supplychainplanning& &in&the&consumer&electronicsindustry · 2013. 6. 29. ·...

Supply Chain Planning in the Consumer Electronics Industry

Real AI 2012

David Lesaint david.lesaint@univ-‐angers.fr

LERIA -‐ Université d’Angers -‐ France

Contents

•  TFT-‐LCD Panels –  Manufacturing operaCons –  Supply chain planning –  SCP as discrete opCmisaCon

•  Constraint Programming Approach –  MulC-‐pass adaptaCve planning –  A generic CP model –  Experiments

•  Summary and Outlook

5 Oct. 2012 Supply Chain Planning in Consumer Electronics 2

Contents





TFT-‐LCD Panel Manufacturing

•  Panel FabricaCon –  etch color filters (CF) and thin-‐film

transistors (TFT) onto glasses –  match and assemble CF and TFT

panels (cell producCon)

•  Module Assembly –  assemble display drivers, back-‐light

units (LEDs), PCB, etc


Manufacturing Process

•  Reentrant flows •  MulC-‐funcConal equipments

•  High uClisaCon 24/7 x 365 •  High contenCon


LCD Panel Fab Layout

•  Reentrant flows •  MulC-‐funcConal equipments

•  High uClisaCon 24/7 x 365 •  High contenCon


(from Choi et al. 2010, KAIST)

LCD Panel Supply Chain

•  Panels for TVs, PCs and mobiles –  about 20 fab and assembly plants –  hubs, vendor-‐managed

inventories and customer warehouses located worldwide

–  air, sea, road or rail shipping

•  Three successive stages mixing producCon and transportaCon acCviCes 1.  Panel fabricaCon 2.  Module assembly 3.  Shipping


Lead Cmes •  3-‐5 days for fab •  1 day for assembly •  1-‐30 days for shipping

Variability •  1K items •  10K routes •  1K resources •  1K inventories

Volume •  1K products •  10K orders / month •  10M panels / month

Contents





Scope of Supply Chain Planning

•  Plan producCon and transportaCon orders –  for the next 4 months [28 daily Cme buckets + 12 weekly buckets] –  every week [rolling plan with 20% refreshment rate] –  plan communicated to plants and suppliers for execuCon and procurement


(from Rohde et al 2000, PPS-‐M

)

Business ObjecCves of SCP

•  Just-‐in-‐Cme and complete delivery –  minimise lateness and earliness

•  Maximise resource capacity uClisaCon –  balance and smoothen uClisaCon

•  Reduce transportaCon costs •  PrioriCse customer volume allocaCon


Build a plan for each confirmed/forecast

sales order

Challenges of SCP

•  Dynamic nature of parts supplies –  eg semiconductor shortages

•  Excessive air freight •  Unbalanced resource uClisaCon •  Inflated demand

–  eg sales mistrust SCP, forecast inaccuracies



sales order

Challenges of SCP

•  Dynamic nature of parts supplies –  eg semiconductor shortages

•  Excessive air freight •  Unbalanced resource uClisaCon •  Inflated demand

–  eg sales mistrust SCP, forecast inaccuracies



sales order

Subop&mal Planning!

Contents





Key Planning Decisions

•  Route(s) selecCon –  mulC-‐level BOMs for products –  alternate routes for each item

•  Resource(s) allocaCon –  single or simultaneous resources –  atomic or aggregate resources

•  Lot-‐sizing –  how much to consume, produce

and store •  Scheduling

–  when to consume, produce and store


100

100

100 100

100

100

17”DF-X

Route1

1

1 use

100 100 init 1/2 1/1

supply 300 0 demand 1/2 1/1

ITC

supply demand

1/2 1/1

use 150 150 init 1/2 1/1

WC1 WC2

2/1 2/2 2/3 2/4 2/5 2/6 2/7 2/8

24

16

8

0

Efficiency

FOR EACH ORDER:

