Report KPP319

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LOGISTIC PROCESS IN H&M WAREHOUSE

KPP319 INDUSTRIAL EXCELLENCE

Authors

Mansour Ahmadi

School of Innovation, Design and Engineering – IDT

Mälardalen University, Eskilstuna, Sweden

KPP319 Industrial Excellence

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Abstract

H&M is big company in Sweden and well-known to worldwide then this is first reason to

motivate and challenge us to work with this big company. As the main problem for H&M

warehouse is the daily number of labor that they have to calculate in everyday then we would

like to find out this problem for them by using simulation model for the optimize solution and

using 3D creation model for more understanding in H&M warehouse process. The paper

introduce the main idea of simulation model and some theoretical background and clarify the

result and conclusion for H&M problem in the last part.


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Contents

INTRODUCTION ................................................................................................................................................................... 4

BACKGROUND AND DESCRIPTION OF SIMULATION CASE..................................................................................................................... 4

OBJECTIVES AND GOALS ........................................................................................................................................................................... 4

PROJECT PLAN ........................................................................................................................................................................................... 4

EXPECTED RESULTS.................................................................................................................................................................................. 6

THEORETICAL BACKGROUND ........................................................................................................................................ 6

SIMULATION AS A TOOL WITHIN PRODUCTION AND LOGISTICS MANAGEMENT .............................................................................. 6

DISCRETE EVENT SIMULATION ............................................................................................................................................................... 6

3D MODELING ........................................................................................................................................................................................... 7

PROCESS DESCRIPTION .................................................................................................................................................... 8

PROBLEM AND REASON FOR SIMULATION ............................................................................................................................................ 8

DATA COLLECTION ................................................................................................................................................................................ 11

MODEL .................................................................................................................................................................................. 11

SIMULATION MODEL CONSTRUCTION................................................................................................................................................ 11

LOGICAL MODEL DESCRIPTION ........................................................................................................................................................... 13

.................................................................................................................................................................................................................. 13

3D MODEL DESCRIPTION ...................................................................................................................................................................... 14

VALIDITY AND RELEVANCE OF THE MODEL ....................................................................................................................................... 15

SIMULATION ...................................................................................................................................................................... 16

DESCRIPTION OF SIMULATION ............................................................................................................................................................. 16

RESULTS FROM SIMULATION ............................................................................................................................................................... 18

RESULTS AND CONCLUSIONS ....................................................................................................................................... 21

REFERENCES ...................................................................................................................................................................... 22

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INTRODUCTION

BACKGROUND AND DESCRIPTION OF SIMULATION CASE H & M Hennes & Mauritz AB (operating as H&M) is a Swedish retail-clothing company, known

for its fast-fashion clothing offerings for women, men, teenagers and children. H&M central

warehouse is located near Eskilstuna.

Modeling is a replica of the system, either physical or mathematical. A model has all the

properties of the system which it represents. Whereas simulation is solely used to describe

computer modeling, modeling is the general term that encompasses both simulation and

modeling.

OBJECTIVES AND GOALS We, as the Group number 1 decided to work on a simulation project based on H&M warehouse.

Our simulation is going to show the picking up process of clothes inside the warehouse.

The lists represent the order lists of customers and the labors are the resources that should pick

up clothes according to orders and finally they delivered finished lists to the reception desks.

Our main purpose of this project is to help H&M warehouse with the Daily Number of Labors, the

Re-Stocking schedule and finding the best place among the possible empty ones for new clothes.

PROJECT PLAN We defined our main tasks as it is shown in figure 1.

1. Specify the Case Study, 2. Gathering information about the warehouse process, 3.

Defining the Problems, 4. Preparing the simulation process flowchart, 5. Implementing

the simulation process, 6. Run the model and gathering the results, 7. Validating the

Result, 8. Verification of the Model.

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Figure 1- Project Plan


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EXPECTED RESULTS This project hopefully is going to help H&M warehouse with the daily number of labors, daily

schedule of Restocking, and finding the best place for new items.

