{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T05:35:03Z","timestamp":1773293703504,"version":"3.50.1"},"reference-count":35,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2020,4,16]],"date-time":"2020-04-16T00:00:00Z","timestamp":1586995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JEIM"],"published-print":{"date-parts":[[2020,12,3]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>In the automotive industry, the high process complexity becomes an important issue because of the increased number of product and process variants demanded by the customers. To avoid quality defects in assembly and losses in such a complex manufacturing environment, new predictive support systems are required. This study aims to develop a multiple attribute decision support system (DSS) for the prediction and quantification of the risk of failures on the workstations of a leading Turkish automotive manufacturing company.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>Initially, the factors affecting the failures in workstations and the attributes to evaluate the factors are identified. Subsequently, the relations among the attributes are specified and priorities of them are calculated. Finally, the risk of failures is calculated and tested in a pilot study and validated with real production data.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>To the best of authors\u2019 knowledge, this is a unique study that computes the risk scores on the workstations via DSS. The DSS has various advantages for improvements of the manufacturing quality: the risk of failures can be detectable and comparable, the effect of changes in the design of new workstations can be observed. Stations that have medium or high complexity scores demonstrated strong correlation with failure rates. A sensitivity analysis is conducted to predict the effect of improvement actions on the riskiness of the workstations.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>High level of production complexity becomes a crucial issue for companies that use various production processes. Considering this fact, it is a requirement for companies to observe and monitor the risk factors, especially in the assembly lines to be able to eliminate failures derived from complexity. Accordingly, to measure risk scores of the workstations in the assembly lines, a decision support for companies aids executives to manage the complexity level in a reliable and effective way. In this study, the authors develop such a DSS for TOFAS, a leading Turkish automotive company. The proposed DSS is verified and applied through a pilot study on a specific basic production unit. A sensitivity analysis is also conducted to see the effects of potential improvements on the risk scores. Additionally, the trend of risk scores for the stations can also give valuable information for tracing the changes in the time horizon. The proposed DSS also enables an opportunity for the executives in their decision of design processes of new production lines by allocating limited resources in an appropriate way based on the risk scores of possible workstations. The proposed DSS is the first and unique proactive failure prevention model developed in a Fiat Chrysler Automobiles (FCA) plant across the world. TOFAS executives also plan to introduce and enlarge the usage of the model to other FCA plants. It may also be possible to apply the model to other assembly lines in any sector. Another plan of the executives of TOFAS is developing a software, which manages each parameter, to constitute data to the DSS to run this system more instantly and effectively. Moreover, they can take integration actions of the software with world-class manufacturing problem management system that is currently in use in TOFAS.<\/jats:p><\/jats:sec>","DOI":"10.1108\/jeim-09-2019-0264","type":"journal-article","created":{"date-parts":[[2020,5,5]],"date-time":"2020-05-05T11:46:32Z","timestamp":1588679192000},"page":"845-880","source":"Crossref","is-referenced-by-count":10,"title":["A decision support system for proactive failure prevention: a case in a leading automotive company"],"prefix":"10.1108","volume":"33","author":[{"given":"Berna","family":"Unver","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5542-309X","authenticated-orcid":false,"given":"\u00d6zg\u00fcr","family":"Kabak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9717-7854","authenticated-orcid":false,"given":"Y. Ilker","family":"Topcu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Armagan","family":"Altinisik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ozcan","family":"Cavusoglu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2020,4,16]]},"reference":[{"issue":"2","key":"key2021061008265837200_ref001","article-title":"Modeling