Simulation Modelling of Warehouse Capacity Re-allocation to

Improve Inbound Logistics Performance: A Study Case

Muhamad Dinaryo, I Nyoman Pujawan, Niniet Indah Arvitrida

Institut Teknologi Sepuluh Nopember

Keywords: Discrete Event Simulation, Warehouse, Warehouse Capacity Re-Allocation

Abstract: The supply chain system in the fertilizer industry is quite complex because it involves many activities such

as internal logistic, material transportation processes, a number of factories, and warehouse management. The

process of raw materials handling that starting from the port to the warehouse is very influential on the

company's ability to carry out operational activities in the production department. To improve the

competitiveness of the company, the case company needs to take a number of strategic steps and to run a

transformation program for becoming an efficient, integrated, and sustainable fertilizer producer. The

problems that occur in the inbound logistics activities at the case company are the raw materials stored in

warehouses often overcapacity, causing a material accumulation when there is new raw material arriving at

the port. Therefore, warehouses need to periodically reorganize their storage in order to keep operating

inefficient manner. This research aims to help overcome the problems that occur in the company by examining

several alternative solutions in inbound logistics activities to reduce material transferred between port,

production warehouses, and buffer warehouses by using model simulation method. The method that is going

to be used to solve this problem is the Discrete Event Simulation (DES), in which the improvement system

does not disrupt the existing system and doesn't cost too much time and money. This research proposed a

scenario of warehouse capacity re-allocation for raw materials by prioritizing the highest frequency of raw

material arrival. By using the improved system, it reduces the total average volume of material transferred

from the port to production warehouses by 13.29%, from port to buffer warehouses by 10.21% and from

buffer warehouses to production warehouses by 17.43%.

1 INTRODUCTION

The supply chain system in the fertilizer industry is

quite complex because it involves many internal

logistics activities, raw material transportation, and

warehouse management. The costs absorbed by

activities in supply chain functions are generally very

dominant so that if the company has an efficiency

program, then the greatest improvement potential is

in activities related to supply chain functions. Thus,

the product can be produced and distributed in the

right amount and timely manner to minimize costs

and provide service satisfaction to consumers with the

aim of achieving a minimum overall cost of the

system and also achieving the desired service level

(Levi, 2000).

Successful supply chain management is a very

complex job because there are numerous players

involved, and each of them has different interests and

goals. Those conditions are a challenge that must be

faced by Pupuk Indonesia Holding Company as a

fertilizer producer on behalf of the Indonesian

Government, an agrarian country where the

agricultural sector plays an important rule that

influences the national economy. PT Petrokimia

Gresik is one of the largest Indonesian fertilizer

producers established under the auspices of PT Pupuk

Indonesia. PT Petrokimia Gresik is the complete

fertilizer producer in Indonesia, which produces

various kinds of fertilizers and chemical products for

agro-industrial solutions that have a cross-country

supply chain network and distribution throughout the

country.

The problem that occurs in the inbound logistics

activities of the case company is the raw materials

stored in warehouses are often overcapacity, causing

a material accumulation when there are new raw

materials arriving at the port. One indication that

causes overcapacity in warehouses is the presence of

several raw materials stored in warehouses that are

Dinaryo, M., Pujawan, I. and Arvitrida, N.

Simulation Modelling of Warehouse Capacity Re-allocation to Improve Inbound Logistics Performance: A Study Case.

DOI: 10.5220/0009403300120020

In Proceedings of the 1st International Conference on Industrial Technology (ICONIT 2019), pages 12-20

ISBN: 978-989-758-434-3

not intended. This has a negative impact because it

can cause the ship that has arrived at the port to have

to wait to unload the material.

Due to seasonal fluctuations in demand or high

inventory levels, initially assigned storage in the

warehouse may become suboptimal from time to

time. Fragmented storage is a particular issue, where

the optimal storage location of a product is not only

dependent on its turn-over rate but also on the storage

locations of items that frequently occur in the same

picking job (Kofler, 2010). Therefore, warehouses

need to periodically reorganize their storage in order

to keep operating inefficient manner.

Experiment with the operation of the real system

to study the effects of the change can lead to a more

efficient and advanced system than the current one.

