Data mining Aided Proficient approach for optimal inventory control in supply chain management

Report as inappropriate Your report has been received

Shared by: ijcsis

Tags

SC cost, SC cost-impact rule, EMA-based inventory, Apriori, Genetic Algorithm (GA), IJCSIS, Computer science issues, google scholar, scirus, Cornell library, journal, research, technology, IEEE

Stats

views:: 143
posted:: 6/10/2010
language:: English
pages:: 8

Public Domain

Document Sample

(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010

Data mining Aided Proficient approach for optimal
inventory control in supply chain management

Chitriki Thotappa Dr. Karnam Ravindranath
Assistant Professor, Department of Mechanical Engineering, Principal
Proudadevaraya Institute of Technology, Hospet. Annamacharya Institute of Technology, Tirupati
Visvesvaraya Technological University, Karnataka, India kravi1949@yahoo.com
thotappa@gmail.com

Abstract— Optimal inventory control is one of the significant supply and demand, globalization, reduction in product and
tasks in supply chain management. The optimal inventory technology life cycles, and the use of outsourcing in
control methodologies intend to reduce the supply chain (SC) cost manufacturing, distribution and logistics resulting in more
by controlling the inventory in an effective manner, such that, the complex supply networks, can lead to higher exposure to risks
SC members will not be affected by surplus as well as shortage of in the SC [8].
inventory. In this paper, we propose an efficient approach that
effectively utilizes the data mining concepts as well as genetic The ultimate goal of every SC is to maximize the overall
algorithm for optimal inventory control. The proposed approach value generated by the chain, which depends on the ability of
consists of two major functions, mining association rules for the organization to fulfill customer orders faster and more
inventory and selecting SC cost-impact rules. Firstly, the efficiently [9]. While the separation of SC activities among
association rules are mined from EMA-based inventory data, different companies enables specialization and economies of
which is determined from the original historical data. Apriori, a scale, many important issues and problems need to be resolved
classic data mining algorithm is utilized for mining association for a successful SC operation which is the main purpose of
rules from EMA-based inventory data. Secondly, with the aid of supply chain management (SCM) [14]. SCM is a traditional
genetic algorithm, SC cost-impact rules are selected for every SC management tool [1] which has attracted increasing attention
member. The obtained SC cost-impact rules will possibly signify in the academic community and in companies looking for
the future state of inventory in any SC member. Moreover, the practical ways to improve their competitive position in the
level of holding or reducing the inventory can be determined
global market [4]. SCM is an integrated approach to plan and
from the SC cost-impact rules. Thus, the SC cost-impact rules
that are derived using the proposed approach greatly facilitate
control materials and information flows [3]. Successful SCM
optimal inventory control and hence make the supply chain incorporates extensive coordination among multiple functions
management more effective. and independent companies working together to deliver a
product or service to end consumers [2]. Inventory control has
Keywords-SC cost; SC cost-impact rule; EMA-based inventory; been considered as a vital problem in the SCM for several
Apriori; Genetic Algorithm (GA). decades [10].

I. INTRODUCTION Inventory is defined as the collection of items stored by an
enterprise for future use and a set of procedures called
Nowadays, supply chains are at the center stage of inventory systems assist in examination and control of the
business performance of manufacturing and service enterprises inventory. The inventory system supports the estimation of
[5]. A SC consists of all parties involved directly or indirectly amount of each item to be stored, when the low items should
and in satisfying a customer request. It includes suppliers, be restocked and the number of items that must be ordered or
manufacturers, distributors, warehouses, retailers and even manufactured as soon as restocking becomes essential. The SC
customers themselves [6]. Because of the intrinsic complexity cost was hugely influenced by the overload or shortage of
of decision making in supply chains, there is a growing need inventories [11]. Since inventory is one of the major factors
for modeling methodologies, which help to identify and that affect the performance of SC system, the effective
innovate strategies for designing high performance SC reduction of inventory can substantially reduce the cost level
networks [5]. Research on supply chains makes an attractive of the total SC [13]. Thus, inventory optimization has
field of study, offering several approach roads to emerged as one of the most recent topics as far as SCM is
organizational integration processes. Some of the problems are considered [11]. Inventory optimization application organizes
considered as most important, which canalize research project the latest techniques and technologies, thereby assisting the
in the area of supply chains that are related to demand enhancement of inventory control and its management across
variability and demand distortion throughout the SC [7]. an extended supply network. Some of the design objectives of
Modern supply chains are highly complex and dynamic; the inventory optimization are to optimize inventory strategies,
number of facilities, the number of echelons, and the structure and thus used in enhancing customer service, reducing lead
of material and information flow contribute to the complexity times and costs and meeting market demand [11].
of the SC [9]. In addition, increases in the uncertainties in

