INESC TEC – Institute for Systems and Computer Engineering, Technology and Science

INESC TEC is a private non-profit research institution, with around 700 integrated researchers (about 350 PhD). Main activities are scientific research and technological development, technology transfer, consulting and advanced training programs in: Industrial and Manufacturing Engineering, Business Networking, Information Technologies, Energy, Telecommunications and Electronics, and Innovation Management. The Centre for Enterprise Systems Engineering (CESE) is composed of about 60 researchers (16 PhD) and includes activity areas related with Operations Management and Enterprise Information Systems, applied to industrial companies and enterprise cooperation networks. The main RTD areas are: Enterprise Cooperation Networks; Operations Management; Industrial Business Analytics; Advanced Planning Systems; Intelligent Logistic Systems and Interoperability. The Centre promotes applied research projects, in partnership with software houses and equipment suppliers, aiming at the development of innovative products in: decision support systems, production planning, scheduling and control, advanced automation and logistics, quality and maintenance management, knowledge management, and integration infrastructures. INESC TEC is a network of research centres, recognised as Associated Laboratory by the science ministry. INESC TEC has a strong link with industry, resulting from a long collaboration in RTD and consulting projects. It is present in the board of the Manufuture European Technology Platform and EFFRA Research Association.

Relevant Expertise for the project:

INESC TEC brings to the project specific expertise in the design and development of decision support systems, business analytics, data mining and forecasting and its application in the predictive maintenance domain. INESC TEC has experience in the design and development of decision support systems, business intelligence and analytics systems as well as data mining and machine learning technology to various problems. In particular, some of the problems which are particularly relevant for the current project are in manufacturing, including production and equipment monitoring and optimisation (modelling equipment functioning for operation parameterisation and machine failure prediction) and tool monitoring and design (modelling tool wear and selection). Other application domains include retail (recommender systems, sales prediction, shelf space allocation), logistics (forecasting), ebusiness (recommender systems) and structural health monitoring (failure identification). In order to solve such a diverse set of problems, INESC TEC can tap into a large network of researchers that have expertise on a set of diversified sub-fields of the area. This means that many different techniques in machine learning and data mining have been employed and their choice depends specifically on the type of problem to be solved. The knowledge about the techniques is complemented with extensive experience in their application to real-world problems and the corresponding know-how in all the steps of the process (including business and data understanding, data preparation and model deployment). This knowledge is complemented with the ability to conduct academic research to develop new approaches when the existing ones are not suitable. This includes, among others, anomaly detection, optimal model selection, meta-learning, transfer learning, learning algorithms specifically designed for streaming data and big data analysis (parallel and distributed data processing).

Role in the project:

Within WP 4 – Analysis and decision making functionalities INESC TEC will participate in the design and development of specific data analysis and forecasting tools especially using techniques such as Data Mining e Machine learning and algorithms to select the best forecasting models for each operating condition. INESC TEC will coordinate the use case implementation at ADIRA with contributions in Tasks 1.2 and WP7. Will also participate in the dissemination and exploitation activities within WP8.