CHAPTER 3
RESEARCH METHODOLOGY
3.1
Research Design
Table 3.1 Research Design
Research
Type of Research
Unit Analysis
Objective
O-1
Time
Horizon
Descriptive &
Inventory of Raw
Longitudinal
Explorative
Material
– Time
Series Data
O-2
Descriptive &
Inventory of Raw
Longitudinal
Explorative
Material
– Time
Series Data
O-3
Descriptive &
Inventory of Raw
Longitudinal
Explorative
Material
– Time
Series Data
Source: Author, 2013
O-1: To know the demand of the product last year and forecast the demand of
the product in the next period (Naive Method, Moving Average, Weighted
Moving Average, Exponential Smoothing, Exponential Smoothing with
Trend, Trend Projection).
O-2: To know the stock of raw materials and set the optimum stock of raw
materials to avoid stock-out (EOQ method).
O-3: To know the inventory cost and minimize the cost of inventory in the
next period.
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3.2
Measurement Instrument
Table 3.2 Measurement Instrument
Variable
Forecasting
Inventory
Control
Sub-Variable
Indicator
Forecast using
Naive Method,
Moving Average,
Weighted Moving
Average,
Exponential
Smoothing,
Exponential
Smoothing with
Trend, Trend
Projection
-
Compare MAD and
MSE values each
method. The best
forecasting method
show the smallest
MAD and MSE
values.
Economic Order
Quantity (EOQ)
-
Total amount
needed.
-
Total amount of
optimum purchasing.
Reorder Point
(ROP)
-
Total amount of
inventory
(maximum) and
reorder point.
Safety Stock
-
Stok available in
order to avoid the
stock-out.
-
Leadtime
-
Average usage of
raw material
Source: Author, 2013
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3.3
Research Data Sources
In this research the data sources divided into two, the first one is
primary research and secondary research, explain as bellow:
1. Primary Data Research
Primary Research Data is a data that collected through interview with the
person who contributed in the company and survey/observation at
PT.Indo Pangan Lestari.
2. Secondary Data Research
Secondary Research Data include the information that already collected to
covered the primary data. This secondary research data collected by
journal, library studies, and website that relevant with this research.
3.4
Data Collection Techniques
Data Collection Techniques in this research divided into two, the first
one is library research and the second one is field studies.
1.
Library Research
The purpose of this library research is to collect the data through books,
literatures, magazines, website, and some articles that support the object
of this research, this research used to do the literature review in this
thesis research.
2.
Field Studies
The purpose of this field studies is to collect the data that happened in
the company. Data collection techniques can be done by doing the
following steps:
a.
Observation/Survey
Observation/Survey is one of data collection technique which is do some
observation/survey directly to the company’s activities that are related
with the objective of this thesis research.
b.
Interview
Interview is one of data collection technique which isdo the conversation
between two or more people where questions are asked by the
interviewer to elicit facts or statements from the interviewee.
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3.5
Company Background
PT.Indo Pangan Lestari is a company which located in Tangerang.
PT.Indo Pangan Lestari is a company which produce Jelly stick. Customer of
PT.Indo Pangan Lestari for the local market has already covered almost the
entire area of Indonesia except east area of Indonesia. According to the table
below that the demand of jelly stick until right now is very dynamic and high.
It means that PT.Indo Pangan Lestari must think the strategy to fullfil the
customer demand of the jelly stick product, from raw material availability
until the production process. The problem of PT.Indo Pangan Lestari now is
they never forecast the demand in the next period, it can result to the
overstock and stock-out. The overstock condition can push to the increasing
costs of the company and the stock-out condition can push to the switching
cost, where the customer try to find another competitors product. Because of
PT.Indo Pangan Lestari never forecast the demand in the next period, they
definitely don’t know the optimal quantity of raw materials that they must
order and also the re-order point of raw material. All of this factors can
impact to the production process of PT.Indo Pangan Lestari. By knowing the
demand in the next period, optimal quantity of raw materials, and re-order
point, PT.Indo Pangan Lestari can reduce the total inventory cost and any
other cost. Beside that PT.Indo Pangan Lestari also can increase their market
share and avoid the customer dissatisfaction.
