How much does Lean Manufacturing need environmental and information technologies?

This paper analyses the role played by Environmental and Information Technologies (ET&IT) in the capability of Lean Manufacturing (LM) to achieve improved industrial performance. In contrast to seminal literature about lean practices, and in view of increasing consumer requirements regarding response times and environmental concerns, we suggest that shop-floor technologies are crucial for transforming lean routines into enhanced performance. Hypotheses were tested in a multisectoral sample of 763 manufacturing plants (NACE codes 15–37) from five different European countries. Results confirm total mediation by both technologies between lean routines and industrial performance, which entails that LM establishes efficient conditions on the shop floor for developing technology-enabled capabilities that can be leveraged to improve industrial performance. From a managerial perspective our findings highlight the need for avoiding short-sighted attitudes and for internalising plant technologies within lean transformation projects. This is important not only because such technologies are determinant for maximising the potential of organisational routines in current manufacturing systems but also because of their intrinsic benefits.Ministerio de Economía y Competitividad | Ref. ECO2016-76625-

Regional scale dryland vegetation classification with an integrated lidar-hyperspectral approach

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems