2nd International Conference on Operations Research
and Enterprise Systems (ICORES) 2013
Barcelona, Spain
―Transforming a Complex, Global Organization:
Operations Research and Management Innovation for the
US Army‘s Materiel Enterprise‖
Greg H. Parlier, PhD, PE
Colonel, US Army, Ret
gparlier@knology.net
18 February 2013
1
Motivating Conditions
•
The changed geopolitical landscape resulting in the Army‘s transition to an
expeditionary, globally deployable organization supported by a new force
management concept (ARFORGEN);
•
The opportunity to consider, adapt, and extend integrating supply chain design
concepts and management principles, and to apply ―advanced analytics‖ methods;
•
A clear understanding of the enabling potential offered by information technology
(so-called ―IT solutions‖) and analytically-based decision support systems (DSS);
•
The DoD mandate for Performance Based Logistics (PBL), a major change in
defense logistics management philosophy;
•
And, an obvious and compelling need driven by current fiscal realities, inevitable
budget cuts, and the search for “efficiencies” . . . along with the ongoing quest for
solvency in US public policy.
A unique opportunity to develop and implement an ―analytical architecture,‖
in conjunction with a newly emerging management innovation paradigm,
to guide Materiel Transformation toward a ―resources to readiness‖ framework
3
―Formulas‖
4
Transforming US Army Logistics: Project Phases
What Happened?
What Could Happen?
A
C
B
Concept
Refinement
2002
Make it Happen!
System
Engineering &
Development
Technology
Development
2003
Phase 2
Phase 3
• Segment the Logistics
Structure & Processes
for Analysis
• Adapt Enterprise
Supply Chain
Framework for
Integration
• Identify "Readiness
Production Function"
• Develop "Mission
Based Forecasting"
• Validate "Readiness
Based Sparing"
• Incorporate
Multi-Echelon
Optimization &
"Synchronized
Retrograde
Operations"
• DDSN & LEWS
• Provide COTS RBS
Solutions for PSI
• Develop Large-Scale
MOD & SIM Capacity
for SC Enterprise
• Implement CILS
Organizational Design
• Strategic outreach &
Research Partnerships
for Continuous
Improvement Task
[$1.0M]
O&S
2005
Phase 1
[~$200K]
Production
Expanded
Market
Opportunities
[$2.2M]
CILS Provides:
• Product Support Integration
• Supply Chain Optimization
• Logistics System Readiness
• AMSAA (RBS)
• MCA
Organization for Research and Analysis
Acquisition
Wholesale
• SNL (RECAP)
• IDA ( Reliability
Design to Readiness)
• UAH (OEM Supplier Analysis)
• LMI (Peak Policy & ICAAPS)
• PNNL (VLD)
• USMC (TOC)
• IDA
Retail
Unit
Demand
• LMI
• AMSAA
• PMs
Reverse
Logistics
• RAND
• LOGSA
• SAIC
• RAND (EDA)
5
Reasons for the Book (from Preface):
1. Resurrect traditional Operations
Research (OR) for the US Army.
2. Apply ―advanced analytics‖ to our
materiel enterprise challenges.
3. Link operational, technical,
educational, scientific, and
analytical communities.
4. Demonstrate ―Management Innovation
as a Strategic Technology‖.
5. Document a case study for:
analytically-driven,
transformational change;
a comprehensive, collaborative
effort by many contributors.
6
Transforming US Army Supply Chains: Strategies for Management Innovation
I. Project Overview
1. Background
2. The Immediate Problem
3. Current Logistics Structure
4. Supply Chain Concepts - Analytical Foundations for Improving Logistics System Effectiveness
II. Multi-stage Analysis of Systemic Challenges
5. Readiness Production Stage
6. Operational Mission and Training Demand Stage
7. Retail Stage
8. Reverse Logistics Stage
9. Wholesale Stage
10. Acquisition Stage
11. Summary
III. Multi-stage Integration for Efficiency, Resilience, and Effectiveness
12. Achieving Efficiency: An Integrated Multi-Echelon Inventory Solution
13. Designing for Resilience: Adaptive Logistics Network Concepts
14. Improving Effectiveness: Pushing the Logistics Performance Envelope
IV. Design and Evaluation: An ―Analytical Architecture‖ to Guide Logistics Transformation
15. Multi-Stage Supply Chain Optimization
16. System Dynamics Modeling and Dynamic Strategic Planning
17. Operational and Organizational Risk Evaluation
18. Logistics System Readiness and Program Development
19. Accelerating Transformation: An ―Engine for Innovation‖
V. Management Concepts for Transformation
20. Organizational Redesign for Army Force Generation
21. Contributions of Information Systems Technology and Operations Research
22. Strategic Management Concepts for a Learning Organization
23. PBL and Capabilities Based Planning for an Expeditionary Army
24. Financial Management Challenges to ―Business Modernization‖
25. Human Capital Investment for a Collaborative Organization
26. Final Thoughts
7
Transforming US Army Supply Chains: Strategies for Management Innovation
―Advanced Analytics‖ = Descriptive + Predictive + Prescriptive Analytics
I. Project Overview
1. Background
2. The Immediate Problem
3. Current Logistics Structure
4. Supply Chain Concepts - Analytical Foundations for Improving Logistics System Effectiveness
II. Multi-stage Analysis of Systemic Challenges
5. Readiness Production Stage
6. Operational Mission and Training Demand Stage
7. Retail Stage
8. Reverse Logistics Stage
9. Wholesale Stage
10. Acquisition Stage
11. Summary
III. Multi-stage Integration for Efficiency, Resilience, and Effectiveness
12. Achieving Efficiency: An Integrated Multi-Echelon Inventory Solution
13. Designing for Resilience: Adaptive Logistics Network Concepts
14. Improving Effectiveness: Pushing the Logistics Performance Envelope
IV. Design and Evaluation: An ―Analytical Architecture‖ to Guide Logistics Transformation
15. Multi-Stage Supply Chain Optimization
16. System Dynamics Modeling and Dynamic Strategic Planning
17. Operational and Organizational Risk Evaluation
18. Logistics System Readiness and Program Development
19. Accelerating Transformation: An ―Engine for Innovation‖
Descriptive Analytics:
Where are we now?
Prescriptive Analytics;
Where do we want to go?
Predictive Analytics:
How can we get there?
V. Management Concepts for Transformation
20. Organizational Redesign for Army Force Generation
21. Contributions of Information Systems Technology and Operations Research
Managing Enterprise
22. Strategic Management Concepts for a Learning Organization
23. PBL and Capabilities Based Planning for an Expeditionary Army
Transformation:
24. Financial Management Challenges to ―Business Modernization‖
What will it take?
