Forecasting and Strategic Inventory Placement for GasTurbine Engine Aftermarket Spares

By

Joshua T. Simmons

Bachelor of Science in Mechanical EngineeringPurdue University, 1999

Submitted to the Department of Mechanical Engineering and the MITManagement on May 11, 2007 in Partial Fulfillment

of the Requirements for the Degrees of

Sloan School of

Master of Science in Mechanical Engineeringand

Master of Business Administration

In Conjunction with the Leaders For Manufacturing Program at theMassachusetts Institute of Technology

June 2007

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Department of Mechanical EngineeringMIT Sloan School of Management

May 2007

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Certified byXoy E. Welsch, Thesis Supervisor

Professor, Statistics & Management Science

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2

Forecasting and Strategic Inventory Placement for GasTurbine Engine Aftermarket Spares

Joshua T. Simmons

Submitted to the Department of Mechanical Engineering and the Sloan School ofManagement on May 11, 2007 in Partial Fulfillment

of the Requirements for the Degrees of

Master of Science in Mechanical Engineeringand

Master of Science in Management

In Conjunction with the Leaders for Manufacturing Program at theMassachusetts Institute of Technology

June 2007

ABSTRACT

This thesis addresses the problem of forecasting demand for Life Limited Parts (LLPs) in

the gas turbine engine aftermarket industry. It is based on work performed at Pratt &

Whitney, a major producer of turbine engines.

The author worked in the Global Materials Solutions program, Pratt & Whitney's latest

business venture, in which they will provide OEM quality materials for the CFM56

engine manufactured by CFM International. The new business venture required a

forecasting method that did not rely heavily on customer input or historic demand. A

forecast was developed using publicly available aircraft utilization history for the LLPs in

the aircraft. In addition, a methodology was proposed for the remaining parts that will be

studied in a future LFM internship. In addition, an inventory placement analysis was

completed for the GMS LLP supply chain.

Thesis Advisor:David E. Hardt, Ralph E. and Eloise F. Cross Professor of Mechanical Engineering

Thesis Advisor:Roy E. Welsch, Professor of Statistics and Management Science

3

ACKNOWLEDGEMENTS

I would like to thank the MIT Leaders For Manufacturing program for providing support

in this project and for the tremendous educational opportunity provided over the course

of two years as an LFM student.

In addition, I would like to thank the United Technologies Corporation and Pratt &

Whitney for proposing the research contained herein. Without the active support of

Larry Hosey and Jim Pennito, this project would not have been successful. I would also

like to acknowledge Tony Danburg and Maury Castanguay, the team of materials

controllers I worked most closely with, for their support of and interest in the project.

They made my time at Pratt & Whitney enjoyable and memorable.

Finally, I would like to thank my wife, Rachel, for her understanding of my time away

from home, encouragement in my studies, and my inspiration for hard work. Without

Rachel, none of this would be possible.

4

TABLE OF CONTENTS

ABSTRACT........................................................................................................ 3

ACKNOW LEDGEM ENTS................................................................................. 4

TABLE OF CONTENTS...................................................................................... 5

TABLE OF FIGURES........................................................................................ 6

TABLE OF TABLES .......................................................................................... 7

Chapter 1: Introduction ........................................................................................ 8

Chapter 1: Introduction........................................................................................ 8

Chapter 2: The Gas Turbine Engine Business ................................................... 10

2.1: The Business M odel............................................................................... 10

2.2 Life-Limited Parts................................................................................... 11

2.3 Comm ercial Airline Aircraft Utilization.................................................. 13

2.4: Strategic Perspective............................................................................. 15

Chapter 3: Forecasting ........................................................................................ 17

3.1: The Pratt & W hitney Forecasting Process............................................. 17

3.2: Gas Path Parts ........................................................................................ 20

3.3: Life Limited Parts ................................................................................. 23

Chapter 4: The Aircraft Utilization Based Forecast ........................................ 26

4.1: The Hypothesis ...................................................................................... 26

4.2 Application to the PW 2000 Fleet............................................................. 30

Chapter 5: GM S Forecast and Inventory Control............................................. 44

Chapter 6: Discussion, Conclusions, and Recomm endations........................... 58

6.1: General Recommendations ................................................................... 58

6.2: Project Specific Recommendations ...................................................... 60

BIBLIOGRAPHY ............................................................................................. 63

5

TABLE OF FIGURES

Figure 1: A nnual A ircraft Landings.................................................................. 14

Figure 2: Inputs to the forecasting process ...................................................... 18

Figure 3: Scrap rate as a Markov Process........................................................ 21

Figure 4: Hypothetical Demand Profile........................................................... 24

Figure 5: Demand From a Single Engine for a 15000 Cycle Part .................... 27

Figure 6: PW2000 3 rd Disk Monthly Demand History.................................... 28

Figure 7: Aircraft Accumulated Cycles .......................................................... 31

Figure 8: Aircraft S/N 23998 Utilization History............................................. 32

Figure 9: Life Limited Part Demand: Expected and Actual ............................. 35

Figure 10: Expected and Actual Demand with Forecast ................................. 37

Figure 11: 15000 Cycle Part Forecast Comparison.......................................... 38

Figure 12: 20000 Cycle Part Forecast Comparison ........................................... 39

Figure 13: 30000 Cycle Part Forecast Comparison ........................................... 39

Figure 14: Forecast and Business Case Volumes ............................................. 48

Figure 15: Scrap R ates ...................................................................................... 49

Figure 16: Forecast and Business Case Volumes with Error........................... 51

Figure 17: The Life Limited Part Supply Chain ............................................... 53

Figure 18: 4 th D isk Poissonness Plot................................................................ 55

Figure 19: MRP Simulation Results ............................................................... 56

Figure 20: Inventory vs. Fill Rate Trade Off.................................................. 57

6

TABLE OF TABLES

Table 1: Mean Absolute Percent Error at Various Stub Times ....................... 34

Table 2: Forecast A ccuracy ............................................................................ 40

Table 3: Stub Tim e Profiling Tool................................................................... 44

Table 4: Results of Inventory Placement Analysis .......................................... 54

7

Chapter 1: Introduction

Pratt & Whitney is a manufacturer of gas turbine engines for commercial and

military airliners. This thesis is largely concerned with Pratt's most recent business

venture, Global Material Solutions, in which Pratt will compete for the aftermarket gas

turbine engine components for rival's engine designs. For engines where Pratt is the

original equipment manufacturer (OEM), forecasts are driven largely by customer input

and demand history. For this new business, these traditional inputs are unavailable. Thus

a new forecasting method is required. This thesis presents a method for Life Limited Part

forecasting developed in cooperation with Pratt & Whitney as part of an MIT Sloan

Leaders For Manufacturing internship.

In Chapter 2, I will discuss Pratt's historic product and current market position. I

will expand and discuss the historic business model followed by the new business model

developed for the Global Material Solutions (GMS) business. In addition, I will

introduce the strategic concerns including discussion of the likely competitive response

of General Electric, the major OEM of the launch product for GMS. In Chapter 3, I will

introduce the problem experienced by GMS in forecasting demand for the new business

venture. I'll discuss Pratt's forecasting methodologies for current products and

demonstrate its lack of applicability for the GMS products. Finally, I will explain the

new forecasting methodology proposed for GMS.

Chapter 4 will discuss the application and validation of the proposed forecasting

methodology by applying it to spare components for which Pratt has owned the complete

market demand for the life of the product. In addition, the forecasts will be contrasted

with forecasts made by Pratt & Whitney for these parts, highlighting the benefits and

limitations of each method.8

Chapter 5 of this thesis will begin with the application of the new forecast method

to the parts planned for manufacture by GMS. Based on this forecast, an inventory

analysis will be completed consisting of optimization of service level, strategic inventory

placement, and finished goods target stock levels. Finally, Chapter 6 will conclude with

recommendations for GMS.

9

Chapter 2: The Gas Turbine Engine Business

2.1: The Business Model

Pratt & Whitney, a United Technologies Company, is a manufacturer of gas

turbine engines for commercial and military aviation applications. For the purposes of

this thesis, I will concentrate on the commercial side of the business. Customers for these

gas turbine engines include airliner manufacturers and airliner operators. The largest

base of customers is the airliner operator. When an operator purchases a new aircraft,

they are generally presented with several powerplant options by the aircraft manufacturer.

The operator then selects the engine that best suits their needs in terms of performance,

reliability, initial cost, and lifetime operating costs. This has led to the common practice

among the gas turbine engine manufactures of selling engines as a loss leader, earning the

bulk of their profits not from the original sale of engines but from aftennarket sales of

parts and service. Pratt & Whitney generates about 60% of revenue from the aftermarket

business (Chenevert 3)

This loss leader practice has been successful for Pratt & Whitney in the past.

Since 1964, more than 13,000 JT8D engines and derivates were manufactured and sold

(JT8D 1). These engines powered the Boeing 727, Boeing 737 - 100/200, Boeing MD-

80, and McDonnell Douglas DC-9 aircraft. In 2005, there were nearly 10,000 of these

engines still in service and generating revenue through the need for spare parts and

services.

