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Inventory simulation model of frozen-meat for food-safety program

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This research discusses the application of inventory simulation and optimization model for food safety program in Indonesia. Frozen meat is a complementary commodity for fresh meat that has an inadequate supply in the country. The disadvantage of frozen meat is that it needs to be stored in a refrigerator to maintain its quality and thus increases the cost. Therefore, an appropriate inventory policy is crucial to minimize the cost. In order to deal with this situation, a Monte Carlo simulation followed by an optimization model is proposed. Specifically, the Moving Average and the Winter Methods are used to predict the demand in the future. The results show the best inventory policy thus the cost is minimized and the national needs are satisfied.
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Communications in Science and Technology 3(1) (2018) 36-43
COMMUNICATIONS IN
SCIENCE AND TECHNOLOGY
Homepage: cst.kipmi.or.id
© 2018 KIPMI
Inventory simulation model of frozen-meat for food-safety program
Moch. Yandra Darajat*, Komarudin, Akhmad Hidayatno
Department of Industrial Engineering, Universitas Indonesia, Depok, Indonesia
Article history:
Received: 3 May 2018 / Received in revised form: 30 May 2018 / Accepted: 31 May 2018
Abstract
This research discusses the application of inventory simulation and optimization model for food safety program in Indonesia. Frozen meat is a
complementary commodity for fresh meat that has an inadequate supply in the country. The disadvantage of frozen meat is that it needs to be
stored in a refrigerator to maintain its quality and thus increases the cost. Therefore, an appropriate inventory policy is crucial to minimize the
cost. In order to deal with this situation, a Monte Carlo simulation followed by an optimization model is proposed. Specifically, the Moving
Average and the Winter Methods are used to predict the demand in the future. The results show the best inventory policy thus the cost is
minimized and the national needs are satisfied.
Keywords: Inventory model; demand forecast; food-safety; simulation; optimization.
1. Introduction
Development of the quality of human resources is one of
the goals of Indonesia's development. It is closely related to
the improvement of the community nutrition, health, and
education level. One source of nutrition is a food of animal
origin in the form of protein derived from beef. The domestic
need for meat will continue to increase along with the increase
in population, the increasing of economic level, the awareness
of the society of nutrition, and the existence of a foreign
community. Beef imports to meet market demand are still
continuing, as Indonesia's local meat production is still unable
to meet domestic demand (Fig. 1). Besides imported meat has
several advantages, namely more tender, a high degree of
marbling so that it is preferred by consumers [1].
The Center for Agricultural Data and Information Systems
in the 2016’s Beef Outlook presents data on national beef
production and consumption as shown in Table 1. It shows the
national meat consumption is still in deficit in 2014 and 2015
at 196 thousand tons and 207 thousand tons and according to
projections will continue to experience supply shortages until
2020 [2].
Based on data from the National Socioeconomic Survey
(SUSENAS) in 2015, Indonesian beef consumption is 2.40
kg/capita/year, that is relatively small compared to the
consumption of developed countries. Indonesian people
generally only eat beef when there is a celebration or religious
holidays. Nevertheless, Indonesia cannot yet become a self-
sufficient state of beef, to meet the demand for beef,
especially in big cities like Jakarta, is still widely obtained
from imports. On the other hand, this deficiency becomes an
obstacle that must be fulfilled by the state to its people [2].
Fig 1. National meat demand in Indonesia for period of 2007 2017
In order to achieve food security, availability is not
enough, the food must also fulfill the principle of affordability
and stability. That is, the food should be within an affordable
price range to be purchased by all levels of society and the
supply can fulfill the demands.
In order to provide adequate supply, the government
continues to open faucet imports of livestock products
including beef. In 2016 the government officially opens
imports of buffalo meat from India as an additional supply
source to meet people's need for meat at an affordable price.
This was performed as well as imported meat from Australia
and New Zealand.
The imported meat from India is in a hope to provide an
alternative for meat consumers in Indonesia because it is
cheaper than the local meat or the imported one from
Australia and New Zealand. As per Ministry of Trade Decree
no. 27/2017 on the pricing of purchases at farmers and
* Corresponding author.
Email: myandrad@gmail.com
Darajat et al. / Communications in Science and Technology 3(1) (2018) 36-43 37
reference prices in consumers, it has set the reference price of
frozen meat at the consumer level of Rp. 80,000,- per
kilogram, and its mandatory for all meat seller both for
traditional and modern markets.
Storage of frozen meat in cold storage costs more than an
ordinary warehouse. Good inventory management can reduce
inventory costs and it can help the sellers to meet the
reference price.
