In 1990 Andrew Siprelle created the modeling approach now called discrete rate simulation, first known as bulk flow. It let engineers model high-speed and bulk manufacturing without tracking every item.
Through Simulation Dynamics, the firm he founded in the early 1990s, the team built SDI Industry and SDI Supply Chain Builder and published at the Winter Simulation Conference from 1995 on. The work moved from the production line to the supply chain, including inventory allocation, run lengths, safety stock and logistics networks.
ChiAha brings that history together with consulting partners from Technology Optimization & Management and our Doctors of Reliability. The research page lists the papers, and customers has the results we can publish. The full story of the method is in the history of discrete rate simulation and its lineage, from Pritsker’s SLAM in 1979 to today.
Educator, consultant and simulation tool developer, and the creator of discrete rate simulation (1990). Andrew has spent his career making operations research, AI and high-performance computing useful on real plant floors. He started ChiAha to put those tools in the hands of the people who run the plants.
Product lead across the ChiAha tool family. Amber shapes the sales, marketing and user experience for ReliaSim®, ReliaStats® and the rest of the ChiAha products.
Retired industrial reliability engineers and R&D leaders with 32 to 36 years each in manufacturing.
Modeling and simulation, reliability engineering, high-performance computing.
Reliability engineering, data analysis, modeling and simulation.
Reliability management, maintenance engineering.
Four rules hold across every tool and every engagement.
Every tool is built on queueing theory and reliability statistics, and the methods are published.
Read the loss tree alone and an AI points at the Labeler. The simulation shows 120 one-minute micro-stops on the Filler cascading through the line. Our chat agents and MCP servers answer from the model's output. See how.
Each tool is built for one kind of problem, such as reliability on high-speed lines, patient flow in clinics or thermal processing, and starts from that field's own data and terms.
A model begins with a question someone has to answer, such as which failure mode to fix first, how big a buffer should be, or where a distribution center should go.
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