Techniques for performance improvement in organisations

Citation

Ho, C.-T.B. and Lenny Koh, S.C. (2007), "Techniques for performance improvement in organisations", Journal of Manufacturing Technology Management, Vol. 18 No. 7. https://doi.org/10.1108/jmtm.2007.06818gaa.001

Techniques for performance improvement in organisations

About the Guest EditorsChien-Ta Bruce Ho is an Associate Professor in the Institute of E-Commerce at National Chung Hsing University. His current research interests include customer relationship management, value chain management and performance evaluation. He has authored and co-authored eight books, 25 refereed journal articles in the performance measurement area, and has presented more than 25 papers at national and international conferences. Samples of his work, could be found in Journal of the Operational Research Society, Journal of Air Transport Management, Industrial Management & Data Systems and Production Planning and Control. He is also the Editor of the International Journal of Electronic Customer Relationship Management.

S.C. Lenny Koh is the Director of the Logistics and Supply Chain Management Research Group and a Senior Lecturer in Operations Management at the University of Sheffield Management School UK. She has 190 publications in journal papers, book, edited book, edited proceedings, edited special issues, book chapters, conference papers, technical papers and reports. Her work appears in some of the top journals, including Journal of the Operational Research Society, International Journal of Production Research and International Journal of Production Economics. She is the Editor in Chief of the International Journal of Enterprise Network Management, International Journal of Value Chain Management and International Journal of Logistics Economics and Globalisation, and serves on editorial boards of some leading journals including Journal of Manufacturing Technology Management.

Techniques for performance improvement in organisations

We are pleased to introduce this special issue of Journal of Manufacturing Technology Management on “Techniques for performance improvement in organisations”. This special issue is one of the important deliverables from the 4th International Conference on Supply Chain Management and Information Systems (SCMIS2006), Taiwan, 5-7 July 2006. Suitable papers were invited to submit to this special issue, and the journal's review process was undertaken.

This special issue contains seven papers, discussing a range of techniques for improving organisational performance. Below is a brief overview of the papers that appear in this issue.

The first paper by Mahamaneerat, Shyu, Ho and Chang, is to provide a novel domain-concept association rules (DCAR) mining algorithm that offers solutions to complex cell formation problems, which consist of a non-binary machine-component (m/c) matrix and production factors for fast and accurate decision support. The proposed DCAR algorithm considers a wide range of production parameters, which makes the algorithm suitable to the real-world manufacturing system settings.

Tan and Noble, in their, propose a “plug and play” approach for decision modelling. The notion of this approach is to build models from components based on a “LEGO block” style of manufacturing simulation and analysis. This approach facilitates managers to rapidly build up models increase communication and decision support efficiency, and improve productivity.

The third paper by Karaev, Koh and Szamosi, reviews the effect of a cluster approach on SMEs' competitiveness. The review focuses on the use of a cluster approach among SMEs as a tool for meeting their challenges related to globalisation and trade liberalisation, as well as investigating its contributing factors in the process of increasing their competitiveness. The findings from this paper enable business managers to make more informed decisions regarding the adoption of a cluster approach and entering into cluster-based relations, as well as to assist policy makers in designing more efficient cluster policies.

The fourth paper by Chung, Wong and Soon, proposes an ANN-enabled decision support system to solve a simple but semi-structured production supply problem in a lens manufacturing environment. A case study approach was used to show how the system is implemented. The authors conclude that a significant improvement in quality level can be achieved by holding the knowledge worker accountable for making the decision to stop the production line, rather than made by default, as is with most traditional operations.

The fifth paper by Song, Platts and Bance, develops a framework of total cost for overseas outsourcing/sourcing in manufacturing industry with input from both academic literature and industrial offshoring practices. An exploratory case study is carried out in a multinational high-tech manufacturer to apply this framework. Practical barriers for implementing this model are discovered.

The development of mobile communication technologies and other information technologies can be used to boost the real-time capability in logistics. The sixth paper by See, presents an application of the wireless wide area network and personal area network technologies in logistic fleet operation management. The result shows a real-world fleet management system that integrates mobile communication and supports real-time logistic information flow management.

The seventh paper by Chiu, Koh and Chang, provides a data framework to support the incremental aggregation of, and an effective data refresh model to maintain the data consistency in, an aggregated centralised database for Taiwan's National Immunization Information System. The authors note that the approach to implement the data refreshment for the aggregation of heterogeneous distributed databases can be more effectively achieved through the design of a refresh algorithm and standardisation of message exchange between distributed databases and central database.

To summarise, this special issue shows that there are a broader range of techniques available for improving organisational performance. It ranges from quantitative-based techniques such as modelling, to qualitative-based techniques such as cluster approach. These techniques spawn to provide a scientific and holistic basis for managerial decision-making for performance improvement in organisations.

The Guest Editors would like to thank all the authors for their contributions to this special issue, the reviewers for their valuable comments, and the Editor of the Journal – Professor David Bennett and the Emerald Editorial Office, for support to make this special issue possible.

Chien-Ta Bruce Ho and S.C. Lenny KohGuest Editors