chapter 2 theoretical research, research framework,...
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CHAPTER 2
THEORETICAL RESEARCH, RESEARCH FRAMEWORK, AND
HYPOTHESIS
2.1 Theoretical Research
2.1.1 Operations Management
One of the four management functions is operations management. Every
company needs operations management to process production factors in the
company holistically, from the planning process until the controlling process.
Operations division is the one with the most cost in a production process in a
company. Thus, there is a big chance to earn more by doing a right efficiency
process. According to Heizer & Render (2014:40) operations management is the
set of activities that creates value in the form of goods and services by transforming
input into outputs.
Jacobs & Chase (2014:3) explains that operation management is defined as
the design, operation, and improvement of the systems that create and deliver the
firm’s primary product and services. Morgan Swink, Steven A. Melnyk, M. Bixby
Cooper, and Janet L. Hartley (2014:4) also explain the definition of operations
management as the management of processes used to design, supply, and deliver
valuable goods and services to customers. R. and Reid dan Nada R. Sanders
(2013:3), explains operations management as the business function that plans,
organizes, coordinates, and controls the resources needed to produce a company’s
goods and services.
Based on the few explanations from the experts, operations management
can be defined as business function covering planning, organizing, coordinating and
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controlling towards every resource so the process of turning input into output could
be done right and therefore produce profit for the company.
2.1.2 Ten Strategic Operations Management Decisions
Operations manager applies management process in decisions known as
ten strategic operations management decisions. The table below will explain all
the decisions that needs to be considered by the manager in making decisions.
Table 2.1 Ten Strategic Operations Management Decisions
No Decision Explanation
1 Design of goods
and services
Defines much of what is required of operations in each
of the other OM decisions. For instance, product design
usually determines the lower limits of cost and the upper
limits of quality, as well as major implications for
sustainability and the human resources required.
2 Managing
quality
Determines the customer's quality expectations and
establishes policies and procedures to identify and
achieve that quality.
3 Process and
capacity
strategy
Determines how a good or service is produced (i.e., the
process for production) and commits management to
specific technology, quality, human resources, and
capital investments that determine much of the firm's
basic cost structure.
4 Location
strategy
Requires judgments regarding nearness to customers,
suppliers, and talent, while considering costs,
infrastructure, logistics, and government.
5 Layout strategy Requires integrating capacity needs, personnel levels,
technology, and inventory requirements to determine
the efficient flow of materials, people, and information.
6 Human
resources and
job design
Determines how to recruit, motivate, and retain
personnel with the required talent and skills. People are
an integral and expensive part of the total system
design.
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7 Supply chain
management
Decides how to integrate the supply chain into the firm's
strategy, including decisions that determine what is to
be purchased, from whom, and under what conditions.
8 Inventory
management
Considers inventory ordering and holding decisions and
how to optimize them as customer satisfaction, supplier
capability, and production schedules are considered.
9 Scheduling Determines and implements intermediate- and short-
term schedules that effectively and efficiently utilize
both personnel and facilities while meeting customer
demands.
10 Maintenance Requires decisions that consider facility capacity,
production demands, and personnel necessary to
maintain a reliable and stable process.
Source: Heizer & Render (2014)
From the above ten strategic operations management decisions, this
research focuses on inventory control, especially on company’s product inventory
control.
2.1.3 Inventory
2.1.3.1 The Concept of Inventory
Company values inventory as one of the strategic aspects. A correct
inventory level could lead a company to minimize the cost incurred. The definition
by Richard B. Chase & Robert Jacobs (2014) explains inventory as the stock of any
item or resource used in organization.
According to the definition, inventory is the items and resources that are
used in a company or in an organization. Inventory is there to fulfil the demand of
the customers of a company. But it has to be acknowledged that an overstocked
inventory would cause an outstanding storage cost while understocked inventory
would hold company production from fulfilling the demand. That is absolute that
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inventory must match the needs and demand of customers, so that it would give the
best benefit to the company.
