# Analysis of the Use of Similarity Coefficients in Manufacturing Cell Formation Processes
## 2 Cell Formation Methods in Multicellular Manufacturing  
It is possible to assess the effectiveness of cellular manufacturing clusters using various performance measures such as grouping efficiency, grouping efficacy, number of exceptional elements, grouping index, and grouping measure. Of particular importance are grouping efficiency and grouping efficacy, which are evaluated using similarity coefficient methods in the cell formation process. Various algorithms and methods have been suggested to improve the efficiency of cell formation in manufacturing systems, including similarity coefficient methods, principal component analysis (PCA), and agglomerative clustering algorithms (ACA) [14]. This approach is significant as the design of machine cells and part families, as well as the reduction of typical setup times, is crucial for the success of cellular manufacturing systems (CMS) [15]. In MCM, cell formation is defined as the organized production process that involves grouping machines into clusters. There are two criteria for cell formation: project and process attributes. Table 1 categorizes possibilities for the cell formation process. The table presents methods and techniques from the field of operations research that are commonly utilized in cell formation processes, along with relevant references that effectively illustrate each technique. The greatest interest in this study's scope lies in clustering methods and hierarchical clustering techniques based on similarity coefficients. In manufacturing studies, assessing the similarity between different components involves looking at a variety of factors, including parts, processes, machinery, material flow, and production volume [24]. Recent research [25] has introduced a coefficient that takes into account the type of batch movement within and between parts when evaluating similarity. When considering part-based similarity, the focus is on grouping parts that require similar or identical operations based on characteristics such as shape, size, material, and required operations. This approach aids in defining production cells based on the common process operations for each family of parts. The process-based similarity is similar to a job shop layout, where each cell is assigned a specific process, such as washing, machining, assembly, welding, or painting. Machinery similarity involves grouping parts based on their machine, tool, and equipment requirements. Parts with similar machine, tool, or equipment needs are grouped together [26]. Flow- and volume-based similarities help to define cells based on material flow and demand, which in turn reduce transportation distances and setup times, respectively [27]. It is important to take into account that each manufacturing method has its distinct advantages and disadvantages. The most suitable method to select depends on specific operational characteristics, such as the variety of products, production volume, and process complexity, among other factors. In many cases, a combination of methods may offer the most benefits. For example, cells can be defined based on production volume. Accordingly, the machines or processing work centers (PWCs) within each cell can be sequenced according to production flow. It is also common to define cells based on processes and then sequence multicells according to flow [28]. Once cells are defined, they can adopt different typologies, including U-shaped, ladder, and centered-robot typologies. Variants such as the U-shaped combination plus centered robot, intermediate storage structures, two inputs, and one output, as well as multicellular layouts, are also widespread in the industry [29]. The focus of this study is particularly located on multicellular layouts. Figures 1-3 display some relevant configurations aligned with this study. The configurations are, respectively, robot-centered multicells, robot-centered multicells with buffers, and U-shaped multicells with robot-centered cells, buffers, two inputs, and one output.