inventory optimization — a lot more than theory

Copyright © 2011 Kinaxis Inc. All Rights Reserved. 2 Copyright © 2011 Kinaxis Inc. All Rights Reserved.

Inventory Optimization: A lot more than theory Trevor Miles director, thought leadership e: [email protected] | m: +1.647.248.6269 | t: @milesahead


Agenda

•  Intro to Kinaxis •  How are we doing? •  Demand and Supply Chain Segmentation •  Classical IO Methods •  Classical Example •  Multi-Echelon Inventory Optimization •  Conclusion


Companies can’t predict the future, …build organizations that will

survive and flourish under …any possible future. Source: McKinsey Quarterly, Dynamic management: Better decisions in uncertain times, December 2009


Market Dynamics

•  Top CPG companies forecast performance Terra Technologies

–  MAPE for a one month lag was 31% + 12% •  Forecast Error Range: 19% - 43%

–  Eight years ago: 36% + 10% MAPE

•  High-Tech/Electronics – anecdotal –  Struggle to get better than 50% MAPE

•  Where will breakthrough performance come from? –  Learning to forecast and plan better? –  Learning to respond profitably to plan variance?


How are we doing?


Inventory Performance – Computer Hardware

ê $10B


Inventory Performance – Household CPG


Inventory Performance – Automotive OEM

é $10B


Inventory Performance – Pharmaceutical


Demand Segmentation


Demand Segmentation

•  Each point is a specific SKU •  Coefficient of variation = Std.Dev./Mean

80% of Volume

80% of Variability


Demand Segmentation


80% of Volume

80% of Variability

How would this graph look for: •  Revenue? •  Margin?


Supply Chain Segmentation


Mak

e-to

-Sto

ck

Configure-to-Order

Pul

l

Make-to-Order Rationalize?


Supply Chain Segmentation for Product Family

Venu Nagali , Procurement Risk Management (PRM) at Hewlett-Packard Company, Stanford Risk Management Roundtable, November 13, 2006 http://www.gsb.stanford.edu/scforum/login/pdfs/HP%20%20PRM%20Nov%2006%20Venu%20Nagali.ppt

Flexible quantity contract

Demand forecast (units)

Time

Fixed quantity contract

0

100

200

300

400 Uncommitted

Hi scenario

Base scenario

Lo Scenario


Supply Chain Segmentation for Product Family

Time 0

100

200

300

400

Hi

Base

Lo

Time

Hi

Base Lo

Time

Hi

Base

Lo

•  Where will breakthrough performance come from?

–  Learning to forecast and plan better? –  Learning to respond profitably to plan variance?

High-Tech/Electronics


Product Life Cycle Segmentation

Toolkit: Frameworks to Design and Enable Supply Chain Segmentation, Matthew Davis, Gartner, 19 May 2011


Classic IO Methods


Common Safety Stock Calculation

•  Demand rate: –  the amount of items consumed by customers, on average, per unit

time. •  Lead time:

–  the delay between the time the reorder point (inventory level which initiates an order) is reached and renewed availability.

•  Service level: –  the desired probability that a chosen level of safety stock will not

lead to a stock out. Naturally, when the desired service level is increased, the required safety stock increases as well.

•  Forecast error: –  an estimate of how far actual demand may be from forecasted

demand. Expressed as the standard deviation of demand.


Common Safety Stock Calculations

•  Service Factor * Forecast Error * √𝐿𝑒𝑎𝑑 𝑇𝑖𝑚𝑒  –  Where’s the representation of supply variability? –  How is demand variability associated with forecast error?

•  (Demand Variability Factor) * (Service Factor) * (Lead-Time Factor) * (Order Cycle Factor) * (Forecast-to-Mean-Demand Factor) –  Where’s the representation of supply variability?

•  Service Factor * √𝜇↓𝐿𝑇 ↓↑2 ∗ 𝛿↓𝐷 ↓↑2 + 𝜇↓𝐷 ↓↑2 ∗ 𝛿↓𝐿𝑇 ↓↑2   –  Includes supply variability

•  What about Multi-Echelon Inventory Optimization? –  Have you looked at the math?


What about Supply Uncertainty?

