Warehouse Simulation: Quick Quick and effectiveand effective · Warehouse Simulation: Quick Quick...
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Warehouse Simulation: Warehouse Simulation:
Quick Quick and effectiveand effective
Alain de Norman et d`Audenhove – Director
Leandro Filippi – Consultant
BELGE - Sao Paulo / Brazil
About Belge
Objectives
Warehouse Environment & Simulation Technology
Agenda
Warehouse Environment & Simulation Technology
Cases & Results
Technology behind & Final Comments
About Belge
Objectives
Warehouse Environment & Simulation TechnologyWarehouse Environment & Simulation Technology
Cases & Results
Technology behind & Final Comments
Belge Engenharia e Sistemas LTDA www.belge.com.br
Belge Engenharia e Sistemas LTDA www.belge.com.br
• Since 1995, Promodel Corp. Distributor for Mercosur(Brazil, Argentina, Paraguay and Uruguay)
• Consulting and software company specialized in
• Since 1995, Promodel Corp. Distributor for Mercosur(Brazil, Argentina, Paraguay and Uruguay)
• Consulting and software company specialized in
About Belge
• Consulting and software company specialized in quantitative methods
• Founded in 1995, originated from the central Engineering Department of Siemens
• Consulting and software company specialized in quantitative methods
• Founded in 1995, originated from the central Engineering Department of Siemens
Some Clients
About Belge
Introduction
Warehouse Environment & Simulation TechnologyWarehouse Environment & Simulation Technology
Cases & Results
Technology behind & Final Comments
Warehouse environments include complexity, high levels of processinterdependency and variability. This makes the planning of distributioncenters an ideal environment for using dynamic simulation, though manylogistics operators continue to rely on static spreadsheets and theiroversimplifications.
WMS systems are excellent for managing operations, but are notsuitable for planning. They have no ability to test layouts, experimentwith different process alternatives, determine the right number of
Warehouse environments include complexity, high levels of processinterdependency and variability. This makes the planning of distributioncenters an ideal environment for using dynamic simulation, though manylogistics operators continue to rely on static spreadsheets and theiroversimplifications.
WMS systems are excellent for managing operations, but are notsuitable for planning. They have no ability to test layouts, experimentwith different process alternatives, determine the right number of
Introduction
with different process alternatives, determine the right number oftransport and human resources, or forecast the impact of differentdemand levels.
We show several ProModel simulations of DCs in large companies suchas Coca-Cola, Unilever, Brasil Foods and Colgate. Our focus is on theresults achieved for both new DC design and productivity improvementin existing facilities.
with different process alternatives, determine the right number oftransport and human resources, or forecast the impact of differentdemand levels.
We show several ProModel simulations of DCs in large companies suchas Coca-Cola, Unilever, Brasil Foods and Colgate. Our focus is on theresults achieved for both new DC design and productivity improvementin existing facilities.
About Belge
Introduction
Warehouse Environment & Simulation TechnologyWarehouse Environment & Simulation Technology
Cases & Results
Technology behind & Final Comments
WarehouseWarehouseWarehouseWarehouse EnvironmentEnvironmentEnvironmentEnvironment & & & & SimulationSimulationSimulationSimulation TechnologyTechnologyTechnologyTechnology
Docks
Reception
Basic flow of a warehouse
Racks / Block
Stacking
Picking
Docks
Parking Area
SimulationSimulationSimulationSimulation considersconsidersconsidersconsiders thethethethe effecteffecteffecteffect ofofofof operationsoperationsoperationsoperations over a over a over a over a periodperiodperiodperiod ofofofof time!!!time!!!time!!!time!!!
How could a static spreadsheet (MS Excel) consider the fact that a forklift
can do several things at once if it does not simulate the operation over
time?
How could a static spreadsheet (MS Excel) consider the fact that a forklift
can do several things at once if it does not simulate the operation over
time?
How long does it take to unload a pallet from a rack?
