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Procedia Economics and Finance 4 ( 2012 ) 312 - 320
2212-5671 © 2012 The Authors. Published by Elsevier Ltd.
Selection and peer-review under responsibility of Parahyangan Catholic U niversity. doi: 10.1016/S2212-5671(12)00346-2 International Conference on Small and Medium Enterprises Development with a Theme (ICSMED 2012) Product Inventory Predictions at Small Medium Enterprise UsingMarket Basket Analysis Approach - Neural Networks
Agus Mansur
a* , Triyoso Kuncoro a aIndustrial Engineering Department ,Universitas Islam Indonesia, Jl. Kaliurang km 14, 5 Yogyakarta 55584, Indonesia
Abstract
One of the key problems in every company, including small and medium enterprises, is how to determine the inventory
level for each product that will be sold to their customers appropriately as it can suppress the build up of inventory as well
as avoid the stock out. This study is aimed to understand the behavior of consumers in purchasing the products so it can be
used to predict the purchasing for the next period. Later, the prediction is used as a decision support in determining the
appropriate amount of inventory for each product. The study was conducted at Karomah Brass, a small and medium
enterbuys from the supplier. The methods that used in this study are the Market Basket Analysis (MBA) and Artificial Neural
Network (ANN) Back propagation. MBA is used to examine the buying behavior of customer while ANN Back customers frequently purchase products that serve as a kind of antique closet accessories and if customer bought that certain
product, then they will also buy similar products in accordance with 21 rules that have been obtained from the mining of
transaction data. Whereas, other result shows the prediction of the amount product inventory requirements/needs for one
year to the next.© 2012 The Authors. Published by Elsevier Ltd.
Selection and/or peer-review under responsibility of Parahyangan Catholic University. Keywords: customer behavior; inventory level; prediction; MBA; ANN; Backpropagation.Corresponding author.
E-mail address: gus_mansur@yahoo.com
Available online at
www.sciencedirect.com© 2012 The Authors. Published by Elsevier Ltd.
Selection and peer-review under responsibility of Parahyangan Catholic U niversity.Open access under CC BY-NC-ND license.Open access under CC BY-NC-ND license.brought to you by COREView metadata, citation and similar papers at core.ac.ukprovided by Elsevier - Publisher Connector
313 Agus Mansur and Triyoso Kuncoro / Procedia Economics and Finance 4 ( 2012 ) 312 - 320
1. Introduction
Inventories are defined as raw materials, works in process (WIP) or the finished products that are stored to
meet the demand (Herjanto, 1999; Baroto, 2002). If the amount of inventory is less than the amount of actual
need, the company will loose the opportunity to maximize their sales, gain new customers, get customer
loyalties and gain the maximum profits. While, if they stock too much inventory, it will increase the costs ofmaintenance and storage, so that it will reduce the profits (Sari, 2010). Because of the great impact of inventory
their biggest investments (Winata & Abbas, 2008).Problems that ment
other products are stock out. These problems occur beca use KB can not read the patterns of their customerbuying behavior. This study aims to answer these problems, especially in understanding the customer buying
behavior and prediction of product inventory needs for the next period.2. Customer Buying Behaviour
David & Bitta (1998) defined customer buying behavior as a decision process that makes people choose and
use the products or services. Customer buying behavior is not easy to find out because of the human behavior
itself. Study conducted by Joseph, Pratikto, & Gerry (2006) prove that Market Basket Analysis (MBA) is a
reliable method in understanding the customer buying behavior.2.1 . Prediction of Inventory.
Prediction, especially in the field of inventory, has attracted the attention of researchers and practitioners
nowadays. Many studies have been conducted, one of them is Pujihastuti (2008). Pujihastuti uses moving
average method in a company that has a fluctuating demand and prove that MA is able to accommodate rapid
changes in information and suitable with the condition of the company that has a high variety of products and
raw materials. However, this method is less appropriate when used to predict long-term predictions (Arsyad,
2001).