2.1.3.2 Functions of Inventory
According to Heizer & Render (2014), inventory has four functions as
follow:
1. To provide item alternatives in order to fulfil customers’ demand and
protecting the company from demand fluctuation.
2. To separate some steps from production process. For example, if a
company’s inventory fluctuates, additional inventory maybe needed to be
able to separate production process from the supplier.
3. For company’s profit because a massive amount of purchase could lower
the distribution cost.
4. To adverse inflation and price rise.
As a comparation, Jacobs & Chase (2014) describe that the inventory functions are:
1. To protect the freedom of operations activity. By having inventory, a
company could predict the average monthly output produced stably.
2. To solve the variance of product demand. If the demand level is known, an
operations manager can easily predict the output needed to be produced, but
not all customers demand is constant so that safety stock could keep the
company from suffering some loss.
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3. For the flexibility on scheduling production. By knowing the inventory
level, a company could estimate lead time and setup time that needs to be
done, so the company could make a scheduling correctly.
4. To provide security feature on raw material delivery time. When ordering
product to supplier, there is a slight chance for tardiness to be happened on
the distribution because of the unexpected things happened during the
distribution. That is why a company must have extra inventory to anticipate
that.
5. To gain profit from the total order. There is cost incurred when ordering. By
having inventory, a company could minimize stock cost because they can
order in an effective and efficient.
2.1.3.3 Types of Inventory
According to Heizer & Render (2014), a company should keep four
different kind of inventory to enable the functions of inventory optimally. The four
functions are:
1. Purchased but not processed raw material inventory. The inventory could
be utilized to filter suppliers from production process.
2. Work in process inventories are components (or raw material) that has
been gone through some change process, but still not processed.
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3. MRO, maintenance repair operating, is the kind of inventory that is
utilized for maintenance, fix or operation tools which are necessary for
keeping the engine and the process still moves.
4. Finished goods inventory, which is the product inventory that is already
done and waiting for the shipping.
2.1.3.4 Costs of Inventory
Heizer & Render (2014) explained about how to make any decision on
inventory. A company will incur costs regarding inventory. The costs are:
1. Holding Cost, it is a cost in respect to storing or holding inventory in certain
period of time.
2. Ordering Cost, it is a cost that covers ordering process, purchasing,
administration and etc.
3. Setup Cost, it is a cost incurred to setup a machine or process to make an
order.
According to Jacobs & Chase (2014:517), deciding inventory size needs
some attention towards the cost incurred, including:
1. Holding or carrying costs. Cost regarding facility, handling, tax,
depreciation, etc.
2. Setup or production change costs. Cost regarding setups.
3. Ordering costs. Cost regarding purchasing to supplier.
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4. Shortage costs. Cost that incurred because of the inventory shortage. This
happens if the company runs out of inventory while there is still demand to
it.
2.1.4 Inventory Management
2.1.4.1 The Concept of Inventory Management
According to Heizer and Barry Render (2014) explains that the goal of
inventory management is to create a balance between the investment in inventory
and customer service. Budi Harsanto (2013) mentioned that inventory management
is a series of decisions or policies to make sure that the company could provide
certain quality, certain amount and certain time.
Based on the theory, inventory management aims to keep the inventory in a fair
level. It is an effort to make sure that no surplus or shortage of inventory that will
happen since that would cause some loss to the company such as overinvestment
or inventory shortage.
2.1.5 ABC Analysis
2.1.5.1 The Concept of ABC Analysis
Heizer & Render (2014) defines ABC analysis as an application of
inventory from Pareto principals, where the principal mentioned that there are “vital
few and trivial many”. ABC analysis is actually an idea of inventory policy that
focuses on the few vital inventory areas and not to the trivial many. Because it won’t
be timely to observe cheap items within the same intensity with the expensive ones.
In order to decide annual cost in ABC analysis, it can be calculated by
multiplying the annual demand from each inventory by the unit cost.
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1. Class A items, items with high annual cost. Around 15% from total
inventory cost but around 70%-80% from total usage. (80-20 category).
