The globalization of markets and the massive influx of new technologies have led to major changes in the manufacturing process. These new technologies have a big impact on the productivity and organization of production in enterprises. In this document, we study, using survey data, the behavior of establishments in the production and logging sectors in terms of advanced technologies. The goal is to gather important information about the technological capabilities of these institutions. In addition, we seek to obtain information on professional skills as a result of introducing advanced technologies, means of acquiring these technologies, sources of information or assistance in mastering technologies. leadership, adoption results, adoption barriers, business practices and success factors.
Information is also collected on the use of new technologies such as geomatics / geospatial technology, biotechnology and nanotechnology. Other general information, such as research and development, as well as innovation by institutions, is also collected. This report is in four parts. The first gives a general portrait of Quebec institutions regarding the introduction of advanced technologies. It presents the types of technologies used in industry, by the size of the institution, as well as by province and territory, investments intended for these technologies, professional skills, types of training that are most often provided by institutions using advanced technologies and the basic methods used by institutions to acquisitions of newest technology.
The second part examines additional information, such as internal and external sources of information that have played an important role in adopting cutting edge technology, the impact of these technologies on enterprises, obstacles to the adoption of these technologies, business practices regularly used by institutions, and factors that have contributed to success. institutions. The third part discusses the use of new technologies in institutions such as geomatics / geospatial technology, biotechnology and nanotechnology. The last part examines the relationship between the use of advanced technology and innovation. The advanced technology, process, organization and marketing innovations of enterprises using these technologies are analyzed. This section will also cover activities and research and development personnel.
What are the most common areas of study for manufacturing enterprises? Technical and computer skills come first. Eight out of ten institutions offer training aimed at improving technical skills, and seven out of ten offer courses to improve computer skills. Security qualifications as well as quality control and development skills are also important, as just over half of the institutions offer this type of training. However, very few institutions offer business continuing education and basic skills.
With regard to training related to improving technical skills, the table shows that all enterprises in the industries related to the production of beverages and tobacco products, as well as enterprises specializing in the production of veneers, plywood and reconditioned wood products, machinery for construction, machinery for trade and services, as well as equipment for radio and television broadcasting and wireless communications offer this training. The main industries that offer the benefits of computer training are the forestry industry, the production of leather and related products, the production of non-metallic mineral products, and the primary processing of metals. At a more disaggregated level, institutions in the following sectors provide the most training for their employees in the computer field: the production of meat products, the production of equipment for trade and services, and the production of measuring and control devices and medical devices.
Note. The complexity of this approach is to develop a machine that can generalize. In fact, after the training data set has been transferred to the machine, the goal is to not be able to identify the numbers contained in the images of this set. This would be trivial to solve by storing and accessing each solution. The goal is for the device to correctly identify the number contained in the image that was not in this set. That is why the performance of a training machine is measured by a set of test data, that is, a set of labeled examples that the machine does not know at the training stage. Thus, a machine with a high level of productivity will be able to find, using examples of a set of training data, a solution function that is sufficiently general to determine the number contained in any image.