed Tom Inventory, or between organizations with
different numbers of SKUs in inventory.
Number of Open Back The "number of open backorders" is the number of unfilled requests for
Orde.rs inventory material that exist at a given point in time. It is calculated by
counting the number of inventory items that have been requested and
are currently unavailable Dom inventory. It is used to focus inventory
management activity and to monitor the status of the availability of
inventory items at a given point In time.
~· en e ae
Time to Fill
Backorder
A smear Indicator is the "average number of open back orders". This
Indicator measures the typical status of Inventory availability by
averaging the number of open back orders at several points in time.
The "time to fig backorders" is the average time it takes to provide an
Inventor item that is unavailable at the time of request. The time to fib
a backorder is the time period beginning with a request for an
unava~iable Inventory item and ending at the time the item is provided
to the requester. These instances are averaged over a period oft~me to
yield the "average time to fib backorders". This indicator measures
inventory management performance in resolving unavailable inventory
items. It can also be used to compare performance across
organizations.
Vehicles Out of Service "Vehicles Out of Service" is the number of times a vehicle is held out
of service due to unavailability of inventory items. A vehicle is counted
each tune it misses a service run, even if the same part is unavailable.
This indicator measures the eject of inventor availability on
transportation service provided by a public transit agency over a
specified period of time.
% of Fleet Out of The "percent of fleet out of service" is the percent of a transit agency's
Service Beet that is held out of service due to unavailable inventory items. It is
calculated by dividing the ``vehicles out of service,' for a service run by
the total number of service vehicles. This indicator can be averaged
over a period of time to provide the average percent of Beet out of
service. This indicator measures the effect of inventory availability on
transportation service, relative to the total fleet size. It can be used to
compare inventory performance across different fleet sizes and different
· -
orgamzatlons.
32
OCR for page 33
Inventory Accuracy Indicators
Dollar Vanance The "dollar vanance" is the difference between the "total inventory
dollars" based on a transit agency's inventory records (book value) and
the "total inventory dollars" based on a physical count of inventory
items. This indicator measures the effect of inventory accuranv bat
_ _ 41 _ _ _ _ . ~ ~t ~ . ~ ·
on one aggregate Sonar value the inventory. It teds how inaccurate an
agency's records are based on total inventory dollars, and is used to
adjust the book value of inventory.
Absolute Dollar The "absolute doDar vanance" is the sum of the doLar variances for
Variance each individual inventory item (SKU). It is calculated by summing the
absolute value of the difference between the dollar value of each item
based on inventory records and the dollar value based on a physical
count of the item. In using the absolute value, variances that are
negative and positive will not cancel each other out. This indicator
provides a more comprehensive picture of the accuracy of Individual
inventory item values and an overall measure of the accuracy of "total
inventory doLar,' records.
% Absolute Dollar The "percent absolute dollar variance" is the "absolute dollar variance"
Variance divided by the "total inventory dollars". It measures the overall
accuracy of inventory values relative to the size of inventory, in doLars.
~ . . .. . . . . .. .
1 IS indicator can be used to compare the overall accuracy of inventory
value between organizations or over time, regardless of the total value
of inventory.
Item Variance
The "item vanance" is the difference between the number of units on
hand of an individual item based on a transit agency's inventory records
and the number of units based on a physical count of the inventory
item. This indicator measures the accuracy of each item's perpetual
balance records, and is used to adjust the "quantity on hand" records
for each inventory item. This measure is not nonnaby summed to give
a variance for the total number of units, however it can be averaged to
give the average variance for an inventory item. This "average item
vanance" measures the average number of units that an item's physical
count varies from the inventory records.
Number of Items Out The "number of items out of balance" is the total number of inventory
of Balance items (SKUs) for which an "item variance" exists. It is determined by
counting the number of items for which the physical count does not
match the inventory records for quantity on hand. While the average
item variance measures the average size of the discrepancy between the
physical count and the inventory records, this indicator measures the
actual number of items that have a discrepancy.
33
OCR for page 34
% of Items Out of The "percent of items out of balance" is the number of items (SKUs)
Balance out of balance divided by the total number of items in inventory. It
measures the overall accuracy of perpetual inventory balances relative
to the size of inventory, in SKUs. This indicator can be used to
compare the overall accuracy of inventory balances between
orgar~tions or over tune, regardless of the number of items stocked
in inventory.