2. Class B items, items with medium annual cost. Around 30% from total
inventory cost but around 15%-20% from total usage. (15-30 category).
3. Class C items, items with intermediate annual cost. Around 55% from total
inventory cost but around 5% from total usage. (5-50 category).
The below image illustrates the ABC analysis explanation:
Figure 2.1 ABC Analysis Ilustration
Source: Author modified
There are many benefits a company can get by making items division into classes.
One of the most crucial is that many policies and controls can be applied to each
class. Heizer & Render (2014) explains that by implementing ABC analysis, a
company would at least have 3 policies regarding inventory, they are:
The policy to buy greater resources for developing suppliers of class A items
compared to the other classes.
0
20
40
60
80
100
120
Category A Category B Category C
Total Cost Total Usage
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The policy to run stricter physical control for class A items. Class A items
may be placed in a safer spot and the record of class A item must be verified
more often.
The policy to give more attention to class A items compared to other classes.
Nur Bahagia (2006) described that the criteria for the level of importance is
subjective. To inventory administrator, the usage speed, which are fast moving or
slow moving is an important indicator and usually becomes the basic for creating
inventory policies. Fast moving means items with high inventory turnover, while
on the contrary, slow moving means items with low inventory turnover
2.1.6 Inventory Model
Heizer & Render (2014) described the two models in terms of inventory.
They are independent inventory and dependent inventory. Independent inventory
model means that the demand for the items is not influenced by any other items’
demand, while dependent inventory model means that the demand for the items is
influenced by other items’ demand.
According to Donald J. Bowersox, David J. Closs, M. Bixby Cooper, John
C. Bowersox (2013), independent demand is often interpreted as “just in case”
inventory because the company needs to anticipate uncertain consumer needs.
Meanwhile, dependent demand can be interpreted as “just in time” inventory
because of the usage of an item dependent to the usage of other items, so that the
quantity needed will always be the same.
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2.1.6.1 Deterministic Model
Jay Heizer & Barry Render (2014:560) mentioned three inventory model
that can be utilized for independent demand, which are:
1) Economic Order Quantity (EOQ)
According to Jay Heizer & Barry Render (2014:561) EOQ is a model
that minimize total order cost and holding cost.
The inventory graphic for the model has a chainsaw tooth shape (Figure
2.2), this is because of the demand that is considered constant. When
inventory level reaches zero, new order can be made so that the inventory
level could come back to Q. The whole process will continue along.
Source: Heizer & Render (2014)
This inventory model goal is to:
a. Minimize Cost
According to Heizer & Render (2014:562), the goal of most inventory
model is to minimize total cost. The influential cost is holding cost and
Figure 2.2 EOQ graph
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setup cost (order cost). Therefore, by lowering or minizing holding cost
and order cost, we could also minimize the whole cost available.
With EOQ model, optimum order quantity will appear where total setup
cost is equal to total holding cost. Based on the statement, the equation
to find the optimum quantity or Q* can directly be found.
Figure 2.3 Total Cost Inventory Graph
Source: Heizer & Render (2014)
By using the below variables, we could determine the order cost and the holding
cost, so the Q* value can be found.
Q = Volume per order
Q* = Optimum volume per order (EOQ)
D = Annual demand of inventory item, in unit
S = Setup cost or order cost for each order
H = Annual holding cost per unit
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1. Annual order cost = (total order made annually) (setup cost or order cost
per order).