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Table 3.3 Demand of Jelly Product in PT. Indo Pangan Lestari
Month
Demand (pcs)
December, 2010
140.400
January, 2011
146.800
February, 2011
151.200
March, 2011
165.600
April, 2011
159.500
May, 2011
160.000
June, 2011
160.500
July, 2011
157.600
August, 2011
150.000
September, 2011
146.500
October, 2011
149.500
November, 2011
160.000
Source : PT.Indo Pangan Lestari, 2011
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Table 3.4 Demand of Jelly Product in PT. Indo Pangan Lestari
Month
Demand (pcs)
December, 2011
144.000
January, 2012
148.500
February, 2012
151.000
March, 2012
164.500
April, 2012
162.000
May, 2012
156.000
June, 2012
180.000
July, 2012
161.500
August, 2012
150.000
September, 2012
155.500
October, 2012
146.000
November, 2012
144.000
Source : PT.Indo Pangan Lestari, 2012
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Table 3.5 Demand of Jelly Product in PT. Indo Pangan Lestari
Month
Demand (pcs)
December, 2012
145.600
January, 2013
168.800
February, 2013
151.600
March, 2013
158.400
April, 2013
162.400
May, 2013
156.800
June, 2013
191.200
July, 2013
196.800
August, 2013
171.600
September, 2013
166.400
October, 2013
160.000
November, 2013
164.000
Source : PT.Indo Pangan Lestari, 2013
Figure 3. 1Graph of Jelly Demand (2011-2013)
Source: PT. Indo Pangan Lestari, 2011-2013
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Based on the chart above, the demand of the product is fluctuative and
high. As we can see on July 2013, the chart show the highest demand, but on
the August 2013, the demand decrease significantly. That similar condition
also happened on 2012. From that situation, the author is interesting to
discuss why it can happened and provide a model of solution of this problem.
3.6
Optimization Model
Based on the condition of PT.Indo Pangan Lestari now, the suitable
optimization model is inventory model. The result of implementing the
inventory model are production cost efficiency and the optimal amount order
quantity of product. In this research, the author decided to use Quantitative
Method for Windows 2 software, to calculate the cost efficiency, order
quantity, and forecast the demand of the product in the next period.
Quantitative Method for Windows 2 is a software that can be used to
calculate the inventory, forecasting, transportation, waiting lines, statistic,
simulation, decisions analysis, assignment, break-even, game theory, goal
programming, integer programming, linear programming, markov analysis,
material requirements planning, mixed integer programming, networks,
project management, and quality control. Quantitative Method for Windows 2
is one of a common helpful software to calculate any other activity.
In this research, the author is using several method such as
Forecasting method with Time Series Analysis and Inventory method. In
order to analyze the data in this research, there are several steps:
1. Calculate the demand of the product in the next period.
2. Make the appropriate inventory model, so that PT.Indo Pangan
Lestari can set the optimal order quantity of raw materials that can
be used in the production activity, set the re-order point, set the
safety stock to avoid the stock-out, and minimize their cost.
In order to forecast the demand in the next period, the author collect
the data from 2011 until 2013. The author use Quantitative Method for
Windows 2 to forecast the demand in the next period. There are several steps
to forecast the demand of the product in the next period, as below:
1. At the main menu choose: Module.
2. At module menu choose: Forecasting.
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3. Choose file – new – time series analysis.
4. Input the title: the title show result at the output of this software.
5. Input the Number of Past Period: Based on the data that have
collected before.
6. Input Row Names: It can be number, month, alphabet, or anything
else.
7. Input the Data: In this research the data is the past demand of the
product (2011 until 2013).
