An Information Sharing Method for Skilled Management Operations
based on Bayesian Network Inference
Takayuki Kataoka
1
, Kazumoto Tanaka
1
, Masakazu Kanezashi
1
and Makoto Hasegawa
2
1
Faculty of Engineering, Kinki University, Takaya Umenobe 1, Higashi-Hiroshima, Hiroshima, Japan
2
LORIA, UMR7503, 54506, Vandoeuvre-les-Nancy, France
Keywords: Bayesian Network, Information Sharing, Management Operation.
Abstract: Given the poor state of the economies all over the world, almost every manufacturing site has been
supported by a lot of part-time, temporary, or mid-career personnel. And expert managers of front-line
workers must design more complex human resource strategies that take into consideration the workers’
skills. However, tacit knowledge existing only in the minds of expert managers is very difficult to capture
with most organizations depending entirely on the explicit knowledge. Therefore, the purpose of our study
is to develop a model with a bayesian network using the operation histories of expert managers, and to
verify some factors that would make it easier for nonexperts to assign human resources. First, the operation
histories are collected. Next, some differences of human resource planning procedures for expert managers
and nonexperts are discussed by dividing into the purposes of either minimizing makespan or workload.
Finally, the effectiveness of the expert managers’ operations is verified by constructing a bayesian network
model based on the operation histories, and is discussed by way of probabilistic inference.
1 INTRODUCTION
Recently, the number of regular, full-time
employees has been decreasing at general
production factories. On the other hand, the number
of part-timers or temporary employees has been
increasing. For a long time, most companies had
recruited, trained and held onto many talented
people as their regular employees. However, it is
difficult to support all of the regular employees
without outsourcing due to poor economic
conditions. Therefore, the leaders of the front-line
workers must assign each worker appropriate tasks
according to the quantity and quality of work.
Mohanty et al. discussed the evolution of a
decision support system (DSS) for human resource
(HR) planning in a petroleum company. The DSS
helped the HR division of the company analyze HR
decisions to overcome many problems, to cut down
on delays in implementing new projects and to
expand the business into new areas (Mohanty,
1997). Further, Parush et al. showed the impact of
visualization and contextual factors on performance
with enterprise resource planning (ERP) systems.
They showed that some graphic information
visualization displays for ERP systems can increase
the probability of a successful implementation and
enhance the capabilities of the human operators
(Parush, 2007).
Furthermore, Abdinnour-Helm et al. studied the
pre-implementation attitudes and organizational
readiness for implementing an ERP system. Despite
an extensive amount of time, money and effort, the
length of time with a firm and position had a greater
impact on attitudes toward ERP capabilities, value,
acceptance and timing than high levels of pre-
implementation involvement (Abdinnour-Helm,
2003). Youngberg et al. discussed the determinants
of professionally autonomous end user acceptance in
an ERP system environment. The study surveyed 66
professionally autonomous end users and gathered
information on their perceptions related to several
technology acceptance factors for a newly installed
ERP system component (Youngberg, 2009).
On the other hand, Corominas et al. studied the
planning of annualized hours with a finite set of
weekly working hours and cross-trained workers
(Corominas, 2007). Lusa et al. also attempted to
determine the most appropriate set of weekly
working hours for planning annualized working
time. Their paper proposes a method for selecting
the most appropriate set of weekly working hours
257
Kataoka T., Tanaka K., Kanezashi M. and Hasegawa M..
An Information Sharing Method for Skilled Management Operations based on Bayesian Network Inference.
DOI: 10.5220/0004116102570260
In Proceedings of the International Conference on Knowledge Management and Information Sharing (KMIS-2012), pages 257-260
ISBN: 978-989-8565-31-0
Copyright
c
2012 SCITEPRESS (Science and Technology Publications, Lda.)
and establishing an annual plan or working time for
each worker as a way of optimizing service levels
(Lusa, 2008).
However, most HR planning systems do not
consider workers’ skills. According to hearing
surveys with some small and medium-sized
enterprises (SMEs), it is enough to have even the
simple support tool that a sub-leader, rather than the
leader, can redesign the current strategy using an
interactive interface.
Thus, it is used that a tool which has already
developed with “An Inference Method of
Management Operations using Bayesian Networks
(Kataoka et al, 2010)” using the Program Evaluation
and Review Technique (PERT) to collect a lot of
operation histories. That is the reason why our
cooperative researcher is a middle iron work’s
company in Japan.