25. Human Capital Investment for a Collaborative Organization
26. Final Thoughts
8
Transforming US Army Supply Chains: Strategies for Management Innovation
Management Innovation as a Strategic Technology (MIST):
OR + BI[MIS + DSS] + TSP[EfI + STAAMP] + IMS = MIST
I. Project Overview
1. Background
2. The Immediate Problem
3. Current Logistics Structure
4. Supply Chain Concepts - Analytical Foundations for Improving Logistics System
Effectiveness
II. Multi-stage Analysis of Systemic Challenges
5. Readiness Production Stage
6. Operational Mission and Training Demand Stage
7. Retail Stage
BI = Business Intelligence
8. Reverse Logistics Stage
DSS = Decision Support Systems
9. Wholesale Stage
10. Acquisition Stage TSP = Transformational Strategic Planning
11. Summary
Operations Research
―Advanced Analytics‖
EfI = Engine for Innovation
STAAMP
Strategic
Architectures for
III. Multi-stage Integration for Efficiency,
Resilience,=and
Effectiveness
12. Achieving Efficiency: An Integrated
Multi-Echelon
Inventory Solution
Analysis, Management,
and Planning
13. Designing for Resilience: Adaptive Logistics Network Concepts
IMS Pushing
= Integrated
Management
Science
14. Improving Effectiveness:
the Logistics
Performance Envelope
TSP[EfI + STAAMP]
BI[MIS + DSS]
IMS
IV. Design and Evaluation: An ―Analytical Architecture‖ to Guide Logistics Transformation
15. Multi-Stage Supply Chain Optimization
16. System Dynamics Modeling and Dynamic Strategic Planning
17. Operational and Organizational Risk Evaluation
18. Logistics System Readiness and Program Development
19. Accelerating Transformation: An ―Engine for Innovation‖
V. Management Concepts for Transformation
20. Organizational Redesign for Army Force Generation
21. Contributions of Information Systems Technology and Operations Research
22. Strategic Management Concepts for a Learning Organization
23. PBL and Capabilities Based Planning for an Expeditionary Army
24. Financial Management Challenges to ―Business Modernization‖
25. Human Capital Investment for a Collaborative Organization
26. Final Thoughts
9
Part I: Project Overview
1. Background
2. The Immediate Problem
3. Current Logistics Structure
4. Supply Chain Concepts - Analytical Foundations for
Improving Logistics System Effectiveness
10
The Immediate Problem: Circa 2002
3000
2800
Requirement
2600
2400
Funded
2200
UFR
200,000
2000
181,047
71% of NMCS
Requisitions were
for parts under $50
150,000
1800
112,946
1600
1400
100,000
1200
54,397
1000
38,202
800
50,000
600
11,212
12,042
2,491
400
200
-400
415
0
<$10
0
-200
2,491
97
98
99
00
01
02
<$50
<$100 <$500
<$1K
<$5K
<$10K <$50K >$50K
03
Cost of Part Requisitioned
11
Assessment: Circa 2002
• Investment is increasing, yet back orders are growing and UFRs are
increasing
• ―Workarounds‖ are increasing, readiness is slowly declining
• Readiness reporting appears suspicious, lacks credibility
• Systems are non-operational for relatively inexpensive parts
―Efficient
Frontier‖
Ao
?
$
Situation: Selected (Anonymous) Comments
Following the Cold-War drawdown, as of late 2002 . . .
―All signs are bad‖
―Huge disconnect between Log & Ops‖
―Wholesale and retail are not integrated‖
―There is growing fear that we do not have enough to ensure readiness; that fear is
accompanied with perceptions of tremendous inefficiencies in our system‖
―We could spend $100M on spares and see no readiness improvement, or we could
spend $10M on spares (differently) and see it improve!‖
―Why am I still throwing billions down this black hole called Spares?‖
―We don‘t believe the aviation spares requirements numbers‖
―The financial system is undermining our ability to do things smart‖
―Our incentives are all in the wrong places…‖
But then . . . .
―The attacks of September, 11th, 2001, opened a gusher of spending
that nearly doubled the base budget over the last decade, not counting the
supplemental appropriations for the wars in Iraq and
Afghanistan. . .‖
And now . . . .
Situation: Selected (Anonymous) Comments
And now (2012), a decade later . . .
―. . . today we face a very different set of American
economic and fiscal realities . . . The gusher has been turned off
and will stay off for a good period of time . . . The culture of endless
money that has taken hold must be replaced by a culture of
savings and restraint‖
―[DoD must] . . . maximize value across the defense enterprise . . . [make]
better use of information technology and inventory management‖
―An era of blank-check defense spending is over . . .‖
Yet . . .
―. . . DoD does not do a world-class job with logistics by any measure . . .
[there] is little cost visibility or performance accountability [and] weapon
system readiness is not linked to supply chain responsibility.‖
―DoD‘s supply chain system has remained stuck in a 20th Century model
because of . . . resistance to change.‖
14
Design of Enterprise Systems:
Theory, Architecture, and Methods
takes a system-theoretical
perspective of the enterprise, and
describes a systematic approach,
called the enterprise design method,
to design the enterprise. The
enterprise design method
demonstrates the principles, models,
methods, and tools needed to design
enterprise systems.
Contact: Ronald Giachetti
Professor, Systems Engineering
Naval Postgraduate School
Monterey, CA 93943
regiache@nps.edu
Enterprise (Engineering) Systems
• Some Definitions:
– An emerging way to think about how to model, analyze, and design large-scale,
complex, socio-technical systems.
– An effort to better integrate engineering with management science, the social
sciences, and the humanities.
– A class of systems characterized by a high degree of technical complexity,
social intricacy, and elaborate processes, aimed at fulfilling important functions
in society.
– Enterprise engineering is the body of knowledge, principles, and practices to
design an enterprise.
– An Enterprise is a complex, socio-technical system that comprises
interdependent resources of people, information, and technology that must
interact with each other and their environment in support of a common mission.
An emerging field at the intersection
of engineering, management, and the
social sciences.
16
Enterprise (Engineering) Systems
• Evolutionary ―Epochs‖:
Structural elements and interactions
– Great inventions and artifacts
cause system attributes, functions, &
– Complex systems
behaviors. Architecture drives behavior,
and governs systems performance and
– Engineering systems
value – both short and long term.
• Characteristics: system “architecture”
• Modeling and Analysis
• Design ―levels‖ (project; enterprise; societal)
• Systems Perspectives:
– abstraction - decomposition for understanding
– “viewing angles” - multidisciplinary views
– perspectives - scale/scope, function, structure, temporality
– properties - the “ilities”: quality; reliability; flexibility; adaptability; agility;
modularity ….. sustainability …..affordability ….. “reversibility”
―Enterprise engineers specialize in integration: the process of making
subsystems work together harmoniously in a way that optimizes the
performance of the entire enterprise.‖
Ron Giachetti
17
II. Multi-stage
Approach
Analysis
Systemic
Part II: Multistage
Analysis–of
Systemic of
Challenges
Abstracting/Decomposition
for Descriptive Analytics
Challenges
Acquisition
Wholesale
Retail
Unit
Demand
Reverse
Logistics
5. Readiness Production Stage
6. Operational Mission and Training Demand Stage
7. Retail Stage
8. Reverse Logistics Stage
9. Wholesale Stage
10. Acquisition Stage
11. Summary
18
Supply Chain Framework: Organization, Process,
Supply
Chain
Framework
and Information ―Views‖ of the
Materiel
Enterprise
ReCap &
ReSet
Demand
Inventory
Vendors
Orders to
Vendors
MATERIAL FLOWS
Inventory
Wholesale
Inventory
Retail
Orders to
Wholesale
Inventory
Unit
Demand
Orders to
Retail
INFORMATION FLOWS
OMA
Funding
Available
AWCF
Payments to
Vendors and
Depots
ReCap &
ReSet
Funding
Obligation
Authority
OMA
Funding
FUNDING FLOWS
19
Supply Variability and Demand Uncertainty:
Army Supply Chain Model
Demand
Uncertainty
Supply Sources of Variability
Acquisition
Stage
Wholesale
Stage
• Supply Depots
• OEM‘s
• SSAs
• Repair Depots
• Suppliers
Unit
Stage
Retail
Stage
―Readiness
Production‖
• ASLs
• OEM‘s
• Training
• Operations
• Retrograde
