Equipment Service & Routine Maintenance
Specialized solutions to manage centralized workforce operations for the Routine Maintenance focused and Equipment Servicing Industries
t3 Routine based Workforce Planning and Allocation solution is an organically integrated solution for workforce capacity planning, demand forecasting, work schedules, rostering and leave management. It is designed to efficiently schedule competent and skilled workforce against the routine maintenance schedule or work order schedules received as an input
Work orders can also be generated within t3 robotically, based on statistical data and applicability rules defined in the RPA – Task Autopilot Set-up. A 360 degree view of labor activities helps boost your responsiveness and service quality while bringing down costs and effort.
The industries that can benefit from t3 Equipment Service and Routine Maintenance solutions are - Airlines & Airports, Trains, Shipping, Transportation, Government, Electricity Distribution. It is powered by Robotic Process Automation and AI to eliminate repetitive and mundane activities, increase efficiencies and enhance productivity.
Routine Maintenance Schedule and Activity Based Workforce Demand Planning and Allocation
Routine and Demand Planning
Setup maintenance routine rules, activities, workforce and competence requirements. Asset types that follow these routines are configured along with the establishment rules that needs to be followed
Define job profile wise competency requirements, Asset linked skills, education and certifications requirements. Detailed competence profile of individuals is also maintained in the system
Workforce fitness and compliance requirements are defined for system to validate Workforce eligibility before scheduling against the demand generated from the maintenance routines or work orders
Robotic Task Allocation
Workforce tasks scheduling is done robotically using t3 Robotic process automation solution which is integral part of the planning and allocation solution. You can configure applicable rules and preferences in the rpa