43 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010

Under the influence of the SCM, conventional inventory confidence from a given database. Association rule mining is a
control theories and methods are no longer adapted to the new two step process [27]:
environment [12]. The optimal inventory control
methodologies intended to reduce the SC cost in the SC • Finding those itemsets whose occurrences exceed a
network. They minimize the SC cost by controlling the predefined threshold in the database, these itemsets are
inventory in an optimal manner and so that the SC members called frequent or large itemsets.
will not be affected by surplus as well as shortage of • Generating association rules from those large itemsets
inventory. In order to control the inventory in an optimal with the constraints of minimal confidence.
manner, we propose an efficient approach with the effective
utilization of data mining concepts as well as GA. The rest of The basic problem in mining association rules is mining
the paper is organized as follows. Section II gives a brief frequent itemsets [30]. Frequent item set mining problem has
introduction about the data mining and generating association received a great deal of attention [28] from its introduction in
rules using Apriori and Section III reviews some of the recent 1993 by Agarwal et al [35]. Frequent item sets play an
related works. Section IV details the proposed approach for significant role in several data mining tasks that tries to
optimal inventory control with required mathematical determine interesting patterns from databases, such as
formulations. Section V discusses about the implementation association rules, correlations, sequences, episodes, classifiers,
results and Section VI concludes the paper. clusters and much more [29]. There have been several various
algorithms developed for mining of frequent patterns, which
II. DATA MINING can be classified into two categories. The first category,
Data mining is one of the newly emerging fields, which is candidate-generation-and test approach, such as Apriori and
concerning the three worlds of Databases, Artificial second category of methods includes FP-growth and Tree
Intelligence and Statistics. The information age has enabled Projection [30].
several organizations in order to gather huge volumes of data. Apriori is one of the most popular data mining approaches
But, the utility of this data is negligible if “meaningful for determining frequent itemsets from transactional datasets.
information” or “knowledge” cannot be extracted from it [15]. The Apriori algorithm is the key basis of several other well-
Data mining has been emerging as an effective solution to known algorithms and implementations [31]. The Apriori
analyze and extract hidden potential information from huge algorithm uses two values for rule construction: 1.) a support
volume of data. The term data mining is used for techniques value and 2.) a confidence value. Depending on the setting of
and algorithms that allow analyzing data in order to determine each index threshold, the search space can be reduced, or the
rules and patterns describing the characteristic properties of candidate number of association rules can be increased.
that particular data. [21]. However, experience is necessary for setting an effective
Usually, data mining tasks can be categorized into either threshold [32]. The basic idea of Apriori algorithm is to
prediction or description [18]. Clustering, Association Rule generate a specific size of the candidate projects set, and then
Mining (ARM) [19] and Sequential pattern mining are few scan the database time’s line counts, to determine whether the
descriptive mining techniques. The predictive mining candidate frequent item sets [33].
techniques involve tasks like Classification [20], Regression III. RELATED WORKS
and Deviation detection [34]. Data mining is utilized in both
the private and public sectors. Data mining is usually used by Some of the recent research works available in the
business intelligence organizations, financial analysts and also literature are described in this section. A. L. Symeonidis et al.,
used for healthcare management or medical diagnosis to [36] have introduced a successful paradigm for coupling
extract information from the enormous data sets generated by Intelligent Agent technology with Data Mining. Considering
modern experimental and observational methods [16] [17]. the state-of-the-art Multi-Agent Systems (MAS) development
and SCM evaluation practices, they have proposed a
Generally, Data mining is used to extract interesting methodology to identify the appropriate metrics for DM-
knowledge that can be represented in several various enhanced MAS for SCM and used those metrics to evaluate its
techniques such as clusters, decision trees, decision rules and performance. They have also provided an extensive analysis of
much more. In these, association rules have been proved to be the methods in which DM could be employed to improve the
effective in identifying interesting relations in massive data intelligence of an agent, agent Mertacor. A number of metrics
quantities [25]. Association Rule Mining (ARM), initially were applied to evaluate their results before incorporating the
introduced by Agrawal et al. [35], is a well-known data mining selected model with their agent. Their mechanism proved that
research field [24]. ARM correlates a set of items with other their agent was capable of increasing its revenue by adjusting
sets of items in a database [23]. It aspires to mine interesting its bidding strategy.
correlations, frequent patterns, associations or casual
structures among sets of items in the transaction databases or Steven Prestwich et al. [37] have described a simple re-
other data repositories [22]. ARM has a extensive range of sampling technique called Greedy Average Sampling for
applications in the fields of Market basket analysis, Medical steady-state GAs such as GENITOR. It requires an extra
diagnosis/ research, Website navigation analysis, Homeland runtime parameter to be tuned, but does not need a large
security and so on [26]. ARM is to identify the association population or assumptions on noise distributions. While
rule which satisfies the pre- defined minimum support and experimented on a well-known Inventory Control problem, it
performed a large number of samples on the best

