INFORMS Award entry for GM Europe successful PLANET System enterprise optimiztion application

1. Developing and communicating an enterprise’s global strategy
through the deployment of optimization technology
Presented at POMS Conference on Competitiveness and Wealth
Creation - the Role of Production and Operations Management;
proceedings published by the University of Cape Town, South Africa;
June, 1998
John M. Lucas Dr. Hernan Wurgaft
john.lucas@eds.com
EDS Global Planning / Marketing Decision Support
750 Tower Dr. - 6D, Troy, Michigan, USA 48098
Abstract
This paper addresses how optimization technology can be applied at the
enterprise level to simultaneously strategize the following:
n selection of specific contracts/products/services to produce/deliver;
n determination of when, where, and how to design, manufacture, deliver,
and market the enterprise’s products/services;
n acquisition, transformation, usage, and/or disposal of specific enterprise
resources;
n timing of critical decisions.
The primary focus will include:
n description of existing cross-functional (product design, marketing,
manufacturing, finance, resource management, and logistics), cross-
enterprise, and cross-geographic optimization modeling capability;
n implications of integrating long term (traditional planning) and short term
(scheduling) planning horizons;
n importance of leveraging optimization technology as a key business
process enabler to develop, deploy, integrate, maintain, and
communicate optimization-based strategy enterprise-wide
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2. Business Situation
Consider an enterprise with:
• •
multiple existing and potential existing in-house product
products or lines of service manufacturing,
• subassembly, and assembly
markets on each continent, each
facilities located in several
with varying product
countries
preferences / requirements /
•
growth potential expensive facility/tooling costs
• •
a 3 year minimum concept to constraining labor agreements at
production cycle some facilities
• •
a 20 year average product life in significant manufacturing
the marketplace complexity
• •
significant opportunities for 50% in-house costs, 50%
alliances and joint ventures outside purchased materials
• •
existing in-house product design costs/revenue in local currencies
•
centers located in several an extensive inbound, interplant,
countries intraplant, and outbound
• purchasable design help logistics system
•
worldwide international trade
considerations
Business Requirement
Assume that the senior leadership wants this enterprise to integrate and
manage the following strategy components:
• global business vision and • global customer-market strategy
• global operations and logistics
strategy
• global product and technology strategy
strategy
Furthermore, assume that management desires a “continuously accessible
decision support model, with optimization capability” representing a detailed
0-10 year plan view of the enterprise that could address the following decision
support issues simultaneously through time:
2

3. • •
continually evaluate existing determine how to best use the
and potential products / in-house or contracted
projects and determine and product and process
recommend which products engineering design resources
•
to keep, launch, design, or determine if engineering
drop (and when) resources should be added,
• evaluate existing and potential eliminated, or retrained
markets and determine a (which & when)
•
deliverable product delivery determine existing and potential
plan, including pricing, for facility requirements,
each selecting usage, disposal,
• determine new tooling and acquisition alternatives
•
requirements and develop a optimize the capital spending
product-tooling acquisition / plan
allocation / disposal plan for • satisfy management policy and
each facility objectives
The objective is to develop and build a “living model” of an enterprise’s
operations that transcends a 10 year, rolling planning horizon (always
spanning the current year +1 through the current year +10). The model is
called a “living model” because, once built, it is intended to be maintained,
updated, and modified continuously to mirror the actual enterprise. During the
course of business, this “living model” can be accessed for a variety of “What
is?” and “What if?” decision support analysis since it reflects the current
enterprise view and most known strategic alternatives
Traditional Barriers to Enterprise-wide Modeling
Traditionally, attempts to satisfy the modeling requirement described above
have been significantly hindered by the enterprise’s functionally protective
immune system (Illustration A). The application of cross-functional, cross-
geographic, and cross-enterprise optimization models with realistic scope,
detail, and size in the manufacturing and services industries has been limited
by:
(Barrier 1) sub-process focus setting which is biased by the scope and
n
organizational responsibility of the study team participants. Few
decision makers seem concerned that functionally focused models are
really significant efforts at trying to sub-optimize only a portion of a
larger whole (E.g. Where to Make?) as opposed to simultaneously
evaluating the impact of more complex interactive decisions (E.G.
What To Make? as a function of Where to Make?).
3

4. Illustration A
Traditional Modeling Within Functional Scope
HOW TO
SALES FINANCIAL
SHOULD IT BE
CAPITILIZE?
STRATEGY MADE? STRATEGY
WHAT TO
WHAT TO
MARKET?