= (Annual demandTotal item quantity per order)
𝑇𝑜𝑡𝑎𝑙 𝑖𝑡𝑒𝑚 𝑞𝑢𝑎𝑛𝑡𝑖𝑡𝑦 𝑝𝑒𝑟 𝑜𝑟𝑑𝑒𝑟 (setup cost or order cost per order)
= (𝐷
𝑄)(S)
= 𝐷
𝑄𝑆
2. Annual holding cost = (average inventory level) (holding cost per unit per
year)
= (𝑇𝑜𝑡𝑎𝑙 𝑜𝑟𝑑𝑒𝑟)
2 (holding cost per unit per year)
= 𝑄
2 H
3. Total optimum order found when annual order cost equal to annual holding
cost:
= 𝐷
𝑄 S =
𝑄
2 H
4. To get Q*, equate both equations, becomes:
2DS = Q2H
Q2 = (2DS )
𝐻
Q* = √ 2DS
𝐻
Then, the total order that is needed to be set along the respective year (N) can
be determined, along with the preferred inter-order time (T):
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Preferred volume = N = Demand
Total unit ordered =
D
Q∗
Preferred inter-order total time = Total working day per week
N 𝑁
Annual inventory cost is the sum of order cost and holding cost:
Total annual cost = order cost + holding cost
Within the context of variabels in EOQ model, we could then calculate the total
cost with the following formula:
TC = D
Q S +
Q
2 H
b. Reorder point
According to Heizer & Render (2014:567), after determined the total
volume that would be ordered, another question that needs to be answered
is when the order will be made. Inventory models assume that company will
wait until the inventory level reaches zero before reordering. But, the period
between order made (lead time), can be in hours, days or even month. So,
the decision of when will the next order be made is usually made by using
reorder point.
Reorder point can be found by:
ROP = (daily demand) (lead time for new order in days)
= d x L
The above equation assumes if the demand is equal and constant. If not,
additional inventory should be added, known as safety stock.
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Daily demand, d, can be found by dividing annual demand, D, by total
workdays per year:
d = 𝐷
𝑡𝑜𝑡𝑎𝑙 𝑤𝑜𝑟𝑘𝑑𝑎𝑦𝑠 𝑝𝑒𝑟 𝑦𝑒𝑎𝑟
Source: Heizer & Render (2014)
2) Production order quantity model
On the previous model, all orders are received in the same time. Even so,
according to Heizer & Render (2014:569), there are times where the orders
are received along the period. This situation requires different kind of
model, a model that doesn’t need order recipient assumption. The figure
below shows inventory level as a function of time.
Figure 2.4 Reoder point graph
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Source: Heizer & Render (2014)
Since this model fits the production environment, the model is more
popular with the “production order quantity” or
“production quantity model” name. This model will benefit the company if
the inventory is always building up. This mode is made by determining
order cost or setup cost equal to holding cost so it can get to Q*. By using
the following symbols, we could determine the equation for annual
inventory holding cost for this model:
Q = Number of units ordered
H = Holding cost per unit
p = Daily production level
d = Daily demand level or usage level
t = production period in days
Order cost = (D/Q) S
Holding cost = 1
2 HQ [1 – (d / p)]
Determine order cost equal to holding cost to get Q*p
Figure 2.5 Changes at inventory level for specific time
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𝐷
𝑄𝑆 =
1
2 𝐻𝑄 [1 − (
𝑑
𝑝)]
𝑄 ∗ = √2𝐷𝑆
𝐻[1 − (𝑑𝑝)]
The equation above, Q*p, can be used for counting total order or
optimum production if the inventory, when consumed, is also produced. We
could also calculate Q*p if the annual data available. If the one used is the
annual data, we could calculate Q*p by:
𝑄 ∗ 𝑝 = √2𝐷𝑆
𝐻[1 − (𝐷𝑃)]
Where:
D = Annual demand level
P = Annual production level
3) Discounted quantity model
To increase sales, many companies offers quantity discount to customers.
Quantity discount is a discount given to customers that purchase an item
within a great quantity. Like other inventory models, the goal of this model
is also to minimize the total cost. But ordering in a big quanity within the
biggest discounted price amount won’t always minimize the total inventory
cost. That is because of the increasing holding cost along with the increasing
order.
To determine total optimum order amount within the discounted
quantity model, we could use an equation that has been explained by Heizer
& Render (2014:572), which is:
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𝑄 ∗ = √2𝐷𝑆
𝐼𝑃
It has to be noted that holding cost is IP and not H. This is because the
product price is factor of annual holding cost. So usually holding cost (I) is
expressed as the percenteage of the constant price per unit per year, P.
