ChiAha to Introduce Cutting-Edge Technology Enhancing Manufacturing Efficiency in October 2024

ChiAha’s innovative technology, to be launched in October 2024, is poised to revolutionize the field of manufacturing optimization.

/EIN News/ -- Farragut, Tennessee, Aug. 28, 2024 (GLOBE NEWSWIRE) -- Achieving optimal overall equipment effectiveness (OEE) is essential in manufacturing. However, it is not a straightforward task. It requires a thorough understanding of numerous variables, including machine speeds, production line configurations, and system improvements. Optimizing OEE is similar to unblocking bottlenecks in a production line, where each element must be meticulously managed to ensure overall efficiency.

One of the challenges in this sector is answering questions about the design, operation, and continuous improvement of production lines. For instance, how does close coupling between machines impact OEE? What are the optimal speeds at which individual machines should operate to maximize output? How can production volumes be increased without compromising quality? Advanced tools and methodologies are required to respond to these queries.

The industry used to rely on traditional methods to gather and analyze production data. Decades ago, the only record of a production system’s performance was scribbled in workbooks. Engineers had to manually conduct time and motion studies to understand their operations’ efficiency. Data collection was laborious, and the insights gained were limited by the numerous variables that needed to be considered. Interpreting this information to build statistical models that could predict the behavior of these machines over time would take months if not years.

Andrew Siprelle, a simulation expert and optimization and modeling consultant with over 30 years of experience, has witnessed the manufacturing industry evolve. He saw how the emergence of automated data collection systems has helped companies monitor every detail of their production lines. This shift in data collection practices paved the way for the development of more sophisticated modeling techniques, such as digital twin simulation.

A digital twin is a virtual replica of a physical production system. It enables engineers to simulate and analyze the performance of a manufacturing line in real time. This capability aids organizations in exploring various scenarios, testing changes to the system, and predicting their impact on OEE without disrupting actual production. Andrew has realized that despite the growth of digital twin technology, there is still a gap in the industry’s ability to leverage these tools for maximum efficiency.

The seasoned professional began his career as an industrial engineer, during his studies he was exposed to capacity prediction and production system modeling, sparking his interest in simulation, particularly discrete event simulation. He followed this passion to the Aluminum Company of America (ALCOA). This interest prompted him to establish Simulation Dynamics, Inc. (SDI) in 1990. He applied simulation technology to various industries, from manufacturing to supply chain management, through his consulting firm.

During the course of his practice, through necessity, Andrew discovered ExtendSIM, formerly known as Extend. After a few months of exploring the simulation program, the expert noticed its lack of a discrete event library. He made it his mission to bridge this gap, proceeding to code an extensive library that could enable rapid simulation processes.

Andrew dedicated himself to improving the simulation for seven years, applying it to various models of food processes. In 2002, the industry expert coined what is known today as the discrete rate simulation (DRS) and presented it at the 2002 Winter Simulation Conference (WSC). This pivotal moment allowed not only him but other industry players to utilize DRS in solving manufacturing problems across diverse sectors.

It is significant to note that DRS strays away from the traditional discrete event simulation, which focuses on individual items moving through a production system. DRS models the flow of materials at a continuous rate. This method is well-suited to high-speed production lines, such as those found in the packaging industry, where thousands of items are processed every minute.

In 2023, Andrew established ChiAha, a company that provides insights into factory flow. The company aims to transform further how production systems are designed and optimized. Its name was derived from the Sanskrit word “chi,” symbolizing flow or energy, and “aha,” an expression representing moments of sudden insight.

ChiAha was built on a foundation of DRS and leverages a multidisciplinary approach. It incorporates industrial engineering, statistics, optimization, machine learning, data analytics, and visualization to deliver powerful insights. The firm is set to launch the ChiAha Express AI toolkit in October, aligning with its mission.

“Our mission is to guide users from raw data to predictive insights,” states Andrew. “Many companies encounter obstacles when applying digital twin technology. It’s because, with the wrong tools, models can run slowly or even fail to reflect real-life conditions accurately. There’s this common misconception that a digital twin will be an exact replica of the physical system, predicting every detail. But there’s an art to modeling—a balance between science and computer modeling. The art is in distilling essential details into a streamlined model, and that’s what we specialize in. It's imperative to have the simplest model to ensure that nothing gets missed. As simple as possible, but no simpler.”

The toolkit has innovative features, including modular model construction, integrated reliability design, and intuitive, no-code model building. These make it accessible even to those who may not have an extensive background in simulation technology. In addition, these features allow the company to cater to various industries, such as mining, electronics, chemical processing, oil and gas, pharmaceuticals, and food and beverage production. Plant managers, production designers, analysts, and engineers involved in high-speed, high-volume operations will benefit from ChiAha’s offerings.

Tom Lange, the founder of Technology Optimization & Management LLC, attests to ChiAha’s effectiveness. Lange serves as the company’s earliest and primary beta tester. His experience with ChiAha illustrates the tool’s ability to shift the focus from only managing machines to understanding the time-related dynamics that drive production efficiency. “Before ChiAha, it would take me up to a month to develop a digital twin for a production line. It’s a very complex and inefficient process. ChiAha streamlines this process, making it faster and more straightforward while still delivering high-quality results,” Lange shares.

Lange identified that the tendency to concentrate on equipment and its physical attributes instead of the broader question of time management—how long machines will run, how long they will be down, and the overall impact on production flow—is one of the major challenges in the manufacturing sector. Traditional manufacturing managers overlook the importance of time as a critical factor in optimizing production. ChiAha addresses this by offering tools that enable them to model and simulate production lines with time-based efficiency.

ChiAha is set to redefine manufacturing optimization. The upcoming launch of its toolkit in October is bound to significantly impact various industries, as it provides users with the insights they need to optimize their operations and achieve higher levels of efficiency.

Media Contact

Name: ChiAha Team

Email: support@chiaha.ai