Supply and Value Chain Support Through Scheduling and Simulation: Applications

to the Semiconductor Industry Dr. James R. Burns, Professor

College of Business AdministrationTexas Tech University

Dr. Onur Ulgen, Professor

Department of Industrial and Systems EngineeringUniversity of Michigan, Dearborn

Dearborn, Michigan 48128

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Introduction

• Simulation Tools for Supply Chain Inventory Analysis are presented

• Reductions in inventory result in• Reductions in cost• Reductions in cycle time• Improvements in quality• Improvements in workflow

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Simulation Models

• Through use of IT to produce enterprise-wide visibility, simulation models show• Significant reductions in uncertainty are possible• This leads to reductions in between supplier inventory• Which leads to reductions in cycle (lead) time

• The models show reductions in information delays through IT investments lead to significantly improved performance

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What are stocks and flows??

• A way to characterize systems as stocks and flows between stocks

• Stocks are variables that accumulate the affects of other variables

• Rates are variables the control the flows of material into and out of stocks

• Auxiliaries are variables that modify information as it is passed from stocks to rates

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Stock and Flow Notation--Quantities

• STOCK

• RATE

• Auxiliary

Stock

Rate

i1

i2

i3

Auxiliary

o1

o2

o3

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Stock and Flow Notation--Quantities

• Input/Parameter/Lookup

• Have no edges directed toward them

• Output• Have no edges directed away from them

i1

i2

i3

Auxiliary

o1

o2

o3

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Inputs and Outputs

• Inputs• Parameters• Lookups

• Inputs are controllable quantities• Parameters are environmentally defined quantities over

which the identified manager cannot exercise any control• Lookups are TABLES used to modify information as it is

passed along• Outputs

• Have no edges directed away from them

Input/Parameter/Lookup

a

b

c

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Stock and Flow Notation--edges

• Information

• Flow

a b

x

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Basic Model Structure

actual inventory

sales

orders in process

prod-trans rate

order rate

production time

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A Two-player Supply Chain Model

• First player (the supplier) provides product to the second player (the firm)

• Second player provides information back to the first

• Each player received orders from its “customer” and replenishes inventory according to its ordering policy

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Inventory Ordering Policy

• Assume continuous replenishment with constant demand, fixed order quantity

• Using the Wilson EOQ model, the optimal order quantity can be calculated to be 2000 widgets

• With annual demand of 6 million, 3000 orders go out every year

• That is an order every 2.9 hours

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We present first The Two-player Supply Chain Model…

• Without information visibility • With discrete ordering policy of ordering

2000 widgets once every 2.9 hours

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actualinventory

sales

orders intransit

prod-trans rate

order rate

production timeOIT unit cost

AI unit cost

actualinventory 0

sales0

customerpurchases

0

orders intransit 0

prod-trans rate 0

order rate0

productiontime 0

OIT unitcost 0

AI unit cost0

Supplier

Firm

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

SupplierHolding Cost

OIT Holding Costper mo

AI Holding Costper mo

monthly HoldingCost

TOTAL HOLDINGCOST

Firm's HoldingCost

Firm's OIT HoldingCost per mo

Firm's AI HoldingCost per mo

Firm's monthlyholding cost

TOTALINVENTORY

ACCUMINVENTORY

Invent rate

ACCUM SALES

sales rate

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The second Two-Player Model Assumes ...

• Instantaneous information about end-customer purchases all the way up and down the supply chain

• orders cost virtually nothing, as opposed to $100 in the earlier model

• an implied order goes out every time a purchase is seen at the customer end

• Otherwise, the two models are identical, structurally

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actualinventory

sales

orders intransit

prod-trans rate

order rate

production timeOIT unit cost

AI unit cost

actualinventory 0

sales0

customerpurchases

0orders intransit 0

prod-trans rate 0

order rate0

productiontime 0

OIT unitcost 0

AI unit cost0

Supplier

Firm

SupplierHolding Cost

OIT Holding Costper mo

AI Holding Costper mo

monthly HoldingCost

TOTAL HOLDINGCOST

Firm's HoldingCost

Firm's OIT HoldingCost per mo

Firm's AI HoldingCost per mo

Firm's monthlyholding cost

TOTALINVENTORY

ACCUMINVENTORY

Invent rate

ACCUM SALES

sales rate

-------------------------------------------------------------------------------------------------------

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Comparing the two models

• Instantaneous ordering model exhibits greater sales (less missed sales)

• Instantaneous ordering models exhibits significantly lower total holding cost--$5,000,000 vs. $13,000,000.

• Results here are approriate for a supplier making product that costs the firm $1000 each and for which there is annual demand of 6,000,000 units a year

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Why the differences with respect to inventory?

• In some cases, the discrete ordering policy “misses” its threshold and does not order more inventory• This results in missed sales (there are some time steps

in which no ordering takes place at all)• Beginning at month four, every other time step is

missed, roughly, so for the last eight months, onl half of the monthly demand of 500,000 units is met.

