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Supporting Affordability-Based Design Decisions in the Presence of Demand Variability

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In the years since the Cold War, the aerospace industry has seen a shift towards affordability-based design from the primarily performance-based designs of the past era. While many techniques, such as IPPD and PLM, have been implemented in support of this shift, recent developments in the industry have led to major cost overruns and production delays. The increased prevalence of demand variability in the aerospace industry and the difficulty to rapidly adapt production plans are a primary cause of these issues. Furthermore, traditional aircraft designers perform detailed manufacturing cost analysis late in the design process when the majority of the program costs are already committed. With the recent shift to more composite aerostructures, historical regressions and cost estimating relationships used to predict cost and manufacturability are no longer accurate, so postponing more detailed cost analyses to later design phases can lead to high costs due to sub-optimal early design decisions. The methodology presented in this paper addresses these problems by providing the ability to conduct multidisciplinary trades in the early stages of design, when a large amount of design freedom and cost savings opportunities exist. To enable these multidisciplinary trades, this paper describes how aircraft performance considerations are integrated with production rate, manufacturing cost, and financial planning metrics into a parametric, visual trade-off environment. The environment, combined with a multi-objective optimization routine, facilitate effective affordability-based trades during the early stages of design. An F-86 Sabre redesigned wingbox using 3 separate manufacturing concepts is used as a proof-of-concept for this research.
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SUPPORTING AFFORDABILITY-BASED DESIGN DECISIONS IN THE
PRESENCE OF DEMAND VARIABILITY
Dennis J.L. Siedlak, Paul R. Schlais, Olivia J. Pinon, and Dimitri N. Mavris
Aerospace Systems Design Laboratory
The Daniel Guggenheim School of Aerospace Engineering
Georgia Institute of Technology
Atlanta, GA, U.S.A.
ABSTRACT
In the years since the Cold War, the aerospace industry has
seen a shift towards affordability-based design from the primarily
performance-based designs of the past era. While many techniques,
such as IPPD and PLM, have been implemented in support of this shift,
recent developments in the industry have led to major cost overruns and
production delays. The increased prevalence of demand variability in
the aerospace industry and the difficulty to rapidly adapt production
plans are a primary cause of these issues. Furthermore, traditional
aircraft designers perform detailed manufacturing cost analysis late in
the design process when the majority of the program costs are already
committed. With the recent shift to more composite aerostructures,
historical regressions and cost estimating relationships used to predict
cost and manufacturability are no longer accurate, so postponing more
detailed cost analyses to later design phases can lead to high costs due
to sub-optimal early design decisions. The methodology presented in
this paper addresses these problems by providing the ability to conduct
multi-disciplinary trades in the early stages of design, when a large
amount of design freedom and cost savings opportunities exist.
To enable these multi-disciplinary trades, this paper describes
how aircraft performance considerations are integrated with production
rate, manufacturing cost, and financial planning metrics into
a parametric, visual trade-off environment. The environment,
combined with a multi-objective optimization routine, facilitate effective
affordability-based trades during the early stages of design. An F-86
Sabre redesigned wingbox using 3 separate manufacturing concepts is
used as a proof-of-concept for this research.
INTRODUCTION
In recent decades, the aerospace industry has seen a major shift
towards affordability-based design from the strictly performance-based
designs of the Cold War era. During the 20th century, aerospace systems
greatly increased in complexity with corresponding increases in cost;
however, the industry is now challenged to continue producing products
with superior performance (higher speed, longer range, reduced fuel
burn, etc.) at a reduced cost [1, 2]. Many new design techniques have
supported this paradigm shift such as Product Lifecycle Management
(PLM) [3], Integrated Product and Process Development (IPPD), and
the Lean Aircraft Initiative (LAI) [2]. These techniques have been
successfully implemented on programs such as the Boeing 777 [2]
and F/A-18 E/F [4], both of which were ahead of schedule and under
budget. Despite these successes in the 1990s, the early 21st century has
seen multiple aerospace programs, such as the F-35 Joint Strike Fighter
[5], Boeing 787, and Airbus A380 [6], plagued with cost overruns
and delivery delays. Boeing and Airbus also have record backlogs
(over 10,000 aircraft combined representing 8 years of production as
of January 2014 [7]).
Many factors contribute to these issues, including (1) the expanded
use of composite materials and (2) a large and increasing amount
of variability and uncertainty in product demand. The increased
usage of composite materials, while enabling weight reductions and
performance improvements, has invalidated the historical regressions
used for estimating costs of conventional aluminum aircraft designs
[8]. Without relevant historical data, traditional techniques using cost
estimating relationships to predict the cost and manufacturability of a
new vehicle during the early stages of design are no longer accurate.
Additionally, demand for aerospace vehicles tends to vary significantly
from year to year, as seen in Figure 1. Consequently, delays arise when
portions of the supply chain are unable to ramp up production to meet
the primary manufacturer’s rate requirements [9]. Finally, while overall
demand is projected to rise steadily over the next 30 years, the demand
variability for individual manufacturers will increase as new competitors
enter the market [10].
Aircraft design traditionally follows a three-phased process,
namely, Conceptual, Preliminary, and Detailed design. As the design
progresses through the phases, the fidelity and sophistication of the
models and analyses increase. In the performance-based design
paradigm, requirements are defined before Conceptual design and
the designers then move to produce a point design that satisfies the
requirements. During the Conceptual and Preliminary stages, cost is
generally only indirectly considered by minimizing the vehicle’s weight
or through regressions to historical data (when available). Then, as
the design moves into the Detailed phase where production plans and
Proceedings of the ASME 2015 International Manufacturing Science and Engineering Conference
MSEC2015
June 8-12, 2015, Charlotte, North Carolina, USA
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Copyright © 2015 by ASME
MSEC2015-9422
FIGURE 1.COMBINED AIRBUS AND BOEING PASSENGER JET
YEARLY ORDERS AND DELIVERIES [11, 12]
drawings are generated, more detailed cost and market analyses occur
[13]. In this paradigm, up to 2
/3of a system’s cost is committed
before Detailed design [14] and 70-90% before production begins [15].
Additionally, by only considering the impact of design decisions on
manufacturing during detailed design, the initial manufacturing choices
may not be able to match the demand or quality required of the product
[16]. In other words, opportunities to design a product for improved
customer satisfaction are lost by pushing detailed cost, manufacturing,
and market trades to the later stages of design.
