Goto

Collaborating Authors

The Magic of Predicting Demand from Data

#artificialintelligence

Sometimes the speed of Amazon's delivery is bewildering. No matter how obscure your order, the retailer frequently promises same-day delivery. Is it that your neighborhood is full of fly-fishing buffs, or whatever your niche interest may be? Instead, it is likely that the company has already shipped the product to your nearest warehouse because it thought that you might order it. Magical as this might sound, it is the application of a technology known as demand sensing.


AWS Announces General Availability of Amazon Forecast HostReview.com

#artificialintelligence

Amazon uses forecasting to make sure that the right product is in the right place at the right time by predicting demand for hundreds of millions of products every day. Amazon Forecast uses this same technology to build precise forecasts for virtually any business condition, including product demand and sales, infrastructure requirements, energy needs, and staffing levels – with predictions that are up to 50% more accurate than traditional methods. Amazon Forecast is easy to use and requires no machine learning experience. The service automatically provisions the necessary infrastructure, processes data, and builds custom, private machine learning models that are hosted on AWS and ready to make predictions. To get started with Amazon Forecast, visit https://aws.amazon.com/forecast/.


How AI-Enabled Demand Forecasting Boosts Logistics?

#artificialintelligence

Demand forecasting is one of the most important aspects of logistics. While some businesses are able to make educated guesses based on previous years' sales, demand forecasting using artificial intelligence (AI) technology can help companies achieve higher degrees of precision when predicting future demand for their products. But how AI-Enabled demand forecasting boosts logistics? Forecasting is a complex task that can be made simpler by using Artificial Intelligence (AI) to analyze historical data about orders placed, the market, shipping routes, and weather. Today, demand forecasting has evolved into what is known as predictive demand planning or forecasting.


Using AI to Solve Complex Global Supply Chain Management Challenges - Liwaiwai

#artificialintelligence

Companies are starting to apply artificial intelligence across global supply chain management to improve efficiency, speed and decision-making in areas such as supply chain planning, warehouse automation, and logistics. The SCM World 2016 Future of Supply Chain Survey found that the importance of artificial intelligence has grown rapidly, with 47 percent of supply chain leaders believing the technology is disruptive to global supply chain management strategies. Market-research firm IDC predicts that by 2020, 50 percent of mature supply chains will use AI and advanced analytics for planning, and to eliminate sole reliance on short-term demand forecasts. Supply chain planning and optimization, including demand forecasting, are among the key areas where AI is already beginning to be deployed. Experts say that global supply chains have become so complex, and are affected by so many variables, that AI may be essential to help identify and predict problems and potential solutions.


AWS announces general availability of Amazon Forecast

#artificialintelligence

Amazon Web Services has announced the general availability of Amazon Forecast, a fully managed service that uses machine learning to deliver highly accurate forecasts based on the same technology that powers Amazon.com. Amazon uses forecasting to make sure that the right product is in the right place at the right time by predicting demand for hundreds of millions of products every day. Amazon Forecast uses this same technology to build precise forecasts for virtually any business condition, including product demand and sales, infrastructure requirements, energy needs, and staffing levels – with predictions that are up to 50% more accurate than traditional methods. Amazon Forecast is easy to use and requires no machine learning experience. The service automatically provisions the necessary infrastructure, processes data, and builds custom, private machine learning models that are hosted on AWS and ready to make predictions.