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1. 19
th
International Conference on Production Research
A HIGH PERFORMANCE MRP PART EXPLOSION PROCESS USING COMPUTATIONAL
GRID IN A DISTRIBUTED DATABASE ENVIRONMENT
Hyoung-Gon Lee
1
, Namkyu Park
2
, Y.S. Hong
1
, Chi-Hyuck Jun
3
, Jinwoo Park
1
1
uCIC (u-Computing Innovation Center), Seoul National Univ., San 56-1, Shillim-Dong, Kwanak-Gu, Seoul,
Republic of Korea
2
Dept. of Industrial & Manufacturing Eng., Wayne State Univ., Detroit, MI 48202, USA
3
Dept. of Industrial & Management Eng., POSTECH, San 31, HyoJa-Dong, Nam-Gu, GyeongBuk, Pohang,
Republic of Korea
Abstract
The part explosion process is one of the key elements of MRP (Material Requirement Planning) systems
which generate plans to provide raw materials and subassemblies in the right amount at the right time for
manufacturing enterprises. However, the process takes up much time as it has to interact with database
intensively, and real time processing was very hard to achieve. Meanwhile, grid computing technology which
aims to utilize unused capacity of computing power internet-wide is being broadly introduced in business.
This paper proposes a grid enabled part explosion process in a distributed database environment and show
the performance improvement by simulation study. In the proposed system, maintenance of MRP consists of
four steps; master data synchronization, job distribution, part explosion, and writing back steps. Particularly,
the part explosion process was found to be much faster using grid resources; accelerated to nearly n times
faster with n nodes when the writing back step is performed afterwards.
Keywords:
Part Explosion, MRP, Computational Grid, Distributed Databases
1 INTRODUCTION
Due to the advances in information technology it has now
become possible for manufacturing enterprises to build
production plans for parts or products for entire supply
chain. Conventionally MRP (Material Requirement
Planning) has been used for efficient planning of material
requirements in a single factory. But more recently,
requirement plans among many production factories are
aggregated to seek for global optimum of a supply network.
To achieve aggregate planning, enterprises have long
struggled with creating, maintaining, integrating, and
leveraging enterprise master data [1]. Issues related to so-
called ‘Master data management’ arose mainly in two
aspects. First, discordance among master data of each
facility units has made it hard to distinguish ‘synonyms’ and
‘homonyms’ for every item. Second, huge size of master
data among the companies in a supply chain has become
hard to manage and resulted in performance degradation in
planning processes. In this research, the latter issue is
considered mainly by improving part explosion process and
former issue is taking into account by assuming all the
master data sets are integrated by same identifications.
The part explosion process in MRP is a repetitive process
which terminates when each requirement plans related to
every parts are traversed. As a result, the process consists
of a series of cycles made up of four steps, namely, netting,
lot sizing, time phasing and BOM explosion as shown in
Figure 1. Detailed explanation on each step follows.
1. Netting: net requirements are computed by subtracting
inventory and scheduled receipts from gross
requirements
2. Lot sizing: net requirements are grouped by batch unit
which appears as lot
3. Time phasing: deciding when to release the order
referring lead time
4. BOM explosion: gross requirements for child items are
calculated according to the requirements received from
former processes for each time bucket
Figure 1: Four steps of every part explosion cycle
From the database operational point of view, the MRP part
explosion process has regular and repetitive query patterns.
And the data source can be separated into two types; the
master data, such as data on parts and BOMs, which
seldom changes in the process, and the transaction data,
which is modified according to the planning result and time
horizon. Basically, the series of part explosion processes
can be completely parallelized if we assume infinite
production capacities, whereas it can be partially
parallelized when finite capacities are considered. These
observations are the primary motivation for applying grid
computing technology for part explosion process.