MAKE? ENGINEERING
HOW TO
STRATEGY
PRICE? WHICH
FINANCE R&D DESIGN?
WHEN TO
WHEN TO
ENTER / LEAVE
DESIGN?
MARKET? PRODUCT
ENGINEERING
SALES HOW TO
MARKETING TRADITIONAL DESIGN?
STRATEGY FUNCTIONAL
VIEW OF
WHICH WHERE TO
STRATEGY
MARKETS MARKETING PROCESS / DESIGN?
TO SATISFY DEVELOPMENT MFG.
& WHEN & ENGINEERING
ALLIANCE?
HOW?
HOW TO MATERIALS WHETHER TO
DISTRIBUTE? MANAGEMENT MAKE OR
MANUFACTURING
BUY?
LOGISTICS
STRATEGY HOW TO MANAGE
RESOURCES?
WHEN TO MAKE /
PURCHASE?
HOW MANY TO MANUFACTURING
PURCHASING WHERE TO
MAKE / STRATEGY
STRATEGY MAKE?
PURCHASE?
(Barrier 2) sub-process objective setting is also biased by the scope and
n
organizational responsibility of the participants. Generating the
functional model of the month seeking only a few optimality
objectives (E.G. maximize profit or minimize investment) is not the
answer.
(Barrier 3) reliance on fixed time frame based analysis instead of time-
n
based analysis where potential decisions in one time period can affect
potential decisions in another, and vice versa across the entire planning
continuum.
(Barrier 4) single usage model design, resulting in “throw away” models
n
that are typically only used once but are frequently re-built in some
fashion because the need to model never really goes away.
(Barrier 5) reliance on “a priori” scenario evaluation in which the
n
modeling is limited to scenarios that management conceives and
excludes scenarios, or combinations of scenarios, that might be
machine generated using a more modular decision modeling approach.
(Barrier 6) fear of data and complexity, in which the vast numbers of
n
variables that could be included in a model is perceived as a huge data
collection and data maintenance requirement when in reality most
models are only sensitive to fewer than 15% of the variables.
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5. Feasibility
The barriers mentioned previously are mostly cultural and organizationally
motivated and can be overcome through senior management support of an
enterprise process approach; today, there are no technical barriers to
developing, managing, and solving these large, complex mathematical
models of the enterprise.
Given that an enterprise’s key/core processes can be described by some
value chain through time (Illustration B), the planning for these same core
processes could collectively and simultaneously be optimized through
time.
Illustration B
Sample Manufacturing Enterprise Core Process Model
Research Concept to Order to Sales to
Support
to Plan Production Cash Reorder
• Provide Leadership • Identify Businesss • Establish Req’ts • Process Orders • Execute Sales Strategies
• Manage Resources - New Business Opport. • Design Products - Develop Schedule • Obtain Sales Order(s)
• Manage Public Rel. - New Products/Services • Design Processes - Schedule Mfg. • Provide Cust. Support
• Manage IT - New Customers • Design Tooling - Schedule Deliverables - Service Customer
• Manage Facilities • Understand Customer • Design Packaging • Mfg. Product - Service Product
• Provide Legal Support - Market Research • Plan Mfg. • Manage Logistics - Manage Warranty
• Manage Finances - Clinics • Manage Sourcing • Manage Suppliers
• Manage Administration • Conduct R&D • Plan Materials • Manage Receivables
• Develop Business Plan(s) • Plan Logistics • Manage Product
- Develop Product Vision • Validate Processes
- Develop Product Plan
- Develop Resource Plans
- Develop Technology Plan
- Develop Marketing Plan
- Develop Production Plan
(These enterprise core process definitions are purely intended for
illustration only. Note that each industry has its own fairly standard, core
process definitions which can be benchmarked against other enterprises;
some of these are unique to an industry, others can be shared by many
industries (such as Order to Cash). The important thing to note is that these
core process descriptions do not represent organizational functions but
enterprise cross-functions.)
With this holistic approach, it is very feasible to simultaneously evaluate
alternatives on “What to Make?” while evaluating “When/Where/How to
Make?” and/or “If to Make?”; cross-functional relationships exist across
these core processes (Illustration C).
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6. Illustration C
Modeling Across the Enterprise’s Core Processes
GLOBAL
HOW TO
GLOBAL SHOULD IT BE
CAPITILIZE?
PRODUCT &
CUSTOMER MADE?
WHAT TO WHAT TO
TECHNOLOGY
/ MARKET MARKET? MAKE?
