Download Comprehensive Evaluating Company HRM Performance Based on BP Neural Network Algorithm

Comprehensive Evaluating Company HRM Performance Based on
BP Neural Network Algorithm *
LIU Zhi Bin, XIONG Feng Shan
Department of Economics & Management, North China Electric Power University, P.R.China, 071003
School of Business, Agricultural University of Hebei, P.R.China, 071001
Abstract As a subsystem in the whole enterprise systems, effective function of human resources
management (HRM) system will determine the existence and development of the enterprises. To
evaluate the effectiveness of HRM and find the existed problems will improve human resources
management and promote the realization of the strategic goals of enterprises. In order to evaluate the
HRM effectiveness scientifically and accurately, according to the principle of BP neural network, this
paper proposes the BP neural network model for the HRM evaluation. The HRM evaluation of 12
enterprises in Hebei Province shows that the results given by this model are reliable, and this method to
evaluate the HRM effectiveness is feasible.
Keywords BP neural network algorithm, Human resource management, Comprehensive evaluation
1 Introduction
Human resources management evaluation is the cost-benefit measurement of the overall human
resources management activities, and compare with the past organization's performance, the similar
organizations' performance and the organizational goals. James Harrington, a manager, once said:" The
measurement is the key. If you can not measure it, you can not control it. If you can not control it, you
can not manage it. If you can not manage it, you can not improve it." In 1984, Wernerfelt first
interpreted the view of "Resource-Based View" (RBV) completely. In 1991, Barney further developed
and expounded this view. In this view, the resources and abilities are heterogeneous, the heterogeneity
decided the performance of different enterprises in the same industry is also different. According to
RBV, the enterprises can create value using a rare and difficult imitated way to theirs competitors. The
human resources are precisely this kind of resources, and when it reaches its way into the operating
system, it is not only able to create value and increase the strength of the enterprises, but this
competitive advantage is hard to imitate. Nowadays, more and more enterprises recognize the
importance of human resources management, but how to measure the performance of human resources
management is a major issue that troubled the enterprises.
Human resources management evaluation is a systematic evaluation process, and a scientific and
quantitative argumentation. There are many methods about the HRM evaluation have been widely
applied, such as: Delphi, the analytic hierarchy process, etc. However, these methods are subject to
stochastic factors in the evaluation, and the evaluation results are influenced by subjective experience
and knowledge limitations easily, which often with personal bias and one-sidedness. In recent years,
with the rapid development of the neural network that has the unique advantages——self-learning,
self-organizing and self-adapting ability, it can overcome the influence of subjective factors and has
been applied widely. This paper will use the BP neural network method to evaluate the HRM
synthetically.
2 The Basic Principle of BP Neural Network
In 1943, American psychologist W McCulloch and mathematician W Pitts presented a simple
neurons mathematical model, namely MP model, which created the theoretical research about the
*
This research was supported by the Scientific Research Foundation for Young Teachers of North
China Electric Power University. The item No. is 200611037.
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artificial neural network model. In 1980s, American physicist J. J. Hopfield proposed the feedback
interlinkage network and defined the energy function, it is the function about the neuron state and
connection weights, which can be used to solve optimization problems and associative memory. In 1986,
D. E. Rumelhart and J. L. McClelland brought forward the back-propagation algorithm of multilayer
feedforward network, called BP network or BP algorithm. This algorithm is used to solve the problems
that perceptron cannot settle, and it is the most widely used algorithm presently.
BP neural network is consist of neurons and the connection between neurons, it can be divided into
input layer, hidden layer and output layer, and it belongs to the learning algorithm with mentor. BP
neural network is composed of positive propagation and the back propagation. In the positive
propagation phase, the state of every layer neurons will only affect the neurons state in the next layer; if
the expected output cannot be gotten in the output layer, the network enters into the error's back
propagation phase. According to the error signal of back propagation, the network changes the
network-connecting of all layers, to find out the best weight set and realize the correct network output.
The output of the input layer neuron is equivalent to the input values.
For the p(p=1,2,…,P) sample, if the output of k(k=1,2,…,K) node (the node is on the output layer
of BP neural network) is Okp, the connection weight values between hidden layer and output layer, input
layer and hidden layer are wkj and vji, and the hidden layer and output layer neurons use the bipolar
compression function as the output function, then the error function E on output layer can be defined as
a square function, namely
1 P K
E= ∑∑ ( d k − O k p )
2 p =1 k =1
2
In the formula, dk denotes corresponding desired output value.
BP algorithm is the guided learning, and the essential of learning is to revise the weight value
constantly so as to the error function to zero, therefore, in accordance with the principle of error gradient
decline, and the adjustment of wkj and vji can be expressed as:
∆w kj = −η
∂E
∂w kj
∆v ji = −η '
∂E
∂v ji
η and η' denote learning rate.
The amending process of weight value is an iterative process, namely:
P

 w kj ( n + 1) = w kj ( n ) + η ∑ δ kp O jp

P =1

P
 v (n + 1) = v (n) + η ' δ O
∑
ji
jp jp
 ji
P =1
δ kp = ( d k − O kp ) O kp (1 − O kp )

