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Material Flow ProcessesTheir strengths and requirementsWhen, what, why and how to usethem? 
Istvan Fekete 
(fekete.istvan.pic@gmail.com) 
Nokia, 2012
How to support oursuccess? 
2 
How to create more responsive flow of values/resources downstream? 
How weserveourcustomers 
How we use our resources 
Shorterresponseto change, demand 
Cheapersupply chain, operation 
More irregular quantitiesin more irregural timing 
More regular quantitiesin more regural timing 
How to create more regular flow of resources/values upstream?
Material Flow Processes and their main aspects
Milkrun 
Top Material 
Center 
Flowrack/ 
CrossDock 
Robust and adaptive Logistics Centers can support any 
Material Flow Process in any Factory 
Materials @ Line/Cell PoU are „made” 
to order or stock items 
BB4.3.2 
BB4.3.2 
BB4.3.2 
BB4.3.2 
BB4.3.2 
BB4.3.2 
BB4.3.2 
BB4.3.2 
BB4.3.2 
Close Supplier 
and/or IHUB 
(Re)filling the trolley/train 
NOK 
OK 
Piece Pick 
Sequence 
Kanban 
Milkrun 
Hand Buffer 
Replacement of 
urgent NOK 
material 
Returning OK leftovers and 
not-urgent NOK materials 
Shipping 
OHUB 
Finished goods, OK leftovers and not-urgent NOK material collection from line/cell 
Material Flow Process 
of a material aligned 
to its behaviour 
OK 
Visual control on ordering 
Hand Buffer material or 
moving back consolidated 
lot to FlowRack
BB4.3.2 
BB4.3.2 
BB4.3.2 
Supplier 
IHUB 
Close 
Supplier 
MRP 
Top Material 
Center 
BB4.3.2 
BB4.3.2 
BB4.3.2 
Flowrack/ 
CrossDock 
Non-Kanban 
Components 
Kanban Card, 
Empty Bin or E-kanban 
Milkrun in 30’ to deliver all 
items to and from cells 
Team Leader 
schedules next 
orders 
Combination of piece-picking and kanbans covers all needs 
efficiently and effectively; creates so-called degenerative 
redundancy, a prerequisite of robustness 
Kanban 
Replenishment
SolutionCardsforinternalSC Network Simulation
RMSpec 
RMCom 
Level 
FORELOG 
GLOBAL ALLOCATION 
KANBAN VMI VENDOR/SUPPLIER MANAGED INVENTORY 
Bin 2/2 
Bin 1/2 
Line Feed Kanban 
Piece Pick to Order 
Pick to Order 
E-ban (electronic kanban) 
PON in use 
Next PON 
PON in use 
Next PON 
Typical case 
Rare case
Hectic 
Stable 
$ 
$ 
To 
BOM 
Quant 
Quant 
From 
Many 
SyncEffect 
Commo- nality Effect 
Size 
Size 
Price 
Price
Extractfrommypre-PhDmaterials
Widespread Pareto-Distribution in supply chain networks, though ‚large’means different in three dimensions 
Irregularity= Coefficientof Variation, NormalizedStandard Deviation 
Numberof Outgoing/downstreamLinks 
Irregularity– distributionof interactionsintime 
Intensityof interactions(UsedvolumePareto)
•X-Axis: Pareto-rankingasper theintensityof interactions(averageusage) 
•Y-Axis:Distributionof interactionsintime(Irregulatity) 
•Bubblesize: numberof outgoinglinks, outgoingdegree(inwhatFG itgoes) 
•Bottom-left: networkelementswithlotof regularinteractions(Signal–areaofoptimization) 
•Top-left: networkelementswithlotof irregularinteractions(Disruption/Perturbation) 
•Top-right openingfan: greatnumberof smallnetworkelements(Noise) 
CombinedQuantity-IrregularityGraphwithallthreeDimensions. Areasof Signal, Noiseand Disruption
•Largeregularnetworkelementhas atleastonelargeoutgoingsub-networkwithouttransversalsync 
•Largeirregularnetworkelementhas largeoutgoingsub-network/s onlywithtransversalsync 
•Smallnetworkelementshas onlysmalloutgoingsub-network/s 
Behaviorof a Network Elementdeterminedbyitsdirectand indirectoutgoingsub-networks 
Largeirregularnetworkelement 
Largeregularnetworkelements 
Smallnetworkelement
4 material flow processes as modular optionsonCombinedQuantityIrregularityGraph 
1.Milkrun-For „fully” common components what go into allFG variants-Replenishmentto fixed location for months 
2.Kanban-For common components of high runner variant group-Replenishmentto fixed location fordays/weeks 
3.Piece-picked-For variant specific components-Deliveryof component in exact quantity 
4.Just-in-Sequence-Onlyifallprocessesaretightlymanaged

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Material Flow Processes and Their Main Aspects

  • 1. Material Flow ProcessesTheir strengths and requirementsWhen, what, why and how to usethem? Istvan Fekete (fekete.istvan.pic@gmail.com) Nokia, 2012
  • 2. How to support oursuccess? 2 How to create more responsive flow of values/resources downstream? How weserveourcustomers How we use our resources Shorterresponseto change, demand Cheapersupply chain, operation More irregular quantitiesin more irregural timing More regular quantitiesin more regural timing How to create more regular flow of resources/values upstream?
