Advanced technologies, such as the Internet of Things (IoT), big data analytics, machine learning, and digital twin modeling, have catalyzed a broader shift, referred to as Industry 4.0. In manufacturing, these digital advancements have empowered manufacturers to gain insights into factory flow, anticipate potential disruptions, and adapt proactively to optimize output.
Still, many manufacturers, particularly small to medium-sized businesses, struggle to utilize these technologies effectively. They recognize the potential of using data to enhance production but lack the expertise, resources, or suitable tools to do so. ChiAha, a leading technology company based in Tennessee, addresses this gap by offering innovative digital twin solutions that simplify complex production modeling, putting high-fidelity, actionable insights within reach for all manufacturers.
Development with a purpose
The forward-thinking firm’s mission is to revolutionize how manufacturing systems are optimized and designed. It developed digital twin software toolkits to help manufacturers create accurate virtual models of their production lines, providing detailed, predictive insights into factory performance.
ChiAha’s models aid in answering questions related to factory flow and productivity and provide predictions within 1% accuracy for production line performance and overall equipment effectiveness (OEE). This high degree of accuracy is possible due to the analysis of event data from the production line and advanced algorithms that simulate real-world manufacturing environments. Essentially, even users with minimal technical expertise can model their production operations and optimize them for reliability and efficiency with ChiAha.
It’s worth noting that Andrew Siprelle, a simulation expert and optimization and modeling consultant, established ChiAha to address several recurring problems in manufacturing optimization and digital twins. Siprelle has realized that simulation tools usually require specialized knowledge and significant time to set up. Many conventional digital twin systems also tend to focus more on 3D animation and visualization. This can be valuable for explaining and improving detailed traffic patterns. However, it adds unnecessary complexity and cost for many manufacturing needs.
Similarly, some systems prioritize visually capturing the 3D layout or movement of materials, which is useful in certain kinematic scenarios but non-essential for high-speed manufacturing. The fact that the primary concern in manufacturing, especially in high-speed, high-volume production lines, isn’t aesthetic animation but timing, flow, and efficiency tends to be forgotten. Siprelle asserts that manufacturers need tools that emphasize time and performance, capturing essential data points like machine uptime, downtime, buffer needs, and failure modes in a way that’s easy to interpret and apply.
Meaningful data is required for true optimization
The founder also recognized that small to medium-sized manufacturers usually lack in-house engineering expertise and have limited budgets for process improvement projects. A custom-built 3D simulation model could take months to create and validate, costing tens of thousands of dollars.
Even manufacturing plants running older or non-standardized equipment are able to collect meaningful data for optimization. However, the challenge is distilling said data into actionable insights.
ChiAha’s mission is to help companies turn raw data into valuable predictions. It has developed the Wishbone Interrupt Modeler and Aidos Performance Predictor—two groundbreaking toolkits set to empower manufacturers with accessible, high-performance solutions that provide in-depth insight into production dynamics and optimization opportunities.
Wishbone, in particular, accurately models process interruptions to improve reliability. Events such as machine breakdowns, safety stops, and scheduled maintenance disrupt the flow in most production lines, impacting efficiency and output. Modeling these interruptions requires an expert to map raw data and manually categorize events based on causes and timing. Wishbone automates this process, using practical AI.
This Wishbone toolkit turns raw line event data into useful distributions for Time to Failure (TTF) and Time to Repair (TTR) — two critical metrics for understanding and improving production reliability. Users can then gain insights on machine-specific or line-wide interruptions, allowing them to address specific causes of downtime and develop targeted maintenance strategies.
Introducing Aidos: The digital twin solution
Meanwhile, Aidos is ChiAha’s high-powered digital twin solution for predicting and optimizing production line performance in high-speed, high-volume environments. It’s built upon Discrete Rate Simulation (DRS) and advanced analytics tools, which enables manufacturers to evaluate complex, interconnected production systems with high accuracy.
“Aidos helps operators, engineers, and managers understand this balance so they can make informed decisions to maximize production efficiency.”
Users can test various scenarios and identify the best options to maximize OEE and throughput with its simulation capabilities. Overall, Aidos helps manufacturers identify high-leverage improvement opportunities, manage buffers and constraints, understand equipment interdependencies, and maximize throughput.
“High-speed packaging lines usually involve a mix of equipment — fillers, sealers, labelers. Each has adjustable operating rates,” Siprelle explains. “These set points vary between shifts, and operators debate the optimal rates. For example, one shift might set the filler rate to 100 cans per minute, while another shift prefers 95. One might think these are just minor adjustments, but they influence production performance, downtime, and even line reliability. This is where simulation becomes incredibly important.”
Aidos allows ChiAha to test various configurations and set points to evaluate their impact on throughput, line reliability, and the buffer capacity needed between equipment units. “Over 30 years, I’ve observed that this ‘buffer-reliability trade-off’ is misunderstood, but it’s crucial for optimizing throughput and capacity,” says Siprelle. “Aidos helps operators, engineers, and managers understand this balance so they can make informed decisions to maximize production efficiency.”
What comes next?
ChiAha’s Wishbone and Aidos toolkits – built on a 30-year foundation across various verticals – are currently being tested by industry partners to ensure robustness and effectiveness. These partners provide real-world feedback, allowing ChiAha to refine the toolkits and tailor them to the practical needs of manufacturing. Both toolkits are set to launch for general availability in 2025.