and analysis of operator effects on process quality and throughput in mixed model assembly systems","volume":"133","year":"2011","journal-title":"Journal of Manufacturing Science and Engineering"},{"key":"key2021061008265837200_ref002","article-title":"A Study of the Effects of Manufacturing Complexity on Product Quality in Mixed-Model Automotive Assembly","year":"2014"},{"issue":"1","key":"key2021061008265837200_ref003","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.ijpe.2005.12.009","article-title":"An analytical network process-based framework for successful total quality management (TQM): an assessment of Turkish manufacturing industry readiness","volume":"105","year":"2007","journal-title":"International Journal of Production Economics"},{"key":"key2021061008265837200_ref004","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1002\/mcda.401","article-title":"A multiple criteria decision making approach for the evaluation of retail location","volume":"14","year":"2006","journal-title":"Journal of Multi-Criteria Decision Analysis"},{"key":"key2021061008265837200_ref005","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1016\/j.procs.2014.05.317","article-title":"Supplier selection: a fuzzy-ANP approach","volume":"31","year":"2014","journal-title":"Procedia Computer Science"},{"issue":"9","key":"key2021061008265837200_ref006","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1080\/0951192X.2015.1130245","article-title":"Manufacturing systems complexity analysis methods review","volume":"29","year":"2016","journal-title":"International Journal of Computer Integrated Manufacturing"},{"issue":"15","key":"key2021061008265837200_ref007","first-page":"4248","article-title":"Proactive assessment of basic complexity in manual assembly: development of a tool to predict and control operator-induced quality errors","volume":"55","year":"2016","journal-title":"International Journal of Production Research"},{"issue":"24","key":"key2021061008265837200_ref008","doi-asserted-by":"crossref","first-page":"7237","DOI":"10.1080\/00207543.2017.1330571","article-title":"Assessment of manual assembly complexity: a theoretical and empirical comparison of two methods","volume":"55","year":"2017","journal-title":"International Journal of Production Research"},{"issue":"3","key":"key2021061008265837200_ref009","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.jmsy.2013.04.011","article-title":"Relations between complexity, quality and cognitive automation in mixed-model assembly","volume":"32","year":"2013","journal-title":"Journal of Manufactuing Systems"},{"key":"key2021061008265837200_ref010","volume-title":"Fiat Chrysler Automobiles","author":"FCA","year":"2019"},{"key":"key2021061008265837200_ref011","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.ijpe.2014.09.027","article-title":"An integrated green supplier selection approach with analytic network process and improved Grey relational analysis","volume":"159","year":"2015","journal-title":"International Journal of Production Economics"},{"issue":"11","key":"key2021061008265837200_ref012","doi-asserted-by":"crossref","first-page":"3105","DOI":"10.1080\/00207540902810551","article-title":"Product quality and plant build complexity","volume":"48","year":"2010","journal-title":"International Journal of Production Research"},{"issue":"1","key":"key2021061008265837200_ref013","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.ijpe.2012.12.023","article-title":"On supply chain competitiveness of Indian automotive component manufacturing industry","volume":"143","year":"2013","journal-title":"International Journal of Production Economics"},{"key":"key2021061008265837200_ref014","article-title":"Lessons learned from implementing configuration management within electrical\/electronic development of an automotive OEM","year":"2004"},{"key":"key2021061008265837200_ref015","unstructured":"Koc (2019), \u201cKo\u00e7\u201d, Retrieved from About Ko\u00e7, available at: www.koc.com.tr\/en-us\/about."