Spreadsheets consider average time
Simulation allows us to consider lift time
AbilityAbilityAbilityAbility to to to to dealdealdealdeal withwithwithwith randomrandomrandomrandom variablesvariablesvariablesvariablesConsidersConsidersConsidersConsiders variabilityvariabilityvariabilityvariability on times and on times and on times and on times and speedsspeedsspeedsspeeds
Simulation allows us to consider lift time variability due to different numbers of levels per rack.
Static Analaysis: PI is input data
Static Analaysis: PI is input data
Dynamic analysis: PI is output data
Dynamic analysis: PI is output data
Eg.: New picking
system
The previous productivity
index was X boxes/hour
Assumes that with the new
The previous productivity
index was X boxes/hour
According to the new times,
resources qty. and the new
Productivity Index (PI)Productivity Index (PI)Productivity Index (PI)Productivity Index (PI)
system Assumes that with the new
method, the productivity
will be increased to Y
boxes/hour
Scales the number of
operators and equipments
with the assumed
productivity
resources qty. and the new
method, the productivity index will
be Y boxes/hour
Evaluates the productivity (meets
or not) according on the amount
of resources and process times
SpreadsheetSpreadsheetSpreadsheetSpreadsheet staticstaticstaticstatic analysisanalysisanalysisanalysis vs.vs.vs.vs.SimulationSimulationSimulationSimulation dynamicdynamicdynamicdynamic analysisanalysisanalysisanalysis
Static analysis Dynamic analysis
Operations sequencing Does not consider Considered
Simultaneous request of
the same resource
Does not consider Considered
Variability in operative
times, speeds, demands
and breakdowns
Does not consider Considered
Productivity indices (eg
pallets/hour qty in
picking and load
assembly
It is an input data, wich
causes error. It is a
mistake to use these
values as a premise,
since it is influenced by
several factors such as:
resource qty, processing
times, arrivals and
departure sequence, etc.
It is an output of the
model. The result of all
its restrictions,
randomness, cycles,
demands, resources,
among others.
Warehouse Environment & Simulation Technology
Warehouse enrivonments include:
Complexity
High level of process interdependency
Variability
Warehouse enrivonments include:
Complexity
High level of process interdependency
Variability
When to simulate?When to simulate?
Warehouse Environment & Simulation Technology
What about WMS?
• Excellent for managing operations, but are not suitable forplanning.
• They have no ability to test layouts, experiment with differentprocess alternatives, determine the right number of transportand human resources or forecast the impact of different demandlevels.
What about WMS?
• Excellent for managing operations, but are not suitable forplanning.
• They have no ability to test layouts, experiment with differentprocess alternatives, determine the right number of transportand human resources or forecast the impact of different demandlevels.levels.levels.
WMS = managementWMS = management SIMULATION = planningSIMULATION = planning
About Belge
Objectives
Warehouse Environment & Simulation TechnologyWarehouse Environment & Simulation Technology
Cases & Results
Technology behind & Final Comments
Some DCSim/ProMocel customers in South America
ObjectiveObjectiveObjectiveObjective::::
Audit 3 different operation logistics proposals and help Unilever to decidewhich 3PL would operate their new biggest DC in South AmericaJoint Warehouse (Foods and HPC / SP) – almost 60% of Unilever Renenue
(Brazil) comes from this DC
ObjectiveObjectiveObjectiveObjective::::
Audit 3 different operation logistics proposals and help Unilever to decidewhich 3PL would operate their new biggest DC in South AmericaJoint Warehouse (Foods and HPC / SP) – almost 60% of Unilever Renenue
