Towards a connected factory: Shop-floor data analytics in cyber-physical environments

Dávid Gyulai, Júlia Bergmann, Viola Gallina, Alexander Gaal
2019 Procedia CIRP  
In today's business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of agile and reconfigurable production systems emerged to cope with various products and product families. To design and optimize production systems as well as to choose the optimal product matches, product analysis methods are needed. Indeed, most of the known methods aim to analyze a product or one product family on the physical level. Different product
more » ... ies, however, may differ largely in terms of the number and nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production system. A new methodology is proposed to analyze existing products in view of their functional and physical architecture. The aim is to cluster these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. Abstract In the era of the industrial digitalization, the availability of shop-floor data is not a question anymore, but rather the exploitation of the underlying information. With advanced sensor technologies, detailed data can be obtained about products, resources and processes in near real time, however, still there are gaps between the collection of the data, and the utilization of it. The greatest current challenge is the use of available data in decisionmaking processes that brings the real business value for companies, to keep their competitiveness and internal efficiency. In the paper, a reference model of an industrial data analytics platform is presented that supports the integration of various analytics solutions with enterprise level decision support tools, such as planning and scheduling systems. The reference model is composed of various layers, supporting the collection, storage and analysis of data coming from various sources. In addition to its business intelligence related dashboarding and visualization functions, it provides the opportunity of linking the analytics results with other software applications. In order to highlight the capabilities of the proposed model, possible application domains and use-cases are presented, reflecting real industrial needs. Abstract In the era of the industrial digitalization, the availability of shop-floor data is not a question anymore, but rather the exploitation of the underlying information. With advanced sensor technologies, detailed data can be obtained about products, resources and processes in near real time, however, still there are gaps between the collection of the data, and the utilization of it. The greatest current challenge is the use of available data in decisionmaking processes that brings the real business value for companies, to keep their competitiveness and internal efficiency. In the paper, a reference model of an industrial data analytics platform is presented that supports the integration of various analytics solutions with enterprise level decision support tools, such as planning and scheduling systems. The reference model is composed of various layers, supporting the collection, storage and analysis of data coming from various sources. In addition to its business intelligence related dashboarding and visualization functions, it provides the opportunity of linking the analytics results with other software applications. In order to highlight the capabilities of the proposed model, possible application domains and use-cases are presented, reflecting real industrial needs.
doi:10.1016/j.procir.2020.01.016 fatcat:hqrdp5u65vbn5f2vzv6n7cgjxi