Recoupling Forecasting with Inventory Control and Supply ... · PDF fileMarket Opportunities ... and Measuring True “Customer Demand” 15 ... N E L UNIT ND Analyzing Root Causes

Recoupling Forecasting with Inventory Control and

Supply Planning: “Readiness-Driven Supply Networks”

Greg H. Parlier

Colonel, US Army, Ret

[email protected]

2017 FORECAST PRACTITIONER CONFERENCE

NCSU IAA 15 November 2017

mailto:[email protected]

-400

-200

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

2600

2800

3000

97 98 99 00 01 02 03

Requirement

Funded

UFR

181,047

112,946

38,202

54,397

11,212 12,0422,491 2,491 415

0

50,000

100,000

150,000

200,000

<$10 <$50 <$100 <$500 <$1K <$5K <$10K <$50K >$50K

Cost of Part Requisitioned

71% of NMCS

Requisitions were

for parts under $50

The Immediate Problem: Circa 2002

Situation: Selected (Anonymous) Comments

“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…”

Following the Cold-War drawdown, as of late 2002 . . .

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 . . . .

And now (2017), a decade and a half later . . .

“. . . we face a very different set of American fiscal realities . . .

The culture of endless money that has taken hold must be replaced by a culture

of savings and restraint”

Situation: Selected (Anonymous) Comments

Yet . . .

“DoD’s supply chain system has remained stuck in a 20th Century model

because of . . . resistance to change.”

“The Army is facing a [readiness] crises . . . Problems will only get worse.”

“An era of blank-check defense spending is over . . .”

“We need to restructure our demand process and change the algorithm to

meet future demand.”

“DoD has not been able to identify relationships between O&S spending

and the readiness of military units.”

Resources-to-Readiness Challenge

• 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

$

?

Aligning Supply to Readiness Driven Demand

Wholesale

ReverseLogistics

Retail Unit Mission Demand

SUPPLY DEMAND

Forecast Actually Used

Acquisition

MATERIAL FLOWS

Inventory

Vendors

Inventory

Wholesale

Inventory

Retail

Inventory

Unit

INFORMATION FLOWS

Orders to

Vendors

Orders to

Wholesale

Orders to

Retail

Demand

FUNDING FLOWS

OMA

Funding

Available

OMA

Funding AWCF

Payments to

Vendors and

Depots

ReCap &

ReSet

Demand

ReCap &

ReSet

Funding

Obligation

Authority

Supply Chain FrameworkSupply Chain Framework: Organization, Process,

And Financial “Views” of the Materiel Enterprise

Wholesale

StageDemand

Stage

Retail

Stage

Unit

StageAcquisition

Stage

• OEM’s

• Suppliers

• Supply Depots

• Repair Depots

• OEM’s

• SSAs

• ASLs

“Readiness

Production”

• Retrograde

Operations

• Training

• Operations

Reverse

Logistics Stage

Supply Sources of VariabilityDemand

Uncertainty

σ2 = LσD+ D2σL2 2

Supply Variability and Demand Uncertainty:

Army Supply Chain Model

“Decentralized

”“Centralized

”

K = # of stages031 542

25

0

20

10

15

5

Suppliers RetailWholesale Unit

qk

Lk

q2

L2

q1

L1

q0 = D

Customer

…the “bullwhip effect”

s2 (qk)

s2 (D)

Capacity, Inventory & Knowledge

Capacity:

What we

can do

What we

knowWhat we

have

Substitutable Ingredients of System

Performance

Knowledge: Inventory:

10

A B C

Production

System

Engineering &

Development

Technology

Development

Concept

RefinementO&S

What Happened? What Could Happen? Make it Happen!

2002 2003 2005

Phase 1• Segment the Logistics

Structure & Processes

for Analysis

• Adapt Enterprise

Supply Chain

Framework for

Integration

[~$200K]

Phase 2• Identify "Readiness

Production Function"

• Develop "Mission

Based Forecasting"

• Validate "Readiness

Based Sparing"

• Incorporate

Multi-Echelon

Optimization &

"Synchronized

Retrograde

Operations"

• DDSN & LEWS

[$1.0M]

Phase 3• 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

[$2.2M]

Expanded

Market

Opportunities

CILS Provides:• Product Support Integration

• Supply Chain Optimization

• Logistics System Readiness

• SNL (RECAP)

• IDA ( Reliability

Design to Readiness)

• UAH (OEM Supplier Analysis)

Acquisition

Reverse

Logistics

UnitWholesale Retail Demand

Task Organization for Research and Analysis

• LMI (Peak Policy & ICAAPS)

