The Role of Overhaul in Reducing
Lifecycle Costs and Maximizing the Return
on Investments to Improve Reliability
William R. Killingsworth
Executive Director, MIT Forum for Supply Chain Innovation
Massachusetts Institute of Technology
77 Massachusetts Avenue, Room 1-176
Cambridge, Massachusetts 02139
billk@ mit.edu
Director, Office for Enterprise Innovation and Sustainability
University of Alabama in Huntsville
301 Sparkman Drive
Huntsville, Alabama 35899
256.824.4434
william.killingsworth@ uah.edu
Stephen M. Speciale
Nelson T. Martin
Office for Enterprise Innovation and Sustainability
University of Alabama in Huntsville
301 Sparkman Drive
Huntsville, Alabama 35899
256.824.2681
stephen.speciale@ uah.edu
martinn@ uah.edu
Abstract
Maintaining military aircraft in a high state of readiness requires a non-stop flow of spare
parts. These replacement parts can either be new parts from procurement or repaired
parts coming from overhaul. The cost of these replacement parts is a major component
of total lifecycle operating and sustainment costs. Improvements in reliability can
potentially reduce removals and these on-going costs. The overall cost reduction
depends upon the interaction over time of any increase in the cost of the new improved
part, the increase in reliability, changing demand levels and the role of overhaul. Three
overhaul scenarios are examined for cases of improved reliability: (i) old parts improved
in overhaul; (ii) old parts not improved in overhaul; and (iii) no overhaul. A system
dynamics supply chain model including financial performance metrics is developed to
investigate these scenarios through simulation. It is shown that all three scenarios
reduce total lifecycle costs and that these reductions can be very significant. The first
overhaul scenario is shown to have the greatest returns but the third scenario is only
slightly lower. All scenarios are shown to have diminishing investment returns and
share a common level of investment that maximizes the percentage reduction in
lifecycle costs.
This research was performed at the University of Alabama in Huntsville.
Introduction
Maintaining military aircraft in a high state of readiness requires a non-stop flow of spare
parts. Almost every part on an aircraft will be replaced, repaired and ultimately scrapped
at some point in time. When parts must be removed, there are two primary sources for
the replacement parts: new parts from procurement or repaired parts coming from
overhaul. The costs associated with the acquisition, overhaul, transportation and labor
to remove and install these parts are a significant part of a system’s total Operations &
Support (O&S) costs. Moreover, these O&S costs generally account for 70% to 80% of
total lifecycle costs, and, as a result, much attention has been directed recently towards
the reduction of O&S costs in Defense budgets (GAO-03-57). One important approach
for reducing O&S costs is to improve reliability. A part with higher reliability is replaced
less often, thus, reducing maintenance labor and the required flow of new and repaired
replacement parts. This reduction in the on-going supply of replacement parts
potentially, but not necessarily, reduces O&S costs. The overall cost impact depends
upon any increase in the cost of the new improved part, the increase in reliability and
the demand level. Even if costs are reduced, it may not be a sound business decision
depending upon the required investment. Business case analyses must answer the
questions: “What are the reductions in lifecycle costs arising from an investment in
reliability improvement and what are the return and payback time for the required
investment?”
Previous efforts (Forbes, McQueary) have investigated the likely payoffs in improved
reliability arising from investments to improve reliability. This research established a
log-log linear relationship between the percent improvement in reliability and the ratio of
the investment to the part cost. Killingsworth, Speciale and Martin have taken a next
step by estimating the cost reductions arising from the improved reliability and the
returns on investment. That research demonstrated that the returns generated by
investments to improve reliability for aviation parts depended upon the cost of the part,
the flight hours per month, and the investment level. Importantly, this prior research
assumed that in the overhaul process, the removed parts could be transformed into the
new design with improved reliability. This transformation, however, may not always be
possible. There are three basic feasible scenarios regarding the old parts and overhaul:
1. Older parts can be transformed during the overhaul process into the new design
with improved reliability. Thus, both the new parts coming from acquisition and
the parts coming from overhaul now both possess the improved reliability. This
was the assumption in the prior research.
2. The older parts cannot be transformed into the new parts but there is either
insufficient production capacity or funding to provide for all new parts. As a
result, the old parts go through overhaul and are re-issued but with the old level
of reliability.
3. The older parts cannot be transformed into the new parts but there is sufficient
funding and production capacity for new parts to make up for the lost overhaul
source. In this case, all old parts are scrapped. As a result, all parts being issued
are new and possess higher reliability.
The objective of this research is to investigate the impacts of these three alternative
overhaul scenarios on the returns generated by investments in reliability improvements.
As noted, many factors affect the potential return: the cost of the new part, cost of the
overhaul, operating hours, improvement in reliability, increase in cost arising from the
improvement, and the investment being made. Moreover, since most aviation system
life spans can exceed two decades, the analysis must address the dynamics of the
supply system over an extended time period. The analysis must capture the interplay of
the many factors over time.
Analytical Approach
System Dynamics is a well-suited tool for understanding the structure and dynamics of
complex supply chain systems. From its very beginning, System Dynamics has been
used to analyze supply chains as a modeling and simulation tool for policy analysis.
Forrester’s (1958) groundbreaking article in the Harvard Business Review demonstrated
fundamental supply chain dynamic behavior such as how small changes in retail sales
and promotional activity can lead to large swings in factory production, i.e., the so called
bullwhip or Forrester effect. Forrester’s model included factory, distribution, and retail
tiers in the supply chain, but no suppliers to the factories. In 2000, J ohn Sterman
expanded on Forrester’s supply chain models, including multiple-tiered suppliers linked
to the factories. Huang and Wang (2007) explored the bullwhip effects in a closed loop
supply chain system. Simchi-Levi (2008) and Lee (1997) addressed the bullwhip effects
from an analytical perspective on complex supply chains. Schroeter and Spengler
(2005) addressed the strategic management of spare parts in closed loop supply
chains. Angerhofer and Angelides (2000) presented an in-depth discussion of system
dynamics modeling in supply chain management. Killingsworth, Chavez, and Martin
(2008) analyzed the government ordering process within a system dynamics in two
forms: including the extended supply chain and excluding the extended supply chain.
Also, Killingsworth, Speciale, and Martin (2009) expanded on the DoD supply chain with
the impacts of improved reliability of total lifecycle costs.