In the early years of any engine program, the engine manufacturer essentially

owns a monopoly on the sale of spare materials for its designs. As the installed base

grows, those aftermarket revenues become attractive to low cost competitors who reverse

10

engineer various parts and begin to compete for the aftermarket business. Historically,

the bolts, brackets, nuts, tubing, and other relatively simple parts are those first copied.

New entrants have lower costs than the OEM as their overhead costs are significantly

lower due to much lower engineering and research and development expenses.

Expansion into more complex parts follows some time later.

2.2 Life-Limited Parts

All the parts in a gas turbine engine operated in the United States must be

approved by the Federal Aviation Administration. Most other countries have adopted the

same or similar standards as those set out by the FAA. Two major classifications of parts

have been defined for gas turbine engines subject to this oversight. These classifications

define whether a part must be replaced on a usage basis, measured in hours or flights, or

on a condition basis.

Life-Limited Parts (LLPs) are those parts in the engine that must be replaced on a

preventive maintenance schedule. The parts defined as LLPs are typically the large

rotating elements in the engine. These parts are subject to fatigue failures and have

potential to take down the aircraft if they were to fail while the engine is rotating. In June

of 2006, a high pressure turbine disk on the number one engine of a Boeing 767 ruptured

during routine maintenance on the tarmac at LAX. The aircraft experienced significant

damage from debris including ruptured fuel lines and fire damage. The level of damage

experienced by the aircraft due to failures of this class of parts motivates the requirement

for replacement as a preventive measure rather than based on engine performance or

monitored condition.

11

Non life limited parts are those that wear at predictable rates and have very low

probability of failing in the catastrophic manner. For instance, the most frequently

replaced parts in gas turbine engines are turbine blades. Blade failure is rarely through

fatigue and fracture. Rather, the blades suffer material loss from the high temperatures

experienced during engine operations. The wear rates are predictable and performance

loss is measurable. The loss of performance motivates replacement by the operators long

before performance degradation would affect the safety of flying the aircraft.

Preventive maintenance schedules for LLPs are detennined by the original engine

manufacturer and define the useful life of the part in terms of number of cycles, or

landings, allowable before replacement is required. Specifications also include an

acceptable number of operating hours on a part acceptable before replacement is

required. However, cycles are usually the limiting factor. The cycle limitation is

determined based on the stress experienced by the part during the highest thrust periods

of operation, takeoffs and landings.

The parts that have been reverse engineered by competitors are subject to the

same Federal Aviation Administration oversight as those of the engine manufacturer.

The responsible companies are issued a Parts Manufacturer Approval (PMA) certification

which controls both product design and production processes (PMA 1). This is compared

to the Type Certificate held by the OEM which fully describes the engine (Design

Approval 1). Prior to Pratt & Whitney Global Materials Solution decision to enter into

the manufacture of LLPs for competitors engines, parts manufacturers had only attempted

to gain approval to make non-LLPs such as airfoils, brackets, and tubing.

This decision to enter the PMA marketplace represents a significant strategic shift

for Pratt & Whitney and a major event in the commercial airliner powerplant industry. It

12

took the aviation industry by surprise when Pratt announced in February, 2006, their

intention to begin manufacturing PMA parts for the CFM56-3. The -3 is one the worlds

most popular turbofans, powering much of the worldwide fleet of Boeing 737 airliners.

Under a tacit agreement, no OEM had entered the PMA space. In fact, they had publicly

decried the existing PMA companies, claiming there parts where not held to the same

standards of testing and manufacturing as OEM parts. (Flint 2007)

2.3 Commercial Airline Aircraft Utilization

The various business models of the world's airlines have significant impact on the

potential future revenues for the OEM engine manufacturers. For life-limited parts, the

highest price and highest margin products in an engine, cycle utilization (number of

landings per day) drives the sale of replacement parts. Thus, an airline with a business

model that requires frequent short flights will use a larger number of LLPs than an

operator flying a similar plane on longer routes for the same amount of time each day.

Figure 1 depicts the distribution of several aircraft annual rate of cycle accumulation. If

we consider a 15000 cycle limit part, a typical preventive maintenance requirement for

many Pratt & Whitney parts, on a CFM56-3 in a Boeing 737 operated by Southwest

airlines, making 6 trips per day and accumulating 2300 cycles per year, and an A320

operated by United Airlines which accumulates only 4 flights per day, its clear that the

requirement for new parts will be 50% higher for the -3 engine. Additionally, Pratt's new

engine sales are for aircraft such as the A380 that will most likely be used for long haul

flight and average only 2 or 3 cycles per day. It will be 15 years or more before sales of

Life Limited Parts are realized for these engines. The decision to focus on the larger

13

engines for larger aircraft has resulted in a much longer delay between the sale of new

engines and the realization of revenues from the aftermarket.

Aircraft Annual Landings

2000-

18001760

16001553

1400

1200

1000 -

800 -

600 6 - ----- 7

400

200

0

737 A320 757 767 747

Aircraft Model

Figure 1: Annual Aircraft Landings

In addition to long delay between the sale of new engines and revenue from sale

of spares, Pratt & Whitney's legacy products with large fleets of existing engines such as

the JT8D are in the twilight phase of the product lifecycle. That is, a large number of

these engines have been retired, additional engines are retired on a regular basis, and

those engines that are in regular use are not utilized as heavily as their modem, more

efficient replacements. As a result, sale of spare parts and service to those engines is in

decline. Thus, Pratt's motivation for entering the PMA business is clear. While Pratt's

future sales of new engines look promising, particularly when one includes the

developmental geared turbofan, targeted to power the next generation of single-aisle

aircraft, revenues from new engines will not be realized for more than a decade. The

14

PMA business offers Pratt & Whitney the opportunity to enter a market with an existing

installed base, capitalizing on the success of it rivals and enhancing its service and

support business with internally developed parts. If successful, these revenues will fill

the gap in over the next decade when sales from legacy products are depressed and sales

from new engines have not yet materialized.

2.4: Strategic Perspective

From a strategic perspective, of particular interest is the competitive response

from the other original equipment manufacturers. Will they enter into the PMA business

as well? Or will they continue to disparage the practice? What is Pratt & Whitney's

competitive advantage in this business? Low cost? High quality of service? Pratt &

Whitney's management did not disclose the answers to these questions during the course

of this project. However, one can expect General Electric's response to include several

avenues:

1. Regulatory - GE will lobby the FAA to not approve Pratt & Whitney as

Parts Manufacturing Authority for Life Limited Parts on the CFM56

engines.

2. Market response - GE will attempt to tie up customers with long term

contracts to provide materials. They will argue they cannot continue to

offer warranty or guarantee performance if engines are assembled with Pratt

made parts

3. Competitive advantage - neither GE nor Pratt wants to compete on the basis

of price for this business. The impact of price competition will be to shift

value captured by the engine manufacturers to the airline operators. Instead,

15

GE will offer its parts as a package of service and materials, guaranteeing a

level of customer service to attract customers over Pratt's lower price points.

4. Respond in kind - It is unlikely that GE will engage in manufacturing parts

for Pratt & Whitney engines. The same lull in expected revenues that

motivated Pratt's entrance into this business will deter GE from responding

in kind. However, Pratt has now set a new course for the industry. By

entering this market as a PMA for Life Limited Parts, Pratt opens the door

for the PMA's who are not OEM's to begin working in this space. When

these competitors gain approval to begin manufacturing Life Limited Parts,

it will add another dimension to the competitive landscape. I believe that

this battle will be fought on a quality of service battlefield as more and more

airline operators outsource their maintenance activities to focus on their core

competencies, providing air transportation.

It is unclear what the outcome of this new competitive environment will be. If

Pratt and GE begin to compete on price for the aftermarket business, the loss leader

business model will suffer. If the companies are no longer able to get the expected return

from the development of new engines in the aftermarket, they will attempt to extract

more value in the service sector or in the original sale of the engine.

Chapter 3: Forecasting

3.1: The Pratt & Whitney Forecasting Process

Like many manufacturing organizations, Pratt & Whitney relies heavily on

demand forecasts in scheduling the production of spare parts. At Pratt, long lead times

for production of parts and relatively short lead times promised to customers results in a

particularly heavy dependency. Accordingly, Pratt & Whitney manufactures spare

materials to stock, according to a forecast, and carries a safety stock of inventory to

protect against forecast error.

Lead times in the aerospace industry have been increasing due to under capacity

in the supply chain for raw materials. Over the last decade, high demand for raw

materials such as steel, aluminum, and titanium from the developing world as well as

traditional industries in the west has overwhelmed the bottlenecks in the raw material

supply chain (Pinkham 3). This results in long waits at the mills for orders of alloys and

is exacerbated by the high degree of specification required for gas turbine engine parts.

At Pratt & Whitney, these delays have resulted in typical spares inventory material lead

times increasing from 60 to 90 to 120 days. Lead times of more than six months are not

unusual in the aerospace industry. For the Life Limited Parts on the CFM56-3, the GMS

programs first engine, estimated lead times based on vendor quotes and current state of

the art processing at Pratt & Whitney's manufacturing facilities average more than 400

days.

