A new methodology to analyze the functional and physical architecture of existing products for an assembly oriented product family identification An automated packaging planning approach using machine learning An automated packaging planning approach using machine learning

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 families, 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 The manufacturing industry is highly affected by trends of mass customization and increasing dynamics of product life-cycles which result in a large set of part variants. Thus, the required effort for logistics planning and, in particular, for packaging planning is increasing. This paper proposes an approach to automate the assignment of packaging for an individual part based on its characteristics using machine learning. We use the historical data of product parts and their packaging specifications to train our two-step machine learning model. Consequently, the model is able to propose a packaging with an accuracy of 84% in comparison with real-world data. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the 52nd CIRP Conference on Manufacturing Systems. Abstract The manufacturing industry is highly affected by trends of mass customization and increasing dynamics of product life-cycles which result in a large set of part variants. Thus, the required effort for logistics planning and, in particular, for packaging planning is increasing. This paper proposes an approach to automate the assignment of packaging for an individual part based on its characteristics using machine learning. We use the historical data of product parts and their packaging specifications to train our two-step machine learning model. Consequently, the model is able to propose a packaging with an accuracy of 84% in comparison with real-world data.