The assessment and examination of anthropometric measures in children are of paramount significance in the advancement and creation of furniture, tools, and toys that specifically addresses the requirements of children as users. The dataset analyzed a total of 354 children from Jordan, divided into six distinct age groups ranging from 6 months to 9 years. The linear static measures included the skeletal dimensions pertaining to the distances between joints in the body, as well as the dimensions of the middle and lower bodies. It consists of 23 anthropometric measurements such as: stature, sitting height, knee height, eye height sitting, chest depth and shoulder breadth and others. The dataset supports the article "Preliminary and Comprehensive Static Anthropometric Measurements of Jordanian Children for Various Age Groups" [1]. The available data may initiate the establishment of a connection between anthropometric measurements and other design attributes used in Jordanian society. Additionally, these data have the potential to provide valuable insights for the development of diverse functional products, including clothing and safety equipment specifically tailored for the needs of children in Jordan.

Multi-response optimization problems investigation is a crucial element in initiatives designed to enhance quality and overall productivity for manufacturing processes. Since no particular algorithm can be employed for all multi-response problems, defining the method that is utilized as a problem-solving technique is a vital step in the process factors optimization. Identifying a formal procedure of implementing the improvement approach in a multi-criteria decision-making problem is a critical need to ensure the consistency and sustainability of the enhancement methods. In this study, a Plan–Do–Check–Act (PDCA) framework is implemented for a case study in the food industry under which a multi-response optimization problem is investigated. The design of experiment (DOE) is used to examine the effect of process parameters on the quality responses by using the Taguchi method to find the optimal setting for each parameter. An orthogonal array (OA) and signal-to-noise (SNR) ratio is employed to investigate the performance characteristics. Each performance characteristic is then converted into a signal-to-noise ratio, and all the ratios are then fed into a fuzzy model to produce a single comprehensive output measure (COM). The average COM values for various factor levels are calculated, and the level that maximizes the COM value for each factor is identified as the optimal level. Results indicated the effectiveness of the applied method to find the optimal factor levels for the multi-response optimization problem under study. The global optimal factor levels that are driven from the fuzzy logic for the studied parameters are 1250, 40, 7.5, and 1:2, for the speed, frying time, cooking time, and the coating ratio, respectively. Means of all the studied quality characteristics were closer to the target values when compared with the initial factors’ settings.