But it is not a wise idea to implement the change

directly in the real system as it can cause unexpected

results, which ultimately disrupts the working of the

system. Therefore, a model is defined as a

representation of a system for the purpose of studying

the system. The model should be built so as to permit

valid conclusions to be drawn about the real system.

Sometimes, different models of the same system are

required to be built to study different aspects of the

real system.

2 LITERATURE REVIEW

This research is a development from some similar

previous researches. The previous researches are

mostly developed on the uncertain scope of inbound

activity. Some similar previous research only

discussed one or two of three the scope of inbound

logistics activities such as ports, warehouses, and

transportation. There is no research that combines

these three scopes of inbound logistic activities.

Therefore, to develop this research using three the

scope of inbound logistics activities such as ports,

warehouses, and transportation.

The purpose of this research is to find the best

alternative solutions in order to improve the

performance of the inbound logistic activity of the

case company. This research is used Discrete Event

Simulation (DES) because the improvement system

does not disrupt the existing system and does not cost

too much time and money. Besides that, DES can

provide some information about the behavior and

performance of real processes in the company and

predict the occurrence of the problem in existing

conditions.

2.1 Logistics and Supply Chain

Management (SCM)

Effective management of infrastructure, materials,

technology, and people is utterly needed to integrate

the flows between material, money, and information

because interruptions in any of the above four flows

affect an organization's raw materials supply

(purchasing), manufacturing (operations) and

marketing (distribution) functions. According to the

Council of Supply Chain Management Professionals

(2010), activities that can be managed in the supply

chain consist of planning, management, and

coordination from procurement, conversion, and

logistics management activities.

2.2 Inbound Logistics

Harrington (2008) explained that focusing on

logistics and inbound logistics planning gives firms

an opportunity for substantial savings and attains

supply chain reliability. Even though there is no

standard definition of inbound logistics, three

significant inbound logistics practices can be

identified, and these are transportation, inventory

control, and warehousing (Baker et al., 2008).

Inbound logistics practices are reliant on existing

infrastructure like warehouse building, terminuses,

highways, communication networks, and energy

supply amenities that need long-term investment

from both governments and the private sector. They

also involve long lead-times to develop and thereafter

maintenance investments over time, which determine

the quality of entire logistics operations (Blecker el

at. 2014).

2.3 System and Model

There are several ways to design, analyze, and

operate a system; one of those is by modeling the

system. System modeling is a very useful tool for

analyzing and designing systems. The model can

show how an operation of the system works and

analyze the problems and potential improvements

that can be made. Model is defined as a logical

description of how the system works or the

components interact. By making a model of a system,

it is expected to be easier to carry out analysis (Law

and Kelton, 1991).

Simulation Modelling of Warehouse Capacity Re-allocation to Improve Inbound Logistics Performance: A Study Case

Figure 1: Various Ways to Learn System

(Law and Kelton, 1991)

Figure 1 shows an overview and an explanation

about how to study the system. The system in this

research can be explained as Experiment Using

Actual System vs. Experiment Using Model of

System. It means if a system physically allows and

does not cost a lot to operate with the conditions

(scenario) that we want, then this method is the best

way because the results of this experiment really fit

the system. But those systems rarely exist, and

stopping the operation of the system for experimental

purposes will be very costly. In addition, for systems

that still not yet exist or systems that are still in

design, experiments with actual systems clearly

cannot be done, so the only way is to use the model

as a representation of the actual system.

2.4 Model Simulation

2.4.1 Discrete Event Simulation (DES)

DES (Discrete Event Simulation) is the process of

codifying the behavior of a complex system as an

ordered sequence of well-defined events. In this

context, an event comprises a specific change

system's state at a specific point in time. DES

modelers often invest a great deal of effort analyzing

historical data to capture process means, variances,

and distributions, but once entered into the model,

these parameters often remain fixed. There is less

emphasis on DES models on identifying events that

might trigger changes in the model's parameters.

The experiment requires the operation of the real

system to study the effects of the change, which can

lead to a more efficient and advanced system than the

current one. But it is not a wise idea to implement the

change directly in the real system as it can cause

unexpected results, which ultimately disrupts the

working of the system. A model is defined as a

representation of a system for the purpose of studying

the system. The model should be built so as to permit

valid conclusions to be drawn about the real system.

