On Optimising Resource Planning in BT plc with FOS

Abstract

The need to move from reactive to proactive resource planning has been highlighted by industry analysts, academia and enterprises. Proactive resource planning provides business users with a view of future jobs, which in turn will help them to plan their workforce utilisation appropriately in order to reduce costs and improve customer satisfaction. This paper presents the application of FOS, an integrated service management system, for managing the resources of BT. FOS incorporates applications for reliable workload forecasting, optimised workforce planning, as well as advance tools for visualising and communicating the outputs to end users.

FOS: An Advanced Planning and Scheduling Suite for Service Operations

“…FieldForecast and FieldPlan are supporting over 5,000 people [11] with plans to extend this to 25,000 within 2006/2007 including our engineering but also parts of our call centre operations. FieldReserve and FieldSchedule which are new modules are currently trialed in the context of challenging real-world scenarios while our previous scheduling system called Dynamic Scheduler is already deployed to over 25,000 personnel [7].…”

“…The FieldForecast application of FOS can be used to build the demand picture of a service organisation both for the short-term and the long-term (see [11] for details). There is a forecasting but also a planning element (e.g.…”

“…day/week/month). FieldPlan adopts a "demand chase" strategy using hill climbing [11] (a heuristic search technique) to identfiy the optimal changes in supply to meet the demand. These changes in supply include people moving between areas, focusing on different skills, invited to work on overtime, allowed taking more annual leave, etc.…”

Analysing the Effect of Demand Uncertainty in Dynamic Pricing with EAs

“…In the type of problems we are analyzing, resource management is the effective workforce utilization for a given calendarised work demand profile, while meeting a set of constraints such as quality of service targets, conflict resolution schemes, such as overtime and borrowing additional workforce [17]. The system described in [18] integrates various Artificial Intelligence and Operational Research techniques in order to forecast demand for specific products and services at regional level, and to optimize the allocation of resources to each one of the region. Our aim is to use evolutionary algorithms as an alternative technique to manage resources by means of effective pricing.…”

“…Our motivation is to use such pricing approach to control demand and manage resources in a service industry [28] [18]. In the type of problems we are analyzing, resource management is the effective workforce utilization for a given calendarised work demand profile, while meeting a set of constraints such as quality of service targets, conflict resolution schemes, such as overtime and borrowing additional workforce [17].…”