Operations
Reverse
Logistics Stage
σ2 = LσD2+ D2σL2
…the ―bullwhip effect‖
qk
Lk
Suppliers
Demand
Stage
q2
L2
q1
L1
Wholesale
Retail
q0 = D
Unit
Customer
25
s2 (qk)
s2 (D)
―Decentralized
‖―Centralized
‖
20
15
10
5
0
0
1
2
3
4
5
K = # of stages
Summary of Systemic Challenges:
Identifying Fundamental Cause-Effect Relationships
To summarize generally, these causal disorders and their respective effects include:
(1) lack of an aviation readiness production function which induces both uncertainty and variability at the point of
consumption in the supply chain resulting in inappropriate planning, improper budgeting, and inadequate
management to achieve readiness objectives;
(2) limited understanding of mission-based, operational demands and associated spares consumption patterns which
contribute to poor operational and tactical support planning and cost-ineffective retail stock policy;
(3) failure to optimize retail stock policy to achieve cost-efficient readiness (customer) objectives which results in
inefficient procurement and reduced readiness;
(4) failure to proactively synchronize and manage reverse logistics which contributes significantly to increased DLR
RO, excess inventory, increased delay times (order fulfillment), and reduced readiness while
simultaneously precluding the enormous potential benefits of a synchronized, closed-loop supply
chain for DLRs;
(5) inability to ―see‖ – and to adapt to and anticipate changes in – actual customer demand, causing inefficient
procurement actions within an unresponsive wholesale stage characterized by abysmal demand plan
forecast accuracy thereby precluding enterprise-wide ―cost-wise readiness‖;
(6) limited visibility into and management control over disjointed and disconnected OEM and key supplier
procurement programs which are vulnerable to boom and bust cycles with extremely long lead times,
high price volatility for aerospace steels and alloys, and increasing business risk to crucial, unique
vendors in the industrial base resulting in diminishing manufacturing sources of materiel supplies, and
growing obsolescence challenges for aging aircraft fleets;
(7) independently operating, uncoordinated and unsynchronized stages within the supply chain creating pernicious
―bullwhip‖ effects including large RO, long lead times, and declining readiness;
(8) fragmented data processes and inappropriate supply chain MOEs focusing on interface metrics which mask the
effects of efficient and effective alternatives, and further preclude an ability to determine ―readiness
return on net assets‖ or to relate resource investment levels to readiness outcomes;
(9) lack of central supply chain management and supporting analytical capacity results in multi-agency, consensusdriven, bureaucratic workarounds hindered by lack of an Army supply chain management science and an
enabling ―analytical architecture‖ to guide Logistics Transformation; and
(10) lack of an ―engine for innovation‖ to accelerate then sustain continual improvement for a learning organization. 21
Officer ORSA (FA49) Strength in AMC
History of FA49 Authorizations in AMC
60
COL
LTC
MAJ
CPT
50
Authorizations
40
30
20
10
0
FY88
FY89
FY90
FY91
FY92
FY93
FY94
FY95
FY96
FY97
FY98
FY99
FY00
FY01
FY02
FY03
COL
5
5
3
3
3
2
2
2
2
1
1
1
0
0
0
0
LTC
9
9
6
6
6
5
5
3
3
1
2
1
0
0
0
0
MAJ
12
15
11
11
11
14
8
5
6
2
1
2
0
0
0
0
CPT
25
24
28
28
28
7
4
4
4
0
0
0
0
0
0
0
Analyzing Root Causes and Prescribing Innovation
Catalysts Across the Supply
ChainTowards
Working
ACQUISITION
WHOLESALE
RETAIL
UNIT
Solutions
DEMAND
RETROGRADE
(1) lack of an aviation readiness production function which induces both uncertainty and
variability at the point of consumption in the supply chain resulting in inappropriate
planning, improper budgeting, and inadequate management to achieve readiness objectives;
Innovation Catalysts:
• Defining the Readiness Equation
• Connect CBM to the Supply Chain
• Mission Based Forecasting
• Readiness Based Sparing
• Readiness Responsive Retrograde
• Leveraging Lessons Learned & Best Practices
23
―Production Function‖: Components of Readiness
Demand Requirements
Supply Availability
Weapon
System
Reliability
• Deployment Missions (DEPTEMPO)
- Patterns of Operation
Duration
Profile
- Environmental Conditions and
Locations
MTBF
- NMCS
Supply
Support
Capability
MLDT
- NMCM
Operational
Availability (Ao)
[ER]
MTTR
Personnel
Manning and
Skill Levels
Training
Resources
(OPTEMPO $)
[AS]
• Training Requirements (OPTEMPO)
- MC
- FMC
- PMC
[TS]
Readiness – related Measures / Metrics
[ER] – Equipment Readiness (Ao)
• FMC
• NMCS
• MC (PMC) • NMCM
[AS] – Assigned Strength
[TS] – Trained Strength
Cost of Part vs Percent of Not Mission Capable Supply/
Anticipated Not Mission Capable Supply Requisitions
(All Army LIF Records Nov00 to Oct01)
Percent 50
NMCS
45
43.6
Low $ Parts Are
Causing Army
Weapon Systems
to Be NMC
40
35
27.2
30
25
20
15
13.1
9.2
10
2.7
5
2.9
0.6
0.6
0.1
0
$
<10
<50
<100
<500
<1K
<5K
<10K
<50K >50K
80% of NMCS/ANMCS Requisitions at
Wholesale Were for Items <$100
Source: AMSAA
Innovation Catalyst: Analyzing the Readiness Equation
Innovation
Catalyst:
Analyzing
Readiness Equation
and Measuring
True
―Customer the
Demand‖
AO =
Labor
=
Uptime
Total Time
MTBF
MTBF + MTTR + MLDT
Where
90% MC
80% MC
MTBF = Mean Time Between
Failures (Reliability)
MTTR = Mean Time To Repair
(Maintainability)
MLDT = Mean Logistics Delay Time
(Supportability)
Research Goals:
- Define and empirically measure the
―readiness equation‖ for Ao
- Determine readiness ―driver‖ marginal
values, and evaluate contributions and
costs for potential solutions.
Capital
Extract from research results:
- The longer the delay, the more likely a
workaround . . .15% of deadline requisitions
for wholesale backorders were satisfied by
workarounds.
- ―Labor‖ (MMH) increasingly substituting for
―Capital‖ . . . If workarounds were eliminated,
readiness would decline by 33%.
- ―Consumption‖ data is not systematically
collected by current MIS
26
Center for Systems Reliability
Readiness & Sustainment Department
Sandia National Laboratories (SNL)
Albuquerque, NM 87185
Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company,
for the United States Department of Energy under contract DE-AC04-94AL85000.
Integration Opportunity:
Connecting CBM to the Supply Chain
―Connecting‖ CBM to the Supply Chain: A Conceptual View
ACQUISITION
WHOLESALE
RETAIL
UNIT
DEMAND
RETROGRADE
GCSS-A
CBM Data Warehouse
PLM+ LMP
In Theater
Maintenance
Platform PMs
Usage & Operations
Condition & Health
Environment
Prognostics
Logistics Req’s
Failure Analysis
Regime Recog
Fleet Managers
IMMC
Historical
Supply
Chain Data
LOGSA
OEMs
29
―Connecting‖ CBM to the Supply Chain: A Mathematical View
Reactive Repair
Proactive Replacement
Reactive Repair
vs.
Proactive Replacement
Downtime
MTBF
Xf
Xf
t2
W
A
OST1
MTBR
Xr
MLDT
t3
Down
time
MTBR
MTTR
OST MTTR
t1
R
OST2
t3
A
U
OST3
?
t2
W
OST1
t1
R
OST2
U
Xr
30
Volatility
the Army
Multi-Stage
Logistics Model
Supply in
Chain
Improvement
Opportunities:
Reducing Demand Uncertainty in the Army Supply Chain Model
Demand
Uncertainty
Supply Sources of Variability
Acquisition
Stage
• OEM‘s
• Suppliers
Wholesale
Stage
• Supply Depots
• Repair Depots
• OEM‘s
Retail
Stage
• SSAs
• ASLs
Reverse
Logistics Stage
• Retrograde
Operations
Unit
Stage
Demand
Stage
―Readiness
Production‖
• Training
• Combat
Missions
• Stability
Operations
How can we drive
down demand?
1. Improve reliability to
extend useful life
2. Use CBM to create
demand signal
3. Improve demand
forecast accuracy
Connect CBM to the supply chain –
Anticipatory Demand
31
Connecting CBM to the Supply Chain:
A Six Month Pilot Project
Purpose: Analyze and link existing Army logistics and CBM datasets to
a new forecasting method/model to better predict DLR replacement
Benefits:
• Improves Forecast Accuracy
- Enables ―right-sizing‖ component inventory levels
• Reduces Operation & Support Costs and Facilitates Efficiencies
- Improve visibility of the reliability state of aircraft components,
giving advance warning of demand
- Reduces inventory footprint and operating costs versus
personnel
• Increases Component Availability/Readiness Levels
- Reduction in NMCS results in improved readiness and cost
avoidance (premium purchases, shipping, etc.)