With the advent of IPPD and PLM, an attempt was made to
consider cost and producibility earlier in the design process. While
these initiatives have had some success, the focus is too often on
reducing costs and not improving affordability [2]. Affordability is
the balance between cost and performance [17], so a more costly
system that provides better capabilities may be more affordable than
a cheaper option. Unfortunately, quantifying a design’s affordability
is often difficult, especially in the early stages of design, because it
requires easily accessible information from various disciplines. This
analysis must include information from the marketing and economics
side of the business in addition to the engineering and manufacturing
areas. Without this collaboration, effectively designing a vehicle
to meet performance requirements with the challenges of increased
globalization and demand variability will remain difficult.
This paper discusses the integration of traditional aircraft
performance design with production and economic considerations
over the lifetime of the vehicle’s production run to facilitate more
informed affordability-based design decisions in the presence of demand
variability. Using a redesigned F-86 Sabre wingbox as a case study,
information from these separate business entities is integrated into a
single, parametric design and visualization environment, permitting
effective design reviews where all disciplines can see the immediate
downstream impact of a decision. In particular, this environment
allows decision makers to rapidly assess the profitability of a design
and production plan for a defined customer demand scenario and
economic assumptions. Trade-off analyses can be conducted (1)
manually, by allowing decision makers to make changes to the wingbox
design and/or production plan and evaluate their impact over the life
of the program or (2) automatically, by leveraging multi-objective
optimization capabilities that allow decision makers to determine the
most profitable design and production plan subject to performance,
economic, and production constraints.
The first section of this paper discusses the methodology and
design environment developed to assess the impact that demand
variability has on production planning and aircraft design. Particular
focus is placed on the development of an optimization routine
that enables rapid exploration of the large design space. The
second section provides a description of the analytical capabilities
that this methodology enables and further discusses the parametric
environment developed to support them. A particular emphasis is
brought on affordability-based trades between program cost and system
capability developed through manual design space exploration. The
subsequent section discusses results from the optimization routine
concentrating on early design trades that are now available. The paper
concludes by discussing potential implications of implementing this
multi-disciplinary design environment and proposed areas of future
research.
PROPOSED METHODOLOGY
Of the factors contributing to recent aircraft programs’ cost
overruns and delays identified above, this work will primarily focus
on driving down the impact of demand variability with implications
for improving relations with suppliers. Demand variability represents
changes in a product’s orders over time. If not properly addressed,
variability in demand can result in periods of excess inventory and
significant backlogs, or in the contrary, can lead to periods of
low activity. Both carry considerable consequences to an aircraft
manufacturer’s market share, profitability, and workforce. Hence, being
able to quickly assess and visualize the impact of changes in wing
geometry and factory configuration on aircraft performance, production
rate, backlog, and profitability is paramount. However, doing so requires
information not typically available during the Conceptual design phase
to be generated and brought forward. To generate such information, the
following capabilities are required:
A parametric formulation that integrates performance,
manufacturing, and production considerations in the early
stages of aircraft design
Integrated process-based costing models to assess the profitability
of a program over its lifespan
The ability to identify vehicle design/factory combinations that
satisfy user-defined performance and production constraints
(maximum backlog, takeoff field length, etc.)
The ability to better understand the sensitivity of performance
and production constraints on wing and factory configuration
variables
An interactive and parametric trade space environment that allows
the decision maker to rapidly assess and visualize the coupling
between aircraft design and performance, factory configurations
(in terms of number of workers, number and type of workstations,
etc.), process efficiencies, cash flow and backlog
Leveraging Past Efforts
This work builds on the Manufacturing-Influenced Design (MInD)
[18, 8] and subsequent Manufacturing-Influenced Design Production
Optimization (MInDPRO) framework [19] developed by the Aerospace
Systems Design Laboratory (ASDL) at the Georgia Institute of
Technology. The MInD framework is a parametric environment that
enables product- and process-based trade studies between design,
structures, performance, and operations in the Conceptual and
Preliminary design phases. The disciplines captured initially in
this methodology include performance, operations, structures, and
process-based manufacturing cost estimation. Models and codes from
each discipline are developed and integrated into a modeling and
simulation environment. Design of Experiments (DoEs) and surrogate
modeling techniques are leveraged to facilitate the integration of the
disciplinary models and tools into an interactive visualization trade-off
environment. This environment allows the user to parametrically define
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FIGURE 2.MInDPRO FRAMEWORK
TABLE 1.DESIGN OF EXPERIMENTS INPUT VARIABLES
Input Variables Min. Max.
Wing Area ft2240 410
Aspect Ratio 3.84 6.24
Range (nmi) 220 1040
Taper Ratio .36 .66
Sweep Angle (degrees) 28 44
Tool Sets (Rib, Spar, and Skin) 2 5
Shifts (Rib, Spar, and Skin) 2 3
Workers/Station (Rib, Spar, and Skin) 1 3
Workstations (Rib and Spar) 1 3
Skin/Stringer Workstations 1 4
Rib NDI Stations 1 5
Spar and Skin NDI Workstations 1 4
various concepts of interest in terms of their geometry, material, and
manufacturing options, and to rapidly compare them with respect to
performance, operations, and structures. The visualization environment
supports an extremely varied amount of analyses, all of which provide
greater insight into the design process.
The MInDPRO framework expands the MInD capabilities by
including a Production Planning Optimization (PRO) component (in
green in Figure 2) to capture production considerations and enable
trades between aircraft design and producibility at the Conceptual
phase of design. It includes a parametric production model for an
advanced composite design concept. This production model, developed
in the Simulation Modeling framework based on Intelligent Objects
(Simio), is capable of estimating production flow statistics (such as
throughput, process flow time, cycle time, waiting time, workstation
utilization rates, etc.) for a wide range of wing geometries and factory
layouts. As in the MInD framework, DoE and surrogate modeling are
used to speed up the production flow analysis and enable rapid data
visualization. A summary of the inputs and outputs of the production
DoE can be found in Tables 1 and 2. The MInDPRO framework
and its visualization environment provide insight into the efficiencies
of the different processes, help quickly identify the critical path, and
support real-time trade studies between factory throughput, flow time,
workstation utilization rates, recurring and non-recurring costs, and
aircraft performance. The reader is invited to consult [19] for a full
description of the MInDPRO framework and its capabilities.
To address the challenges resulting from demand variability and
to enable more informed, affordability-based design, the following
additional capabilities were developed, as discussed in the ensuing
sections.