Results would be probably in the shape of tables and diagrams which would be gotten by sample

order lists.

THEORETICAL BACKGROUND

SIMULATION AS A TOOL WITHIN PRODUCTION AND LOGISTICS MANAGEMENT Simulation is the imitation of some real thing, state of affairs, or process. The process of

simulation involves building a dynamic model of a process or system, then performing what-if

analysis to see how changes would affect the actual process. And simulation in only used to

describe computer modeling; modeling is the general term that contain both simulation and

modeling. Then the benefits for using simulation in production and logistics are faster change

implementation and reduce lead time, eliminating waste and bottlenecks, increase capacity and

also better resource utilization and reduce operating costs and lower capital expenditure.

DISCRETE EVENT SIMULATION Discrete event simulation is a methodology that based on assumption of the changing of

simulation at discrete time interval by using a mathematic or logical model of a physical system

to investigate the changing at specific point in simulated time. There are many components that

use in discrete event simulation model:

- Clock: The simulation has to keep track of the current simulation time, in whatever

measurement units are suitable for the system being modeled.

- Queue: Items wait here for downstream capacity. We can assign the length of queue and

calculate the waiting costs. We can select the 2D and 3D animation of item also.

- Set: It assigns properties to the items passing through. We have to list down the

properties, in our case we list down the page number and process time as properties.

- Equation: Compute an equation when an item arrives and outputs the results. For this we

have to assign input variables and output variables.

- Resource Pool: It actually store resources for use in the model. Assign the number of

resources (number of warehouse workers), allocation resources to qualified items by

logics such as FIFO (first in first out). We can also assign the cost of resource or cost per

use as well.

- Random Number: Generates random integers or real numbers based on the selected

distribution


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- Read: Reads data from a database when items arrive.

- Get: Display the value of item properties. We have to select from the properties

(attribute, priority, etc) which ones value we need.

- Batch: Batch items into a single item.

- Resource Item: Stores items and allows them to be removed. We can assign the initial

number of items, its name and waiting cost and cost per usage.

- Activity: Processes one or more items simultaneously and outputs each item as soon as it

is finished. We define the capacity like maximum items in activity and specify the

processing time, processing cost.

- Unbatch: Output multiple items for each input item. We have to specify the quantity to

unbatch and how to define property values of outgoing items.

- Resource Pool Release: Releases the specified number of resource pool units.

- Conveyor Item: Move items on a conveyor from one block to another. We can define the

processing cost per unit time and cost per item.

- Plotter, Discrete Event: Display plots and tables of data for up to four value inputs for

continuous models. Can be used to input it results to another section of the model or to

another model.

3D MODELING 3D modeling is the procedure of developing a 3D model and mathematical representation by

using specialized software. It is a process of creating a wireframe model that represents a three

dimensional object. That object can be alive or inanimate. A three dimensional model is created

using a set of points in 3D space, which are connected by various geometric data such as lines,

and curved surfaces. Models may be created automatically or manually.

Today, 3D modeling is engaged in a wide variety of activities such as video game industry uses

various three dimensional modeling methods to create realistic character models for video

games. In medical field, detailed models of human body organs are created using various

advanced 3D modeling software. Engineering community uses 3D computer aided design CAD

programs to create three dimensional models of new devices, vehicles and structures.

Three dimensional models are created using four popular methods:

Polygonal modeling: is a method of creating a 3D model by connecting line segments through

points in a 3D space. These points in space are also known as vertices. Polygonal models are very

flexible and can be rendered by a computer very quickly. One cannot, however, create an exact

curved surface using polygonal 3D modeling technique.


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Primitive modeling: This is the simplest way of modeling three dimensional objects. Using

geometric primitives such as cylinders, cones, cubes and balls, complex models are created. This

approach ensures easy construction as the forms are mathematically defined and precise.

Primitive modeling is mainly used in developing 3D models of technical applications.

NURBS modeling: The NURBS (Non-uniform rational B-spline) modeling method can be found in

popular software like Maya. The developer can create smooth-surfaced 3D models using this

modeling technique. Unlike polygonal modeling techniques which can only approximate curved

surfaces using numerous polygons, NURBs modeling can truly create smooth curved surfaces.