},{"key":"key2021061008265837200_ref016","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.promfg.2016.08.014","article-title":"Prediction of defect propensity for the manual assembly of automotive electrical connectors","volume":"5","year":"2016","journal-title":"Procedia Manufacturing"},{"key":"key2021061008265837200_ref017","volume-title":"Structural Complexity Management: An Approach for the Field of Product Design","year":"2009"},{"key":"key2021061008265837200_ref018","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.procir.2012.07.068","article-title":"Testing complexity index \u2013 a method for measuring perceived production complexity","volume":"3","year":"2012","journal-title":"Procedia CIRP"},{"issue":"1","key":"key2021061008265837200_ref019","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1504\/IJMR.2014.059602","article-title":"Comparing quantifiable methods to measure complexity in assembly","volume":"9","year":"2014","journal-title":"International Journal of Manufacturing Research"},{"key":"key2021061008265837200_ref020","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.cie.2018.02.032","article-title":"Mixed-model sequencing problem under capacity and machine idle time constraints in JIT production systems","volume":"118","year":"2018","journal-title":"Computers and Industrial Engineering"},{"key":"key2021061008265837200_ref021","article-title":"Measurement of assembly system complexity based on the task differences induced from product variety","year":"2015"},{"key":"key2021061008265837200_ref022","article-title":"Part assurance in a mixed-model assembly line","year":"2014"},{"key":"key2021061008265837200_ref023","volume-title":"Multicriteria Decision Making: The Analytic Hierarchy Process","year":"1980"},{"key":"key2021061008265837200_ref024","volume-title":"Decision-making with Dependence and Feedback: the Analytic Network Process","year":"1996"},{"key":"key2021061008265837200_ref025","volume-title":"Theory and Applications of the Analytic Network Process","year":"2005"},{"issue":"11","key":"key2021061008265837200_ref026","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1080\/0951192X.2010.511652","article-title":"A model for measuring products assembly complexity","volume":"23","year":"2010","journal-title":"International Journal of Computer Integrated Manufacturing"},{"issue":"1","key":"key2021061008265837200_ref027","first-page":"135","article-title":"Complexity mapping of the product and assembly system","volume":"32","year":"2012","journal-title":"Assembly Automation"},{"key":"key2021061008265837200_ref028","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cirpj.2015.05.007","article-title":"Complexity-oriented ramp-up of assembly systems","volume":"10","year":"2015","journal-title":"CIRP Journal of Manufacturing Science and Technology"},{"issue":"1","key":"key2021061008265837200_ref029","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1109\/TSMCA.2009.2033030","article-title":"A systematic study of the prediction model for operator \u2013 induced assembly defects based on assembly complexity factors","volume":"40","year":"2010","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans"},{"key":"key2021061008265837200_ref030","first-page":"11990","article-title":"A review on transition in the manufacturing of mechanical components from conventional techniques to rapid casting using rapid prototyping","volume":"5","year":"2018","journal-title":"Materials Today: Proceedings"},{"key":"key2021061008265837200_ref031","volume-title":"TOFAS T\u00fcrk Otomobil Fabrikas\u0131 A.\u015e","author":"TOFAS","year":"2019"},{"issue":"1","key":"key2021061008265837200_ref032","first-page":"83","article-title":"An AHP based prioritization model for risk evaluation factors in the automotive industry","volume":"10","year":"2018","journal-title":"International Journal of Analytic Hierarchy Process"},{"key":"key2021061008265837200_ref033","article-title":"Analysis of design for X methodologies for complex assembly processes: a literature review","year":"2014"},{"issue":"15","key":"key2021061008265837200_ref034","first-page":"4620","article-title":"Measuring complexity in mixed-model assembly workstations","volume":"51","year":"2013","journal-title":"International Journal of Production Research"},{"issue":"5","key":"key2021061008265837200_ref035","doi-asserted-by":"crossref","first-page":"051013","DOI":"10.1115\/1.2953076","article-title":"Modeling of manufacturing complexity in mixed-model assembly lines","volume":"130","year":"2008","journal-title":"Journal of Manufacturing Science and Engineering"}],"container-title":["Journal of Enterprise Information Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/JEIM-09-2019-0264\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/JEIM-09-2019-0264\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:32:01Z","timestamp":1753396321000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/jeim\/article\/33\/5\/845-880\/223955"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,16]]},"references-count":35,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,4,16]]},"published-print":{"date-parts":[[2020,12,3]]}},"alternative-id":["10.1108\/JEIM-09-2019-0264"],"URL":"https:\/\/doi.org\/10.1108\/jeim-09-2019-0264","relation":{},"ISSN":["1741-0398"],"issn-type":[{"value":"1741-0398","type":"print"}],"subject":[],"published":{"date-parts":[[2020,4,16]]}}}