The intent of the current research is to analyze the importance of overhaul
(maintenance and repair) over the life span of an aviation system. Incorporating
overhaul, total lifecycle costs, and improvements in reliability within a supply chain, a
system dynamics model can capture both the multitude of variables and the dynamics
of time. By using appropriate discount and inflation rates, cumulative and annual costs
are measured in relation to investment amounts to weigh the overall benefits for
reliability improvement and total overhaul costs within the government supply chain. By
modeling the three specific overhaul scenarios for aviation parts in the supply chain
over the system's life span, total costs (both constant and current dollars), benefit
investment ratios, and payback periods can be determined for comparison.
Model Description
An overview of the supply chain for high-value aviation parts is shown in Figure 1. This
diagram illustrates the flow of parts from new production and overhaul to the final
customer. Also shown is the reverse logistics path in which removed parts may be
returned to the overhaul facility for repair. The overall supply chain process is managed
in a feedback form by the government's ordering or requirements determination
process. (Killingsworth, Chavez, Martin, 2008) These algorithms are typically embedded
in a computerized process utilized by item managers, such as the Army’s Supply
Control Study. Based upon the calculated recommendations, repair action or
procurement action will be initiated. This process, or something similar, is used by most
government and defense supply chains for high-value parts. (Rosenman, 1964)
FIGURE 1: Overview of Supply Chain Model
Funding
‘Supply Control Demand Information
i Ordering Process SSE eee
H ¥ ¥ 1
leo
1 1 1 '
' H Leese ‘ Operation Hours __ ie
1 I & Reliability 1 4
| { 4
' 1 1 4
H Distribution H ei
1 Center for Regional Paria | Removals
ne Sarviceable Distribution on
! lnventary Center Aircraft
in Fiekd
L-->) Qverhaul ------ |
Retumed for Repair
—_—- INFORMATION/DATA FLOW
—— PARTS/MATERIALS FLOW
—— FUNDING FLOW
By monitoring levels of inventory, due-ins, due-outs, and historical demand levels, the
ordering process determines the recommended number of buys and repairs. The supply
of parts comes from three possible sources: production of new items, commercial
overhaul of worn or damaged parts, and government overhaul of worn or damaged
parts. Once the production or overhaul process is completed, parts are transferred to a
central distribution inventory. Geographical regions have an inventory of key spare
parts, and these inventories are replenished from the central distribution inventory. The
4
demand for a part is driven by the total number of installed parts, the monthly operating
hours, and the failure rate per part per operating hour, sometimes expressed as a mean
time between failure (MTBF). Removed parts that are excessively damaged and
deemed unfit for repair may be scrapped in the field and not returned. Most high-dollar
parts, however, are returned to a maintenance depot for evaluation. Those parts not
scrapped upon evaluation will then be sent either to a government or commercial
overhaul facility. Parts coming from both new production and overhaul are delivered to
the central inventory and are then available for shipment to the regional inventory
centers.
The primary external drivers for the Financial and Supply Chain Model are listed in
Figure 2.
Figure 2: Primary Demand Drivers for Supply Chain and Financial Model
Mean Time Between Failure (MTBF)
Monthly Flying Hours
Number of Aircraft
The mean time between failures is an engineering measure that specifies the amount of
time (measured in hours) the part can be in operation before it is expected to fail. For
this model, the initially assumed MBTF is 1,390 hours. This number is increased to
examine those cases with improved reliability. The monthly flying hours specifies the
hours the aircraft and associated part are assumed to operate per month. For this
analysis, fourteen (14) flying hours per month are assumed. The number of aircraft in
this case is 463 and there are three installed parts of interest on each aircraft. There
are thus 19,446 total monthly operating hours for the part of interest (14 flight hours per
month)(463 aircraft)(3 installed parts per aircraft). With 1,390 hours between failures,
the total average failures per month are fourteen. This is the basic demand level and
the number of removals each month.
The key assumed parameters within the model are presented in Figure 3.
Figure 3: Important Parameters for Supply Chain and Financial Model
Administrative Lead Time (ALT) Inflation Rate
Repair Lead Time (RLT) Discount Rate
Production Lead Time (PLT) Scrap Rate in Field
Cost of New Part Scrap Rate at Depot
Cost of Overhauled Part
Before the production/repair process begins, contracts must be approved at various
levels within the government; this elapsed time is known as administrative lead time
(ALT), and typically ranges from a few weeks to six months. For this model, ALT is
assumed to be one month. After contracts are in place, the repair or production process
begins. The elapsed time between the contract(s) approval and part delivery to the
government is called either the repair lead time (RLT) or production lead time (PLT).
For this model, an 11-month RLT and 22-month PLT are assumed. The cost of a new
part and cost of an overhauled part are the cost of a new part through procurement and
the cost of a part repaired through overhaul, respectively. For this model it is assumed
the cost of a new part is $250,000 and $187,500 for a repaired part. The inflation rate
allows for forecasted growth in the price of all future expenditures. This model assumes
a 6% inflation rate. The discount rate allows for analysis of a present value of expected
costs within the simulation. Simply, this measure calculates the present value of total
costs to be incurred in the future. A discount rate of 4% is used in this model. The
scrap rate in the field is the percentage of parts scrapped in the field and deemed
unserviceable and incapable of repair. For Scenarios 1 and 2, the scrap rate in the field
is 15%. For Scenario 3, the scrap rate in the field is 100%, meaning no parts are
returned to the depot for evaluation. The scrap rate at the depot is the percentage of
parts that are scrapped at the depot facility following evaluation. For Scenarios 1 and 2,
this percentage is 35%. As noted, in Scenario 3, no parts are returned for evaluation at
the depot.
The intent of this research is to determine how investments made in the supply chain for
improved reliability reduce total lifecycle costs under alternative overhaul scenarios. It
is assumed in the model that the investment occurs over a three year period that
includes design, manufacturing, test, and certification. Shorter or longer investment are
easily included and examined in the model structure.
The investment in reliability impacts the spending amounts for each year after the new
improved part is introduced, depending on the degree of reliability improvement for the
part and any changes in the unit cost of the part. It may very well be the case that the
improved part will have a higher production cost and the model enables the
investigation of tradeoff between improved reliability, higher unit cost, and reduced
demand.
The three overhaul scenarios are described in greater detail below:
1. In this first scenario, the older parts that are removed are transformed during the
overhaul process into the new design with improved reliability. Thus, new parts
coming from acquisition and also parts coming from overhaul now both possess
the improved reliability. This was the assumption in the prior research effort. For
this scenario, the scrap rate in the field is 15% and the scrap rate at the depot
facility is 35%. Figure 4 shows an overview of the model structure for this
scenario.
Figure 4: Overview of Model Structure for Scenario 1
Reliability of Older Parts Upgraded During the Overhaul Process
—
‘Supply Control |, ere ree
‘Ordering Process 7
t F i
1 i \
"sm"
i ion Hours:
! Inventory | ‘Rellabilty 1
Dent 1 ot
1 tot
1 tot
i tot
H vii
Distribution Regional Parts Removals
Centerfor |»! Distribution |—p| on —> Sermpped in
Serdosebie Contars Atroratt
Inventory
Retumed for Repair
ceeoe INFORMATIONIDATA FLOW
—— PARTSIMATERIALS FLOW
FUNDING FLOW
2.
In the second scenario, the older parts that are removed from the aircraft cannot
be transformed in overhaul to the new design with higher reliability. These older
design parts undergo overhaul but are returned to the supply system with the
prior reliability levels. Thus the population is a renewing mix of new and old
designs. The newly procured parts operate at the new and improved reliability
levels, but the old, refurbished parts will remain in operation at original reliability
levels. In this case, it takes much longer to reach overall improved reliability
levels and reduced demands. For this scenario the scrap rate in the 15% and the
scrap rate at the depot facility is 35%. Prior research has not examined this
scenario.
Figure 5: Overview of Model Structure for Scenario 2
Reliability of Older Parts Not Upgraded During the Overhaul Process
t
‘Supply Control Demand Information
Ordering Procesa tn nn nnn nana
+ F H
Hl 1 H
ed a
' —
H oan” | 3 Rellablty 4 H
|) _—i-»| ' Vit
i Hl 4
i ht
Distrtbution ae
Parts Removals
>| ‘cenertor |__| ppeteren |—s| on > Somppad in
‘Serviconbie Centers Aboraft Field
Inventory
—_——
J
Returned for Repair
Sialaeien INFORMATICNIDATA FLOW
——— PARTS/MATERIALS FLOW
FUNDING FLOW
3. In this scenario, the older parts cannot be transformed into the new improved
reliability part. This is similar to the assumption in Scenario 2. It is assumed,
however, in this scenario that there is sufficient funding and production capacity
for an increase in the production rate of new parts to make up for the overhaul.
In this case, all old parts are scrapped. A key assumption for this case is that the
supply chain has the capacity and resources to manufacture the new parts fast
enough to fill all orders and overcome the lack of overhaul supply. For this
scenario the scrap rate in the field is 100%, as all parts removed from the aircraft
will be replaced with only newly procured parts. Figure 6 presents the model
overview for this case in which there is no reverse logistics of old parts following
the introduction of the new part. Prior research has not examined this scenario.
Figure 6: Overview of Model Structure for Scenario 3
Only New Parts are Issued, No Overhaul Process
After Introduction of Improved, More Reliable New Part
Funding
ne Supply Control |, Demand Information
‘a Procurement Ordering Process i
' sete 7 OF aA H
1
I 1 i 1
a
1 1
1 : H Inventory | 4 — cae |
ic Dain | oe
i i Pit
| ! ' og
1 ! ' Ela
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t 1 Distribution
Lepl NewSpare }------- a Regional Parts Removals
>) Rem Seee [o-oo oo" | centertor |»! piattbution |—»| on > Scrapped
Upgraded Serviceable Centers Alrcreft
Inventory
Setaieiet INFORMATION/DATA FLOW
PARTS/MATERIALS FLOW
FUNDING FLOW
Financial Analysis and Supply Chain Behavior
Because of the strong tie between reliability and sustainment costs, the DoD Director of
Operational Test and Evaluation sponsored research to investigate the empirical
relationships between reliability investments, improvements in reliability and life-cycle
support costs. In this research, a preliminary relationship between investment in
reliability (normalized by average production unit cost) and achieved reliability
improvement was developed. This relationship is presented in Figure 7 and is taken
from a presentation delivered by Mr. Charles E. McQueary, Director, Operational Test
and Evaluation, Office of the Secretary of Defense.
Figure 7: Empirical Relationship Between Reliability Improvements
& Reliability Investments
Linear regression of In (Improvement Ratio) on In (Investment/APUC) gives: ZI APG E3 v1
y =.4718684 Log (Investment/APUC) — 1.0053 ad Ss
10- 5 etzent Confidence Interval = ee
8 me _-7 F100 bot
6 F a -
5 oo
4 2 “ “toaiesse~ * A-10 GPS
3 ie - ee _
aes
275 a
£ “CAT aera aetQ eee
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148 ~opcesin ; e
ost = - "OBIGES (Phedatey” “Apache Gyro
£ : _0isbal Hawk --
0.6 B Lee -
015 | nies BT ‘
0.4 a =
034 a ; 2.419
a = Investment = ini lids = XAPUC
0.3659
T T T T
0.6 0.8 4 2 3 4567849 20 30 405060 80499 200 -300.400.500700 1000 ©2000
Investment/ APUC
Source: Life Cycle Cost Savings by Improving Reliability, Dr. Charles E. McQueary
Director, Operational Test and Evaluation, J anuary 15, 2009
www.qw-itea.org/.../McQuearyGW-ITEAluncheonP resentation] an2009.ppt
As an illustrative data point on the graph of Figure 7, the research determined that the
Predator program invested a total cumulative amount of $39.1 million in reliability
investments over a nine year period. The ratio of this investment to the Average
Production Unit Cost (APUC) of $4.2 million is 9.3 and is the value of the x axis for the
Predator data point. The research also determined that the overall failure rate of the
Predator was reduced by 48.1 percent, resulting in an overall improvement in MTBF
from 40 hours in FY 98 to 77 hours in FY 06, or a 92.5 percent improvement in reliability.
This is the y-axis point for the Predator. The other data points on this graph reflect the
results of similar analysis.
For the analysis presented in this research, cases are evaluated for Investment/APUC
ratios of 20 (Case 1), 30 (Case 2), and 40 (Case 3). Fora part costing $250,000, these
cases require investments of $5, $7.5 and $10 million. Correspondingly, these
scenarios had reliability improvement ratios of 1.5, 2.0, and 2.25, respectively. The
improvement ratios of 1.5 (150%), 2.0 (200%), and 2.25 (225%) may be viewed as
generating percent reductions in failure rate per flight hour of 60% (Case 1), 66.7%
(Case 2), and 69.2% (Case 3). These three cases are used in analysis of the three
overhaul scenarios.
