This article is a review of work published in various journals on the topics of Enterprise Resource Planning (ERP) between January 2000 and May 2006. A total of 313 articles from 79 journals are reviewed. The article intends to serve three goals. First, it will be useful to researchers who are interested in understanding what kinds of questions have been addressed in the area of ERP. Second, the article will be a useful resource for searching for research topics. Third, it will serve as a comprehensive bibliography of the articles published during the period. The literature is analysed under six major themes and nine sub-themes.

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Purpose-To develop a methodology to augment enterprise resource planning (ERP) systems with the discrete event simulation's inherent ability to handle the uncertainties. Design/methodology/approach-The ERP system still contains and uses the material requirements planning (MRP) logic as its central planning function. As a result, the ERP system inherits a number of shortcomings associated with the MRP system, including unrealistic lead-time determination. The developed methodology employs bi-directional feedback between the non-stochastic ERP system and the discrete event simulation model until a set of converged lead times is determined. Findings-An example of determining realistic production lead-time data in the ERP system is presented to illustrate how such a marriage can be achieved. Research limitations/implications-The research demonstrates that the limited planning functionality of the ERP system can be complemented by external system such as discrete event simulation models. The specific steps developed for this research can be adopted for other enhancements in different but comparable situations. Practical implications-The organizations who have been using the discrete event simulation in their planning and decision-making processes can integrate their simulation models and the ERP system following the steps presented in this paper. The ideas in this paper can be used to look for automatic data collection process to update or build the simulation models. Originality/value-The ERP implementation is a significant investment for any corporation. Once the ERP implementation is completed successfully, the corporations must look for ways to maximally return on their investment. The research results may be used to enhance the implemented ERP systems or to fully utilize the capabilities in a corporation.

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3D printing, or additive manufacturing, is a key technology for future manufacturing systems. However, 3D printing systems have unique vulnerabilities presented by the ability to affect the infill without affecting the exterior. In order to detect malicious infill defects in 3D printing process, this paper proposes the following: 1) investigate malicious defects in the 3D printing process, 2) extract features based on simulated 3D printing process images, and 3) an experiment of image classification with one group of non-defect infill image and the other group of defect infill training image from 3D printing process. The images are captured layer by layer from the top view of software simulation preview. The data extracted from images is input to two machine learning algorithms, Naive Bayes Classifier and J48 Decision Trees. The result shows Naive Bayes Classifier has an accuracy of 85.26% and J48 Decision Trees has an accuracy of 95.51% for classification.