Sometimes, different models of the same system are

required to be built to study different aspects of the

real system.

A discrete-event simulation model is both

stochastic and dynamic with the special property that

the changes occur at discrete times only. A stochastic

simulation model has one or more random variables

as inputs. Random inputs lead to random outputs.

Since the outputs are random, they can be treated only

as approximations of the true characteristics of a

model.

2.4.2 Arena

The arena is a model builder program and also as a

simulator. The arena is a mixture of two categories, a

combination of ease of use that is owned by high-

level programs and flexibility that characterizes

general-purpose simulation language. Arena users

can build models, templates and can even create their

own modules if needed using the help of programs

such as Visual Basic, FORTRAN, and C / C ++.

2.4.3 Verification and Validating

The verification and validation process is a simulation

phase that is carried out to test whether the simulation

model and the conceptual model have represented the

system in its actual state. In the process of converting

conceptual models into simulation models, there are

many possible errors.

2.5 Similar Research

The previous researches are mostly developed on the

uncertain scope of inbound activity. Some similar

previous research only discussed one or two of three

the scope of inbound logistics activities such as ports,

warehouses, and transportation. There is no research

that combines these three scopes of inbound logistics

activities, such as:

1. Jean Philippe et al., 2007. “A Simulation

Model to Improve Warehouse Operations."

The scope is the warehouse. Develops a DES

model of the logistic operations that could help

managers in assessing the performance of

different storing and picking strategies and

evaluate the specific strategies to share the

storage space.

2. Lauri Lattila, 2012. “Improving Transportation

and Warehousing Efficiency with Simulation-

Based Decision Support System." Scopes are

transportation and warehouse. Argued that

ICONIT 2019 - International Conference on Industrial Technology

simulation-based decision support systems

need to take various issues into account to

make a functioning decision support system.

3. Mariam Kotachi et al., 2013. “Simulation

Modelling and Analysis of Complex Port

Operations with Multimodal Transportation."

The scope is port. Analyze the complex

operations involved, as well as the utilization

of resources. The outcome of the port flow is

various scenarios motivated by changes in

different inputs to measure their impact on the

outputs that include throughput, resource

utilization, and waiting times.

This research is a development from some similar

previous researches and discussed three the scope of

inbound logistics activities such as ports, warehouses,

and transportation. This research is to find the best

alternative solutions in order to improve the

performance of the inbound logistic activity of the

fertilizer company.

3 RESEARCH METHODOLOGY

3.1 Preliminary Study Phase

The preliminary study is performed for studying the

system element in the research object. The system

elements are system objectives, system boundaries,

input and output, and system activities.

3.2 Data Collection & Processing Phase

The collection of data is done to support the

conducted research. Data is collected through

historical data of the case company, interviews with

related parties, and supporting sources on the internet.

All data that has been collected then processed to

find the type of distribution fitting. The results of this

data processing are the results of distribution fitting

for material arrival and material transfer. Numerical

data that need to be done with the fitting distribution

before became the input variable are the inter-arrival

time of raw material in port, raw material supply, and

raw material demand. Fitting distribution needed to

describe the uncertain event, so the distribution will

describe the frequencies of future repetitions of the

experiment. Fitting distribution is done by input the

two years of historical data into the input analyzer in

Arena Software. The distribution will follow the best

fit distribution or the theoretical distribution, which

has been used before for certain conditions.

3.3 Simulation Phase

At this stage, the design of the conceptual model and

simulation model is designed, which describes the

actual process activities. The real system is built into

a model through several processes.

3.3.1 Conceptual Model Development

A conceptual model is made in accordance with the

current conditions of ship unloading system at the

Port of company case, the transportation scheme of

raw materials to storage warehouses, and material

transfers that occur between production warehouses

and buffer warehouses. This conceptual model will

later be used as a reference for the simulation model

carried out on Arena software.

3.3.2 Simulation Model Development

This research uses Arena Software to run the

simulation. Based on the modules chosen in the

simulation model built, the data that has been

collected acts as the input in the development of the

model.

3.3.3 Verification

There are three ways to do the verification process.

First, debug the model to know whether the model has

a syntax error. Second, by observing the animation

movement between models. Third, check the output

reasonableness from the simulation result.