While lead times for raw materials and the production of components has been

increasing, quoted lead times to customers has not. Pratt & Whitney's standard promise

to customers is seven days. This combination of quick delivery requirements with a slow

17

supply chain has placed considerable burden on the Spares Organization at Pratt to

increase the accuracy of their forecasts to avoid increasing inventory levels or decreasing

service levels. Additionally, as with many manufacturing organizations, there is pressure

to continually reduce the amount of inventory carried.

Most forecasting processes at Pratt & Whitney begin with the Shop Visit

Forecast. In a sense, this is the master forecast, from which all other forecasts are

derived. It is measured in engine visits to repair facilities per unit time. Of typical

interest is the number of visits expected over the next year and the expected distribution

of those visits across the year.

Inputs to the Forecasting Process

Historic DemandIndustry

Fuel Prices

World Events

Chapter 5 Lives

Aircraft Utilization

Statistical Analysis and Business Judgment

Customer DataOverhaul Visits

Scrap RatesBuild StandarRoute Changes

New RepairsBudget/Financials

Figure 2: Inputs to the forecasting process'

Figure 2: Inputs to the forecasting process, was created by Pratt & Whitney

18

A.A

Shop visit forecasts are developed with several inputs but key among them is

surveys of customers. Controllers in the spares organization spend much of their time

communicating with customers, obtaining data from maintenance and operations on

which aircraft engines are expected to be removed for overhaul over the next time period.

This is coupled with Pratt's own knowledge of the fleet including total number of engines

on wing, new service bulletins specifying redesigned parts, and the financial state of their

customers, to estimate the number and timing of visits over some future period. From

these inputs, a rate of engine inductions over the next 18 months is projected that includes

the number and intensity of engine overhauls expected.

Generally, the shop visit forecast is fairly accurate for mature fleets; mature fleets

being those with a large installed base and a comparatively low rate of new engines being

manufactured. The military spares organization claims an accuracy of within two engine

inductions per month for the Fl 17, the military version of the PW2000. While the

specific engines that are serviced may not match exactly to those that arrive, the quantity

of engines services is accurate.

Pratt uses the Shop Visit Forecast (SVF) as the basis for part level forecasts by

applying a Scrap Ratio. The Scrap Ratio is the fraction of engines serviced that are

expected to require that part. For example, the SVF for PW2000 engines in 2005 was

298 engines. The historic Scrap Ratio for part number 1A8707, a compressor blade, is

0.315. Thus the Pratt forecast for part number 1A8707 for 2005 is 298 x 0.315 = 94

blades.

Clearly there are two sources of error in this forecast. First is the error in the

SVF. While the military spares organization claims a high degree of accuracy, the data

tells another story. In 2005, there were 391 actual inductions. The forecast was 24%

19

low. Unfortunately, at the time of this project, Pratt did not maintain a record of historic

forecasts with which to do further analysis. Their practice is to update forecasts

regularly, effectively erasing the previous data points. In light of this project, a new

policy of capturing a snapshot each month of the forecasts and maintaining that record

has been established for future work.

3.2: Gas Path Parts

The second source of error is the scrap ratio. Standard practice is to use the actual

scrap rate from the prior year. This historic scrap rate is assumed to be constant.

However, interviews with representatives from the spares organization and the Cheshire

Engine Center reveal that this is not actually the case. The scrap rate evolution they

described is a Markov process.

Consider a ring of 12 identical airfoils in a hypothetical engine. The airfoils are

not required to be replaced on a time basis but have standards for replacement and repair

based on condition. The engine is released new into the field and has 12 new airfoils.

After 3000 flights, a typical service interval for a PW2000, the engine is brought in for its

regularly scheduled inspection service. Three of the 12 airfoils are determined to be in

unrepairable condition due to excessive material loss, five are sent out for refurbishment,

and the remaining four are deemed fit for continued service. The scrap rate for this first

shop visit as Pratt & Whitney measures it is 0.250 scrapped parts per visit. Returning to

the field are 3 new pieces, and 8 refurbished or reused pieces.

The engine returns to the field and is flown an additional 3000 flights and before

another regularly scheduled inspection. Of the 3 new pieces, one is replaced and two are

reused. Of the 9 2"n run pieces, 4 are refurbished and 5 are replaced resulting in 6 new

20

pieces, 2 2 "d run pieces and 4 3 d run pieces returning to the field. As per Pratt's

measurements, the scrap ratio for this visit is 50%. We can continue this example and

find that the scrap rates changes based on the status of the engine.

This probability chain is commonly known as a Markov Process and is

graphically depicted in Figure 3. It is characterized by a chain of discrete states and

discrete times when those states can change. Each time the engine returns to the shop for

service, there is some probability that an airfoil will advance from one state to the next.

There is also a chance that the airfoil will require replacement, thus returning to the base

state, a 1 't run airfoil. The probability of advancing and the probability of replacement

must total 100% for each service event.

Anecdotal descriptions of the scrap rates for airfoils indicate that the probability

of an airfoil advancing to 4th run from 3 rd is less than the probability of advancing to 3 rd

run from 2 d. Based on the scrap rates described in interviews, approximate steady state

scrap rates would be reached by the 8t' engine shop visit. This is equivalent to about

24,000 flights or 20 years for an average PW2000 installed on a Boeing 757. Thus, over

time, the error from the scrap ratio should diminish in the total forecast error.

1st 2nd 3rd 4thRun Run Run Run

Figure 3: Scrap rate as a Markov Process

Unfortunately, data from the engine service centers that could be used to validate

this theory was not available. While data concerning the number of airfoils consumed

per shop visit is captured, there is not a ready connection between the service event21

information and the general age of the engine in cycles or shop visits in Pratt &

Whitney's databases. As such, developing a forecasting method for these parts has been

deferred. Instead, Pratt uses the most recent 12 months of history to estimate the scrap

rate. If the Markov Process model accurately describes scrap rates, during the early years

of the engine program forecasts to be less accurate than later in the product life cycle.

Pratt & Whitney specifies forecasts for parts such as these on a per shop visit

basis. That is, the forecast specifies the number of parts expected to be used by each

shop visit captured through contract by Pratt & Whitney. Some contracts are written for

parts only and others for parts and service. Nearly all of the airline operators engage in a

long term contract with a provider for parts, service, or both.

It is within the conditions of these contracts that Pratt is able to collect and

identify much of the information that it uses to develop forecasts. For instance, when

forecasting life limited part consumption, Pratt uses the current condition of the engines

which the airline operator expects to bring in for maintenance over a period of time to

estimate demand from that customer. That is for a given engine with 27 life limited parts,

the operator will provide a list with the current total accumulated cycles on the engines,

and the expected date and expected accumulated cycles on the engine parts at the time of

service. From these data, it is relatively easy to extract a forecast. Some assumptions

must be made regarding the operators minimum acceptable remaining life for any part in

an engine as it exits service, but these are typically shared by the operator with Pratt &

Whitney.

22

3.3: Life Limited Parts

Forecasts for Life Limited Parts are not so easily generated. Trending does not

yield adequate infornation for parts with long lives in the engines. Observation of recent

past might indicate that volume for a particular spare part is next to nothing and motivate

Pratt to carry only a small amount of inventory. However, as will be shown, this is

simply a matter of timing. By using what is known about the way operators are flying

aircraft, we can make some reasonable estimates of what the total volume of demand will

be over the lifetime of the aircraft and, based on current status of the aircraft, forecast the

timing of that demand.

Consider a 15000 cycle limit part in a fleet of 100 engines, introduced in one year.

The planes are flown, on average, 1000 cycles per year. With no variation from the

mean, the hundred engines will require 100 new parts in exactly 15 years. Introducing

random deviation from the mean annual cycles and cycles remaining at replacement

changes the demand profile as shown in Figure 4. Clearly, when working with a demand

scenario as depicted above, applying scrap information from the prior year will not

provide a good estimate of expected demand in the coming year.

23

Hypothetic Demand Scenario (15000 Cycle Limit Part)

70 -

60 - - - - -

50

40 -4- -

20 -_-_- -

1 0 - - -- - - -- --

0- -11 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Year

Figure 4: Hypothetical Demand Profile

With the defined replacement conditions regulated for Life Limited Parts, one

would expect forecasting demand to be routine. If a part must be replaced every 15000

flights, one might think it is simply a matter of bookkeeping to estimate demand. This

would be true if the placement of engines and parts in the fleet were static. If an engine

were installed on an aircraft and that aircraft, engine, and parts combination remained

together throughout its life and the operators extracted all of the useful life from each

part, the forecast would only require estimating when the aircraft were to reach specified

intervals at which point a replacement would be required. However, the fleet is not static.

In the experience of managers at Pratt & Whitney, operators routinely make decisions to

pull parts from an engine based on build standards, strategic business concerns, and

unanticipated maintenance. In addition, spare engines are kept on hand. When an engine

is removed from an aircraft for service, spares are installed such that the plane is removed24

from service only for the length of time required to remove and install an engine, and

complete any non-engine related service. The engine that was removed from the aircraft

for overhaul then becomes a spare engine after service is completed. Thus Pratt &

Whitney has difficulty knowing the current status of any particular engine.

