Warehouse Simulation: Quick and effective Alain de Norman et d`Audenhove – Director Leandro Filippi – Consultant BELGE - Sao Paulo / Brazil
Agenda
About Belge
Objectives
Warehouse Environment & Simulation Technology
Cases & Results
Technology behind & Final Comments
About Belge
Objectives
Warehouse Environment & Simulation Technology
Cases & Results
Technology behind & Final Comments
About Belge
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 quantitative methods
•
Founded in 1995, originated from the central Engineering Department of Siemens
Some Clients
About Belge
Introduction
Warehouse Environment & Simulation Technology
Cases & Results
Technology behind & Final Comments
Introduction Warehouse environments include complexity, high levels of process interdependency and variability. This makes the planning of distribution centers an ideal environment for using dynamic simulation, though many logistics operators continue to rely on static spreadsheets and their oversimplifications. WMS systems are excellent for managing operations, but are not suitable for planning. They have no ability to test layouts, experiment with different process alternatives, determine the right number of transport and human resources, or forecast the impact of different demand levels. We show several ProModel simulations of DCs in large companies such as Coca-Cola, Unilever, Brasil Foods and Colgate. Our focus is on the results achieved for both new DC design and productivity improvement in existing facilities.
About Belge
Introduction
Warehouse Environment & Simulation Technology
Cases & Results
Technology behind & Final Comments
Warehouse Environment & Simulation Technology
Reception Docks
Picking
Basic flow of a warehouse Parking Area
Racks / Block Stacking
Docks
Simulation considers the effect of operations over a period of 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?
Ability to deal with random variables Considers variability on times and speeds
How long does it take to unload a pallet from a rack?
Spreadsheets consider average time Simulation allows us to consider lift time variability due to different numbers of levels per rack.
Productivity Index (PI)
Static Analaysis: PI is input data The previous productivity index was X boxes/hour
Eg.: New picking 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
Dynamic analysis: PI is output data The previous productivity index was X boxes/hour
According to the new times, 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
Spreadsheet static analysis vs. Simulation dynamic analysis
Static analysis
Dynamic analysis
Operations sequencing
Does not consider
Considered
Simultaneous request of the same resource
Does not consider
Considered
Does not consider
Considered
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.
Variability in operative times, speeds, demands and breakdowns Productivity indices (eg pallets/hour qty in picking and load assembly
Warehouse Environment & Simulation Technology Warehouse enrivonments include: Complexity High level of process interdependency Variability
When to simulate?
Warehouse Environment & Simulation Technology What about WMS? • Excellent for managing operations, but are not suitable for planning. • They have no ability to test layouts, experiment with different process alternatives, determine the right number of transport and human resources or forecast the impact of different demand levels.
WMS = management
SIMULATION = planning
About Belge
Objectives
Warehouse Environment & Simulation Technology
Cases & Results
Technology behind & Final Comments
Some DCSim/ProMocel customers in South America
Case Unilever Objective: Objective: Audit 3 different operation logistics proposals and help Unilever to decide which 3PL would operate their new biggest DC in South America Joint Warehouse (Foods and HPC / SP) – almost 60% of Unilever Renenue (Brazil) comes from this DC Expansion - DHL
Greenfield - DHL
Greenfield - Mclane
Case Unilever
Case - Unilever Interactive analysis to define the number of resources required: Inbound and outbound results X targets
Outbound target: 5800 t
Inbound target: 4000 t
Eliminating bottlenecks and reducing resources idle
Case Unilever Errors:: Sizing in Excel vs Simulation Errors
Case Unilever Layout winner
Results: Results: Defined the mininum number of resources necessary (we detected oversizing and subsizing) Costs were reduced by US$ US$ 130, 130,000 / month (reducing operators and forklifts) Identified and eliminated bottlenecks. Otherwise the DC would have a backlog of almost 35% on peak days Guarantee ability to attain the level of service required. Start-up operations ran very well
Case Brasil Foods
Case - Sadia
OBJECTIVE: Sizing the number necessary Capacity planning
of
resources
RESULTS Headcount reduced in 35% Based on improvements suggested by DCSim, the client could increase the DC capacity by 20% without additional resources or investment Invesment could be reduced by almost 30% Flexible model: the client can test new scenarios any time, quickly and effectively
Case Colgate
Case - Sadia OBJECTIVE: Capacity planning Sizing head-count Identify the best operation model RESULTS Possibility to reduce the number of forklifts by 55% For future demand, it is not necessary to increase the number of all resources. Increasing only the number of checkers (real bottleneck), they could increase capacity for the next few years Avoid spending money by changing rack positions Identified the DC capacity (how long this DC can operate in current configuration) and when they should invest in a new DC or in an expansion.
