Evidence, methods, and operating insight for decisions that must work in the field.
A missing submarine and airliner, a codebreaking war fought on paper, and navigation to the Moon. Through dramatic episodes in history, discover how priors, likelihoods, posteriors, and the weight of evidence change the next judgment.
The Dreyfus affair and the authors of The Federalist, insurance premiums and spam filters, combined models and superforecasters. Follow Bayesian reasoning in everyday life as small pieces of evidence are compared, accumulated, and used to revise judgments.
There is a gap between a busy day and a day spent completing important work. Through ExaRing+’s Rings and execution records, its Bayesian feedback loop, and Jordan’s story, this article examines how judgments about oneself change the next action.
Follow the course of world history to explore the birth of knowledge and the choices people made. An introduction to why EXA Business Science reads business and civilization through history.
BA03. [On-Time Material Inbound: Bayesian MCMC] The Real Game in Business is the Fight Against Uncertainty
Change the inspection records in a short-shot investigation and see how new evidence changes the judgment about its cause. Move between experiments and investigation notes to distinguish this evidence from the overall judgment, and build a basis for discussing the next inspection and action.
A business narrative explaining the decision-making value of Exa Omni+'s transaction data, dashboard, Bayesian Risk, Ontology AI-Agent, and Auto-Tuner through a local production-subsidiary executive's day of assessing delivery, material, quality, inventory, and shipment risks.
This story depicts the process of resolving chronic chaos on the manufacturing floor through EXA's advanced Bayesian algorithm and production scheduling engine. Moving away from the indiscriminate push-style production methods of the past, it introduces data-driven simulation and backward scheduling to precisely control process bottlenecks. Through real-time data learning, the system sets dynamic buffers and reorders priorities toward optimizing schedules based on bottleneck process capability for due-date compliance rather than simple utilization. As a result, by suppressing unnecessary WIP and securing protective capacity, the factory undergoes an innovative transformation in which profitability and due-date hit rate rise even while physical machine operating time decreases. It shows the completed form of a demand-driven Pull production system realized by combining human intuition with cold data computation.