To deal with this issue, Pratt relies heavily on customer input to estimate future

demand for life limited parts. Customers provide this information in a variety of ways.

Generally, at the beginning of a contract, current status of engines including the

accumulated cycles on all the LLP's is provided. Contracts are long term, typically 10

years or more and written for parts, service, or both. Over the course of the contract,

Pratt is not usually able to get updated engine condition reports. Instead they rely on the

customers anticipated maintenance plans including the engines they expect bring in and

their anticipated parts consumption. However, the data provided by customers is

typically limited to the next 12 months. With part lead times at in excess of one year,

Pratt becomes dependant on expediting and high levels of inventory to provide adequate

service levels.

The forecasting methods described above rely on two major data sources:

customer input and engine condition. This is a source of great difficulty for the GMS

program. This new engine program is intended to service customers who are currently

under contract with competitors and will provide parts to a mature fleet for which Pratt &

Whitney has no history. Thus the need for a new forecast methodology that relies only

on generally available industry information.

25

Chapter 4: The Aircraft Utilization Based Forecast

4.1: The Hypothesis

At the beginning of this project, we proposed the hypothesis that we could

estimate future demand based on aircraft status rather than engine status. The

accumulated cycles on an airframe is publicly available data. In addition, the preventive

maintenance schedules required for the engines that power the airframes is published in

Chapter 5 of the engine maintenance manuals as specified by the OEM in accordance

with the FAA. Pratt & Whitney has access to these engine manuals as a maintenance

service provider for competitor's products (most of the OEM's service each others

engines).

Airframe status data was collected through a subscription service to the Aircraft

Analytical Systems database provided by Flight (http://www.flightglobal.com). The

ACAS database captures data on a monthly basis and includes the monthly history of

cycle accumulation on all airframes manufactured in the west. The data includes the

engine model installed on the aircraft, current and previous aircraft operators, engine

utilization history in hours, and a variety of other airline market information.

Mathematically, the forecast model developed is very simple. If one assumes that

aircraft operators are basically rational and, as rational businesspeople, extract as much

useful life from life-limited parts as possible such that they are disposed of with near zero

remaining life, then the total fleet accumulated cycles divided by the cycle limits of a part

is the total expected historic demand for that part. The underlying driver for demand,

then, is the simply the number of cycles on the aircraft engine. That is to say, if we were

26

to follow the life of any single engine, and Life Limited Parts were replaced only at

expiry, the demand process for a 15000 cycle parts appears as in Figure 5.

Demand for a 15000 Cycle Limit Part

1.2

1 -

0.8

0.4

Accumulated Engine Cycles

Figure 5: Demand from a Single Engine for a 15000 Cycle Part

Summing a series of engines, which are introduced at varying times and

accumulate cycles at different rates results in the apparently complex demand pattern

seen by Pratt & Whitney material controllers (Figure 6). By identifying this underlying

driver of demand, or approximating it using aircraft accumulated cycles as a proxy for

accumulated cycles on a part, a model that will describe future demand is generated.

Forecasting models often use statistical analysis of historic data to estimate future

trends through various analyses. (Montgomery, Johnson, and Gardiner 8). Models are

used to simulate seasonality, product life cycles, linear trends, etc. However, the demand

model we will build is a transformation of another forecast. That is to say, the forecast

27

generated is of aircraft utilization, rather than demand. We then transform the forecast

mathematically as a causal model based on preventive maintenance requirements.

PW2000 3rd Disk Monthly Demand

E__

Year

Figure 6: PW2000 3 rd Disk Monthly Demand History

Of course, there are inefficiencies in the demand process that will yield error in

this forecast model. One such inefficiency is the early replacement of parts. Operators

are not allowed by regulation to fly with an aircraft that has a part beyond its preventive

maintenance limit. They schedule their maintenance with a buffer of time before the hard

expiration of the part. The life remaining on a part is referred to as the stub time. In

addition, parts do not have identical lives. To minimize total maintenance costs, an

operator might remove a part early when servicing the engine for other reasons, to avoid

bringing the engine in at a later date. This has the impact of bringing demand in early.

In addition to early replacement, there is cannibalization of parts from other

engines. After the terrorist attacks on September 1 1 th, world wide air travel fell by more

28

than 30% (Smallen 2002). Airline operators found themselves over capacity with more

available seat miles than the demand for air travel could support. They began a

rationalization program wherein many older, less efficient aircraft were retired before

their age would have necessarily dictated. By August 2002, they had successfully

reduced the available seat miles (avg number of seats x number of planes x number of

flights x avg miles per flight) by 6% compared to the prior year.

The MD80, powered by the Pratt & Whitney JT8D is one of the older, less

efficient aircraft that was affected by the rationalization program. Over the following six

years, Pratt has experienced significantly lower demand than expected. Operators were

using the engines and parts from retired aircraft to maintain the aircraft still in service. In

addition, a secondary market for these parts exists. When an aircraft is retired, if there is

remaining useful life in the components, they are removed and put up for sale. This has

the effect of delaying demand.

The secondary market for used components does not affect the net demand over

the lifetime of an engine fleet. Those components that are removed early from one

engine and used to satisfy a timed out replacement on another have potential to shift the

timing of demand. That is, the purchasing customer can delay the acquisition of a new

part indefinitely if enough used material is available. However, the supply of used

material is limited to either retired engines that have useful life remaining or engines that

are still in service. Clearly, the supply from retired engines is limited and will increase as

the fleet ages in a generally predictable manner. For engines that are still in service the

net effect of introducing components into the secondary market is to accelerate one

demand (the engine providing the part) and delaying another (the engine which accepts

the used component).

29

While the net effect of these errors might be to shift demand from one source to

another, or to delay demand through the use of components from retired engines, and

might be small when applied to an entire fleet of engines, there is potential for very large

errors if applied to small subsets. For instance, if we were to consider the behavior of a

major airline in its efforts to emerge from bankruptcy over the past several years, we

would find clear evidence of this difficulty. This airline made significant efforts to cut

costs immediately and retain as much cash as possible during its emergence from

bankruptcy. One of the sources of cash that they were able to utilize in this effort was

purchasing used parts from the secondary market for use in their engine overhauls. As

reported to Pratt & Whitney, the airline changed its maintenance schedules, reducing the

time between services significantly. By shortening these maintenance intervals, they

were able to use materials from the secondary market with low remaining cycles till

required replacement. The prices for these used materials are typically prorated from the

original purchase price based on the remaining life thus allowing the airline to conserve

cash. The consequence of using this decision is higher lifetime maintenance costs due to

an increased number of engine overhauls. Survival in a competitive environment at

sometimes necessitates suboptimal decisions. When transforning the utilization forecast

to a demand forecast, we will attempt to modify the transform to correct for these

inefficiencies.

4.2 Application to the PW2000 Fleet

As stated in Chapter 3, the goal of this project was to develop a method to

forecast Life Limited Part demand based on commonly available industry data rather than

the high labor content customer obtained data typically used by Pratt & Whitney in

30

forecasting. Key among the available data is aircraft utilization as measured in cycles per

time period. A cycle is defined as an aircraft landing. In 2002, United Airlines

scheduled 650,000 flights for its fleet of 554 aircraft, targeting an aircraft cycle utilization

of 1174 cycles per year.

100 -

90-

80-

70 -

60-

50 -

50

30

20

10

0 -

Aircraft Accumulated CyclesAugust 2006

Figure 7: Aircraft Accumulated Cycles

Through the ACAS database, utilization of individual aircraft is available. Figure

7 is a snapshot of the age of the PW2000 fleet measured in cumulative engine cycles in

August of 2006. Using the utilization history, one can estimate the future usage based on

the history for individual aircraft. The utilization history for a typical PW2000 powered

aircraft is shown in Figure 8. This Boeing 757-200 is owned and operated by Delta

Airlines, and is powered by twin PW2037's, the most common PW2000 derivative. The

usage pattern, xt, is modeled well with a basic 5 year moving average. That is to say, our

best estimate of xt is given by the equation x, = b + e, where our estimate of b is

31

iT

b L x, and c, is normally disturbed, random error term at time t. There is a clearT j

shift in the utilization of aircraft S/N 23998 likely resulting from the terrorist attacks of

September 11 . Our moving average will over predict utilization following a shift such

as this. However, over predicting utilization by several hundred cycles per year will not

greatly affect the demand forecast because this over prediction is such a small fraction of

the accumulated cycles necessary to trigger a demand point. More sophisticated models

such as exponential smoothing were explored and can better detect a shift in utilization.

However, the simple moving average is computationally efficient and is effective for our

purposes.