Connecting CBM to the Supply Chain
Project Process
Develop
Prognostic
Simulation
Project/Process Flow:
Analyze Existing,
Actual Data
Validate
Algorithm
Baseline
Metrics
DA Form
DSC
2410
Develop
Predictive
Algorithm
ASAP Data
RIMFIRE
LMP
SSA
Inventory
Prognostic Simulation Tool
Failure Rate Curve: Component Health
Connecting CBM to the Supply Chain
Prognostic Algorithms
Engineering and Logistics Components
• Reliability State Algorithms
– Predicting Supply Chain Performance
– Velocity of Supply (VoS)
– Based on supply chain history
Supply Response
• Logistics Side Algorithm
E (T f ± d f )
E (Td ± dd )
Goal:
Component arrives
before actual failure
Component Condition
– Identifying current reliability state (Rvs) to predict
time to failure
– Predicting Failures (DSC data)
– Associating to maintenance events (2410 data)
Time
Connecting CBM to the Supply Chain
Remaining
Useful
Life
CBM “Alert”(Ta)
(Ta)
Prognostic
Normal Distribution
Component
Replacement (T
(Tr)
r)
density
From
Prognostic
Algorithm
= Time to Replacement
Failure
Rate
Curve
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
λ0.1
0
Early demand
signal can drive
down retail
stockage
Available
ost
Tr
Ta
Tf
Tr – Ta > OST
Time
-10
-8
-6
-4
-2
0
Reliability improvements & Time on Wing Extensions
x
2
4
6
Connecting CBM to the Supply Chain
CBM Prognostics Simulation Model - Initial Results
Expected Results Based on Improved Predictive Ordering
# of Times an Aircraft was Down for More Than One Day
Days Ordered Early (compared to historical requisition times)
Inventory
Reduction
0%
2%
5%
10%
15%
20%
25%
0
110.7
130.1
150.7
196.4
230.7
256.2
287.4
2
4
6
8
77.4
99.6
120.5
155.6
196.4
232.8
265.4
48.1
66.3
81.9
113.4
166.4
201.8
244.1
34.9
45.8
52.0
89.2
134.8
180.2
223.7
22.8
27.1
36.6
61.1
105.4
147.7
180.6
12
7.4
12.0
13.1
27.3
51.9
78.7
128.0
16
1.6
2.9
5.6
9.2
22.3
44.9
59.2
Actual
Inventory
Level
37
36
35
33
31
29
27
Inventory
Cost
Savings
$0
$51,801
$103,602
$207,204
$310,806
$414,408
$518,010
Calibrated using actual 2410 data for AH-64D Nose Gear Box
•
2 Variables, 7 levels each, 49 options, 90 simulation runs per option = 4410 total runs
Analyzing Root Causes and Prescribing Innovation
Working
Catalysts Across the Supply
Chain Towards
ACQUISITION
WHOLESALE
RETAIL
UNIT
Solutions
DEMAND
RETROGRADE
(2) limited understanding of mission-based, operational demands and associated spares
consumption patterns which contribute to poor operational and tactical support planning and
cost-ineffective retail stock policy;
Innovation Catalysts:
• Defining the Readiness Equation
• Connect CBM to the Supply Chain
• Mission Based Forecasting
• Readiness Based Sparing
• Readiness Responsive Retrograde
• Leveraging Lessons Learned & Best Practices
37
Continuous front
Front
Continuous
Analyzing Operational Forms and Empirical Patterns
Main attack
Secondary attack
Effects-based operational forms:
Fix
Fix
3 observed force-on-force forms
Secondary attack
Main attack
Continuous fronts
Disruptions
Fix
Disruption
Disruption (high-order)(high-order)
Disintegrations
Fix
Main attack
Fix
+ Stability operations
Main attack
Fix
Fix
Secondary attack
Disintegration
Disintegration
Main attack
Fix
Fix
Main attack
Fix
Secondary attack
Fix
Fix
Fix
Secondary attack
Main attack
Fix
P A G E 38
Stability Opn
STRATIFIED SAMPLING
POPULATION OF SIZE N DIVIDED INTO K STRATA
RANDOM SAMPLING:
x

Pˆ RSM
n
STRATIFIED SAMPLING:
xk
Pk  n
k
1
THEN:
k

N k Pk
i
1
Pˆ STRAT  N
1
USUALLY:
Var
ˆ STRAT  VarPOP  Var
ˆ RSM 
Innovation Catalyst: Mission-Based Forecasting (MBF)
Innovation
Catalyst: Mission Based Forecasting (MBF)
Research Goal:
Our major hypothesis states: ―If empirically-derived Class IX usage patterns, profiles and/or trends
can be associated with various operational mission types and environmental conditions, then
operational planning, demand forecasting, and budget requirements can be significantly improved
to support a capabilities based force‖.
111
84
111
CONUS vs IraqStab
CONUS vs IraqMCO
2833
2833
88%
Note: original Views
(2005 data)
29%
IraqMCO
CONUS
AH-64D
AH-64D
73%
12%
71%
6
5
4
3
2
1
0
1.0
87
27%
59%
IraqStab
CONUS
6
5
4.4 x CONUS
3.9 x CONUS 4
3
2
Ratios
1
RatiosofofDemands:
Demands:Common
CommonParts
Parts
0
41%
1.0
40
Note: New (2006) data
Note: New (2006) data
Note: New (2006) data
Tested MBF Method Using Real Data from Four Operational Cases
Operation (OpTempo)
Duration
AH-64D UH-60
Case 1
Stability Ops (high threat )
6 months
24 tails 22 tails
Case 2
Stability Ops (mid-level)
6 months
79 tails 10 tails
Case 3
Case 4
Stability Ops (mid-level)
12 months
104 tails
Garrison (Training, CONUS)
12 months
54 tails 40 tails
MBF
compared to
MBF uses maintenance data
A
B
C
D
Current forecast methods use requisition data
Comparing Forecast Approaches:
Accuracy versus over- and under-forecast
Accurate
Found
Error
Actual NSNs Used
(over-forecast)
Error
(under-forecast)
Forecasted NSNs
The Analysis addresses two perspectives
Actual
Forecast
Part
Analysis
Quantity
Analysis
Errors
Found
(over-forecast)
Forecasted
Quantity
Errors
(under-forecast)
Forecasted
Quantity
Actual
Quantity
Actual
Quantity
NSN lines
[―breadth‖]
Cost
Quantity
[―depth‖]
45
AH-64D Parts Count Forecast (Breadth of NSNs):
MBF Compared
to(Lines)
Current
Methods
Phase 2 Membership
- Case 3
JLAT (3 hr)
Case 3, Stability Ops without
(mid-level
threat), 12 months, 104 tails
2,500
MBF reduces part over-forecast, and under-forecast
& improves forecast part-accuracy
2,000
Part Count
% ―Found‖ out of total forecast parts (breadth)
1,500
Over-forecast
Found (correctly forecast)
Under-forecast
1,000
500
0
ODDP
MBF
G4
A
B
OSRAP
C
CIF
D
JLAT (1 hr)
Method
These current methods (A, B, C, D)
use supply requisitions data
AH-64D Parts Quantity Forecast (Depth of NSNs):
Phase 2 Apache Quantity
(Parts)
- Case 3 Methods
MBF Compared
to
Current
without JLAT (3 hr)
Case 3, Stability Ops (mid-level threat), 12 months, 104 tails
90,000
MBF improves forecast parts quantity-accuracy
80,000
Total Part Quantity
70,000
60,000
Over-forecast
Found (predicted qty)
Found (actual qty)
Under-forecast
50,000
40,000
30,000
20,000
10,000
0
ODDP
MBF
A
G4
B
OSRAP
C
CIF
D
JLAT (1 hr)
Method
These current methods (A, B, C, D)
use supply requisitions data
AH-64D Parts Quantity Forecast (Depth of NSNs):
MBF Compared to Actual On-Hand Stocks
Phase 2 Cost (Parts) - Case 3
$250,000,000
Savings:
for 1 year
$200M
$200,000,000
(breadth & depth)
Excess $ for incorrectly predicted parts
Excess $ for correctly predicted parts
= $62M
Shortage of $ for missed parts
Shortage of $ for correctly predicted parts
Correctly predicted actual $
$150,000,000
$138M
$100,000,000
$50,000,000
Subset: 4 Bn PLLs
(Bn-level stocks)
$0
ODDP
Combo
PLL+ASL
Actual
ODDPOn-hand
Forecasted
Parts rollup Rollup
for
for
4Bn‘s
104 tails
(96 tails)
Combo PLL
PLL1
PLL
Rollup
for
4Bn‘s
Bn
(24 tails)
(96 tails)
PLL2
Bn
(24 tails)
PLL3
Bn
(24 tails)
PLL4
Bn
(24 tails)
48
Analyzing Root Causes and Prescribing Innovation
Working
Catalysts Across the Supply
Chain Towards
ACQUISITION
WHOLESALE
RETAIL
UNIT
Solutions
DEMAND
RETROGRADE
(3) failure to optimize retail stock policy to achieve cost-efficient readiness (customer)
objectives which results in inefficient procurement and reduced readiness;
Innovation Catalysts:
• Defining the Readiness Equation
• Connect CBM to the Supply Chain
• Mission Based Forecasting
• Readiness Based Sparing
• Readiness Responsive Retrograde
• Leveraging Lessons Learned & Best Practices
49
Supply Variability and Demand Uncertainty:
Army Supply Chain Model
Demand
Uncertainty
Supply Sources of Variability
Acquisition
Stage
Wholesale
Stage
• Supply Depots
• OEM‘s
• SSAs
• Repair Depots
• Suppliers
Unit
Stage
Retail
Stage
―Readiness
Production‖
• ASLs
• OEM‘s
• Training
• Operations
• Retrograde
Operations
Reverse
Logistics Stage
σ2 = LσD2+ D2σL2
…the ―bullwhip effect‖
qk
Lk
Suppliers
Demand
Stage
q2
L2
q1
L1
Wholesale
Retail
q0 = D
Unit
Customer
25
s2 (qk)
s2 (D)
―Decentralized
‖―Centralized
‖
20
15
10
5
0
0
1
2
3
4
5
K = # of stages
Adopting Mission Based Forecasting (MBF):
Key enabler for a ―readiness-driven‖ supply network (RDSN)
Advanced Commercial Supply Chain
Focus:
Bottom-up, POS
based approach
geared toward
meeting customer
demands
Commodity flow
Distribution
Center
Supplier
Data flow
Store
Room
Army Supply Chain
Focus:
Top-down
approach geared
toward meeting
inventory level
targets
Current Forecast
Data Source
Current Forecast
Data Source
Point
of Sale
(POS)
Recommended
Forecast Data Source
Parts flow
Data flow
Supplier
Data flow
―Wholesale‖
supply, aggregated
orders (requisitions)
―Retail‖
supply
(inventory across
multi-echelons)
Consumption
(parts in
aircraft
maintenance)
51
Army Consumption Data=Commercial POS Data
Guiding Principles for Readiness-Driven Demand
1. The purpose of the materiel enterprise is to sustain current readiness and generate
future capability.