Splines and patches modeling: These methods are similar to the NURBS modeling procedure.

They depend on curved lines to identify the visible surface.

PROCESS DESCRIPTION

PROBLEM AND REASON FOR SIMULATION In everyday H&M warehouse will receive orders from customers and lead time for all orders is 2

days. The amount of orders that the H&M warehouse receives in each day are unequal. In some

day permanent labours in the warehouse are not enough which this cause the H&M warehouse

has to calculate the number of labours in every day and hire temporary labours to pick up the

clothes in the warehouse.

For the picking up process; first of all the labours have to pick up the order lists at the desk

which the page number of order lists for each labour is 6 or the remained pages of current

customer. Usually each page has 30 items and each item could have any quantity. Figure 2 shows

a real order list.


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Inside the warehouse has 20 corridors and each corridor side will divide into 50 blocks (100

blocks in both sides) and each block has 4 levels then in one corridor will have 400 places for the

labours to pick up the clothes.(Figure 3)

Figure 2- An Order List


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While the number of order items is defined, calculating the number of required labours is not

easy since they stay in queues inside the corridors and calculating the waiting time is not easy.

So the first problem which this simulation model is going to solve is number of labours.

Figure 3- Warehouse Corridor


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The clothes quantity which is in each place, the stock, should be refilled during the day. But the

re-stock time is unclear and usually labours should check the place several times. Our simulation

model will help the warehouse to know approximately the Re-stocking time.

Another problem is to specify one place for the new cloth which comes to the warehouse.

Choosing the place among the possible idle places is important since it can affect the queue

length inside the corridors. Therefore our Model is going to help the H&M with this problem

also.

DATA COLLECTION The most important Data was the order lists which we generated them since it doesn’t play any

role in the gained results and the generated orders are probably the same as real orders.

Actually different customers select among existing clothes and this process is the same as

generating order lists.

Another input data was picking-up time which was calculated by Stop-Watch method and is 8

seconds/piece for B&C levels and 12 seconds/piece for A&D levels. It should be added that these

times included the transportation times inside the corridors.

MODEL

SIMULATION MODEL CONSTRUCTION The H&M Model consisted of nearly 6000 single blocks. Block types are mostly Extendsim

ordinary blocks which are modified to match the picking up process inside the warehouse.

The whole model contained 20 hierarchical blocks-called GANG-which represents the main

corridors of the warehouse. Each of those above-mentioned blocks consisted of 50 hierarchical

blocks-called PLATS- in which there are 6 single blocks that simulate two positions inside the

warehouse.

Single Blocks are H&M CatchItem, H&M SelectItemIn. H&M SelectItemOut, H&M ThrowItem,

H&M Queue, and H&M Activity which all were customized. A hierarchical block called H&M office

also contained the single blocks which represent the Desk and Conveyer inside the warehouse.

Figure 4 shows the WBS of Model.

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Figure 4- H&M Model WBS

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LOGICAL MODEL DESCRIPTION As figure 5 shows that the logical description of the Model are almost the same when a labour

thinks to pick up an order list in the real world.

As you can see in the figure 5, a labour should find at least 4 empty places sequentially in front of

corresponding place in order to go further. Otherwise s/he should wait in the queue until they

become free.

Figure5-Logical Model Description


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3D MODEL DESCRIPTION Actually 3D model of H&M warehouse wasn’t as effective as its 2D simulation since there is no

defined rate process inside the warehouse.

However in order to only show how labours work and how whole system proceed, a part of

warehouse and its picking up process depicted by 3D Max software.

In 3D Model 1 labour with his cart walks through different corridors in order to pick up clothes.

Although it is only two corridors shown in the model, process is the same for the others also. In

addition its whole warehouse 3D model would become much heavy if all the corridors were

shown.

Figure 6-3D Model Picture


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VALIDITY AND RELEVANCE OF THE MODEL Well this part is the Achill’s hill of this report since modeler did not have availability to the

source of warehouse information. Therefore some results of this report should ponder carefully!