10
For each scenario, a base case projection is compared to several alternative cases that
include reductions in failure rate per flight hour of 60.0%, 66.7%, and 69.2%. Each case
has an associated investment amount for its specific improvement in reliability. Annual
spending is used to determine the payback ratio in years (break-even) and the total
benefit for the predetermined investment amount. Additionally for each case, the total
annual costs and benefit per defined investment are determined. These results indicate
those investment amounts that are most appropriate for achieving the greatest cost
savings and total benefit.
Overhaul Scenario 1: Old Parts Undergo Overhaul, are Upgraded in Overhaul and
are Re-Issued with New Improved Level of Reliability
For Scenario 1, overhauled parts are capable of being upgraded to the new level of
improved reliability. Once the improved part becomes available at the beginning of year
four, all parts being issued have improved reliability. For this scenario, three cases of
improved reliability are examined. Figures 8, 9, 10 and 11 present the simulation
results for the case with the greatest reliability improvement, a 69.2% reduction in
failures per flight hour. Figure 8 presents the recurring monthly demands for this case.
With constant flight hours, demand is constant at fourteen per month until the new parts
begin to be introduced. Each time an older part is removed, a part with improved
reliability is installed, and, as a result, the overall average meantime between failures
begins to decline reflecting the mix of new and old parts. After approximately eight
years, all of the parts are the improved parts with higher reliabillity and demand has
dropped to a new steady level. Since parts are lasting longer, fewer parts are
demanded. Figure 9 illustrates that when the total net assets drop below the
procurement reorder point, a procurement action is initiated ordering new parts. The
total net assets are calculated by summing the available wholesale inventories, the
items due in from procurement and repair processes, and subtracting the number of
items due out. As may be seen in Figure 9, there is an eight year period where total net
assests exceed the reorder point. This creates an eight year period where no parts are
ordered through new procuremen. This period of time with no new orders can be
explained by reviewing the graph of inventories presented in Figure 11. The growth in
inventories is largely the result of long time lags in the system. First, forecast demands
used to calculate the reorder point are often based upon a two year rolling average of
demand. Thus, as the improved parts begin to reduce demand, that reduction is only
slowly taken into account in the rolling average and the forecast. Secondly, the
production lead time is two years so a significant pipeline of production work in progress
exists. This pipeline empties into the supply system at the same time that demand is
falling and, as a result, inventories increase. With the growth in inventories, new
production is essentially halted for several years and only repaired parts are needed to
sustain operations. Figure 10 illustrates the completion rates for overaul and new
procurement. Both completion rates decline once the reliability improved parts are
introduced. The halt in new production is not a good thing and, in fact, could be quite
troublesome. The industrial base for many defense aviation parts is quite small and this
type of gap could well lead to a loss of suppliers. Integrated planning and careful
forecasting of inventories could prevent the excess supply and the cutback in orders.
11
Figure 8: Recurring Demands for Scenario 1
20
10
a
0 24 48 72 96 120 144 168 192 216 240
Months
Recurring Demands
Figure 9: Procurement Action for Scenario 1
400
200
O LUM Wee ne
0 24 48 72 96 120 144 168 192 216 240
Months
Total Net Assets
Procurement Reorder Point
Procurement Action
Figure 10: Overhaul and New Procurement Production Completion Rates
20
15
10
Total Overhaul Completion Rate
New Procurement Completion Rate
12
Figure 11: Inventories for Scenario 1
100
50
essai
0
0 24 48 72 96 120 144 168 192 216 240
Months
Serviceable Inventory
Available Inventory at Regions
Figure 12 presents the financial results for Scenario 1. This chart presents the current
dollar annual expenditures for the base case and three cases with improved reliability.
Note that the spend column for each case includes the investment being made over the
first three years in which the new part is developed. Thus, for Case 1, the negative
savings (expressed in current dollars) of $1.6, $1.8, and $1.8 million are equal to the
initial investment amount of $5 million. Once the improved part is introduced at the
beginning of year four, positive annual savings begin to accrue. For Cases 1, 2, and 3,
the cumulative lifetime savings are $655.6 million, $725.7 million and $752.0 million
respectively. It is important to note that these large savings are arising from an
investment in reliability for a single part with a monthly demand of fourteen (14) and a
cost of $250,000 per unit. This indicates the very large potential in lifecycle savings that
are possible from improved reliability. All of the breakeven payback points are between
3 and 3.4 years, a fairly rapid payoff for the investments. However, as may be seen in
the lower section of Figure 12, the ratio of benefits to investment has a much broader
range going from 131 for Case 1 to 97 for Case 2 and dropping to 75 for case 3 with the
highest investment of $10 million. This clearly indicates the very real potential for
diminishing returns on higher levels of investment.
It is important to note that in the three cases of improved reliability discussed above, the
cost of the improved part with higher reliability remains at $250,000. _ Killingsworth,
Speciale and Martin (2010) have examined the impacts on the financial benefits of
increases in part cost due to the new design. This analysis showed that such a cost
increase would reduce the savings and the benefit to investment ration, but under
reasonable assumptions for cost increase, the financial benefits remained very
attractive.