Furthermore, our paper has also already
suggested an inference method of management
operations using bayesian networks (Kataoka et al.,
2011). However, it is not enough that the
effectiveness of an inference method is shown in
only a sample data of human resource planning.
Therefore, this paper researches to develop a
model with a bayesian network using the operation
histories of expert managers, and to verify some
factors that would make it easier for nonexperts to
assign human resources.
2 BAYESIAN NETWORK
A Bayesian network is one of the probabilistic
models which express the dependence between
random variables by a conditional probability.
Figure 1: Bayesian network model.
Based on a sample data, each variable has
already classified into three categories and each
causal relationship has also already specified in our
paper as shown in Figure.1 (Kataoka et al., 2011).
Firstly, some factors like worker’s skill or
operational targets are arranged as parent nodes.
Next, operational processes from 1st operation to
10th are arranged with causal relationships to the
increase or decrease value of makespan and
workload. Every operational process is recorded to
show the difference between the expert manager and
nonexperts clearly. Lastly, some results are arranged
with causal relationships to operational processes.
3 SKILLED OPERATIONS
3.1 Makespan and Workload
In the operation purpose to minimize makespan, it is
verified that the result of the expert manager who
will greatly decreases makespan time at the first
stage is better. Figure 2 shows a graph where the
mean value of the increase or decrease in workload
when makespan times of each operation decrease by
one unit is shown. There are not so many differences
in the increase or decrease of both experts and
nonexperts at the first stage. However, the expert
manager greatly decreases the workload in the latter
half. As a result, it can be judged that the expert
manager is finally obtaining a great result for both
makespan and workload.
Figure 2: The increase or decrease ratio of reworks to a
decrease per unit time.
3.2 Reworks
Figure 3 shows the mean value of makespan and
workload in the case that some reworks will occur or
not to minimize workload. The decision to minimize
Results
Factors
T : Makespan
W: Workloa
d
Operator Criteria
1stT
1stW
2ndT
2ndW
3rdT
3rdW
4thT
4thW
5thT
5thW
6thT
6thW
7thT
7thW
8thT
8thW
9thT
9thW
10thT
10thW
Total W
Last T
WConst
Back
T : Makespan
W: Workload
Operational Process
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workload is done without decreasing makespan.
Therefore, workload is approached to zero at the last
stage, and makespan is shortened. As a result, many
reworks will occur because operators fall greatly
into disorder.
Figure 3: Difference of procedure in reworks.
4 EXPERIMENT
4.1 Sample Data
As a comprehensive experiment, 4 sample data is
prepared as shown in Table 1. Some small scale
projects might be defined to have 100-200 hours as
total operation time (TepStep, 2011).
Therefore our study assumes that the number of
maximum process is 35 and the number of minimum
process is 14 in case of 7 hours per process.
Table 1: Sample data for comprehensive experiment.
4.2 Experimental Results
Modes of experimental results for each sample are
shown in Table 2. CPT means Conditional
Probability Table. In case of many processes
(Sample 0 and 1), experts could operate the tool
better than nonexperts. Especially, it seems that
nonexperts could not understand the way to decrease
total workload as shown in the results of ‘Minimum
Total Workload’.
In case of less workers (Sample 1 and 3), it
seems that nonexperts tend to rework a lot.
Table 2: The first result of comprehensive experiment.
4.3 Instructions to Nonexperts
Instructions expected to be effective for the
improvement are shown in the following.
<Common instructions>
Operate to decrease the schedule from the left or
the right point to avoid overlaps on critical path.
Operate up to 1st-4th operation (Sample0:1,
Sample1:2, Sample2: 4, Sample3: 3) to minimize
makespan as a first object.
<Only to sample 0 and 1>
Assign a worker who takes a long operation time
for a task in case of ‘Minimum Total Workload’.
<Only to sample 3>
Operate to decrease the most overlapped
workload for a task in case of ‘Minimum Total
Workload’.
4.4 Verified Results
The result that nonexperts try it again based on the
instructions of 4.3 is shown in Table 3.
As a result, almost samples are improved.
Additionally, nonexperts got to tend not to rework as
shown in Table 4.
4.5 Inductive Modelling
An information sharing model (:inductive
modelling) based on our experimental results is
shown in Figure 4.
