Inventory Guru Introduction. © 2013 LLamasoft, Inc. All Rights Reserved Introducing the New Inventory Guru ADAPTIVE INTELLIGENT INVENTORY OPTIMIZATION

Inventory Guru Introduction

© 2013 LLamasoft, Inc. All Rights Reserved

Introducing the New Inventory Guru

ADAPTIVE INTELLIGENT INVENTORY OPTIMIZATION (AI+IO)

INVENTORY GURU

DEMAND ANALYSIS

DEMAND CLASSIFICATION

MULTI-ECHELON INVENTORY OPTIMIZER

DISCRETE EVENT INVENTORY SIMULATION

NETWORK OPTIMIZATION


What is new in AI+IO?

Demand analysis and advance demand statistics• Mean and std. dev. of nonzero demand and inter-demand interval

mean• Propagation of these advance demand statistics

Demand classification• Automatically analyze and classify up to 10 different types of non-

normally distributed demand• Demand classification for each facility in a given network

Inventory policy recommendations• Summary and detailed inventory outputs

3 different definitions of service level• Type 1 (cycle service level)• Type 2 (fill rate): Improved formulation• Type 3 (ready rate)


What is new in AI+IO?

Better inventory modeling with various distributions• Normal, Poisson, Negative Binomial, Gamma, and Mixture Distributions• Use of historical demand or forecast data to determine a right

distribution based on characteristics

Dynamic programming approach for the solution of Guaranteed Service Time Algorithm• Dramatic improvements in run time.• Ability to handle complicated network/BOM structures.• Provide a framework for adding new constraints /features.

Set service time for different destinations. Takes into account more than one inbound transportation modes.


What’s Different in Inputs?

Products: None Sites: None Demand: None Sourcing Policies

• MOQ is required since it’s used to calculate both the Q and demand variability

Transportation Policies • Minimum Shipment Quantity— similar to MOQ but considers Mode• Minimum/Maximum Service – similar to service time in IP but considers Mode

Inventory Policies• Safety Stock Rule has been removed since it is now obsolete

Multi-Period Inventory Policies• Service Requirement is now supported

User Defined Customer/Facility Demand Profile• Inputs added for Non-Zero Demand Mean, Non-Zero Demand Std Dev and Inter-

Demand Interval Mean


What’s Different in Outputs

Safety Stock Details • Now replaced by Inventory Policy Details and Inventory Policy

Summary• Added recommended control policies• Added additional statistics• Added additional approximated operating parameters

Customer/Facility Daily Demand• Replaced by Aggregated Customer Demand and Aggregated

Customer Facing Facility Demand

Customer/Facility Demand Profile• Added demand classification columns and additional statistics

Inventory Specific Tableau Outputs


What’s Different In Settings

Full Scenario Support Control over thresholds Control over outlier handling Single/Multi-Echelon setting is now at

global level


What happens when I upgrade?

All input tables except for User Defined are intact

Both User Defined tables are cleared SSO outputs from IO2 are cleared Demand profiles and daily demand details

are preserved As usual, a backup of the IO2 model will be

made automatically


New Demand Propagation


What is demand propagation?

Warehouse

Facility1

Facility2

Customer1

Customer2

Demand Series1

Demand Series2

Propagated Demand

External Supplier


How does demand propagation work?

Warehouse

Facility1

Facility2

Customer1

Customer2

𝝁 ,𝝈𝝁𝑵𝒁 ,𝝈𝑵𝒁 ,𝒑

Demand Series1

Demand Series2

Demand MeanDemand StdDevNZ Demand MeanNZ Demand StdDevDemand Interval Mean



External Supplier


Example: Demand Propagation

12

1

3

2

7

6

5

4 Customer demand(𝑇 4 ,𝑀 4 )

(𝑇 5 ,𝑀5 )

(𝑇 6 ,𝑀 6 )

(𝑇 7 ,𝑀7 )

(𝑇 2 ,𝑀2 )

(𝑇 3 ,𝑀 3 )

ExternalSupplier

StockpointDemand Demand StatisticsReplenishment Order Statistics (𝑇 𝑖 ,𝑀𝑖 )

Demand Propagation

Demand Propagation

(𝜇 ,𝜎 ,𝝁𝑵𝒁 ,𝝈𝑵𝒁 , 𝒑 )

,







Customer demand

Customer demand

Customer demand


Why we need advance demand statistics?

To take into account batching effect• Batch size (Q) might have a great impact on the variability for

the upstream echelon

To perform demand classification• , are used for algorithm


Example-1: Impact of Batching on Demand Propagation


Demand Process in CentralWarehouses

Product Facility Intermittency VariabilityDemand

ClassClumpiness

Demand Mean

Demand StdDev

Demand Size Mean

Demand Size StdDev


Product1 CentralWarehouse Intermittent HighlyVariable Slow - 49.70 70.66 100.23 4.83 2.02

Demand Process in CentralWarehouses

Product Facility Intermittency VariabilityDemand

ClassClumpiness

Demand Mean

Demand StdDev

Demand Size Mean

Demand Size StdDev


Product1 CentralWarehouse Intermittent HighlyVariable Slow - 49.30 70.22 100.00 4.34 2.03

IO2 Solution: Demand Propagation with Batch Size (Q) = 100

AI+IO Solution: Demand Propagation with Batch Size (Q) = 100

Simulation Verification

Demand Propagation: IO2 vs. AI-IO

Demand Process in CentralWarehousesProduct Facility

Demand Mean

Demand StdDev

Product1 CentralWarehouse 49.70 33.76


Demand Classification

16

LumpySlowIntermittent

ErraticSmooth

Non-Intermittent

Demand

Cut-off value

Cut-off value

© 2013 LLamasoft, Inc. All Rights Reserved 17

Demand Classification

ErraticRegular Smooth(fast)