Core Combinatorial Model


Resource

Route Inventory Inventory

Route Inventory

Route

... Resource Resource


Inventory

alternaCve resources

aggregate resources

Material

BOR BOR BOR

BOR-‐set

BOR

p-‐BOM

p-‐BOM

p-‐BOM

c-‐BOM c-‐BOM

c-‐BOM

c-‐BOM

c-‐BOM

c-‐BOM

c-‐BOM c-‐BOM c-‐BOM

c-‐BOM c-‐BOM

Core Constraints


Resource


Route Inventory

Route



Inventory


aggregate resources

Material

BOR BOR BOR

BOR-‐set

BOR

p-‐BOM

p-‐BOM

p-‐BOM

c-‐BOM c-‐BOM

c-‐BOM

c-‐BOM

c-‐BOM

c-‐BOM


c-‐BOM c-‐BOM

•  Cme-‐phased yield •  priority, cost, proporCon

Core Constraints


Resource


Route Inventory

Route



Inventory


aggregate resources

Material

BOR BOR BOR

BOR-‐set

BOR

p-‐BOM

p-‐BOM

p-‐BOM

c-‐BOM c-‐BOM

c-‐BOM

c-‐BOM

c-‐BOM

c-‐BOM


c-‐BOM c-‐BOM


•  consumpCon rate •  priority, cost, proporCon

Core Constraints


Resource


Route Inventory

Route



Inventory


aggregate resources

Material

BOR BOR BOR

BOR-‐set

BOR

p-‐BOM

p-‐BOM

p-‐BOM

c-‐BOM c-‐BOM

c-‐BOM

c-‐BOM

c-‐BOM

c-‐BOM


c-‐BOM c-‐BOM


•  capacity consumpCon rate •  cycle-‐Cmes •  Cme-‐phased efficiency •  lot size min & increments

•  consumpCon rate •  priority, cost, proporCon

Required Features

5 Oct. 2012 Supply Chain Planning in Consumer Electronics

Id Feature 1 Due date fence 2 Material constraints 3 Route details & calendars 4 Shipping lead Cmes 5 Time-‐phased yield 6 Time-‐phased qty consumpCon 7 Efficiency rates 8 Resource capacity calendar 9 Resource down-‐Cmes 10 Over-‐prod. resource capacity 11 Resource capacity uClizaCon 12 ObjecCve-‐based simulaCon 13 Disconnected inventories 14 Resource-‐less routes 15 Incomplete BOMs 16 MulCple due dates (RTF) 17 Build-‐ahead days constraints 18 Demand/trans. mode pegging 19 EffecCve dates 20 Product life-‐cycles 21 Planned orders creaCon rule

Id Feature 22 Resource pooling 23 MulC-‐resource sales orders 24 MulC-‐bucket sales orders 25 MulC-‐route sales orders 26 Time-‐phased safety stock 27 Stock keeping-‐Cme 28 Policies -‐ delivery, demand priority 29 Policies -‐ cost-‐driven rouCng 30 Policies -‐ resource uClizaCon

maximizaCon & smoothing 31 Alternate materials 32 WIP projecCon 33 WIP pegging (Nenng) 34 Stock pegging (Nenng) 35 Time-‐phased line/plant assignmt. 36 Shipping schedule constraints 37 Time-‐phased efficiency 38 Time-‐phased takt Cme 39 Lot size 40 Net change producCon planning 41 Frozen plan, NOH handling

IN SCO

PE

OUT-‐O

F-‐SCOPE

19

RepresentaCve Instance Size

•  Data Model –  10s of DB tables and columns –  100K records –  1mn to load an instance

•  RepresentaCve Instance Size


•  20,000 BOMs •  10,000 c-‐BOMs •  10,000 p-‐BOMs

•  6,000 inventories •  10,000 routes •  1,500 resources

•  16-‐weeks long horizon •  28 daily buckets + 12 weekly buckets

•  1500 items (30 materials) •  10 item groups

•  10,000 sales orders •  12,000 planned orders

•  4,500 stock deliveries •  7,500 WIPs

•  12,000 BORs •  20 BOR-‐sets

Other Requirements

•  Acceptable response Cmes

•  Adaptable to problem instance characterisCcs –  eg, seasonal demand, new product launch

•  Decision-‐support –  support what-‐if analysis –  diagnose failure and subopCmality –  repair or improve parts of a plan

•  Plan stability

•  System integraCon with MRP, ERP, etc


Contents





ExisCng SCP System


Rule-‐based order-‐by-‐order planning

SequenCal process 1. WIP projecCon 2.  stock order planning 3.  inventory nenng 4.  planned order planning

Incremental strategy •  1 stock/sales order at a Cme

Greedy algorithm •  pre-‐ranking orders, routes, resources •  backward-‐planning for planned orders •  forward-‐planning for stock orders

ExisCng SCP System


Rule-‐based order-‐by-‐order planning

No search •  alternaCves are not explored •  may miss soluCons •  may miss improvements

Same model for all orders •  manually reconfigured aqer each run

No inference •  does not prune search space using

constraints before and during search •  may explore useless parts of search

space

Analysis

•  MathemaCcal (Linear and Mixed Integer) Programming unsuitable –  low-‐fidelity models –  prohibiCve response Cmes

•  Problem decomposiCon is a must –  instance size –  data-‐rich –  orthogonal decisions –  heterogeneous requirements ...