Table 1. Projection of surplus/deficit meat for period of 2014 2020
Period
Production (000 Tons)
National Consumption
(000 Tons)
Carcas Production (000 Tons)
Meat Production (000 Tons)
2014
497.67
398.14
595.11
2015
506.66
405.33
613.11
2016*)
524.11
419.29
623.48
2017**)
531.21
424.97
636.39
2018**)
540.13
432.1
641.33
2019**)
549.05
439.24
642.76
2020**)
557.96
446.37
644.73
Growth (%)
1.93
1.93
1.35
Source: Outlook Beef. Center for Agricultural Data and Information Systems, 2016.
Information :
*) 2016 Production of Temporary Figures, DG PKH (Directorate General of Husbandry and Veterinary)
**) Consumption of Pusdatin estimation
Frozen meat storage is a separate issue in the meat supply
chain in Indonesia. This is because long time the people of
Indonesia still prefer to buy meat that is served in fresh
condition, so the storage infrastructure of frozen product is
still limited. Nevertheless, the government continues to
encourage people to consume beef in frozen form. The
policies issued increasingly lead to the handling of
slaughterhouses with cold supply chain, such as Regulation of
the Minister of Agriculture no. 13 of 2010 on the requirements
of slaughterhouses and meat cutting plants where modern
slaughterhouses (RPH) should be equipped with chiller, blast
freezer and cold storage facilities [13].
Inventory management is a method of streamlining
inventory control and helping companies to determine when
and how many purchases are made. Good inventory
management enables companies to reduce their inventory
levels while maintaining a good level of service [3]. Inventory
control at the desired level in the company's operational
activities is an important activity to meet market needs and
control inventory costs [4].
The cost of storage determined by the amount of inventory
that controlled within a certain time determined by the time
order (reorder point) and the ordering size [5]. Effective or not
a company's inventory management can affect the
performance of the company [6]. The optimum inventory
level affected by the low purchase cost in the procurement
period and with the number of purchases that adjust to the
demand (demand) in the same period. Fig. 2 shows the
inventory level and cost relation.
One of the objectives of inventory control is to minimize
costs arising from the availability of such inventories [7]. The
costs are:
a. Holding cost, is the cost incurred by the storage of
inventory in the warehouse at certain period of time,
including the cost of insurance, depreciation, interest and
others.
b. Ordering cost, is the cost incurred by the activity of
ordering supplies in one message, eg: forms, supplies,
ordering and administration process; as long as material /
goods are not available for further processing.
c. Stock out cost, is the loss due to unmet demand at certain
period such as: loss of sale, loss of customer, special
ordering cost, the difference of price, disruption of
operation, and additional expenditure of managerial
activity.
Fig 2. Inventory level and cost relation
There are two important decisions in an inventory model:
1. How much amounts should be ordered for a particular
supply of goods? 2. When the optimal time to order the item
back so that the inventory can reach the optimum point?
Every decision taken has an effect on the cost of inventory. To
make easier decisions, models for inventory management
developed. The demand model is divided into two types i.e.
deterministic demand and probabilistic demand [7].
Several previous studies have found a formula that can
help in determining inventory models that can form the basis
of determining inventory policies. Li et.al (2013) [8]
conducted a reorder point optimization research on
manufacturing spare parts systems under permit conditions
and random leadtime. With demand assumed to be normally
distributed. His research aims to find the best order strategy to
minimize inventory costs.
Wang et.al (2006) [9] examined the optimal ways to
minimize the annual inventory cost by determining the
38 Darajat et al. / Communications in Science and Technology 3(1) (2018) 36-43
production lot size, reorder point, and isvestation to reduce
setup cost. The study considers historical data of normally
distributed and exponential demand. El-Wakeel et.al (2013)
[10] conducted a probabilistic system backorder inventory
with order cost is a function of order quantity. The study is to
minimize the total annual cost with demand following the
normal distribution.
From the description of the problem in the background,
there is no research that discusses the problem of meat supply
model in Indonesia, therefore this research is trying to fill the
gap. Limitations of the problem in this study:
1. Import quota constraints. The import quota granted by the
government must be realized 100%.
2. The object of research is imported frozen meat as a
complementary commodity of beef / buffalo in Indonesia.
3. The scope of research covers the territory of the Unitary
State of the Republic of Indonesia.
4. The inventory control period undertaken for 2018.
5. The selling price of the commodity has the upper limit
(the applicable ceiling price) according to the Minister of
Trade Regulation no. 27 year 2017. Therefore, it is done
optimization efforts at the level of importers to obtain the
minimum management cost in order to achieve the
expected retail price at the consumer level.
