Infor Upgrade Uses Adaptive Learning Framework to Enhance Revenue Management

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Infor Upgrade Uses Adaptive Learning Framework to Enhance Revenue Management

04/12/2016
Infor has announced the completion of a transformative upgrade to Infor EzRMS (version 20.x), marking a new era of forecasting and optimization that fortifies its hospitality domain expertise with cross-industry science.  The upgrade is the first to reflect the collaboration between the Infor EzRMS science team and Infor Dynamic Science Labs, an internal think tank recently formed to infuse machine learning and big data analytics into all Infor applications. While Infor EzRMS modeling has always used advanced econometric methods to factor in both historical and current patterns, modeling is now based on an adaptive learning framework, gleaning even more granular observations from the data it is already collecting.
 
Through this upgrade, Infor EzRMS has clearer visibility into data trends that can help indicate whether current or historical patterns should be given more weight in forecasting.  Another science enhancement is dynamically adaptive forecasting, through which the solution continues to test which combination of forecasting methods tends to achieve the best accuracy, at each stage in the booking curve.  The result is that each property attains its own custom combination of forecasting methods that can continually adapt to its changing market mix, seasonality, and booking curve.
 
Infor EzRMS now addresses the industry reality that not all booking systems can fully support bid price, deltas, and other more sophisticated yield controls.  Rather than just translating optimized restrictions into the simpler ones, such as length of stay, that are supported by the reservation system, Infor EzRMS now uses “Restriction- Aware Optimization” that factors in the available restriction types from the outset. 
 
This newest version also contains science that is designed to help revolutionize extended stay modeling by not only forecasting more length of stay buckets, but by also forecasting renewals.  Enhanced overbooking logic is designed to achieve a new level of optimization during periods of excess demand, which not only can help minimize the risk of having to relocate guests, but can also assist with minimizing the equally serious risk of having rooms go unsold on nights when demand and rates are at their highest.
 
Infor is also committed to redefining the user experience, and this iteration boasts a beautiful, consumer-grade interface that is designed to help users to work the way they live.
 
Part of the Infor Hospitality suite of solutions, Infor EzRMS is designed to help hoteliers better optimize revenue by automatically calculating demand forecasts and recommending appropriate selling strategies based on data such as day-of-week patterns, lead time figures and the number of guests per room.