LumpySlow

=4

Demand size variability

Intermittent

Non-Intermittent

Low

High

=1.32

Squared coefficient of variation

=4

Low Variable

Hig

hly

Varia

ble

Low

Var

iabl

e

Hig

hly

Varia

ble

Low High

Low HighDemand size variability

Low High

Clum

ped

Slow

0 𝜎𝑁𝑍 ≅ 0

𝐶𝑉 𝑁𝑍2 =0.49

Regu

lar

Slow

𝐶𝑉 𝑁𝑍2 =

𝜎𝑁𝑍2

𝜇𝑁𝑍2


Use of Demand Classification for

Safety Stock Placement


Lead Time Demand Modeling

Why is the demand class important?Based on the demand class, we identify a distribution to model lead time demand (a LTD Distribution). This LTD Distribution is then used in the multi-echelon SS optimization to more accurately represent the lead time demand - compared to the typical assumption that lead time demand follows a normal distribution

20

Demand Class LTD DistributionSmooth NormalErratic Mixture of DistributionsSlow-LowVariable Poisson/Mixture of DistributionsSlow-HighlyVariable Poisson/Mixture of DistributionsLumpy Negative Binomial


Control Policy Mapping Based on the demand class, we recommend an inventory

control policy

21

Demand Class Inventory Policy

Smooth (Normal) (r,Q)Erratic (s,S)

Slow-Low Variable Base-Stock

Slow-Highly Variable (s,S)

Lumpy (T,S)

AI+IO reports optimal policy parameters.


How Does AI+IO Compare?


Lumpy Demand Data

Intermittency Variability Demand Class Mean Variance Demand

Size MeanDemand Size

VarianceInter-Demand Interval Mean

Intermittent Highly Variable Lumpy 22.84 5492.31 56.06 11632.64 2.46

1 46 91 136 181 226 271 316 361 406 451 496 541 586 631 676 721 766 811 856 901 946 991 10360

100200300400500600700800900

1000

Three-Year Demand Series

Day

Dem

and

Size

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 490

100200300400500600700800900

1000

Fifty-Day Demand Series

Day

Dem

and

Size


Observed Density AI-IO Assigned Density* Normal Assigned Density

L =

3

L =

5

0 200 400 600 800 1000

0.0

00

0.0

05

0.0

10

0.0

15

Density estimate of data

N = 640 Bandwidth = 12.77

De

nsi

ty

0 200 400 600 800 1000

0.0

00

0.0

02

0.0

04

0.0

06



De

nsi

ty

-400 -200 0 200 400

0.0

00

00

.00

05

0.0

01

00

.00

15

0.0

02

00

.00

25

0.0

03

0

x

y

-400 -200 0 200 400 6000

.00

00

0.0

00

50

.00

10

0.0

01

50

.00

20

0.0

02

5

x

y

0 100 200 300 400 500 600

0.0

00

.02

0.0

40

.06

x

dn

bin

om

(x, siz

e =

0.4

77

32

46

19

, p

rob

= 0

.00

41

62

36

1)

0 100 200 300 400

0.0

00

.05

0.1

00

.15

0.2

0

x

dn

bin

om

(x, siz

e =

0.2

86

39

47

71

, p

rob

= 0

.00

41

62

36

1)

* Negative Binomial Distribution

Lead Time Demand Densities for Different Lead Times


L =

10

L =

20

0 500 1000

0.0

00

00

.00

05

0.0

01

00

.00

15

0.0

02

00

.00

25

0.0

03

0



De

nsi

ty

0 500 1000 1500 2000

0.0

00

00

.00

05

0.0

01

00

.00

15



De

nsi

ty

-500 0 500 1000

0.0

00

00

.00

05

0.0

01

00

.00

15

x

y

0 500 1000 1500 2000

0.00

000.

0005

0.00

100.

0015

x

dnbi

nom

(x, s

ize

= 1.

9092

9847

6, p

rob

= 0.

0041

6236

1)

-500 0 500 1000 1500 2000

0.0

00

00

.00

02

0.0

00

40

.00

06

0.0

00

80

.00

10

0.0

01

2

x

y

0 200 400 600 800 1000

0.0

00

0.0

01

0.0

02

0.0

03

0.0

04

0.0

05

x

dn

bin

om

(x, s

ize

= 0

.95

46

49

23

8, p

rob

= 0

.00

41

62

36

1)

Lead Time Demand Densities for Different Lead Times (Cont’d)

Observed Density AI-IO Assigned Density* Normal Assigned Density


IO3: Negative Binomial DistributionLe

ad T

ime

= 3

Safety Stock Results for 95% FillrateLe

ad T

ime

= 5

Metric Approximation Simulation

Type2 95% 94%

Safety Stock 537


Type2 95% 98%

Safety Stock 683


Type2 95% 95%

Safety Stock 590


Type2 95% 99%

Safety Stock 788

Optimal safety stock = 581

Normal Distribution


AI+IO IO2 / Current Gen Tools


Safety Stock Results for 95% Fillrate

Lead

Tim

e =

10

Lead

Tim

e =

20


Type2 95% 95%

Safety Stock 698


Type2 95% 99%

Safety Stock 984


Type2 95% 96%

Safety Stock 862


Type2 95% 100%

Safety Stock 1257



Normal Distribution

AI+IO IO2 / Current Gen Tools

Documents

Inventory Guru Introduction. © 2013 LLamasoft, Inc. All Rights Reserved Introducing the New Inventory Guru ADAPTIVE INTELLIGENT INVENTORY OPTIMIZATION