With the commodity price fluctuation and the uncertain
demand for commodities in each period, the purchasing model
should be adjusted to the price level occurring in a period by
following the demand rate for a given period. In other words,
the question arises: whether to buy with more amounts when
the price is low, but at the same time demand at low position?
Or do more purchases still be made on the eve of high demand
despite high commodity purchase price?
Under existing condition there will be a trade-off between
the purchase amount at a low purchase price and the amount
of purchase at the time of high demand. Thus, the purchase
pattern and inventory level will follow the pattern as in Fig. 2.
The purpose of this study is to determine the appropriate
inventory policy scenario to support the fulfillment of meat
requirements in Indonesia by streamlining the cost of
purchasing and supplies; thus this generally can be realized in
the provision of meat at affordable prices for the community.
2. Research Method
Some methods below are used to analyze the data:
2.1. Monte Carlo
Monte Carlo simulations defined as all statistical sampling
techniques used to estimate solutions to quantitative problems
(Risk Glosary, 2008). This method used to evaluate business
situations where there are uncertainties and random situations
(Leong, 2007). In a Monte Carlo a model is built on the actual
system. Each variable in the model has a value that has a
different probability, indicated by the probability distribution
or commonly called the probability distribution function (pdf)
of each variable. The Monte Carlo method simulates the
system over and over, hundreds or even thousands of times
depending on the system reviewed, by choosing a random
value for each variable of the probability distribution. The
result of the simulation is a project distribution.
The research will be done by comparing the model
obtained from previous research and simulating the model
with the condition of frozen meat stock in Indonesia. The
model that generates the minimum total inventory cost will be
selected. Model simulation will be done by taking the
procurement and inventory data from the company engaged in
the field of food logistics. In order to validate the inventory
model obtained will be a sensitivity analysis.
2.2. Forecasting Method
Data demand considered the trend of seasonal data where
the demand for meat is usually high in the days leading up to
the big religious days. Demand forecast with Weighted
Moving Average method and Holt-Winter Technique
conducted to determine the demand in the future.
2.3. Moving Average (WMA)
Moving average is a general forecasting method and it is
easy to use the tools available for technical analysis. Moving
averages provide a simple method for smoothing past data.
This method is useful for forecasting when a trend occurs. If
there are trends, use different estimates to consider.
2.4. Holt-Winter (HM)
The Holt-Winters method is an extension of two Holt
parameters. The Holt-Winters method is a time series
prediction method that can handle seasonal behavior on a data
based on past data [12].
2.5. Simulation Scenario
Five scenarios were created to simulate the control of
frozen meat stocks:
Scenario 1. Purchase of 10,000 tons per month, arrival in
stages per month without price contract;
Scenario 2. Total purchase of optimization result with safety
stock 10% without price contract;
Scenario 3. Total purchase of optimization result without
safety stock without price contract;
Scenario 4. Total purchase of optimization result with 2-
month price contract;
Scenario 5. Total purchase of optimization result with 3-
month price contract.
From the simulation result with some scenarios above, we
get the total cost which different each scenario.
3. Result and Discussion
Fluctuating purchasing and demand prices led to the need
for simulations to determine the policy of frozen meat stock
control. Data fluctuations in the price of monthly frozen meat
purchases in 2017 at Jakarta can be seen in Fig. 3.
The fluctuation of national meat requirement caused by the
pattern of meat consumption of Indonesian people who
consume more meat at certain times such as Idul Fitri,
Darajat et al. / Communications in Science and Technology 3(1) (2018) 36-43 39
Christmas and New Year, as indicated in Fig. 4 and Fig. 5
(Source: PUSDATIN (reproduced)).
After the transformation of data then tested its stationery,
the next step is to test the normality of data. In this study,
normality test using Anderson Darling (AD) approach.
Anderson Darling Test is a test of normality that resembles
the Kolmogorov Smirnov Test and Cramer Von Mises Test,
which are both based on Empirical Distribution Function
(EDF).
Fig. 6 shows that the data spreads around the diagonal line
and follows the direction of the diagonal line. From the value
of Anderson Darling (AD) of 0.533 with p-value 0.170, the
comparison of p-value and AD value with α is 0.170 and
0.533> 0.05 (alpha used in the study) so that the data is
normally distributed.
3.1. Forecast Result
3.1.1. Moving Average
Fig. 7 is the output of forecast using the Moving Average
method with length of 12, found that the MAPE value of 2.91,
the MAD value of 0.256 and the MSD value of 0.118.
3.1.2. Winters Method
Fig. 8 is a forecast processed output using the Winters
Method of smoothing constants, where each level (α), trend
(γ) and seasonal (δ) are 0.2. It was found that the MAPE value
was 1,371, the MAD value was 0.117 and the MSD value was
0.04838.