2 LITERATURE REVIEW
2.1 Performance improvement for MRP systems
Past studies for improving the performance of MRP can be
divided into two types; those for improving the part
explosion process and those for reducing MRP
nervousness. These issues are interrelated, and the MRP
nervousness used to be the key problem to be resolved in
MRP. Most previous studies attempted to solve this
problem by the lot-sizing method. However, the lot-sizing
method often needs to be tailored to the specific

2. manufacturing environment involved [2, 3], and there is no
guarantee that an optimal solution can be found [4].
However, there have been few studies dealing with the
computational process or methodology required to execute
MRP part explosion. Recently, a main memory based part
explosion process considering the hierarchical structure
among part requirements was proposed claiming a
remarkable performance improvement, but critical
performance decline was observed above the level 13 of
BOM [5]. Due to the overload of CPU capacity, parallelizing
and balancing the computational load among distributed
resources was remained for further research.
2.2 Computational grid
In recent years, the notions of grid computing have
emerged. A grid [6] is a very large scale, generalized
distributed network computing system that can be scaled to
internet-size environments with computers distributed
across multiple organizations and administrative domains.
Krauter [7] placed grid systems into three categories:
computational grid, data grid, and service grid. The three
different design objectives behind those groups are:
improving application performance, data access, and
enhanced services, respectively. Since this paper mainly
focuses on improving the speed of the MRP process, the
computational grid area is mainly discussed.
From the parallel computation algorithms perspective, a
computational grid cannot be seen as a brand-new concept,
since the basic idea incorporates the procedure to let
multiple processors cooperate in solving a huge
computational process. Thus, theoretical issues that occur
in the parallel computing field still remain in the
computational grid. One of the famous and well-known
theories regarding the performance of parallel computing is
Amdahl’s law [8].
of domain knowledge is needed, since parallelization at the
level of compiler is very limited [12].
Figure 2: Speedup limit proposed by Amdahl’s law
Most research on the grid involves the development of
several types of grid middleware [13, 6] capable of solving
the various problems caused by sharing resources through
the wide area network. In particular, the Master-Worker
(MW) paradigm [14] is often involved for application studies
concerning computational grid. A Master machine delegates
tasks to Worker machines, which report the results of these
tasks back to the Master. Tree search algorithms [15],
genetic algorithms [16], parameter analysis for engineering
design [17], and Monte Carlo simulations [18] are just a few
examples of natural MW computations. Most of all, a study
by Anstreicher [19], to solve the large quadratic assignment
S p = 1
<
1
f +
1 − f f
p
(1) problems, is regarded as a monumental result regarding the
huge problem to be resolved by computational grid
employing the MW paradigm.
On the other hand, the major difference between a data grid
Ideally, p number of processors employed might accelerate
a given process p-fold faster. However, Amdahl argued for
the presence of a certain speedup limit (or upper bound),
defined by above formula, where f refers to the
unparallelizable portion of a given process, as shown in
Figure 2. For example, according to the law, if the f ratio is
given as 0.05, even infinite processors would only produce
a speedup ratio (Sp) of 20 times.
Nevertheless, many reports found that the ratio f is not
independent of the input size n and is usually diminished
with increasing input size [9]. Furthermore, an extremely
data intensive process such as the MRP part explosion
might show some other patterns against Amdahl’s law.
Led by the popularity and visibility of SETI@home [10],
other new projects and companies have sprung up in the
field of grid computing. Today, grid technology has evolved
to the point where it is no longer a theory but a proven
practice. It represents a viable direction for corporations to
explore grid computing as an answer to their business
needs within tight financial constraints.
The rationale that leads to computational grid application on
conventional problems can be summarized from two
perspectives. First, all personal computers have completely
unused capacities of more than 90 % [11]. Hence, utilizing
this idle capacity to support computationally expensive
procedures has become a crucial issue concerning the IT
infrastructure of an enterprise. Second, human effort to
parallelize the problem based on a thorough understanding
and a computational grid is the specialized infrastructure
provided to applications for storage management and data
access. Nowadays, major ERP vendors such as SAP and
Oracle are offering to provide server consolidation solutions
to their customers by means of data grid technologies [20].