TABLE 2.DESIGN OF EXPERIMENTS RESPONSES AND
JUSTIFICATION
Response Justification
Flow Time
- Provides meaningful comparisons between
components’ fabrication times
- Enables Gantt chart to visualize critical path in the
production flow
Processing Time
- When used in conjunction with flow time, provides
insight to waiting times, queues, and efficiencies
throughout the factory
Workstation
Utilization Rates
- Enables user to quickly see constraining production
steps
Yearly Factory
Throughput
- Major response to be traded with performance and
cost
Worker
Utilization Rates
- Enables user to predict the number of workers
required to support the factory layup stations
Development of Additional Production Model
Concepts
In addition to the advanced composite wingbox design considered
in [19], two further parametric production and scheduling models were
developed in Simio (one for a machined aluminum design and one for
a conventional composite design). A summary of the three concepts
modeled can be found in Table 3. These two new models further
expand the capabilities to trade between factory settings and wingbox
design parameters for different design concepts (and materials). As an
example, by including the additional models, trades between improved
performance of a composite wingbox against the decreased cost and
improved producibility of the aluminum design are now available.
The production inputs and outputs for the three design concepts
considered are summarized in Table 4. These inputs and outputs are for
the Production Flow Model block in Figure 2 only; additional responses
related to manufacturing cost, backlog, and aircraft performance
are calculated within the visualization environment using previously
generated surrogate models [18, 20].
Development of a Parametric Cash Flow Formulation
A parametric cash flow formulation, in the form of cost equations
for equipment acquisition, labor rates, material costs, loan payments,
and taxes, was also implemented and integrated into the MInDPRO
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TABLE 3.SUMMARY OF MANUFACTURING CONCEPTS MODELED
Aluminum Design Conventional Composite Design Advanced Composite Design
Automated Machining Advanced Process Advanced Process
Facilities and Tools CNC Machining Automated Fiber Placement (AFP) Hand Lay-up (HLU)
Autoclave Cure VaRTM Cure
Integration Very High Integration Medium Integration High Integration
Structural Data Industry Practices & First Industry Practices & First Industry Practices & Structural
Order Structural Analysis Order Structural Analysis Optimization using Hypersizer
Joining Fasteners (Assembly Only) Co-Bonding Co-Bonding/Paste Bonding
Fasteners Fasteners (Assembly Only)
TABLE 4.PRODUCTION MODEL INPUTS AND OUTPUTS
Aluminum Conventional Composite Advanced Composite Model Outputs
- 7 wing geometry variables - 7 wing geometry variables - 5 wing geometry variables - Throughput
- Stretch forming station capacities - AFP station capacities - HLU station capacities - Work station utilization rates
- Machining capapcities - NDI station capacity - NDI station capacity - Component/Batch flow times
- Number of shifts - Number of shifts - Number of shifts
- Number of workers - Number of tools available - Number of tools available
- Number of workers
framework to assess the profitability of a program over its lifespan.
The cost estimating relationships were developed from a combination
of publicly available cost data (such as aerospace technician rates)
and benefits information, surveys of equipment and material providers,
and process-based costing models developed within SEER-MFG.
Process-based costing models are required to accommodate the
expanded usage of composite materials that lack information for
accurate weight-based cost estimates. With this information, an estimate
of total production cost per wingbox produced for a selected wing
geometry and factory layout is available. The visualization environment,
described in the following section, combines these calculations with
user defined market assumptions (such as interest rate, tax rate, facility
depreciation rate, and salvage value) to produce a cumulative cash flow.
The user may get a sense of the proposed program’s robustness to a
range of market conditions by varying the economic assumptions to
quickly see how the cash flow and other economic factors are affected.
The reader is invited to consult [21] for a full description of how the cost
estimations were developed.
Development of Multi-Objective Optimization
Capabilities
Much research has been devoted to developing optimization
frameworks to bring forward multiple disciplines in the design
process such as structures, weights, manufacturing costs, and lifecycle
costs, then using the resulting models to perform multi-disciplinary
optimization with aircraft performance simultaneously [22]. Kaufmann,
Zenkert, and Wennhage proposed a methodology to optimize both
composite and aluminum wing skin/stringer components by minimizing
a combination of aircraft performance and manufacturing cost [23].
Zhao, Curran, and Verhagen conducted a similar study for an aluminum
concept by integrating performance, operating costs, and structural
weight relations as objective functions [24]. These studies are
representative of current optimization efforts to integrate manufacturing
cost considerations into multi-disciplinary aircraft design. One aspect
missing from these optimization formulations is the impact of customer
demand and production over time. Optimizing the manufacturing cost
for a single component may not be the best solution when a design with
higher production rate agility may lead to higher profitability over the
lifetime of production despite a higher per-unit cost.
Optimization Problem Description The simultaneous
wing geometry and production planning optimization problem contains
both continuous and discrete variables and many constraints. The
goal is to minimize (or maximize) some metric of value, such as
cost, performance, or backlog health. The parameters being varied are
high-level wing geometry variables and factory configuration variables
for each year of production. Multiple types of constraints need to
be considered: side, aircraft sizing, performance, and production
constraints. The side constraints are dictated by the surrogate models
for performance and production. The surrogate models for performance
and production were created using bounds on the input variables, so
allowing variables to have values outside those bounds would result
in extrapolation outside of the domain of verified prediction accuracy.
Sizing constraints dictate the maximum weights allowed for the entire
aircraft. Performance constraints enforce various point performance
characteristics for the aircraft, such as landing field length and rate of
climb, set either externally by the customer or derived by the designer.
Lastly, the production constraints enforce certain business philosophies
through the way that the factory is operated and backlog managed.
The formal optimization problem statement is defined as follows:
Minimize f (X,Y)
Subject to:
gper f ormance (X)Rper f or mance
gbacklog (X,Yi)backlogmax ,i=1,...,nyears
ginventory (X,Yi)inventorymax ,i=1,...,nyears
nyears1
i=1
Yi+1,jYi,j
max
Yi+1,jYi,j
,1
changesmax,j,
j=1,...,nFactoryVariabl es
XLXXU
YLYiYU,i=1,...,nyears
A summary of the optimization problem inputs and constraints is
presented in Table 5.
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TABLE 5.SUMMARY OF OPTIMIZATION PROBLEM DESIGN
VARIABLES AND CONSTRAINTS
Design Variables
Wing Geometry
Planform area, Aspect ratio
Taper ratio, Sweep angle
Design range, Rib angle, Rib spacing
Factory Configuration Workstations, workers
Tools, shifts
Constraints
Side Constraints Surrogate model definitions
Sizing Max fuel weight, Empty weight
Takeoff gross weight
Performance
Takeoff field length, Landing field length
Turn rate, Stall speed
Rate of climb, Endurance
Production Max backlog, Inventory
Number of factory changes allowed
Optimization Algorithm Selection Many factors need
to be considered when selecting a suitable optimization algorithm for
this problem. The design variables determining factory configuration
are discrete, which eliminates most gradient-based algorithms. Some
of the constraints, such as backlog, are highly non-linear functions.