First of all, the positioning of clothes inside the warehouse is not exactly the same as the reality.

Secondly the order list which used is not a real one and surely a real daily order list looks like

differently.

Thirdly only one sample was examined in this report and confidence level is quite low with only

one sample.

Fourthly the picking up time considered as deterministic while in reality it is stochastic.

Finally modeler could not compare the model results with reality. It could be a good idea for

further research. In addition there are still some bugs in the program. The given restock

information is sometimes false and didn’t match the real inventories. These faults were

happened in some special number of labors which shows that program has problem with this

test cases and should be debugged.

Figure 7-3D Model Picture


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SIMULATION

DESCRIPTION OF SIMULATION This Model was developed in order to solve three main problems: firstly the daily number of

labors, Secondly the restock schedule and thirdly finding the best position for the new item in

the warehouse.

It was supposed that there are 80 items in each place at the beginning of the simulation and

whenever it needs to be replenished, 80 items would be added to the position quantity.

Model shows one department of H&M warehouse and a usual daily number, 30000 items was

supposed to be picked up. Although at the beginning the modeler thinks that it takes much time

and probably an enormous PC would be needed, it takes only a few minutes for 30000 items to

simulate the picking up process. Figure 6 shows the Model picture.

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Figure 8-H&M Simulation Model

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RESULTS FROM SIMULATION 1. Daily number of labors: Usually it is said that each labor can pick up 6 pages per hour;

however it completely depends on the quantity of Items in the list. In addition the queue

inside the corridors can affect it.

Here the simulation model was run for different number of labors in order to see the

result. A normal daily pick-up, 30000 was supposed to be done in 8 hours. If we want to

calculate the rough number of labors according to 6 Page/Hour rule, we would get 21! (It

was supposed that each page has 30 items.) But what happens in reality for our sample

order list?

The below chart shows the result of running simulation model for a range of labor

numbers (21-40) while it was always the same sample order list!

As it is shown in the chart, the required number of labors for this sample is around 34-35

labors which is obviously more than estimation!

Figure 9-Labor Chart


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Another interesting issue is the slope of the chart. This chart shows that although the

more labor the faster picking up process, the chart slope which is the time difference

decreases as the number of labors increases.

With the help of Excel a trend line was found to match with mentioned chart;

This exponential equation tells us that the slope of the diagram never gets Zero and still

there is time difference in high quantity of labors, but probably not worthy in

comparison with labor cost!

2. Restock Schedule: Another issue which this model can help with is replenishing times

during a day!

As simulation model runs, the place inventory would decrease and must replenish. The

simulation file also shows us at what time approximately shall restock. The following

restocking scheme is for example for a model with 34 labors.

Figure 10-Exponential trendline


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Field Names Tid Gang Plats Niva

