SciELO - Scientific Electronic Library Online

 
vol.106 número11-12Repeat-punctured superorthogonal convolutional turbo codes on AWGN and flat Rayleigh fading channelsMorphological evaluation of genetic evidence for a Pleistocene extirpation of eastern African impala índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados

Articulo

Indicadores

Links relacionados

  • En proceso de indezaciónCitado por Google
  • En proceso de indezaciónSimilares en Google

Compartir


South African Journal of Science

versión On-line ISSN 1996-7489

Resumen

SHAO, C; WANG, L; XIAO, L  y  WU, J. Qualitative phase space reconstruction analysis of supply-chain inventory time series. S. Afr. j. sci. [online]. 2010, vol.106, n.11-12, pp. 1-7. ISSN 1996-7489.  http://dx.doi.org/10.4102/sajs.v106i11/12.422.

The economy systems are usually too complex to be analysed, but some advanced methods have been developed in order to do so, such as system dynamics modelling, multi-agent modelling, complex adaptive system modelling and qualitative modelling. In this paper, we considered a supply-chain (SC) system including several kinds of products. Using historic suppliers' demand data, we firstly applied the phase space analysis method and then used qualitative analysis to improve the complex system's performance. Quantitative methods can forecast the quantitative SC demands, but they cannot indicate the qualitative aspects of SC, so when we apply quantitative methods to a SC system we get only numerous data of demand. By contrast, qualitative methods can show the qualitative change and trend of the SC demand. We therefore used qualitative methods to improve the quantitative forecasting results. Comparing the quantitative only method and the combined method used in this paper, we found that the combined method is far more accurate. Not only is the inventory cost lower, but the forecasting accuracy is also better.

Palabras clave : data mining; phase space; qualitative forecasting; quantitative forecasting; supply chain management.

        · texto en Inglés     · Inglés ( pdf )

 

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License