Another studies on the application of another forecasting methods are such as Exponential Smoothing (Tanuwijaya, 2008), Brown method (Winata & Abbas, 2008), Exponential Smoothing Winter (Paramita &Tanuwijaya, 2010), and Box-Jenkins (ARIMA) (Naibaho, 2009), but due to the limitations of each method
above, it is difficult to apply in the KB. Pratama (1999) uses ANN Backpropagation to predict inventory and
proves that ANN offers so many advantages, it is very accurate and the computation can be done with computer in an efficient algorithm so that the calculation does not take much time.Based on the previous studies above, the ANN method is seen as the most appropriate method to predict the
amount of product inventory needs in KB, so the prediction inventory process in this study will only focus by
using this method.2.2 Market Basket Analysis
MBA is a method in data mining that focus on identification of products that are purchased at the same time
on each transaction. Output of MBA is a set of rules that indicate the products that are purchased on the same
time. This output will be used as input for the prediction of inventory. More detail, the rules generated by MBA are associatio n rules that have form "If antecedent (A), thenconsequent (B)". each rule is equipped with a support level that indicates the number of transactions containing
314 Agus Mansur and Triyoso Kuncoro / Procedia Economics and Finance 4 ( 2012 ) 312 - 320
A and B and confidence level that is a measure of accuracy which is the rule of association rules. Each rule is
also equipped with an expected condition and a lift. For each antecedent (A) and consequence (B), the support,
confidence, expected confidence, and lift are formulated as follows (Yusuf, Pratikto, & Gerry, 2006):
Support = Number of transaction contains A & B/Number of transactions [1] Confidence = Number of transaction contains A & B/Number of transactions contain A [2] Expected Confidence = Number of transaction contains B/Number of transactions [3]Lift = Confidence/Expected confidence [4]
2.3 Artificial Neural Network (ANN)
ANN is a model that mimics the workings of biological neural networks and has been widely applied to theprediction problems (Eliyani, 2007). Backpropagation (BP) is an ANN that has capability to train a network to
be able to give correct response based on the input pattern (Maru'ao, 2010). It is necessary to select input data
(P) or target (T) for this study because the training process that is used is supervised training. The learning
algorithms used in ANN training for this study is the Levenberg Marquardt algorithm. We choose this algorithm because this algorithm is faster (Kusumadewi, 2004).3. Research Methodology
The research methodology of this study briefly describe in figure 1 below. StartProblems Identification
EndLiterature Study
Data Colection
Data Processing:
1. MBA
2. ANN Backpropagation
Discussion and Conclution
A AFig. 1. Research Methodology
We use software named XL miner to handle the MBA process, as shown in Fugure 2.315 Agus Mansur and Triyoso Kuncoro / Procedia Economics and Finance 4 ( 2012 ) 312 - 320
No.Trans Product Freq
items sets (support) Product Probability (confidence) Product1 H13 Malang,
Black couplers
K, H13 3 H13 Malang, Black
couplers K, H13 100% H13 Malang,Black couplers
K, H13
2Keyhole, Key 1.5
cm 5Keyhole, Key 1.5 cm
100%Keyhole, Key
1.5 cm
3Keyhole, Key 1.5
cm 3Keyhole, Key 1.5
cm 100%Key 1.5 cm
Fig. 2. MBA process
There are two steps on the Association Rule Algorithm (Tama, 2010), first find the Frequent Item sets
(probability of combination that frequently appear from a set of items). Second, find the association rules of a set of Frequent Item sets. StartCompute the
feedforward EndCompute the Jacobian
matricsCompute the changes in
the weights and biasesRecompute the
feedforwardRecompute the MSE
New MSE
Min? No YesFig. 3. Detailed of ANN Backpropagation
316 Agus Mansur and Triyoso Kuncoro / Procedia Economics and Finance 4 ( 2012 ) 312 - 320
Fig. 4. Network Architecture
In the fig. 4., shown that the network only has 1 input cell unit, because it only uses 1 variable, namely
number of product. The network also has 1 hidden layer with 5 cell units, it is intended to speed up the
computation process. The output layer only has 1 output cell, because it only has 1output variable, namely
number of product.The Matlab commands are shown as below:
Input and Target Data
P = [160 259 28 146 90 36 108 184 189 110 161 94]; T = [165 261 30 150 95 40 110 190 195 115 165 100];Building the Feed Forward Network
net = newff([28 259],[5 1],{'tansig' 'purelin'},'trainlm');Training Process
net = train(net,pn,tn) TRAINLM-calcjx, Epoch 0/1000, MSE 0.311041/0.001, Gradient 0.363677/1e-010 TRAINLM-calcjx, Epoch 1/1000, MSE 0.300253/0.001, Gradient 0.838133/1e-010 TRAINLM-calcjx, Epoch 2/1000, MSE 0.071804/0.001, Gradient 0.226616/1e-010 TRAINLM-calcjx, Epoch 3/1000, MSE 0.0692973/0.001, Gradient 0.334378/1e-010 TRAINLM-calcjx, Epoch 4/1000, MSE 0.0151635/0.001, Gradient 0.472178/1e-010 TRAINLM-calcjx, Epoch 5/1000, MSE 0.00220436/0.001, Gradient 0.0363479/1e-010 TRAINLM-calcjx, Epoch 6/1000, MSE 0.00109866/0.001, Gradient 0.0118473/1e-010 TRAINLM-calcjx, Epoch 7/1000, MSE 0.000788252/0.001, Gradient 0.00680927/1e-010TRAINLM, Performance goal met.