Management Cost Indicators
% Inventory Carrying The "percent inventory carrying cost" is the cost of maintaining
Cost inventory divided by the total dollar value of the inventory. The cost of
maintaining inventor includes the following components:
storage cost, the cost of storage space and equipment
insurance cost, the cost, if any, of Ensuing inventory
obsolescence, the cost of items that become obsolete
(e.g. due to changes In Beet series)
shrinkage, the cost of inventory items that become
missing, damaged, spoiled, decayed or otherwise
unusable
capital cost, the opportunity cost associated with
investing dollars in inventor rather in other assets
This indicator measures the "overhead" costs Involved In maintaining
inventory. One or more of the above components may be excluded
Dom the calculation if it does not apply. For example, in some cases
storage space is absorbed by other transit functions (such as vehicle
maintenance).
% ObsoleteInventory The "percent obsolete inventory?' is the cost of obsolete inventory
items divided by the total dollar inventory value. Although this
indicator is also a component of canying cost, many organizations
track it separately. Items may become obsolete to a transit agency due
to changes in the mix of vehicle series in the fleet, changes In parts
design, changes in part quality specifications, etc. If these items remain
in inventory, the transit agency will incur the cost of carrying items that
it cannot use. This indicator assists in measuring the degree to which
inventory management anticipates and reacts to changes in fleet mix
and parts storage requirements.
Inventory Dollars per "Inventory dollars per person" is the total inventory dollars divided by
Person the number of people with inventory management and control
34
OCR for page 35
responsibility. Inventory management and control personnel are the
entire inventory staff, including stores personnel, inventory planners,
clerical personnel, etc. This indicator provides a measure of Inventory
management and control staffing levels relative to the size of the
inventory, In doDars. It can be used to compare staffing levels across
different organizations.
Inventory Dollars to "Inventory dollars to personnel dollars" is the ratio of total inventory
PersonnelDoRars dollar value to the total cost (salary and fringe) of the personnel
charged with managing and controlling the inventory. This indicator
provides a measure of the cost of inventor management and control
personnel relative to the size of the inventory, in doldars, that is being
managed. It can be used to c~mnare Tiffing Alec Urn Ai~PrP"t
-
organizatio'L.
~;, ~__ _~_ _~
Inventory Transactions "Inventory transactions per person" is the average number of inventory
per Person transactions (issues, receipts, transfers, returns) per person for
individuals with inventory management and control responsibility. This
indicator measures the activity level of inventory material now relative
to the number of people in the inventory organization. It can be used
to compare relative workload of inventory personnel across different
organizations.
4.2.3 Inventory Performance Indicators Used for Benchmarks
Many of the inventory performance indicators defined above are absolute measures that are
best used to monitor a specific inventory over a period of time, e.g., total inventory dollars. Other
indicators measure performance relative to a standard factor, such as inventory dollars per vehicle.
These indicators better lend themselves to comparing performance between different inventories and
are more meaningfi~! as benchmarks. In addition, the survey data was unavailable or inconsistent for
some indicators, such as carrying cost and percent of fleet out of service for parts. Further, the survey
quantifies some indicators by mode (bus, rail, and other). Bus and rail responses were acceptable;
however. responses in the "other'' category were veal inr.On~iCtPnt ATOP `^ Honda A;~O ;-
, ~ ~ J ~ J ·~---I__- Am_ ~ Van me- ~ ~1 ~11~O ~1
. . . · . .
Interpretation by respondents. As a result, benchmark values for the following inventory management
performance indicators will be quantified.
1.
2.
3.
4.
5.
6.
7.
8.
Bus Inventory Dollars per Vehicle
Rail Inventory Dollars per Vehicle
Annual Bus Inventor Tumover
Annual Rail Inventory Turnover
Bus Percent Demand Filled Mill Rate)
Rail Percent Demand Filled (Fill Rate)
Percent of Items Stocked Out per Week
Average Days to Fill Bus Inventory Backorders
35
OCR for page 36
Average Days to Fib Rail Inventory Backorders
Percent of Items Out of Balance
Percent Obsolete Bus Inventory
Percent Obsolete Rail Inventory
Total Inventory Dollars per Person
Total Inventor Dollars to Personnel Dollars
Total Inventory Transactions per Person
4.3 TE1: EFFECT OF TRANSIT AGENCY AND FLEET ClIARACTERISlICS ON
PERFORMANCE INDICATORS
4.3.! Objectives for Analyzing Agency and Fleet Characteristics
Inventory management indicators can be used to monitor and evaluate inventory management
performance. Furthermore, benchmark values for the indicators can serve as a yardstick for comparing
inventory management performance between departments, organizations, and entire industries. As part
of this comparison, it is important to identify any effects that relate to the characteristics of the
organizations being compared. In particular, for comparing benchmark values between public transit
agencies, the characteristics of the public transit agency or the agency's fleet may have an impact on
inventory performance. For example, the population of the agency's service area, the number of
annual passenger miles, the percent of foreign manufactured vehicles, or the average age of the Beet
may have identifiable elects on inventory perfo~nance indicators. These effects should be taken into
account when using benchmarks.
The objectives of analyzing the effects of agency and Beet characteristics are to:
(~) identifier which characteristics, if any, affect which inventory management indicators;
(2) isolate and quantify the effects; and
(3) develop a decision modeling guide to assist a public transit agency In identifying the
appropriate benchmark values that pertain to the agency, based on the agenc,r's
characteristics and Beet profile.
For example, an agency with a Beet size greater than 1000 vehicles that serves urban and suburban
ridership may have different benchmark values for inventory turnover than an agency with less than 50
vehicles serving a suburban and rural area.
36
OCR for page 37
4.3.2 Methodology for Analyzing Agency and Fleet Characteristics
The data provided In survey Sections I and II, Agency Profile and Fleet Profile respectively,
were used for agency and fleet characteristics. Data from survey Section V, Inventory Management
Performance, were used to calculate values for inventory management indicators.
Two primary statistical methods were used for the analysis, based on whether the characteristic
being examined was numerical (e.g. annual passenger molest or divided into "categories" (e.g. rural,
suburban, urban, etc.~.
4.3.2. 1 Correlations for Numencal Data
The conception coeff-cienf and the associated coe~fcienf of determination were used to
analyze numerical data. The correlation coefficient (r) measures the degree to which variables vary
together. The value of r ranges from -1 for variables that are negatively correlated (as one variable
goes up, the other goes down), to O for variables that are not correlated at all, to +1 for variables that
are positively correlated. Values In between indicate the degree of correlation. The closer to +1 (or
1), the higher the correlation between the variables. The coefficient of determination (d) equals r2 and
measures the percent of variance in one variable that can be associated with variance In the correlated
variable.
For example, annual bus passenger miles can be correlated with bus inventory dollars per
vehicle to quantify the degree of the relationship between these two variables. The resulting value of r,
0.253, measures the degree of the relationship. Although the variables are positively correlated, the
degree of correlation is low, since the value of r is not close to I. The coefficient of detenrunation, d, is
rid, or .064. This indicates that only 6.4% of the variance in bus inventory dollars can be associated
with the variance In annual bus passenger miles. This correlation is not high enough to consider annual
bus passenger miles as a factor when monitoring bus inventory dollars per vehicle.
4.3.2.2 l-Tests for Category Data
Some sunrey questions asked the respondent to check a category, such as whether the
agency's service area was urban, suburban, rural, or some combination. In addition, some numerical
responses were grouped into categories indicating ranges, for example, service area population
between 500,001 md 1,000,000. Since the number of survey responses for most individual categories
was less than 30, the standard normal distribution could not be used to test the difference between
category averages. In addition, the number of responses in each category were different and do not
represent "paired" data. Therefore, the t statistic for small, unequal sample sizes was used to test
whether average values of inventory performance indicators are different for different response
categories.
The methodology for the t-test is to hypothesize that there is no difference in inventory
performance indicators between categories (null hypothesis), calculate the t statistic, and determine the
37
OCR for page 38
confidence level with which the nub hypothesis can be rejected. In other words, if the confidence level
is high enough (e.g. 90%), one can conclude that there is a difference In performance indicators for
different categones.
