US-based hotel-specific software provider Inn-Flow has launched its improved tool, Labor Management 2.0, to address complex hotel challenges.
More than 15,000 individual users across the globe have deployed Inn-Flow’s original management tool. The upgraded version serves as an all-in-one labour solution with new features that helps users to streamline operations, drive performance and manage expenses.
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Inn-Flow CEO and founder John Erhart said: “Without a doubt, we know that 2020 proved to be one of the most trying and difficult years for the hospitality industry. And while our labor management tool has always been important, it’s more critical than ever as hotels begin the road to recovery.
“Our new software was built with this in mind and is aimed at helping hotels minimize expenses while maximising time-savings, which ultimately impacts the bottom line and allows hotel staff to get back to what they love doing – taking care of guests.”
Inn-Flow’s Labor Management 2.0 tool helps hotels to tackle all aspects of operations such as budgeting and forecasting, scheduling, time-tracking, analytics, integrations, housekeeping management.
The upgrade version of the tool helps in controlling overtime. By setting minutes per occupied rooms for housekeeping and sending out “overtime at risk” alerts to management, hotels can control labour costs.
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By GlobalDataIts smart scheduling feature helps hotels to create schedules with just one click. They will be able to use real-time performance to forecast upcoming schedules.
New features to the upgraded version such as drag-and-drop, bulk editing and smart shift reassignments aid in minimising the risk of overtime for staff and enables them to manage schedules.
The Biometric Time Clock feature comes with a user-friendly interface and provides a seamless experience for users to view schedules whilst also seeking time off.
The time clock also has a new proprietary facial recognition technology, called Smile ID. Equipped with this facility, based on facial landmarks, hoteliers can streamline the punch in/punch-out process with machine learning predictions.