44 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010

chromosomes yet only a small number on average, and was IV. THE PROPOSED APPROACH FOR OPTIMAL INVENTORY
more effective than the other four tested techniques. CONTROL
Mouhib Al-Noukari et al [38] have explained a data In the proposed approach for optimal inventory control,
mining application in car manufacturing domain and two major functions are included, namely, association rules
experimented it. Their application results demonstrated the mining for inventory and recognizing optimal inventory rules
capability of data mining techniques in providing important to be maintained. Prior to perform the two aforesaid functions,
analysis such as launch analysis and slow turning analysis. a database of historical data has to be maintained. The
Such analysis helped in providing car market with base for database holds the historical record of inventory over N p
more accurate prediction of future market demand.
periods in N s SC members, say, [ I ij ] N P × N S ; 0 ≤ i ≤ N P − 1
Tao Ku et al. [39] have presented a complex event mining
and 0 ≤ j ≤ N S − 1 . Initially, the Exponential Moving
network (CEMN) and defined the fundamentals of radio-
frequency identification (RFID)-enabled SC event Average (EMA) is determined for the historical data as
management. Also, they have discussed how a complex event follows
processing (CEP) could be used to resolve the underlying
architecture challenges and complexities of integrating real- I ema lj = I prev lj + α ( I (l + n) j − I prevlj ) ; 0 ≤ l ≤ N P − (n + 1) (1)
time decision support into the supply chain. Finally, a
distributed complex event detection algorithm based on
where,
master-workers pattern was proposed to detect complex events
and trigger correlation actions. Their results showed that their
approach was more robust and scaleable in large-scale RFID  I ema (l −1) j ; if l > 0
application. 

I prevlj =  1 n −1 (2)
Se Hun Lim [40] has developed a control model of SCM
sustainable collaboration using Decision Tree Algorithms

n i=0
∑I ij ; otherwise

(DTA). He has used logistic regression analysis (LRA) and
multivariate determinate analysis (MDA) as a benchmark and
compared the performance of forecasting SCM sustainable The EMA values of the original historical data for N P − n
collaboration through three types of models LRA, MDA, periods, [ I emalj ] ( N P − n) × N S from (1), where,
DTA. Forecasting SCM sustainable collaboration using DTA
was considered as the most outstanding feature. The obtained α = 2 /(n + 1) (termed as constant smoothing factor), is
result has provided useful information of SCM sustainable subjected for a decision making process as follows
collaboration determining factors in the manufacturing and
distributing companies. shortage ; I
 ema lj < I th
Shu-Hsien Liao et al. [41] have investigated functionalities ' 
that best fit the consumer’s needs and wants for life insurance I ema lj = balance ; I ema lj = I th (3)
products by extracting specific knowledge patterns and rules 
excess ; I ema lj > I th
from consumers and their demand chain. They have used the 
apriori algorithm and clustering analysis as methodologies for
data mining. Knowledge extraction from data mining results As given above, EMA-based inventory data
was illustrated as market segments and demand chain analysis '
on life insurance market in Taiwan in order to propose [ I emalj ] ( N P − n)× N S is obtained in which the original
suggestions and solutions to the insurance firms for new historical data is converted into three different states of
product development and marketing. inventory which include shortage, balance and excess.
Xu Xu et al. [42] have proposed an approach that Subsequently, the association rules for inventory are mined
combines expert domain knowledge with Apriori algorithm to from the previously obtained EMA-based inventory data.
discover the pattern of supplier under the methodology of A. Mining Association rules for inventory using Apriori
Domain-Driven Data Mining (D3M). Apriori algorithm of
One of the two major functions of the approach, mining
data mining with the help of Intuitionistic Fuzzy Set Theory
association rules for inventory is described in the sub-section.
(IFST) was employed during the process of mining. The
Mining the association rules for inventory is to find the
obtained overall patterns help in deciding the final selection of
relationship between the inventories of the SC members. In the
suppliers. Finally, AHP was used to efficiently tackle both
proposed approach, we utilize Apriori, a classic algorithm for
quantitative and qualitative decision factors involved in
learning the association rules. Let,
ranking of suppliers with the help of achieved pattern. An
 ' ' ' '  be the itemset taken from
example searching for pattern of supplier was used to  I ema , I ema , I ema , L , I ema 
demonstrate the effective implementation procedure of their  l1 l2 l3 lN S 

method. Their method could provide the guidelines for the
decision makers to effectively select their suppliers in the
the EMA-based inventory data I ema { ' }
lj ( N − n )× N
P S
. The itemset

current competitive business scenario.

45 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010

' {
and the dataset I emalj }( N P − n)× N S
are subjected to Apriori rules are obtained where each set has R 'j number of rules and
for mining association rules. Initially, the Apriori finds the they need not to be in equal number. From the N s set of rules,
frequent itemsets with a minimum support threshold s min , and a rule per each SC member (i.e. a rule per set) is selected using
determines the rule which states the probabilistic relationship GA. The rules are chosen in such a way that they have major
between the items in the frequent itemsets with a minimum impact over the SC cost.
confidence of c min .
B. Selecting SC cost-impact rules using GA
The Apriori determines the association rules from the The obtained rules from apriori are the frequently occurred
frequent itemset by calculating the possibility of an item to be events in the past and so they illustrate that they have a good
present in the frequent itemset, given another item or items is impact over the SC cost, but not strongly. To identify the rules
that have strong impact over the SC cost (SC cost-impact
present. For instance, considering a frequent itemset, I ' ,
emal1 rules), it is essential to consider the shortage cost and holding
cost. It is already known that the SC cost increases, when
I ' and I ' in which a rule may be derived as when either of the shortage and holding costs increases. Hence, by
emal 2 emal 3
considering the shortage or holding cost in the GA, SC cost-
the inventory in I ' and I ' are excess in a period impact rules can be obtained. The process of selecting SC
emal1 emal 2
cost-impact rules using GA is explained as follows
'
l ; l ∈ (0, N P − (n + 1)) , then the inventory in I ema is likely
l3 Step 1: Generate initial
to be shortage. The general syntax of the rule for the aforesaid chromosomes, X a = [ x (a ) x ( a ) x ( a ) L x ( a ) ] ; 0 ≤ a ≤ N pop − 1 ,
0 1 2 N −1
example is given S

