# Analysis of the Use of Similarity Coefficients in Manufacturing Cell Formation Processes
## 5 Conclusions  
In contemporary manufacturing systems, cellular manufacturing has emerged as a relevant concept, especially in the context of layout design and its relationship with the production mix and flexibility of part fabrication [43]. In the current context, the adoption of a cellular layout may be necessary for manufacturers striving to maintain a competitive edge. Cell formation methods can effectively organize production cells, providing the capacity for adaptation to the specific needs of customers [44]. This study explored the application of the similarity coefficient in the cell formation process in multicellular manufacturing systems. This research delved into the utilization of the similarity coefficient in the context of cell layout formation and the implementation of group technology in multicellular manufacturing, highlighting their role in augmenting efficiency, adaptability, and quality within production processes. It examined various techniques for cell formation and the key components of GT, underscoring their role in managing efficiency, flexibility, and quality in production processes. In manufacturing processes, both CM and GT techniques offer an organized method for classifying machines and parts in order to balance part flow and reduce resource idleness and time to order completion. According to the bibliometric search, six of the twenty similarity coefficients evaluated are more frequently cited. The Jaccard coefficient was the most cited in the search. However, regardless of the selected coefficient, the cell formation method should consider the distinct features of every industrial environment, including factors such as product diversity, production volume, and process intricacy. In essence, this study explored the utilization of cellular layout and cluster technology in manufacturing, emphasizing the use of the Jaccard coefficient for similarity calculation. This article makes a technical contribution, as it introduces a systematic approach to supporting a cell formation process in manufacturing, utilizing a similarity coefficient. The initial step calculates the similarities between all machines by using a matrix [machines X parts] that allocates parts to machines. Next, based on the similarities, a dendrogram is created manually or supported by software. The dendrogram is a visual tool that provides multiple options for cell formation according to the desired number of cells. Once a specific cell configuration is selected, the matrix's rows and columns are rearranged to cluster relevant incidences around the diagonal, promoting more organized groupings of machines and parts. It should be recognized that the resultant classification might contain exceptions--cases where specific occurrences do not fit within the defined categories. These exceptions are opportunities for continuous improvement in both the production process and the project. Finally, the paper discusses how to evaluate the efficiencies of the feasible solutions, supporting the final choice for the cell formation process. As a suggestion for future research, alternative similarity coefficients and methods for assessing cluster efficiency in the same case should be compared. It is also suggested that multiple similarity coefficients be combined into a single optimized index, utilizing multicriteria methods to assess the weights [45] when project and process attributes hold differing levels of significance in the multicell formation process. Additionally, exploring methods grounded in fuzzy logic [38] presents an avenue for future research. Another possibility is to evaluate how the Jaccard similarity coefficient performs in comparison with other similarity coefficients regarding computational complexity and ease of implementation in diverse manufacturing systems. Finally, a last possibility is to examine the influence of variety and volume in the choice of the similarity coefficient. Even if they are not independent variables, their combination may influence the applicability of the various similarity coefficients.