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Evaluating Inventory Segmentation Strategies for Aftermarket Service Parts in Heavy Industry using Linked Discrete-

Event and Monte Carlo Simulations

Author: Randolph L. Bradley

Advisor: Jarrod Goentzel

Sponsor: Heavy Industry

MIT SCM ResearchFest May 23-24, 2012

1 MIT SCM ResearchFest

Agenda

• Overview

• Hypothesis

• Methodology

• Data Analysis

• Simulating Baseline Strategy

• Simulating Alternate Strategy

• Stocking Policy

• Executive Summary


Overview & Hypothesis


Bernt A. Lilliestråle • Vice President, Shuttle Operation, Teekay Norway

• Teekay Petrojarl is a global leader in oil & gas shipping & offshore production

• Tech data, manuals, and standards often non-existent

• Focus on maintenance rather than maintaining data

The Persona of Heavy Industry

“My biggest fear is that I have a

ship down for maintenance, the

part is somewhere in Teekay’s

spares chain, and I don’t know it.”

– Bernt Lilliestråle

• Small fleet

‒ Five Floating Production, Storage and Offloading (FPSO) vessels

‒ Two shuttle tankers ‒ One storage tanker

• Knows his support challenges are systemic across heavy industry

Definitions

• Customers want all their parts when needed…

• Fill Rate

• Percent of parts shipped from stock on hand

• Objective function of inventory optimization

• Perfect Order Fulfillment (POF)

• Percent of orders shipped “on time and in full”

• Order Fulfillment Lead Time (OFLT)

• Average time from order placement to customer receipt

• Measures speed of service

• OFTL as contractual metric = POF within delivery window

• Modeled with simulation

MIT SCM ResearchFest 5

Hypothesis

Baseline AB Inventory Segmentation Strategy

• Group on price

• A=Parts ≤ $2,000

• B=Parts > $2,000

• Min 50% Fill Rate


Alternate ABCDE Inventory Segmentation Strategy

• Group on annual use

• A=Parts 80% Annual Use

• B=Parts 15% Annual Use

• C=Parts 5% Annual Use

• D=$25 ≥ Parts > $1

• E=$1 ≤ Parts

Hypothesis: The alternate ABCDE inventory segmentation

strategy for consumable parts will outperform the baseline

AB strategy when measured on Order Fulfillment Lead

Time and inventory investment.

Baseline AB vs. Alternate ABCDE Inventory Segmentation Strategies

0%10%20%30%40%50%60%70%80%90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

% C

um

ula

tive

Par

t C

ou

nt

% Cumulative Annual Use (Demand * Unit Cost)

Segment % Annual

Use % Part Count

A: 80% Usage & >$25 79% 7%

B: 15% Usage & >$25 14% 14%

C: 5% Usage & >$25 4% 42%

D: $1<Unit Cost≤$25 3% 30%

E: Unit Cost≤$1 0% 7%

Total 100% 100%


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

$0

$2

$5

$9

$1

4 $

19

$2

6 $

37

$5

2 $

73

$1

00

$1

43

$2

00

$2

81

$3

81

$5

45

$7

74

$1

,123

$1

,689

$2

,779

$5

,161

Cu

mu

lati

ve %

Par

t C

ou

nt

Unit Cost

Baseline AB Inventory Optimization Unit Cost vs. Cum. % Part Count

Segment % Annual

Use % Part Count

A: ≤$2,000 37% 92%

B: >$2,000 63% 8%

Total 100% 100%

Alternate ABCDE Inventory Segmentation Part Count vs. Annual Use

Alternate ABCDE Inventory Segmentation

E Consumables, 7% Parts,

$0<=Cost<=$1, 99% FR

C Consumables, 42% Parts, 5%

Annual Use, 99% FR

B Consumables, 14% Parts, 15%

Annual Use, 92% FR A Consumables, 7% Parts, 80%

Annual Use, 90% FR

D Consumables, 30% Parts,

$1<Cost<=$25, 99% FR

Inventory optimization uses a

“greedy algorithm” to select the

lowest cost quantity and mix of

parts achieving fill rate

constraint


Pe

rce

nta

ge

Pe

rce

nta

ge

Pe

rce

nta

ge

Cost Cost Cost P

erc

en

tag

e

Cost

Pe

rce

nta

ge

Cost

90% Fill

Rate Goal

Average

Days Delay

“Availability”

Methodology


Warehouse

Simulation Discrete

event

warehouse

simulation

New buys

arrive in lead

time

Student’s t-

test validates

sim

Fill Rate

Over Time

Statistical

Evaluation Ao, Fill Rate, and Avg Delay

Availability

Fill Rate

Avg. Delay ** Scaled to 10.00 = 100

Percentage

Cost

0

20

40

60

80

100

0 10000000 20000000 30000000 40000000 50000000 60000000

Inventory Optimization

Model B

Data Cleaning + Integration

Extract, Xform & Load (ETL)