' ' '
as ( I ema l1 = excess, I ema l 2 = excess ) → ( I ema l 3 = Shortage) where N pop is the population size. The j th gene of the
; c ≥ c min . Hence, by using the apriori, the association rules chromosome x (ja ) ; 0 ≤ j ≤ N S − 1 is an arbitrary integer in the
are mined with a minimal confidence c min based on the
interval (0, | R 'j | −1) , where, R 'j is the cardinality of the rule
frequent itemset with a minimal support s min .

The mined rules are given as { A}q → {B}q ; set belongs to the j th SC member.
0 ≤ q ≤ N r − 1 , where, { A}q and {B}q are the antecedent and Step 2: Determine fitness of the chromosomes present in
the population pool using the fitness function
th
consequent of the q rule respectively and N r be the
number of association rules generated. The antecedent and 1
consequent consists of one or more items that belongs to the f (a) = (6)
N S −1
 

itemset  I '

ema lj 

(i.e. { A} q ⊆  I '

emalj 

, {B} q ⊆  I '

emalj 
) ∑  C I × µ ema ( R 'j ( x ( a ) )) × c ' ( a ) 
 j j j R j (x j ) 
      j =0  
and also it satisfies { A}q ∩ {B}q = φ . After obtaining the
where,
association rules, they are allocated for j th SC member based
on the consequent of the rules. The final rules after allocation S ; if µ ' (a)
are obtained as follows  cj ema j ( R j ( x j )) < 0

 (a)
CI j =  H c j ; if µ ema j ( R 'j ( x j )) > 0 (7)
R 'j = R j − φ (4) 
0 ; if µ ' (a)
 ema j ( R j ( x j )) = 0
where, 

∑
1
{ A} → {B} ; if I ' ∈ {B}q µ ema j ( R 'j ( x (ja ) )) = I ema k (8)
 q q emalj F (a ) j
Rj =  (5) R 'j ( x j ) k ∈(0, N P − ( n +1))
φ
 ; else
In (6), f (a ) is the fitness value of a th chromosome, C I j
Using (5), the rules R 'j which have the element I ' in
ema lj (determined using (7)) is the inventory cost incurred by the
(a )
the consequent are assigned to the j th SC member. Each SC j th SC member, µ ema j ( R 'j ( x j )) ( determined using (8)) is
member has its own rules that illustrate its inventory’s state the mean EMA value of the I ema l that are taken only from
with respect to other SC member or members. So, N s set of j

46 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010

(a )
the pattern which satisfies the rule R 'j ( x j ) and c ' ( a ) N S −1
C × µ ' best 
R j (x j ) SC best
= ∑  Ij

ema ( R j ( x j )) 

(9)
(a )
is the confidence of the rule R 'j ( x j ) . In (7), S c j is the j =0

shortage cost incurred for a unit of shortage in j th SC N S −1
∑ c R j ( xbest )
1
member, H c j is the holding cost incurred for a unit to hold in c best = (10)
NS j
j =0
the j th SC member. In (8), F ' ( a ) is the frequency of
R j (x j ) Similarly, the mean SC cost and the mean confidence are
(a )
occurrence of data pattern that satisfies the rule R 'j ( x j ) and determined for all the remaining rules in the rule set R 'j . { }
Then, the efficacy is compared by determining the difference
I emak is the EMA value of inventory in j th SC member that between the SC cost and confidence of the final SC cost-
j
impact rule and the mean SC cost and the mean confidence of
  the remaining rules, respectively.
are available in the data pattern, where, I emak ∈  I emal  .
j  j
V. RESULTS AND DISCUSSION
Step 3: Select the best N pop / 2 chromosomes, which
The proposed approach for optimal inventory control has
have minimum fitness, from the population pool. been implemented in the working platform of JAVA (version
Step 4: Crossover the selected chromosomes with a JDK 1.6) and the results are discussed in this section. The
inventory data (weekly data) has been simulated for five years
crossover rate of CR so as to obtain N pop / 2 children
(i.e. N p = 260 ) by considering five SC members
chromosomes.
(i.e. N s = 5 ), an agent A1 and four retailers, R1 , R2 , R3
Step 5: Mutate the children with a mutation rate of MR and R4 . In the simulated inventory data, the negative and
which leads to N pop / 2 new chromosomes.
positive values represent the shortage amount of inventory and
excess amount of inventory respectively. All the SC members
Step 6: Place the N pop / 2 new chromosomes and
have been considered to have the shortage cost and holding
N pop / 2 parent chromosomes in the population pool. cost as S c = Rs.2.50 and H c = Rs.1.00 respectively. The
'
I ema determined from the simulated data with n = 7 is given
Step 7: Go to step 2, until the process reaches a maximum
number of iterations N g . Once the process reaches N g , in the Table I.
terminate it and select the N pop / 2 best chromosomes, which TABLE I. A SAMPLE OF EMA-BASED INVENTORY DETERMINED FROM THE
have minimum fitness value. SIMULATED DATA

