bayesian
Bayesian EXAWin-Rate Forecaster
Precisely predict sales success by real-time Bayesian updates of subtle signals from every negotiation. With EXAWin, sales evolves from intuition into the ultimate data science.
The Line PQC Case: Managing Defects as Evidence, Not Assumptions
A supply chain business scenario based on the primary manufacturing line as the reference process. It explains how an NG detected during roaming line PQC is connected to LOT, line, position, defect type, disposition status, supplied-part risk, and delivery impact. It also shows how paper- and Excel-based quality records are standardized inside Exa Omni+, and how repeated evidence updates risk judgment.
The Moment One POP Entry Continues All the Way to Shipment: A Day of Line Execution Control
A business scenario describing how an operator's POP result entry on an electric blanket production line continues into line-side inventory, WIP status, WMS inventory transactions, production risk alerts R/Y/G, and Japan head-office monitoring. It explains how the field uses Exa Omni+ around work orders, actual results, and risk signals rather than around complex algorithms.
From First-floor Inbound to the External Finished-goods Warehouse: How Inventory Remains a Company Asset
A real-time inventory transaction synchronization scenario that follows raw materials from first-floor inbound, second-floor IQC inspection, Keeping location management, Picking, process input, and storage in an external 3PL finished-goods warehouse. It explains how field exceptions such as miscellaneous issue, scrap, returns, and stock-count reconciliation are aligned with enterprise inventory accuracy.
The Head-office Screen Is Support, Not Surveillance: A Real-time Control Scenario for the Vietnam Production Subsidiary
A business scenario clarifying how Japan head office introduces Exa Omni+ to synchronize the production, material, quality, inventory, and shipment status of overseas subsidiaries in real time, and to realize proactive support and multinational decision-making rather than passive surveillance. It highlights how multilingual operating infrastructure removes the language barrier among head-office executives, local Japanese managers, and local operators.
A Plant Director’s Day: Judging Delivery, Materials, and Quality Through One Execution Ledger
A business scenario explaining the decision-making value provided by the Exa Omni+ execution ledger, dashboard, Bayesian Risk, Ontology AI-Agent, and Auto-Tuner through the daily scene of a Vietnam production subsidiary executive judging delivery, materials, quality, inventory, and shipment risk.
![BA04-1. [Novel] Probability in Saigon — The Day Data Beat Intuition (Part 1)](/_next/image?url=%2Fstatic%2Fimages%2FBA041-saigon-probability-1.png&w=3840&q=75)
BA04-1. [Novel] Probability in Saigon — The Day Data Beat Intuition (Part 1)
The ultra-high-rise condo pre-sales market in Ho Chi Minh City. A showdown between an intuition-driven ace salesman and a data-driven rookie. This novel format explains how the EXAWin Bayesian engine becomes a tool for victory in the Southeast Asian real estate sales competition. Part 1: The calm before the storm — two salesmen in Saigon.
![BA04-2. [Novel] Probability in Saigon — The Day Data Beat Intuition (Part 2)](/_next/image?url=%2Fstatic%2Fimages%2FBA042-saigon-probability-2.png&w=3840&q=75)
BA04-2. [Novel] Probability in Saigon — The Day Data Beat Intuition (Part 2)
The conclusion of the 480-unit condo pre-sales war in Ho Chi Minh City. President Phan's contract, Tuấn's awakening, and the turnaround led by Park Jun-hyuk's EXAWin. The showdown between intuition and data finally reaches its conclusion.
BA111. Dynamic Buffers and Backward Scheduling: How to Reconfigure the Factory Around Due Dates
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.
BA024. The Evolution of EXAWin Bayesian Engine: The Day Data Tuned Its Own Parameters
The EXA Bayesian Engine calculated win probabilities, but its precision depended on manually configured initial parameters. When 100 historical deals accumulated, the engine was ready to evolve on its own. Grid Search, MCMC Ensemble Sampling, and Cross-Validation — three mathematical pillars working in concert to find optimal parameters. Told as a story.