The system proposed in this paper takes advantage of
computational grid dominantly to gain the aggregated CPU
power for parallelized processes. Furthermore, the
enhancement of this improvement is demonstrated by
assuming the provision of an intelligent data grid.
3 GRID ENABLED PART EXPLOSION SYSTEM
Our first goal is to develop a parallel part explosion
processor that can efficiently harness the available
resources of a computational grid. For the convenience of
the present discussion, several terms are defined as listed
in Table 1.
A notable property behind this design is that the
Requirements Structure has a hierarchical characteristic
similar to that of Global BOM. As a result, the transaction
data bottled in the Requirements Structure and distributed
by the Master Task Pool to Workers are completely used in
parallel without any communication among them, assuming
an absence of any capacity limit for production resources.
The whole mechanism is illustrated in Figure 3.

3. 19
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International Conference on Production Research
Table 1: Several terms to describe a grid enabled part
explosion system
Terms Description
Global BOM BOM that contains every parent-
child relationship among all the
parts involved in the supply chain.
By the way, since part explosion process is merely a part of
MRP engine, implementation issue for master data
management or maintenance has to be resolved as well. To
design the MRP engine realistic which operates through the
supply chain with highlighting the excellence in the
improvement of part explosion performance, several
assumptions have been made to design and implement the
MRP engine on the grid.
Requirements
Structure
Master Task
Pool
Data objects, which include
transaction data for each cycle
element of the part explosion
processes
List structure, which includes
tasks that are ready to be sent to
Workers from a Master in MW
paradigm
• Master DB is installed for every Worker in the supply
chain.
• Master DB is integrated in its semantic aspect.
• Master DB keeps its consistency when any single node
of Worker alters their master DB considering that any
modification of master DB has to be agreed among all
the participants in the supply chain.
Figure 3: Parallelized part explosion process on the grid
Including the part explosion process, the proposed MRP
engine consists of four other processes: 1) master DB
synchronization process, 2) distributing the part explosion
process, 3) the part explosion process for each Worker, and
4) the transaction DB report process or writing back
process, as shown in Figure 4. Initially, the master data has
to be established by distribution and this is not regarded as
a regular MRP processes.
Another prototype for the MRP engine is proposed, which
neglects the writing back process during the part explosion
process. Transaction data, which are produced from
Workers during the part explosion process, are not
delivered back to the Master instantly, considering that the
data grid service is provided.
In this prototype, the participants in the supply chain are
assumed that they do not need to search all the
requirements yielded by the part explosion. Furthermore, if
they need to search for other results, they can find them
easily since the transaction data possess their order
pegging code (order id, item id), included for themselves,
which indicates the order and item information, as shown in
Figure 5. Thus, the data grid, which manages the
distributed DBs through the supply chain, is able to respond
to the Worker’s request to seek for transaction data when
asked for, as depicted in Figure 6.
4 OVERVIEW OF THE EXPERIMENTS
4.1 Data set
A binary-tree type BOM was configured, in which one
parent part had two child parts, as shown in Figure 6. The
Figure 4: Four processes of the MRP engine including the part explosion process

4. Figure 5: Transaction data with their own pegging information
Figure 6: Part explosion process enhanced by data grid
depths were set in the range from 11 to 13, since a data set
with a certain degree of depth was needed to observe the
impact of applying the computational grid. Although it is
unrealistic to assume BOM as a binary type there are
several reasons for the choice. First, it is easy to generate
and experiment on at BOM levels even higher than 10.
Second, estimating the performance is simple because the
sub-nodes are evenly distributed. Third, separating the
nodes for job distribution is comfortable.
Figure 6: Binary tree type BOM
4.2 N*Grid Environment
Here are described some of the attractive features offered
by N*Grid which featured the following advantages [21].