• PNNL (VLD)

• USMC (TOC)

• IDA

• RAND

• LOGSA

• SAIC

• RAND (EDA)

• LMI

• AMSAA

• PMs

• AMSAA (RBS)

• MCA

Transforming US Army Supply Chains (TASC): Project Phases

11

Working Towards Solutions

Innovation Catalysts:• Defining the Readiness Equation

• Mission Based Forecasting

• Connect CBM to the Supply Chain

• Readiness Based Sparing

• Readiness Responsive Retrograde

• Leveraging Lessons Learned & Best Practices

RETROGRADE

ACQUISITION DEMANDWHOLESALE UNITRETAIL

Analyzing Root Causes and Prescribing Innovation

Catalysts Across the Supply Chain

(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;

Supply Availability Demand Requirements

MTBF

MLDT

MTTR

- NMCS

- NMCM

Operational

Availability (Ao)

[ER] - MC

- FMC

- PMC

[AS] [TS]

• Deployment Missions (DEPTEMPO)

- Patterns of Operation

Duration

Profile

- Environmental Conditions and

Locations

• Training Requirements (OPTEMPO)

Readiness – related Measures / Metrics

[ER] – Equipment Readiness (Ao)

• FMC • NMCS

• MC (PMC) • NMCM

[AS] – Assigned Strength

[TS] – Trained Strength

Personnel

Manning and

Skill Levels

Weapon

System

Reliability

Supply

Support

Capability

Training

Resources

(OPTEMPO $)

“Production Function”: Components of Readiness

La

bo

rL

ab

or

CapitalCapital

90% MC90% MC

AO =

=

Uptime

Total Time

WhereMTBF = Mean Time Between

Failures (Reliability)

MTTR = Mean Time To Repair

(Maintainability)

MLDT = Mean Logistics Delay Time

(Supportability)

MTBF

MTBF + MTTR + MLDT

80% MC80% MC

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

Research Goals:

- Define and empirically measure the

“readiness equation” for Ao

- Determine readiness “driver” marginal

values, and evaluate contributions and

costs for potential solutions.

Innovation Catalyst: Analyzing the Readiness EquationInnovation Catalyst: Analyzing the Readiness Equation

and Measuring True “Customer Demand”

15








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;

3 observed force-on-force forms

Effects-based operational forms:

Continuous fronts

Disintegrations

Disruptions

+ Stability operations

P A G E 16

Secondary attack

Fix

Fix

Fix

Main attack

Main attack

Secondary attack

Continuous FrontContinuous front

Stability OpnMain attack

Fix

Fix

Main attack

Main attack

Fix

Fix

Fix

Secondary attack

Disruption (high-order)

Disruption(high-order)

Main attack

Secondary attack

Fix

Fix

Main attack

Secondary attack

Fix

Fix

Fix

Fix

Disintegration

Disintegration

Analyzing Operational Forms

and Empirical Patterns

STRATIFIED SAMPLING

POPULATION OF SIZE N DIVIDED INTO K STRATA

n

xPRSM

ˆRANDOM SAMPLING:

STRATIFIED SAMPLING:

nx

Pk

kk1

THEN:

NPN

P

k

ikk

STRAT

1

1

ˆ

USUALLY:

ˆˆRSMPOPSTRAT

VarVarVar

“. . .compare forecasting methods [to establish] regions of

superior performance, then categorize demand patterns

[in order to] select the most appropriate estimation procedure.”

From “On the Categorization of Demand Patterns”, by

Syntetos, Boylan, and Croston JORS, 2005

1

8

Mission Demand

Operation Type/Duration

Environmental Conditions

Force Size/Composition

Center for Systems ReliabilityReadiness & Sustainment DepartmentSandia 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.

Innovation Catalyst: Mission Based Forecasting (MBF)

CONUS vs IraqStabCONUS vs IraqMCO

59% 41%

73% 27%

CONUS IraqStab

59% 41%59% 41%

73% 27%73% 27%

CONUS IraqStab

Ratios of Demands: Common PartsRatios of Demands: Common Parts1

2

3

4

0

4.4 x CONUS5

6

1

2

3

4

0

4.4 x CONUS5

6

1

2

3

4

0

3.9 x CONUS

5

6

1

2

3

4

0

3.9 x CONUS

5

6

12%88%

CONUS IraqMCO

71% 29%

12%88%

CONUS IraqMCO

12%88% 12%88%

CONUS IraqMCO

71% 29%71% 29%

111 84 87111

AH-64DAH-64D

1.01.0

Note: original Views

(2005 data)

Note: original Views

(2005 data)

2833 2833

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”.