In this formula: 
 K

δ jp =  ∑ δ k p w kj  O jp (1 − O jp )
 K =1


BP algorithm steps are as follows:
(1) Initialize weight value, give the random number between 0-1 to all the weight value; (2) Input
the samples, specify the output layer neuron's expectations; (3) Calculate the actual output of every layer
neurons in sequence; (4) Amend the weight value, starting from the output layer until hidden layer
gradually; (5) Return to step 2, the network study concluded in less than a given error.
3 BP Neural Network Model of the Human Resources Management
Comprehensive Evaluation
3.1 The comprehensive indicators system of the human resources management evaluation
The indicators system of human resources management evaluation is the foundation to evaluate the
human resources management and its contribution to the competitiveness of enterprises. When
evaluating the HRM, we should keep to the systemic, consistency, comparability and feasible principles,
build a comprehensive HRM evaluation indicators system. The following evaluation indicators are taken
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into account when we evaluate the human resources management.
3.1.1 The quality evaluation indicators
The quality evaluation indicators reflect the current quality situation of human resources and its
development potential in the future, including: Average age evaluation indicator (U1), Average
education level indicator (U2), Professional titles indicator (U3), Enterprise staff holding rate (U4).
3.1.2 The recruitment evaluation indicators
Through evaluating the recruitment work of the enterprises, we may hold the work quality and results of
the new staff, which can provide an effective guarantee for the sustainable development of human
resources, including: Average time to fill the jobs post (U5), Average passing rate for employees (U6),
Labor contracts maturity rate (U7), Labor contract rate (U8).
3.1.3 The training and development evaluation indicators
The training and development evaluation indicators used to evaluate the incentive effect of the
training and development system for the enterprise staff, including: Training methods coverage rate (U9),
Staff training rate (U10), Employees innovation evaluation indicator (U11), Self-development and
implementation evaluation indicator (U12).
3.1.4 The performance assessment evaluation indicators
Performance assessment refers to evaluate the completed status of the work target, the
implementation degree of the work duty of the employees, and feed back the above assessment results to
them, including: Knowing degree of the staff performance assessment (U13), Leading quality evaluation
indicator (U14), Work quality evaluation indicator (U15), Work efficiency evaluation indicator (U16).
3.1.5 The incentive evaluation indicators
The so-called incentive is to inspire, guide, maintain and regulate the behavior of the organization's
members using the certain conduct criterion and punitive measures. The incentive evaluation
indicators include: Employee salary satisfaction indicator (U17), Competitiveness evaluation indicator of
enterprises salary system (U18), Employee satisfaction indicator about the safety (U19), Employees active
degree indicator (U20).
3.2 BP neural network method of human resources management evaluation
Many of the evaluation indicators are qualitative factors among U1-U20 that impact the human
resources management. So for the indicators of U1, U2,…,U20, we use the method of expert scoring, as
follows: 1.0, 0.7, 0.5, 0.3, 0.1, five grades. Experts assess the influence factors of the human resources
management, and then give the scores of each evaluation indicators.
For the question of human resources management evaluation, we may look it as the non-linear
mapping from inputting the evaluation indicators of human resources management to exporting the final
evaluation values. Thus, when we use 3-layer BP network, the input layer is the evaluation value of the
influencing factors, a total of 20 neurons, the neuron number of hidden layer can be determined by need
and the output layer is only one neuron, is a numerical value, the range is [0,1], which denotes the
comprehensive evaluation result for the human resources management. The higher the score is, the
better all the evaluation indicators is, accordingly the benefit of the human resources management is
higher; Conversely, the lower the score is, the lower the benefit of the human resources management is.
