A comparison of strategies to dampen nervousness in MRP systems
Management Science
Integrating artificial neural networks with rule-based expert systems
Decision Support Systems - Special issue on neural networks for decision support
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm
The Journal of Machine Learning Research
Stability-oriented evaluation of rescheduling strategies, by using simulation
Computers in Industry
Expert Systems with Applications: An International Journal
Fuzzy neural based importance-performance analysis for determining critical service attributes
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A two-stage analysis of the influences of employee alignment on effecting business-IT alignment
Decision Support Systems
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Understanding how the various factors in a supply chain contribute to the overall performance of its operation has become an important topic in management science research nowadays. In this paper, we propose and apply a two-stage methodology to an industrial survey data set to investigate relations among the key factors in a supply chain model. Precisely, we use the PC-algorithm to discover the connectivity relation among the factors of interest in the supply chain model. Critical factors in the model are then identified, and we then utilize the neural network to quantify the relative importance of some of the factors in predicting the critical factors. An advantage of our proposed method is that it frees up the researcher from making subjective decisions in his or her analysis, for example, from the needs of specifying plausible initial path models required in a structural equation modeling analysis (which is usually used in business and management research) and of selecting factors for the subsequent predictive modeling. We envision that the analysis results can aid a decision maker in optimizing the system performance by suggesting to the decision maker which ones of the factors are the important ones that he or she should devote more resources and efforts on.