(Brazil) comes from this DC
Case Unilever
Expansion - DHL Greenfield - DHL Greenfield - Mclane
Case - UnileverInteractive analysis to define the number ofresources required:
Inbound and outbound results X targets
Case Unilever
Eliminating
bottlenecks
and reducing
resources
idle
Outbound target: 5800 t
Inbound target: 4000 t
ErrorsErrors: : SizingSizing in Excel in Excel vsvs SimulationSimulation
Case Unilever
ResultsResultsResultsResults::::
Defined the mininum number ofresources necessary (we detectedoversizing and subsizing)
Costs were reduced by USUSUSUS$$$$ 130130130130,,,,000000000000 ////monthmonthmonthmonth (reducing operators and forklifts)
ResultsResultsResultsResults::::
Defined the mininum number ofresources necessary (we detectedoversizing and subsizing)
Costs were reduced by USUSUSUS$$$$ 130130130130,,,,000000000000 ////monthmonthmonthmonth (reducing operators and forklifts)
Case Unilever
Layout winner
Identified and eliminated bottlenecks.Otherwise the DC would have a backlog ofalmost 35% on peak days
Guarantee ability to attain the level ofservice required. Start-up operations ranvery well
Identified and eliminated bottlenecks.Otherwise the DC would have a backlog ofalmost 35% on peak days
Guarantee ability to attain the level ofservice required. Start-up operations ranvery well
Case - SadiaOBJECTIVE:OBJECTIVE:OBJECTIVE:OBJECTIVE:OBJECTIVE:OBJECTIVE:OBJECTIVE:OBJECTIVE:
Sizing the number of resourcesnecessaryCapacity planning
RESULTSRESULTSRESULTSRESULTSRESULTSRESULTSRESULTSRESULTSHeadcount reduced in 35%Based on improvements suggested by
DCSim, the client could increase the DC
Case Brasil Foods
DCSim, the client could increase the DC capacity by 20% without additional resources or investmentInvesment could be reduced by almost
30%Flexible model: the client can test new
scenarios any time, quickly and effectively
Case - SadiaOBJECTIVE:OBJECTIVE:OBJECTIVE:OBJECTIVE:OBJECTIVE:OBJECTIVE:OBJECTIVE:OBJECTIVE:
Capacity planningSizing head-countIdentify the best operation model
RESULTSRESULTSRESULTSRESULTSRESULTSRESULTSRESULTSRESULTSPossibility to reduce the number of forklifts
by 55%For future demand, it is not necessary to
Case Colgate
For future demand, it is not necessary toincrease the number of all resources.Increasing only the number of checkers (realbottleneck), they could increase capacity forthe next few yearsAvoid spending money by changing rack
positionsIdentified the DC capacity (how long this
DC can operate in current configuration) andwhen they should invest in a new DC or in anexpansion.
16 Bottlers in Brazil16 Bottlers in Brazil
Objective:
Development of simulation models to optimize the
layout, flows and storage of the new area of the DC in
Taguatinga do Sul of BRASAL.
The model contemplates the storage area expansion
and through the results analysis it was possible to
identify some operational restrictions in the system.
Example: Idleness level of each resource in each shift.
Objective:
Development of simulation models to optimize the
layout, flows and storage of the new area of the DC in
Taguatinga do Sul of BRASAL.
The model contemplates the storage area expansion
and through the results analysis it was possible to
identify some operational restrictions in the system.
Example: Idleness level of each resource in each shift.
Scope Scope
Case Coca-Cola - BRASAL
Scope
The model considered the following processes:
Storage Area;
Picking Area;
Loading/Unloading Tunnel;
Receiving and Expedition of Product.
Scope
The model considered the following processes:
Storage Area;
Picking Area;
Loading/Unloading Tunnel;
Receiving and Expedition of Product.