3625
3750
3650
3800 3850 3875
3625 3575
3475
3550
36253600
3000
3500
4000
1 2 3 4 5 6 7 8 9 10 11 12
Price (USD/MT)
Month
Fig. 3. Fluctuation of buying price of frozen meat in cost and freight (CIF) term
9,597.54
9,384.90
9,340.03
9,406.49
9,800.90
10,347.18
9,591.29
9,250.84
8,944.10
9,422.96
9,554.37
9,909.59
8,500.00
8,700.00
8,900.00
9,100.00
9,300.00
9,500.00
9,700.00
9,900.00
10,100.00
10,300.00
10,500.00
1 2 3 4 5 6 7 8 9 10 11 12
Demand (in MT)
Month
Fig. 4. Fluctuation demand of frozen meat in Indonesia with year period of 2017
From the two forecast methods, Winters Method yields
MAPE, MAD and MSD values lower than the Moving
Average method. This means that Winters Method can
produce better forecasts than the Moving Average Method,
because the margin of error between the forecast and the real
number is smaller. Therefore, we use forecasting data
produced by Winters Method for simulation. Table 3 shows
consumption data per month resulted from forecast for the
year of 2018.
40 Darajat et al. / Communications in Science and Technology 3(1) (2018) 36-43
6,500,000.00
7,000,000.00
7,500,000.00
8,000,000.00
8,500,000.00
9,000,000.00
9,500,000.00
10,000,000.00
10,500,000.00
JAN FEB MA R APR MAY JUN JUL AUG SE P O C T N O V D E C
Demand (kilogram)
Month
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
Fig. 5. National frozen meat demand with year period of 2007 2017
Fig. 6. Normality test of demand data output from Minitab 18 software
Fig 7. Forecast result using moving average output from Minitab 18 software
Darajat et al. / Communications in Science and Technology 3(1) (2018) 36-43 41
Fig. 8. Forecast result using winters method output from Minitab 18 software
Table 3. Consumption data per month resulted from forecast
Month
Consumption
January
9,742,164.66
February
9,516,818.88
March
9,462,392.64
April
9,521,362.98
May
9,912,535.56
June
10,457,180.82
July
9,686,566.26
August
9,336,828.42
September
9,022,086.36
October
9,500,372.46
November
9,628,821.36
December
9,983,556.36
Total
115,770,687
3.2. Monte Carlo Simulation for Purchase Price
Monte Carlo simulation is applied to determine the
purchase price data in the year to be simulated, the price data
is taken from the history of purchase price 3 years earlier to
determine the price in the year to be simulated. Price history
data are presented in Table 4. The data in Table 4 is made
interval value, as shown in Table 5.
Based on data on Tables 4 and 5, the possible distribution
data for the purchase price can be constructed. The result is
listed in Table 6. Further, it is generated in random number to
make uncertainty of purchase price [14]. The random number
data can be seen in Table 7. The probability interval data is
based on the cumulative probability data in Table 8. Table 9
shows data of purchase price simulation for 12 months.
3.3. Inventory Control Simulation
From the simulation, we get the total cost values which are
different each scenario. Scenario III results in the lowest total
cost of Rp. 6,038,499,272,931,- compared to total cost
resulted by Scenario I of Rp. 6.074.162.437.002,- Scenario II
is Rp. 6.052.312.201.581,- Scenario IV is Rp.
6,106,848,070,522,- Scenario V is Rp. 6,045,421,679,444,-.
The complete result for each scenario is listed in Table 10.
This shows that the difference of purchasing policy will
affect the total cost incurred to buy and store meat, so the
policies taken by the company and government in the effort to
provide frozen meat for the community must be done
effectively and efficiently in order to meet the needs of the
community at a price affordable.
Table 4. Purchase price data (2015-2017)
2015
2016
2017
3,600
3,575
3,625
3,750
3,675
3,750
3,600
3,625
3,650
3,825
3,750
3,800
3,775
3,800
3,850
3,875
3,825
3,875
3,625
3,700
3,625
3,575
3,550
3,575
3,525
3,525
3,475
3,575
3,575
3,550
3,575
3,575
3,625
3,600
3,625
3,600
*) Unit USD per Metrix Ton
Table 5. Data interval value
Interval No.
