Greg H Parlier PhD PE Presented by Tom McLaughlin ValuLytics Inc November 3 2015 Background National Research Council Board on Army Science and Technology Report Force Multiplying Technologies for Logistics Support to Military Operations 2014 ID: 706314
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Slide1
Connecting Prognostics to the Supply Chain
Greg H. Parlier, PhD, PE
Presented by
Tom McLaughlin
Valu-Lytics, Inc
November 3, 2015
Slide2
Background
National Research Council, Board on Army Science and Technology Report:
Force Multiplying Technologies for Logistics Support to Military Operations (2014)
Rebuilding analytical capacity for Army Logistics Synchronized retrograde process Connect CBM to the Supply Chain Sustainment Maturity Model (Appendix G) Engine for innovation (EFI) (Appendix F)
Transforming U.S. Army Supply Chains – Strategies for Management Innovation by Greg H. Parlier (2011) Dynamic Strategic Logistics Planning Enterprise Integration and Transformational Change Conditioned Based Maintenance Sustainment Readiness Levels (SRLs) Engine for Innovation (EFI) PBL/CLSSlide3
Guiding Principles for Readiness-Driven Demand
Align the Class IX supply chain to “real” customer demand, measure
tactical
forecast accuracy, then pursue Continuous Performance Improvement efforts and initiatives focusing on “Cost-Wise Readiness” for Army Materiel Transformation
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). Slide4
Supply Chain Engineering:
Catalysts for Innovation
Mission Based Forecasting (MBS)Readiness Based Sparing (RBS)
Multi-Echelon RBS (MERBS)Readiness Responsive Retrograde (
R3)Conditioned Based Maintenance (CBM)
Intermittent DemandLogistics Readiness Early Warning System (LREWS)CBM PrognosticsSlide5
AMRDEC CBM+ Diagnostics to Prognostics
Remaining Useful Life (RUL) Process
Prognostic Early Warning is achieved thru Raw Data Collection, Feature Extraction, Condition Indicators (CIs), and OPTEMPO. Supports
Supply Chain Optimization.
Supply Chain
USG AMRDEC Material DISTRIBUTION A APPROVED FOR PUBLIC RELEASE; DISTRIBUTION IS UNLIMITED; October 2015Slide6
RUL
(DAYS)
Forecast time (days) to replacement
Transmit credible Demand Signal to the Supply Chain
Allows Supply Chain to optimize inventory and delivery
AMRDEC CBM+ Diagnostics to Prognostics Remaining Useful Life (RUL
) Process CI/RUL DIAGNOSTICSPROGNOSTICS
Supports
Cost-Wise
Readiness
Enables
Readiness-Driven
Demand Network
USG AMRDEC Material DISTRIBUTION A
APPROVED FOR PUBLIC RELEASE; DISTRIBUTION IS UNLIMITED; October 2015Slide7
Supply Chain Improvement Opportunity
Wholesale Stage
Demand Stage
Retail Stage
Unit Stage
Acquisition Stage
OEM’s
Suppliers
Supply Depots
Repair Depots
OEM’s
SSAs
ASLs
“Readiness Production”
Retrograde
Operations
Training
Combat Missions
Stability Operations
Reverse Logistics Stage
Supply Sources of Variability
Connect Prognostics to the Supply Chain
for anticipatory demand
How can we improve demand forecast accuracy?