•  Constraint Programming ... –  ensures high-‐fidelity models –  proven track-‐record on real-‐life

planning/scheduling problems

•  ... But no one-‐size-‐fits-‐all soluCon! –  use different CP models for

different subproblems –  leverage COMET to this effect –  develop a run-‐Cme configurable

CP engine


CP versus LP/MIP


FEATURE Mathema&cal Programming Constraint Programming

Relaxa&on Yes No

GAP measure Yes No

Op&mality Proof Yes Yes

Modeling Limita&ons QuadraCc problems limited to PosiCve Semi Definite problems and Second Order Cone Programming problems

Discrete problems

Specialised Constraints No Yes

Logical Constraints Yes Yes

Theore&cal Grounds Algebra AI, Graph Theory, Algorithms

(from ILOG 2009, OPL manual)

Problem DecomposiCon Approach

•  MulC-‐pass planning –  each pass iterates over a class of orders –  one or more orders planned at each iteraCon –  failed orders revisited in subsequent passes

•  CP model –  created at each iteraCon and customised for each (set of) order(s) –  uses COMET global constraints and –  porwolio of saCsfacCon/opCmizaCon algorithms


Problem too large for global approach Too much variability

for a single CP model

configure CP model

plan orders with CP model

select set of orders

next pass

MulC-‐Pass Planning Procedure


soluCon?

build CP model

solve

select orders

save soluCon

record failure

next itera*on

pass [CP solver]

yes

mulC-‐pass [Meta-‐Solver]

no

Contents





Search Space

•  RouCng search space is an OR-‐AND-‐tree –  inventories = OR-‐nodes

•  choose a single upstream route –  routes = AND-‐nodes

•  follow all upstream inventories –  a plan is a tree of acCviCes

•  Other decision points at each node –  quanCCes and Cming of

producCon/consumpCon –  resource allocaCon –  stock consumpCon/creaCon –  order shortage, etc


... ...

...

Materials

Inventory

Resource

Route

OR-‐node AND-‐node

Pazern

Search Space


...

...

... ...

...

...

...

...

... ...

... ...

...

Pazern

Search Space

Materials

Inventory

Resource

Route

OR-‐node AND-‐node

SoluCon Plans


...

...

... ...

...

...

...

...

... ...

Search Space

SoluCon Plan

CP Model Build & Solve

•  Declare finite domains •  Create decision variables •  Post constraints •  Search for a soluCon •  Save soluCon or return failure

•  All steps traverse the OR-‐AND-‐tree –  either visit each node once –  or visit each node mulCple Cmes


...

...

... ...

...

...

...

...

... ...

Search space

Variable CreaCon

•  At each node by traversing supply net top-‐down

•  Variables indexed by order ids, node ids and/or object ids

•  Each variable models a specific aspect –  rouCng decision, quanCty to

produce, stock to consume, start date of acCvity, etc

•  Finite domain variables –  symbolic –  integers –  booleans


...

...

... ...

...

...

...

...

... ...

... ...

...

Variable CreaCon Example


// order o!// inventory i!// RTE(i) = 1..#upstream_routes_of_i!!xRoute{<o,i>} = new var<CP>{int} = var<CP>{int}(_cp,RTE(i));!

... ...

...



// order o!// inventory i!// POR(o) = set of planned orders for o!// RTE(i) = 1..#upstream_routes_of_i!// HOZ = 1..#buckets_in_planning_horizon!!xAddBck{<o,i>} = new var<CP>{int}[p in POR(o), r in RTE(i)]! = var<CP>{int}(_cp,HOZ);!

... ...

...



//order o!// route r!// POR(o) = set of planned orders for o!// RSC(r) = 1..#resources_of_r!// CPY(r) = 0..#max_capacity_consumable_on_r!!xCsdCpy{<o,r>} = new var<CP>{int}[p in POR(o),w in RSC(r)]! = var<CP>{int}(_cp,CPY(r));!

Constraint PosCng

•  At each node (visited once) •  Each constraint addresses a

specific requirement or rule –  match stock ins and outs –  RTF/DDF –  safety stocks –  route possible Cmes –  item SOL/EOL –  capacity constraints –  yield rate –  etc

•  Finite domain constraints –  arithmeCc, logical –  indexing, etc


...

...

... ...

...

...

...

...

... ...

... ...

...

Constraint PosCng Example


// order o!// RTF of o rtf!!if (_cfg.solver().enableOrderRTF()) {! if (! _cfg.solver().enableJustInTimeDelivery()) {! _cp.post(xSrlsBck{o} <= rtf,! onBounds);! } else {! _cp.post(xSrlsBck{o} == rtf,! onBounds);! }!}!

... ...

...

Constraint PosCng Example


// order o!// inventory i!// si = <o,i>!// POR(o) = set of planned orders for o!// RTE(i) = 1..#upstream_routes_of_i!!forall(p in POR(o)) {! _cp.post(xRaddStk{si}[p,route{<o,i>}] == xIaddStk{si}[p],!