Innovation Catalyst: Mission-Based Forecasting (MBF)

Note: New (2006) data



Actual NSNs Used

Comparing Forecast Methods:Accuracy versus over- and under-forecast

Cost

Found

Accurate

Error(over-forecast)

Error(under-forecast)

Forecasted NSNs

NSN lines [“breadth”]

Errors Found Errors(over-forecast) (under-forecast)

Forecast Actual

Part

Analysis

Actual

QuantityQuantity

[“depth”]

Quantity

Analysis

Forecasted

Quantity

Forecasted

Quantity

Actual

Quantity

The Analysis addresses two perspectives

Phase 2 Membership (Lines) - Case 3

without JLAT (3 hr)

0

500

1,000

1,500

2,000

2,500

ODDP G4 OSRAP CIF JLAT (1 hr)

Method

Pa

rt C

ou

nt

Over-forecast

Found (correctly forecast)

Under-forecast

AH-64D Parts Count Forecast (Breadth of NSNs):

MBF Compared to Current MethodsCase 3, Stability Ops (mid-level threat), 12 months, 104 tails

MBF reduces part over-forecast, and under-forecast

& improves forecast part-accuracy% “Found” out of total forecast parts (breadth)

MBF A B C D

These current methods (A, B, C,

D)

use supply requisitions data

Measuring Forecast Accuracy:Reducing Error Sources

Improving Forecast Accuracy: Reduces Forecast Errors, Increases Readiness, Reduces Excess, and Minimizes Burden

$0

$50,000,000

$100,000,000

$150,000,000

$200,000,000

$250,000,000

ODDP Combo

PLL+ASL

Combo PLL PLL1 PLL2 PLL3 PLL4

Phase 2 Cost (Parts) - Case 3

Excess $ for incorrectly predicted parts

Excess $ for correctly predicted parts

Shortage of $ for missed parts

Shortage of $ for correctly predicted parts

Correctly predicted actual $

AH-64D Parts Quantity Forecast (Depth of NSNs):

MBF Compared to Actual On-Hand Stocks

$200M

$138M

Bn

(24 tails)

Bn

(24 tails)

Bn

(24 tails)

Bn

(24 tails)PLL

Rollup

for

4Bn’s(96 tails)

Actual

On-hand

Rollup

for

4Bn’s(96 tails)

ODDP-

Forecasted

Parts rollup

for

104 tails

Subset: 4 Bn PLLs

(Bn-level stocks)

Savings:

for 1 year

(breadth & depth)

= $62M

Intermittent Demand

Professional Judgment

Moving Average/Exponential Smoothing

Poisson Methods (Croston)

Markov Bootstrap (Smart-Willemain)

Ongoing Research

Varieties of Intermittent Demand

7/24/2015 29

0 5 10 15 20 25 30 35

02

46

8

company 17 item 221

Index

y[i,

]

0 5 10 15 20 25 30 350

10

20

30

company 17 item 222

Index

y[i,

]

0 5 10 15 20 25 30 35

02

46

8

company 17 item 223

Index

y[i,

]

0 5 10 15 20 25 30 35

050

150

company 17 item 224

Index

y[i,

]

0 5 10 15 20 25 30 35

020

60

company 17 item 225

Index

y[i,

]

0 5 10 15 20 25 30 35

010

20

company 17 item 226

Index

y[i,

]

0 5 10 15 20 25 30 35

040

80

company 17 item 227

Index

y[i,

]

0 5 10 15 20 25 30 35

0.0

0.4

0.8

company 17 item 228

Index

y[i,

]

0 5 10 15 20 25 30 35

0.0

0.4

0.8

company 17 item 229

Index

y[i,

]

0 5 10 15 20 25 30 35

02

4

company 17 item 230

Index

y[i,

]

0 5 10 15 20 25 30 35

01

23

4

company 17 item 231

Index

y[i,

]

0 5 10 15 20 25 30 35

040

80

company 17 item 232

Index

y[i,

]

0 5 10 15 20 25 30 35

05

15

company 17 item 233

Index

y[i,

]

0 5 10 15 20 25 30 35

02

46

company 17 item 234

Index

y[i,

]

0 5 10 15 20 25 30 35

0.0

1.0

2.0

company 17 item 235

Index

y[i,

]

0 5 10 15 20 25 30 35

0400

1000

company 17 item 236

Index

y[i,

]

0 5 10 15 20 25 30 35

02

46

8

company 17 item 237

Index

y[i,

]

0 5 10 15 20 25 30 35

0.0

0.4

0.8

company 17 item 238

Index

y[i,

]

0 5 10 15 20 25 30 35

0.0

1.0

2.0

3.0

company 17 item 239

Indexy[i,

]

0 5 10 15 20 25 30 35

0.0

1.0

2.0

company 17 item 240

Index

y[i,

]

Unclassified. Smart Software, Inc.