Aircraft Utilization S/N 23998

2000 30000

1800 -_-_--

250001600

U)1400 - --

20000

1200 -+- Utilization-5 Year Average

2 1000 -*- Cumulative Cycles 15000 U

800 - -_-

E10000 E

U 600 -

4 400 - - --5000

200

0 01990 1992 1994 1996 1998 2000 2002 2004

Year

Figure 8: Aircraft S/N 23998 Utilization History

With the expected future utilization of aircraft established, it is a simple matter to

generate the expected replacement of Life Limited Parts. As stated in Chapter 2, the

32

starting assumption is that parts are used completely. In addition, it is assumed that all

accumulated aircraft cycles must be applied to a life limited part and that parts are

replaced according to their regulated life limits. So, one would expect Aircraft S/N

23998 to have required a 15,000 cycle limit part in 1996 and a 20,000 cycle limit part in

2000. In addition, one expects an additional 15,000 cycle limit part and the first 30,000

cycle limit part to be required in 2007.

An expected demand history for the Life Limited Parts on the PW2000 for all

PW2000 powered aircraft was generated in this fashion and the results compared to the

actual demand history. The error remaining after this best fit between historic and actual

usage is error for which the source cannot be identified with current data. This error

likely comes from several sources, key among them the actual remaining life at time of

replacement. This in turn is driven by many of the factors addressed in Chapter Two

including the ability of operators to move engines from aircraft to aircraft, the presence of

spare engines in the fleet, and the management of maintenance operations. Some of these

sources tend to delay demand and others tend to accelerate it.

Completing the best fit of historic demand and expected demand based on aircraft

utilization history yielded the following results for Life Limited Parts on the PW2000.

33

Estimated Stub Time

Cycle Limit Part Description 0 500 100015,000 1 stg Disk 33% 35% 40%

15,000 2nd Hub 61% 86% 121%

20,000 16 stg disk 43% 38% 41%

20,000 17 stg Disk 41% 46% 46%

20,000 6th stg front Hub 51% 57% 42%

20,000 7-15 Stg Drum 44% 46% 27%

v U 709%420,000 Fan Hub 28% 27% 31%

20,000 3rd disk 28% 32% 44%

20,000 4th disk 43% 35% 30%

20,000 5th Disk 24% 26% 28%

20,000 6th Disk 34% 43% 56%

20,000 7th Disk 25% 29% 30%

!L ~ L 91% ~~30,000 Rear Hub 76% 41% 90%

Mean Absolute Percent Error (MAPE)

Table 1: Mean Absolute Percent Error at Various Stub Times

It's clear that some of the parts reviewed in this study are not well represented by

the expected historic demand model. The most striking examples are the 2-5 stage hub

and the Low Compressor Long Shaft. As it turns out, both of these parts have

experienced a significant service event in their history. The parts were showing signs of

failure earlier than expected and were redesigned by Pratt & Whitney. The entire fleet

that had been originally manufactured with the problem design was refitted with new

parts, and could not be introduced into the serviceable market. Therefore, the

assumptions regarding that efficient secondary market place that results in most life being

consumed before final disposition of the part, does not hold for these cases. Thus, these

parts have been dropped from further analysis. In addition, Life Limited Parts that were

not similar to those on CFM56-3 that Pratt & Whitney intended to manufacture were not

included in the analysis.

The total error expected for forecasts of the Pratt & Whitney parts will include

error from the sources estimated above as well as error in the estimation of future annual

34

cycle accumulation. This second source of error is quite insignificant compared to the

first and will be ignored in the application of the forecast model. The average annual

landings experienced by at PW2000 engine over the past 5 years is 1030 cycles with a

standard deviation of 6 cycles. Clearly, the 1% error from our estimate of the future rate

of cycle accumulation is insignificant compared to other sources of error.

There are two engine models within the PW2000 engine family, each specified by

the maximum thrust the engine is capable of producing. In the interest of simplicity,

clarity, and to protect the interests of Pratt & Whitney, I have aggregated the demand and

the parts for each of the separate models. To validate the forecast model, I applied the

forecast model to the PW2000 engine fleet accumulated cycle history and compared the

results to the demand history for those parts. As Pratt is the sole provider of Life Limited

Parts for the PW2000, they own the complete demand history.

All PW2000 LLP DemandExpected and Actual

700 -- -_

-- Actual Demand-G- Expected Demand

600 -Correlation Factor = 0.91Total Error = -15%Mean Absolute Percent Error (MAPE) =

500 29%Mean Absolute Deviation (MAD) 75 units

400 -

E4)0U 300 -

200 - -- - - - -

100 - --

01992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Figure 9: Life Limited Part Demand: Expected and Actual

35

Figure 9 depicts the results of the application of the forecast method to historic

aircraft utilization. However, this is not a forecast. This is expected demand based on

the actual aircraft utilization rather than projected forecast utilization. The visual

correlation between the expected demand based on the aircraft utilization history and the

actual demand history is significant. The coefficient of correlation is calculated as 0.91.

Of particular concern are the large errors noted in 1998, 1999, and 2001. We will attempt

to identify the sources of these errors and, if possible, correct the model to account for

them.

In addition to the difference between the expected and actual demand, the overall

shape of the demand curve is of interest. A question that might occur is what caused the

decline in demand for Life Limited Parts from the PW2000 fleet beginning in 2001.

When the product managers at Pratt were interviewed regarding this curve, they stated

that this reflected the life cycle of the fleet and events in the industry. The fleet was new

and growing in the early 90's and demand grew with it till it reached maturity in

1999/2000. At this time, production of new PW2000 was reduced as production of the

Boeing 757 tapered off. 2001, 2002, and 2003 experienced depressed demand due to

world events including the terrorist attacks of September 11, 2001 and the medical scares

of SARS and the avian flu. The question that we posed was whether our model, which

reflected depressed demand based on the actual aircraft utilization during these years,

have predicted reduced demand given data available only through the years 2000.

To test the robustness of the methodology and to determine if the manager was

correct in believing that the changes in demand were due to events in the airline industry

that could not be anticipated, or if the depression was predictable and the result of the

status of the fleet, data from December 2000 was loaded into the model. Aircraft

36

accumulated cycles as of the end of December were used as a starting point. The rate of

accumulation of cycles was modeled as the average of the previous 5 years of utilization

for each aircraft. The resulting output can be seen in comparison with the actual demand

and the expected demand based on actual utilization in Figure 10.

All PW2000 LLP DemandExpected and Actual

700 -- -- ---4-*Actual Demand-- Expected Demand

600 - -- Dec. 2000 FCST

Correlation Factor = 0.91Total Error = -15%

500- Mean Absolute Percent Error (MAPE) = 29%Mean Absolute Deviation (MAD) = 75 units

400

E0

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Figure 10: Expected and Actual Demand with Forecast

The yellow line in the Figure represents the forecast generated through the aircraft

utilization model for total LLP demand for the PW2000 made using December 2000 data.

The model predicts significantly lower demand in 2001 than in 2000 indicating that the

depressed demand experienced during this time was not a result of industry events and

decreased aircraft utilization but was a product of the status of fleet at the time. Of

particular interest is the success in forecasting the lowest demand year in 2004. In the

words of the PW2000 spares manager, "We're pretty good at forecasting when we're

37

following a trend, but we get in trouble when we have to turn a corner." The value to this

manager in forecasting the timing of swings in future demand is significant.

This is one example of an apparent success in forecasting using aircraft utilization

and a generic profile of parts replacement. It is important at this point to develop a frame

of reference for success or failure in forecasting for these parts. Figures 11 - 13 compare

forecasts made using the aircraft utilization based method with forecasts generated for the

same classes of parts by Pratt & Whitney at the same point in time. Unfortunately, Pratt

has not made a practice of maintaining forecast records for posterity so there are only a

few points in time that can be compared. However, the results indicate that the utilization

based forecast is comparable in quality to those forecasts generated with heavy customer

input.

PW2000 15000 Cycle Limit Parts

120 -

+15000 cycle part average demand

-E- Utilization Based Fcst 2003

100 - -A Pratt Forecast - 2003

-0- Pratt Forecast - 2004

-N- Pratt Forecast - 2005

80 -*- Pratt Forecast - 2006

E060-

40

20-2Forecast Based on 2003 Data

01999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Figure 11: 15000 Cycle Part Forecast Comparison

38

PW2000 20000 Cycle Limit Parts

100

90

80 -

70 -

60 -

50 -

40 -

30 -

20 -

10 -

0 -2002 2003 2004 2005 2006 2007 2008 2009 2010

Figure 12: 20000 Cycle Part Forecast Comparison

PW2000 30000 Cycle Parts

-'4-30000 cycle part average dem-U- Utilization Based Fcst 2003--- Pratt Forecast - 2003-- Pratt Forecast - 2004-*- Pratt Forecast - 2005-#-- Pratt Forecast - 2006

and

Forecast Based on 2003 Data

1999 2000 2001. . . 2 . 2

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Figure 13: 30000 Cycle Part Forecast Comparison

39

-4--Actual Demand

Pratt Forecast - 2003Pratt Forecast - 2004

Pratt Forecast - 2005

Pratt Forecast - 2006

Utilization Based 2003-E

'UE0

1999 2000 2001

IForecast Based on 2003 Data

45-

40-

35 -

30-

25-E

0-U) 20-

15-

10-

5-

0--

C::

Pratt & Whitney uses two measures of forecast accuracy that will be familiar to

those readers involved in forecasting. First is Mean Absolute Deviation (MAD). This is

the average difference between the forecasted value and the realized value for the period

of interest. Second is Mean Absolute Percent Error (MAPE). This is related to MAD but

presented as a percentage with the actual value as the basis for a percentage measure,

making comparison of accuracy between parts easier. The following Table compares the

accuracy of the utilization based forecast with the forecasts generated by Pratt & Whitney

from the previous figures. In the figures above, only a single utilization based forecast is

presented for clarity. In the calculation of MAPE and MAD, forecasts were made using

data available through 2003, 2004, and 2005 to ensure that the comparisons are well

founded.