Inventory Optimization

Model A

Set fill rate

goal

Extract

Model A

inputs and

outputs

Reuse data

for multiple

models

Model B vali-

dates Model

A fill rate

Strategic Inventory Optimization

Monte Carlo Demand Categorization & Metrics

Simulation

Discrete Event Warehouse Simulation

Metrics

Simulation

Categorization

& metrics

simulation

OFLT w/

days delay

Days Delay

OFLT Over Time OFLT w/

Confidence

Interval


Simulation Methodology

70%

72%

74%

76%

78%

80%

82%

84%

86%

UCL21Sims

LCL21Sims

UCL4Sims

LCL4Sims

How much is enough? Optimal # warehouse discrete event simulations

Time

74%

76%

78%

80%

Tran

sact

ion

s in

OFL

T W

ind

ow

Sims vs. 80% Confidence Level Consumables Hi-Pri OFLT Jun-12

UCL Mean LCL

78%

79%

80%

81%

82%

Tra

nsa

ctio

ns

in O

FLT

W

ind

ow

Sims vs. 80% Confidence Level Consumables Hi-Pri OFLT Dec-12

UCL Mean LCL

80%

82%

84%

86%

Tran

sact

ion

s in

OFL

T W

ind

ow

Sims vs. 80% Confidence Level Consumables Hi-Pri OFLT Jun-13

UCL Mean LCL

Tra

ns

ac

tio

ns in

OF

LT

Win

do

w

80% confidence

interval similar from

4 to 21 iterations

Sweet spot for warehouse

discrete event simulation =

4 repetitions (+/- 1½%)


+/- 1% +/- 1% +/- 1½%

Data Analysis


Average Shipping Delay (Days) Consumable Parts High Priority 1.74 Consumable Parts Low Priority 1.94

Empirical Shipping Delay Distributions

23%

29% 27%

8% 7% 3% 1% 0% 0% 0% 0% 0% 0% 0%

0%

10%

20%

30%

40%

0 1 2 3 4 5 6 7 8-10 11-15 16 17-2728-38 SD39+

Empirical Shipping Delay CDF*

33%

14%

19%

13% 10% 9%

1% 0% 0% 0% 0% 0% 0% 0% 0%

5%

10%

15%

20%

25%

30%

35%

0 1 2 3 4 5 6 7 8-10 11-15 16 17-2728-38 SD39+

Empirical Shipping Delay CDF*

Hig

h P

rio

rity

L

ow

Pri

ori

ty

Co

nsu

mab

les


Co

nsu

mab

les

Days

Days

*CDF = Cumulative Density Function

Perc

enta

ge

Perc

enta

ge

Simulating Baseline AB Inventory Segmentation Strategy


Baseline AB Inventory Segmentation for High Priority Consumables


70%

75%

80%

85%

90%

95%

100%

OF

LT

Goal

Conf High

Mean

Conf Low

Simulated ==> <== Historical

Open orders at end

of simulation cause

phantom spikes…

Rightsizing Inventory for Consumable Parts Baseline AB Inventory Segmentation Strategy


Optimal inventory with

baseline AB inventory

segmentation stock levels

Optimal - Active 40%

Inactive 14%

Excess - Active 26%

Burned Down Years 1-5 - Active

20% Backorder Obligation

0%


One-off Lay-in 4%

Manage to Optimal Inventory 44%

Current Customer Inventory 100%

Identify

Optimal

Inventory

Simulating Alternate ABCDE Inventory Segmentation Strategy


65%

70%

75%

80%

85%

90%

OF

LT

Goal

Alternate ABCDE Inv Seg Conf High

Alternate ABCDE Inv Seg Mean

Alternate ABCDE Inv Seg Conf Low

Baseline AB Conf High

Baseline AB Mean

Baseline AB Conf Low

Alternate ABCDE Inventory Segmentation for High Priority Consumables

Ostensibly* 50% confident of

achieving metrics goal Aug 2013

Note that stock levels assume

Continuous Resupply!

*Must also address Stocking Policy: Continuous Resupply vs. Periodic Resupply


Alternate ABCDE

Inventory Segmentation

Baseline AB Inventory

Segmentation

Open orders at end

of simulation cause

phantom spikes…


Inactive 13% Excess - Active

40%

Burned Down Years 1-5 - Active

19% Backorder Obligation

0%


One-off Lay-in 4%

Manage to Optimal Inventory 36%

Identify

Optimal

Inventory

Current Customer Inventory 100%

Rightsizing Inventory for Consumable Parts Alternate ABCDE Inventory Segmentation


Optimal inventory with

alternate ABCDE inventory

segmentation stock levels

Stocking Policy for Baseline Stock Levels


Stocking Policy for High Priority Consumable Parts

60%

65%

70%

75%

80%

85%

90%

95%

OF

LT

Goal

Continuous Replenishment

30 Day Periodic Resupply




LEGEND • OFLT Goal

• Continuous Resupply o Inventory Optimization Strategy

Assumes Continuous Resupply

• 30-Day Periodic Resupply o Spares Order Review Board

Means 30 Day Periodic Resupply

• 90-Day Periodic Resupply o Quarterly Acquisition Funding


• 360-Day Periodic Resupply o Annual Acquisition Funding


Executive Summary


Executive Summary

• Simulation reveals tomorrow’s supply chain today • Resolves disconnects between models and measures

• Inventory segmentation improves service parts mix


Segmentation Strategy

Date OFLT Goal Achieved

Inventory Investment

Baseline AB Never 100%

Alternate ABCDE Mar-2013 80%

• Successful spares chain execution requires aligned stocking, review, and acquisition policies

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