SEATTLE--(BUSINESS WIRE)--Today, Amazon Web Services, Inc. (AWS), an Amazon.com company (NASDAQ: AMZN), 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. To get started with Amazon Forecast, visit https://aws.amazon.com/forecast/.
Forecasting is the science of predicting the future. By examining historical trends, organizations can make a call on what might happen and when, and build that into their future plans for everything from product demand to inventory to staffing. Given the consequences of forecasting, accuracy really matters. If a forecast is too high, customers will over-invest in products and staff, which ends up as wasted investment, and if the forecast is too low, they will under-invest, which leads to a shortfall in raw materials and inventory; creating a poor customer experience. Today, companies try to use everything from simple spreadsheets to complex financial planning software to generate forecasts, but high accuracy remains elusive for two reasons. First, traditional forecasts struggle to incorporate very large volumes of historical data, missing out on important signals from the past that are lost in the noise. Second, traditional forecasts rarely incorporate related but independent data, which can offer important context (such as sales, holidays, locations, marketing promotions, etc.). Without the full history and the broader context, most forecasts fail to predict the future accurately.
Amazon has a wealth of knowledge in building accurate forecasts using machine learning from over 20 years of experience operating the world’s largest ecommerce business. Delivering billions of packages per year, with a multitude of delivery options in more than ten thousand zip codes, Amazon has developed advanced forecasting capabilities that incorporate the full product history and overlay context from related business activities, such as promotions and pricing changes. Due to this diverse and large-scale forecasting experience at Amazon, customers have asked AWS to share this knowledge with them to help make their own forecasts more accurate.
Today’s general availability of Amazon Forecast provides a major step toward putting the power of Amazon’s deep experience in forecasting into the hands of everyday developers in virtually every industry. Amazon Forecast produces private, custom models that can help developers make predictions that are up to 50% more accurate than traditional methods. Using machine learning, Amazon Forecast automatically discovers how variables such as product features, seasonality, and store locations affect each other. These complex relationships can be difficult to spot using traditional forecasting methods, but Amazon Forecast uses the machine learning developed at Amazon to quickly recognize complex patterns to improve forecast accuracy. Amazon Forecast automatically sets up a data pipeline, ingests data, trains a model, provides accuracy metrics, and performs forecasts. Developers do not need to have any expertise in machine learning to start using Amazon Forecast, and can use the Amazon Forecast Application Programming Interface (API) or easy-to-use console to build custom machine learning models in less than five API calls or clicks. With Amazon Forecast, customers can achieve accuracy levels that used to take months of engineering in as little as a few hours.
“Amazon Forecast now offers the forecasting expertise from Amazon’s first 25 years of building the world’s largest ecommerce business in a managed service for any company to leverage,” said Swami Sivasubramanian, Vice President, Amazon Machine Learning. “We’ve built sophisticated, machine learning forecasting algorithms over many years that our customers can now use in Amazon Forecast without having to know anything about machine learning themselves. We can’t wait to see how our customers use the service to reduce operating expenses and inefficiencies, ensure higher resource and product availability, deliver products faster, and lower costs to delight their customers.”
Amazon Forecast is available today in US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Singapore), and EU (Ireland) with more availability zones coming soon.
Puget Sound Energy (PSE) is the state’s largest utility, supporting 1.1 million electric customers and 825,000 natural gas customers in communities in 10 Washington counties. “At PSE, we’ve used Amazon Forecast to forecast electric and gas consumption at a typical residence. We found that even with a very limited set of historical consumption and weather data, Amazon Forecast performed very well at forecasting 30 days out with virtually no manual effort. With the increased emphasis on environmentally-friendly energy solutions, the ability to produce more accurate energy usage projections at each of our customers’ homes and businesses will be essential for energy service providers like PSE. With these enhanced analytical capabilities, PSE will be able to identify custom energy saving programs and services, ultimately reducing customer bills,” said Paul Johnson, Sr. Cloud Architect at PSE.
Accenture is a leading global professional services company, providing a broad range of services and solutions in strategy, consulting, digital, technology, and operations. “We are relentlessly focused on driving innovation and bringing increased value to our key clients,” said Takuya Kudo, a managing director and global data science innovation lead at Accenture Applied Intelligence. “Amazon Forecast is simple to operationalize for production workloads, allowing us to experiment with multiple state-of-the-art deep learning models for forecasting across our business.”
OMOTOR helps businesses improve through AI by providing them with the best of machine learning algorithms, computer vision techniques and cognitive bots that can communicate via WhatsApp and others platforms. "At OMOTOR, we use AI to innovate on behalf of our customers, so access to the most cutting-edge deep learning technologies from AWS is imperative to our client's success. Using Amazon Forecast gives us the ability to create and refine various forecasts from time series data without having to build and train a model manually every time. We forecast real sales for the next 12 months, so we can adequately plan for inventory, estimate future profitability, track market share gain or loss, and other insights. This means we can use more contextual data, optimize more frequently, generate forecasts with upwards of 50% improvements in accuracy, and operate at a great speed. For example, we're helping customers in the automotive industry predict sales across 185 vehicles in Brazil,” said Marcio Rodrigues, CEO, OMOTOR.
CJ Logistics is a leading integrated transportation and logistics service provider for individuals and companies in Korea. “Amazon Forecast has been applied to CJ Logistics’ parcel volume forecasting process to optimize the amount of human resources, transportation, and warehouse space we provision to meet demand,” said YoungSoo Kim, Vice President of TES Strategy Unit for CJ Logistics. “Amazon Forecast allows us to use sophisticated machine learning-based forecasting techniques without building our own system. We have a clear method for increasing our operational efficiency by using Amazon Forecast.”
Daemon Solutions is a consultancy helping clients get the most out of technology in business. “One of the most common challenges facing our retail partners is the ability to accurately predict product demand across different SKUs and store locations. Given the ease with which we have been able to use Amazon Forecast to create advanced ML models that meet the accuracy bar when it comes to forecasting as well as account for variables such as promotions and price with our test data sets, we are excited to deploy the service on behalf of our clients and solve real-life problems,” said Akram Dweikat, Head of Machine Learning and Artificial Intelligence at Daemon Solutions.
About Amazon Web Services
For 13 years, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud platform. AWS offers over 165 fully featured services for compute, storage, databases, networking, analytics, robotics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, virtual and augmented reality (VR and AR), media, and application development, deployment, and management from 69 Availability Zones (AZs) within 22 geographic regions, spanning the U.S., Australia, Brazil, Canada, China, France, Germany, Hong Kong Special Administrative Region, India, Ireland, Japan, Korea, Middle East, Singapore, Sweden, and the UK. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com.
About Amazon
Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfillment by Amazon, AWS, Kindle Direct Publishing, Kindle, Fire tablets, Fire TV, Amazon Echo, and Alexa are some of the products and services pioneered by Amazon. For more information, visit amazon.com/about and follow @AmazonNews.
