Preventing user mistakes by using machine learning

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Odoo Experience 2020

Talks

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Location: Studio 7 - 9/30/20, 9:30 PM - 9/30/20, 10:00 PM (Europe/Brussels) (30 minutes)

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Preventing user mistakes by using machine learning

Faisal Basar

CTO & Co-Founder at Bista Solutions Inc.

Faisal Basar

CTO & Co-Founder at Bista Solutions Inc.

With over 18 years of international experience in the technology industry, Faisal Basar is a software expert who co-founded Bista Solutions, which is amongst the top partners of Odoo. As the CTO of Bista Solutions, Faisal has gone to the length and breadth of the world to evangelize Odoo as the to-go platform for integrated business needs.

As the CTO he is responsible for the global delivery of all the projects which are underway at Bista Solutions, while also optimizing and implementing the new technologies that can help transform businesses.

Odoo provides efficient stocking methods to organize your warehouse and enable smart double inventory systems. But what happens if someone makes an incorrect entry in your inventory management system?
Human beings are always prone to making errors, however, if this happens, errors in your inventory can cause unprecedented disruptions in your supply chain. More often than not, it means that an unusual occurrence has taken place. This could be a human data-entry error, a tagging error or even corporate espionage. Ultimately causing major changes in your balance sheet and reflecting poorly on your finances and balance sheets. If your inventory balance is incorrect, it could in turn affect the cost of goods sold and profits, having dramatic effects on your business performance.
The talk focuses on a module that we designed to safeguard you against all these errors. This module understands your data, identifies anomalies and helps you prevent damages. From warehouses in manufacturing factories to delivered products in retail, this cutting-edge automation can be used across all industries. If your business relies on inventory or raw materials, this module can help you avoid errors in stock inventory. After this, we use the tested data, which is a matrix of current stock moves. This data is used to find a pattern of transactions from the trained data to be compared to.