3.3.4 Number of Replication Calculation

The number of replications and half-width should be

determined so that the simulation results in a good

estimation interval that is convincing for decision

making. Determining the number of replications is

started by setting the initial number of replication (n).

After the result of running n replications is obtained,

the half-width (hw) should be calculated. Then the

desired half-width (hw’) should be calculated by

setting the absolute error (e) to be compared later.

3.3.5 Validation

Validation is done by comparing the results of the

demand fulfillment material in the simulation running

model with the historic data of 2017-2018 demand

fulfillment material obtained from the Port

Department. Paired-t-test is done to know whether the

real system and simulation model are not significantly

Simulation Modelling of Warehouse Capacity Re-allocation to Improve Inbound Logistics Performance: A Study Case

different so that the simulation model can be declared

as valid.

3.3.6 Scenario Design and Run Experiments

This research proposes a scenario for improvement

that will be used as a recommendation for

improvement in the systems. The scenario made into

a condition to prioritizing the allocation of materials

that have the highest frequency of arrival.

The scenario is developed by taking into account

the frequency of raw materials arrival. Raw materials

that have the highest frequency of arrival will be

prioritized to be stored in the nearest Production

warehouse, and if possible, it not stored in buffer

warehouses.

3.3.7 Output Analysis

The output result of the scenarios is calculated as the

total volume of material transferred. Then, Paired-t-

test or ANOVA hypothesis analysis is used to know

whether the scenario shows a significant difference

with the existing condition. Paired-t-test or ANOVA

hypothesis testing analysis is performed based on the

number of population or scenario compared.

4 MODEL

CONCEPTUALIZATION

4.1 Overview of Inbound Logistic

Process

The raw material used to produce fertilizer is

Phosphate Rock High Grade (PR HG), Phosphate

Rock Medium Grade (PR MG), Red KCl, White KCl,

DAP, and ZA. When raw materials arrive at the port,

the company receives raw materials and carries out

unloading operations using port facilities. After being

dismantled, the raw materials are then distributed

using transportation modes in the form of conveyors

and trucks. The choice of transport mode prioritizes

the use of a conveyor. Since not all warehouses are

connected with conveyors and if the conveyor line

has been used to transport another raw material, then

the transportation mode used in the truck.

The raw material will later be transported to the

production warehouse in each production unit to be

used as a basic material in the fertilizer production

process. The Production Warehouse, which is located

directly in contact with the factory, has the highest

priority to receive the arrival of raw materials from

the port. If these Production Warehouses are unable

to accommodate the incoming raw materials, the

Buffer Warehouse is used as a second-choice

destination for raw materials that cannot be

accommodated in the Production Warehouse. The

detailed process of raw material handling at inbound

logistics in case the company is shown in Figure 2.

Figure 2: Raw Material Handling Process Flow

ICONIT 2019 - International Conference on Industrial Technology

4.2 Element of System

Elements of systems include entities, activities,

resources, and controls.

1. The entity used in this system is the raw

material used for fertilizer production. The type

of raw material are, Phosphate Rock High

Grade (PR HG), Phosphate Rock Medium

Grade (PR MG), Red KCl, White KCl, DAP,

and ZA.

2. Activities observed in the system are systems

for raw materials handling from unloading in

ports, transportation from ports with conveyors

or trucks, to placement in raw material

warehouses.

3. The resources used in this system are facilities

at the port (material unloading facilities;

Kangaroo Crane and CSU), transportation

modes (conveyors and trucks) and raw material

warehousing facilities

4. The control used in this handling system is the

assignment of allocating material carried out

from port to the raw material warehouse, which

has the highest availability of space and

readiness by considering the volume

transported.

4.3 Variable of Systems

The variables of systems in this research's simulation

consist of decision variables and response variables.

More detailed, as seen in Table 1.

Table 1: Variables System for Raw Material Handling

Scope

Decision

Variables

Response

Variables

Transporting to

Warehouses

Raw Material

Allocation

Volume

The volume of

Transferred

Raw Materials

Warehouse

Allocation

5 SIMULATION MODEL AND

ANALYSIS

5.1 Model Verification

The model's verification implies that the simulation

model is error-free, and according to the conceptual

model, the logic is right. To check the syntax error in

the simulation model is by debugging in the software.