2.1.6.2 Probabilistic Model
Deterministic inventory model uses the assumption that the demand of a
product is constant and certain. Sometimes when the demand is unknown, to get
over it, probabilistic model can be utilized. According to Heizer & Render
(2014:575) probabilistic model is a statistic model that can be utilized when the
demand of a product or other variables are unknown, but can be determined by
using probability distribution.
The important thing that the management should put some attention is to
keep the sufficient service level to face the uncertain demand. Service level is the
complement from the stock-out probability. For example, if the stock-out
probability is 0.04, then the service level is 0.96.
Reorder Point = d x L
Uncertain demand increases the stock-out probability. One of the methods
that can be utilized to decrease stock-out is to keep additional units in the inventory,
popularly known as safety stock. With safety stock available, the ROP equation is
as follows:
ROP = d x L + ss
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According to Heizer & Render (2014:579), if the lead time data is
unknown, the above formula couldn’t be used. Even so, there are other models that
can be used based on certain situation.
1. ROP model with various demand level and constant lead time
ROP = expected usage during lead time + safety stock
= (ū x LT) + (z x σu x √𝐿𝑇 )
Where safety stock = (z x σu x √𝐿𝑇 )
σu = standard deviation from daily demand
2. ROP model with constant demand level and various lead time
ROP = (u x 𝐿𝑇̅̅̅̅ ) + (z x u x σLT)
Where safety stock = (z x u x σLT)
σLT = standard deviation from lead time in days
3. ROP model with various demand level and various lead time
ROP = (ū x 𝐿𝑇̅̅̅̅ ) + z√[ (𝐿𝑇̅̅̅̅ )(𝜎𝑢)2] + [(𝑢)̅̅ ̅2 (𝜎𝐿𝑇)2
2.1.7 Forecasting
According to Jay Heizer & Barry Render (2014:113), forecasting is an art
and science of predicting the future event. Forecasting will involve historical data
collection and projecting it to the future using math models.
According to Jacobs & Chase (2014:443) forecasting is the base of
company’s planning and controlling process. To choose the forecasting kind that
will be used, we need to learn the goal of the forecasting first.
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Strategic forecasts. It is a long to mid-term forecasting that is used for
taking a strategy-related decision and aggregate planning.
Tactical forecasts. It is a short-term forecasting that is used for making
daily decisions regarding demand level.
Jacobs & Chase (2014:444) explained that forecasting can be categorized
into four base type: qualitative, time series analysis, causal relationships and
simulation. Qualitative method is based on the experts and needs many opinions.
Time series analysis is based on the past demand data and will be used as the
prediction of the future. Causal relationship is based on the regression analysis
where the factors in surrounding environment affects future demand. Model
simulation is based on the forecaster that uses his assumption on conditioning the
foreacast.
According to Jacobs & Chase (2014:446), time series forecasting model
tries to predict the future based on the data from the past, such as sales. Short term
forecasting goes around 3-12 months, mid-term forecasting goes for 1 to 2 years,
meanwhile the long-term forecasting goes for more than 2 years. This is the guide
for choosing forecasting methods:
Table 2.2 Guidance to choose forecasting method
Forecasting
Method
Total historical
data needed
Data Pattern Forecasting
Result Period
Simple Moving
Average
6 to 12 months Constant Short
Weighted Moving
Average and
Simple
Exponential
Smoothing
5 to 10 observation Constant Short
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Exponential
Smoothing with
Trend
5 to 10 observation Constant with
trend
Short
Linear Regression 10 to 20
observation
Constant, with
treand,
seasonal
Short to medium
Trend and Seasonal
Models
2 to 3 observation
every season
Constant, with
treand,
seasonal
Short to medium
Source: Jacobs & Chase (2014:447)
Simple moving average is used when the demand for a product doesn’t grow
and doesn’t have seasonal characteristic. The method is very useful to remove
random fluctuation in a forecast.