The best chromosomes obtained from the GA indicate Sl.
A1 R1 R2 R3 R4
N pop / 2 set of rules in which each set has N s rules (one rule No
1 Excess Shortage Excess Excess Excess
per SC member). From the rule obtained for a particular SC
member, it can be decided that 2 Excess Shortage Excess Shortage Shortage
3 Excess Shortage Excess Shortage Shortage
• The inventory will likely to be as in the rule given for
the inventory of the associated SC members. 4 Excess Shortage Excess Shortage Excess

• Either by reducing or by increasing the holding level The first major function of the proposed approach, mining
of inventory (can be decided from the rule) in the SC association rules for inventory using Apriori has been
member, an optimal level of inventory can be implemented with the aid of data mining software WEKA
maintained in the upcoming days. (version 3.7). The Table II and the Table III consist of some
Hence, by the optimal inventory control, the SC member frequent itemsets with s min = 10% that are discovered from
will not be suffered either by increased shortage cost or by '
the I ema and some of the association rules generated from the
increased holding cost. This ultimately helps to keep the SC
cost in a controlled manner. discovered frequent itemset respectively. The rules that are
categorized based on the consequent are shown in the Table
C. Evaluation of Rules IV.
The efficacy of the rules is demonstrated by comparing the
obtained rules with all the remaining rules. To accomplish
this, the SC cost and the confidence of the rule associated to
the best chromosome are determined as

47 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010

TABLE II. SOME FREQUENT ITEMSETS DISCOVERED FROM
'
I ema AT LENGTHS L1 , L2 , L3 AND L4 , AND THEIR SUPPORT.

Length of
Frequent itemset Support %
the itemset
R1=Shortage 0.536 53.6
L1 R1=Excess 0.476 47.6
R2=Excess 0.6 60
R1=Shortage, R2=Excess 0.316 31.6
L2 R1=Shortage, R2=Shortage 0.22 22
R1=Shortage, R3=Excess 0.224 22.4
R1=Shortage, R2=Excess, R3=Excess 0.128 12.8
L3 R1=Shortage, R2=Excess, R3=Shortage 0.188 18.8
R1=Shortage, R2=Excess, R4=Excess 0.132 13.2
R1=Shortage, R2=Excess, R3=Shortage, R4=Shortage 0.1 10
L4
R1=Shortage, R2=Excess, R3=Shortage, A1 =Shortage 0.104 10.4

TABLE III. SOME GENERATED ASSOCIATION RULES WITH c min = 30% AND THEIR CONFIDENCE

Sl.
Association Rules Confidence %
No
1 R2=Excess, R4=Shortage, A=Excess ==> R1=Shortage 79
2 R1=Excess, A=Excess ==> R3=Shortage 76
3 R1=Excess, R4=Excess, A=Shortage ==> R2=Excess 75
4 R1=Shortage, R4=Shortage, A=Excess ==> R2=Excess 72

TABLE IV. SOME OF THE RULES THAT ARE CATEGORIZED BASED ON THE CONSEQUENT OF THE RULES

Sl. No Rule for A1 Rule for R1 Rule for R2 Rule for R3 Rule for R4
(R1=Excess, (R2=Excess, (R1=Excess, (R1=Excess
(R1=Excess,
R3=Excess) → R4=Shortage, R4=Excess, ,R2=Excess,
1 A1=Excess) →
A1=Shortage A1=Excess) → A1=Shortage) R3=Shortage)
R3=Shortage
R1=Shortage → R2=Excess → R4=Excess
(R1=Shortage, (R4=Excess, (R1=Excess,
(R2=Shortage, (R3=Excess,
R4=Shortage, A1=Excess) → R2=Excess) →
2 R3=Excess) → A1=Excess) →
A1=Excess) → R3=Shortage R4=Excess
A1=Shortage R1=Shortage
R2=Excess
(R1=Shortage, (R3=Excess, (R1=Excess, (R2=Shortage, (R1=Shortage
3 R2=Shortage) → R4=Shortage) R4=Excess) → A1=Excess) → ,R3=Excess) →
A1=Shortage → R1=Shortage R2=Excess R3=Shortage R4=Shortage
(R1=Excess, (R1=Shortage,
(R2=Shortage (R2=Excess, (R2=Shortage,
R3=Shortage, R2=Excess,
4 ,R4=Shortage )→ R4=Shortage) R3=Excess) →
R4=Excess) → A1=Shortage)
A1=Shortage → R1=Shortage R4=Shortage
R2=Excess → R3=Shortage

In selecting the SC cost-impact rules, the GA has been TABLE V. THE RULES ASSOCIATED TO THE CHROMOSOME, WHICH IS GIVEN IN
THE FIG. 1.
initialized with a chromosome length = 5
(i.e. number of genes = 5 ), N pop = 10 and N g = 50 . The Gene. no Associated rules
generated initial chromosome and the rules that are associated 0 R4 = -12.46 --> R1 = -11.7, R3 = -8.64
to the chromosome are given in the Fig. 1 and the Table V, 1 R3 = -11.32 --> R2 = -9.09
respectively. 2 R4 = -12.46 --> R1 = -11.7, R3 = -8.64
3 R2 = -8.84 --> R4= 6.26
4 R1 = -11.91 --> A1 = -45.6

Figure 1. An initial chromosome of length ‘5’ with random values in their The generated chromosomes have been subjected to
genes
crossover with CR = 0.6 and the obtained children have been

48 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010

subjected to mutation with MR = 0.4 . In the mutation, the The final SC cost-impact rules that are associated to the
gene values in the mutation point are changed arbitrarily so obtained best chromosomes are given in the Table VI.
that new chromosome is obtained from the child chromosome.
TABLE VI. SOME OF THE FINAL SC COST-IMPACT RULES ASSOCIATED TO THE BEST CHROMOSOMES OBTAINED FROM GA.