The verification of semantic error is done by

verifying whether the logic in the model is in

accordance with the logic it is supposed to be.

Checking the reasonableness in the simulation

report in the research is done by checking the total

demand fulfillment in the factory for two years, where

it must be less than or equals with total entities created

by creating a module as the factory demand.

Another way to check for reasonableness in the

simulation report is to see if there's an entity that

needs to be allocated to one of the production

warehouses, it should be allocated to the one that is

the nearest one from the port. In a simulation report,

it can be seen from the results in two years that there

is no allocation of entities in the production

warehouse that skips the order of the nearest

warehouse. For example, red KCl is allocated in

warehouse 09A.650, 09B.650, and 02.650 for

production warehouse IIA, arranged from the nearest

one, respectively. It is impossible if in two years total

red KCl entities are allocated in warehouse 09B.650

is equal to zero while the total red KCl entities

allocated in warehouse 02.650 is more than zero.

5.2 Number of Replications

The number of replications is determined to know

how many replications are needed in the simulation,

so the simulation results will represent different

patterns of supply and demand every day. The method

used to determine the number of replications in this

research is using the total demand fulfillment of raw

material. In this research, the method that is used to

determine the number of replication is an absolute

error with α = 5%.

The simulation is run with ten replications (n) and

resulting the total demand fulfillment for one year in

ten replications. Then the half-width of the result of

ten replications simulation is calculated with equation

1 for all raw materials with hw’ has the desired error

value does not exceed 5% from historical data as

shown at equation 2. The number of replications (n’)

is calculated with equation 3. The result of the

calculation can be seen in Table 2.

Based on the calculation in Table 2, every raw

material has a different value of n’. To fulfill the

requirement for every raw material, then the highest

value of n’ is used as the number of replications for

simulation. Therefore, the number of replications

used in this research is 10 replications.

𝒉𝒘

𝒕

𝑵𝟏,

𝜶

𝟐



𝑺

√

𝒏

(1)

𝒉𝒘



𝟓% total demand fulfillment

(2)

Simulation Modelling of Warehouse Capacity Re-allocation to Improve Inbound Logistics Performance: A Study Case

𝒏







𝒁

𝜶

𝟐



𝑺

𝒉𝒘′



𝟐

(3)

Table 2: Result of Demand Fulfilment Simulation with 10 Replications

Replication

Demand Fulfillment (Tons)

DAP Red KCl

White

KCl

PR HG PR MG ZA

IIB IIA IIB IIB IIIB IIIA IIA IIA IIB

Existing 182,586 318,630 249,964 84,552 538,697 501,567 294,716 251,833 190,140

1 186,600 330,700 254,800 85,150 472,550 516,250 313,650 257,200 192,700

2 171,500 333,200 251,150 87,100 479,900 509,700 296,350 208,700 160,400

3 168,350 301,550 246,100 90,000 526,100 481,500 301,650 250,900 193,500

4 179,450 324,550 252,600 85,650 512,400 504,250 295,050 239,600 178,650

5 183,050 296,850 226,150 90,450 543,300 505,900 256,250 250,050 178,750

6 166,250 323,650 245,150 80,950 481,900 509,600 296,300 238,900 180,500

7 165,650 323,100 248,350 80,050 557,250 528,350 288,100 254,250 190,800

8 178,000 286,000 252,700 84,300 553,200 511,300 248,800 252,650 182,850

9 147,950 264,400 232,100 84,100 530,750 481,750 292,300 254,000 185,100

10 194,150 323,450 242,800 80,200 543,800 464,650 275,150 258,600 194,300

Average 174,095 310,745 245,190 84,795 520,115 501,325 286,360 246,485 183,755

Standard

Deviation

13,076 22,636 9,366 3,718 31,834 19,270 20,369 14,837 10,250

hw 0.05 0.05 0.03 0.03 0.04 0.03 0.05 0.04 0.04

hw' 9,129 15931.48 12498.08 4227.60 26934.84 25078.33 14735.80 12591.66 9506.99

n’ 10 10 3 4 7 3 10 7 6

5.3 Validation

The method used for validation is using Paired-t-test

hypothesis testing with a parameter used for

validation is the demand fulfillment of each raw

material in each factory. The demand fulfillment of

each raw material in each factory considered can

capture the whole behavior of the system, starts from

the supply up to the demand. The recapitulation of

total raw material demand fulfillment in factories is

presented in Table 2.

