BISTel
Predictive analytics for the Manufacturing Landscape

The manufacturing landscape is changing from within. As part of this transition, Industry 4.0 technologies are finding utility in factories across the world. Technologies such as IoT, cloud AI, and big data have long since stopped being mere buzzwords, and are being actively exploited by shop floor managers, as the industry switches to 4.0 ecosystems. Amid this, solution providers are seizing the initiative and filling in where there’s a need for automation and data exchange on the shop floor.

BISTel, an IoT solution provider of engineering and automation solutions for semiconductor, flat panel display, electronics assembly manufacturing, is now spreading its wings and venturing into new manufacturing sectors that include automotive, LEDs, biotech and steel. “While many companies are now just jumping to big data-like systems, we’ve been dealing with IoT, AI, and other data management technologies, for many years. That’s why we decided to bring our expertise to the general manufacturing areas, where there is a dire need for Industry 4.0 technologies in production processes,” explains W.K. Choi, CEO, BISTel.

Upon offering clients a Proof of Concept (PoC), BISTel walks side-by-side with customers throughout the entire production process. To kick-start, BISTel collects all possible data from a client’s facility, which includes sensors, machines, and processes. After contextualizing this data, the service provider delivers real-time monitoring to identify potential quality or productivity issues. If an error is detected, BISTel offers comprehensive analytics solutions to determine the root source of the mishap. Based on the analytics, the team designs and integrates a predictive analytics module into the client’s systems, to ensure that future issues can be prevented beforehand.

According to Choi, the strong infiltration of analytics has forced organizations to focus on the optimization of manufacturing processes beyond the machine level. “Even five years ago, manufacturers were more focused on productivity and quality. Now, everyone is pretty much on the same level in terms of quality, and the priority has shifted to efficiencies and optimization. By getting the most out of each asset, our customers enjoy a bigger impact on year-end financial outcomes.”

With BISTel, we leverage our manufacturing domain expertise to help our customers get the most out of each asset, helping them to enjoy a bigger impact on year-end financial outcomes

In its ongoing transition from semiconductor manufacturing to other sectors, BISTel has noticed the steep rise in demand for AI-enabled applications. “Our three biggest global customers— Samsung, Toshiba, and SK Hynix—are all asking for more AI-capable applications, in order to optimize their production processes even further. Previously, we used to connect our system with their production machines, monitor them, and provide analytical capabilities. But that’s not enough now,” says Choi.

Owing to their years of expertise in big data, BISTel is able to differentiate the needs of manufacturing clients and offer customized solutions. For example, clients in the automotive industry have only 20 percent of data available, as compared to semiconductor manufacturing companies that have a large share of data at their disposal. There are industries such as biotech that have data volumes of approximately 30 percent data available. Since these industries use fewer incremental machines in their production processes, they can benefit massively from analytics that is applied to most of the manufacturing processes.

Though BISTel’s pre-deployment process varies based on the industry, its fundamental functions are rooted in the same philosophy. Even its next-wave solution, titled Dynamic Fault Detection (DFD), which conducts AI-enabled real-time monitoring of sensor trace data to improve yield and engineering productivity, is ultimately designed to optimize a client’s manufacturing processes. According to BISTel, even engineers who were previously reluctant to let go of general legacy solutions, are now willingly embracing a move to the Industry 4.0 paradigm.