Solution Best SC cost-impact Rules
no. A1 R1 R2 R3 R4
R4 = -11.84 → R4 = -10.33 → R4 = 9.67 →
R4 = -11.62 → R3 = -8.55 →
1 R2 = -10.89, A1 = R1 = -12.02, A1 R1 = 14.12, R3
R2 = -10.02 R4 = -12.11
-45.89 = -46.68 = -12.52
R4 = -11.84 → (R2 = 10.15, A1 R4 = 9.67 →
R4 = -11.62 → R3 = -8.55 →
2 R2 = -10.89, A1 = = 25.94) → R1 R1 = 14.12, R3
R2 = -10.02 R4 = -12.11
-45.89 = -11.56 = -12.52

All the obtained rules in a solution provide their combined impact rules. It could also be decided, whether the inventory
contribution in the SC cost. The SC cost given by the solution has to be reduced or increased in the particular SC member.
was very high in the past records and so, by considering those Also, an EMA level of inventory to be reduced or increased
rules in the solution, the SC cost can be reduced in the future. can also been determined from the obtained SC cost-impact
The cost reduction can be accomplished by inverse holding of rules. Thus, the SC cost will be reduced proficiently by the
inventory that has been obtained as a rule for a particular SC proposed optimal inventory control approach that paves the
member. From Table VI, by keeping 46, 12, 10, 13 and 12 way for effective SCM.
(approximately) units of products additionally in the SC
member A1 , R1 , R2 , R3 and R4 , respectively, the SC cost will REFERENCES
[1] Duangpun Kritchanchai and Thananya Wasusri, "Implementing Supply
be reduced in the future. For evaluation, the SC best , c best Chain Management in Thailand Textile Industry", International Journal
of Information Systems for Logistics and Management, Vol.2, No.2,
(from solution I), SC mean SC mean, c mean have been pp.107-116, 2007.
determined and tabulated in the Table VII. [2] Jennifer Blackhurst, Christopher W. Craighead and Robert B, "Towards
supply chain collaboration: an operations audit of VMI initiatives in the
TABLE VII. COMPARISON OF THE OBTAINED SC-COST IMPACT RULE AND THE electronics industry", Int. J. Integrated Supply Management, Vol. 2, No.

REST OF THE RULES IN THE RULE SET {R 'j } BASED ON THE SC COST AND THE
1/2, pp. 91-105, 2006.
[3] Shen-Lian Chung and Hui-Ming Wee, "Pricing Discount For A Supply
CONFIDENCE OF THE RULE. Chain Coordination Policy With Price Dependent Demand", Journal of
the Chinese Institute of Industrial Engineers, Vol. 23, No. 3, pp. 222-
Sl. Efficacy Factor SC Cost- Rest of the 232, 2006.
no. Impact Rule set {R 'j } [4] Xiande Zhao, Jinxing Xie and Janny Leung, "The impact of forecasting
rule model selection on the value of information sharing in a supply chain",
European Journal of Operational Research, Vol.142, pp.321–344, 2002.
1 Total SC Cost (in Rs.) 222.50 145.40
[5] Shantanu Biswas And Y. Narahari, "Object oriented modeling and
2 Mean Confidence (in %) 51.93 38.60 decision support for supply chains", European Journal of Operational
Research, vol. 153, No. 3, pp. 704-726,2004.
[6] M. Zandieh and S. Molla- Alizadeh- Zavardehi, "Synchronized Production
From Table VII, it can be demonstrated that the SC cost- and Distribution Scheduling with Due Window", in proceedings of
impact rule which is obtained from best chromosome claims Journal on Applied Sciences, vol. 8, no. 15, pp: 2752- 2757, 2008.
more SC cost as well as more frequency of occurrence rather [7] Francisco Campuzano Bolarín, Andrej Lisec and Francisco Cruz Lario
than the all the other rules. Hence, by considering the rule, the Esteban, "Inventory Cost Consequences of Variability Demand Process
optimal inventory can be maintained in all the SC members within A Multi-Echelon Supply Chain", Journal of Logistics and
Sustainable Transport, vol. 1, No.3, 2008.
and so SC can be reduced effectively.
[8] Vasco Sanchez Rodrigues, Damian Stantchev, Andrew Potter and
VI. CONCLUSION Mohamed Naim and Anthony Whiteing, "Establishing a transport
operation focused uncertainty model for the supply chain", International
In the paper, an efficient approach for optimal inventory Journal of Physical Distribution & Logistics Management, Vol. 38 No.
control using Apriori and GA has been proposed and 5, pp. 388-411, April 2008.
implemented as well. For experimentation, we have utilized [9] Mustafa Rawata and Tayfur Altiokb, "Analysis of Safety Stock Policies in
the EMA-based inventory data determined from the simulated De-centralized Supply Chains", International Journal of Production
Research, Vol. 00, No. 00, pp. 1-22, March 2008.
data. The results have shown that the effectual association
[10] Mileff, Péter, Nehéz, Károly, “A new inventory control method for
rules are mined from the EMA-based inventory data using supply chain management”, 12th International Conference on Machine
Apriori. Then, the rules have been categorized based on their Design and Production, 2006.
consequent, followed by the selection of SC cost-impact rules [11] P. Radhakrishnan, V.M. Prasad and M.R. Gopalan, "Optimizing
using GA. The fitness function devised for the GA has Inventory Using Genetic Algorithm for Efficient Supply Chain
performed well in selecting the rules that have high impact on Management," Journal of Computer Science, Vol. 5, No. 3, pp. 233-241,
the SC cost. It could be decided that, the upcoming inventory 2009.
in any SC member will likely be as in the obtained SC cost- [12] Guangyu Xiong and Hannu Koivisto, "Research on Fuzzy Inventory
Control under Supply Chain Management Environment," in proceedings