For example, the average bus inventory turnover for the 24 responding transit agencies serving
only urban areas is 2.00, with a standard deviation of 2.43. The average for the 1 5 responding agencies
that serve both urban and suburban areas is 2.62, with a standard deviation of 2.~. The nub
hypothesis is that there is no significant statistical difference between the category averages, 2.00 and
2.62. In other words, the nub hypothesis implies that the difference between the average bus inventory
turnover from the survey sample is due to chance and does not represent a real difference between
transit agencies. The resulting confidence level for rejecting the nud hypothesis indicated by the t-
statistic is 59.4%. Therefore, there is only 59.4% confidence that the difference between the sample
averages, 2.00 and 2.62, represents a real difference in bus inventory turnover. Conversely, there is a
40.6% confidence that the difference observed from the survey is due to chance. A minimum
confidence level for rejecting the null hypothesis is usually 90%. Therefore, based on this test, we
cannot conclude that there is a difference between bus inventory turnover for agencies serving only
urban areas and those serving both urban and suburban areas. Even though there is an observed
difference of 0.62 in the category averages, the large standard deviations indicate that there is enough
variance In both categories to prevent this difference Tom being statistically significant.
4.3.3 Effect of Transit Agency Characteristics on Inventory Performance Indicators
The following transit agency characteristics from survey Section I (Agency Profile) were
analyzed:
Service Area Characterized as Urban/Suburban/Rural
Service Area Population
Agency Operating Cost
Annual Material Purchases by Mode (Bus, Rail)
Annual Passenger Miles by Mode (Bus, Rail)
4.3.3. I Service Area - Urban/Suburban/Rural
The survey responses for urban/suburban/rural were analyzed four ways. First, l-tests were
conducted using each unique category of response. Then three sets of l-tests were conducted to
exaIT,ine the inclusion or exclusion of urban, suburban, and rural areas. The following categories were
used:
Category Set ~
Urban Only
Suburban Only
Rural Only
UrbarJSuburban
UrbanlRural
38
OCR for page 39
Suburbar~ral
Urban/Suburban/Rural
Category Set 2
Category Set 3
Category Set 4
Urban
No Urban
Suburban
No Suburban
Rural
No Rural
Average values ofthe inventory performance indicators listed in section 4.2.3 for each category
within a set were tested against others in the set. This resulted in 24 l-tests for each of 15 inventory
performance indicators, for a total of 360 t-tests. In order to conclude that the average values of the
inventory performance indicators were different between categories, a category had to have at least
three (3) responses and the t statistic confidence level had to be at least 90%. Based on these criteria,
only a few isolated categories have different average inventory performance indicators. The
performance indicators and the categories are listed below with the number of responses (in
parentheses) and the confidence level.
CategoIy Set 1:
Bus dollars/vehicle ~ Urban Only (31) and Suburban Only (5) - 98.7°/O
Bus dollars/vehicle-Urban Only (3 1) and Urban/Suburban/Rural (9)-94.3°/O
Bus dollars/vehicle-Suburban Only (5) and Urban/Suburban/Rural (9) - -99.7%
Bus dollars/vehicle Urban/Suburban (18) and Urban/Suburban/Rural (9) - 97.~%
% items out of balance-Urban Only (20) and Urban/Suburban (14) - 94.~%
InventoIy transactions/person-Urban Only (22) and Suburban Only (6) - 94.4%
CategoIy Set 2:
Category Set 3:
Bus % obsolete items - Urban (41) and No Urban (7) - 96.3%
Inventory dollars/person-Urban (57) ant! No Urban (a) - 92.6%
Inventory transactions/person-Urban (49) and No Urban ha) - 99.6%
Rail dollars/vehicle-Suburban (10) and No Suburban (3) - 90.~%
Stockout % of SKUs-Suburban (25) anti No Suburban (15) - 96.~%
% items out of balance ~ Suburban (27) and No Suburban (23) - 98.4%
39
OCR for page 40
Category Set 4:
Bus turnover -- Rural (13) and No Rural (42) -- 94.9°/0
Bus dollar~ve~cle -- Rural (13) and No Rural (53) -- 96.0%
In summary, ondy 14 of the 360 l-tests resulted In categories that had statistically significant differences
In perfo~ance indicators. Among the 14 categories, there was no consistent pattern. Although bus
dollars per vehicle was affected by five categories, the results were not sufficient to draw a conclusion.