13
FIGURE 12: Financial Results for Scenario 1 - Cases Base, 1, 2, and 3
Current Dollars Annual Spending Amounts
($Millions)*
Percent Reduction in Failure Rate Per Flight Hour
Base Casel Case 2 Case 3
Year Spend Spend oe Z Spend Case é Spend Case 3
0% 60% avings 66.7% Savings 69.2% Savings
4 $43.4 $43.4 1 $0.0 $43.4 ' $0.0 $43.4 1 $0.0
5 $46.0 $45.9 | $0.1 $45.9 | $01 $45.9 | $01
6 $48.8 $44.7 1 $4.1 $44.1 ' $4.7 $43.9 ! $4.9
7 $51.8 $40.5 1 $113 $39.2 ' $12.6 $38.7 © $13.1
8 $55.1 $36.1 | $19.0 $34.1 | $21.0 $33.4 | $21.7
9 $58.5 $32.5 | $26.0 $28.2 1 $30.3 $28.7 | $29.8
10 $62.1 $29.1 1 $33.0 $25.4 ' $36.7 $24.2 1 $37.9
ae $65.9 $25.9 | $40.0 $211 | $44.8 $19.9 | $46.0
12 $70.0 $22.2 1 $47.8 $17.0 1 $53.0 $15.4 1! $54.6
13 $74.3 $18.8 1 $55.5 $13.6 ' $60.7 $11.6 1 $62.7
14 $78.9 $20.7. | $58.2 $15.3 | $63.6 $13.3. | $65.6
15 $83.8 $26.3! $57.5 $19.7 ! $64.1 $17.7 1! $66.1
16 $89.0 $32.0 1 $57.0 $24.3 ' $64.7 $21.6 1 $67.4
17 $94.5 $36.3. | $58.2 $29.2 : $65.3 $25.2 | $69.3
18 $100.3 $39.3 1 $61.0 $32.5 ! $67.8 $29.2 ' $71.1
19 $106.5 $42.4 1 $64.1 $35.3 ' $71.2 $32.3 1 $74.2
20 $113.1 $45.1 | $68.0 $40.0 | $73.1 $35.0 | $78.1
Cumulative $1,357.4 $701.8 ' $655.6 $631.7 =! $725.7 $605.4! $752.0
[ investment Over Three Year Period (Years 1-3)" |
esting Break-Even (Y ears) 3.08
ahem Break-E ven (Y ears) 3.21
sii Break-Even (Y ears) 3.38
Ratio of Benefits to Investment**
Case 1 $5 Million 131.0
Case 2 $7.5 Million 96.8
Case 3 $10 Million 75.2
*Annual spending amounts include investment spending during first three years
*B reakeven period is the time required to recapture the investment through savings after
the investment period
**Benefits are the total cumulative savings
14
Overhaul Scenario 2: Old Parts Undergo Overhaul But Are Not Upgraded in
Overhaul and Are Re-Issued with Old Reliability
For Scenario 2, it is assumed that the overhaul process cannot upgrade older parts to
perform at the same reliability levels as the new parts. It is assumed, moreover, that
there is neither the funding nor production capacity to do away with overhaul and only
supply new parts. As a consequence, at the beginning of year four, new parts with
improved reliability are being introduced into the system along with older parts coming
from overhaul that have the historical reliability level. Figures 13, 14, 15, and 16
present the simulation results for this overhaul scenario. Figure 13 illustrates the level
of recurring demands over time. Significantly different from Scenario 1, the level of
recurring demands requires over sixteen years to reach the new lower level. This is
because in Scenario 1, all parts used for replacements had the new higher level of
reliability. In contrast, in Scenario 2, many of the parts being used have the older level
of reliability. It thus takes longer for the changeover in the population. Figure 14
presents the procurement actions over time. As the new parts are introduced, the time
between orders lengthens and the order size becomes somewhat smaller. Similar to
Scenario 1, a lower total level of net assets is required over time, as more newly
improved parts enter the supply chain and demand drops. Figure 15 illustrates the total
overhaul and new procurement completion rates. For this scenario, both completion
rates decline correspondingly once the new parts are introduced. Lastly Figure 16
shows that inventory levels increase upon the introduction of the reliability improved
parts, and then decline for the remainder of the simulation. Importantly for this case,
with demand falling much more slowly, the inventories do not grow to the same extent
as in Scenario 1 and there is no period without orders for new parts as was seen in
Scenario 1. This is a positive development for the stability of the supplier base.
Figure 17 presents the financial results for Scenario 2 including the base case and
reliability improvement cases 1, 2 and 3. Note that the reliability improvements and the
investments are the same as in Scenario 1 yet total cumulative savings are much lower,
the payback years are higher, and the benefit to investment ratio is lower for all three
cases compared to Scenario 1. Recall that in Scenario 1, all older parts were upgraded
to the higher reliability design during the overhaul process. This means that starting in
year four of Scenario 1, all parts being supplied have higher reliability and demand
drops fairly quickly over a period of eight years. In contrast, in Scenario 2, the older
parts cannot be upgraded and after overhaul are returned to service with the lower
reliability. As a result, turnover of parts occurs more slowly and demand does not drop
quickly, requiring approximately sixteen years to reach the new lower level. As a result,
savings do not accrue so rapidly, and this scenario has lower financial returns and is not
as attractive as Scenario 1.
15
Figure 13: Recurring Demands for Scenario 2
20
15
10
5
0
0 24 48 72 96 120 144 168 192 216 240
Months
Recurring Demands
Figure 14: Procurement Action for Scenario 2
400
200
O POMEL pdt
0 24 48 72 96 120 144 168 192 216 240
Months
Total Net Assets
Procurement Reorder Point
Procurement Action
16
Figure 15: Overhaul and New Procurement Production Completion Rates
20
15
10
Total Overhaul Completion Rate
New Procurement Completion Rate
Figure 16: Inventories for Scenario 2
100
ee
CE ee ||
0
0 24 48 72 96 120 144 168 192 216 240
Months
Serviceable Inventory
Available Inventory at Regions
17
FIGURE 17: Financial Results for Scenario 2 - Cases Base, 1, 2, and 3
Current Dollars Annual Spending Amounts ($Millions)*
Percent Reduction in Failure Rate Per Flight Hour
Base Casel Case 2 Case 3
Year Spend Spend | owe Spend ae Spend | ene
0% 60% 66.7% 69.2%
4 $43.4 $43.4 I $0.0 $43.4 i $0.0 $43.4 $0.0
5 $46.0 $46.0 4 $0.0 $46.0 1 $0.0 $46.0 4 $0.0
