Automatic Identification of Similarities Across Products to Improve the Configuration Process in ETO Companies

Engineer-To-Order (ETO) companies making complex products face the challenge of delivering highly customised products with high quality, affordable price and a short delivery time. To respond to these challenges, ETO companies strive to increase the commonality between different projects and to reuse product-related information. Therfore, ETO companies need to retrieve data about previously designed products and identify parts of the design that can be reused to improve the configuration process. This allows companies to reduce complexity in the product portfolio, decrease engineering hours and improve the accuracy of the product specifications. This article proposes a framework to identify and compare products’ similarities. The framework (1) identifies the most important product variables available in the Product Configuration System (PCS), (2) retrieves data of previously designed products in an Enterprise Resource Planning (ERP) system, (3) identifies a method to compare products based on the main products variables and (4) sets up an IT system (database) with data of the previously designed products to integrate with the PCS. The proposed approach (the framework and the IT system) is tested in an ETO company to evaluate the application of the framework and the IT system. We retrieved the needed data from the ERP system at the case company and developed the IT system in Microsoft Excel, which is integrated with the PCS.

[1]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[2]  John L. Burbidge,et al.  The introduction of group technology , 1975 .

[3]  Raghu Ramakrishnan,et al.  Database Management Systems , 1976 .

[4]  Roy Davies,et al.  The Creation of New Knowledge by Information Retrieval and Classification , 1989, J. Documentation.

[5]  Karl T. Ulrich,et al.  Fundamentals of Product Modularity , 1994 .

[6]  Heikki Mannila,et al.  A database perspective on knowledge discovery , 1996, CACM.

[7]  Ronald J. Brachman,et al.  The Process of Knowledge Discovery in Databases , 1996, Advances in Knowledge Discovery and Data Mining.

[8]  Fritz H. Grupe,et al.  Using domain knowledge to guide database knowledge discovery , 1996 .

[9]  Tu Bao Ho,et al.  Discovering and using knowledge from unsupervised data , 1997, Decis. Support Syst..

[10]  Deborah L. McGuinness,et al.  Conceptual modelling for configuration: A description logic-based approach , 1998, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[11]  Gunnar Erixon,et al.  Controlling Design Variants: Modular Product Platforms , 1999 .

[12]  Kevin Otto,et al.  Identifying Product Family Architecture Modularity Using Function and Variety Heuristics , 1999 .

[13]  Parisa Ghodous,et al.  Product Family Manufacturing Plan Generation and Classification , 2000, Concurr. Eng. Res. Appl..

[14]  Javier P. Gonzalez-Zugasti,et al.  Modular product architecture , 2001 .

[15]  S. Okamoto,et al.  Effective Decision Support for Product Configuration by Using CBR , 2001 .

[16]  Laurent Geneste,et al.  Search and Adaptation in a Fuzzy Object Oriented Case Base , 2002, ECCBR.

[17]  Alea M. Fairchild,et al.  Coding standards benefiting product and service information in E-Commerce , 2002, Proceedings of the 35th Annual Hawaii International Conference on System Sciences.

[18]  Jörg Leukel,et al.  A modeling approach for product classification systems , 2002, Proceedings. 13th International Workshop on Database and Expert Systems Applications.

[19]  Elliot Bendoly,et al.  Theory and support for process frameworks of knowledge discovery and data mining from ERP systems , 2003, Inf. Manag..

[20]  Magnus Persson,et al.  Analysis and improvement of product modularization methods: Their ability to deal with complex products , 2003 .

[21]  Timothy W. Simpson,et al.  Product platform design and customization: Status and promise , 2004, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[22]  Ashok K. Goel,et al.  Design, innovation and case-based reasoning , 2005, The Knowledge Engineering Review.

[23]  Timothy W. Simpson,et al.  Commonality indices for product family design: a detailed comparison , 2006 .

[24]  Fabrizio Salvador,et al.  Product Information Management for Mass Customization: Connecting Customer, Front-office and Back-office for Fast and Efficient Customization , 2006 .

[25]  Heidi A. Taboada,et al.  Multi-objective scheduling problems: Determination of pruned Pareto sets , 2008 .

[26]  Alexander Felfernig,et al.  Constraint-based recommender systems: technologies and research issues , 2008, ICEC.

[27]  Lars Hvam,et al.  The impact of product configurators on lead times in engineering-oriented companies , 2011, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[28]  Flávio Sanson Fogliatto,et al.  Selecting the best clustering variables for grouping mass-customized products involving workers' learning , 2011 .

[29]  C. Forza,et al.  Product configurator impact on product quality , 2012 .

[30]  Lars Hvam,et al.  OBSERVED BENEFITS FROM PRODUCT CONFIGURATION SYSTEMS , 2013 .

[31]  Linda L. Zhang,et al.  Product configuration: a review of the state-of-the-art and future research , 2014 .

[32]  Alexander Felfernig,et al.  Knowledge-Based Configuration: From Research to Business Cases , 2014 .

[33]  Alexander Felfernig,et al.  4 CHAPTER Benefits of Configuration Systems , 2014 .

[34]  Roger Jianxin Jiao,et al.  A system level product configurator for engineer-to-order supply chains , 2015, Comput. Ind..

[35]  Sergio Segura,et al.  An assessment of search-based techniques for reverse engineering feature models , 2015, J. Syst. Softw..

[36]  Lars Hvam,et al.  The documentation of product configuration systems: A framework and an IT solution , 2017, Adv. Eng. Informatics.