Young B. Moon

Enterprise Resource Planning (ERP): a review of the literature

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.

An unified group technology implementation using the backpropagation learning rule of neural networks

Abstract Two engineering problems in implementing Group Technology are part family formation and part classification. Regardless of the approach adopted for the formation and classification, a critical problem is how to maintain consistency. The consistency problem can be addressed most effectively if the formation and classification is a single procedure rather than two separate procedures. A feedforward neural network using the Backpropagation learning rule is adopted to automatically generate part families during the part classification process. The spontaneous generalization capability of the neural network is utilized in classifying the parts into the families and creating new families if necessary. A heuristic algorithm using the neural network is described with an illustrative example.

Enhancing ERP system’s functionality with discrete event simulation

Purpose – To develop a methodology to augment enterprise resource planning (ERP) systems with the discrete event simulations 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 step...

Forming part-machine families for cellular manufacturing: A neural-network approach

This paper presents a new approach to forming group-technology part families for cellular manufacturing. The approach is based on neural-network technology, which mimics the way biological brain neurons perform to generate intelligent decisions. A procedure of forming part families using parallel and simple artificial neurons is described with examples. The implications and advantages of using neural networks in group technology are discussed.

A comprehensive clustering algorithm for strategic analysis of supply chain networks

Inventory management in supply chain networks involves keeping track of hundreds of items spread across multiple locations with complex interrelationships between them. However, it is not computationally feasible to consider each item individually during the decision making process. The use of clusters of items is preferred for the evaluation of these decisions. In addition, the use of groups of items provides management with more effective methods for characterizing and controlling system performance and results in cost savings such as group discounts. In this research, we introduce a comprehensive clustering methodology for supporting inventory management in supply chain networks. All product characteristics which have a significant impact on the performance of the supply chain are taken into account. The nodes in the network are split into subnodes prior to clustering to reduce the complexity. The average linkage clustering algorithm and the Calinski and Harabasz index are used to identify clusters of similar items. In addition, a set of heuristics is used to capture the relationships between items as specified in the bill of materials for the products. Examples are presented to demonstrate the effectiveness of the clustering methodology as well as the performance of the heuristics, by comparing the results obtained with the optimal solution.

A framework for managing engineering change propagation

Engineering Changes (ECs) are facts of life for any company developing and introducing new products, despite a commonly held notion that they are distractions from normal operation. Companies can become more innovative by utilising ideas from the ECs or by learning how to handle the ECs. This paper presents a framework to manage the ECs effectively, particularly the issue of EC propagation. An EC seldom confines itself to a single change, but triggers other changes in different components. The framework is designed to identify the affected components automatically, capture the required knowledge during the design phase of the product life cycle, and use it during the Engineering Change Management (ECM) process.

Learning group-technology part families from solid models by parallel distributed processing

This paper presents a new approach to part classification in group technology. It advocates the introduction of a feature-based solid-modelling scheme for part representation which, in turn, helps in identifying features of interest. The extracted features of the part are then used to determine the part family to which the part belongs. A parallel distributed processing (PDP) model has been utilised in developing a learning module for the part-classification problem. The proposed model has been implemented in the Unix environment of a Sun work-station. The usefulness of the proposed model has been validated with an example of 16 parts in three part families.

Enhancing systems integration by incorporating business continuity drivers

Purpose – The purpose of this paper is to present a framework for developing an integrated operating environment (IOE) within an enterprise information system by incorporating business continuity drivers. These drivers enable a business to continue with its operations even if some sort of failure or disaster occurs.Design/methodology/approach – Development and implementation of the framework are based on holistic and top‐down approach. An IOE on servers side of contemporary business computing is investigated in depth.Findings – Key disconnection points are identified, where systems integration technologies can be used to integrate platforms, protocols, data and application formats, etc. Downtime points are also identified and explained. A thorough list of main business continuity drivers (continuous computing (CC) technologies) for enhancing business continuity is identified and presented. The framework can be utilized in developing an integrated server operating environment for enhancing business contin...

Establishment of a neurocomputing model for part family/machine group identification

The part family/machine group identification (or formation) is the crux of implementing Group Technology, and a well-studied subject. However, most of the approaches have neglected the original family concept proposed by Burbidge, that there are already ‘naturally occurring’ families existing. A desirable approach should be that of identifying these families rather than forcing to form the families. This paper describes a neurocomputing model which is inspired by the way the biological neuronal systems reach intelligent decisions. A comprehensive survey of previous approaches is presented. The simulation results from an example are provided to show how the model is used to identify the part families and the machine groups. The advantages of the neurocomputing model and future directions are discussed.

Hybrid modeling and simulation for innovation deployment strategies

Purpose – The purpose of this paper is to provide a simulation model for assessing innovation deployment strategies by evaluating and comparing their outcomes using a hybrid modeling and simulation of agent‐based modelling and simulation (ABMS) and system dynamics (SD). Since successful deployment of innovations in any organization is as important as the innovations themselves, how to choose a suitable deployment strategy and assess its effectiveness before actual implementation is a critical task.Design/methodology/approach – This paper adopts a hybrid modeling and simulation approach combining the advantages of agent‐based modeling and system dynamics to study the activities and strategies involved in innovation deployment. The developed model was verified and validated with the data from GMs OnStar project.Findings – The research demonstrates that evaluating various deployment strategies for desirable results through hybrid modeling and simulation is possible and useful by finding critical factors and...