6 $48.8 $47.0 | $18 $46.7 | $2.1 $46.6 | $2.2
7 $51.8 $46.2 4 $5.6 $45.6 u $6.2 $45.3 $6.5
8 $55.1 $45.7 4 $9.4 $44.7 1 $10.4 $44.3 1 $10.8
9 $58.5 $45.6 | $12.9 $441 | $14.4 $43.5 | $15.0
10 $62.1 $45.7 $16.4 $43.7 I $18.4 $43.0 1 $19.1
11 $65.9 $45.8 1 $20.1 $43.6 1 $22.3 $42.9 1 $23.0
12 $70.0 $46.0 | $24.0 $43.6 | $26.4 $42.6 | $27.4
13 $74.3 $46.3 \ $28.0 $42.9 ! $31.4 $41.6 ! $32.7
14 $78.9 $46.1 1 $32.8 $42.3 1 $36.6 $41.2 1 $37.7
15 $83.8 $45.6 | $38.2 $41.7 | $42.1 $39.8 | $44.0
16 $89.0 $45.4 '§ $43.6 $40.1 ' $48.9 $38.6 '§ $50.4
7 $94.5 $445 1 $50.0 $39.1 1 $55.4 $368 1 $57.7
18 $100.3 $43.3 | $57.0 $37.2 | $63.1 $34.6 | $65.7
19 $106.5 $42.3 4 $64.2 $34.9 ‘ $71.6 $32.3 1 $74.2
20 $113.1 $40.5 1 $72.6 $32.5 1 $80.6 $29.4 1 $83.7
Cumulative | $1,357.4 $886.0 | $471.4 $835.5 1 $521.9 $817.9 | $539.5
[investment Over Three Year Period (Years 1-3)" |
Case 1 Break-Even (Years) 3.57
$5 Million
Case 2 3.87
$7.5 Million Break-Even (Years)
Case 3
$10 Million Break-Even (Years) 42
Ration of Benefit to Investment **
Case 1 $5 Million 94.3
Case 2 $7.5 Million 69.6
Case 3 $10 Million 54.0
*Annual spending amounts include investment spending during first three years
*Breakeven period is the time required to recapture the investment through savings after
the investment period
**Benefits are the total cumulative savings
18
Overhaul Scenario 3: All Old Parts are Scrapped and Do Not Undergo Overhaul,
New Production Ramps up to Overcome Loss of Overhaul Supply Stream
In Scenario 3, it is assumed that all of the removed parts with the old design are
scrapped and do not undergo overhaul. A highly important assumption for this scenario
is that funding and production capacity are sufficient to do away with the overhaul of the
older design part. Thus, at the beginning of year four, only parts from new procurement
are introduced as replacements in the system. Over time, as these new parts are
removed, they are then returned for overhaul and maintain the new higher reliability
levels. However, it takes some time for those parts to begin appearing in the reverse
logistics flow. Figures 18, 19, 20, and 21 present the simulation results for Scenario 3.
Figure 18 illustrates the recurring demands level over time. Similar to Scenario 1, once
the improved reliability parts are introduced, the recurring demands decline over
approximately eight years as the part mix goes from all older parts to all newly designed
parts. The overall dynamics of this scenario are more complex than in the first two
scenarios. Figure 19 shows a decline in the procurement reorder point similar to that
seen in Scenario 1. This decline in the “target” reorder point is due to the reduced
demands arising from the more rapid introduction of improved parts than in Scenario 2.
Figures 19 and 20 show a substantial growth in new procurement since overhaul parts
are not used for some period of time. As in Scenario 1, the pipeline of improved parts
(ordered two years previously in an era of higher demand) enters the supply system as
demand is falling. As a result, as may be seen in Figure 21, there is a buildup of
inventories. This leads then to a period of time with no orders for new parts. On the
other hand, the total overhaul completion rate drops off completely for a few years
because the older parts are not undergoing overhaul. After roughly six years, a
sufficient number of new parts are being returned for overhaul and that overhaul
program largely supports the demands for a period of time. Lastly, note in Figure 21
that the serviceable inventory level drops significantly at the beginning of year four
because all of the old design parts are scrapped and production of new parts has not
ramped up as quickly. Because only newly improved parts are entering the supply
chain, there is a lag before the serviceable inventory begins to rise. The long lead times
and lag periods account for the difficulty in planning and the complex dynamics.
Figure 22 presents the financial results for Scenario 3 including the base case and
reliability improvement cases 1, 2 and 3. Note that the benefit to investment ratio is
similar to Scenario 1 but much better than Scenario 2. Recall that in Scenario 1, all
older parts were upgraded to the higher reliability design during the overhaul process.
This means that starting in year four of Scenario 1, all parts being supplied have higher
reliability and demand drops fairly quickly over a period of eight years. In Scenario 3, all
new parts are being introduced with a corresponding rapid drop in demand. The key
difference between Scenarios 1 and 3 is the higher cost of a new part compared to the
cost of an overhauled part. This difference creates slightly lower savings for Scenario 3.
Both Scenarios 1 and 3 have greater returns than Scenario 2 with the slow introduction
of parts with improved reliability.
19
Figure 18: Recurring Demands for Scenario 3
20
15
10
0
0 24 48 72 96 120 144 168 192 216 240
Months
Recurring Demands
Figure 19: Procurement Actions for Scenario 3
400
200
O LANNE LL vheberses
0 24 48 72 96 120 144 168 192 216 240
Months
Total Net Assets
Procurement Reorder Point
Procurement A ction
20
Figure 20: Overhaul and New Procurement Production Completion Rates
20
10
ov
0 24 48 72 96 120 144 168 192 216 240
Months
Total Overhaul Completion Rate
New Procurement Completion Rate
Figure 21: Inventories for Scenario 3
100
50
0
0 24 48 72 96 120 144 168 192 216 240
Months
Serviceable Inventory
Available Inventory at Regions
21
FIGURE 22: Financial Results for Scenario 3 Cases Base, 1, 2, and 3
Current Dollars Annual Spending Amounts ($Millions)*
Percent Reduction in Failure Rate Per Flight Hour
Base Casel Case 2 Case 3
Year Spend Spend cae 1 Spend &e 2 Spend ence 3
0% 60% ings 66.7% avings 69.2% avings