2. Since readiness is ―produced‖ by tactical (and training) units, these tactical
―consumers‖ represent the ultimate ―customer‖.
3. Actual consumer demand needed to produce ―readiness‖ for training and
operational missions should drive the materiel enterprise - these are customer
―requirements‖ .
4. These requirements must be systematically measured and accurately forecasted at
the ―point of sale‖ where readiness is produced by the consumer.
5. Demand planning across the enterprise must focus on meeting these requirements
(for effective performance) while reducing forecast error (efficient performance).
Align the Class IX supply chain to ―real‖ customer demand,
then pursue Continuous Performance Improvement efforts and initiatives
focusing on ―Cost-Wise Readiness‖ for Army Materiel Transformation
52
Readiness Based Sparing (RBS)
Item
Ao
$ Spares
Marginal Analysis Includes:
• Cost of Parts
• Frequency of Use/Need
• Part Impact on Readiness
Shopping List
Unit
Added
Total
Cost Aircraft/
Cost
($)
$10K
($)
Availability
Rate
(%)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
6th A
1,600
0.388
101.600
66.67
11th B
2,300
0.352
103.900
66.69
2nd C
10, 400
0.312
114.300
66.74
12th B
2,300
0.283
116.600
66.76
1st D
13,800
0.154
130.400
66.78
7th A
1,600
0.144
132.000
66.79
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Analytical Demonstration:
Readiness Based Sparing: 101st ABN DIV UH-60
Readiness
Target
Results
100
$4.8M
95
$2.2M
90
?
RBS Curve:
―The Efficient Frontier‖
$1.2M
x
85
$.8M
80
$.6M
75
$.5M
70
65
0
1
2
3
4
5
ASL Investment in $M
Source: AMSAA
Ft. Rucker Readiness Based Sparing
RBS ASL Fill Rate vs ILAP RAND EDCB
• Provided first RBS ASL in 2003 (MG Dodgen) and continue to provide yearly updates
• Ft. Rucker winner of 2006 and 2009 Supply Excellence Award for CAT IV (Large Group) SSA
Ft. Rucker consistently has
the highest fill rates in the Army
Fill Rates 2002-2009
Initial
Implementation
70%
2005
Update
2006
Update
2007
Update
2008
Update
Fill Rate
80%
2004
Update
60%
1
81%-90%
0
71%-80%
Ft. Rucker
25
41%-50%
30%
Rucker (W0H) fill_rate
Knox (AXB) fill_rate
10%
10
51%-60%
DA Goal
of 30%
Fill
20%
number of RICs
2
61%-70%
50%
40%
91%-100%
Sill (AY5) fill_rate
37
31%-40%
45
21%-30%
44
11%-20%
22
1%-10%
17
Bragg (WAU) fill_rate
0%
0
ILAP performance rates as of March 20, 2009
20
40
number of RICs
60
ILAP performance for June 2009
12
Dec
2011 55
Analyzing Root Causes and Prescribing Innovation
Working
Towards
Catalysts Across the Supply
Chain
ACQUISITION
WHOLESALE
RETAIL
UNIT
Solutions
DEMAND
RETROGRADE
(4) failure to proactively synchronize and manage reverse logistics which contributes significantly
to increased DLR RO, excess inventory, increased delay times (order fulfillment), and reduced
readiness while simultaneously precluding the enormous potential benefits of a synchronized,
closed-loop supply chain for DLRs;
Innovation Catalysts:
• Defining the Readiness Equation
• Connect CBM to the Supply Chain
• Mission Based Forecasting
• Readiness Based Sparing
• Readiness Responsive Retrograde
• Leveraging Lessons Learned & Best Practices
56
Retrograde Challenge:
Depot
LevelLevel
Reparables
(DLRs)
TheThe
Retrograde
Challenge:
Depot
Reparables
• Represents the Army’s “Value Recovery Effort” – the “Feedback
Loop”
• Accounts for 13% of customer orders, but 88% of Sales Value
Wholesale
Retail
Unit
Demand
―Reverse
Logistics‖
• The FY00 average reverse pipeline delay was 80 Days
• AMCOM shortfall for DLR maintenance was $1.4B in 02
• Increasing obsolescence challenges will further stress RL and
illuminate RCT inefficiencies, e.g. PATRIOT TWTs during OIF
57
Innovation
Catalyst:
Readiness
ResponsiveBased
Retrograde
Innovation
Catalyst:
Readiness
Retrograde
Conditions:
Represents ―Value Recovery Effort‖ – the ―Feedback Loop‖. Accounts for 13% of customer orders, but 88% of
sales value.
Research Results:
Tremendous potential for reducing retrograde delay: DLR RO can then be reduced, or used for other purposes, and
readiness improved. However, TRANSCOM metrics focus on transportations costs, not readiness outcomes.
January 2003 – June 2004
Days
N
350
12,000
FY00 study
300
250
OIF return data still
truncated, and many
measured from the GS
base rather than from
the originating unit
95%
10,000
75%
50%
mean
8,000
N
200
6,000
150
4,000
100
2,000
50
0
0
EUROPE
KOREA
USARPAC
END OUT OF OIF
THEATER
58
Evaluating Retrograde Efficiency:
Readiness Return on Net Assets
Retrograde Efficiency Measures for management focus:
Minimize: transportation costs? DLR inventory cost per aircraft?
Maximize: materiel availability? DLR turnover ratio?
59
Example of IntermittentlyDemanded Service Part
Compact Pneumatic Assembly M7
60
© Smart Software, Inc. 2013, All Rights Reserved
Evolution of Intermittent Demand (ID)
Forecasting Methods
• 1st Generation: Subjective (still most used)
• 2nd Generation: ID-ignorant stat methods (USAF D200)
– Exponential smoothing, Moving average
• 3rd Generation: ID-aware stat methods (USA LMP)
– Croston’s method, Poisson and extensions
• 4th Generation: State of the art ID-aware stat methods
– Markov bootstrap
• 5th Generation (future): Advanced ID statistical methods
– Mining large sets of stochastic process data
© Smart Software, Inc. 2013, All Rights Reserved
61
4th Gen – ID State of the Art:
Markov Bootstrap
• US Patent 6205431 B1 to Smart Software, Inc.