Output of Markov Bootstrap

307/24/2015 Unclassified. Smart Software, Inc.

Markov Bootstrap Algorithm

1. Code historical demands as 0 or X>0.2. Fit a 1st- order binary Markov model to data.3. Use Markov model to project demand

sequences over the replenishment lead time.4. Replace X’s in scenarios with random samples

from the set of observed nonzero demands (“bootstrap”).

5. Sum the projected lead time demands.6. Repeat steps 3-5 many times to build empirical

estimate the distribution of lead time demand.


Step 1: Parse History into Two Pieces

• Piece #1: Sequence of zero/nonzero demands.

7/24/2015

• Piece #2: List of all nonzero demand values.

Unclassified. Smart Software, Inc.

Step 2: Estimate Transition Probabilities


Step 3: Generate Demand Scenarios


Output of Markov Bootstrap

357/24/2015

Unclassified. Smart

Software, Inc.

Assessing ID Forecast Accuracy•ID is not “unforecastable”.•Goal is to support inventory management by forecasting the sum of ID over an item’s replenishment lead time

Need a “distribution forecast” not “point forecast”.•Traditional forecasting metrics not applicable.•Accuracy should be measured by the “calibration” of the forecast distribution.

Ex: If weather forecast says “80% chance of rain”, then it should rain on 80% of days with that forecast.Ex: After ID forecasts identify 95%iles of lead time demand, 95% of items should have demand <= their predicted 95th %tiles.

•Markov Bootstrap algorithm has excellent calibration.









RETROGRADE




Prognostic Demand

Condition Based Maintenance (CBM)

“Connecting” CBM to the Supply Chain

Remaining Useful Life (RUL)

39

RETROGRADE


CBM Data Warehouse

Regime Recog

Failure Analysis

Logistics Req’s

Prognostics

Environment

In Theater

PLM+ LMP

GCSS-A

Fleet Managers

IMMC

Platform PMs

LOGSA

Historical

Supply

Chain Data

OEMs

Condition & Health

Usage & Operations

Maintenance

Integration Opportunity:

Connecting CBM to the Supply Chain“Connecting” CBM to the Supply Chain: A Conceptual View

40

t3 t2 t1

A W R U

t3 t2 t1

A W R U

Downtime

Xf

MTBF

MLDT MTTR

Xf ?OST1 OST2 OST3

Reactive Repair Proactive Replacement

MTBR

OST MTTR

Xr

Down

timeMTBR

XrOST1 OST2

Reactive Repair Proactive Replacementvs.

“Connecting” CBM to the Supply Chain: A Mathematical View

Remaining

Useful

Life

Available

ost

Mean,Std. dev.

0,1

Normal Distribution

x

de

nsi

ty

-10 -8 -6 -4 -2 0 2 4 6

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time

TaTr

λ

Tf

CBM “Alert”(Ta)

Prognostic

Component

Replacement (Tr)

Tr – Ta > OST

Failure

Rate

Curve

From

Prognostic

Algorithm

Connecting CBM to the Supply Chain

(Tr)

(Ta)

Early demand

signal can drive

down retail

stockage

= Time to Replacement

Benefits of “Connecting” CBM to Forward Supply Chain

Wholesale

ReverseLogistics

RetailRDA UnitMissionDemand

CBM+

•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

CBM+ = Early Warning

SSA

Inventory

LMP

RIMFIRE

Connecting CBM to the Supply ChainProject Process

Project/Process Flow:

Analyze Existing,

Actual Data

Baseline

Metrics

Develop

Prognostic

Simulation

Validate

Algorithm

Develop

Predictive

Algorithm

ASAP Data

DA Form

2410

DSC

Failure Rate Curve: Component HealthPrognostic Simulation Tool

Actual InventoryInventory Inventory CostReduction 0 2 4 6 8 12 16 Level Savings

0% 110.7 77.4 48.1 34.9 22.8 7.4 1.6 37 $0

2% 130.1 99.6 66.3 45.8 27.1 12.0 2.9 36 $51,801

5% 150.7 120.5 81.9 52.0 36.6 13.1 5.6 35 $103,602

10% 196.4 155.6 113.4 89.2 61.1 27.3 9.2 33 $207,204

15% 230.7 196.4 166.4 134.8 105.4 51.9 22.3 31 $310,806

20% 256.2 232.8 201.8 180.2 147.7 78.7 44.9 29 $414,408

25% 287.4 265.4 244.1 223.7 180.6 128.0 59.2 27 $518,010

Days Ordered Early (compared to historical requisition times)