Also, due to the hundreds of independent variables associated with
lifetime factory configuration, the design space most likely has many
local optima. To begin to approach the global optimum, an algorithm
that has the ability to sample across the entire design space despite
the presence of local optima is essential. Finally, the objective and
constraint functions are defined via surrogate models, so function calls
are computationally inexpensive.
These problem characteristics make a heuristic search algorithm
that effectively handles discrete variables and a complex design space
appropriate for the problem. Because of the extremely fast objective
function evaluations, a genetic algorithm was chosen over other heuristic
search strategies for this problem. The GA parameters used for the
optimization studies presented in the Optimization Results section are
summarized in Table 6. A pattern search algorithm was then added for
local optimization and for more control of the continuous variable search
resolution after the GA finds a solution, similar to the implementation
in [24].
TABLE 6.GENETIC ALGORITHM PARAMETERS
Population size 1200
Generations 500
Crossover fraction 80%
Mutation rate 1%
Objective Function Selection One of the most
influential factors in the selection of an optimum design is which
objective function is used. However, the metric used as a basis
to designate the “best” design is highly subjective. Performance,
manufacturer economics, or customer economics could all be legitimate
objective functions depending on the specific design philosophy and
current circumstances. To accommodate subjectivity, multiple objective
functions need to be defined and developed for the MInD environment.
A very common objective function in design problems, especially
for commercial applications, is maximizing profit on each unit sold. If
the selling price of the product is fixed, maximizing profit is equivalent
to minimizing total expenses. However, it may be favorable to optimize
a design in terms of a subcategory of cost rather than total cost. For
example, material costs may be a major contributor to the total cost of
a design. If an expensive material is imported from another country
and economic ties between nations are deteriorating, additional costs
associated with imports or blocking the supply altogether would cause
a major cost increase on the product as a whole. Minimizing material
cost, rather than total cost, would provide a more robust solution in this
scenario. In a similar way, other categories that could be minimized
could be labor, equipment, or recurring costs.
Another objective function particularly relevant to the aerospace
industry could be indicators of backlog health. For many major
aerospace companies today, one metric closely watched by analysts
in the financial sector is the value of orders tied up in backlog and
how many years of production that backlog represents. The choice on
how to manage backlog in terms of what is “acceptable” is a business
preference, not a deterministic rule. As such, two objective functions
representing different philosophies were added: maintaining a desired
level of backlog determined by the user, and attempting to reduce the
customer wait time between order and delivery to as much as possible.
By allowing the user to investigate multiple objectives, the
optimization routine enables the designers to identify a family of
alternatives that may then be compared based on the current market
requirements. With these alternatives defined, more informed,
affordability-based design may be performed with considerations for
demand variability. Following the presentation of the visual trade
space environment in the ensuing section, selected results from the
optimization routine will be presented with a focus on illustrating the
affordability trades it enables.
TRADE SPACE ENVIRONMENT
A successful multi-disciplinary design exercise requires that all
parties involved, in this case the vehicle design, production, and
manufacturing engineers along with the marketing and management
personnel, have access to data at an appropriate level of detail to
provide effective decision support. In recent years, much research has
been focused on developing Visual Analytics as a means to effectively
present high dimensional, multi-disciplinary data to aid collaborative
design reviews [16,25]. A more detailed discussion of visual analytics
is presented in [26]. Therefore, to enable the various disciplines to
effectively communicate and assess the impact of design decisions, a
visual trade space environment was developed within JMP from the SAS
Institute.
The following subsections will describe the capabilities of the
environment with a focus on the visualizations created to investigate
the impact of demand variability on the aircraft’s design, production
plan, and program profitability in support of affordability-based trades.
The following environment builds on work that is specifically related
to aircraft design and production planning as presented in [19]. A
parametric Gantt chart and utilization chart is shown in Figure 3
alongside an aircraft performance constraint plot (Figure 4). Both were
created in a previous version of the trade space environment [19]. While
the utilization chart in [19] was labeled as a Value Stream Map, the
label was changed in this work to be consistent with current literature;
the analysis capabilities remain the same. These visuals are linked via
surrogate models to wing planform inputs and a defined production floor
layout. In this way, the impact of design decisions on both the aircraft’s
performance (through Figure 4) and producibility (through Figure 3)
can be viewed concurrently, allowing for trades that are generally
unavailable in the earlier design phases. While the previous work is
an important first step, it lacks the ability to assess production over the
entire manufacturing run in the presence of demand variability. The
previous iteration of this environment also does not contain sufficient
costing information to enable true profitability trades. The following
section describes the capabilities added to this environment in order to
address these deficiencies.
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FIGURE 3.PARAMETRIC GANTT CHART AND UTILIZATION CHART
FIGURE 4.PARAMETRIC AIRCRAFT PERFORMANCE
CONSTRAINT PLOT
Environment Capabilities
Demand and Production To help assess the impact of
production layout and wing planform design on profitability over the life
of the vehicle, an interactive demand profile was developed (Figure 5).
The demand profile allows the user to define a projected order history
(blue shaded region) derived from market research. The environment
has order histories from many major aircraft (Figure 5 presents the
747 order history as an example) that may be selected to populate
the demand profile. Additionally, the user may create a custom
demand scenario by dragging points within the interactive plot to set
the projected year to year demand. Overlayed onto the demand profile
is the current proposed yearly production plan (red dashed line). The
production plan is determined by first choosing a wing planform design
(wing area, aspect ratio, taper ratio, etc.) and then defining the year
to year factory layout. The user does this by selecting a production
year, modifying the factory layout (workstations, technicians, shifts,
and tools for the subcomponent lines), and applying the changes. The
environment then takes these inputs and calculates the yearly production
rate with the surrogate models developed using data from the Simio
models. A full description of how the production rate is calculated
and the modeling behind the data can be found in [19]. This yearly
production rate is then used for all subsequent years (the flat portions
of the red dashed line in Figure 5) until a new factory configuration is
defined by the user in an ensuing year (the jumps in the production plan
line). Once all orders have been filled, the factory is shut down and the
production rate goes to zero (as in year 51 in Figure 5).
A plot of the cumulative order backlog in terms of number of
wingboxes is also provided, as shown in Figure 6. The user can
easily see when the backlog exceeds an allowable threshold during the
production run and may use this information to help decide when to
make changes to the factory layout. These visuals, along with the
production and performance plots described in [19], enable interactive,
concurrent aircraft geometry and production plan design. The next
section describes how the designer would use the environment to design
a wingbox to meet performance requirements while maintaining a
healthy order backlog.