STRATEGY
STRATEGY
RESEARCH WHICH
HOW TO
TO PLAN DESIGN?
PRICE?
WHEN TO
DESIGN?
WHEN TO
HOW TO
ENTER / LEAVE
SALES DESIGN?
ENTERPRISE
MARKET?
PROCESS VIEW OF CONCEPT
TO
WHERE TO
TO
REORDER STRATEGY DESIGN?
DEVELOPMENT PRODUCTION
WHICH
MARKETS ALLIANCE?
TO SATISFY
& WHEN & WHETHER TO
HOW? MAKE OR
BUY?
ORDER TO
HOW TO
CASH HOW MANY TO
DISTRIBUTE?
MAKE /
PURCHASE?
WHEN TO MAKE /
PURCHASE?
HOW TO
MANAGE
WHERE TO
GLOBAL OPERATIONS & RESOURCES?
MAKE?
LOGISTICS STRATEGY
PLANETS™ Modeling Technology Availability
The actual optimization model of a global enterprise through time will
require both linear (LP) and mixed integer (MIP) formulations. Mixed
integer models, using bivalent variables, are very effective in the long
range planning domain where go/no-go policy decisions and investments
are required; linear programming or network models are more than
adequate for the short term planning horizon.
While many business evaluations can be supported by mathematical
models given enough time, knowledgeable modeling resources, and
modeling ingenuity using commercial optimization packages, a proprietary
modeling aid that easily allows enterprise process owners to describe their
business as a model has been developed by EDS. This modeling tool is
called PLANETS™ (Electronic Data Systems Inc.) and it provides the
enterprise team with the means to:
n use generic “building block” entities (Illustration D), with appropriate
variable attributes (duration, availability, capacity, status, linkages,
financials, quantity, efficiency, etc.), to describe and capture the
essence of any business enterprise and develop a detailed “living
model” representing all or part of an enterprise, describing business
problems in business terms;
n automatically translate this “building block” representation into a
mathematical representation of the business;
n automatically solve the optimization model and convert the output to
business legible reports;
n maintain and update the model as a representation of the enterprise.
6

7. Illustration D
PLANETS™ Building Blocks
POLICIES /
OBJECTIVES
LOGISTICS FACILITIES
MARKET
VENDORS / SOURCES
PRODUCTS /
SUB - PRODUCTS
MATERIALS EXISTING
RESOURCE CENTERS
ROUTINGS
POTENTIAL
RESOURCE CENTERS
GEO-POLITICAL
FACTORS
STRATEGIES
TIME / FINANCIALS
PLANNING HORIZON
Model Structure
This “living model” of the entire enterprise is intended to be represented as
a single mathematical programming construct and is easily structured in
PLANETS™ using the available “building blocks”. Virtually all variables
that PLANETS™ can consider are time-based; this means that these
variables have dependencies in all relative past, present, and future time
periods; these variables can vary over time, across the model’s planning
horizon, providing the ability to dynamically create and evaluate unique
scenario combinations as they relate to current and future conditions or
potential decisions (with independent, dependent, contingent, or mutually
exclusive cross-dependencies). This large, ongoing, reusable model is
structured around interactive sub-models of the four core processes and is
designed to evaluate all of the stated short term and long term planning
issues:
Research to Plan Sub-Model:
n
Which markets to target? Evaluate different marketing opportunities;
n
evaluate market diversions/cannibalization and select from different
product set price-volume alternatives; evaluate different
industry/competitor counter and counter-counter market strategies.
n When to remove a product from a marketplace? Determine when it
becomes a better strategy to remove a product from a market.
n When to launch a product in a marketplace? Actually have the model
determine the time period to launch a new product in each market; this
may be a planned replacement for an aging product line or a new entry
altogether; evaluate time market-product-time diversion strategies.
n Should it be made? Determine the mix of products or engineering design
projects to include in the optimum product portfolio; provide a go/no-
go decision in any given decision time-frame.
Concept to Production Sub-Model:
n
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8. What to make? Determine the optimum product (project, service)
n
portfolio that maximizes DCF/ROI and is engineering resource
compliant through time. Select from existing alternative
product/service alternatives, some of which may be dependent or
contingent upon other existing or potential products.
Which design? Select from alternative designs that require different
n
investment, materials, design/manufacturing lead times and processes,
levels of engineering design resource.
When to design? Actually have the model select the starting time period
n
for the product design project and targeted manufacturing startup;
evaluate time diversion alternatives.
