Complexity mapping of the product and assembly system

Samy, S. N.; ElMaraghy, Hoda A.

doi:10.1108/01445151211212299

Cited by 43 publications

(27 citation statements)

References 30 publications

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“…Schuh et al (2015) declared that the product complexity strongly affects the quality losses (81-88 percent agreed by the industry experts), and many researchers have described the assembly complexity as one of the most complex process in a manufacturing company in Germany. Samy and ElMaraghy (2012) utilized regression analysis to reveal the effect of part complexity on assembly equipment complexity, and they found a positive relation as expected. Falck et al (2017) compared the two methods, i.e.…”

Section: Manufacturing Complexitymentioning

confidence: 59%

A decision support system for proactive failure prevention: a case in a leading automotive company

Unver

Kabak

Topçu

et al. 2020

JEIM

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PurposeIn 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.Design/methodology/approachInitially, 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.FindingsTo the best of authors’ 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.Originality/valueHigh 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.

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Section: Manufacturing Complexitymentioning

confidence: 59%

A decision support system for proactive failure prevention: a case in a leading automotive company

Unver

Kabak

Topçu

et al. 2020

JEIM

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“…AlGeddawy and ElMaraghy [36] proposed a hierarchical clustering (cladistics) technique to find an optimum granularity level of a modular product architecture in assembly process. Then, Samy et al [37] applied the technique to find an appropriate granularity level for balancing two sources of complexity in an assembly system: equipment and layout. Since then, AlGeddawy et al [38] integrated cladistics with both a matrix-based tool and the measure they introduced before [33] in order to balance modularity and complexity.…”

Section: B Design For Complexity Managementmentioning

confidence: 99%

Product Interface Design for Complexity Management in Assembly Systems

Kim²,

Kim

et al. 2020

IEEE Access

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“…Hanafy [9] developed a novel model for concurrent design of product families and their corresponding assembly systems where a global product families and platforms formation mathematical model which fully integrates assembly task assignments, assembly sequence, and assembly cost was developed. Samy and ElMaraghy [10] introduced and implemented a method for defining and mapping products and their corresponding assembly systems in order to help system designers develop less complex assembly system. Bukchin et al [11] addressed the design of an assembly system facility involving multiple assembly lines of different layouts in which it is required to find the minimum area of the facility and to maximize the efficiency of the material handling system using a mixed integer programming.…”

Section: Introductionmentioning

confidence: 99%

Assembly system synthesis using association rule discovery

ElMaraghy

Kashkoush

2015

Int J Adv Manuf Technol

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Assembly is the final process for product realization in which various parts, modules, and sub-assemblies are put together to build a product. Designing an assembly system typically involves many issues such as assembly equipment selection, assembly sequence planning, system layout, task assignments, and line balancing. Assembly system synthesis, specifically the choice of assembly resources/equipment, is examined in this paper. An association rule discovery model is employed to extract association relationships between existing and/or previous products and the systems used to assemble them. The extracted knowledge is used to synthesize assembly systems for new products that fall within the scope of their predecessors. The developed method is demonstrated using two examples from the automotive industry. System synthesis results that are consistent with the used assembly data instances are obtained in both examples. The presented systematic system synthesis method supports the utilization of existing legacy data in developing assembly systems for new product generations. This will lead to a significant reduction of time and effort needed to design assembly systems, particularly in applications that feature frequent design changes and updates, as in the automotive industry.

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Complexity mapping of the product and assembly system

Cited by 43 publications

References 30 publications

A decision support system for proactive failure prevention: a case in a leading automotive company

A decision support system for proactive failure prevention: a case in a leading automotive company

Product Interface Design for Complexity Management in Assembly Systems

Assembly system synthesis using association rule discovery

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