• Positives
– Highly accurate on real-world data
– Explicitly accounts for key data features
• Integer nature of demand
• Large % of zero values
• High variability in nonzero demand values
• Autocorrelation of successive demands
• Negatives
– Assumes no trend or seasonality
– … but an even better version is coming (patent pending)
62
© Smart Software, Inc. 2013, All Rights Reserved
Markov Bootstrap Methodology
63
© Smart Software, Inc. 2013, All Rights Reserved
ID Demand Forecast Over Lead Time
64
© Smart Software, Inc. 2013, All Rights Reserved
Analyzing Root Causes and Prescribing Innovation
Working
Towards
Catalysts Across the Supply
Chain
ACQUISITION
WHOLESALE
RETAIL
UNIT
Solutions
DEMAND
RETROGRADE
(5) inability to ―see‖ – and to adapt to and anticipate changes in – actual customer demand,
causing inefficient procurement actions within an unresponsive wholesale stage characterized
by abysmal demand plan forecast accuracy thereby precluding enterprise-wide ―cost-wise
readiness‖;
Innovation Catalysts:
• Defining the Readiness Equation
• Connect CBM to the Supply Chain
• Mission Based Forecasting
• Readiness Based Sparing
• Readiness Responsive Retrograde
• Leveraging Lessons Learned & Best Practices
65
Innovation
Catalyst: Multi-Echelon
Readiness Based
Sparing
Innovation
Catalyst: Readiness
Based
Sparing
Conditions:
Analytical Demo 101st ABN DIV UH-60 Results
100
Readiness
Target
$4.8M
95
90
$1.2M
85
RBS Curve:
―The Efficient
Frontier‖
$2.2M
x
$.8M
80
$.5M
70
65
0
Research Results:
- Analytical Demo revealed significant potential to reduce
costs and relate investment levels to Ao. . . RBS later
adopted at Fort Rucker.
$.6M
75
- Low $ parts were causing Army weapon systems NMC
- ―Readiness Based Sparing‖ (RBS), developed at RAND
and LMI, had not been tested for Army Aviation
1
2
3
ASL Investment in $M
4
5
Source: AMSAA
- Multi-Echelon RBS exhibits tremendous potential for
cost savings and relating resources to Ao fleetwide.
Impact of Increased Investment at Wholesale on
Blackhawk Equipment Readiness at 101st Airborne
Percent
Increase
In
Readiness
5
RBS Impact?
4
3
Baseline:
2
1
Fill Rate Safety Level Readiness
70
189M
84.7%
75
210M
85.9%
80
256M
86.7%
85
340M
87.3%
90
505M
87.7%
95
857M
88%
0
0
50
100
150
200
250
300
350
400
Percent Increase in
Investment at Wholesale
Source: AMSAA
66
ICAAPS: Intelligent Collaborative Aging
Aircraft Parts Support (LMI)
SIX SIGMA, LEAN, AND THEORY OF CONSTRAINTS:
CONTRIBUTIONS IN THE COST-PERFORMANCE TRADESPACE
Six Sigma – improving product quality (fewer defects) by reducing process variation (variation reduction)
Lean – synchronizing process flow (―takt‖ time) by removing excess WIP (inventory reduction)
Theory of Constraints – improving cost effectiveness by strengthening weak links (constraint reduction)
“Performance” - Availability
3
2
Improving
Effectiveness
1
Lean
Improving
Efficiency
“Investment Costs” – $
“TOC”
Six Sigma
1
2
3
IMPACT of SIX SIGMA, LEAN & THEORY OF CONSTRAINTS
USMC Maintenance Depot, Albany, GA
Analyzing Root Causes and Prescribing
WorkingInnovation
Towards
Catalysts Across the Supply Chain
ACQUISITION
WHOLESALE
RETAIL
UNIT
Solutions
DEMAND
RETROGRADE
(6) limited visibility into and management control over disjointed and disconnected OEM and key
supplier procurement programs which are vulnerable to boom and bust cycles with extremely
long lead times, high price volatility for aerospace steels and alloys, and increasing business risk
to crucial, unique vendors in the industrial base resulting in diminishing manufacturing sources
of materiel supplies, and growing obsolescence challenges for aging aircraft fleets;
Innovation Catalysts:
• Defining the Readiness Equation
• Connect CBM to the Supply Chain
• Mission Based Forecasting
• Readiness Based Sparing
• Readiness Responsive Retrograde
• Leveraging Lessons Learned & Best Practices
71
CH-47 Sync Shaft Assembly Monthly Capacity
Chicago Mag Finishing, coating
and paint (15)
(casting)
80 (M)
15 (M)
Alcoa Fast120 (M)
Ruscomb
(studs)
Timken
135 (M)
(material)10 (M) w/LTA
Bolt (114D3053-1)
40 (M) N/A
Carpenter 10 (M)
Ruscomb
(material)
50 (BA) N/A
Support Assembly (114D3058-8)
30 (M)
50 (BA)
Bearing (145DS301-3)
Subcontract 20 (M)
Machining
N/A
200
60 (M) 265
50 (BA) 150/mo
MRC
Tube (145D3400-34)
AMI
(material)
150 (M)
Service Steel100 (M)
(material)
Outside
processing
Outside
processing
30 (M) 260
50 (BA)
100-150/mo
Washer (114D3044-1)
40 (M)
Fluid Mech
10 (M)
Jamco
22 (M) 197
15 (A) 50 (BA)
N/A
Mount (234DS301-2)
Awaiting data
(material)
Notes: Purple denotes monthly capacity without
impact to normal through put
(M)
10 (A) 150
Lord Corp 90 (M) 50 (BA)
N/A
Plate Assembly (114DS344-1)
Rexnord
110 (M) 160
50 (BA) N/A
Boeing
Findings and Concerns
• Supply chain plagued by extremely long and growing lead times
• Companies in the supply chain are averse to risk and investment resulting
in little or no inventories
• There are very few long term contracts
• Essentially no visibility of downstream demand in supply chain
• Suppliers are very concerned about demand uncertainties and financial
viability over the next 5 years.
• Continuous improvement programs are limited to localized manufacturing
processes
• Specific vulnerabilities:
– raw material price escalation
– union contract negotiations
– sole suppliers for many parts and required specialty steels
• Common issues across multiple aviation platforms
“Instead of protecting the 20th Century defense industrial base , government and
industry need to transform it into a 21st Century industrial base that can justify its
existence by providing needed military equipment at an affordable price. This requires
an across-the-board transformation – including infrastructure, equipment, workforce,
and the defense industry at large.”
From Democracy’s Arsenal: Creating a 21st Century Defense Industry, by Jacques S.
Gansler, former Under Secretary of Defense for Acquisition, Technology, and Logistics
1997-2001
Part III. Enterprise Integration: Prescriptive Analytics for
Efficiency, Resilience, and Effectiveness
12. Achieving Efficiency: An Integrated Multi-Echelon Inventory Solution
13. Designing for Resilience: Adaptive Logistics Network Concepts
14. Improving Effectiveness: Pushing the Logistics Performance Envelope
Increasing “Effectiveness” in the
Cost -Availability Tradespace
Achieving “Efficiency” in the Cost Availability Trade Space
Cost Benefits Alternatives:
2
3
―Efficient Frontier‖
4
Ao
Gain in
―Efficiency‖
Ao
1
1. Improved effectiveness
with increased costs
2. Improved effectiveness at
same costs
―Efficient Frontier‖ 3. Improved effectiveness at
reduced costs
4. Same effectiveness at
significantly reduced costs
… however, magnitude of each
depends upon where you are on
the current efficient frontier!