How to design? Select the design process and the product and process
n
engineering design resources to be applied to this product/project that
meet targeted level of resource compliance /utilization; select from in-
house or contracted resources.
How to utilize manpower/skills (long term)? Select the optimum
n
resource acquisition, skill retraining, transition, relocation/mobility,
and/or rationalization strategies.
Whether to make or buy (long term)? Evaluate make-versus-buy
n
alternatives; select optimum strategies.
How to make (long term)? Select the best production process(es) /
n
routings throughout the entire planning horizon; select from existing,
reconfigured, or totally new / “greenfield” production facilities and
allocating specific production, in time, to each.
How many to make (long term)? Determine the optimal quantity of
n
product to be assigned to each production / delivery resource in each
time period in the entire planning horizon.
When to make (long term)? Actually have the model select the time
n
period(s) for the targeted manufacturing startup, facility closure,
facility reconfiguration, or new facility construction; optimally
determine the best production tooling purchase / installation / salvage /
reconfiguration / relocation strategies throughout the entire planning
horizon; actually assign production build schedules to each
manufacturing resource throughout the entire planning horizon; plan
inventory build and usage strategies; evaluate market-product-time
diversion alternatives.
Where to make (long term)? Determine the facility locations to be
n
allocated production over the entire planning horizon; if a new facility,
optimize the site selection process.
How, when, and where to purchase (long term)? Select from different
n
in-house manufacturing/delivery alternatives and/or select from
different vendor material/component price/volume sourcing
alternatives; develop long-term vendor sourcing strategies; determine
optimal geographic sourcing patterns (inbound shipments,
transhipments); determine vendor tooling capitalization strategies and
strategies involving vendor value-added product development and/or
sub-production.
8

9. Order to Cash Sub-Model:
n
How to utilize manpower/skills (short term)? Select the optimum
n
resource acquisition, process/project assignment, utilization, and/or
rationalization strategies.
How to make (short term)? Select the best production process(es) /
n
routings throughout the entire planning horizon; this involves selecting
from existing, reconfigured, or totally new / “greenfield” production
facilities and allocating specific production to each.
When to make (short term)? Actually have the model select the time
n
period(s) for the targeted manufacturing startup, and tooling ramp-up /
ramp-down; actually assign production build schedules to each
manufacturing resource throughout the entire planning horizon;
evaluate time market-product-time diversion alternatives; determine
inventory build and usage strategies.
Where to make (short term)? Determine the facility locations to be
n
allocated production over the entire planning horizon.
How many to make (short term)? Determine the optimal quantity of
n
product to be assigned to each production / delivery resource in each
time period in the entire planning horizon.
How, when, and where to purchase (short term)? Select from different
n
in-house manufacturing/delivery alternatives and/or select from
different vendor material/component price/volume sourcing
alternatives; develop short-term vendor sourcing strategies; determine
optimal geographic sourcing patterns (inbound shipments,
transshipments).
Sales to Reorder Sub-Model:
n
Which markets to satisfy and how? Determine which products get
n
assigned to specific markets; determine the optimal quantity of product
to be assigned to each market per time period in the entire planning
horizon; determine which product / delivery facilities will deliver to
which markets and in what quantities and when.
n How to distribute? Determine optimal interplant and outbound logistics
patterns throughout the entire planning horizon; select the optimal
product-market distribution volume allocation and pattern; select the
optimal packaging/boxing (reusable or throwaway container)
strategies; develop short term and long term logistics strategies and
shipping patterns; determine whether product should be shipped direct
from production or from inventory; evaluate warehouse / PDC
strategies.
n When to enter / leave a market? Determine market entry and exit
strategies; develop market penetration ramp-up strategies.
n How to price? Select the optimum pricing strategy for each product in
each market, over time; evaluate price/volume elasticities and market /
time diversion alternatives.
This single global enterprise model could address the following objectives:
9

10. • • •
Maximize market Minimize, or target Maximize
penetration / a set number of, production
•
share product Minimize number
• delivery
Maximize of facilities
families
DCF/ROI
• • •
Maximize resource Evaluate currency Minimize
utilization exchange investment
sensitivity
• • •
Minimize losses Minimize least unit Minimize total
• cost costs
Minimize
inventory
• • •
Maximize profit Minimize tooling Maximize resource
• utilization
Minimize overtime
Technology / Operating Environment
The code for the PLANETS™ modeling shell has been on various mainframe
(timeshared) computer platforms since 1974. PLANETS™ for DOS, the first
stand alone PC PLANETS™ tool introduced by EDS in 1994, was the result of
a migration effort from a Honeywell MULTICS mainframe, that included
converting almost two million lines of PL/1 code to a fourth generation
development language, Clarion Professional developer. PLANETS™ for
Windows is the current, newly released for 1998, version of the evolutionary
PLANETS™ product, replacing the PC PLANETS™ for DOS version; it is
designed to operate on a stand-alone PC platform utilizing a 32-bit Windows
environment.