4 $40.9 $40.9 | $0.0 $40.9 | $0.0 $40.9 | $0.0
5 $37.6 $375 ' $0.1 $375 ' $0.1 $37.5 ' $0.1
6 $53.0 $51.1 1 $1.9 $50.9 1 $2.1 $50.8 4 $2.2
7 $63.2 $56.2 | $7.0 $55.2 | $8.0 $54.9 | $83
8 $71.2 $53.6 | $17.6 $51.5 | $19.7 $50.7 : $20.5
9 $78.3 $45.6 1 $32.7 $42.2 1 $36.1 $41.0 1 $37.3
10 $77.3 $36.4 | $40.9 $32.4 | $44.9 $30.9 | $46.4
11 $74.5 $27.7 1 $46.8 $22.5 4 $52.0 $20.5 $54.0
12 $75.9 $24.6 1 $51.3 $18.5 1 $57.4 $16.3 1 $59.6
13 $77.3 $19.8 | $57.5 $13.9 | $63.4 $12.0 | $65.3
14 $79.4 $206 ! $58.8 $15.1 ! $64.3 $13.2! $66.2
15 $82.5 $25.1 1 $57.4 $19.6 1 $62.9 $17.6 1 $64.9
16 $86.3 $30.7 | $55.6 $23.6 | $62.7 $21.6 | $64.7
17 $92.8 $35.9 1 $56.9 $27.9 1 $64.9 $24.5 1 $68.3
18 $98.5 $39.3 1 $59.2 $32.1 1 $66.4 $28.3. 1 $70.2
19 $104.8 $42.2 | $62.6 $35.3 | $69.5 $32.2 | $72.6
20 $112.0 $45.0 ' $67.0 $37.6 ' $744 $34.8 = ' $77.2
Cumulative $1,420.9 $752.8 + $668.1 $680.1 + $740.8 $653.7 *§ $767.2
Casel
$5 Million Break-Even (Years) 3.43
Case 2
$7.5 Million Break-Even (Y ears) 3.66
Case 3
$10 Million Break-Even (Years) 3.93
Ratio of Benefits to Investment**
Case 1 $5 Million 133.6
Case 2 $7.5 Million 98.8
Case 3 $10 Million 76.7
*Annual spending amounts include investment spending during first three years
*B reakeven period is the time required to recapture the investment through savings after
the investment period
**Benefits are the total cumulative savings
22
Sensitivity and Comparative Analysis
Figure 7 presented an empirically derived relationship between percentage
improvement in reliability and the ratio of investment to part cost. As indicated earlier,
this relationship was used to determine the reliability impacts on a part costing $250,000
arising from investments of $5, $7.5 and $10 million to improve reliability. From Figure
7, for a part costing $250,000 these investments would lead to reliability improvements
of 150%, 200% and 225%. These improvement levels were the alternative cases used
for the three overhaul scenarios. Figures 23, 24 and 25 present reduction in lifecycle
spending and the benefit to investment ration for a wide range of investment ratios,
going from zero to 800, that is, an investment 800 times the cost of the product. Results
are presented for the three overhaul scenarios. These charts present the investment
ratios, the resultant reliability improvement ratios, and from the simulation results, the
total cumulative spending, the percent reductions in lifecycle spending and the benefit
(savings) to investment ratios for both constant and current dollars.
FIGURE 23: Scenario 1
Old Parts Undergo Overhaul, are Upgraded in Overhaul and are Re-Issued with
New Improved Level of Reliability
Constant Dollars Current Dollars
Reliability | tots1 spend Percent Benefit/ Total Percent Benefit/
Investment) | Improvement | (<iniions)s+ |. Reduction in | Investment | Spend Reduction in| Investment
APUC Ratio* Spending (%)** | Ratio | ($millions) | Spending (%) Ratio
0 (Base) 0.0 $724 : : 1,357 : :
20 (Case 1) 15 $423 42% 60.2 $702 48% 131.0
50 25 $372 49% 28.2 $582 57% 62.0
100 35 $353 51% 148 $521 62% 33.4
200 5.0 $359 50% 73 $507 63% 17.0
400 7.0 $393 46% 33 $523 61% 8.3
800 9.0 $478 34% 12 $600 56% 38
FIGURE 24: Scenario 2
Old Parts Undergo Overhaul But Are Not Upgraded in Overhaul
and Are Re-Issued with Old Reliability
Constant Dollars Current Dollars
Reliability | total spend Percent Benefit/ Total Percent Benefit/
Investment | improvement | (<Tiigncye | Reduction in | Investment | Spend Reduction in| Investment
APUC Ratio* Spending (%)*** | Ratio | ($millions) | Spending (%) Ratio
0 (Base) 0.0 $724 : - 1,357 : -
20 (Case 1) 15 $516 29% 416 $886 35% 94.2
50 25 $483 33% 19.3 $798 41% 447
100 35 $474 35% 10.0 $757 44% 24.0
200 5.0 $484 33% 48 $750 45% 12.1
400 7.0 $521 28% 2.0 $772 43% 5.9
800 9.0 $610 16% 0.6 $855, 37% 25
23
FIGURE 25: Scenario 3
All Old Parts are Scrapped and Do Not Undergo Overhaul, New Production
Ramps up to Overcome Loss of Overhaul Supply Stream
Constant Dollars Current Dollars
Reliability | tots spend Percent Benefit/ Total Percent Benefit/
Investment! | Improvement | (<initions)s* |. Reduction in | Investment | Spend Reduction in| Investment
APUC Ratio* Spending (%)** | Ratio | ($millions) | Spending (%) Ratio
0 (Base) 0.0 $763 : : 1,421 : :
20 (Case 1) 15 $455 40% 61.6 $753 47% 133.6
50 25 $403 47% 28.8 $631 56% 63.2
100 35 $382 50% 15.2 $568 60% 34.1
200 5.0 $388 49% 75 $551 61% 174
400 7.0 $419 45% 34 $564 60% 8.6
800 9.0 $505 34% 13 $638 55% 3.9
*Derived from Figure 7
*Investment amount included in Total Spend amount
*+Calculated by dividing total benefit (savings) by Base Case Total Spend amount
***C alculated by dividing total benefit (savings) by investment amount
The results in Figures 23, 24 and 25 are presented as graphs in Figures 26 through 29.
Figures 26 and 27 clearly show that the maximum percent reduction in spending is
achieved at an investment ratio of approximately 100. Below this level of investment,
potential benefits are being left on the table. Above this ratio, diminishing returns are
evident in that the higher and higher investments are not generating the sufficient
benefits to overcome the large investments. Figures 28 and 29 present the return on
investment defined as the ratio of benefits (savings) to investment. As may be seen,
these returns fall off sharply as the investment increases. Note that these are benefit
ratios, not percentages, so that for an investment ratio of 100 the benefit ratio ranges
between ten and fifteen (using constant dollars) for a return percentage of 1,000% to
1,500%. The returns are so substantial because twenty year lifecycle spend reductions
are very large, ranging from $700 to $900 million for a single part that costs $250,000.
These results dramatically illustrate the benefits that are possible by improving
reliability.