  • 3. Material Flow Processes and their main aspects
  • 4. Milkrun Top Material Center Flowrack/ CrossDock Robust and adaptive Logistics Centers can support any Material Flow Process in any Factory Materials @ Line/Cell PoU are „made” to order or stock items BB4.3.2 BB4.3.2 BB4.3.2 BB4.3.2 BB4.3.2 BB4.3.2 BB4.3.2 BB4.3.2 BB4.3.2 Close Supplier and/or IHUB (Re)filling the trolley/train NOK OK Piece Pick Sequence Kanban Milkrun Hand Buffer Replacement of urgent NOK material Returning OK leftovers and not-urgent NOK materials Shipping OHUB Finished goods, OK leftovers and not-urgent NOK material collection from line/cell Material Flow Process of a material aligned to its behaviour OK Visual control on ordering Hand Buffer material or moving back consolidated lot to FlowRack
  • 5. BB4.3.2 BB4.3.2 BB4.3.2 Supplier IHUB Close Supplier MRP Top Material Center BB4.3.2 BB4.3.2 BB4.3.2 Flowrack/ CrossDock Non-Kanban Components Kanban Card, Empty Bin or E-kanban Milkrun in 30’ to deliver all items to and from cells Team Leader schedules next orders Combination of piece-picking and kanbans covers all needs efficiently and effectively; creates so-called degenerative redundancy, a prerequisite of robustness Kanban Replenishment
  • 7. RMSpec RMCom Level FORELOG GLOBAL ALLOCATION KANBAN VMI VENDOR/SUPPLIER MANAGED INVENTORY Bin 2/2 Bin 1/2 Line Feed Kanban Piece Pick to Order Pick to Order E-ban (electronic kanban) PON in use Next PON PON in use Next PON Typical case Rare case
  • 8. Hectic Stable $ $ To BOM Quant Quant From Many SyncEffect Commo- nality Effect Size Size Price Price
  • 10. Widespread Pareto-Distribution in supply chain networks, though ‚large’means different in three dimensions Irregularity= Coefficientof Variation, NormalizedStandard Deviation Numberof Outgoing/downstreamLinks Irregularity– distributionof interactionsintime Intensityof interactions(UsedvolumePareto)
  • 11. •X-Axis: Pareto-rankingasper theintensityof interactions(averageusage) •Y-Axis:Distributionof interactionsintime(Irregulatity) •Bubblesize: numberof outgoinglinks, outgoingdegree(inwhatFG itgoes) •Bottom-left: networkelementswithlotof regularinteractions(Signal–areaofoptimization) •Top-left: networkelementswithlotof irregularinteractions(Disruption/Perturbation) •Top-right openingfan: greatnumberof smallnetworkelements(Noise) CombinedQuantity-IrregularityGraphwithallthreeDimensions. Areasof Signal, Noiseand Disruption
  • 12. •Largeregularnetworkelementhas atleastonelargeoutgoingsub-networkwithouttransversalsync •Largeirregularnetworkelementhas largeoutgoingsub-network/s onlywithtransversalsync •Smallnetworkelementshas onlysmalloutgoingsub-network/s Behaviorof a Network Elementdeterminedbyitsdirectand indirectoutgoingsub-networks Largeirregularnetworkelement Largeregularnetworkelements Smallnetworkelement
  • 13. 4 material flow processes as modular optionsonCombinedQuantityIrregularityGraph 1.Milkrun-For „fully” common components what go into allFG variants-Replenishmentto fixed location for months 2.Kanban-For common components of high runner variant group-Replenishmentto fixed location fordays/weeks 3.Piece-picked-For variant specific components-Deliveryof component in exact quantity 4.Just-in-Sequence-Onlyifallprocessesaretightlymanaged