Joaquín Bienvenido Ordieres Meré

Prediction models for ozone in metropolitan area of Mexico City based on artificial intelligence techniques

Ozone is one of the worst harmful pollutants nowadays which affects the public health, so it is necessary to predict ozone level accurately in order to prevent the public from exposing to the pollution when it exceeds the limits. This study aims to predict daily maximum ozone concentrations in the metropolitan area of Mexico City by using four individual artificial intelligence techniques: multiple linear regression, neural networks, support vector machine, random forest, and two ensemble techniques: linear ensemble and greedy ensemble. Results from the comparison among different artificial intelligence techniques clearly showed that ensemble models, especially linear ensemble model, outperformed the individual artificial intelligence techniques. Moreover, it is concluded that the performance of models is influenced by the time ahead factor for the predictors. The errors of prediction models related to the data of current day are only around 50% of ones corresponding to the data of the previous day. In addition, in order to select the input variables properly, analysis of variance (ANOVA) based on multiple linear regression models was performed. Best model prediction capability also depends on the ranges of input variables.

Resilience for lean organisational network

In the literature, when lean is associated with resilience the focus is mainly on developing leaner and more resilient supply chains underrating the importance of organisations as communicating entities. Although people are the heart of a company, their impact on resilience is only marginally considered in the literature. In this study, we address these gaps by developing and testing a model that can calculate the resilience of a lean organisation while considering the organisational topology as well as the learning capacity and attitudes of its workforce. The proposed methodology consists of four macro-steps: identification of a Lean Structural Network (LSN), modelling of nodes, nodes characterisation and analysis of Resilience. A case study is used to explain the proposed model to assess the resilience of the intrinsic structure of a company against two major effects: (a) unexpected shortages in key performance indicators; (b) replacement of a process owner with another having different individual characteristics (different learning curve and attitude). The results show that the proposed methodology allows quantification and prediction of the local and global impacts of unexpected (i.e. failures or other disruptions) and expected events (i.e. cross-training, personnel relocation) in companies under the LSN paradigm.

A data-driven manufacturing support system for rubber extrusion lines

A better control of extrusion processes offers clear advantages in the manufacturing of rubber profiles for the automotive industry. This work reports our experience in developing a support system aimed to ease the work of the extruder machinist while improving the quality of the profiles obtained. In order to build the system, an approach based on facts was adopted, following ISO 9000 standard quality principles. The data warehouse service available provided a wealth of information on the conditions of the running processes. The collected data, after being analysed with the appropriate data-mining techniques, allowed us to gain a better understanding of the process and to identify the main causes of variance. In particular, principal components analysis, Sammon projection and several classification techniques were applied for exploratory purposes. Different behaviours could be described for the extrusion process, allowing for the definition of a control strategy and, eventually, the development of a manufacturing support system. The estimates displayed by the system greatly improve the responsiveness of the machinist when the process departs from expected behaviour. The results of using this system in a local factory proved highly satisfactory and encouraging.

A Multi-agent Data Mining System for Defect Forecasting in a Decentralized Manufacturing Environment

This paper reports an experience on setting a multi-agent system to control a complex production environment, a steelmaking manufacturing plant. The decentralized character of such a plant fits perfectly with the approach of a control system by means of a multi-agent configuration. The agents devoted to rendering the superficial and internal defects maps, to developing and maintaining the learning context, to evaluating the coils entering the pickling line and to forecasting the remaining defects on the coil are described. Data mining techniques are used by the agents to gain access to the actual status of the manufacturing process, thus helping in the decision-making processes. This proves to be a great aid in improving the quality of the products and reducing both costs and the environmental footprint of the manufacturing process. The results of using such a system reinforce our belief in the approach presented.

The moderating effects of corporate and national factors on lean projects barriers: a cross‐national study

Abstract Despite companies’ efforts to develop lean thinking in their industrial context, the implementation of many lean projects has not been consistently successful, often resulting in delay, failure, abandonment or rejection. Whereas some authors emphasised that company characteristics, like the product demand profile, are significant factors in lean projects, other studies analysed the impact of national culture. This paper aims to study the combined effect of various factors related to national culture and company characteristics of lean implementation barriers in order to determine whether the environmental context in which the company operation can affect the outcome of lean project implementation. A survey has been conducted to collect information about companies of various sizes (small–medium–large) in all industry fields. Data from companies, including manufacturing firms, in 23 different countries, were analysed in depth by a combination of Association Rules and Network Analysis. The results show that some national culture dimensions, such as Performance Orientation and Gender Egalitarianism, influence lean management success and help to maintain a lean culture. Maintaining a lean culture is even more critical than developing it. Furthermore, if the implementation of lean practices is an arduous task for large organisations, it becomes even greater for SMEs. Other cultural factors of individuals such as Uncertainty Avoidance, Future Orientation and Institutional Collectivism also help to support a lean culture and overcome human and cultural barriers.

Insights into the Prevalence of Software Project Defects

This paper analyses the effect of the effort distribution along the software development lifecycle on the prevalence of software defects. This analysis is based on data that was collected by the International Software Benchmarking Standards Group (ISBSG) on the development of 4,106 software projects. Data mining techniques have been applied to gain a better understanding of the behaviour of the project activities and to identify a link between the effort distribution and the prevalence of software defects. This analysis has been complemented with the use of a hierarchical clustering algorithm with a dissimilarity based on the likelihood ratio statistic, for exploratory purposes. As a result, different behaviours have been identified for this collection of software development projects, allowing for the definition of risk control strategies to diminish the number and impact of the software defects. It is expected that the use of similar estimations might greatly improve the awareness of project managers on the risks at hand.