Production Use Case For this case, the demand history
is shown in Figure 5, and a requirement that the backlog must remain
under 300 wingboxes will be enforced. Using the advanced composite
redesigned F-86 wingbox and a wing geometry that meets customer
defined performance goals (verified using the constraint plot in Figure
4), the baseline production rate (red dashed line in Figure 5 from years 1
to 13) is sufficient to remain under the backlog constraint until year 14.
At this point, the production rate must be increased to keep the backlog
below 300 orders. The provided Gantt and utilization charts (Figure
3) can be used to intelligently make changes and avoid ineffective
investments in the production plan. Figure 3 depicts results for the
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FIGURE 5.DEMAND PROFILE POPULATED WITH THE 747
ORDER HISTORY AND A PROPOSED PRODUCTION PLAN
FIGURE 6.CUMULATIVE ORDER BACKLOG FOR THE DEMAND
AND PRODUCTION DEFINED IN FIGURE 5
baseline factory. The Gantt chart shows that there is a long delay time at
the rib non-destructive inspection (NDI) step, while the utilization chart
shows that the rib NDI station is almost fully utilized. This indicates
that the NDI station is not able to meet the demands of the rest of the
factory. Therefore, an additional station is added in year 14 to increase
the production rate, as seen by the step in production at year 14 in Figure
5. Doing so helps ensure that the backlog remains under 300 orders.
Further details about how to effectively increase yearly throughput using
the visualization environment can be found in [19]. A similar procedure
is followed throughout the rest of the production run to maintain an
acceptable backlog as it approaches the constraint. Figures 5 and 6
illustrate the overall production plan defined through this exercise.
Economic Analysis The previous section focused on
adapting the production rate by making changes to the factory layout
to maintain a specified backlog. However, since there are a myriad of
factory configurations that will have similar production rates, the cost
of each configuration must be jointly considered to determine the best
configuration. Therefore, various economic statistics and visuals are
also provided to allow the decision makers to instantaneously view the
economic impact of varying a decision variable (wing area, rib NDI
workstations, etc.) alongside the vehicle performance and production
rates.
Using surrogate models developed in [21] for the same advanced
composite F-86 wingbox and user defined economic assumptions, such
as the aircraft selling price, buyer down payment percentage, loan
interest rate, and depreciation information, a cumulative cash flow
FIGURE 7.CUMULATIVE CASH FLOW DIAGRAM
diagram (Figure 7) is generated from the time of the factory investment
to the end of production. Using the demand and production rates from
Figure 5, the cash flow diagram illustrates that the program is mostly
profitable during its mature phase of higher production from year 14
to 28. This is partially because more volume simply means more
cash inflows, but the factory is also more efficient when producing at
higher volumes. As seen in Table 7, which summarizes the economic
impact of each change to the factory configuration during the production
run, the cost per wing during the periods of higher production is less
than the baseline factory. This is due to the allocation of fixed and
overhead costs over a larger quantity of products during the periods of
higher production and more efficient utilization of some workstations
throughout the flow.
TABLE 7.FACTORY LAYOUT CHANGE SUMMARY
Year Non-Recurring Recurring Cost/Wing
Investment Cost Produced
1 $198,872,000 N/A $430,000
14 $3,000,000 $10,810,000 $386,000
22 $3,000,000 $10,457,000 $358,000
27 $0 -$14,017,000 $365,000
28 $0 -$7,250,000 $430,000
The final economic visual (Figure 8) shows the breakeven year
and return on investment (ROI) for the chosen wing geometry and
production plan against the selling price per wing. The breakeven
year describes the time before the program will be profitable, which
helps to indicate risk and the time before other investments may be
made. ROI is a key performance indicator used to evaluate a program
or division’s profitability [27], and it may be compared against the
company’s Minimum Acceptable Rate of Return (MARR) to determine
whether or not to continue the program [28]. The marketing team will
have a price range at which they believe they can sell the vehicle, so
determinations can quickly be made as to whether the proposed design
will meet the company’s MARR. Therefore, by providing access to
economic, performance, and production information concurrently, this
environment enables true affordability-based design. A summary of the
performance, production, and economic responses available through the
visualization environment is presented in Table 8. The results from
the optimization routine, described in the next section, will further
illustrate the interactions between wing geometry, production, and
demand variability and their impact on affordability-based design.
7
Copyright © 2015 by ASME
FIGURE 8.BREAKEVEN YEAR AND RETURN ON INVESTMENT
VS. PRICE PER WING
TABLE 8.SUMMARY OF RESPONSES AVAILABLE FROM
VISUALIZATION ENVIRONMENT
Performance Production Economics
Fuel weight Production rate Capital cost
Gross weight Workstation utilization rate Tooling and machine cost
Take-off field length Component flow time Material cost
Landing field length Component waiting time Labor cost
Turn rate Component processing time Overhead cost
Stall speed Technician utilization Return on investment
Rate of climb Yearly Backlog Cumulative cash flow
Range/Endurance Excess Inventory Breakeven year
OPTIMIZATION RESULTS
The previous section described the manual trade space exploration
that is very helpful in determining data trends during design reviews;
however, because of the sheer size of the space and high dimensionality,
manual identification of an optimal solution is impractical. Therefore,
this section will present results from different optimization studies
using the developed environment. The first study investigates how the
optimum wing geometry changes for different cost objective functions
using a simple trapezoidal demand profile. The next section presents
results from a screening test to determine which performance and
backlog constraints are most influential to overall production cost and
wing area. Finally, results from a multi-objective optimization for an
aircraft performance metric (Stall Speed) and total material cost are
presented.
Optimum Wing Geometry For Various Cost Objective
Functions
The metric chosen for the objective function can have a large
impact on the optimum design, especially if certain metrics are inversely
correlated. To investigate this effect, the wing geometry and factory
settings were optimized for the unconstrained case over a trapezoidal
demand profile to represent a simple ramp-up, ramp-down order
scenario. Some representative results are shown in Figure 9.
The optimal wing geometry changes based on the objective
function used, as expected. Figure 9 shows the wing geometry from
three different cost rollup objective functions overlaid on the original
F-86 Sabre geometry. The wing area when minimizing material cost is
the smallest of the three geometries at 240 ft2, while the labor and total
production costs have wing areas of 259 ft2and 296 ft2, respectively.
A smaller area means smaller components and less material. The labor
FIGURE 9.OPTIMUM COMPOSITE WING GEOMETRY FOR
DIFFERENT COST OBJECTIVE FUNCTIONS
cost design has a smaller aspect ratio than the other costs, 4.42 versus
5.45 for the material cost design. Decreasing the aspect ratio reduces
the number of ribs; through past experience with the MInD composite
model, the rib production line and its corresponding NDI stations are
often on the critical path. Alleviating this bottleneck through a smaller
aspect ratio wing makes the overall production flow more efficient with
less idle workers, translating into the lowest cost devoted to labor.
