About SISTAM

About SISTAM

SISTAM is Thailand's premier B2B event dedicated to smart industrial safety and advanced maintenance solutions. An industry collaboration underpinned by strong industry support, SISTAM 2024 is organized by exhibition management specialists - expoSis, together with co-hosts, the Technology Promotion Association (Thailand-Japan) and the Thai Institute of Chemical Engineering and Applied Chemistry (TIChE), and our distinguished knowledge partner - Chula Engineering.

As Thailand embraces smart factories and digital technologies to create more flexible and agile manufacturing processes, the need for industrial safety practices and predictive maintenance initiatives will become increasingly vital. Together, SISTAM sets the stage for forging a path towards safer, more sustainable, and technologically advanced industrial practices, elevating Thailand's commitment to global excellence and innovation. SISTAM 2024 comes against the backdrop of Thailand’s 4.0 strategy to revolutionise the manufacturing and engineering sectors and increase productivity efficiency, and competitiveness.

SISTAM also attracts a highly targeted audience and decision-makers across various industries, from automotive, petrochemical, oil and gas, food & beverage, and more. It provides a direct gateway for your company to gain potential leads and clients with a specific interest in industrial safety and advanced maintenance solutions. Beyond exhibition opportunities, SISTAM also enables you to engage with fellow industry leaders, subject experts, and influencers. These interactions can pave the way for new business opportunities, partnerships, and collaborations that drive business growth, and innovation.

MARKET INSIGHTS

shaping the industrial safety and advanced maintenance landscape in 2024 and beyond

With an increasing focus on predictive maintenance and moving away from reactive maintenance, industries are increasingly adopting predictive maintenance strategies. This shift is driven by the cost-effectiveness and efficiency of predicting equipment failures before they occur.