LOGISTICS SIMULATIONLOGISTICS SIMULATION

Simulation Service

22

Introduction - Who we are

Logistics Simulation Ltd.Logistics ConsultancyStarted in 1993Currently 9 PeopleBased in Poynton - South Manchester

Supply Chain Group Member

Logistics Simulation Ltd, Phone +44 (0)1625 850 919, Web: www.logsim.co.uk, email:info@logsim.co.uk

33

Introduction - What we do

Warehouse DesignNew Facilities, Extension, Site Review, Automate

British Library, Hozelock, Guinness, ColArt (Winsor and Newton Artist Materials)

Warehouse SimulationReal orders data, 3D Animation.

Details later

Network ModellingNumber and location of distribution centres

EMI, Levi, Masterfoods (Mars/Uncle Ben/Pedigree Petfoods)

Inventory ModellingFull stock modelling, typically over longer time frame

Dairy Crest, Hozelock, Banta

We don’t just do Warehouse Simulation – but that is what we will talk about today.

44

Simulation – Who have we worked for?

Highly AutomatedHilti, Bettenwelt

Hanging Garment New Look (left)

Semi-Automated Distribution CentresB&Q, Arco

Production DPC, Elkes, Lever

Conveyor PickingRandom House, Royal Mail, Estee Lauder

Cross DockRoyal Mail, Rogers

Traditional Pallet Storage & Picking B&Q, Frigoscandia, Dunlop, Somerfield

55

Overview - Simulation

What is simulation?Good uses of simulation

Why simulate?Benefits of simulation

Our simulation Show case studies – Understand the simulation process

Simple demonstration of programmeSo you can get a better feel

66

What is Simulation?Time based simulation is a model that steps though a period of time replicating the warehouse activity

Other models can be used for warehouse design, for example spreadsheets that calculate the number of moves per hour that a truck is capable of. Time based simulation looks at the interactions between different pieces of equipment as well as queuing and marshalling.

Good uses of simulationVerification of final designEquipment specifications and quantitiesHigh level WMS rulesIdentification of congestion & bottlenecks

Bad uses of simulationDecision between different technologies: Do not try and use it to decide between major design differences e.g. automation or traditional. This can usually be achieved using spreadsheet models at far lower cost. If there is insufficient data and the expectation is an accurate representation then simulation should be avoided as it leads to a false sense of security.If the equipment works in isolation with little interaction then a simple simulation will suffice.

77

Why Simulate?

Benefits of simulationThe obvious applications are where there is a high capital spend in automation. As well as ensuring a workable operation, simulation can avoid unnecessary expenditureIf there is to be a large number of operatives e.g. pickers, then reduction in head count will justify the use of a detailed simulation

InterdependenciesDifferent parts of a warehouse interact.More automation / complicated WMS rules leads to more interdependencies.

Time based effectsPeaks and troughs effect overall resultCut off and deadlines (vehicle departures)

Visual AidSimulation can be used as a presentation device to show others how a new facility will look.

Additional benefits of process of simulationAsking how everything should work can highlight areas not thought all the way through.The process can be used to focus people together

88

Simulation Detail

The equipment in the simulation is modelled in detailMHE speeds (Loaded, Unloaded), AccelerationsConveyor accumulation, priorities, routings, speeds.

Activities Levels of DetailRandom Data

Careful use of random data can give useful results but relying too much on averages can give a incorrect view on the system.

Real DataThe most detailed simulation is one where each individual product is assigned a pick location and actual orders are used.

Future DataManipulation of recent orders to reflect likely future orders. Sensitivities used to find boundaries

What if? – ScenariosShift patternsVehicles schedules and services levels.Equipment manufacturesWMS rules and priorities

99

Logistics Simulation’s Simulation

Our own, custom built, softwareDesigned for warehouses

Basic structures RackingAislesTruckConveyorsDocksRoads

Basic LogicPallets flow round the warehouseCustomer ordersVehicle schedules

Software demonstration 1

1010

Simulation ResultsSummary Spreadsheet available

Truck moves per hourPicker lines per hourOverall utilization

Detailed log can be analysed for any results required

1111

Basic Warehouse ExampleSmall warehouse, few trucks, no Warehouse management system

1212

Complex Warehouse Management Systems (WMS)

The rules governing the simulation are important Can have a large effect on the outcome.

We have built in rules for managing the systemGeneral Rules for work release, conveyor logic

For more complex or specific WMS rulesThe simulation can communicate with a database

1313

WMS Example – EMI

A traditional warehouse with modern WMSPicking CDs from shelves and carton live racking into despatch cartons.A Picker has a picking trolley and can batch pick many despatch cartons at the same time.