Previous LayoutPrevious Layout
Case Coca-Cola - BRASAL
Production
Lines
Storage area“Docks” – 16
loading points
Space idleness
Picking
StagingPicking area was not working
New Proposed LayoutNew Proposed Layout
Case Coca-Cola - BRASAL
Storage area
Production
Lines
Loading points
Picking
Replenishment
Picking
Staging area
(new concept)
Loading points
(8)
Case Coca-Cola - BRASAL
6 a.m.: all trucks must be loaded
Layout
proposed
Staging area
required
Baseline:
several trucks
loaded after the
deadline
Case Coca-Cola - BRASAL
Picking: Baseline Picking: New Layout1 2
New product positions
3Decrease of 1.5
min/pallet
Case Coca-Cola - BRASAL
Reduction: 12.5%
Example: Loading Time – Vehicle“Mercado” Baseline
Mean time ���� 1.6 HR
Layout Proposed
Mean time ���� 1.4 HR
- New staging area
- Defined the number of resources required
- Increased picking productivity
Production –
pallets produced
during the day
loading
day night
Case Coca-Cola - BRASAL
Pallets for
Picking
replenishmentReplenishment
Pallets
From
Picking
Results:
Through the simulation results, bottlenecks were
identified, as operation problems and the breakpoint
of the DC in the current situation;
Based on the simulation results, a new layout was
proposed, improving storage capacity by 20%;
Results:
Through the simulation results, bottlenecks were
identified, as operation problems and the breakpoint
of the DC in the current situation;
Based on the simulation results, a new layout was
proposed, improving storage capacity by 20%;
Case Coca-Cola - BRASAL
The operation strategy of the DC was changed,
reducing vehicle loading times by almost 26%;
A new picking configuration was proposed, reducing
the picking time and inspection in the stages.
The operation strategy of the DC was changed,
reducing vehicle loading times by almost 26%;
A new picking configuration was proposed, reducing
the picking time and inspection in the stages.
Goal:
Development of simulation models that allow the
optimization the stock area of Porto Alegre Unit.
Determine the best layout considering some enlargement
area possibilities, identifying which layout best
accommodates growing demand until 2018.
Goal:
Development of simulation models that allow the
optimization the stock area of Porto Alegre Unit.
Determine the best layout considering some enlargement
area possibilities, identifying which layout best
accommodates growing demand until 2018.
Case Coke 2
Comparision of the proposed scenarios
% Customer Service Level
40m
Ideal
Summer
Tunnel
30m
Tunnel
30m
Lateral
Dock
60m
Frontal
Dock
60m
Lateral
Dock
2010 98.04% 77 97.83% 77 97.97% 77 99.00% 78 97.77% 77
2011 96.07% 81 97.95% 83 99.00% 84 96.02% 81
2012 91.92% 84 95.93% 87 98.00% 89 96.03% 87
2013 90.13% 89 93.78% 91 90.23% 89 94.01% 92
2014 87.06%* 93 - 88.85%* 95 84.87% 90 94.22%** 101
Results:
Case Coke 2
* with 18 forklifts (10
simple)
** adjusting resale window and route
service time
2014 87.06%* 93 - 88.85%* 95 84.87% 90 94.22%** 101
2015 - - 90.22%** 105
2016 - - 76.46%**
Goal:
Improve the Distribution Center capacity in order to
optimize stock areas and flows, considering the
business dynamics, seeking to decrease the
operational costs and improve the asset’s applications.
Simulation Period:
Goal:
Improve the Distribution Center capacity in order to
optimize stock areas and flows, considering the
business dynamics, seeking to decrease the
operational costs and improve the asset’s applications.
Simulation Period:
Case Coke 3
Simulation Period:
2009 to 2012;
Scope:
Storage areas, DC internal flows, picking area,
loading/unloading docks, DC Gates, factory output.
Simulation Period:
2009 to 2012;
Scope:
Storage areas, DC internal flows, picking area,
loading/unloading docks, DC Gates, factory output.
Results:
--18% Reduction of 18% Reduction of forklift forklift displacement distance (considering the same demand)
14% Reduction by: better positioning of products.
4% Reduction by: loading/unloading by forklifts’ circuit.