3.3 Application example
In this paper, we take the human resources management evaluation of 12 enterprises in Hebei
Province as an example, which are shown in table 1.
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Table 1 Expert Evaluation Data
Number
U1
U2
U3
U4
U5
U6
U7
U8
U9
U10
1
0.5
0.5
0.5
0.7
0.7
0.5
1
0.7
1
0.7
2
1
0.7
1
1
1
1
1
0.7
1
1
3
0.5
0.5
0.3
0.5
0.3
0.3
0.3
0.5
0.5
0.7
4
0.7
0.7
1
0.7
0.7
0.5
0.7
0.5
0.7
0.7
5
0.7
0.7
1
0.7
0.7
0.5
1
0.7
1
0.7
6
0.7
1
1
0.7
0.7
1
0.7
0.7
1
1
7
0.5
0.7
0.5
0.7
0.7
0.5
0.5
0.7
0.5
0.7
8
0.5
0.5
0.5
0.5
0.7
0.3
0.3
0.5
0.5
0.7
9
0.7
1
1
1
0.7
1
1
0.7
1
1
10
0.7
0.7
0.3
0.5
0.3
0.5
0.3
0.5
0.5
0.5
11
0.7
0.7
0.5
0.7
0.7
0.5
0.7
0.5
0.5
0.7
12
0.7
0.7
0.5
1
0.7
1
1
0.7
1
1
Continued Table
Number
U11
U12
U13
U14
U15
U16
U17
U18
U19
U20
score
1
0.7
1
1
0.7
0.7
0.7
0.7
0.7
0.7
0.1
0.713
2
0.7
1
1
0.7
1
0.7
1
0.3
0.7
1
0.931
3
0.7
0.5
0.3
0.5
0.7
0.7
0.7
0.7
0.3
0.5
0.488
4
0.7
0.5
0.7
0.7
0.5
0.7
0.7
0.7
0.7
0.5
0.683
5
0.7
1
1
0.7
0.7
0.7
1
0.7
0.7
0.1
0.727
6
0.7
1
0.7
0.7
0.7
0.7
1
1
1
1
0.861
7
0.7
0.5
0.7
0.5
0.7
0.7
0.7
0.7
0.7
0.5
0.604
8
0.7
0.5
0.3
0.5
0.7
0.7
0.7
0.7
0.3
0.5
0.488
9
0.7
1
0.7
0.7
0.7
0.7
0.7
0.7
1
0.5
0.817
10
0.7
0.5
0.3
0.5
0.7
0.5
0.7
0.3
0.3
0.5
0.460
11
0.7
0.5
0.7
0.7
0.7
0.7
0.7
0.3
1
1
0.630
12
0.7
1
1
0.7
0.7
0.7
0.7
0.7
0.7
0.5
0.766
We use the MatLab to realize the software program, establish the three-layer BP neural network
structure of human resources management, the given study accuracy ε = 0.0001, and we select 7
network neurons for the hidden layer. We take 1-8 group evaluation indicators data and evaluation
results in table 1 as the training set, train the network, and carry through the simulation evaluation using
the evaluation indicators data of the four residual groups and trained the network. In table 2, the network
training results and the actual comprehensive evaluation results in the human resources management
process are shown. The simulation results about the 4 test sets and the actual evaluation results, as
shown in table 3. The results in the table 2 and table 3 show that not only all the training samples is very
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close to the actual evaluation value, but the results of the four simulation test sets is also very close to
the actual evaluation.
To better understand the evaluation results visually, we staple the evaluation results. The evaluation
scores between 0.8-1.0 show that the human resources management benefit is very high; The scores
between 0.7- 0.8show that the human resources management benefit is high; The scores between 0.5-0.7,
we say that the human resources management benefit is general; The scores below 0.5, we say that the
benefit is low.
Table 2 The Actual Evaluation Results Compared with the Network Training Results
Number
1
2
3
4
5
6
7
8
The actual evaluation results
0.7130
0.9310
0.4880
0.6830
0.7270
0.8610
0.6040
0.4880
Network training results
0.7185
0.9193
0.3408
0.6890
0.7196
0.8612
0.6029
0.4894
Table 3 The Actual Evaluation Results Compared with the simulation Results and the Benefit Classification
Number
1
2
3
4
The actual evaluation results
0.8170
0.4600
0.6300
0.7660
The simulation results
0.8369
0.4509
0.6614
0.7567
Experts classification
Very high benefit
Low benefit
General benefit
High benefit
Simulation classification
Very high benefit
Low benefit
General benefit
High benefit
4 Conclusion
The human resources management evaluation is associated with many factors, it need large
numbers of statistical calculation, and the factitious factors can be mixed into easily, which make the
evaluation work is difficult. In this paper, we build the BP neural network model about the human
resources management benefit evaluation based on the analysis of the influence factors. It can simulate
the evaluation made by the experts and avoid the subjective mistakes. However, when BP neural
network evaluates the human resources management, we use the computer to simulate the experts,
which requires some samples to train the network, so the selection of the training samples need to be
improved further.
References
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Evaluation. Journal of Xi’an Institute of Finance and Economics, 2003, 16 (4): 19 22
[4]Liu Ying. Research on Appraisal Index System of Human Resource Management in Enterprise.
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