4.3.3.2 Service Area - Population
Survey responses for each of the following service area population categories were analyzed
against the others for each ofthe 15 inventory performance indicators, resulting In 225 t-tests:
Over 1,000,000
500,001 - 1,000,000
200,001 - 500,000
100,001 - 200,000
50,001 - 100,000
Less than 50,000
Using the same cntena, a minimum of three responses per category and a 90% confidence
level, the following differences were found:
Bus dolIa~IveWcle - 2-500K (15) and 50-1OOK (6) - 92.6%
Bus % obsolete - >1~1 (14) and 1-200K (10) - 99.7%
Bus % obsolete - 50~100K (4) and 1-200K (10) -- 97.5%
% out of balance - 500k-1~! (7) and 1-200K (10) ~ 90.1%
Inventory dollardperson ~ >154~4 (21) and 2-500K (15) -- 95.3%
[nventor~v dollar~person -- >1~1 (21) and 1-200K (13) ~ 98.8%
[nventor~v dolIardperson -- >1~! (21) and SO-NOOK (4) -- 92.1%
Inventory transactiondperson -- 2-SOOK (13) and SO-NOOK (3) ~ 92.7%
Only ~ of the 225 l-tests resulted in statistically significant differences In perfonnance
indicators. Of these 8. inventory dollars per person appears to be different for agencies serving
populations over 1 million than for the other categories. However, upon closer exan~mat~on, this
difference was due to the fact that most properties with rail service are In this category. (As will be
shown later, raid properties have higher inventow levels.] When the analysis was corrected for the
eject of rail properties, there was no significant
, _ in, .
_ . as. difference due to the population area.
40
OCR for page 41
4.3.3.3 Agency Operating Cost
, ,
Survey responses for total transit agency operating cost serve as one measure of the size of a
transit agency. The total operating cost responses were correlated with each of the 15 inventory
performance indicators with the following results:
Number of Correlation Coefficient of
Variable Responses (n) Coefficient (r) Determination (r2)
Bus inventory turnover 52 .0747 .0056
Rail inventory turnover 13 .0578 .0033
Stockout % of SKUs 35 .1368 .0187
Bus inventory dolIars/vehicle 59 .0002 .0000
Rail inventory dodars/vehicle 12 -.2695 .0726
% items out of balance 47 -.0654 .0043
Bus % fill rate 46 .0756 .0057
Rail % fill rate 10 .3946 .1557
Bus % obsolete items 42 .0333 .1100
Rail % obsolete items 9 .5783 .3344
Bus days to fill backorders 42 -.0995 .0099
Rail days to fill backorders 6 -.2506 .0628
Inventory dollars per person 59 .2368 .0561
Person dolIars/Invento~y dollars 38 .0629 .0040
Transactions per person 52 .0599 .0036
The above correlation coefficients reveal that there is no significant correlation between agency
operating cost and inventory perfonnance indicators.
4.3.3.4 Material Purchases by Mode
Bus material purchases and rail material purchases were correlated with bus and rail inventory
~· · .
perfonnance Indicators with the toDow~ng results:
Number of Correlation Coefficient of
Variable ResponseS(n! Coefflcienttr! Deterrninationfr
Bus inventory turnover 44 .2912 .0848
Bus inventory dolIars/vehicle 52 .0043 .0000
Bus % fin rate 40 .1 157 .0134
Bus % obsolete items 37 .0150 .0002
Bus days to fig backorders 35 -.0687 .0047
41
OCR for page 42
Rail inventory turnover 10 .083 ~
Rail inventory dollars/vehicle ~ ~.01 19
Rail % fill rate 7 .2293
Rail % obsolete items 7 .9237
Rail days to fill backorders 4 -.421 ~
.0069
.0001
.0526
.8532
.1773
Only one inventory perfonnance indicator, the percent of obsolete items for rail inventory, has a
significant correlation with material purchases (r = .92~. The small sample size of seven reduces the
significance of this isolated result. (Removing one data point would reduce r to .74.) There is no
consistent pattern of correlation between material purchases and inventory performance indicators.
