Manufacturing vision and competitiveness

Discusses a refinement to the process by which manufacturing strategy is created. Builds on an existing strategy process (Platts, 1990) and adapts it to fit more closely within the dynamic manufacturing vision. The method for creating a manufacturing vision allows a business to do this in a two- to three-week period as part of a 10-12 week manufacturing strategy project. A conceptual model of manufacturing vision has been developed that enables practitioners to explore the factors that influenced the potential competitive contribution of manufacturing and to agree an explicit direction for change. Describes the successful application of the process in six manufacturing organizations and highlights the practical limitations of the approach.

Phrase-based statistical language generation using graphical models and active learning

Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to inform the generation decision process. Both approaches rely on the existence of a handcrafted generator, which limits their scalability to new domains. This paper presents BAGEL, a statistical language generator which uses dynamic Bayesian networks to learn from semantically-aligned data produced by 42 untrained annotators. A human evaluation shows that BAGEL can generate natural and informative utterances from unseen inputs in the information presentation domain. Additionally, generation performance on sparse datasets is improved significantly by using certainty-based active learning, yielding ratings close to the human gold standard with a fraction of the data. © 2010 Association for Computational Linguistics.