! onBounds);! _cp.post((sum(r in xRaddStk{si}.getRange(1)) xAddStk{si}[p,r]) == xIaddStk{si}[p],!

! onBounds);!}!

Search Algorithm

•  Different labelling strategy per class of variables –  rouCng, allocaCon, stock, quanCty, Cme

•  Each class has –  a priority

•  0..5 (0 ó disabled)

–  a traversal direcCon •  backward or forward

–  value selecCon heurisCcs •  domain-‐specific or not •  greedy or exhausCve •  randomized or systemaCc •  single value or domain splinng •  min first or max first


...

...

...

...

... ...

Search Strategy Example


...

...

...

...

... ... 1. Rou

Cng + AllocaCo

n



...

...

...

...

... ...

2. Q

uant

ities



...

...

...

...

... ...

3. T

ime

Problem DecomposiCon Strategy (Meta-‐Solver)

•  Cluster orders using any criteria combinaCon –  unsolved orders –  same customer –  same module-‐out inventory –  similar due dates –  contenCon on resources –  contenCon on materials –  etc

•  Use best-‐fit model for each class of orders


Model and Solver ConfiguraCon

•  ReconfiguraCon aqer each pass –  switch on or off problem features/constraints

•  allow lateness, disable aggregate resources, do not fragment sales orders, shorten sales orders, etc

–  switch on or off algorithmic opCons •  aspect prioriCes, heurisCcs, search type, etc

•  Other algorithmic opCons –  maximum number of failures allowed –  don’t explore non-‐contribuCng subtrees –  saCsfy or opCmize –  restarts (eg, Large-‐Neighbourhood Search)


Example of Strategy

•  Quickly plan the bulk of the demand in first passes –  using efficient but constraining CP model

•  eg, no order fragmentaCon, no lateness, no shortening

•  Tackle hard orders and perform fine-‐grained planning in subsequent passes –  using more flexible model with all features turned on

•  MulC-‐criteria opCmizaCon may be carried out –  at iteraCon-‐level

•  eg, using weighted objecCve funcCon inside CP model –  at pass-‐level

•  eg, different passes focus on different KPIs


Contents





Experiments

•  Tests on representaCve dataset –  similar structure and size to customer’s instances

•  Measures –  on-‐Cme delivery (OTD) and fulfillment for orders and quanCCes –  assembly resource capacity uClizaCon –  run-‐Cme

•  Comparison of COMET with exisCng rules system –  lazer fragments a sales order's plan, former does not

•  over different routes and dates and resources on a route


Search Strategy


CLASS PRIORITY

rou&ng 1

resource alloca&on 1

stock 0

quan&ty 2

&me 3

HEURISTIC dom-‐spec all-‐random

greedy-‐min

greedy-‐max

all-‐min all-‐max dicho-‐tomic

rou&ng x

resourcing x

stock x

quan&ty x

&me x

CLASS TRAVERSAL

rou&ng backward

resource alloca&on backward

stock none

quan&ty backward

&me forward

COMET vs. Rule-‐based Planner


total orders to plan #orders fulfilled #orders on-‐Cme COMET 7102 6658 5588

total quanCCes #quanCCes delivered #quanCCes on Cme COMET 21,909,458 18,550,314 16,411,630

run-‐Cme COMET 764 sec

#orders delivered #orders on Cme Improvement + 9.7% + 6.7%

#quanCCes delivered #quanCCes on Cme Improvement + 2.0% + 3.1%

Contents





Summary

•  DecomposiCon approach based on CP –  high-‐fidelity model –  solver leverages COMET propagaCon and search capabiliCes –  "best-‐fit model" approach through run-‐Cme configurable model

•  Results very encouraging –  much bezer soluCons than Rules system –  acceptable perfs: 10mns / run (30 orders/sec)

•  Project –  implementaCon enCrely in COMET –  10 man-‐month effort end-‐to-‐end –  30K lines of code –  core model 3K lines of code


Outlook

•  Missing features should further improve OTD, fulfillment and resource uClisaCon by tapping in remaining pockets of capacity and exploiCng inventories


Id Feature 22 Resource pooling 23 MulC-‐resource sales orders 24 MulC-‐bucket sales orders 25 MulC-‐route sales orders 26 Time-‐phased safety stock 27 Stock keeping-‐Cme 28 Policies -‐ delivery, demand priority 29 Policies -‐ cost-‐driven rouCng 30 Policies -‐ resource uClizaCon maximizaCon & smoothing 31 Alternate materials

should improve OTD, fulfillment and resource uClizaCon

supplychainplanning& &in&the&consumer&electronicsindustry · 2013. 6. 29. ·...

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