4.3.3.5 Annual Passenger Miles by Mode
Selected "Section 15" data was gathered using the survey for reference and comparison
purposes. The data most likely to affect inventory performance was the annual passenger miles. The
bus and rail annual passenger miles was correlated with bus and rail inventory performance indicators
with the following results:
Number of
Variable Responses (n)
Correlation Coefficient of
Coefficient (r) Determination (r2)
Bus Inventory turnover 42 .0697 .0049
Bus inventory doDars/vehicle 49 .2534 .0642
Bus % Fill rate 37 . ~ 198 .0143
Bus % obsolete items 3S .2840 .0806
Bus days to fill backorders 36 -.OSS3 .0078
Rail inventory turnover ~ ~-.0353 .0012
Rail inventory dollars/vehicle 12 -.2356 .0555
Rail % fill rate ~.3313 .1098
Rail % obsolete items ~.6682 .4465
Rail days to fib backorders ' 5 -.2819 .0795
The only indicator showing a correlation of any size is the percent of obsolete items for rail inventory (r
= .66). As with material purchases, this small sample size reduces the significance ofthis result. Based
on the above, there is no significant pattern of correlation between annual passenger miles and
inventor performance indicators.
4.3.3.6 Summary of Agency Profile Effects on Inventory Performance Indicators
The analysis of the effects of agency profile data (survey Section I) against the inventory
performance indicators shows only scant isolated effects with no consistent patterns. Only 22 out of
42
OCR for page 43
585 l-tests resulted in statistically significant differences in inventory performance indicators, and only 2
out of 35 correlations resulted in high correlation coefficients (.66 and .92). Furthermore, the
significant differences appear random and may be the result of chance. For example, at the 90%
confidence level used for the t-tests, one could expect that as many as 10% of the tests (58) could
randomly result in significant differences. Therefore' agency profile characteristics have no significant
effect on inventory performance indicators.
4.3.4 Effect of Fleet Profile Characteristics on Inventory Performance Indicators
The following fleet profile characteristics from survey Section ~ (Fleet Profile) were analyzed:
Service Mode (Bus versus Rail)
Fleet Size ~ Number of Vehicles
% of Fleet that is Manufactured in the US
Number of Different Vehicle Models
Average Vehicle Age -- Years
Average Vehicle Age ~ % of Expected Life
Average Annual Miles
4.3.4. 1 Differences Between Bus and Rail (Mode!
The average value of bus and rail inventory performance indicators were compared using t
tests with the following results:
Confidence Level
Perfonnance Indicator Bus Rail for Difference
Annualinventoryn~nover 1.74 0.71 99.998%
Inventory dollars per vehicle $5,027 $37,497 99.1%
°/0 fin rate 89.0% 86.1% 35.3%
%obsoleteinventory 9.2% 6.1% 74.8%
Days to fig backorders 16.4 2S.3 64.C9/o
As the results show, transit agencies carry significantly more inventory per vehicle to support rail
service than bus service. In addition, rail inventory is turned over significantly fewer times per year
than bus inventory. The differences in other inventory performance indicators are not statistically
significant at the 90% confidence level. However, the confidence levels are also not low enough to
conclude that there is not a difference.