# of Times an Aircraft was Down for More Than One Day

Expected Results Based on Improved Predictive Ordering

• 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

Connecting CBM to the Supply ChainCBM Prognostics Simulation Model - Initial Results

Actual InventoryInventory Inventory CostReduction 0 2 4 6 8 12 16 Level Savings

0% 110.7 77.4 48.1 34.9 22.8 7.4 1.6 37 $0

2% 130.1 99.6 66.3 45.8 27.1 12.0 2.9 36 $51,801

5% 150.7 120.5 81.9 52.0 36.6 13.1 5.6 35 $103,602

10% 196.4 155.6 113.4 89.2 61.1 27.3 9.2 33 $207,204

15% 230.7 196.4 166.4 134.8 105.4 51.9 22.3 31 $310,806

20% 256.2 232.8 201.8 180.2 147.7 78.7 44.9 29 $414,408

25% 287.4 265.4 244.1 223.7 180.6 128.0 59.2 27 $518,010

Days Ordered Early (compared to historical requisition times)

# of Times an Aircraft was Down for More Than One Day

Expected Results Based on Improved Predictive Ordering

Calibrated using actual 2410 data for AH-64D Nose Gear Box

CBM Prognostics Simulation Model –Initial Results








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;



Benefits of “Connecting” CBM to Reverse Pipeline

RDA Wholesale

ReverseLogistics

Retail UnitMissionDemand

CBM+

• 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

CBM+ = Early Warning

Benefits of “Connecting”CBM to Demand Signal

RDA Wholesale

ReverseLogistics

Retail UnitMissionDemandCBM+

•Capture consumption/replacement data at unit

•Adopt point-of-effect demand segmentation

•Forecast Demand = f (Mission Based

Forecasting + Intermittent Demand + CBM+)

Contributes to Readiness Driven Supply Network (RDSN)

The Benefits of ConnectingCBM+ to the Supply Chain

CBM+

ReverseLogistics

Wholesale RetailRDA Unit Demand

Forward Supply Chain

Demand Signal

Reverse Pipeline

Contributes to Achieving Cost-Wise

Readiness

Contributes to Readiness Driven Supply Network

Contributes to Synchronized Retrograde

Process

Benefits

Quantifying the Benefits

Metrics Forward Supply Chain Reverse Pipeline Demand Signal

Readiness Return on Net Assets

Operational Availability

Materiel Availability

Backorders

Forecast Error


Inventory (RO)

Inventory Value/Aircraft

Inventory Turns

Excess

Forecast Error

READINESS

INVENTORY

BURDEN


Workarounds

Forecast Error

Draft Pre-Decisional Working Papers

Ultimate Goal

51

Improved alignment between maintenance and supply will

reduce excess and improve performance

Reduce Reduce

Right Part

Right Time

Backo

rders

Excess P

arts

Readiness

Risk

Underestimated

Demand

Overestimated

Demand

Fiscal

Risk

Army Consumption Data=Commercial POS Data

Data flow

Consumption

(parts in

aircraft

maintenance)

Recommended

Forecast Data Source

52

Adopting Mission Based Forecasting (MBF):

Key enabler for a “Readiness-Driven” Supply Network (RDSN)

Supplier Distribution

CenterStore

Room

Point

of Sale

(POS)

Supplier “Wholesale”

supply, aggregated

orders (requisitions)

“Retail”

supply(inventory across

multi-echelons)

Advanced Commercial Supply Chain

Army Supply ChainCurrent Forecast

Data Source

Current Forecast

Data Source

Focus:

Top-down

approach geared

toward meeting

inventory level

targets

Focus:

Bottom-up, POS

based approach

geared toward

meeting customer

demands

Commodity flow

Data flow

Parts flow

Data flow

Guiding Principles for Readiness-Driven Supply Networks

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








RETROGRADE




Readiness Based Sparing (RBS)

Ao

$ Spares

Marginal Analysis Includes:

