Abstract Manufacturing has been identified as a key pillar of growth in many Southeast Asian (ASEAN) economies. However, in the last decade many countries have become keen competitors for foreign direct investments. Many countries are trying to improve their total business capabilities by encouraging computerisation of small and medium sized enterprises (SME). Manufacturing SMEs (M-SMEs) are tasked to adopt technologically advanced programmes. With an improving public education system and more literate work force, more SMEs are better positioned to tap into the knowledge-based economy. There is tremendous amount of knowledge intensive activities within the multi-flows of the M-SMEs. Although the concept of ERP systems and artificial intelligence (AI) techniques have been around for more than two decades, this has largely remained the domain of the larger companies. ASEAN M-SMEs have been slow to implement it. In this paper, the various strategic and operational requirements of regional M-SMEs are presented and a knowledge-based resources planning model making use of AI techniques is proposed. This improved AI model makes use of the large amount of accumulated knowledge typically found in the M-SMEs, especially those in the electronics and precision engineering sectors. This includes a case study of how an electronics precision engineering company adopted the proposed AI model.
[1]
F. W. Kellaway,et al.
Advanced Engineering Mathematics
,
1969,
The Mathematical Gazette.
[2]
Sukhdev Khebbal,et al.
Intelligent Hybrid Systems
,
1994
.
[3]
Behrokh Khoshnevis,et al.
Integration of machine requirements planning and aggregate production planning
,
1996
.
[4]
Perry A Trunick.
ERP -- PROMISE OR PIPE DREAM?
,
1999
.
[5]
Kazem Abhary,et al.
Internal supply chain planning determinants in small and medium‐sized manufacturers
,
2002
.
[6]
William L. Berry,et al.
Manufacturing Planning and Control Systems
,
1984
.
[7]
Seongwon Cho.
Parallel, self-organizing, hierarchical neural networks with fuzzy input signal representation, competitive learning and safe rejection schemes
,
1992
.