IFS, the global enterprise applications company, announced today at the IFS World Conference 2016 the launch of IFS Mobile Workforce Management version 5.8.
Among the enhancements in the new version are:
Automatic shift generation—The new version supports automatic shift planning and rostering for individuals or groups based on constraints such as number of work hours, number of work days, and permissible shift patterns. With only one click, the solution then automatically plans shifts that are optimised for the company’s requirements and constraints.
Self-learning scheduling—Leverages an extended data archive to allow for more educated and informed scheduling decisions based on historical data. The solution automatically learns to produce more accurate work schedules by analysing a broad range of data including average job duration by activity type, customer and contract.
New cloud deployment options—Features such as capacity planning and target-based scheduling can now run in scalable, multi-tenant Microsoft Azure environments to support occasional users and ensure maximum system availability for the solution’s Dynamic Scheduling Engine (DSE).
Big data optimisation—The new version offers even better support for large datasets, including support for aggregation and de-aggregation of planning data, extended options for filtering tasks and time-slicing as well as intelligent support for allocating processor time.
Enhanced visual insights—A new cockpit view empowers users with customisable key performance indicators for quick and easy data access, driving efficiency and a positive user experience.
IFS product director for service-, asset-, and project-based solutions Jørgen Rogde added: “The new version of our dynamic work scheduling solution represents a major product investment aimed at helping our clients streamline and automate their scheduling processes while maintaining and enhancing the flexibility of the solution through new options for cloud deployment and big data management. It will help our customers use their resources more effectively, ultimately saving them time and money.”