•  Types of supply uncertainty: –  Lead-time uncertainty –  Yield uncertainty –  Inspection failure –  Disruptions

•  Strategies for dealing with supply uncertainty –  Safety stock inventory –  Dual sourcing –  Improved forecasts

•  Has big effect on inventory levels


Dimensionless Analysis

•  Safety Stock = Service Factor * √𝜇↓𝐿𝑇 ↓↑2 ∗ 𝛿↓𝐷 ↓↑2 + 𝜇↓𝐷 ↓↑2 ∗ 𝛿↓𝐿𝑇 ↓↑2  

•  Safety Stock = Service Factor * 𝜇ͿԤԥԦԧԨԩԪԫԬԭԮԯՠֈ֍֎֏ࢪࢩࢨࢧࢦࢥࢤࢣࢢࢡࢠࡪࡩࡨࡧࡦࡥࡤࡣࡢࡡࡠ࡞࡛࡚࡙ࡘࡗࡖࡕࡔࡓࡒࡑࡐࡏࡎࡍࡌࡋࡊࡉࡈࡇࡆࡅࡄࡃࡂࡁࡀ࠾࠽࠼࠻࠺࠹࠸࠷࠶࠵࠴࠳࠲࠱࠰࠭ࠬࠫࠪࠩࠨࠧࠦࠥࠤࠣࠢࠡࠠࠟࠞࠝࠜࠛࠚ࠙࠘ࠗࠖࠕࠔࠓࠒࠑࠐࠏࠎࠍࠌࠋࠊࠉࠈࠇࠆࠅࠄࠃࠂࠁࠀ߿߾߽ٟؠׯ𝐿𝑇 * 𝜇↓𝐷  * √𝐶𝑜𝑉↓𝐷 ↓↑2 + 𝐶𝑜𝑉↓𝐿𝑇 ↓↑2   –  CoV = 𝛿⁄𝜇 

•  Safety Stock / (𝜇ͿԤԥԦԧԨԩԪԫԬԭԮԯՠֈ֍֎֏ࢪࢩࢨࢧࢦࢥࢤࢣࢢࢡࢠࡪࡩࡨࡧࡦࡥࡤࡣࡢࡡࡠ࡞࡛࡚࡙ࡘࡗࡖࡕࡔࡓࡒࡑࡐࡏࡎࡍࡌࡋࡊࡉࡈࡇࡆࡅࡄࡃࡂࡁࡀ࠾࠽࠼࠻࠺࠹࠸࠷࠶࠵࠴࠳࠲࠱࠰࠭ࠬࠫࠪࠩࠨࠧࠦࠥࠤࠣࠢࠡࠠࠟࠞࠝࠜࠛࠚ࠙࠘ࠗࠖࠕࠔࠓࠒࠑࠐࠏࠎࠍࠌࠋࠊࠉࠈࠇࠆࠅࠄࠃࠂࠁࠀ߿߾߽ٟؠׯ𝐿𝑇 * 𝜇↓𝐷 ) = Service Factor * √𝐶𝑜𝑉↓𝐷 ↓↑2 + 𝐶𝑜𝑉↓𝐿𝑇 ↓↑2   –  Safety Stock = Units

–  𝜇ͿԤԥԦԧԨԩԪԫԬԭԮԯՠֈ֍֎֏ࢪࢩࢨࢧࢦࢥࢤࢣࢢࢡࢠࡪࡩࡨࡧࡦࡥࡤࡣࡢࡡࡠ࡞࡛࡚࡙ࡘࡗࡖࡕࡔࡓࡒࡑࡐࡏࡎࡍࡌࡋࡊࡉࡈࡇࡆࡅࡄࡃࡂࡁࡀ࠾࠽࠼࠻࠺࠹࠸࠷࠶࠵࠴࠳࠲࠱࠰࠭ࠬࠫࠪࠩࠨࠧࠦࠥࠤࠣࠢࠡࠠࠟࠞࠝࠜࠛࠚ࠙࠘ࠗࠖࠕࠔࠓࠒࠑࠐࠏࠎࠍࠌࠋࠊࠉࠈࠇࠆࠅࠄࠃࠂࠁࠀ߿߾߽ٟؠׯ𝐿𝑇 = Periods

