Enterprise resource planning: Analysing the impact

Describes a study that set out to evaluate the performance impact of a SAP R3 implementation. The SAP system was implemented by a major multinational business in four of its European plants. Qualitative and quantitative data were collected over a two-year period, through surveys and interviews with systems users and by accessing company records. While users were able to identify the operational benefits of SAP, they were still doubtful at the end of the evaluation process whether the system had resulted in any significant positive financial benefits for the business. Two themes related to this observation are explored. First the time lag between operational improvements and subsequent financial impact. Second the importance of learning as a means of reducing the time lag. Learning in this context is a multi-dimensional concept and covers learning how to use the system, learning how to improve the system and learning how to improve the implementation process.

Simulation-based optimal sensor scheduling with application to observer trajectory planning

The sensor scheduling problem can be formulated as a controlled hidden Markov model and this paper solves the problem when the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. The aim is to minimise the variance of the estimation error of the hidden state w.r.t. the action sequence. We present a novel simulation-based method that uses a stochastic gradient algorithm to find optimal actions. © 2007 Elsevier Ltd. All rights reserved.