First, the MW paradigm is supported with ease. Second,
platform independency, plug-in type application installation,
and web service resource framework (WSRF) are
supported. Third, it is the first commercially successful, grid
middleware that operates on heterogeneous operating
systems in Korea. The number of Workers was limited into
8 nodes, because the N*Grid that is supported was an
academic version.
4.3 Experimental Treatments
Table 2: Several terms to describe a grid enabled MRP
Treatment Description
Current A single resource executes part explosion
process by itself.
grid-CMCT MRP process is performed by several
Workers with retrieving master data from
Master’s DB, and then store the
transactional DB to the Master’s DB also.
grid-DMCT Master data is distributed to Workers in
advance, and then MRP process is
performed by those Workers. Resulting
transactional data yielded are stored into
Master’s DB at every stage of the process.
grid-DMDT Master data is distributed to Workers in
advance, and then MRP process is
performed by those Workers. Resulting
transactional data yielded are stored into
Worker’s temporary DB at every stage of
the process.
In the case of the grid enabled part explosion process with
no capacity constraint, we conducted a series of
experiments to verify that our prototype improves the
process. Four independent groups are designed for
different design rules: current, grid-CMCT (Centralized
Master Data; Centralized Transactional Data), grid-DMCT
(Decentralized Master Data; Centralized Transactional
Data), and grid-DMDT (Decentralized Master Data;
Decentralized Transactional Data). The current model
performed the MRP process in a single server without
utilizing other grid resources in the manner that
conventional MRP does. Grid-DMCT and grid-DMDT were
implemented for centralized writing back and decentralized
writing back respectively. Grid-CMCT was devised to test

5. 19
th
International Conference on Production Research
BOM
11
No. of Workers
Current
time
144.7(Sec.)
Sp
--
time
144.7
Sp
--
time
144.7
Sp
--
Grid(2) 129.1 12.08% 125.3 15.48% 49 195.31%
Grid(4) 117.2 10.15% 109.2 14.74% 24.9 96.79%
Grid(8) 107.7 8.82% 96.5 13.16% 12.8 94.53%
12 Current 298.6 -- 298.6 -- 298.6 --
Grid(2) 233.6 27.82% 223.3 33.72% 83.9 255.90%
Grid(4) 207.9 12.36% 194.5 14.81% 43.3 93.76%
Grid(8) 188 10.59% 173 12.43% 22.9 89.08%
13 Current 613.9 -- 613.9 -- 338.1 --
Grid(2) 441.1 39.17% 420.9 45.85% 88.7 295.81%
Grid(4) 393.2 12.18% 368.3 14.28% 60.1 91.48%
the speedup effect by distributing the master data through
the grid resources. These four treatments are summarized
in Table 2.
4.4 Experimental Results
The part explosion duration time among the number of
nodes involved is compared according to the BOM depth
level for (a) grid-CMCT, (b) grid-DMCT, and (c) grid-DMDT.
Increasing the number of Workers reduces the MRP
processing time. However, the speedup scale among the
three cases shows variation. The speedup ratios (Sp) from
the Amdahl’s formula are presented in Table 3.
Grid-CMCT and grid-DMCT obeyed Amdahl’s law correctly.
They exhibited a speedup far from the ideal of 100%, and
the ratio decreased with increasing number of Workers. The
results from grid-DMDT presented interesting patterns. The
speedup ratio between current and grid(2) exceeded 100%,
and other improvements approached 100%. Further
sophisticated analysis will be required to investigate the
abnormal speedup breakthrough between current and
grid(2). It is presumed that neglecting the congestion flow
into Master and the speed variance between Master’s DB
and Worker’s DB operation resulted in a flash improvement.
In the case of the remaining speedup effect for a multiple
number of Workers, the result shows that the most time
consuming operation was writing the transactional data
back into Master’s DB. As the MRP part explosion is a data
intensive process which results in sensitive behavior
according to the data flow congestion, rather than merely a
simple calculation, this notable result for grid-DMDT group
can be reasonably and intuitively expected as well.
