Connecting Prognostics to the Supply Chain Greg H. Parlier, PhD, PE Presented by Tom McLaughlin Valu-Lytics, 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) 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/CLS

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).

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 Demand Logistics Readiness Early Warning System (LREWS) CBM Prognostics

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 2015

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 DIAGNOSTICS PROGNOSTICS Supports Cost-Wise Readiness Enables Readiness-Driven Demand Network USG AMRDEC Material DISTRIBUTION A APPROVED FOR PUBLIC RELEASE; DISTRIBUTION IS UNLIMITED; October 2015

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 Uncertainty

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 Warning

CBM Prognostics Simulation Model - Initial Results Calibrated using actual 2410 data for AH-64D Nose Gear Box

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 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 WarningSupplemental Information Prior field maintenance records Diagnostics Location / Environment Age / Usage Prior field maintenance records Diagnostics Location / Environment Age / Usage Acquisition

ICCAPS: Intelligent Collaborative Aging Aircraft Parts Support (LMI)

Aligning Supply to Readiness Driven Demand Wholesale Reverse Logistics RetailUnit Mission Demand SUPPLY DEMAND ForecastActually Used Acquisition

Mission Based Forecasting for Readiness Driven Demand

Benefits of “Connecting” Prognostics to Demand Signal Wholesale Reverse Logistics Retail Unit Mission Demand 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) Acquisition

Connecting Prognostics to the Supply Chain – Improved Forecasting Improving Forecast Accuracy: Reduces Forecast Errors, Increases Readiness, Reduces Excess, and Minimizes Burden

Downtime XfXf MTBF MLDT MTTR MTBR OST MTTR XrXr Down time MTBR Reactive Repair Proactive Replacement vs. “Connecting” Prognostics to the Supply Chain: A Mathematical View MTBR MLDT =

The Benefits of Connecting Prognostics to the Supply Chain Reverse Logistics Wholesale Retail RDA UnitDemand Forward Supply Chain Demand Signal Reverse Pipeline Contributes to Achieving Cost-Wise Readiness Contributes to Readiness Driven Demand Network Contributes to Synchronized Retrograde Process Benefits

Quantifying the Benefits MetricsForward Supply ChainReverse PipelineDemand Signal Readiness Return on Net Assets Operational Availability Materiel Availability Backorders Forecast Error MetricsForward Supply ChainReverse PipelineDemand Signal Inventory (RO) Inventory Value/Aircraft Inventory Turns Excess Forecast Error READINESS INVENTORY BURDEN MetricsForward Supply ChainReverse PipelineDemand Signal Workarounds Forecast Error

How might one design, test, and pursue connecting prognostics to the supply chain? Establish a Materiel Enterprise Engine for Innovation (EFI).

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 policies

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/CLS

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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