In recent years, “Smart Factory” has become one of the most frequently mentioned keywords in the manufacturing industry. However, many businesses are still wondering: What exactly is a Smart Factory? Is it simply a matter of buying more robots or automated machines? Or does it require investing in a complex software system? And more importantly:

In every second of operation, modern factories generate a massive amount of data — from PLCs, sensors, packaging lines, quality control systems to personnel actions on the shopfloor. Yet in many businesses, this crucial data remains scattered across disparate systems, individual Excel files, or manual end-of-shift reports. The consequences are: Limited operational visibility Delayed decision-making

  In many factories, OEE data is still being manually recorded using paper or Excel spreadsheets, resulting in inaccuracies, delays, and difficulties in conducting root cause analysis. i-MAG OEE was developed to address this problem by automatically collecting data directly from machines in real time. So how does the i-OEE software collect data? 1. Direct

In the fast-moving consumer goods (FMCG) production line, where output is high, profit margins are low, and production is stable, every minute of downtime incurs costs. However, many factories still struggle to effectively manage downtime, leading to unexplained productivity losses. So, where does downtime come from, and how can it be better controlled? Read more

In many factories, workplace safety is still primarily managed through regulations, signage, scheduled training programs, and manual monitoring by the EHS/SHE department, along with post-incident response. This approach isn’t inherently wrong, but its limitations are becoming apparent as factories grow, shifts increase, production processes become more complex, and risks rise. Furthermore, the pressure to comply

For many years, factory safety monitoring relied primarily on traditional cameras and human observers. However, this model increasingly revealed its limitations: it couldn’t provide continuous monitoring, easily missed risks, and resulted in slow responses to hazardous situations. The emergence of behavioral analysis AI has completely transformed how businesses approach workplace safety, shifting from passive monitoring

According to OSHA (Occupational Safety and Health Administration), falls, tripping, slipping, and falling down stairs account for 15-20% of all workplace accidents related to working at height in factories. Although specific statistical data for Vietnam is not as widely published as in the United States, applying global procedures and international standards, we can conclude that

In factory logistics and import/export operations, weighing trucks is a crucial but potentially risky step: manual record-keeping can lead to data inaccuracies, making verification difficult and resulting in significant delays. i-AccessMaster solves this problem by directly integrating an automated truck weighing system into the access control process, enabling transparent data, standardized operations, and reduced reliance

In the context of rising production costs and increasingly stringent delivery requirements, controlling equipment performance and reducing downtime has become a top priority for manufacturing plants, especially in the fast-moving consumer goods (FMCG) and food & beverage (F&B) industries. However, many businesses still operate based on manual, inaccurate, and outdated data, leading to flawed and

In industrial production environments, workplace accidents don’t usually happen “unexpectedly”; they are often preceded by risky behaviors such as entering restricted areas, climbing on equipment, not wearing personal protective equipment (PPE), or performing tasks incorrectly, etc. However, continuous human monitoring to detect these risks is almost impossible. This is where artificial intelligence (AI) becomes a