49 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010

of Applied Simulation and Modelling, pp. 907–916, September 3 – 5, [33] Han Feng, Zhang Shu- Mao and Du Ying- Shuang, "The analysis and
Marbella, Spain, 2003. improvement of Apriori algorithm", in proc. of Journal on
[13] Guangshu Chang, “Supply Chain Inventory Level with Procurement Communication and Computer, vol. 5, no. 9, Sept. 2008.
Constraints”, International Conference on Wireless Communications, [34] S.Shankar, T.Purusothaman, “Utility Sentient Frequent Itemset Mining
Networking and Mobile Computing, 2007, WiCom 2007, p.p. 4931- and Association Rule Mining: A Literature Survey and Comparative
4933, DOI. 10.1109/WICOM.2007.1208. Study”, International Journal of Soft Computing Applications, ISSN:
[14] Peter Trkman and Ales Groznik, “Measurement of Supply Chain 1453-2277 Issue 4 (2009), pp.81-95
Integration Benefits”, Interdisciplinary Journal of Information, [35] R. Agrawal, T. Imielinski, and A.N. Swami, “Mining association rules
Knowledge, and Management Volume 1, p.p. 37-45, 2006. between sets of items in large databases”, In Proceedings of the 1993
[15] Yehuda Lindell and Benny Pinkas, "Privacy Preserving Data Mining", ACM SIGMOD International Conference on Management of Data,
journal of Cryptography, vol. 15, no. 3,2002. pages 207{216, ACM Press, 1993.
[16] David L. Iverson, "Data Mining Applications for Space Mission [36] L. Symeonidis, V. Nikolaidou and P. A. Mitkashave, "Sketching a
Operations System Health Monitoring", NASA Ames Research Center, methodology for efficient Supply chain management agents enhanced
Moffett Field, California, 94035, 2008. through Data mining", International Journal of Intelligent Information
and Database Systems Vol. 2, Issue. 1, pp: 49-68, 2008.
[17] Ping Lu, Brent M. Phares, Terry J. Wipf and Justin D. Doornink, "A
Bridge Structural Health Monitoring and Data Mining System", in [37] Steven Prestwich, S. Armagan Tarim, Roberto Rossi and Brahim Hnich,
Proceedings of the 2007 Mid-Continent Transportation Research "A Steady-State Genetic Algorithm With Resampling for Noisy
Symposium, Ames, Iowa, August 2007. Inventory Control", Lecture Notes in Computer Science, Parallel
Problem Solving from Nature – PPSN X, 2008.
[18] Tibebe Beshah Tesema, Ajith Abraham And Crina Grosan, "Rule Mining
And Classification of Road Traffic Accidents Using Adaptive [38] Mouhib Al-Noukari and Wael Al-Hussan, "Using Data Mining
Regression Trees", In Proc. Of I. J. On Simulation, Vol. 6, No. 10 and Techniques for Predicting Future Car market Demand," in proceedings
11, 2008. of the 3rd International Conference on Information and Communication
Technologies: From Theory to Applications, pp. 1 - 5, 7-11 April, 2008.
[19] F. Coenen, Leng, P., Goulbourne, G., “Tree Structures for Mining
Association Rules”, Journal of Data Mining and Knowledge Discovery, [39] Tao Ku, YunLong Zhu and KunYuan Hu, "A Novel Complex Event
Vol 15, pp: 391-398, 2004. Mining Network for Monitoring RFID-Enable Application," Pacific-
Asia Workshop on Computational Intelligence and Industrial
[20] Hewen Tang, Wei Fang and Yongsheng Cao, "A simple method of Application, Vol. 2, pp. 925-929, 19-20 December, 2008
classification with VCL components", proceedings of the 21st
international CODATA Conference, 2008. [40] Se Hun Lim, "The Design of Controls in Supply Chain Management
Sustainable Collaboration Using Decision Tree Algorithm", in proc. of
[21] Gerhard Münz, Sa Li, and Georg Carle, “Traffic anomaly detection using Intl. Journal on Computer Science and Network Security, vol. 6, no. 5A,
k-means clustering”, in proceedings of GI/ITG-Workshop, Hamburg, May 2006.
Germany, September 2007.
[41] Shu-Hsien Liao, Ya- Ning Chen and Yu- Tia Tseng, "Mining demand
[22] Sotiris Kotsiantis and Dimitris Kanellopoulos, "Association Rules
chain knowledge of life insurance market for new product development",
Mining: A Recent Overview", GESTS International Transactions on
in proc. of Intl. Journal on Expert Systems with Applications, vol. 36,
