Abstract
Retailers need demand forecasts at different levels of aggregation in order to support a variety of decisions along the supply chain. To ensure aligned decision-making across the hierarchy, it is essential that forecasts at the most disaggregated level add up to forecasts at the aggregate levels above. It is not clear if these aggregate forecasts should be generated independently or by using an hierarchical forecasting method that ensures coherent decision-making at the different levels but does not guarantee, at least, the same accuracy. To give guidelines on this issue, our empirical study investigates the relative performance of independent and reconciled forecasting approaches, using real data from a Portuguese retailer. We consider two alternative forecasting model families for generating the base forecasts; namely, state space models and ARIMA. Appropriate models from both families are chosen for each time-series by minimising the bias-corrected Akaike information criteria. The results show significant improvements in forecast accuracy, providing valuable information to support management decisions. It is clear that reconciled forecasts using the Minimum Trace Shrinkage estimator (MinT-Shrink) generally improve on the accuracy of the ARIMA base forecasts for all levels and for the complete hierarchy, across all forecast horizons. The accuracy gains generally increase with the horizon, varying between 1.7% and 3.7% for the complete hierarchy. It is also evident that the gains in forecast accuracy are more substantial at the higher levels of aggregation, which means that the information about the individual dynamics of the series, which was lost due to aggregation, is brought back again from the lower levels of aggregation to the higher levels by the reconciliation process, substantially improving the forecast accuracy over the base forecasts.

Abstract
Purpose The purpose of this paper is to identify patterns of project risk management (PRM) practices' adoption, and provides empirical evidence concerning the importance (and key attributes) of organizational PRM maturity to the use of risk-related practices and project performance. Design/methodology/approach The research involved two phases: interviews with five project managers, and a worldwide survey of project managers that resulted in the analysis of 865 valid questionnaire responses. Cluster analysis was used to classify PRM practices' use, factor analysis to detect the structure of the relationship between the variables measuring PRM practices' use and a multiple regression analysis (with canonical correlation) to further reveal the different degrees to which PRM practices and organizational maturity are associated. Findings The identified patterns of risk practices' adoption indicate that different contexts of organization PRM maturity and project complexity influence practices selection. The PRM practices related with targets (e.g. time-phased budget plan) are the most used, and those related to tools and techniques (e.g. S-curve) are the least used. Additionally, the obtained results confirm that organizational PRM maturity influences risk practices' usage, moderated by project complexity, and organizational PRM maturity influences project performance. Originality/value Empirical methods were used to investigate the relationship between organizational PRM maturity and a large set of PRM practices with project complexity as a moderator. Gaps in the use of PRM practices (i.e. areas where more PRM knowledge and training are needed) were identified. Finally, this work identifies the attributes of organizational maturity with implications in practices' usage and project performance.

Abstract
This study presents an assessment of the main research problems addressed in the literature on New Product Development (NPD) and its methodologies, for the pharmaceutical industry. The work is particularly focused on the establishment of an evolutionary perspective of the relevant modelling approaches, and on identifying the main current research challenges, considering the fast-changing business context of the industry. Main findings suggest a generalized misalignment of recent studies with today's technological and market trends, highlighting the need for new modelling strategies.

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Abstract
This work proposes a mathematical programming model for jointly scheduling of production and transport in flexible manufacturing systems considering alternative job routing. Although production scheduling and transport scheduling have been vastly researched, most of the works address them independently. In addition, the few that consider their simultaneous scheduling assume job routes as an input, i.e., the machine -operation allocation is previously determined. However, in flexible manufacturing systems, this is an important source of flexibility that should not be ignored. The results show the model efficiency in solving small -sized instances.

Abstract
This work proposes a biased random key genetic algorithm (BRKGA) for the integrated scheduling of manufacturing, transport, and storage/retrieval operations in flexible manufacturing systems (FMSs). Only recently, research on this problem has been reported; however, no heuristic approaches have yet been reported. The computational results show the BRKGA to be capable of finding good quality solutions quickly.