… and the expansion trace of the
improved frontier
$
$
(7) independently operating, uncoordinated and unsynchronized stages within the supply chain creating
pernicious ―bullwhip‖ effects including large RO, long lead times, and declining readiness;
(8) fragmented data processes and inappropriate supply chain MOEs focusing on interface metrics which
mask the effects of efficient and effective alternatives, and further preclude an ability to determine
―readiness return on net assets‖ or to relate resource investment levels to readiness outcomes;
74
Achieving
Multi-Echelon
Integration
Achieving
MaterielEfficiency:
Enterprise Efficiency:
Multi-echelon
Integration
SingleEchelon
Supplier
MultiEchelon
Supplier
Lead
Time
Lead
Time
Supplier
Wholesale
Wholesale
Inventory Drivers
Inventory Drivers
Lead
Time
Retail
Supplier
Lead
Time
Lead
Time
Wholesale
Replenishment
Optimization
Wholesale
Inventory Drivers
Lead
Time
Lead
Time
Inventory Drivers
Demand
Retail
Retail
Inventory Drivers
Inventory Drivers
Demand
Customer
Customer
Inventory Drivers
Retail
Replenishment
Optimization
Demand
Customer
Sequential Approach
Network
Replenishment
Optimization
Retail
Inventory Drivers
Demand
Customer
Multi-Echelon Approach
75
Current Vertical Supply ―Chain‖ Structure
Design
for Resilience:
Demand
Driven
Supply
Design
for Structural
Resilience: Readiness
Driven
Supply
Network
Network (DDSN)
SSA
SSA
ASL
ASL
SSA
DDOC
SSA
NICP
ASL
SSA
DDOC
DDOC
SSA
SSA
SSA
RBS
Cost/Item
List
• RBS reduces cost
• Inventory pooling reduces both
A
cost and risk
• Lateral supply decreases
requisition delay time & increases
Ao
SSA
RBS
Stock
List
$
Hi CostLow Demand
DLRs
• Low Cost
Consumables
• Hi Demand
Parts
77
Pursuing Cost-Effective Readiness:
Pushing the Performance Envelope
Increasing “Effectiveness” in the
Cost -Availability Tradespace
Cost Benefits Alternatives:
2
3
1
4
―Efficient Frontier‖
Ao
1. Improved effectiveness
with increased costs
2. Improved effectiveness at
same costs
3. Improved effectiveness at
reduced costs
4. Same effectiveness at
significantly reduced costs
… however, magnitude of each
depends upon where you are on
the current efficient frontier!
… and the expansion trace of the
improved frontier
$
78
Part IV. Predictive Analytics for Design and Evaluation:
An ―Analytical Architecture‖ to Guide
Materiel Enterprise Transformation
15. Multi-Stage Supply Chain Optimization
16. System Dynamics Modeling and Dynamic Strategic Planning
17. Operational and Organizational Risk Evaluation
18. Logistics System Readiness and Program Development
19. Accelerating Transformation: An ―Engine for Innovation‖
Capacity
(What we can do)
Knowledge
(What we know)
Inventory
(What we have)
(9) lack of central supply chain management and
supporting analytical capacity results in multiagency, consensus-driven, bureaucratic
workarounds hindered by lack of an Army supply
chain management science and an enabling
―analytical architecture‖ to guide Logistics
Transformation;
(10) lack of an ―engine for innovation‖ to accelerate
then sustain continual improvement for a learning
organization.
79
Improving System Efficiency: Across the System
of Stages and within each Stage
Acquisition
Wholesale
Retail
Unit
Demand
10
1.1
Numeric Stockage Objective
1.0
Repair Cycle
Stock Due Out
8
3.4
6
Procurement Cycle
Production Lead Time
Administrative Lead Time
4
2
2.1
Safety Level
0.5
Below Depot SSF RO
0.8
0.3
1.4
0
War Reserve
$160,000
Reverse
Logistics
$140,000
Safety Stock
AMC Requirements Objective ($ in Billions)
12
Because inventories are
managed to the computed
RO, reducing the value of the
RO calculated by AMC’
AMC’s
models is a critical first step
to reducing inventories.
Baseline Cost
Optimized Cost
$120,000
$100,000
$80,000
$60,000
$40,000
$20,000
$0
50%
60%
70%
80%
Service Level
90%
100%
80
―Optimizing‖ the System: Applying a
Dynamic (Multi-Stage) Programming Model
N
$
2
$
Wholesale
Stage
1
$
Retail
Stage
5
Unit Production
Stage
Ao
100
$4.8M
95
4
$2.2M
90
$1.2M
2
85
$.8M
80
$.6M
75
$.5M
1
80% MC
70
0
90% MC
Labor
3
Readiness
Target
Acquisition
Design Stage
Percent Increase In Readiness
$
3
65
0
50
100
150
200
250
Percent Increase in
Investment at Wholesale
300
350
400
0
1
2
3
Investment in $M
4
5
Capital
81
Supply Chain Framework
Supply Chain Framework
ReCap &
ReSet
Demand
Inventory
Vendors
Orders to
Vendors
MATERIAL FLOWS
Inventory
Wholesale
Inventory
Retail
Orders to
Wholesale
Inventory
Unit
Demand
Orders to
Retail
INFORMATION FLOWS
OMA
Funding
Available
AWCF
Payments to
Vendors and
Depots
ReCap &
ReSet
Funding
Obligation
Authority
OMA
Funding
FUNDING FLOWS
82
Logistics Readiness and Early Warning System
Logistics Readiness Early Warning System
Feedback
Automated Monitoring
- Readiness trends and forecasts
- Supply chain metrics
- Logistics system readiness parameters
Alert
Policy Response
- HQDA reviews
- Analyze and implement cost-effective options
- Minimize recognition and response bags
- PPBES implications (resources-to-readiness)
Management
Assessment
Warning
- Corroborate and validate alerts
- Assess near and long-term implications
- Integrate empirical evidence with human judgment
The regression relating Mission Capable rates (MC) to age lagged 5 months, shown in
the equation below, indicates that a one-month increase in backorder average age
leads to a reduction of 2.8 percentage points in MC rate 5-months hence. The
coefficient is highly significant (at the one percent level), and the R2 is 63 percent.