1 1/1/2011 3:21 E 39 A

2 1/1/2011 3:45 H 39 B

3 1/1/2011 4:13 J 15 A

4 1/1/2011 4:27 B 10 B

5 1/1/2011 4:33 A 70 A

6 1/1/2011 4:39 H 32 A

7 1/1/2011 4:44 L 73 A

8 1/1/2011 4:46 C 30 B

9 1/1/2011 4:53 K 71 A

10 1/1/2011 5:10 F 16 A

11 1/1/2011 5:10 J 85 B

12 1/1/2011 5:15 M 23 A

13 1/1/2011 5:17 J 26 A

14 1/1/2011 5:27 F 50 A

15 1/1/2011 5:36 A 34 B

16 1/1/2011 5:37 G 10 B

17 1/1/2011 5:40 R 56 A

18 1/1/2011 5:43 K 52 B

19 1/1/2011 5:48 Q 62 A

20 1/1/2011 5:55 F 27 B

21 1/1/2011 6:00 G 15 A

22 1/1/2011 6:00 P 2 A

23 1/1/2011 6:01 P 78 B

24 1/1/2011 6:05 K 44 A

25 1/1/2011 6:06 N 56 A

26 1/1/2011 6:07 O 71 A

27 1/1/2011 6:09 M 43 A

28 1/1/2011 6:10 B 2 B

29 1/1/2011 6:14 I 54 B

30 1/1/2011 6:16 G 87 B

31 1/1/2011 6:16 S 67 A

32 1/1/2011 6:17 D 44 B

33 1/1/2011 6:21 S 77 A

34 1/1/2011 6:26 H 92 A

35 1/1/2011 6:26 J 70 A

36 1/1/2011 6:28 B 8 A

37 1/1/2011 6:28 M 81 A

38 1/1/2011 6:29 B 32 B

39 1/1/2011 6:29 R 1 A

40 1/1/2011 6:34 D 77 B

41 1/1/2011 6:36 F 57 B

42 1/1/2011 6:37 T 10 A

43 1/1/2011 6:38 M 64 B

44 1/1/2011 6:39 Q 3 A

45 1/1/2011 6:45 B 64 B

46 1/1/2011 6:49 A 30 A

47 1/1/2011 6:50 A 32 A

48 1/1/2011 6:50 E 71 B

49 1/1/2011 6:50 O 51 B

50 1/1/2011 6:55 A 42 A

51 1/1/2011 6:55 J 35 A

52 1/1/2011 7:00 K 59 A

53 1/1/2011 7:01 E 81 A

54 1/1/2011 7:02 N 56 B

55 1/1/2011 7:03 F 90 A

56 1/1/2011 7:03 N 14 B

57 1/1/2011 7:05 N 18 B

58 1/1/2011 7:07 B 80 A

59 1/1/2011 7:10 H 94 B

60 1/1/2011 7:10 J 65 A

61 1/1/2011 7:16 I 1 A

62 1/1/2011 7:20 N 92 B

63 1/1/2011 7:21 J 34 B

64 1/1/2011 7:21 S 5 A

65 1/1/2011 7:21 T 98 A

66 1/1/2011 7:24 M 35 B

67 1/1/2011 7:28 E 17 A

68 1/1/2011 7:28 L 16 A

69 1/1/2011 7:30 F 39 A

70 1/1/2011 7:31 E 24 A

71 1/1/2011 7:40 G 85 B

72 1/1/2011 7:41 E 43 A

73 1/1/2011 7:41 I 52 A

74 1/1/2011 7:45 H 41 A

75 1/1/2011 7:48 I 66 A

76 1/1/2011 7:55 P 82 B

Figure 11-Restocking Schedule


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3. Finding the best position for new items in the warehouse: There are always new clothes

which are coming to the warehouse and therefore empty places should allocate to them.

In addition there are always empty places inside the warehouse which could be an

option for new items. Hereinafter a number of clothes with high demands were relocated

in order to assess the time difference among different alternatives.

In summary, there are 8000 positions inside the warehouse while there are only 3600

types of clothes. The Modeler thought it would take less time if 10 of most demanded

clothes move to empty places in empty corridors, but the results shows vice-versa!

The following chart shows the result for two states, 32 and 33 labors, and as it depicted,

both take 5 minutes more!

Discussing about the reasons of this problem is more than the capacity of this report! It

seems another measurement than only demand must be considered for selection of the

items which should be relocated.

RESULTS AND CONCLUSIONS The mentioned model shows how simulation can facilitate the estimation for complex models

like a warehouse. With this model, assessment of different scenarios takes only a few minutes.

Here we talked about the daily number of labours, the restocking schedule and the best position

for new clothes.

Figure 12-Relocation Assessment


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There are still more issues which could be solved by this model. The effect of any relocation,

calculation of wasted time in the queues, finding the bottle neck in the warehouse and evaluation

of new solutions like having two places for same clothes and so on!

REFERENCES Erik Dahlquist, November 2006, Process technology and process simulation, Mälardalen

University.

Extendsim 8 Developer Reference, 2007, Imagine That Inc.

Extendsim 8 User Guide, 2007, Imagine That Inc.

Photos from H&M warehouse.

Report KPP319

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