16 Bottlers in Brazil
Case Coca-Cola - BRASAL 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 The model considered the following processes: Storage Area; Picking Area; Loading/Unloading Tunnel; Receiving and Expedition of Product.
Case Coca-Cola - BRASAL Production Lines
Space idleness
Previous Layout “Docks” – 16 loading points
Staging
Storage area
Picking area was not working
Picking
Case Coca-Cola - BRASAL New Proposed Layout
Production Lines
Storage area
Loading points (8)
Staging area (new concept)
Picking Replenishment Picking
Case Coca-Cola - BRASAL 6 a.m.: all trucks must be loaded
Layout proposed
Baseline: several trucks loaded after the deadline
Staging area required
Case Coca-Cola - BRASAL 1
2 Picking: Baseline
Decrease of 1.5 min/pallet
Picking: New Layout
3 New product positions
Case Coca-Cola - BRASAL Example: Loading Time – Vehicle“Mercado”
Baseline Mean time 1.6 HR Reduction: 12.5%
Layout Proposed Mean time 1.4 HR
- New staging area - Defined the number of resources required - Increased picking productivity
Case Coca-Cola - BRASAL day
night
Production – pallets produced during the day loading
Pallets From Picking Pallets for Picking replenishment
Replenishment
Case Coca-Cola - BRASAL 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%;
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.
Case Coke 2
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 Results: 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 2012 2013
96.07% 91.92% 90.13%
81 84 89
83 87 91
99.00% 98.00% 90.23%
84 89 89
96.02% 96.03% 94.01%
81 87 92
2014
87.06%*
93
88.85%* 95
84.87%
90
2015
-
-
90.22%** 105
2016
-
-
76.46%**
* with 18 forklifts (10 simple)
97.95% 95.93% 93.78% -
94.22%** 101
** adjusting resale window and route service time
Case Coke 3
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: 2009 to 2012;
Scope: Storage areas, DC internal flows, picking area, loading/unloading docks, DC Gates, factory output.
Case Coke 3 Results: -18% Reduction of 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% Increase in storage capacity Previous Capacity : 15.833 pallets Current Capacity : 17.233 pallets
-12% Decrease of distributor loading time Previous average time: 1:32 hours Later average time: 1:21 hours
Some Results at Coke
Objectives Objetivos
Assis Brasil
Results 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
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
Some Results at Coke
Objectives Objetivos Set the best layout for future and increasing demands
Results Resultados Obtained the optmized layout for the DC
Set the best operation strategy
Charging system dramatically modified
Set the best layout for increasing volumes
Increase of 20% in warehouse area availability
ANC
Define the picking and stagin area Improvement in the picking area
Truck loading time reduced by 40% Definition of a new layout for picking area
Some Results at Coke
Objectives Objetivos Define the best layout for a new DC Identify the investiments required in the future years
Results Resultados Defined warehouse area, restaurant, parking area, etc
Investiment plan per year Sized the mininum number of human Sizing the number of resources resources and equipments
Flow optimization
Increase of 45% in warehousing capacity when compared with the original proposed layout 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
Define the best layout for a new DC
Testimony
“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 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
“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.”
Heitor Ferreira Perez Villar Logístics Analyst
About Belge
Introduction
Warehouse Environment & Simulation Technology
Cases & Results
Technology behind & Final Comments
What is DCSim • • •
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
DCSim uses ProModel Technology • World state-of-art in simulation • Clear for all users (does not requires any previous knowledge about simulation)
Routing Simulator Language
How it works? Input Data: • Resources • Times • Demand • Shifts • etc
DCSim Model
Cockpit – Input Datas
Subroutines • Inbound • Outbound • Picking • Conference • Storage area • etc
Results
44
Conclusions Simulation is helping several companies to avoid typycal sizing errors (compared to simple MSExcel usage)
Conclusions According to our experience in several DC projects, we can point to some significant improvements, such as: • • • •
Up to a 40% increase in outbound capacity, made possible through the identification and modification of system bottlenecks Minimized startup errors in new and modified DCs Up to a 30% reduction in human resource requirements during peak days Operational cost reductions of up to 35%
Thank You !!!
Questions ? Alain de Norman et d´Audenhove -
[email protected] Leandro Filippi –
[email protected] +55 (11) 5561-5353 www.belge.com.br
