Luiza Zeltzer

Comparing quantifiable methods to measure complexity in assembly

In order to measure complexity and stay competitive, manufacturing companies need to be able to quantify production complexity. For this reason, two methods were developed within the context of two concurrent research projects are compared: the Belgian Complexity Calculator, CXC, measures objective complexity and the Swedish Complexity Index, CXI, focuses on subjective complexity, as experienced by operators in the stations. This paper presents a comparative analysis of the two methods by comparing them to seven relevant existing quantitative methods and by examining results from case studies. It is observed that the two methods can be used as a compliment to one another, where CXC can be used for scanning data automatically CXI can be used for in-depth analysis. In addition, the comparison of existing methods provides insight on how to measure complexity depending on need and scope.

Workload balancing and manufacturing complexity levelling in mixed-model assembly lines

To effectively react and meet the current ever growing demand for individualised motor vehicles, built to customer specific requirements, automotive industry has accelerated its transition towards mass-customisation. As a result, the number of new model introductions has drastically increased over the past three decades. To cope with this intensified customisation, the current automotive assembly platforms are designed to assemble a wide range of relatively different models, and are turned into mixed-model assembly lines (MMALs). This implies that the set of tasks to be performed on each workstation is no longer stable but varies highly with the model-mix. As a consequence, the manufacturing complexity increases at the workstations and throughout the whole assembly system. This paper proposes a method to monitor manufacturing complexity at each workstation while the MMAL is being balanced. An entropy-based quantitative measure of complexity, which incorporates the variability of each task duration, is developed. This measure is used to monitor the manufacturing complexity level at each workstation. An integrated mixed-line balancing and complexity monitoring heuristic is proposed, to determine workload balance solutions, in which manufacturing complexity is levelled throughout the workstations composing the line. This procedure is tested on a real data-set provided by an automotive manufacturer. The results are reported and thoroughly discussed.

Balancing Mixed-Model Assembly Lines in Real World Complex Workstations

In an attempt to react to the increasing customization, the same mixed-model assembly line is required to assemble a large variety of products. As a consequence, workstations become more complex due to the resulting variability of the assembly tasks. Thus, the need for new mixed-model assembly line balancing solutions, taking this complexity into account is raising. This paper discusses the issue of mixed-model line balancing of complex workstations using real data from some automotive assembly lines. Using an earlier developed complexity analysis model, the complexity of each workstation was evaluated. Then an optimization Mixed Integer Linear Program (MILP) model was developed and used to determine a line balance that optimizes overloads. The complexity results and the overload of each workstation are analysed to determine how these two models are related. The complexity model measures complexity of each workstation based on collected data and the MILP model determines an optimal balance. The complexity and overload results are then analysed and opposed to determine where improvements are required.