8. Choose the method.
9. Click solve button.
As below, there is a forecasting model demand of the product
Table 3.6 Forecasting Model
Month
Demand (box)
December
January
February
Maret
April
Mei
June
July
August
September
Oktober
November
Source: QM for Windows
In Quantitative Method for Windows 2, there are 3 basic method, which are,
Time Series Analysis method, Least Squares – Simple and Multiple
Regression, and Regression Projector.
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Figure 3.2 QM for Windows (Forecasting)
Source: QM for Windows
In each method, there are also a several method that can be used to calculate a
Quantitative Method for business activity. In this research, the author used
Time Series Analysis basic method. In Time Series Analysis basic method,
the author used several method such as, Naive Method, Moving Average,
Weighted Moving Average, Exponential Smoothing, Exponential Smoothing
with Trend, and Trend Projection.
Input the data into the forecasting method in Quantitative Method for
Windows 2
Figure 3.3 Create Forecasting Data
Source: QM for Windows
In column title fill the title of forecasting. At the left column “Number of Past
periods” is the amount of data collected in the past periods. At the right
column is to show the type of the data. In this research, the author choose
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month (January, February, March, April, …) because the data that the author
want to input is the data in previous month.
Figure 3. 4 Input Data
Source: QM for Windows
At the right column, input the data of product sales in the previous periods,
that already collected from the company.
Figure 3.5 Forecasting Results
Source: QM for Windows
The picture above show that the result of forecasting calculation using
Quantitative Method for Windows 2. Next period row show that the result of
forecasting calculation in the next period. The picture above also show the
forecasting error.
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Figure 3.6 Forecasting Method
Source: QM for Windows
The picture above show that the several method in Time Series Analysis
(forecasting) in Quantitative Method for Windows 2.
The next step is calculate the optimal order quantity using inventory method,
to calculate the inventory model, the author use Quantitative Method for
Windows 2. There are some steps as below:
1. At main menu choose: Module.
2. At module menu choose: Inventory.
3. Choose file – new – Economic Order Quantity (EOQ) model.
4. Input the title: the title show result at the output of this software.
5. Checklist the Compute Reorder Point
6. Input the data: demand rate, setup/ordering cost, holding cost, unit
cost, days per year, lead time, safety stock, and daily demand rate.
7. Click solve button.
Inventory method in Quantitative Method for Windows 2 starts with
choosing the model.
Figure 3.7 QM for Windows (Inventory)
Source: QM for Windows
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In this research, the author using Economic Order Quantity (EOQ) model.
Figure 3.8 Create Inventory Data
Source: QM for Windows
In column title fill the title of Economic Order Quantity (EOQ) model.
Choose the compute reorder point.
Figure 3.9 Input Data
Source: QM for Windows
Input the data at Value column. The data include: demand rate, setup/ordering
cost, holding cost, unit cost, days per year, lead time, safety stock, and daily
demand rate. After input the data correctly, click solve button, then the
optimal order quantity and the company cost result will come up.
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3.7
Implication Design of Solution
In this research, the author use several method to calculate and to
know the result of the demand in the next period, optimum order quantity,
and cost efficiency of PT.Indo Pangan Lestari. In this forecasting method,
there are 6 different method and to make the decisions of the result, there are
some criteria in each method that used in this research. As below, there are
the criterias that will be used in each method.
-
In order to forecast the demand of the product in the next period,
the company use forecasting method. In the forecasting method, there
are 6 method, and each method show the different number of the
result. To consider to make the decisions, the author will find out the
smallest error values to get the result of the demand in the next period.
This result will help the company to forecast the demand of the
product in the next period.
-
To maximizing the inventory of the company, the suitable method
is inventory method, where this method can calculate the optimal
order quantity, cost efficiency, re-order point, lead time, and safety
stock. With this informations, that will help the company to reduce the
cost and gain the higher profit. The author use EOQ analysis, which
will display the new condition of the company’s raw materials
inventory and reserve, to tackle excessive lead times and soaring
customer demand.