5 CONCLUSION
Duration time for part explosion process was considerably
reduced by applying computational grid activated on
resource suppliers. Furthermore, if the assumption that
every requirement plan has to be stored in a single
database is violated, the performance could even reach to n
times better for n nodes of participants. Although it could be
claimed that this is not a genuine improvement in the sense
that n processors can only give an n-fold increased
performance, the proposed method to harness unused
computing resources to enhance the process efficiency has
fair practical importance as such resources are manifestly
available and well suited to the demanding computational
nature of the MRP process. Proposed system also provides
a solution for synchronizing master data, so that efforts for
managing them may be reduced. The part explosion
process that does not consider capacity constraints could
be parallelized completely, but this is not easy for finite
capacitated MRP which remains to be studied further
6 ACKNOWLEDGMENTS
This research was primarily supported by the project,
'Development of a Knowledge-based Collaborative
Manufacturing System', one of the 'Next Generation New
Technology Development' programs funded by the MOCIE
(Ministry of Commerce, Industry and Energy), Republic of
Korea. We would also like to acknowledge financial and
administrative supports from ‘Seoul R&BD Program’
sponsored by the Seoul Metropolitan Government, ‘Brain
Korea 21 Project’ sponsored by the Korean Research
Foundation, and ASRI (Automation and Systems Research
Institute) in Seoul National University.
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Table 3: Speedup ratio test among grid enabled MRP models
Depth
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m Grid(8) 354.3 10.98% 328.7 12.05% 44.3 86.21%

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7. [8] Amdahl G., 1967, Validity of the single processor
approach to achieving large scale computing
capabilities, Proceedings of the American Federation
of Information Processing Societies.
[9] Berman K. A., Paul J. A., 2005, Algorithms:
sequential, parallel, and distributed, Boston: Thomson.
[10] Korpela E., Werthimer D., Anderson D., Cobb J.,
Lebofsky M., 2001, SETI@home – Massively
distributed computing for SETI, Computing in Science
& Engineering, 3, pp. 78-83.
[11] Miller M., 2001, Discovering P2P, San Fransisco:
Sybex.
[12] Allen J. R., Kennedy K., 1984, PSC: A program to
Convert FORTRAN to parallel form, Supercomputers :
Design & Application, IEEE Computer Science Press.
[13] Litzkow M., Livny M., Mutka W., 1988, Condor – A
Hunter of Idle Workstations, Proceedings of the 8
th
International Conference on Distributed Computing
Systems, pp. 101-111.
[14] Goux J., Kulkarni S., Yoder M., Linderoth J., 2001,
Master-Worker: An Enabling Framework for
Applications on the Computational Grid*, Cluster
Computing, 4, pp. 63-70.
[15] Kumar V., Ramesh K., Rao, V. N., 1988, Parallel best-
first search of state-space graphs: A Summary of
results, Proceedings of the 1988 National Conference
on results, Proceedings of the 1988 National
Conference on Artificial Intelligence, pp. 122-127.
[16] Cantu-Paz E., 1998, Designing efficient master-slave
parallel genetic algorithms, Proceedings of the 3
rd
Annual Conference of Genetic Programming.
[17] Abramson D., Sosic R., Giddy J., 1995, Nimrod: A tool
for performing parameterised simulations using
distributed workstations, Proceedings on High
Performance Distributed Workstations.
[18] Basney J., Raman R., Livny M., 1999, High throughput
Monte Carlo, Proceedings of the 9
th
SIAM
Conferences on Parallel Processing for Scientific
Computing.
[19] Antreicher K., Brixius N., Goux J., Linderoth J., 2002,
Solving large quadratic assignment problems on
computational grids, Mathematical Programmiong, 91,
563-588.
[20] Sun, 2004, Sun Infrastructure Solution for N1 Grid for
SAP Solutions, Whitepaper of Sun Microsystems.
[21] National Grid, 2006, N*Grid Middleware, Brochure of
the National Grid.