No abstract

No abstract

PurposeThis work reports on a developing method time measurement system for measuring manufacturing and assembly processes automatically. This automatic system enables the production engineers and management to detect, process, and display concise and accurate information about the operations in real time.Design/methodology/approachThis system is based on Internet of things technology and RFID-antenna. This methodology consists of seven main steps and one final optimization step. Mainly, the operator is equipped by RFID reader, and the work station tools and devices are provided by RFID tags. Responding the RFID tags to the reader will refer to the certain operations, the difference time between start and end of the operations will be collected immediately and calculated by the microprocessor of the system.FindingsThis automatic system is promising, considering the accurate time measurements and recommendations that obtained from the case study which includes measuring manual assembly operations to be followed in order to overcome the limitations which are not only technical but also managerial, legal and organizational.Research limitations/implicationsThe acquired data about timing and duration of individual operations are anonymized to guarantee the compliance with respect to the privacy laws (GDPR and Italian work's laws).Originality/valueThis work presents a unique system to measure the time instead of traditional methods in the factories environment and satisfies the requirements to study the recommendations in order to overcome the challenges.

Companies are dealing with many cognitive changes with the introduction of the Industry 4.0 paradigm. In this constantly changing environment, knowledge management is a key factor. Dialog systems, being able to hold a conversation with humans, could support the knowledge management in business environment. Although, these systems are currently hand-coded and need the intervention of a human being in writing all the possible questions and answers, and then planning the interactions. This process, besides being time-consuming, is not scalable. Conversely, a dialog system, also referred to as chatbot, can be built from scratch by simply extracting rules from technical documentation. So, the goal of this research is designing a methodology for automatic building of human-machine conversational system, able to interact in an industrial environment. An initial taxonomy, containing entities expected to be found in maintenance manuals, is used to identify the relevant sentences of a manual provided by the company BOBST SA and applying text mining techniques, it is automatically expanded. The final result is a taxonomy network representing the entities and their relation, that will be used in future works for managing the interactions of a maintenance chatbot.

Data, information and knowledge are strongly involved in Engineering Design (ED) process. Despite the crucial role played by data in the design process, there is a lack of studies about how different data are used and generated by the various phases of the ED process. This study is a first attempt to fill this gap by mapping which data types are involved in the different ED phases from a research perspective.In order to achieve this objective, we used a methodology based on Text Mining. Firstly, we retrieve a corpus of scientific papers related to ED; then, we build two lexicons to recognize ED phases and data types; finally, we collect these entities within ED papers and map the relations between them.The methodology application allows the building of a network graph for visualizing the relations among data lexicon and ED lexicon. Then, we investigate the specific relations among data types and ED phases by building a heatmap to investigate data types from 3 different perspective.The insight coming from our analysis shows that ED studies have a great potential in the usage of many data sources, but also that there exist some gaps to be solved in order to reach a more effective data usage in the context of ED.