317 Agus Mansur and Triyoso Kuncoro / Procedia Economics and Finance 4 ( 2012 ) 312 - 320
Fig. 5. Network Training Process
In the Fig. 5. shown that the network training process is stop at the 7 epoch, it because the MSE of network
training (valued 0.000788252) < error goal that has been defined (valued 0.001). It means that the network
performance is well. In the fig. 6. shown that the Target graphic is closed to the Output graphic, it means that
a = Columns 1 through 10166.7375 255.9069 35.6269 151.5184 91.1725 41.7922 110.0364 191.8998 196.9273 112.1806
Columns 11 through 12
167.8133 95.2874
Fig. 6. Comparison of Target and Output
318 Agus Mansur and Triyoso Kuncoro / Procedia Economics and Finance 4 ( 2012 ) 312 - 320
4. Discussion and Conclusion
The data that we used for this research were 564 sales transaction data on a small enterprise that sell antique
furniture accessories, named Karomah Brass, starting from the September 2010 through August 2011. Table 1
contains examples of the data.Table 1 Example of the raw data
Date TransNo Receipt
NoQty Description
Unit Price
(Rp) Total Price (Rp) September 02, 2010 1 124 8 Iron Hinges 3" 3500 280004 Q3 K 7500 30000
4 K8 5000 20000
2 125 6 K4 4500 27000
6 K2 3000 18000
3 126 12 Iron Hinges 2" 8000 96000
Date TransNo Receipt
NoQty Description
Unit Price
(Rp) Total Price (Rp) September 10, 2010 4 127 100 Thick Hinges 3 " 19000 190000096 Thick Iron Hinges 3 " 7000 672000
5 128 6 Key 2.5 cm 7700 46200
6 H13 7500 45000
10 Mini Bolt 1800 18000
Later, the sales transactions data were processed using MBA technique. We used a software named XL Miner Version 3 to generate the rules. We set 3 as the minimum support value and 80% as the minimum confidence. The results were 21 rules, as shown in the table 2 (example) below. From the rule, we can draw the conclusions about the customer buying behavior at KB, such as thealso buy keyhole and key 1.5 cm. From these knowledge, we can suggest to the KB to offer black couplers and
H13 to any customer that buy H13 Malang, and so on.Table 2. The example of MBA generated rules
Rule # Conf.Antecedent (a) Consequent (c)
Support
(a) Support (c) Support (a U c)Lift Ratio
1 100H13 Malang, Black couplers
KH13 3 26 3 21.69231
2 100 Key 1.5 cm 5 60 5 9.4
3 100 Key 1.5 cm 3 60 3 9.4
4 85.71 Iron bolt K. Keyhole Key 1.5 cm 7 60 6 8.057143
5 100 H13. H13 Malang Black couplers K 3 83 3 6.795181
From our observations, all of the products in the generated rules were antique cabinet accessories so that
they were the most frequent products purchased by consumers. We took these items as our main concern for
the next step, inventory prediction, because the most frequent product must be never in stock out condition if
319 Agus Mansur and Triyoso Kuncoro / Procedia Economics and Finance 4 ( 2012 ) 312 - 320
bolt B, iron bolt K, mini bolt, H10, H13, H13 Malang, K1, K2, K4, klik klok, key 1.5 cm, key 2.5 cm, keyhole,
P8 K, black couplers K, and white couplers.
Later, we used ANN backpropagation to predict the need of these items for next year, starting fromSeptember 2011 to August 2012. We used ANN backpropagation for the predictions. The network architectures
were 1 neuron of input layer , 1 layer with 5 neurons of hidden layer, and 1 neuron of output layer. The MSE
ue to the zero because if we set it to zero, the maximum epoch will be reached before we reach the MSE goal, the result is shown in table 3 below. Table 3. The quantity predictions of each item based on ANN BackpropagationNo Items Qty
1 D8 30
2 Iron bolt B 49
3 Iron bolt K 48
4 Mini bolt 54
5 H10 33
6 H13 54
7 H13 Malang 15
8 K1 117
9 K2 302
10 K4 282
11 65
12 Key 1.5 cm 162
13 Key 2.5 cm 232
14 Keyhole 295
15 P8 K 37
16 Black couplers K 1622
17 White couplers 437
5. Future Works
This study is limited to these limitations:
We are not considering the cost of each the product procurement from the supplier. We are not considering the costs that occur as long as the product is in inventory. We are not considering the selection of neural network architectures to be the optimal.So that, we suggest to next research to consider the procurement cost and apply the costs that occur because of
inventory is employed as the input to the determination of the optimum amount of product that will be bought and stored.References
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