Computer Science and Engineering, vol. 32, no. 1, pp: 71- 82, 2006.
no. 5, pp: 9422- 9437, July 2009.
[23] Huebner, Richard A., "Diversity- Based Interestingness Measures for
[42] Xu Xu, Jie Lin and Dongming Xu, "Mining pattern of supplier with the
Association Rule Mining", in proc. of ASBBS Annual Conference, vol.
methodology of domain-driven data mining", in proc. of IEEE
16, no. 1, Feb. 2009.
International Conference on Fuzzy System, pp: 1925- 1930, 20- 24 Aug.,
[24] Yanbo J. Wang, Qin Xin and Frans Coenen, "Hybrid Rule Ordering in 2009.
Classification Association Rule Mining", Transactions on Machine
Leaning and Data Mining, vol. 1, no. 1, pp: 1- 15, 2008.
Chitriki Thotappa received the B.E (Mech) and M.E
[25] Rahman AliMohammadzadeh, Sadegh Soltan and Masoud Rahgozar,
(Production Management) degrees from the Department of
"Template Guided Association Rule Mining from XML Documents", in
Mechanical Engineering from Gulbarga and Karnataka
proceedings of the 15th international conference on World Wide Web,
Universities, Karnataka, INDIA in 1991 and 1994
pp: 963- 964, 2006.
respectively, he is currently pursuing the Ph.D. degree in
[26] M.H.Margahny and A.A.Mitwaly, "Fast Algorithm for Mining the field of Supply Chain Management and closely
Association Rules", in proc. of AIML Conference, 19- 21 December working with his research supervisor Dr. Karnam
2005. Ravindranath. He is presently working as a Assistant
[27] Kamrul Abedin Tarafder , Shah Mostafa Khaled , Mohammad Ashraful Professor in the Department of Mechanical Engineering, Proudadevaraya
Islam , Khandakar Rafiqual Islam, Hasnain Feroze, Mohammed Institute of Technology, Hospet. Visvesvaraya Technological University,
Khalaquzzaman and Abu Ahmed Ferdaus, "Reverse Apriori Algorithm Karnataka India and also visiting faculty for Diploma, and P.G Courses. And
for Frequent Pattern Mining", in proc. of Asian Journal on Information is a member for Professional bodies like MISTE and MIE.
Technology, vol. 7, no. 12, pp: 524- 530, 2008.
[28] Bart Goethals, "Memory issues in frequent itemset mining", Proceedings Dr. Karnam Ravindranath received the B.E ( Mech),
of the 2004 ACM symposium on Applied computing, Nicosia, Cyprus, M.E (Industrial Engg.) Degrees from Sri Vekateshwara
pp: 530-534, 2004. University, Tirupati Andra Pradesh India in 1971 and 1976
[29] Bart Goethals, "Survey on Frequent Pattern Mining", Technical report, respectively and later completed his Ph.D from Institute of
Helsinki Institute for Information Technology, 2003. Technology, Delhi in 1985. He worked as a Professor and
Head, Department of Mechanical Engineering and also
[30] M.H.Margahny and A.A.Shakour, "Scalable Algorithm for Mining
Principal of Sri Venkateshwara College of Engineering, Sri
Association Rules", in proc. of AIML Journal, vol. 6, no. 3, Sept. 2006.
Venkateshwara University, Tirupati Andra Pradesh INDIA. During this period
[31] E. Ansari, G.H. Dastghaibifard, M. Keshtkaran and H.Kaabi, "Distributed he has visited Pennsylvania University, USA, and Hamburg University,
Frequent Itemset Mining using Trie Data Structure", in proc. of Intl. Germany and presented papers in International conference. He has awarded
Journal on Computer Science, vol. 35, no. 3, 21 August 2008. best teacher by the Govt. of Andhra Pradesh in 2007, and having a teaching
[32] Ayahiko Niimi and Eiichiro Tazak, "Rule Discovery Technique Using experience of 32 years, he worked as a Dean faculty of Engineering, Chairman
Genetic Programming Combined with Apriori Algorithm", Lecture Board of Studies (UG and PG) and also Dean of Examinations. He has more
Notes In Computer Science, Springer-Verlag, vol. 273- 277, London, than 70 research paper publications in International and National journals in
UK, 2000. his credit. He has produced 7 Ph.D scholars and another 8 are in pipeline.
Presently working as a Principal of Annamacharya Institute of Technology,
Tirupati. And is a member for Professional bodies like MISTE and MISME.

50 http://sites.google.com/site/ijcsis/
ISSN 1947-5500