We are building a series of fast, visually accessible, cross.-sectional, hence static urban models for large metropolitan areas that will enable us to rapidly test many different scenarios pertaining to both short-term and long-term urban futures. We call this framework SIMULACRA which is a forum for developing many different model variants which can be finely tuned to different problem contexts and future scenarios. The models are multisector, dealing with residential, retail/service, and employment location, are highly disaggregate, and subject to constraints on land availability and transport capacities. They have an explicit urban economic focus around transport costs, incomes, and house prices and thus encapsulate simple market-clearing mechanisms. Here we will briefly outline this class of models, paying particular attention to their structure and the way physical flows and locations are mirrored by economic flows in terms of costs and prices. Several versions of the model now exist, but we will focus, first, on the simplest 'one-window' desktop pilot version with the most obvious graphical interface; and, second, on a much more elaborated framework developed for web access, extensible to web service architectures and other related services. To demonstrate its flexibility and intelligibility, we define the various interfaces and demonstrate how the aggregate model can be calibrated to the wider London region to which it is applied. We will demonstrate the model, albeit briefly with respect to the rapid assessment of different urban futures-"what-if " scenarios, based on the impact of new London airports in the Thames Estuary. The key feature of this entire project is that the model and its variants can be run in a matter of seconds, thus entirely changing the traditional dialogue associated with their use and experimentation.

In Mexico, the automotive industry is considered to be strategic in the industrial and economic development of the country because it generates production, employment and foreign exchange. Good demand forecasts are needed for better manufacturing management. The time series modelling tools applied to the monthly demand forecasting of automobile spare parts in Mexico are assessed, for the case of a transnational enterprise, considering affordability. The classic methods of moving averages, final value and exponential smoothing, the prestigious autoregressive integrated models of moving averages (ARIMA), the rarely implemented artificial neural networks (ANNs) and the very little explored ARIMA-ANNs hybrid models are compared. A good performance of the models involving ANNs is observed, but they were not as steady as the ARIMA models in the post-sample periods. The mean absolute percentage error (MAPE) was reduced from an original 57% to 32.65%. The obtained results could help demonstrate the importance of improving industrial forecasting methodologies for better planning.