• Cost of Parts

• Frequency of Use/Need

• Part Impact on Readiness

-

-

-

6th A

11th B

2nd C

12th B

1st D

7th A

-

-

-

-

-

-

1,600

2,300

10, 400

2,300

13,800

1,600

-

-

-

-

-

-

0.388

0.352

0.312

0.283

0.154

0.144

-

-

-

-

-

-

101.600

103.900

114.300

116.600

130.400

132.000

-

-

-

-

-

-

66.67

66.69

66.74

66.76

66.78

66.79

-

-

-

Item UnitCost($)

AddedAircraft/$10K

TotalCost($)

AvailabilityRate(%)

Shopping List

65

70

75

80

85

90

95

100

0 1 2 3 4 5

Readiness

Target

ASL Investment in $M

$.5M

$.6M

$.8M

$1.2M

$2.2M

$4.8M

Source: AMSAA

RBS Curve:

“The Efficient Frontier”

Results

x

?

Analytical Demonstration:

Readiness Based Sparing: 101st ABN DIV UH-60

57

Innovation Catalyst: Readiness Based Sparing

65

70

75

80

85

90

95

100

0 1 2 3 4 5

Readiness

Target

ASL Investment in $M

$.5M

$.6M

$.8M

$1.2M $2.2M

$4.8M

Source: AMSAASource: AMSAA

RBS Curve:

“The Efficient

Frontier”

Analytical Demo 101st ABN DIV UH-60 Results

xx

Conditions:

- Low $ parts were causing Army weapon systems NMC

- “Readiness Based Sparing” (RBS), developed at RAND

and LMI, had not been tested for Army Aviation

Research Results:

- Analytical Demo revealed significant potential to reduce

costs and relate investment levels to Ao. . . RBS later

adopted at Fort Rucker.

- Multi-Echelon RBS exhibits tremendous potential for

cost savings and relating resources to Ao fleetwide.

0

1

2

3

4

5

0 50 100 150 200 250 300 350 400

Percent

Increase

In

Readiness

Percent Increase in

Investment at Wholesale

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%

Baseline:

Impact of Increased Investment at Wholesale on Blackhawk Equipment Readiness at 101st Airborne

Source: AMSAASource: AMSAA

RBS Impact?

Innovation Catalyst: Multi-Echelon Readiness Based Sparing

Part III. Enterprise Integration: Prescriptive Analytics for

Efficiency, Resilience, and Effectiveness

Achieving “Efficiency” in the Cost -

Availability Trade Space

“Efficient Frontier”

Ao

$

Gain in

“Efficiency”

Increasing “Effectiveness” in the

Cost -Availability Tradespace


Ao

$

4

32 1

Cost Benefits Alternatives:

1. Improved effectiveness with increased costs

2. Improved effectiveness at

same costs


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

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

59

NICP

DDOC

SSA

ASL

ASL

ASL

A

Design for Resilience: Demand Driven Supply

Network (DDSN)

DDOC

DDOC

SSA SSA

SSA

SSA

SSA

SSA

SSA

SSA

• RBS reduces cost

• Inventory pooling reduces both

cost and risk

• Lateral supply decreases

requisition delay time & increases

Ao

RBS

Stock

List

$

Hi Cost-

Low Demand

DLRs

• Low Cost

Consumables

• Hi Demand

Parts

RBS

Cost/Item

List

Design for Structural Resilience: Readiness Driven Supply Network

60

Pursuing Cost-Effective Readiness:

Pushing the Performance Envelope

Increasing “Effectiveness” in the

Cost -Availability Tradespace


Ao

$

4

32 1

Cost Benefits Alternatives:

1. Improved effectiveness with increased costs


same costs


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

61

“Optimizing” the System: Applying a Dynamic (Multi-Stage) Programming Model

0

1

2

3

4

5

0 50 100 150 200 250 300 350 400

65

70

75

80

85

90

95

100

0 1 2 3 4 5

Acquisition

Design Stage

Wholesale

Stage

Retail

Stage

Unit Production

Stage

$$ $$ $$ $$ AAoo

Perc

en

t In

cre

ase

In

Read

iness

Percent Increase in Investment at Wholesale

Re

ad

iness

Targ

et

Investment in $M

$.5M

$.6M

$.8M

$1.2M

$2.2M

$4.8M

Lab

or

Lab

or

CapitalCapital

90% MC90% MC

80% MC80% MC

112233NN

Automated Monitoring

Management

Assessment

Policy Response

Feedback

Alert Warning

- Readiness trends and forecasts

- Supply chain metrics

- Logistics system readiness parameters

- Corroborate and validate alerts

- Assess near and long-term implications

- Integrate empirical evidence with human judgment

- HQDA reviews

- Analyze and implement cost-effective options

- Minimize recognition and response bags

- PPBES implications (resources-to-readiness)