Optimization Constraint Screening Tests
With the optimization algorithm and objective functions in place,
the design space can be studied by determining which performance and
production constraints are the most influential on the choice of wing
geometry and factory configuration. One way to quantify sensitivities
is through the use of a factor screening experiment. This type of
experiment is typically performed to identify factors that have large
effects on the response [29]. In this application, the factors are the
performance and production constraints imposed on the design, whereas
the responses are the optimum design variable values for the given
constraint scenario.
A major observation from the factor screening study is that the
backlog requirement has major cost implications and can even drive
the values of certain wing design variables. For both aluminum and
composite designs, the optimal design for objective functions related
to the various breakdowns of cost (total production, material, labor,
equipment, recurring, and breakeven year) were most impacted by
the backlog threshold. As an example, the screening results for the
total production cost associated with the composite designs are shown
in Table 9. The backlog-cost relationship is negative in direction,
indicating the relationship is negatively correlated; increasing the
backlog requirement to a higher, less stringent value tends to lower the
total production cost. This effect makes sense, as a lower tolerance to
large backlogs means production rate changes will be more common,
implying higher costs due to more equipment and personnel acquisitions
that are not necessary if the backlog is allowed to be a larger value.
Another observation showcased in Table 9 is how the original
performance requirements set for the design can have a large impact
on production costs. After backlog, the next most influential constraints
on total production cost are landing field length, stall speed, and takeoff
field length, respectively. These constraints are direct drivers of wing
area: increasing the value of these constraints allows for a smaller wing.
A smaller wing means fewer raw materials and less manufacturing
time necessary to build the wing, decreasing the total production cost.
8
Copyright © 2015 by ASME
TABLE 9.INFLUENTIAL CONSTRAINTS FOR COMPOSITE
OPTIMUM TOTAL PRODUCTION COST
Constraint Relative t-Ratio Plot p-Value
Backlog <.0001
LFL <.0001
Stall speed <.0001
TOFL 0.0128
Knowing the performance sensitivities on high-level cost parameters
early in the design process allows the designer to understand the cost
implications of certain requirement changes and react appropriately.
In addition to the objective function values, the impacts of the
overall requirements on the optimal design parameter selections can be
studied. The sensitivities for the wing area of the aluminum concept
optimized for total production cost is shown in Table 10. The results
show what is expected: performance constraints such as landing field
length, takeoff field length, and stall speed are major drivers of the
resulting wing area. However, the interesting result is that backlog is the
main driver of wing area. Wing area is a major driver for the dimensions
of each component within the wingbox structure; scaling up the size of
the wing increases the necessary thickness of each component, as well
as lengthening the spars, lengthening the ribs, and potentially increasing
the number of stringers. Having a very low backlog tolerance requires
the factory to have the ability to shift to higher production rates very
quickly, and a smaller wing would ease the transition to those higher
production rates because the components will be smaller (and therefore
have a shorter manufacturing time) than larger wings.
TABLE 10.INFLUENTIAL CONSTRAINTS FOR ALUMINUM WING
AREA OPTIMIZED FOR MINIMUM TOTAL PRODUCTION COST
Constraint Relative t-Ratio Plot p-Value
Backlog <.0001
LFL <.0001
TOGW <.0001
TOFL 0.0002
Stall Speed 0.0005
The impact of constraints on the choice of initial factory layout is
not as clear. Through past experience with the MInD environment and
its surrogate models, the main factory settings to target when requiring
a larger throughput is the number of assembly workers and rib NDI
stations for the aluminum and composite structures, respectively [19].
The factor screening results show that there are no specific constraints
that dominate what selection is made. Previous experience with the
surrogate models has shown significant dependency of factory settings
on each other when throughput changes. There are many variables that
have little impact on throughput individually, but a large impact when
changed along with other settings. Factory setting interdependencies
may be a reason why the outside influences of high-level constraints do
not have as direct of an effect on factory settings as they do on wing
geometry.
Multi-objective Optimization Results
For many design problems, multiple criteria are considered to
determine the design to select. When multiple criteria are critical to
the performance and desirability of a design, especially for competing
objectives, optimizing in a multi-objective way provides insight into the
best “compromise” designs. For example, maximizing the performance
of a vehicle may come at a higher production cost. Examining the most
efficient designs in terms of criteria value tradeoff gives the designer
multiple solutions to consider in deciding the design to select, based on
corporate strategy and preferences.
Multi-objective capabilities were created for this environment
using a multi-objective formulation of genetic algorithms, NSGA-II.
The algorithm was modified to accept any two objective functions
relating to production costs, backlog management, and aircraft
performance. An example of this capability is shown in Figure
10. This specific example shows how stall speed, a performance
requirement that directly influences takeoff and landing capabilities,
can be traded against total material cost for the aluminum production
concept. Decreasing the stall speed requires a larger wing area and
increases the raw materials required for manufacturing. Pareto frontiers,
such as Figure 10, are powerful tools for designers because they display
the frontier of non-dominated solutions, which enable compromise
tradeoffs. Additionally, the Pareto frontiers display the best value a
metric can achieve when the other metric is held constant. For example,
if the aluminum design has a stall speed of 70 ft
/s, the absolute best
material cost that can be achieved for the given stall speed is $44
million. If the stall speed requirement is relaxed to 100 ft
/s, the best
material cost is reduced to $41.2 million. Multi-objective optimization
can provide insight on the impact of changing requirements on the best
design possible with respect to cost, production, or performance metrics,
which is the primary goal of affordability-based design and acquisition.
FIGURE 10.REQUIREMENT VS. COST PARETO FRONTIER FOR
AN ALUMINUM CONCEPT
CONCLUSIONS AND FUTURE RESEARCH
While implementations of PLM, IPPD, and the LAI during the
1990s and early 2000s provided a major step towards affordability-based
design, the struggles of more recent programs illustrate that there is
room for improvement. Furthermore, recent changes to the aircraft
manufacturing industry, including the transition to the usage of more
advanced composites and the increase in aircraft demand variability,
have exacerbated the challenges faced by designers and manufacturers.
The traditional design paradigm saw most manufacturing and cost
analysis performed in the later stages of design when only small design
changes and on-line process improvements are possible; however,
because a significant amount of the manufacturing cost is locked in
during the earlier stages of design, understanding the downstream cost
and manufacturability implications of early decisions is paramount to
designing an affordable product.