24
FIGURE 26: Constant Dollars Percent Reduction in Spending as a Function of
Investment in Improved Reliability
60%
50%
40%
Percent 39%
Reduction
in Spending
(%) 20%
10%
0%
—Scenario 1
—™Scenario 2
0 200
400
Investment/APUC
600
800
te Scenario 3
FIGURE 27: Current Dollars Percent Reduction in Spending as a Function of
Investment in Improved Reliability
70%
60%
50%
Percent 40%
Reduction
in Spending30%
(%)
20%
10%
0%
Scenario 1
—®Scenario 2
800
_— —j—
—a
0 200 400 600
Investment/APUC
—te—Scenario 3
25
FIGURE 28: Constant Dollars Benefit/Investment Ratio as a Function of
Investment in Improved Reliability*
=—Scenario 1
Benefit/ 40.0: 9
Inv Ratio
Scenario 2
20.0 tS cenario 3
10.0
0.0
0 200 400 600 800
Investment/APUC
*NOTE: Scenario 1 and 3 overlap in the figure above.
FIGURE 29: Current Dollars Benefit/Investment Ratio as a Function of Investment
in Improved Reliability*
160.0
140.0
120.0
100.0
Benefit/ 0.0
: Scenario 1
Inv Ratio
60.0 + —t—Scenario 2
ate Scenario 3
40.0
20.0
0.0 : T T
0 200 400 600 800
Investment/APUC
*NOTE: Scenario 1 and 3 overlap in the figure above.
26
Conclusion
Maintaining military aircraft in a high state of readiness requires a non-stop flow of spare
parts. When parts must be removed, there are two primary sources for the replacement
parts: new parts from procurement or repaired parts coming from overhaul. The costs
associated with the acquisition, overhaul, transportation and labor to remove and install
these parts are a significant part of a system’s total Operations & Support (O&S) costs.
Moreover, these O&S costs generally account for 70% to 80% of total lifecycle costs,
and, as a result, much attention has been directed recently towards the reduction of
O&S costs in Defense budgets. One important approach for reducing O&S costs is to
improve reliability. A part with higher reliability is replaced less often, thus, reducing
maintenance labor and the required flow of new and repaired replacement parts. This
reduction in the on-going supply of replacement parts potentially, but not necessarily,
reduces O&S costs. The overall cost impact depends upon any increase in the cost of
the new improved part, the increase in reliability, the demand level and whether older
parts can be transformed to the new more reliable design through overhaul. Even if
costs are reduced, it may not be a sound business decision depending upon the
required investment. Business case analyses must answer the questions: “What are
the reductions in lifecycle costs arising from an investment in reliability improvement
and what are the return and payback time for the required investment and what role
does overhaul play in determining lifecycle returns?”
Three overhaul scenarios have been examined to evaluate the payback and returns
generated by investments to improve the reliability of certain aviation parts. These
scenarios are:
1. Older parts can be transformed during the overhaul process into the new design
with improved reliability. Thus, both the new parts coming from acquisition and
the parts coming from overhaul now both possess the improved reliability.
2. The older parts cannot be transformed into the new parts but there is either
insufficient production capacity or funding to provide for all new parts. As a
result, the old parts go through overhaul and are re-issued but with the old level
of reliability.
3. The older parts cannot be transformed into the new parts but there is sufficient
funding and production capacity for new parts to make up for the lost overhaul
source. In this case, all old parts are scrapped. As a result, all parts being issued
are new and possess higher reliability.
A system dynamics supply chain and financial model was developed to investigate
these scenarios through simulation. This model incorporates the requirements
determination process that controls many government supply chains in a feedback
fashion. It is shown using the model that all three scenarios reduce total lifecycle costs
and that these reductions can be very significant. The system dynamics supply chain
and financial simulation model demonstrates how these lifecycle cost reductions
depend upon the levels of reliability, investment amounts and the role of overhaul.
27
The lifecycle simulations show that the financial results are somewhat similar for
Scenarios 1 and 3. These scenarios are similar because, after year four, all parts being
issued have the improved reliability. It must be noted that Scenario 1 can be difficult to
implement because it assumes the older parts can be upgraded to the new improved
reliability level during overhaul. If design changes are significant this may not be
possible. Scenario 3 assumes all older parts are scrapped and that production capacity
can be increased to make up for the lost overhaul. Again, this scenario may be difficult
to implement either because of funding limitations or because of the production
constraints of the US industrial base. In general, overhaul Scenario 1 financially
outperforms the other two scenarios, although Scenario 3 is often very competitive.
Since in Scenario 3, all parts being issued are newly procured parts, the annual
spending amounts are higher based on the original assumption that new parts are more
expensive that repaired parts. Scenario 2 offers the lowest potential for cost savings
since old parts emerge from overhaul with the old level of reliability. This delays the
realization of lower demands and the financial benefits. In Scenarios 1 & 3 the
changeover between old and new parts in the population requires about eight years. In
Scenario 2, the changeover requires about sixteen years since older parts are re-issued
from overhaul with the old reliability level.
Although all scenarios have relatively quick payback ratios, Scenario 1 recaptures its
investment the quickest. Since Scenario 1 allows initial parts to be upgraded and
returned for service at the new reliability level, fewer funds are used to buy solely new
procured parts (Scenario 3). This unique capability allows the overhaul process of
Scenario 1 to endure greater cost savings immediately after the investment period as
compared to Scenario 3.
All scenarios illustrate that improvements in reliability can greatly reduce the total costs.
However, it is noted that the possibility for cost reductions cannot continue to increase
forever. Diminishing returns exist as the investment amount grows past a certain
amount. If the investment amount is too substantial in size, costs savings can still be
achieved but not in the most efficient form. O & S costs may be greatly decreased in
size, but excessive investment and production costs will counteract the main goal of
reducing total lifecycle costs. The analysis shows clearly that the maximum percent
reduction in spending is achieved at an investment ratio of approximately 100. Below
this level of investment, potential benefits are being left on the table. Above this ratio,
diminishing returns are evident in that the higher and higher investments are not
generating the sufficient benefits to overcome the large investments. It is shown that
returns fall off sharply as the investment increases. Note that these are benefit ratios,
not percentages, so that for an investment ratio of 100 the benefit ratio ranges between
ten and fifteen (using constant dollars) for a return percentage of 1,000% to 1,500%.
The returns are so substantial because twenty year lifecycle spend reductions are very
large, ranging from $700 to $900 million for a single part that costs $250,000. These
results dramatically illustrate the benefits that are possible by improving reliability and
the important role that overhaul plays in achieving these benefits.
28
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