Interval Values
Mean Interval Values
1
3,475
3,542
3508
2
3,543
3,609
3576
3
3,610
3,677
3644
4
3,678
3,745
3711
5
3,746
3,812
3779
6
3,813
3,875
3844
4. Conclusion
The result of simulation can provide optimal value to
control the procurement of frozen meat for meeting national
needs. The scenario that has minimum cost is Scenario #3
(total purchase of optimization result without safety stock and
without price contract). Future research can be performed by
42 Darajat et al. / Communications in Science and Technology 3(1) (2018) 36-43
increasing the complexity of the problems in the simulation,
e.g. by considering other forecasting methods and comparing them against one and another, as well as analyzing the results.
Table 6. Distribution of probability
Interval no.
Interval values
Frequency
Probability of Occurrence
Cumulative Probability
Mean values
1
3,475
3,542
3
0.08
0.08
3,508
2
3,543
3,609
13
0.36
0.44
3,576
3
3,610
3,677
8
0.22
0.67
3,644
4
3,678
3,745
1
0.03
0.69
3,711
5
3,746
3,812
6
0.17
0.86
3,779
6
3,813
3,875
5
0.14
1.00
3,844
Table 7. Random number generated for Monte Carlo simulation
Table 8. Probability data interval
Interval no.
Prob. Intervals
Mean values
1
0.00
0.08
3,508
2
0.09
0.44
3,576
3
0.45
0.67
3,644
4
0.68
0.69
3,711
5
0.70
0.86
3,779
6
0.87
1.00
3,844
Table 9. Data of purchase price simulation for 12 months
INTERVAL NO.
1
2
3
4
5
6
7
…..
49
50
ROW. AVG
1
3644
3711
3576
3779
3576
3576
3644
3644
3576
3614
2
3576
3644
3644
3644
3644
3576
3844
3576
3844
3548
3
3576
3779
3779
3644
3844
3576
3508
3576
3576
3587
4
3576
3844
3576
3508
3576
350
3644
3779
3576
3508
5
3779
3576
3576
3576
3644
3779
3576
3844
3576
3614
6
3779
3844
3508
3644
3844
3576
3508
3576
3576
3548
7
3779
3844
3844
3644
3644
3576
3844
3844
3779
3587
8
3576
3644
3779
3644
3844
3779
3644
3644
3779
3508
9
3844
3844
3576
3844
3644
3508
3576
3644
3576
3614
10
3844
3576
3779
3644
3644
3644
3508
3576
3576
3548
11
3844
3576
3844
3644
3576
3644
3779
3576
3576
3587
12
3576
3508
3644
3644
3576
3644
3576
3576
3508
3508
Table 10. Result of simulation with each scenario
Scenario
Purchasing Cost
Transportation Cost
Holding Cost
Total Cost
I
5,892,670,508,130
150,000,000,000
31,491,928,872
6,074,162,437,002
II
5,862,973,061,557
150,000,000,000
39,339,140,024
6,052,312,201,581
III
5,861,568,782,476
150,000,000,000
26,930,490,437
6,038,499,272,913
IV
5,951,931,052,797
150,000,000,000
4,917,017,725
6,106,848,070,522
V
5,890,504,661,719
150,000,000,000
4,917,017,725
6,045,421,679,444
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RN
1
2
3
4
5
6
7
…..
49
50
Jan
0.6867
0.4274
0.4795
0.6444
0.2447
0.7306
0.7692
……
0.9992
0.1415
Feb
0.3840
0.0096
0.4890
0.0487
0.9467
0.0950
0.7849
……
0.0657
0.6848
Mar
0.8036
0.5219
0.0343
0.9144
0.0423
0.8979
0.9308
……
0.5050
0.3653
Apr
0.4927
0.0072
0.2182
0.5262
0.6944
0.5363
0.1688
……
0.0693
0.4760
Mei
0.6867
0.4274
0.4795
0.6444
0.2447
0.7306
0.7692
……
0.9992
0.1415
Jun
0.3840
0.0096
0.4890
0.0487
0.9467
0.0950
0.7849
……
0.0657
0.6848
Jul
0.8036
0.5219
0.0343
0.9144
0.0423
0.8979
0.9308
……
0.5050
0.3653
Ags
0.4927
0.0072
0.2182
0.5262
0.6944
0.5363
0.1688
……
0.0693
0.4760
Sep
0.6867
0.4274
0.4795
0.6444
0.2447
0.7306
0.7692
……
0.9992
0.1415
Okt
0.3840
0.0096
0.4890
0.0487
0.9467
0.0950
0.7849
……
0.0657
0.6848
Nov
0.8036
0.5219
0.0343
0.9144
0.0423
0.8979
0.9308
……
0.5050
0.3653
Des
0.4927
0.0072
0.2182
0.5262
0.6944
0.5363
0.1688
……
0.0693
0.4760
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