The simulation was to test the pick assignment rules

1414

WMS Example – EMI Order pool

Orders are received throughout the day.Pick assignment can be made up of cartons from different orders :

by selecting cartons from the available work (order pool).

This process has to be done while the simulation is running. No way to know in advance what orders will be available.

Order Lines in pool

0

1000

2000

3000

4000

5000

6000

6 8 10 12 14 16 18 20

Time (hr)

Ord

er li

nes

in p

ool

0

20

40

60

80

100

120

140

160

180

Pic

k ra

te li

nes/

hr

Order lines in pool Pick rate line/hr

1515

WMS Example - EMI - Movie

Click picture to play/pause movie (2 mins 40 secs) “Esc” key to stop

1616

Case Study - Arco

Arco are the UK’s leading supplier of safety clothing and equipment – From gloves and work shoes to ladders.

An extension to their existing warehouse had been designed by a systems integrator.

Arco wished to have the design tested against their proposed business plans.

1717

Case Study – Arco – Different Zones

VNA bulk for picking and replenishment of carton live

Wide Aisle picking of large & awkward items

For Small and Medium products:

Pallet live racking for large orders of fast products

Carton live system 1 for top 80% of fast picks

Carton live system 2 bottom 20% of fast picks

Gondola system for slow picks

1818

Case Study – Arco – Plan View

8. Goods out7. Pack Stations6. Buffer Towers5. Gondola picking

4. Pallet Live3. Wide Aisle2. VNA1. Goods in

Mezzanine levels 1. Carton Live Fast 2. Carton Live Medium

1919

Case Study – Arco – Mezzanine Levels

Level 1: Carton Live racking –pick to conveyor

TO 2nd MEZZ

MEZZANINE FLOOR +4600

CARTON LIVE STORAGE SYSTEM

TO GROUND FLOOR

MEZZANINE LEVEL ONE

270

02

700

TO 2nd MEZZ

ESSIRS

340

04

860

340

0

145

0

MEZZANINE LEVEL TWO

CARTON LIVE STORAGE SYSTEM FROM 1st MEZZ

FROM 1st MEZZ

TO 1st MEZZ

SSRS

Level 2: Carton Live racking –batch picking with trolleys

2020

Case Study – Arco Simulation

Click picture to play/pause movie (2 minutes) “Esc” key to stop

2121

Case Study – Arco - Experiments

Two design profiles:Orders received on day one for day three delivery

Start day with all orders available

Orders received on day one for day two deliveryOrders received throughout the day

Activity:Average day: 7000 orders and 18000 order linesPeak day: 9000 orders and 22500 order lines

2222

Case Study – Arco – Results 1

Activity completion times

19.1317.3017.5018.5219.13Peak: Day one for day two

18.3017.5617.3218.1718.31Peak: Day one for day three

Last setdown to despatch gridReplenishmentPallet

PickingTote PickingSimulation Design

Cumulative Orders at despatch

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

6 8 10 12 14 16 18 20

Time hours

Perc

enta

ge o

f ord

ers

2323

Case Study – Arco – Results 2No. of pack benches in use

02468

10121416182022

6 7 8 9 10 11 12 13 14 15 16 17 18

Tme (5 min snaps)

No.

of b

ench

es

Cumulative totes in & out of towers and totes stored

0

25

50

75

100

125

150

175

200

225

250

6 7 8 9 10 11 12 13 14 15 16 17 18 19

Hour (5min snaps)

Tote

s st

ored

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Cum

ulat

ive To

tes In

& O

ut

STOREDINTOOUT

2424

Case Study – Arco – Results 3

We can record many different type of statistics for the resultsThe overall utilisation for the two types of carton live pickers

Carton Live Picker Utilization

0

10

20

30

40

50

60

70

80

90

100

6 8 10 12 14 16 18 20

Time Hours

Per

cent

age

Wor

king

Carton Live 1Carton Live 2

2525

Software demonstration 2

Conveyor example

2626

Other examples – Random House

Click picture to play/pause movie (1 minutes) “Esc” key to stop

2727

Processes for a Detailed Simulation

CAD Layout

SKU & OrderData

WarehouseLogic

EquipmentSpecifications

VehicleSchedule

Outputs

Detailed layout, need to know how everything connects!

Need to know how an order is pickedSplit each order line into pallet picks, case picks, etc

Need to know where an order line is picked:Different types of product picked in different ways

Need to know how an order is consolidated:Picks into cartons, cartons into pallets, pallets to vehicle loads

Can the proposed specifications meet the throughputs

Service Level Agreements effect the demand over time

Clients specific result requirements calculated

2828

Other examples – Hilti

Click picture to play/pause movie (2:20 minutes) “Esc” key to stop