Abstract
Life cycle cost(LCC) is an important content of equipment integrated logistics support. While the LCC includes the whole life cycle of equipment from development, production, service and maintenance to retirement, in order to effectively manage and control the LCC and better develop integrated logistics support, it is necessary to analyze and predict it. The unbiased grey markov model(UGMM) was introduced into the LCC prediction in the paper, in order to check model accuracy, the posterior difference method(PDM) was used, also the influence by the number of state intervals in UGMM on the prediction accuracy is analyzed and studied. The result indicate that UGMM can be used to predict the LCC, also have the highest prediction accuracy comparing with unbiased grey model and grey separating model, and in order to ensure the prediction accuracy, the state interval should be divided according to the number of sequence.
Highlights
Life cycle cost(LCC) of equipment was first proposed by the U
Base on previous research results [11, 12], this paper introduces unbiased grey markov model(UGMM) into LCC prediction and the results were checked by posterior difference method(PDM)
The value of C are 0. 1609, 0. 1580 and 0. 1539, all of them are less than 0. 35, and the C of UGMM have the least value, it indicated that the three models can be used to LCC prediction, and the UGMM has the highest prediction accuracy
Summary
Life cycle cost(LCC) of equipment was first proposed by the U. S. Department of defense in the 1960s, it is defined as the sum of all costs paid for the demonstration, development, production, service, maintenance, support, and retirement of equipment within the life cycle, its main purpose is to reveal the law of the occurrence and development of LCC, so as to take effective methods to control it [1, 2, 4]. The control of LCC is one of the important contents of equipment integrated logistics support(ILS). Base on previous research results [11, 12], this paper introduces unbiased grey markov model(UGMM) into LCC prediction and the results were checked by posterior difference method(PDM). The influence of the number of state interval divisions on the prediction accuracy was studied
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