–  𝜇↓𝐷  = Units/Period

–  Safety Stock / (𝜇ͿԤԥԦԧԨԩԪԫԬԭԮԯՠֈ֍֎֏ࢪࢩࢨࢧࢦࢥࢤࢣࢢࢡࢠࡪࡩࡨࡧࡦࡥࡤࡣࡢࡡࡠ࡞࡛࡚࡙ࡘࡗࡖࡕࡔࡓࡒࡑࡐࡏࡎࡍࡌࡋࡊࡉࡈࡇࡆࡅࡄࡃࡂࡁࡀ࠾࠽࠼࠻࠺࠹࠸࠷࠶࠵࠴࠳࠲࠱࠰࠭ࠬࠫࠪࠩࠨࠧࠦࠥࠤࠣࠢࠡࠠࠟࠞࠝࠜࠛࠚ࠙࠘ࠗࠖࠕࠔࠓࠒࠑࠐࠏࠎࠍࠌࠋࠊࠉࠈࠇࠆࠅࠄࠃࠂࠁࠀ߿߾߽ٟؠׯ𝐿𝑇 * 𝜇↓𝐷 ) = 𝑈𝑛𝑖𝑡𝑠 /𝑃𝑒𝑟𝑖𝑜𝑑𝑠 ∗ 𝑈𝑛𝑖𝑡𝑠⁄𝑃𝑒𝑟𝑖𝑜𝑑   = 𝑈𝑛𝑖𝑡𝑠 ∗ 𝑃𝑒𝑟𝑖𝑜𝑑/𝑃𝑒𝑟𝑖𝑜𝑑 ∗𝑈𝑛𝑖𝑡  = 1


Classical Example


Effect of Lead Time Variability on SS


Multi-Echelon Inventory Optimization


An Approximate Method

•  Assume that each stage carries sufficient inventory to deliver product within S periods “most of the time” –  Definition of “most” depends on service level –  S is called the committed service time (CST)

•  We simply ignore the times that the stage does not meet its CST –  For the purposes of the optimization –  Allows us to pretend LT is deterministic


Net Lead Time

•  Each stage has a processing time T and a CST S

•  Net lead time at stage i = Si+1 + Ti – Si

3 2 1

T3 T2 T1

S3 S2 S1

“bad” LT “good” LT

Prof. Larry Snyder, Multi-Echelon Inventory, Lehigh University, June 15, 2006


Net Lead Time vs. Inventory

•  Suppose Si = Si+1 + Ti –  e.g., inbound CST = 4, proc time = 2, outbound CST = 6 –  Don’t need to hold any inventory –  Operate entirely as pull (make-to-order, JIT) system

•  Suppose Si = 0 –  Promise immediate order fulfillment –  Make-to-stock system



Net Lead Time vs. Inventory

•  Precise relationship between NLT and inventory:

•  NLT replaces LT in classical formula •  Ignores effect of supply variability •  Choose S (committed service level) at each stage •  Efficient algorithms exist for finding optimal S values

–  Minimize holding cost while meeting customer service –  Optimal for only a few stages to hold inventory

•  Essentially decomposes multi-echelon problem into multiple stages

NLTzNLTy σµ α+×=*



A Hybrid Push-Pull System

•  Part of system operated produce-to-stock, part produce-to-order

•  Moderate lead time to customer •  Influenced by postponement strategy

PART 1 DALLAS ($260)

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PART 2 CHARLESTON ($7)

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PART 4 BALTIMORE ($220)

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PART 3 AUSTIN ($2)

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PART 5 CHICAGO ($155)

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push/pull boundary



Practical & Pragmatic, but …

•  Ignores –  Multi-sourcing –  Alternate parts –  Alternate routing –  ECO/ECN –  Product life-cycle stage

•  Especially new product introduction

•  …and what about –  Changes in product mix? –  NPI effect on component requirements? –  Transportation costs? –  Labor costs?


Conclusion


Be Practical & Pragmatic

•  Some observations on Inventory Optimization –  More about SC design than Operations –  All ‘theories’ based upon rule-of-thumb –  Effort to maintain should be less than effort to deploy –  Demand changes more quickly than inventory policy

•  Some practical suggestions –  Start with segmentation

•  Customers •  Products

–  Use theories to set ball park inventory levels –  Convert these to periods of supply (DOS, WOS, …)

•  Adjusts automatically for seasonality and life-cycle stage


Thank you! Questions? Trevor Miles | e: [email protected] | m: +1.647.248.6269 | t: @milesahead

inventory optimization — a lot more than theory

Technology

specific sku

supply chain

net lead time

echelon inventory

service level

supply variability

supply uncertainty

practical