In this hypothesis testing Paired-t-test, the null

hypothesis is that there is no significant difference

between the average results of the simulation results

with the existing condition. The alternative

hypothesis is that there is a significant difference

between the average results of the simulation results

with the existing condition.

H0: 𝝁

𝟏

𝝁

𝟐

H1: 𝝁

𝟏

𝝁

𝟐

The recapitulation of hypothesis testing using the

Paired-t-test is simplified in Table 3. Based on the

result of hypothesis testing, the null hypothesis is

accepted because the t stat falls between the range -

2.101 < t Stat < 2.101. Such a condition shows that

there is no significant difference between the actual

and the simulation system.

Table 3: Validation Hypothesis Testing Result

Validation

Parameter

t Stat

t Critical two-

tail

Conclusion

Lower

Limit

Upper

Limit

DAP at IIB -2.054 -2.101 2.101 Accept Ho

Red KCl at

IIA

-1.101 -2.101 2.101 Accept Ho

Red KCl at

IIB

-1.611 -2.101 2.101 Accept Ho

White KCl

at IIB

0.207 -2.101 2.101 Accept Ho

PR HG at

IIIB

-1.864 -2.101 2.101 Accept Ho

PR HG at

IIIA

-0.040 -2.101 2.101 Accept Ho

PR MG at

IIA

-1.297 -2.101 2.101 Accept Ho

ZA at IIA 0.104 -2.101 2.101 Accept Ho

ZA at IIB -1.970 -2.101 2.101 Accept Ho

ICONIT 2019 - International Conference on Industrial Technology

6 SCENARIO DEVELOPMENT

AND ANALYSIS

6.1 Scenario Development

The proposed scenario in this study aims to minimize

the total transferred volume of raw material that

occurs between the warehouses, especially in the

buffer warehouses, by finding the best configuration

of raw material composition. Scenarios are developed

by taking into account the variables that cause the

accumulation of raw materials when in port,

transportation, and storage. This is in the spotlight

because to place raw materials in buffer warehouses

requires a considerable distance of transportation

while the production warehouse capacity is

insufficient to accommodate all existing raw

materials. The scenario condition design in this

research is by prioritizing raw material movement

based on the highest frequency of shipment arrival.

The scenario is developed by taking into account

the frequency of raw materials arrival. Raw materials

that have the highest frequency of arrival will be

prioritized to be stored in the nearest Production

warehouse, and if possible, it not stored in buffer

warehouses. Table 4 shows the rank of the material's

arrival frequency until August 2018.

For scenario development, Phosphate Rock High

Grade must be prioritized to be stored in the nearest

production warehouse from the port and has a larger

storage portion than the other raw materials. The raw

material in the next priority then placed in the nearest

production warehouse, which still has storage

capacity for its type as the previous scenario

adjustment method. Table 5 shows the new raw

material allocation.

Table 4: Rank of Raw Material Arrival Frequency

Arrival Frequency

Phosphate Rock HG 66

ZA 38

Red KCl 35

DAP 13

Phosphate Rock MG 12

White KCl 5

Table 5: New Raw Material Capacity Allocation

6.2 Scenario Significance Test

A significance test between the existing system and

scenario is performed to provide better decision

making on the selected scenario. Simulation of

scenario resulting in 3 parameters of volume

transferred material, which will be tested by using

ANOVA (Analysis of Variance). ANOVA Test is the

method used to compare the scenarios with

confidence level 95%. This significance test is to use

the hypothesis statement as follows.