The methodology presented in this paper integrates multiple
aircraft design disciplines with various economic and financial metrics
to enable trades between aircraft performance, production rates, and
manufacturing costs throughout the program’s production run in the
presence of demand variability in the early phases of design. While
9
Copyright © 2015 by ASME
affordability is primarily related to defense acquisition, the ability
to balance performance and cost during design is imperative for
commercial applications as well. By leveraging visualization techniques
and a multi-objective optimization routine, design engineers and
financial, marketing, and management personnel can effectively work
together to produce an affordable design.
The trade space environment presented in this paper was developed
to facilitate effective communications across the various business units
during design reviews. By immediately projecting the downstream
impact of various decisions on performance, cost, production, and
cash flow/profitability, trades that are usually hidden during early
design are now apparent. Additionally, through the incorporation of
an optimization routine, efficient exploration of the design space and
identification of influential requirements and constraints is possible.
Therefore, through this methodology and visualization/optimization
environment, cost reduction design decisions can be identified early to
enable more affordable designs in the presence of demand variability.
Future work will focus on expanding the capabilities of the
optimization routine to enable more multi-objective trades, especially
between the various manufacturing concepts and performance
requirements. Additionally, focus will be turned to increasing the
design’s robustness to manufacturing variability. By their nature,
aerospace vehicles require demanding manufacturing tolerances, so
identifying design decisions that can reduce these demands and
assessing the impact on cost and time will be an important next step
towards improving the early stages of vehicle design.
ACKNOWLEDGMENTS
This research is funded by Boeing Research and Technology
(BR&T). We would like to thank Howard Appelman, Steven Wanthal,
Keith Rupel, Adam Graunke, and Gabriel Burnett from The Boeing
Company for their expertise and support. We would also like to
thank Todd Schmidt, Ben Murdock, C´
eline Bonicel, and Young-Jin
Kim for helping with the development of the models and visualization
environment. Finally, we would like to thank the previous MInD Grand
Challenge teams for developing some of the earlier models as well as
for providing invaluable guidance.
NOMENCLATURE
AFP Automated Fiber Placement
DoE Design of Experiments
GA Genetic Algorithm
HLU Hand Layup
IPPD Integrated Product and Process Development
LAI Lean Aircraft Initiative
MARR Minimum Acceptable Rate of Return
MInD Manufacturing Influenced Design
NDI Non-Destructive Inspection
PLM Product Lifecycle Management
ROI Return on Investment
Rper f ormance Performance requirements
S Wing planform area
TOGW Take-off Gross Weight
XWingbox design variables (Sref, AR, TR, Sweep, Rib Spacing, Rib
Angle, Design Range)
XLWingbox design variable lower bounds
XUWingbox design variable upper bounds
YLFactory configuration design variables lower bounds
YUFactory configuration design variables upper bounds
Yi,jFactory configuration design variable j (workstations, workers,
tools, schedules) for the ith production year
changesmax,jMaximum number of factory configuration changes allowed for a
specific factory design variable (j)
f(X,Y)Objective function (cost, performance, backlog, etc.)
nFactoryVariable Number of factory configuration variables for any given production
year
nyears Number of years the factory is in production
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... Project and process planning and scheduling can be classified into multiple levels based on the level of detail and abstraction considered [60,132,136,164,165,175]. is designed to help the transition from the middle/short-range (also referred to as ...
Thesis
Full-text available
The knowledge and value gained from collecting data and being able to monitor vehicles’ performance, safety, reliability, etc. have resulted in a sharp increase in the number of sensors being installed on modern aerospace vehicles. Sensor installations, which are commonly performed manually, lead to increased risk for installation errors and quality issues. These disruptions, in turn, contribute to the program cost overruns, increased schedule risk, and production delays seen throughout the industry. As such, reducing the risk and impact of manual installation tasks on aerospace production flows is becoming increasingly important for such highly schedule- and cost-constrained vehicles. Robust scheduling methodologies, which aim to build schedules with reduced risk of cost or time overruns by minimizing the impact of disruptions, have the potential to meet the requirements of these scheduling problems. Despite the benefit to be gained by implementing robust, detailed project scheduling methodologies, traditional, deterministic strategies still tend to dominate the industry. Two research challenges must be overcome to support the implementation of such robust scheduling techniques in an industrial setting: First, the project scheduling methodologies in use today struggle to model and optimize real-world systems. The increasing complexity of modern aerospace vehicles is only going to exacerbate these difficulties. Second, the transition of new planning and scheduling practices from academia to an industrial setting is commonly challenging. Moreover, this transition is not generally discussed alongside the development of new methods. To address these challenges, this dissertation focuses on the development, implementation, and evaluation of a new planning methodology, named PORRTSS: Production Optimization to Reduce Risk Through Simulation-based Scheduling. A representative case study is used to test the methodology’s capability to model a real production environment and search for improved scheduling options. The case study involves planning sensor installation processes within a provided production schedule to reduce the risk of production delays. Traditional scheduling techniques provide a strong framework to plan and optimize, at a medium level of detail, the completion of primary production processes (e.g. structural assembly, system integration, etc.). However, fully defining the interactions and logic required to evaluate the impact resulting from the sensor installations in this scheduling framework is challenging. The discrete-event simulation paradigm simplifies the definition of these production rules and constraints; however, DES models commonly require too much detail, modeling effort, and optimization time/resources to be useful during pre-production planning. The developed methodology addresses this gap by integrating the process optimization strengths of scheduling with the modeling flexibility of simulation. This enables the fast generation of a limited fidelity simulation that can evaluate the impact of sensor installations to support simulation-based schedule optimization. Even with an optimization framework in place, the deployment of scheduling methodologies developed in academia to an industrial setting remains challenging. A primary barrier that limits the implementation of developed scheduling practices is poor interactions between the system and the human planners. The developed methodology works to overcome these challenges by: 1) increasing the transparency of the planning process, 2) improving collaboration among the stakeholders, and 3) enabling the stakeholders to directly modify the sensor installation plan. Increased transparency and improved collaboration is achieved by developing a decision-support tool that provides both system- and detailed-level views of the planning results. Finally, this research does not claim to provide the answer, but instead, recognizes that there may be additional “soft” constraints. As such, it also provides planners with the capability to make manual modifications to the optimized production plans. This ultimately leads to a more implementable and beneficial planning methodology when compared to the many rigid methods developed in academia. The PORRTSS methodology begins by identifying process constraints contained within a provided medium-level production plan. This schedule is accepted as truth, and the identified process constraints are utilized to automatically generate a baseline discrete-event simulation. The DES model contains process logic to control sensor installation processes, and using this logic, the simulation can estimate the impact of a parametrically defined sensor installation plan. With a parametric model in place, a multi-objective, meta-heuristic optimization algorithm (Non-dominated Sorting Genetic Algorithm-II) is linked to the simulation. The optimizer sets the locations within the primary production plan during which each sensor is installed. The flexible nature of the optimization routine enables the