MC = 0.97 – 0.028 (Age lagged 5 months)
83
An ―Engine for Innovation‖:
The Center for Innovation in Logistics Systems (CILS)
FFRDC‘s
Non-Profits
Academia
(1)
Magnet, Filter and
―Repository‖
for
―Good Ideas‖
Corporate
Research
•
•
•
•
•
Tactical Units
Organizational Design
Supply/Value Chain
Workforce Development
Technology Implications
Innovation & Productivity
Gain
• R&D
DoD
Organizations
Professional
Societies:
INFORMS
(2)
Modeling,
Simulation
& Analysis of
Complex
Systems
Academic
Institutions
Commercial
Sector
• System Dynamics Modeling
• Large Scale System
Design, Analysis, and
Evaluation
• Systems Simulation,
Modeling and Analysis
• Repository for validated
models & analytical tools
Public
(3)
Transforming
Organizations &
Managing
Change
Private
• Cost Benefit Analyses
• Risk Reduction & Mitigation
• Research, Studies, and
Analysis
• Education & Training
• Technical Support
• Change Management
84
Part V. Management Concepts for
Materiel Enterprise Transformation
20. Organizational Redesign for Army Force Generation
21. Contributions of Information Systems Technology and Operations Research
22. PBL and Capabilities Based Planning for an Expeditionary Army
23. Financial Management Challenges to ―Business Modernization‖
24. Human Capital Investment for a Collaborative Organization
25. Strategic Management Concepts for a Learning Organization
26. Final Thoughts
Civilian ―ORSA‖ (1515) Strength in AMC
Officer ORSA (FA49) Strength in AMC
History of FA49 Authorizations in AMC
60
2,500
COL
LTC
MAJ
CPT
50
ALL ARMY
2,000
AMC
30
1,500
20
10
FY98
FY99
FY00
FY01
FY02
FY03
1
1
1
0
0
0
0
LTC
9
9
6
6
6
5
5
3
3
1
2
1
0
0
0
0
MAJ
12
15
11
11
11
14
8
5
6
2
1
2
0
0
0
0
CPT
25
24
28
28
28
7
4
4
4
0
0
0
0
0
0
0
500
31.84%
FY97
2
32.61%
FY96
2
33.66%
FY95
2
34.29%
FY94
2
37.02%
FY93
3
37.75%
FY92
3
37.21%
FY91
3
40.41%
FY90
5
39.92%
FY89
5
41.00%
FY88
42.81%
0
COL
44.10%
1,000
41.99%
Authorizations
40
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
0
85
Integration Opportunity:
ARFORGEN Synchronization - RBS
Integration Opportunity – RBS for ARFORGEN
3
2
90% MC
Labor
Ao
60% MC
Capital
1
Available
Ready
Reset
80% MC
$
86
Integration
Opportunity:
ARFORGEN
Synchronization
Integration
Opportunity:
RBS and MBF for
the
Army‘s new Regionally
Aligned
Force Concept
MBF and
RBS
Europe
CONUS
111
84
111
87
CONUS vs IraqStab
CONUS vs IraqMCO
2833
2833
PMO
Apache
88%
71%
Pacific
29%
RBS Curve:
―The Efficient
Frontier‖
27%
59%
SWA
CONUS
IraqMCO
CONUS
41%
IraqStab
Available
Ready
90% MC
Labor
Ao
73%
12%
Reset
80% MC
60% MC
87
$
Capital
Integration Opportunity: Logistics Support for
Integration Opportunity: ―Advanced Analytics‖ for a Capabilities Based Force
Capabilities Based Planning
CONUS vs Iraq Stab
73%
27%
59%
CONUS
Defense Planning Guidance
Scenarios
41%
IraqStab
MISSION
SCENARIOS
Readiness
Equation
REVISED CLASS IX STOCKAGE POLICY
*EG: IDEEAS, JANUS,
JCATS CASTFOREM,
JTLS
88
Integration
Opportunity:
Product
Integration
Integration
Opportunity:
ProductSupport
Support Integration
for (PSI) for PBL
Performance Based Logistics (PBL)
Aligning PBL Incentives to Readiness Outcomes
The Fallacy of ‗Fill Rate‘ as an Incentive
for SC Performance
Maximum difference
between Total Revenue
and Total Cost or
Maximum Profit
Total Cost Function
Value of
Cost or
Output
(Return)
Ao/FR/Score
Production Function
(A0 * VPC)
Tangents
of equal
slope
Avg. Delay
1.0
100
.9
Steep here means
shallow here
Decision or Stopping Points
(Iterations)
PBL Contract Scoring Regime Results
.8
90
What the
customer
wanted 80
.7
70
.6
60
.5
50
.4
40
($ 000,000s)
10
.3
Legend
Award Fee
Cost
Profit
Max Profit
9
8
Legend
What the
customer got
.2
Ao
Fill Rate
Avg. Delay
Max Profit
Score
7
.1
6
30
20
10
0
0
5
0
5
10
15
20
25
30
35
40
45
50
4
Inventory Value* ($ 000,000s)
3
2
1
0
0
5
10
15
20
25
30
35
40
45
50
Inventory Value* ($ 000,000s)
89
Management Innovation as a Strategic Technology
Management
Innovation:
Technology
Innovation:
Customer
Needs
•MERBS1
•CBM6
•MBF2
•RFID7
•R33
•TAV8
•DSLP4
•LREWS5
1Multi
Methodology
Advancement
Technology
Enablers
•ERP9
Echelon Readiness Based Sparing
2Mission
Based Forecasting
3Readiness
Responsive Retrograde
4Dynamic
Strategic Logistics Planning
5Logistics
Readiness and Early Warning
System
6Condition
7Radio
8Total
Based Maintenance
Frequency Identification
Asset Visibility
9Enterprise
Resource Planning
90
Contributions of Information Systems Technology and Operations
Research: ―Advanced Analytics‖ for Enterprise Strategy and Integration
Vision &
Strategy
Programs &
Resources
Vision
Quantitative
& Qualitative
"Analysis"
Strategy
Decision Support System
ERP (SALE)
Databases
"Readiness"
• Traditional Weapon System "A 0"
• Program Sustainment
• Capability to Meet Operational Needs
91
Linking Processes and Systems with Operational
and Financial Performance
Mature
Immature
Business Processes
Lack of System Support
Planning Practices and Systems
Aligned
Improved bottom line profitability
27% improvement potential
B
Significant improvement on
operational performance
75% higher profitability for BIC
C
Single Site, Informal and Manual
Planning Processes
Below average business performance
Systems are not Complemented by
Planning Practices
Significant Inefficiencies
A
Immature
D
IT Systems
Mature
As of FY 2011: GAO found DoD ERP implementation delays ranging
from 2-12 years with cost increases of nearly $7 billion; for the Army,
LMP reported more than $10.6 billion in ―abnormal balances‖ within the
Procure-to-Pay general ledger accounts.
89
Common Expectations and the Reality of Change
Performance
Anticipated
Performance
C?
B?
Historical Performance
A
The Reality of Change
Disruption
D?
Source:
Professor Rebecca Henderson
MIT Sloan
Time
Mature
Immature
Business Processes
Linking Business Processes and IT Systems
with Operational and Financial Performance
Advanced planning practices
Immature IT Systems
Advanced planning practices
& advanced IT systems—aligned
B
C
Single sites, Informal and
Manual Planning Processes
Advanced IT Systems — not
complemented by advanced planning
practices
A
D
Immature
Mature
IT Systems
Performance
Common expectations and the reality of change
Anticipated
Performance
C
B
Historical performance
The Reality of Change
A
Disruption
Time
D
Sustaining Innovation While
Linking Execution to Strategy
A.D. 80
A Keystone in Time:
The voussoir arch in
the Roman Empire
A.D. 2012
Current Global ―Tectonic Stresses‖
& ―Grand Challenges‖ for
Engineering Systems:
Population
Energy
Environmental
Climate
Economic
• Operations Research
• ―Advanced Analytics‖
• MIST
― . . .the organizational ‗glue‘ needed
to coordinate, orchestrate, and pull
the enterprise together to keep it
focused and continuously learning,
precluding chaos during a period of
transformational change.‖
Confronting the ―Engenuity Gap‖: Operations Research
and Management Innovation for Enterprise Systems
96
2nd International Conference on Operations Research
and Enterprise Systems (ICORES) 2013
Barcelona, Spain
―Transforming a Complex, Global Organization:
Operations Research and Management Innovation for the
US Army‘s Materiel Enterprise‖
Greg H. Parlier, PhD, PE
Colonel, US Army, Ret
gparlier@knology.net
18 February 2013
97
―Formulas‖
1. ―Advanced Analytics‖ = Descriptive + Predictive + Prescriptive Analytics
2. Management Innovation as a Strategic Technology (MIST):
MIST = OR[MIS + DSS] + TSP[EfI + STAAMP] + IMS where:
(see handout)
3. f(D x VL x F) > R
where:
f = ―forcing‖ function (for organizational change)
D = dissatisfaction
VL = visionary leadership
F = first steps -- the compelling analytical argument for change
R = resistance -- bureaucratic and/or organizational
98
―Literature Search‖ – A Few Suggestions
• MIT Series on Engineering Systems:
– Engineering Systems: Meeting Human Needs in a Complex Technological
World; deWeck, Roos, and Magee
– Flexibility in Engineering Design; deNeufville and Scholtes
– Design Structure Matrix Methodology and Applications; Eppinger and
Browning
• Design of Enterprise Systems: Theory, Architecture, and Methods; Giachetti
• Transforming US Army Supply Chains; Parlier
• The Engenuity Gap: How can we solve the problems of the future? Homer-Dixon
• Consilience: The Unity of Knowledge; EO Wilson
• Operational Research in the RAF; Her Majesty’s Stationery Office, 1963
• Democracy‘s Arsenal: Creating a 21st Century Defense Industry; Gansler
99
Traditional (Incremental) vs. Transformational Strategic Planning
Transformational Vision
Imagineering Back to Present
Business
Growth
Horizon 3
See & „Architect‟
Architect‟
Options for Future
Business Growth
Horizon 2
Drive Growth in
Emerging New
Businesses
Horizon 1
Defend and Extend Existing Business
Time
Leading from the Present
Leading from the Future
100
The Aggregate Impact of Both Uncertain
Demand and Variable Supply
Impact of
Variable Supply Lead Times
RO
Impact of
Variable Demand Rates
RO
L1
ROP
L2
L3
D3
ROP
D2
D1
Time
Time
RO
=LxD
ROP
ºº
F(x) = y(x/t)g(t)dt
º
σ2 = LσD2+ D2σL2