• Instead of selling 6,000,000 units, only 4,000,000 were sold

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Why the differences with respect to holding cost?

• Overall, the inventory in the pipeline in the instantaneous ordering model is significantly less.

• Discrete pipeline approach to upstream information dissemination results in larger inventories• Discrete pipeline scenario starts with much higher

initial inventories--500,000 versus only 100 for the enterprise visibility approach.

• The high initial inventories are needed to compensate for the missed sales and does so until about month four

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Cycle times and Little’s Law

• According to Little’s Law• Cycle time = inventory / throughput• Inventory was reduced by 58%• Cycle time would be similarly reduced

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Reduced inventory leads to...

• reduced cycle (lead) times• less rework and scrap due to smaller lot

sizes

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What about a large order quantity?

• 500,000 once a month would do it• results are worse that orders of 2000 a

month

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ACCUMULATIVE SALES

8 M

4 M

0

0 1 2 3 4 5 6 7 8 9 10 11 12

Time (Month)

Discrete Pipeline Approach

Enterprise Visibility Approach

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TOTAL HOLDING COST

2 M

1 M

0

0 1 2 3 4 5 6 7 8 9 10 11 12

Time (Month)

ENTERPRISE VISIBILITY APPROACH

DISCRETE PIPELINE APPROACH

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ACCUMULATIVE SALES 8 M

6 M

4 M

2 M

0 0 1 2 3 4 5 6 7 8 9 10 11 12

Time (Month)

Discrete Pipeline 2k run Discrete Pipeline 500k run Enterprise Visibility Approach

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TOTAL HOLDING COST 8 M

6 M

4 M

2 M

0 0 1 2 3 4 5 6 7 8 9 10 11 12

Time (Month)

Enterprise Visibility Approach Discrete 500k run Discrete 2k run

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A Three-Player Supply Chain

• Each player is modeled as a first-order balancing loop structure

• Customer orders run 30 per time steps, but this happens randomly in only halfof the time steps.

• This model is looked at in both of two contexts--a delayed information approach and the enterprise-wide instantaneous information approach

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First-order Balancing loop structure

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actual inventory

desired inventory adjustment time

order/ship rate

information delay

actual inventory 0

desired inventory 0 adjustment time 0

order/ship rate 0

information delay 0

actual inventory 1

desired inventory 1 adjustment time 1

order/ship rate 1

information delay 1

demand rate

demand rate 0

demand rate 1

Customer orders

Delay time

<adjustment time>

<adjustment time>

First Supplier

Second

Supplier

Firm

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Actual Inventories with one-week information delays

20,000

0

-20,000

0 10 20 30 40 50 60 70 80 90 100

Time (Month)

actual inventory at first supplier actual inventory 0 (at second supplier) actual inventory 1 (at the firm)

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Actual Inventories with two-week information delays

40,000

0

-40,000

0 10 20 30 40 50 60 70 80 90 100

Time (Month)

actual inventory at the first supplier actual inventory 0 (at the second supplier) actual inventory 1 (at the firm)

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Actual Inventories with one-month information delays

200,000

0

-200,000

0 10 20 30 40 50 60 70 80 90 100

Time (Month)

actual inventory at the first supplier actual inventory 0 (at the second supplier) actual inventory 1 (at the firm)

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actual inventory

desired inventory adjustment time

order/ship rate

actual inventory 0

desired inventory 0 adjustment time 0

order/ship rate 0

actual inventory 1

desired inventory 1 adjustment time 1

order/ship rate 1

demand rate

demand rate 0

demand rate 1

Customer orders

Adjustment First

Supplier

Second

Supplier

Firm

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Actual Inventories Without Information Delays

1,000

500

0

0 10 20 30 40 50 60 70 80 90 100

Time (Month)

actual inventory of the first supplier actual inventory 0 (at the second supplier) actual inventory 1 (at the firm)

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The last figure

• exhibits a rapid ascent to the desired inventory on the part of all three players, to the desired inventory, with no overshoot--very well behaved

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These models were created using the VENSIM tool

• www.vensim.com• a product of Ventana Systems, Inc.

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Translation of these models to commercial simulations

• These models can be setup to be driven by flight simulator front ends with sliders and dials, meters and such

• Users would decide upon • Amount of work in process• Ordering policy• Ordering parameters (quantity, time between

reviews, lead time, safety stock, etc.)

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Summary

• Continuous dynamic simulations explain much of the behavior we see in enterprise systems and supply chains

• They can be useful tools for deciding• What effect IT will have on the supply chain

• The actual structure of the simulation tools can be preprogrammed

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Summary, Continued

• The only thing the user has to do is use the simulation model to make decisions about• Ordering policy • Order quantities• Order frequency• Order lead time• Amount of work in process• Etc.

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Questions from the AUDIENCE???

•Thank you for coming!!!

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