Firstly, a set of experts’ skilled operation
histories is prepared. Next, nonexpert’s operations
are compared with a set of skilled operations in real
time, and some instructions are given to a nonexpert
based on differences and causal relationships of
bayesian networks. Lastly, a bayesian database is
Process Worker
Sample0 35 10
Sample1 35 4
Sample2 14 10
Sample3 14 4
Mode CPT Mode CPT Mode CPT Mode CPT
Sample0 Makespan 136~140 0.72 136~140 0.30 216~220 0.67 206~210 0.30
Workload 131~135 0.25 136~140 0.08 21~25 0.24 46~50 0.07
Sample1 Makespan 131~135 0.68 141~145 0.25 206~210 0.29 216~220 0.14
Workload 176~180 0.08 196~200 0.04 91~95 0.21 151~155 0.05
Sample2 Makespan 66~70 0.69 66~70 0.53 66~70 0.50 66~70 0.38
Workload 0 0.69 0 0.35 0 0.08 0 0.73
Sample3 Makespan 96~100 0.90 96~100 0.90 111~115 0.53 111~115 0.53
Workload 11~15 0.92 11~15 0.92 1~5 0.71 1~5 0.71
Minimum Makespan
Expert Nonexpert Expert Nonexpert
Minimum Tota l Workload
T : Makespan
W: Workloa
d
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259
updated, and next operation is considered with an
object.
Table 3: The result of comprehensive experiment.
Table 4: The total number of reworks.
Figure 4: Information sharing model.
5 CONCLUSIONS
This paper showed an information sharing model
with a bayesian network using the operation
histories of expert managers, and verified some
factors that would make it easier for nonexperts to
assign human resources.
In the future, the decision of HRP to assume all
situations needs to be verified, and the improvement
of further inference accuracy is requested.
ACKNOWLEDGEMENTS
This work was supported by KAKENHI 23710186.
REFERENCES
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Parush, A., Hod, A. and Shtub A., 2007, Impact of
visualization type and contextual factors on
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Issue 1, 133-142.
Abdinnour-Helm, S., Lengnick-Hall, M. L. and Lengnick-
Hall, C. A., 2003, Pre-implementation attitudes and
organizational readiness for implementing an
Enterprise Resource Planning system, European
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273.
Youngberg, E., Olsen, D. and Hauser, K., 2009,
Determinants of professionally autonomous end user
acceptance in an enterprise resource planning system
environment, International Journal of Information
Management, 29, Issue 2, 138-144.
Corominas, A., Lusa, A. and Pastor, R., 2007, Planning
annualized hours with a finite set of weekly working
hours and cross-trained workers, European Journal of
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Lusa, A., Pastor, R. and Corominas, A., 2008,
Determining the most appropriate set of weekly
working hours for planning annualized working time,
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Issue 2, 697-706.
Kataoka, T., Tanaka, K., and Hasegawa, M., 2010,
Interactive Model for Human Resource Planning In
Operating a group of Different Cycle Time,
Proceedings of The 21st IASTED International
Conference -MODELLING AND SIMULATION-, July
15-17, Banff, Canada, pp.322-329.
Kataoka, T., Kanezashi, M., Morikawa, K. and Takahashi,
K., 2011, An Inference Method of Management
Operations using Bayesian Networks, Proceedings of
21st International Conference on Production Research,
July 31-August 4, Stuttgart, Germany, p.230(6P).
TepStep Japan, 2011, Definition of Operation - Decision
of Project Scale, p5/13.
Expert
Before After
Makespan 146.4 141.8 137.6 3.1%
Total Workload 153.4 152.4 140.3 0.6%
Makespan 205.4 214.8 216.9 4.5%
Total Workload 79.9 36.2 32.3 54.6%
Makespan 148.2 131.9 132.8 10.9%
Total Workload 203.3 161.9 176.1 20.3%
Makespan 210.6 170.6 196.1 18.9%
Total Workload 163.8 120.4 100.7 26.4%
Makespan 67.4 66.0 66.0 2.0%
Total Workload 3.6 0.0 0.0 100.0%
Makespan 88.1 68.1 66.7 22.7%
Total Workload 0.3 0.0 0.2 100.0%
Makespan 92.3 97.2 96.0 5.3%
Total Workload 46.2 15.1 14.5 67.3%
Makespan 129.4 113.2 112.3 12.5%
Total Workload 19.9 6.7 5.5 66.4%
Sample 3
Minimum
Makespan
Minimum
Total Workload
Sample 1
Minimum
Makespan
Minimum
Total Workload
Sample 2
Minimum
Makespan
Minimum
Total Workload
Nonexpert
Improvement
Rate
Sample 0
Minimum
Makespan
Minimum
Total Workload
Before After
Sample0 050
Sample1 0 31 0
Sample2 040
Sample3 3 33 0
Expert
Nonexpert
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