150002000030000

14%36%40%

19%17%34%

, , ,M ," ddo atdtb

15000 26% 14% 16 9

20000 25% 13% 7 4

30000 44% 1 36% 1 12 9

15000o 20% 2% 14

35000 30% 29% 14 12

iparison,,MAD-

nnt Tools, utiliz. tior10 158.4 59 5

Table 2: Forecast Accuracy

One can see from the table that the aircraft utilization based is neither better nor

worse than the forecasts made by Pratt & Whitney in terms of the raw accuracy numbers.

However, there are several things to note in observing the results of the two methods.

40

First is the general shape of the utilization based forecasts (and historic expectation) as

compared to realized demand. When compared to the Pratt & Whitney forecasts, it

seems that the UB forecasts reflect the shape of actual demand better. From the position

of a materials manager for a part a good forecast of the shape can be very valuable. As

the materials manager at Pratt said, "we're not very good at turning corners." A forecast

that predicts major inflection points in the demand profile can go a long way to

improving Pratt's performance in this area.

In addition, you will notice that the error terms in Table 2 are lower than those

estimated based on the historic best fit. This is a result of the aggregation of parts with

the same life limits for purposes of protecting Pratt & Whitney's private data. By

aggregating, the errors tend to cancel out yielding a more accurate, albeit less useful,

forecast.

The next observation concerns the falling nature of Pratt & Whitney's forecasts.

In almost every case, Pratt's year over year forecasts decline. That is, the forecast for

two years out is less than the forecast for next year. This is an artifact of the forecasting

system. The MRP system at Pratt is set up to automatically make a projection for

demand based on time weighted average of historic demand. The materials managers

then update this forecast based on their own research and knowledge of the market for

particular parts. As one would expect, the materials managers ability to forecast based on

customer input improves as time goes on such that next years forecast is the most up to

date and most accurate in the three year forecast. In a time period of generally increasing

demand, the years further from the forecast point that were populated based on historic

demand will lag the current trend. One would expect that if Pratt were to enter a period

when demand is generally falling, those further out years would also lag and show a

41

rising forecast when the trend is actually declining. Clearly this artifact of the system is

detrimental to capacity planning, raw material procurement, and supply chain planning,

and places pressure on the manufacturing organization to respond to sometimes dramatic

year over year changes in the forecast.

In summary, the utilization based forecast methodology provides similar accuracy

to the more traditional customer survey based forecasting that Pratt & Whitney has been

engaging in. However, it does with much less effort than the hundreds of hours spent in

phone calls and data collection than is currently required. The results presented in this

chapter are all aggregated results. It is possible to disaggregate on two parameters of

interest, namely specific customers and specific part numbers. As one would expect,

disaggregation yields less accurate results. Pratt did not have records of customer

specific forecasts or demand history to investigate the degree of this decline in accuracy

on this dimension. However, it is expected to be significant. The assumption in

generating these forecasts is that the efficiency of the market makes the many customer

specific decisions irrelevant as in the case of build standards.

Part specific forecasts, on the other hand, are a necessary part of doing business.

While an aggregate market forecast is interesting to the materials managers, a distribution

across specific part numbers is necessary for making planning decisions. These are not

presented here in order to protect Pratt's interests. However, developing part specific

forecasts yielded one key learning regarding the utilization based forecast. Parts that

have experienced a major service issue, for which the response was to replace all parts in

the fleet, cannot be forecasted using the utilization based methodology without resetting

the replacement profile to account for the shift. Unfortunately, this can be quite

42

cumbersome due to the timing of a service issue in the maturity of the fleet and the

specific part revisions affected.

In addition, the part specific forecasts were been adjusted based on historic

correlation of actual and expected demand to yield the current forecasts. The number of

cycles to replacement has been adjusted from the published Chapter 5 preventive

maintenance limits to give a best fit with historic demand. The parameter in the fleet

reflected in this adjustment are the build standard of customers. In other words, parts are

not used up completely, thus requiring replacement before one would expect resulting in

shorter average part lives. Data is not available to calculate an actual average part life so

we use the correlation of actual and expected demand to approximate the part life going

forward. The remaining error after this adjustment, plus error introduced by the

uncertainty in the future rate of aircraft cycle accumulation is then applied to the

projection, yielding a forecast with error. Forecasts with error were an unfamiliar idea at

Pratt & Whitney when we began this project. By the end of the six month project, some

early adoption of the inclusion of an error in forecasts, yielding a range of expectations

for the manufacturing organization, had begun.

43

Chapter 5: GMS Forecast and Inventory Control

The application of the forecast model to GMS parts is similar to that of the Pratt

& Whitney parts with one exception. Pratt does not have data on the demand history for

these parts. Therefore, a best fit of historic data to identify the estimates of remaining

stub life at replacement cannot be completed. Two alternatives were proposed to

compensate for this lack of data. One proposal is to create a profiling tool that

incorporates operator's typical maintenance intervals, the Chapter 5 Limits for the life

limited parts. An example of this profiling tool is shown below.

Stub Life PlotLLP Life 1 1 1 2 1 3 | 4 | 5 1 6 1 7 8 1 9 | 10

Fan Disk 30000 - - - 0 - - 0Booster Spool 30000 - - - 0 - 0

Fan Shaft 30000 - - - 0 - - 0HPC Forward Shaft 20000 - 2500 - - 2100 _ -

HPC 1-2 Spool 20000 - 2500 - - 2100 _-

HPC Stage 3 Disk 20000 - 2500 - - 2100 - -HPC Stage 4-9 Spool 18824 - 1324 - - 924 4224 -

HPC Rear CDP Seal 17980 - 480 - - 80 3380 -HPT Front Shaft 18323 - 823 - - 423 3723 -

HPT Front Air Seal 18000 - 500 - - 100 3400 -

HPT Disk 20000 - 2500 - - 2100 - -HPT Rear Shaft 20000 - 2500 - - 2100 - -

LPT Stage I Disk 25000 - - 0 - - 0 -LPT Stage 2 Disk 25000 - - 0 - 0 -LPT Stage 3 Disk 25000 - - 0 - - 0 -

LPT Stage 4 Disk 25000 - - 0 - - 0 -LPTShaft 30000 - - - 0 - 0

LPT Rotor Support 25000 - - 0 - - 0 -

LPT Stub Shaft 25000 - - 0 - - 0 -

Maintenance Invervalsj 10000 1 7500 1 7500 1 5000 5400 J 7100 1 1 4600 1 1 7500Accumulated Engine Cycles 10000 17500 25000 30000 35400 42900 50000 55400 60000 67500 75000

Build Standard 7500

Table 3: Stub Time Profiling Tool

Three inputs are required to generate such a profile. First are the LLP Chapter 5

limits. Second is the build standard to which the engines will be maintained or the

minimum acceptable remaining cycles on a part after maintenance. Lastly are the

expected maintenance intervals of the engine during its lifetime. In practice, these inputs

44

are very operator specific. Some operators manage this activity to minimize the lifetime

operating costs of the asset. Others focus on minimizing current costs. Some operators

manage this process with little sophistication and simply define a maintenance interval

that is convenient for their operations. In aggregate though, the varying standard and the

presence of an efficient secondary market yield few parts that are disposed of with

remaining life in excess of 2000 cycles. This profiling tool was expanded to include the

revenue generated by the sale of parts into the secondary marketplace when they are

removed with significant life remaining and optimized to provide the operator the lowest

lifetime operating costs.

The second proposal for estimating the remaining stub life for LLP's on the

CFM56-3 engine was to use a fixed percentage of the life limit of the part. Five percent

is the average observed stub life experienced at the Pratt & Whitney engine repair

centers. However, both these proposals fail to recognize that the forecast model stub life

term is not just a reflection of the life remaining on parts when they are scrapped. Rather,

it reflects this remaining life as well as the impact of other inefficiencies in the system

such as the impact of spare engines in the fleet. Instead, we elected to use a zero stub life

in our forecasts. Most of the PW2000 parts showed a best fit between expected demand

and actual demand over the course of the engines history with a using zero stub life. In

addition, we know that in reality, no parts will be consumed completely. By assuming

that they will be, we generate a more conservative forecast. Given the uncertainty in the

business (at the time of this project, Pratt had not yet gained FAA approval to

manufacture these parts) a conservative forecast seemed prudent.