PLANETS™ for WINDOWS also makes use of self-contained licensed
technology provided by third party vendors to generate and solve
mathematical programming models. The modeling user only operates within
the PLANETS™ input, output, and control screen hierarchy and is never
exposed to PLANETS™ calls to third-party matrix building / solving
technology. Earlier versions of PLANETS™ made calls to MPS, MPSX,
SCICONIC, and other third party model building technology. Today,
PLANETS™ for WINDOWS currently makes calls to MathPro 2000™
(MathPro Inc.) and XPRESS-MP™ (Dash Associates Ltd.).
MathPro 2000™ is an Integrated Mathematical Programming Development
Environment that provides capabilities to develop and operate mathematical
programming models. MathPro 2000™ supports a full range of mathematical
programming constructs including all forms of mixed integer variables.
MathPro 2000™ also includes comprehensive case management, a relational
database, and facilities to import and export data. An important feature of
MathPro 2000™ is its use of Matrix Schematic representation of mathematical
programming models. The PLANETS™ for WINDOWS “behind the scenes”
mathematical model has been developed using the MathPro 2000™ Matrix
Schematic representation.
10

11. The MathPro 2000™ Matrix Schematic, a mathematical programming model
abstraction, is based on the observation that LP and MIP matrices are made up
of a number of homogeneous submatricies. The Matrix Schematic is a
spreadsheet-like display in which each cell contains an icon for the submatrix
that occupies that cell. The icons represent data tables, logical connectors, and
blanks (void areas in the matrix). The PLANETS™ for WINDOWS
mathematical model maintenance and development is greatly simplified by the
use of the Matrix Schematic representation.
The process of solving a PLANETS™ for WINDOWS model simply requires
the PLANETS™ user to “click” on a Generate/Solve icon which automatically
conducts the following internal data table generation / population, matrix file
generation, matrix solving, and output capture procedures:
• The generation of the data tables associated with the icons in the Matrix
Schematic representation involves pulling the data from the
PLANETS™ for WINDOWS database and automatically putting it in a
format that MathPro 2000™ can import to populate the tables in the
Matrix Schematic.
• Once the MathPro 2000™ tables are populated, the Matrix Schematic is
used to generate the matrix file to be read by an optimizer. MathPro
2000™ provides generators that interface with the more popular
commercial optimizers.
• PLANETS™ for WINDOWS use the XPRESS-MP™ optimizer, one of the
best available commercial solvers. The XPRESS-MP™ optimizer fully
supports Mixed Integer Programming including binary variables,
general integer variables, semi-continuous variables, and special
ordered sets. It has state-of-the-art branch and bound search strategies
modifiable by the user. It also has high performance Newton Barrier
Interior Point linear and quadratic optimizers complementing the
advanced Simplex Mixed Integer Programming solver.
• MathPro 2000™ provides the facilities to interpret the solution provided by
XPRESS-MP™ and put it in a format that PLANETS™ for WINDOWS
can use to generate end-user business reports (typically in Excel).
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12. Performance Assessment
Using the MathPro 2000™ generator and the XPRESS-MP™ optimizer,
PLANETS™ for WINDOWS has been able to generate and solve problems
with the order of 100,000 continuous variables and 30,000 constraints, and
hundreds of integer variables, in a few minutes. The ability to serve as a
PC-based modeling platform to develop and solve mathematical
representations of a global enterprise model is a reality.
Conclusion
Due to space limitations, this report has only been able to:
• describe the global enterprise optimization modeling opportunity,
state that specific technology (PLANETS™ ) exists to deploy these
•
models,
• describe the single global model’s cross-functional and cross-geographic
analysis capability.
If the reader desires more information, the authors may be contacted through
e-mail; a global enterprise case study, operable models, and descriptive
presentation material are available for limited review.
For more information on EDS, please contact our Web site (www.eds.com).
For more information regarding Dash Associates Ltd., please contact their
Web site (www.dash.co.uk).
For more information regarding Math Pro, Inc., please contact their Web site
(www.sundown-vmp.com).
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