43
OCR for page 44
4.3.4.2 The Effect of Fleet Size -- Number of Vehicles
Correlations were performed between the number of vehicles and inventory performance
indicators. Those indicators relating to bus and rail were correlated with the number of buses and rail
vehicles, respectively. The results were as follows:
Number of Correlation Coefficient of
Variable Responses (n! Coefficient fr) Determination (r2)
Bus inventory turnover 55 .1533 .0235
Bus Inventory doLar~ve~cle 66 .1231 .0152
Bus TO fig rate 52 .1182 .0140
Bus TO obsolete items 48 .2494 .0622
Bus days to fin backorders 48 -.0897 .0081
Rod inventory turnover 1 1 -.0557 .0031
Red Inventory doDar~veWcle 13 -.3015 .0909
Rod °/0 fig rate 9 .5429 .2948
Rod °/0 obsolete items 9 .5477 .3000
Rod days to Backorders 6 -.3401 .1157
Stockout % of SKUs 40 . ~ 117 .0125
°/0 items out of balance 50 -.0349 .0012
Inventory dollars per person 65 .2385 .0569
Person doDar~r~nventory dollars 40 .0534 .0029
Transactions per person 61 .1422 .0202
The above results show that there is no significant correlation between the number of vehicles and
inventory performance indicators. To test this conclusion further, separate correlations were run
between inventory performance indicators and subsets of the survey respondents based on the four
categories defined by number of vehicles:
Small Properties
Medium Properties
Large Properties
Very Large Properties
50 or fewer vehicles (3 ~ survey respondents)
51 - 300 vehicles (33 survey respondents)
301-2000 vehicles (IS survey respondents)
over 2000 vehicles (7 survey respondents)
In addition to correlations, l-tests were performed on the average values of inventory
performance indicators for each of these categories. The results were the same within these categories
as for the entire survey sample. There is no significant correlation between the number of vehicles and
inventory performance indicators within any of the above size categories. Furthermore, there is no
pattern of statistically significant differences between average inventory performance indicator values
for the above groups.
44
OCR for page 45
4.3.4.3 The Effect ofthe TO of Fleet that is Manufactured in the US
Correlations were performed between the percent of the agency's fleet that was manufactured
in the United States and the inventory performance indicators. Those indicators relating to bus and rail
were correlated with the percent of US manufactured buses and rail vehicles, respectively. All
correlation coefficients are below .35. Correlations were also performed for the four categories based
on number of vehicles with similar results. There is no significant correlation between the percent of
US manufactured vehicles and the inventory perfonnance indicators.
4.3.4.4 The Elect ofthe Number of Different Vehicle Models
Correlations were performed between the number of different vehicle models in an agency's
Beet and the inventory performance indicators. In addition, l-tests were run for the average value of
the indicators for each value of the number of vehicle models. Most of the correlation coefficients
were very low. Only two were over .35; the percent fill rate (.47) and the dollars per vehicle (.56) for
rail inventory. These coefficients are not high enough to be significant. In addition, the l-tests did not
show a statistically significant difference at the 90% confidence level between the number of models
and the inventory performance indicators. Therefore, there is no statistically significant relationship
between the number of vehicle models in an agency's fleet and the inventory performance indicators.
4.3.4.5 The Effect of Average Vehicle Age
The effect of the average age of a transit agency's fleet was examined separately for two
indicators of age; average years old and percent of expected life expended. In both cases, there were
no significant correlations between the average age of an agency's fleet and the inventory perfommance
indicators.
4.3.4.6 Average Annual Miles
Average annual miles was the final fleet profile characteristic that was examined. As with the
others, there was no significant correlation between average annual miles and inventory performance
Indicators.
4.3.4.7 Summary of Fleet Profile Effects
The results of the analysis of fleet profile characteristics (survey Section ~ against inventory
performance indicators are that fleet profile characteristics, except for mode, have no statistically
significant effects on inventory performance indicators. The difference between the bus and rail
inventory performance indicators for turnover and dollars per vehicle are significant at the 99%
confidence level.
45
OCR for page 46
4.3.5 Benchmark Decision Modeling Guide
The development of a benchmark decision modeling guide was contingent upon isolating the
relationships between agency and fleet profile characteristics and the inventory performance indicators.
The goal was to develop different benchmark values based on significant relationships. For example, if
inventory turnover were significantly correlated with agency operating cost, the benchmark value for
Hanover would depend on the agency's operating cost.
However, as the analyses summarized in this section show, there were no significant effects
between an agency's characteristics and fleet profile and the inventory performance indicators.
Therefore, there is no need for a benchmark decision modeling guide. The differences that were found
relating to mode (bus and rail) win be accommodated by using different benchmark values for bus and
rail.