Jacobs & Chase (2014:447) mentioned that the formula for the method is
as follows:
𝐹𝑡 = (𝐴𝑡 − 1) + (𝐴𝑡 − 2) + (𝐴𝑡 − 3) + ⋯ + 𝐴𝑡 − 𝑛
𝑛
Where
Ft = forecasting for the upcoming period
n = average total period
At-1 = the actual record on the period before
Weighted Moving Average uses weight that is put in every element, where
the total weight must be 1. This method forecasting result would be more significant
compared to the previous data because of the usage of weight. The method formula,
according to Jacobs & Chase (2014:449) is:
𝐹𝑡 = (𝑤1)(𝐴𝑡 − 1) + (𝑤2)(𝐴𝑡 − 2) + ⋯ + (𝑤𝑛)(𝐴𝑡 − 𝑛)
Exponential Smoothing is a forecasting method that uses weight that
derivates exponentially in every period in the past. This is the most used method
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because it produces a more accurate forecasting result. The formula for this method,
according to Jacobs & Chase (2014:450) is:
𝐹𝑡 = (𝐹𝑡 − 1)+ ∝ ((𝐴𝑡 − 1) − (𝐹𝑡 − 1))
∝ is smoothing constant or preferred response number.
Exponential Smoothing with Trend uses smoothing constant delta (𝛿) where
this is the additional parameter that is used in exponential smoothing that is a part
of adjustment with trend. The formula is:
𝐹𝑡 = 𝐹𝐼𝑇𝑡 − 1 + 𝛼((𝐴𝑡 − 1) − 𝐹𝐼𝑇𝑡 − 1)
𝑇𝑡 = 𝑇𝑡 − 1 + 𝛿(𝐹𝑡 − (𝐹𝐼𝑇𝑡 − 1))
𝐹𝐼𝑇𝑡 = 𝐹𝑡 + 𝑇𝑡
Linear Regression Analysis creates straight line relationship between
variables. According to Jacobs & Chase (2014:452), the form of this method is Y
= a + bt. Where Y is the dependent variable value that will be counted, a is Y
intercept, b is slope, and t shows time period of forecasting.
After doing the forecasting, it is better to then calculate the error of the
forecasting. According to Jacobs & Chase (2014:462), error in a forecasting is if
there is difference between the real demand and the demand forecasted. There are
few error calculations that can be done.
Mean Absolute Deviation (MAD) is the average of absolute value compared to
actual forecasting. 𝑀𝐴𝐷 = ∑|𝐴𝑡−𝐹𝑡|
𝑛 t is the number of periods. At is actual demand
in t period. Ft is forecasted demand in t period. n is total period.
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Mean Absolute Percent Error (MAPE) is error average that is measured in
percentage. 𝑀𝐴𝑃𝐸 =𝑀𝐴𝐷
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐷𝑒𝑚𝑎𝑛𝑑
Mean Square Error (MSE) is the error average quadrat in the forecasting. 𝑀𝑆𝐸 =
∑ 𝑒𝑖2
𝑛 e is error in i period.
2.1.8 Decision Tree
According to Jay Heizer & Barry Render (2011:706), a decision tree is a
graphical picture of a decision process that shows various alternative decisions,
possibilities and impact from the combined alternative decision. Expected
Monetary Value (EMV) is the criterion used in decision tree analysis. EMV can be
found by multiplying probability and the impact caused from a probability.
The five steps in decision tree analysis according to Jay Heizer & Barry
Render (2011:706):
1. Define the problem
2. Draw the decision tree within the right structure
3. Determine probability on available possibilities
4. Consider the impact of each alternate combination
5. Solve the problem by counting the EMV
These steps should be done backwards.