Logistics Readiness and Early Warning SystemLogistics Readiness Early Warning System

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)

CONUS Europe

Pacific

PMO

Apache

SWA

Ao

Reset

Available

Ready

$

Integration Opportunity: ARFORGEN Synchronization -

MBF and RBS

CONUS vs IraqStabCONUS vs IraqMCO

59% 41%

73% 27%

CONUS IraqStab

59% 41%59% 41%

73% 27%73% 27%

CONUS IraqStab

12%88%

CONUS IraqMCO

71% 29%

12%88%

CONUS IraqMCO

12%88% 12%88%

CONUS IraqMCO

71% 29%71% 29%

111 84 87111

2833 2833

La

bo

r

Capital

80% MC60% MC

90% MC

RBS Curve:

“The Efficient

Frontier”

Integration Opportunity: RBS and MBF for the

Army’s new Regionally Aligned Force Concept

Integration Opportunity: Logistics Support for

Capabilities Based Planning

REVISED CLASS IX STOCKAGE POLICY

*EG: IDEEAS, JANUS,

JCATS CASTFOREM,

JTLS

MISSION

SCENARIOS

ReadinessEquation

CONUS vs Iraq Stab

73% 27%

CONUSIraqStab

59% 41%

Integration Opportunity: “Advanced Analytics” for a Capabilities Based Force

Defense Planning Guidance

Scenarios

Integration Opportunity: Product Support Integration (PSI) for PBL

Aligning PBL Incentives to Readiness Outcomes

Value of

Cost or

Output

(Return)

Decision or Stopping Points

(Iterations)

Maximum difference

between Total Revenue

and Total Cost or

Maximum Profit

Total Cost Function

Production Function

(A0 * VPC)

Steep here means

shallow here

Tangents

of equal

slope

PBL Contract Scoring Regime Results

0

0 5025

10

2015105 403530 45

9

3

2

1

6

5

4

8

7

($ 000,000s)

Inventory Value* ($ 000,000s)

Legend

Award Fee

Cost

Profit

Max Profit

The Fallacy of ‘Fill Rate’ as an Incentive

for SC Performance

0

0 5025

1.0

2015105 403530 45

.9

.3

.2

.1

.6

.5

.4

.8

.7

Ao/FR/Score

Inventory Value* ($ 000,000s)

0

100

90

30

20

10

60

50

40

80

70

Legend

Ao

Fill Rate Avg. Delay

Max Profit

Score

What the

customer got

What the

customer

wanted

Avg. Delay

Integration Opportunity: Product Support Integration for

Performance Based Logistics (PBL)

Draft Pre-Decisional Working Papers

MVC Principles

Point of Effect

Data

Point of Effect

Maintenance

Planning and

Forecasting

Inventory

Demand

Planning

Close

Collaboration

Between Supply

& Maintenance

Functions

Supply Chain

(Asset) Visibility

Performance

Measures

Utilized

Level

1

Collect data at point

of effect using ad hoc

data collection

systems

Forecasting

scheduled

maintenance using

OPTEMPO as

primary driver

Forecast based

predominantly on

historical demand

Supply and

Maintenance

functions act nearly

independently;

limited comms

between functions

No visibility of

inventory levels

across wholesale

and retail levels

Metrics that only

focus on wholesale

supply goals (i.e. Fill

Rate)

Level

2

Authoritative data

sources, made

available to

authorized users;

quality improvements

to data collected

Level 1 +

Forecasting

unscheduled

maintenance using

mission based

forecasting (MBF)

Forecasting models

plus analytics for

different types of

demand at different

echelons

Scheduled

maintenance

forecasts are shared

with supply planners

Inventory visibility

across unit and retail

levels, within units

supported by SSA

Metrics that focus on

supply goals at all

echelons

(operational, retail

and wholesale)

Level

3

Systemic processes

to enhance

collection, cleansing,

and auditing; Data is

used to inform

decision making at

multiple levels

Level 2 +

Forecasting

unscheduled

maintenance using

segmented MBF (i.e.

CBM+, intermittent

demand, etc)

Driven by Point of

Effect Level 3

forecasting

Systemic feedback

processes to monitor

progress,

performance, and

cross-functional

effectiveness

Transparency of

inventory levels

across all echelons

of supply chain,

including In-transit

Visibility

Metrics focused on

organizational goals

(i.e. readiness);

Metrics that

incentivize cost

savings

DRAFT MVC Principles Maturity Model

67

Bringing it All Together

Other Uses of the MLNPS

• Plan Fuel Networks

• Reverse Logistics

Logistical Planning tool is needed

• Must support fast-paced, frequently

changing expeditionary operations.

