There’s little doubt that small and medium enterprises (SMEs) must accelerate their digital transformation, but to do so successfully, they need to understand the process and know what technologies to invest in and when, says Dr Somkiat Tangkitvanich, President, Thailand Development Research Institute (TDRI).
Blockchain is an example of such hype. It has been 10 years since the world first encountered blockchain technology, and the hype surrounding its potential has been extraordinary. Eventually, it proved impractical, offering no legitimate use cases.
The second is to discard technologies too early after they fall short of expectations. They might become effective later.
The third is reluctance to deploy new technology. If enterprises wait until a technology is commonly used, it will not help increase business competitiveness.
Finally, enterprises must constantly keep up with and upgrade their technology models to remain competitive.
Gartner Hype Cycles provide a graphic representation of the maturity and adoption of technologies and applications, and their potential relevance to solving real business problems and exploiting new opportunities. Gartner's Hype Cycle methodology gives enterprises a view of how a technology or application will evolve over time.
Dr. Somkiat recommended that SMEs start the digital transformation with simple technologies including ERP (Enterprise resource planning), a software system that helps organizations streamline their core business processes including finance, manufacturing, supply chain, sales, and procurement.
Others include RFID (Radio Frequency Identification) to identify people or objects, Dashboard to display a comprehensive overview of the whole factory through the most important information, and a censoring system.
A newly-developed Digital Temperature Indicator (DTI) by a Thai startup, Cleantech & Beyond, is an interesting technology to improve preventive maintenance within the factory.
DTI is an irreversible temperature indicator label that enables temperature tracking at item-level and displays the device status in both visual and digital formats.
DTI offers a cost-effective solution for an item-level temperature monitoring system in logistics and industrial applications. The advantages of the technology are compatible with the existing RFID smart label production, RFID devices, standards, infrastructure, NFC devices and QR-code technology.
DTI-RFID can continuously track the operating temperature at component-level of tools and equipment in industrial plants and factories.
Ideally suited for preventive maintenance, DTI-RFID allows the effective monitoring and management of temperature-related factors to prevent equipment failures and optimize operational efficiency.
TDRI also presented how Japanese manufacturers in Thailand use AI.
A survey among 168 manufacturers by the Japanese Chamber of Commerce (JCC), Bangkok last year revealed that visual inspection remains the most essential technique for detecting defects, followed by anomaly detection for preventive maintenance, sorting and management, demand forecasting, and equipment inspection.
AI is a game changer for today's fast-changing business landscape, but SMEs should have a clear picture of the types of AI they want to adopt to fully reap its benefits.
Addressing three stages of artificial intelligence, Dr. Somkiat explained that Deep Learning (a subset of machine learning) emerged about 10 years ago. Now a common technology, it has shown itself to be very useful for manufacturing.
The second stage is Generative AI or GenAI, which was developed a few years ago, and the third stage is Agentic AI or AI Agents.
He believed that a quick win for SMEs is to use Deep Learning due to the maturity of the technology. Unfortunately, a lot of SMEs have yet to adopt Deep Learning even though it can be used in a wide variety of applications.
With a robust image recognition system, Deep Learning can identify objects and features in images, such as people, animals, and places.
In addition to Agentic AI, TDRI anticipated that Large language models (LLMs) would revolutionize the industry in the near future. Large Behavior Models are expected to be a new frontier in AI, offering profound insights into complex human, social, and even organizational behaviors.