The resulting forecast is shown below in Figure 14. Again, to avoid released of

specifics of Pratt's business plans, I have aggregated the data based on some common

45

characteristics. In this case, they are aggregated based on the engine module of the parts.

The four modules of the CFM56-3 engine (and most gas turbine engines for commercial

aircraft) are the Low Pressure Compressor (LPC) or Fan Module, the High Pressure

Compressor (HPC), the High Pressure Turbine (HPT) and the Low Pressure Turbine

(LPT).

The estimate of error included in the forecast is based on the observed error for

the Pratt & Whitney parts. The average error for individual parts was about 40%. This

was included in the individual part forecasts and is aggregated in the module forecasts.

The charts show the forecast applied over the course of the business case developed by

Pratt & Whitney when making the decision to enter the market. This business case was

developed with a ten year planning horizon.

LPC Volumes Forecast and Business Case

El Utiliz0 Busi

00.00

.00 00U) -0"0 .10 04

D

.1 0,0 oh 00

00

0/."

T

1

.10

410

ation Based Forecastness Case

/Je

//R

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

46

HPC Volumes Forecast and Business Case

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

O2 Utilizaiton Based Forecast

o Business Case

7 ~ UtlztoAasdFrcs

AOAO

00/

/W

E3Utilization Based Forecast

M Business Case

210

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

U0

4.00

HPT Volumes Forecast and Business Case

U)

47

LPT Volumes Forecast and Business Case

0Utilization Based Forecast

0Business Case

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Figure 14: Forecast and Business Case Volumes

The aircraft utilization based forecast is displayed side by side with the volumes used to

justify entering the business. For the high and low pressure compressors, the business

case is within the expected error of the forecast. This is not true for the turbine side of

the engine. For these modules, the forecast comes in significantly lower than the

business case, albeit not significantly until 2010.

The business case was developed by Pratt & Whitney using information from

their engine repair facilities. Much like the forecasts used regularly for Pratt parts, scrap

rate data was collected from engine overhauls. Then this scrap rate was applied to an

expected number of shop visits that Pratt forecasted market wide for the CFM56-3

yielding an expected number of parts consumed. This was then adjusted based on the

fraction of the market Pratt expects to capture over the next decade. The business case

forecast has the same problem described in Chapter 2 regarding using scrap rates to

48

project Life Limited Part demand; namely that this method does not reflect the periodic

nature of demand for LLPs. In addition to this inherent problem, the data set was limited

to 33 engine overhauls. With this limited data set, it is impossible to make projections

without a significant error term.

Consider the following randomly generated data set generated with a probability

of encountering a part that needs replacement of 30%. Three samples of 11 engines are

taken. We calculate a scrap rate for each sample and find the average and standard

deviation of the samples. The resulting scrap rate is normally distributed with mean and

standard deviation of 33% and 10% respectively.

0 0 00 1 00 0 11 0 01 0 01 1 00 0 11 0 01 0 00 1 00 0 1

Scrap Rate 45% 27% 27%

Mean Scrap Rate 33%1

Standard Deviation 10%1

Figure 15: Scrap Rates

Based on these figures, we can only say with confidence that the true mean is between

13% and 53% (a = 0.05). Including an error term estimated based on this hypothesis of

approximately three standard deviations in the business case brings the aircraft utilization

based forecast and the business case into much closer agreement. The failure of the

business case to include the dynamic nature of the demand process accounts for the

residual differences.

49

LPC Volumes Forecast and Business Case

O UO B

U)

400

0 go

2012 2013 2014 2015 2016 2017 2018

HPC Volumes Forecast and Business Case

5 Utilization Based ForecastO Business Case

00

:0,

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

50

2008 2009 2010 2011

tilization Based Forecastusiness Case

A0 / 0

HPT Volumes Forecast and Business Case

U Utilization Based Forecast* Business Case

LWd

K/

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

LPT Volumes Forecast and Business Case13 Utilization Based Forecast

II Business Case

/Z

iiI~I~l~l- _

#1

/

/i

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Figure 16: Forecast and Business Case Volumes with Error

51

U)

I-/

/

U,

C

-ii0I-

'I I

T TIT Ill

IF

With forecast in hand, we now turn to the issue of inventory control policy. The

aerospace industry has suffered from increasing lead times. With expected lead times of

more than a year for the Life Limited Parts Pratt & Whitney will be manufacturing for the

CFM56-3 and forecast errors on the order of 40%, high levels of inventory are to be

expected. However, looking into Pratt's supply chain points to opportunities to reduce

inventories beyond what would be necessary if Pratt applies its traditional inventory

control methods to these new parts.

The primary opportunity for inventory reduction comes in the fonn of strategic

inventory placement. Pratt's supply chain for Life Limited Parts consists of four stages

from raw material through finished machinings. Some parts require assembly after

machining adding a fifth stage to the chain. The first stage is the procurement of raw

material or ingot. Pratt places orders for raw material and enters a queue for fulfillment.

Lead times for this stage are commonly measured in months. The average for the GMS

LLP's is nearly six months. Next stage is billet where the raw ingot is transformed into

standard size stock. This process averages two months. The billet material is sent on for

forging with a lead time of two and a half months. Finally, the forging is machined into a

finished part. The total lead time from beginning to end of this supply chain is more than

13 months.

Most Life Limited Parts at Pratt & Whitney are ordered as rough forgings. That

is, Pratt places an order with a supplier for a rough forging, receives the forging, and then

machines the part internally. With the GMS program, existing relationships and the

current business climate motivated Pratt to use smaller suppliers.. Many of the first tier

suppliers were hesitant to work with Pratt on equivalent parts to those they were currently

manufacturing for GE. The second tier aerospace suppliers selected were not in the

52

position to purchase raw material for Pratt parts, therefore, Pratt owns the raw material

much further back in the supply chain than is normally the case. This presents Pratt an

opportunity to take a global look at inventory placement within the supply chain, and

evaluate where in the process it is optimal to keep inventory to provide the desired

service level.

To consider this opportunity, the inventory demand process was modeled as

normally distributed with the demand rate equal to the forecasted demand in 2010

variance equal to the expected forecast error of 40%. We assumed that the inventory

control process would a continuous review policy and the desired service level to be

95%. While the assumption of normally distributed demand does not hold when we look

at the demand process over a period of a number of years is not accurate, it is

computationally efficient. Additionally, this model is not intended provide exact results,

simply to identify where in the supply chain safety stock might be positioned to minimize

costs.

Ingot i Billet Forging Machining

Buffer Buffer Buffer Finished Goods

Figure 17: The Life Limited Part Supply Chain

In order to find the optimal positioning of safety stock, the stock necessary to

satisfy a 95% service level was calculated under a number of scenarios. In all, it was

assumed that finished goods inventory would be held as the lead time for the final

echelon of the supply chain is longer than Pratt's seven day delivery commitments. The

scenarios are as follows. In addition to finished goods, inventory is held:

1. nowhere

53

2. as forgings

3. as billet and forgings

4. as ingot billet and forgings

5. as billet

6. as ingot

7. as ingot and billet

8. as ingot and forgings

Table 4 shows the results of this analysis for a representative part. The total value

of safety inventory is minimized when material is held as billet in addition to finished

goods. The total value of safety stock carried is 23% lower when maintaining a buffer as

billet when compared to than holding finished goods only. Cases 6 and 7 also achieve

significant reductions in the total value of safety stock.

Inventory Units Held After Piocessing Stage

Process Stage LT Net Cost Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Profile 6 Profile 7 Profile 8Stage (weeks) after Stage (0001) (0011) (0111) (1111) 10i011 (1001) (1101) (1011)

Ingot 28 $ 5,500 0 0 0 6 0 6 6 6Billet 10 $ 7,500 0 0 7 4 7 0 4 0Foige 12 $ 21,500 0 8 4 4 0 0 0 6

Machine 18 $ 30,000 10 5 5 5 6 7 6 5Safety Stock $ $ 300,000 $ 322,000 $ 288,500 $ 299,000 $ 232,500 $ 243,000 $ 243,000 $ 312,000

Table 4: Results of Inventory Placement Analysis

The next task to be completed with a demand forecast was to determine

appropriate inventory targets. Of particular interest to the Pratt & Whitney managers are

the expected average inventory levels for the GMS program as this metric is included in

their annual performance targets. Because the program is new, there are no established

metrics. The estimates we generated as part of this project will become the targets for the

first year of production.

54

To estimate the required inventory, Pratt & Whitney's typical target service level

of 95% was used. This fill rate target is based on Pratt's experience with customers. As

one Pratt manager put it, "when we drop below 95%, that's when the pain begins."

These difficult times stem from the efforts of expediting, putting customers on allocation,

repurchasing materials from customers that have material in their own inventory, and

other efforts made to satisfy new orders. We then simulated demand as a Poisson process

for three years. The rate of demand arrivals was set at the forecasted annual demand rate.

The Poisson assumption was based on examination of Pratt & Whitney demand history

for the PW2000. Using a Poissonness Plot developed by Hoaglin (1980) shows that

demand for LLP's is reasonably well described by the Poisson process. Figure 18 shows

such a Poissonness Plot for the PW2000 4h disk. The straightness of the line

characterizes the degree to which the sample data fits the Poisson.