• Must rapidly determine capability

requirements as operational

requirements change.

RAND Study

MLNPS

ERP

System

Expeditionary

Operation

Mission-

Based

Forecasting

Current State

of System

Network

MDMP

Constraints

Mission

Specific

Demand

Forecast

DP

• Decision Point for Commander

• Forecasted Information for Subordinate

Units & Critical Nodes

• Reduce Uncertainty (Fog of War)

MLNPS as the app

There’s an app for that!

Sustaining Innovation While

Linking Execution to Strategy

Capacity, Inventory & Knowledge

Capacity:

What we

can do

What we

knowWhat we

have

Substitutable Ingredients of System

Performance

Knowledge: Inventory:

Management Innovation as a Strategic Technology

Customer

Needs

Methodology

Advancement

Technology

Enablers

Management

Innovation:

•MERBS1

•MBF2

•R33

•DSLP4

•LREWS5

Technology

Innovation:

•CBM6

•RFID7

•TAV8

•ERP9

6Condition Based Maintenance

7Radio Frequency Identification

8Total Asset Visibility

9Enterprise Resource Planning

1Multi Echelon Readiness Based Sparing

2Mission Based Forecasting

3Readiness Responsive Retrograde

4Dynamic Strategic Logistics Planning

5Logistics Readiness and Early Warning

System

71

Academia

Corporate

Research

Tactical Units Academic

Institutions

Commercial

SectorPrivate

(1)

Magnet, Filter and

“Repository”

for

“Good Ideas”

(2)

Modeling,

Simulation

& Analysis of

Complex

Systems

Public

(3)

Transforming

Organizations &

Managing

Change

DoD

Organizations Professional

Societies:

INFORMS

FFRDC’s

Non-Profits

• Organizational Design

• Supply/Value Chain

• Workforce Development

• Technology Implications

• Innovation & Productivity

Gain

• R & D

• System Dynamics Modeling

• Large Scale System

Design, Analysis, and

Evaluation

• Systems Simulation,

Modeling and Analysis

• Repository for validated

models & analytical tools

• Cost Benefit Analyses

• Risk Reduction & Mitigation

• Research, Studies, and

Analysis

• Education & Training

• Technical Support

• Change Management

An “Engine for Innovation”:

The Center for Innovation in Logistics Systems (CILS)

http://www.nap.edu/catalog/18832/force-multiplying-technologies-

for-logistics-support-to-military-operations

http://www.nap.edu/catalog/18832/force-multiplying-technologies-

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.

Recoupling Forecasting with Inventory Control and

Supply Planning: “Readiness-Driven Supply Networks”

Greg H. Parlier

Colonel, US Army, Ret

[email protected]

2017 FORECAST PRACTITIONER CONFERENCE

NCSU IAA 15 November 2017

mailto:[email protected]

Mission Based ForecastingMBF BENEFITS

•Ability to accurately predict tactical-level demand

•Measure the actual cost of operational requirements

•Align/manage inventory to readiness-driven demand

•“Connect” CBM prognostics to forward supply chain

•Synchronize retrograde and depot repair operations

•Defend resources needed for mission Ao requirements

•Reduce tactical-level “burden” and work-arounds

•Enable “early warning” for sustainment enterprise

•Cost savings estimates: tens of billions $

•MBF investment ROI of several orders of magnitude

•Significantly improve tactical unit-level operational Ao

TECHNOLOGY SOLUTION

To identify potential “catalysts for innovation”, the US Army established the project to Transform Army Supply Chains (TASC). Mission Based Forecasting (MBF), a critical enabling catalyst, is a new concept for demand planning which capitalizes on big data, the Internet of Things, and predictive analytics to support military operations. MBF testing suggests order-of-magnitude reductions in forecast error, inventory savings of billions of dollars, and reduction of manpower-intensive work-arounds at the tactical level.

PROBLEM STATEMENT

The US Department of Defense (DoD) operates the

most complex global supply chains in the world.

However, effectively integrating production

planning, maintenance operations, inventory

systems, and distribution policies has been a

strategic challenge. According to the GAO, DoD

supply chain management is both wasteful and hi-

risk due to poor demand forecasting, ineffective

inventory management, and inadequate strategic

planning.

Documents

Recoupling Forecasting with Inventory Control and Supply ... · PDF fileMarket Opportunities ... and Measuring True “Customer Demand” 15 ... N E L UNIT ND Analyzing Root Causes