Use Prognostics to create demand signal
Demand UncertaintySlide8
Benefits of “Connecting” Prognostics to Forward Supply Chain
Wholesale
Reverse
Logistics
Retail
Acquisition
Unit
Mission
Demand
Anticipatory requisitioning for proactive maintenance
Supply Forecasting - Readiness Based Sparing (RBS)
Reduced Enterprise Requirement Objective (RO) for Cost-Wise Readiness
Contributes to Achieving Cost-Wise Readiness
Prognostics = Early WarningSlide9
CBM
Prognostics Simulation Model -
Initial Results
Calibrated using actual 2410 data for AH-64D Nose Gear BoxSlide10
Benefits of “Connecting” Prognostics to Reverse Pipeline
Wholesale
Reverse
Logistics
Retail
Unit
Mission Demand
Improve DLR induction forecast
Forecast consumable Class IX requirements maintenance workload
Enable synchronized closed loop supply chain for Maintenance Repair & Overhaul (MRO) depots
Contributes to Synchronized Retrograde Process
Prognostics = Early Warning
Supplemental Information
Prior field maintenance records
Diagnostics
Location / Environment
Age / Usage
AcquisitionSlide11
ICCAPS
: Intelligent Collaborative Aging Aircraft Parts Support (LMI)Slide12
Aligning Supply to Readiness Driven Demand
Wholesale
Reverse
Logistics
Retail
Unit
Mission
Demand
SUPPLY
DEMAND
Forecast
Actually Used
AcquisitionSlide13
Mission Based Forecasting for
Readiness Driven DemandSlide14
Benefits of “Connecting” Prognostics to Demand Signal
Wholesale
Reverse
Logistics
Retail
Unit
MissionDemand
Capture consumption/replacement data at unit
Adopt point-of-effect demand segmentation
Forecast Demand =
f
(Mission Based Forecasting + Intermittent Demand +
CBM+)
Enable Readiness Driven Demand Network (RDDN) - relate resources to A
O
Contributes to Readiness Driven Demand Network (RDDN)
AcquisitionSlide15
Connecting Prognostics to the Supply
Chain – Improved Forecasting
Improving Forecast Accuracy: Reduces Forecast Errors, Increases Readiness, Reduces Excess, and Minimizes BurdenSlide16
Downtime
X
f
MTBF
MLDT
MTTR
MTBR
OST
MTTR
X
r
Down
time
MTBR
Reactive Repair
Proactive Replacement
vs.
“Connecting” Prognostics to the Supply Chain: A Mathematical View
MTBR
MTBR
MLDT
= Slide17
The Benefits of Connecting
Prognostics to the Supply Chain
Reverse
Logistics
Wholesale
Retail
RDA
Unit
Demand
Forward Supply Chain
Demand Signal
Reverse Pipeline
Contributes to Achieving Cost-Wise Readiness
Contributes to Readiness Driven
Demand Network
Contributes to Synchronized
Retrograde Process
BenefitsSlide18
Quantifying the Benefits
Metrics
Forward Supply Chain
Reverse Pipeline
Demand Signal
Readiness Return on Net AssetsOperational AvailabilityMateriel Availability
BackordersForecast ErrorMetrics
Forward Supply Chain
Reverse Pipeline
Demand Signal
Inventory (RO)
Inventory
Value/Aircraft
Inventory Turns
Excess
Forecast
Error
READINESS
INVENTORY
BURDEN
Metrics
Forward Supply Chain
Reverse Pipeline
Demand Signal
Workarounds
Forecast ErrorSlide19
How might one design, test, and pursue connecting prognostics to the supply chain?
Establish a Materiel Enterprise Engine for Innovation (EFI
). Slide20
Center for Innovative Logistics Support (
CILS)
Accelerating Innovation for the Materiel Enterprise
Establish an advanced analytics test bed for innovative concepts, strategic technologies, and management policiesSlide21
Supporting Sources
National Research Council, Board on Army Science and Technology Report:
Force Multiplying Technologies for Logistics Support to Military Operations (2014)
Rebuilding analytical capacity for Army Logistics Synchronized retrograde process Connect CBM to the Supply Chain Sustainment Maturity Model (Appendix G) Engine for innovation (EFI) (Appendix F)
Transforming U.S. Army Supply Chains – Strategies for Management Innovation by Greg H. Parlier (2011) Dynamic Strategic Logistics Planning Enterprise Integration and Transformational Change Conditioned Based Maintenance Sustainment Readiness Levels (SRLs) Engine for Innovation (EFI) PBL/CLSSlide22
Questions?Slide23
BackupsSlide24
CBM
Prognostics Simulation Model -
Initial Results
2 Variables, 7 levels each, 49 options, 90 simulation runs per option = 4410 total runs
Calibrated using actual 2410 data for AH-64D Nose Gear Box