4.4 BENCHMARK VALUES FOR PERFORMANCE INDICATORS
The benchmark values for the inventory performance indicators are based on the survey
responses. Rather than using the average value alone as the benchmark value, the following are
presented for each inventory performance indicator:
Mean - the average value
Median-the middle value (equal number of respondents above and below)
Maximum-the highest value
Minimum-the lowest value
20th percentile-the value greater than 20% of the responses
SOth percentile ~ the value greater than 80°/0 ofthe responses
~ Performance Indicator. ~ ~ ~..~ Crimean ~ ~:~ Median ~ Max i. ~ ~ ~. Min Act: ~ ~ ~: 20% :. : ~ ~ .~ 80% ~ : i- ~
_ . . . . . . _ . . .. :
Bus inventory turnover 1.74 1 1.43 7.36 0.13 0.75 2.54
~:
PI inventory turnover 0.71 ~0.56 1.43 0.29 0.51 0.99
Stockout % Of SKUs 1.52% 1 0.17% 20.0% .013% .047% 1.54%
1 1
Bus inventory Stvehicle S5,027 S4,604 S 15,384 S281 S2,566 S7,234
~1
PI inventory S/veWcle S37,498 S27,418 $ 139,286 S6,785 S 12,660 S47,688
1
/Oitems out ofbalance ~783% ~5.0% ~: 60% 11 ·005% 1~ 1.42% H 10%
us%fillrate 11 89.0% nH 95.0% 11 100% . 10% r 85% T 98%
1 1 .
Rai1%fillrate 1 86.1% 1 90.2% 1~ 100% 1 40% 1~ 84.4% 1 98.3% L
Bus % obsolete items 9.2% 5% 60% .01% 2% 13.8%
1 . .
Rail%obsoleteitems H 6.1% H 5% 11 20% 11 1/o 1 1.12% ~10%
46
OCR for page 47
~ , :. . . : . . , . . . . . 1 . ; . ; .: 1 .. :; . .; :: . : . : .
~ Penance indicator: ~ ~ 1l ~ ~Mea:n~-~1l~: ~Median:: 1l : ~ AM ~ ~i ~Mimi ~:~-1t 20%:~ ~- ~1l - -801% --of
Bus days to fill backorder 16.4 10 90 | 1 3 30
~ ~
Rail days to fill backorder ~25.3 ~18 ~56 ~1 ~14 ~45
Inventory S per person 11 S217,980 ~ ~S146,000 11 1,300,000 11 S32,418 11 S84,302 11 S250,5?8
Person S/Inventory S 11 SO.31 11 S0.25 11 S1.05 11 S0.05 ~SO.15 11 S0.44 ~
~798 _ 11 5 11 61.1 11 225.6 !
4.5 INVENTORY PERFORMANCE INDICATORS IN OTlIER INDUSTRY S
The following table shows innovation on inventor perfonnance indicators in other industries
as compared to transit. This information was extracted Dom the articles included in the literature
search conducted at the beginning ofthis study.
Electnc Utility Fleet Spare Parts
Beverage Distnbutor Fleet Spare Parts -- All
Beverage Distributor Fleet Spare Parts ~ Sof`Drink
Beverage Distnbutor Fleet Spare Parts-Beer
Retail Disinbution Center Product Inventor
American Airlines
Public Transit-Bus
Public Transit-Rail
Fleet Spare Parts Inventory (mixture of all fleet types)
I~25 vehicles
Fleet Spare Parts Inventory (mixture of all fleet types)
2~99 vehicles
Fleet Spare Parts Inventory (mixture of ad! fleet types)
100 or more vehicles
Annual Inventory
Tumover
3.3
6.1
7.8
3.4
52
1.74
0.71
2.7
Inventory Doldars
per Vehicle
$15,793
$13,271
$22,710
$1,800,000
$5,027
$37,498
$26,040 -$65,100
5.1 $10,010 -$38,1 15
5.7
$37,330
Maximum
The above chart compares public transit bus and rail annual inventory turnover and inventory dollars
per vehicle to the same indicators in other industries. The "fleet spare parts" data is from a survey of
Beet managers who manage a variety of public and private sector fleets. The annual turnover for a
47
OCR for page 48
ram dl~u110n Operas pawn Memos is not compile 10 Spay pans Memos but Is Include
far Salon. ~-se, 1be down per veDlcle far ~edc~ Edges Is Include.
In the new chapter me discuss the rel~lons~ps between pe~=ce measures Ad
o~a~z~loni1 profiles. Included are discussions of the appropd~e thresholds far mode
o~s~=lonal Lectures or Hang other Reps to lucrease account~dby Ad contact
48
Representative terms from entire chapter:
inventory performance