inclusion of a variety of objective functions, including process time and heuristic risk metrics. Once convergence is achieved, the resulting non-dominated points are fed into a data analysis and decision support environment. The decision making system is included to support the implementation of the methodology. An initial system-level view and ranking algorithm enable SMEs to quickly identify points of interest. These can then be compared in more detail to identify similarities and differences between the selected plans. A detailed Gantt chart is also utilized to improve transparency and help understand the reasons for potential problems. Finally, the user is able to make manual modifications to a selected plan to include any additional knowledge or understanding. With the PORRTSS methodology in place, the following experiments are performed to test its ability to overcome the aforementioned research challenges. Focusing on the first research challenge, the difficulty in modeling and optimizing the systems of interest, the appropriateness of the simulation logic and model generation strategy is tested. This is accomplished by generating a model from a schedule for a major sub-assembly of a “real-world” aerospace vehicle. The simulation is shown to appropriately match the baseline schedule and estimate the impact of parametrically defined sensor installations. The appropriateness of the optimization integration is then tested by linking the NSGA-II to the simulation model. An initial experiment conducted with deterministic simulation evaluations is shown to improve the objectives of interest in a short time, which demonstrates that the optimization strategy is effective for the problem of interest. This experiment is then expanded to investigate the impact of directly evaluating the risk in a schedule. This is accomplished by allowing for uncertainty in the simulation and running multiple replication per case to estimate the output distribution of the process time. The optimization is then seeded with generation 500 from the deterministic run and evaluated with this additional information. When comparing the optimization runs with and without the robustness information, the optimization considering robustness is shown to make immediate improvements to the population, especially in the process time risk. While this indicates that the additional information is effectively utilized by the optimization routine, a significant number of further runs are needed to make a generalized conclusion. Despite this, these experiments demonstrate that the developed methodology is able to effectively model and efficiently optimize the sensor installation plan. To address the second research challenge, strategies to improve the “implementability” of the developed methodology are investigated. To test the scalability of the methodology, the modeling and optimization strategy are applied to a range of problem sizes and complexities. The results demonstrate that the methodology is capable of handling models representative of the largest size expected to be seen in an industrial setting. Alternative, point-solution optimization algorithms are also investigated to attempt to improve the optimization’s speed. These are shown to perform adequately when optimizing the deterministic model; however, when considering the stochastic model, their performance does not appear to leverage the schedule risk evaluations provided. Therefore, it cannot be shown that the point-solution algorithms expand the applicability of the methodology. A final experiment is conducted to investigate whether the decision-making environment increases the acceptance and deployability of the methodology. Results generated by the NSGA-II for a real-world planning problem are propagated to the decision-making tool. This tool and results are then provided to the industrial engineers, manufacturing engineers, and avionics experts to down-select to a final plan for execution. Following this exercise, the SMEs confirmed the feasibility of the provided plans and leveraged the decision-making system to down-select and compare scenarios of interest. Overall, the real-world implementation demonstrates that the inclusion of the decision-support environment increased transparency and acceptance of the methodology. From these results, the PORRTSS methodology is shown to overcome the two identified research challenges. The modeling and optimization strategy enables the automatic generation and evaluation of feasible alternative installation plans. The inclusion of heuristic robustness metrics and process time risk enables the identification of more robust installation plans. Then, the transparency and freedom provided by the decision-support system is shown to increase the approachability and deployability of the methodology. Ultimately, this methodology enables the replacement of a manual planning process with one that can better estimate and reduce the system-level impact of small installation steps, which can be large contributors to the time to complete a schedule.
... Some effort has already been made in order to leave the design-for-performance layer, and to integrate more business-related disciplines. Design for manufacturing [8][9][10] incorporates the manufacturing aspects of design and tries to minimize the manufacturing costs of a particular architecture. Frank et al. 11 address the problem of evolving requirements in the design of unconventional aerospace vehicles. ...
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Lean Thinking is the paradigm that enabled Toyota to rise to the best and largest auto company in the world. The paradigm includes not only Lean manufacturing but also extraordinary effective Product Development and Systems Engineering, as well as a culture based on Respect for People. Systems Engineering is regarded as a technically sound process but often burdened with waste and inefficiencies. Lean Systems Engineering is a new body of knowledge applying the wisdom of Lean Thinking to Systems Engineering. Systems Engineering and Lean have overlaps and differences, but both represent processes that evolved over time with the common goal of delivering product or system lifecycle value to the customer. Lean Systems Engineering represents synergy of the two, leading to superior systems engineering process. Most emphatically, Lean Systems Engineering is not a re-packaged FBC or Acquisition Reform“. Lean Systems Engineering does not mean ”less Systems Engineering”; it means more and better Systems Engineering, with better preparations, planning, front-loading, training, and more common sense, leading to better program execution. Lean Enablers for Systems Engineering is a product designed by 14 experts from industry, academia, and U.S. and foreign governments, supported by 115+ strong Lean Systems Engineering Working Group of INCOSE. Lean Enablers are formulated as 194 “do's” and “don'ts” of Systems Engineering practice focused on Mission Assurance/Product Success and elimination of waste. The workshop will cover three parts:
Conference Paper
This paper presents a Multidisciplinary Design Optimization (MDO) method to minimize the Direct Operating Cost (DOC) and maximize the Surplus Value (SV) for aircraft components restricted by structural constraints. A parameterized DOC/SV analysis is proposed to link cost performance and the structural design properties. This analysis is based on component design parameters including geometric sizes, material types and process plans. The geometric parameters are used as the optimization variables for both the cost/value and the structural performance evaluation. The optimization algorithm is coupled with Genetic Algorithm and gradient based optimization with user supplied gradient values. A series of optimization studies are performed for weight minimization, cost minimization and value-driven optimization separately. A further comparison incorporating the problem definition, optimal design solutions and computation time is conducted to evaluate the presented method.
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World-wide markets are becoming increasingly competitive, and in order to sustain market share organisations are developing a customer-oriented approach for designing and producing high-quality, high-value products. The philosophy of simultaneous engineering has been proposed as a potential means of improving product development practice. This philosophy involves simultaneously satisfying the functionality, reliability, manufacturability, and marketability concerns of new products in order to reduce product development time and cost, and to achieve higher product quality and value. In this paper the concept, objectives and principles of simultaneous engineering are introduced. The past and present research into computer-aided systems for the support of simultaneous engineering is present, reviewed and classified. This will both allow the current state of the art to be assessed, and enable the identification of future research that will contribute to the realisation of computer aided support for Simultaneous Engineering.