Average distance by Loaded Pallet (before simulation): 178m
Average distance by Loaded Pallet (after simulation): 153m
+8,9% +8,9% Increase in storage capacity
Case Coke 3
+8,9% +8,9% Increase in storage capacity
Previous Capacity : 15.833 pallets
Current Capacity : 17.233 pallets
--12% 12% Decrease of distributor loading time
Previous average time: 1:32 hours
Later average time: 1:21 hours
Objetivos Resultados
Optmize flows and
displacements
Increase the storage area utilization by
8.9%
Space optmization
Decrease of 18% in vehicls service
times
Size the number of resources
required for the next years Correct sizing of the resources required
Objectives Results
Some Results at Coke
Define the best layouts for
future scenarios
Required Investment reduced by
USD 9 million
Define the best operation
strategy
Best ‘summer plan’ of Coke/Vonpar
history
Set the best increasing strategy High precision and quality
Assis Brasil
Objetivos Resultados
Set the best layout for future and
increasing demands Obtained the optmized layout for the DC
Set the best operation strategy Charging system dramatically modified
Increase of 20% in warehouse area
Objectives Results
ANC
Some Results at Coke
Set the best layout for increasing volumes
Increase of 20% in warehouse area
availability
Define the picking and stagin area Truck loading time reduced by 40%
Improvement in the picking area
Definition of a new layout for picking
area
Objetivos Resultados
Define the best layout for a
new DC
Defined warehouse area, restaurant,
parking area, etc
Identify the investiments
required in the future years Investiment plan per year
Sizing the number of resources
Sized the mininum number of human
resources and equipments
Increase of 45% in warehousing capacity
Objectives Results
Some Results at Coke
Define the best layout for a
new DC
Increase of 45% in warehousing capacity
when compared with the original
proposed layout
Flow optimization
Defined the picking method, type of
storage structures, staging areas and set
the best regions for each SKU
Size the number of resouces Defined the minumun qty of resouces
“We could, thanks to the project made by Belge
in Vonpar, choose the best scenario considering a
Layout Definition and Optimizing the flows. The
result was a great increase in the operation level of
“We could, thanks to the project made by Belge
in Vonpar, choose the best scenario considering a
Layout Definition and Optimizing the flows. The
result was a great increase in the operation level of
“A unique learning opportunity, where we
evolved a lot in the knowledge of our inner
processes, and mainly to know which results can be
pursued and leveraged.”
“A unique learning opportunity, where we
evolved a lot in the knowledge of our inner
processes, and mainly to know which results can be
pursued and leveraged.”
Testimony
result was a great increase in the operation level of
our DC concerning storage, displacement and service
level to our clients.”
“We can say that it was the best summer plan
in the history of Vonpar."
Sandro Soares
Logistics Coordinator
result was a great increase in the operation level of
our DC concerning storage, displacement and service
level to our clients.”
“We can say that it was the best summer plan
in the history of Vonpar."
Sandro Soares
Logistics Coordinator
pursued and leveraged.”
Heitor Ferreira Perez Villar
Logístics Analyst
pursued and leveraged.”
Heitor Ferreira Perez Villar
Logístics Analyst
About Belge
Introduction
Warehouse Environment & Simulation TechnologyWarehouse Environment & Simulation Technology
Cases & Results
Technology behind & Final Comments
• Developed as an in-house tool for quickly modeling DCs
• Dramatic reduction in time required
• Customer requests to continue using the tool on their own
What is DCSim
DCSim uses DCSim uses ProModelProModel Technology Technology
• World state-of-art in simulation
• Clear for all users (does not requires any previous knowledge about simulation)
Simulator
Language
Routing
DCSim Model
Subroutines• Inbound• Outbound• Picking• Conference• Storage area• etc
Input Data:• Resources• Times• Demand• Shifts• etc
How it works?
44
Cockpit –Input Datas
Results
Conclusions
Simulation is helping several companies to avoid typycal sizing errors (compared to simple MSExcel usage)
According to our experience in several DC projects, we can point to somesignificant improvements, such as:
• Up to a 40% increase in outbound capacity, made possible through theidentification and modification of system bottlenecks
• Minimized startup errors in new and modified DCs
• Up to a 30% reduction in human resource requirements during peak
Conclusions
days
• Operational cost reductions of up to 35%
ThankThankThankThank YouYouYouYou !!!!!!!!!!!!
QuestionsQuestionsQuestionsQuestions ????
Alain de Norman et d´Audenhove - [email protected]
Leandro Filippi – [email protected] Filippi – [email protected]
+55 (11) 5561-5353
www.belge.com.br