4th Disk Poissoness Plot

14

12 -

10

80

4

2

00 1 2 3 4 5 6 7 8 9

k (monthly demand)

Figure 18: 4th Disk Poissonness Plot

55

The inventory control process used for the simulation is an adaptive base stock

policy as described by Graves (1999). This policy is similar to the policy employed by

Pratt & Whitney in their MRP system. We simulated the response of the system over the

three year period during which Pratt expects to build up its capture of the CFM56-3

aftermarket. The base stock level adapts to reflect the demand forecast over the lead time

and the current level of inventory. In addition, the level of safety stock changes over time

to reflect an expected increase in volatility as the level of demand increases. The results

for a typical part are shown in Figure 19. The horizontal column in measured in

multiples of the safety stock calculated above in the inventory placement calculations.

LPT Stub Shaft Simulation Results

$300,000 - - 99.5%

-1- 99.0%$250,000 1

$200,000

$150,000

$100,000

$50,000

-4-Average Inv (2008 - 2010)- Lost Sales

-A- Fill Rate

98.5%

98.0% 0)

ii97.5% U-

97.0%

96.5%

I96.0%1.00 1.10 1.20 1.30 1.40 1.50 1.60 1.70 1.80 1.90 2.00

Multiples of Calculated Safety Stock

Figure 19: MRP Simulation Results

The results shown demonstrate the impact of increasing safety stock, and thus

average inventory has on Fill Rate and Lost Sales. For the LPT 1" Stage Disk, increasing

Fill Rate from 96.5% to 99% requires an increase in safety stock by 70% and average

56

inventory by 40% (~$200,000). The increase in fill rate yields an increase in expected

annual revenue by only $12,000 for this part. This tool will be useful for managers to

evaluate the financial costs and benefits of changes in target fill rate. In summary, we

found that to achieve the targeted fill rate, Pratt & Whitney would need to carry

approximately $5.8 million. Figure 20 shows the trade off for the sum of all the GMS

Life Limited Parts.

LLP Inventory vs Service Level

$10,000,000

$9,000,000

$8,000,000

$7,000,000

$6,000,000

$5,000,000

$4,000,000

$3,000,000

$2,000,000

$1,000,000

40% 50% 60% 70% 80% 90% 100% 110%

Fill Rate (Fraction of demand satisfied)

Figure 20: Inventory vs. Fill Rate Trade Off

57

Chapter 6: Discussion, Conclusions, andRecommendations

The results of this project completed at Pratt & Whitney's East Hartford, CT

campus include a novel forecasting methodology for Life Limited gas turbine engine

parts based on aircraft utilization, application of that methodology to Pratt & Whitney's

Global Material Solutions business, a strategic inventory placement analysis for GMS,

and target inventory requirements to achieve desired fill rate. Over the course of the

project, the author had the opportunity to work in partnership with Pratt & Whitney

employees and spent a great deal of time discussing the potential implementation and

future use of the work as well as the GMS business in general. These discussions are also

relevant to these conclusions and recommendations.

6.1: General Recommendations

Six months on site with Pratt & Whitney provided the opportunity to experience

the benefits and problems inherent in organizational design. Pratt operates as a matrix

organization with the goal being a workforce that is focused on globally optimal decision

making. That is, if reporting both within a functional hierarchy and work within product

focused team, employees will be more likely to work together with employees from other

functions and make decisions with a broad perspective. This vision has not been fully

realized. Much of the decision making is myopic, focused on ones functional objectives.

Much of the evidence of this is anecdotal but did have impact on this project when a

recommendation to introduce an inventory buffer of raw billet material was made. This

analysis, while well received and understood, was not implemented. It seems that in

Pratt's current organization structure and incentives, Manufacturing owns work in

58

process inventory. An inventory buffer would be considered work in process and

therefore, manufacturing would own the inventory. The benefit of carrying that

inventory would be reduced finished goods inventory resulting from a quicker response

time to new orders. This benefit would be had by the spares organization.

Conflicting incentives such as these that are not aligned with company objectives

were talked over water coolers and discussed at lunch but no changes were made in them

during the course of this project. Based on these observations, Pratt could benefit by

developing a team that will focus on global supply chain initiatives that is empowered to

make changes in the system. This might take the form of a Logistics Services

organization that works for the engine programs and must sell its services. By creating

such an organization that is required to sell its services, they will be motivated to do the

global analysis, demonstrating to their constituents the value in making the changes they

propose.

One of area of focus for such a team is Pratt's strategic reserve of raw materials.

Currently, Pratt maintains some inventory of raw materials. However, this is not

considered to be a buffer inventory but is held in the event that suppliers are unable to

obtain material on the market to satisfy Pratt & Whitney orders. Suppliers make requests

to access the Pratt reserve when market prices are too high or queues lengthen to the

point that will be unable to manufacture the components on time. By taking a more

holistic, total supply chain perspective, there is potential for Pratt to capture more value

for both UTC and its suppliers.

59

6.2: Project Specific Recommendations

The aircraft utilization based forecasting methodology we developed should prove

useful for Pratt & Whitney. However, there are several caveats to this statement. First, it

is a market forecasting tool and relies on certain efficiencies of the marketplace to reduce

expected error. Attempting to apply this model for customer specific forecasts without

additional refinement is likely to yield unsatisfactory results.

Consider the launch customer for the Global Materials Solutions business, United

Airlines. Over the course of three years, in efforts to emerge from bankruptcy in

February 2006, United began charging for meals on flights, renegotiated its labor

contracts reducing labor rates by 30%, cancelled its employee pensions, cut wages again,

and took on $3 Billion in new debt. Over the course of those three years, the only thing

on the mind of United's managers was survival. They put any part they could buy into

their engines regardless of how long it would last before they were required by the FAA

to replace it. Many of these parts were purchased from the secondary marketplace rather

than from the OEM, increasing lifetime maintenance costs but minimizing short term

expenses. The accumulated cycles on the parts in United's engines most likely are no

longer reflected by the accumulated cycles on their aircraft. Our method will not work

when applied to this subset of the industry because of this behavior.

To apply this method to a specific customer, Pratt must first obtain detailed

information about the current status of their engines. By gaining this data the model

could be modified to provide a customer specific forecast. I recommend that Pratt

consider purchasing this information from customers or including the provisions for

collecting it in contracts for service and materials.

60

While at Pratt, when the need for data from United or other customers was

discussed, the common response was that the airline operators engineers were probably

too stretched by the climate of layoffs and downsizing to collect and provide such data

regularly. Given this situation, Pratt might consider placing its own people on site with

the airline operators or consider paying the operators through reduced prices or promised

service levels to collect this data. Experiments would have to be undertaken in which this

data is available, a customer specific forecast generated, and the accuracy of this forecast

compared to the accuracy of a forecast without this additional data. The increased

accuracy will translate to a reduced level of safety stock required to support the desired

service level thus allowing Pratt to value the information.

In addition to the forecast for Life Limited Parts, Pratt has need for a forecasting

methodology for the non Life Limited or gas path parts. The Markov Process described

by Pratt managers was interesting and will provide an excellent starting point for a future

Leaders For Manufacturing internship. In this area, I recommend placing the intern with

the group that will have the best access to data rather than within the Spares or GMS

organizations. Muench (2003) described a data collection and cleansing process in his

thesis that could provide much of the data necessary to complete this analysis. However,

it seems that Pratt abandoned the effort shortly after Muench left the project. Renewing

efforts in this area could provide valuable data not only to the GMS program but also to

Pratt's Spares Organization and engine centers.

Regarding the specific forecast and inventory target recommendations developed

as part of this project, Pratt would do well to put a rigorous framework around the

determination of service level and fill rate in the Spares organization. Fill rate is one of

the key measures that the managers are held responsible for in this area but there seems to

61

be little thought into what the specific target should be. As a sidebar to our efforts, we

completed a newsvendor type analysis for the GMS parts, demonstrating that the

expected return for keeping a high service level for low margin products is much less

beneficial than maintaining a similar service level for high margin products. While the

specific business model at GMS, where high service level has potential to win long term

customers is not the area at Pratt where experiments in this regard should begin, there is

potential for Pratt to reduce its inventory holdings and improve its profitability by adding

rigor to the Fill Rate target selection process. One area to begin such an effort would be

for parts on Pratt's legacy engines where PMA's have developed suitable alternative

supply. By concentrating on these parts, Pratt could reduce its inventory position without

increasing significantly the risk to customers that they will be unable to locate a needed

part.

In conclusion, the forecasting method developed for Life Limited Parts at Pratt at

Pratt & Whitney is robust and effective when applied to the entire market. In many

industries, this would be of little value. However, for the commercial gas turbine engine

business, OEM's have a defacto monopoly on Life Limited Parts through the regulatory

restrictions of the FAA. Therefore, the forecasting method will continue to provide

value. In its current form it is limited to market wide use but with additional, customer

specific data, the functionality could be extended to provide customer oriented output.

62

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