H0: 𝜇



𝜇



H1: 𝑎𝑡 𝑙𝑒𝑎𝑠𝑡 𝑜𝑛𝑒 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛

𝑖𝑠 𝑠𝑖𝑔𝑛𝑖𝑓𝑖𝑐𝑎𝑛𝑡 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡

The result of the parameters significance test

between existing conditions and scenarios can be seen

in Table 6. It shows that the F value is larger than F

critical only on Parameter 1. It can be said, there is at

least one population is significantly different in

Parameter 1. Meanwhile, for Parameter 2 and 3, there

is no significant different. Therefore, scenario can

only be performed for Parameter 1

Dept Warehouses

New Ca

acit

Allocation

(

Tons

)

(

)

(

)

Red KCl White KCl ZA DAP

IIA 09A.650

16,000

IIA 09B.650

2,000 23,000

IIA 02.650

7,000 9,000

IIA PF-I

40,000

IIB Z

500

IIA CURING PF-I

4,000

IIB CURING PF-II

3,197 4,803

IIIA PA 110,000

IIIB DOME 50,000

IIB NP

-I & 2

14,000 2,000 2,000

Buffe

PF-II

45,000

Buffe

BS BELERANG

8,000

Buffe

BUFFER A 10,850 12,934 24,216

Buffe

BUFFER B

19,262

19,301

Buffe

BUFFER C

32,500

Simulation Modelling of Warehouse Capacity Re-allocation to Improve Inbound Logistics Performance: A Study Case

Table 6: Parameter Validation Hypothesis Testing Result

6.3 Scenario Analysis

The scenario model is developed by modifying the

raw material allocation in each production warehouse

and buffer warehouse. There is an obvious interest in

identifying the best out of a group of scenarios by

inspecting the mean results for each scenario. Table 7

shows that the proposed scenario gives the decreasing

result for the mean value for material transferred from

buffer warehouse to production warehouse.

Table 7: Comparison of Mean Value

Parameters

3 2 1

Existing 2,012,385 1,665,415 425,130

Scenario

1,744,895 1,495,345

351,050

-13.29% -10.21% -17.43%

The proposed scenario shows the minimum result

of the total volume of materials transferred under the

same condition of supply, demand, and material

handling equipment resources are available. The

scenario might show different performances if the

input of the system changes.

7 CONCLUSIONS AND

RECOMMENDATIONS

7.1 Conclusions

Based on calculations and discussions that have been

done, it can be concluded that:

1. The best warehouse capacity allocation is by

prioritizing the raw material that has the highest

frequency of shipment arrival to be allocated in

the nearest production warehouse and nearest

buffer warehouse from the port.

2. The improvement in warehouse allocation is

reduced an average of 17.43% of raw material

transferred from buffer warehouse to

production warehouse compared to the existing

condition.

3. The improvement in warehouse allocation has

reduced an average of 10.21% of raw material

transferred from the port to buffer warehouse

compared to the existing condition.

4. The improvement in warehouse allocation is

reduced an average of 13.29% of raw material

transferred from port to production warehouse

compared to the existing condition.

7.2 Recommendations

Recommendation for future similar research are:

1 Consider the improvement based on the cost

that occurs during the material transferred

activity.

2. Develop scenarios by investing additional

handling equipment such as conveyor line and

discharging equipment at the port.

REFERENCES

Blecker, T., Kersten, W., and Ringle, W. (2014). Innovative

Methods in Logistics and Supply Chain Management.

1st Edition. Hamburg, Berlin.

Council of Supply Chain Management Professionals.

(2010). Supply Chain Management Terms and

Glossary.

http://cscmp.org/sites/default/files/user_uploads/resour

ces/downloads/glossary.pdf. Lastly visited October 8th

2018.

Harrington, L. (2008). The Evolution of Inbound Logistics-

Choosing Inbound: Natural Selection.

http://www.inboundlogistics.com/cms/article/the-

evolution-of-inboundlogistics-choosing-inbound-

natural-selection/. Lastly visited October 8th 2018.

Kofler, M., Beham, A., Wagner, S., Affenzeller, M., and

Reitinger, C. (2010). Reassigning Storage Locations in

a Warehouse to Optimize the Order Picking Process.

Technical Report HEAL-2010-181. Rusia.

Law, A.M., and Kelton, W.D. (1991). Simulation

Modelling and Analysis. 2nd Edition. McGraw-Hill

International.

No Paramete

F F crit Conclusion

Material

Transferred from

Buffer

Warehouse to

Production

Warehouse

4.467 2.579 Reject Ho

Material

Transferred from

Port Warehouse

to Buffer

Warehouse

2.033 2.579 Accept Ho

Material

Transferred from

Port Warehouse

to Production

Warehouse

2.079 2.579 Accept Ho

ICONIT 2019 - International Conference on Industrial Technology