2.2 Research Position on Past Research
The following are the summary, similarity and differences between this
research and the past research that is used as references on doing this research. The
first journal is a national journal entitled “Analisis Pengendalian Persediaan Barang
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berdasarkan Metode EOQ di Toko Era Baru Samarinda (2015)”, from eJournal
Ilmu Administrasi Bisnis, 2015, 2(1):162-173 ISSN 2355-5408. This was
researched by Rudi Wahyudi. The result of the research is that Era Baru Citra Niaga
store Samarinda should have used EOQ model to place their order because of their
total inventory cost calculated with EOQ model is lower than the conventional
method that they have been using. The similarity with this research is that the
research also uses EOQ and ROP model to calculate the total inventory cost, and it
also observe the same type company that only re-sell stuffs. But the research doesn’t
use forecasting method, only calculate the EOQ directly using the sales and
purchasing data. The journal is the main reference in this research because of the
same object observed in both this and the research mentioned.
The second journal is an international journal entitled “Strategy of
Optimizing Inventory: Case Study in Private Manufacturing in Construction Field
Company in Indonesia (2014)”. From Journal of Applied Science 14(24): 3538-
3546, 2014 ISSN 1812-5652, it is researched by Lim Sanny and Monica Felicia.
The result of this study states that by using EOQ, the total inventory cost is reduced
and by the application of EOQ, the company’s income is increasing. The similarity
between the research is that the research uses forecasting method first to forecast
the demand and continued by calculating error MAD, MSE and MAPE. After that,
the author did EOQ calculation and utilized decision tree to make final decision,
whether it is better to use the forecasted data or not.
The next journal is entitled A Literature Review on Models of Inventory
Management Under Uncertainty (2015), ISSN 2029-8234. From Business System
and Economics Vol. 5(1), researched by Serhii Ziukov. The result of the journal is
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that efficiency from inventory management becomes one of the main topics in
business. The fuzzy model from inventory control based on its’ classification can
be divided to EOQ, EPQ, and Lot sizing models. Every model has its flaws and
advantage but shares the same goal which is to minimize the inventory cost. The
similarity between the research is that one the method used is EOQ. EOQ is
Persamaannya adalah salah satu metode yang digunakan adalah EOQ. Meanwhile,
the difference is that this research talks about another method used in inventory
management such as EPQ, Joint Economics Lot Sizing, Single Period, and Multi
Period Model.
2.3 Research Framework
Operations management is a business function from planning, organizing,
coordinating and controlling toward all resources so it can change the input become
output in form of goods or services that are valuable to consumer and to the
company. Operations management focuses on the effective and efficient production
activity. If the production activity of a company is already effective and efficient,
then it would increase the competitiveness of a company. The increase of
company’s competitiveness is an important thing to do so that a company could
compete.
In a company, there will be surplus or shortage in inventory, which is a
normal thing. But that normal thing often becomes a thing that causes loss to the
company. Inventory control would help the company removing the risk of staked
items, late inventory or inventory shortage, etc. Inventory control would answer
when and where and how many items that a company needs. That answer is
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necessary to increase the company’s profit. The maximum profit can actually
increase company’s competitiveness.
Inventory control must be done according to the attributes of the materials.
It can be dependent or independent to other items. In retail company, a demand of
an item is independent to other items, so it is called independent demand. The
uncertain level of demand from the customer should also be considered.
Judging from the data above, the ABC method is necessary to analyze the
most expense-influencing product type in a company, so then forecasting can be
done to get the demand level of a product in the next period. After doing the
forecasting, the control system is counted from the independent inventory method,
and besides, this research has no daily production level so the method used in this
research is Economic Order Quantity (EOQ), Safety Stock (SS), and Reorder Point
(ROP). After doing the calculation, a company could get the total inventory cost
needed and compare the inventory efficient level before and after using those
methods afterwards. By doing so, the best method for ordering would be known.
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Operations Management
(Jay Heizer & Barry
Render, 2014)
Inventory Management
(Jay Heizer & Barry
Render, 2014)
Dependent (Jay
Heizer & Barry
Render, 2014)
Independent (Jay
Heizer & Barry
Render, 2014)
Deterministic (Jay
Heizer & Barry
Render, 2014)
Probabilistic (Jay
Heizer & Barry
Render, 2014)
Economic
Order Quantity Safety Stock Reorder Point
Total Inventory Cost
Grand Theory
Mid Theory
Applied Theory
Figure 2.6 Research Paradigm