1. The problem involves analyzing fabric output in textile manufacturing based on multiple influencing factors such as shift, machine type, fabric type, automation level, operator skill, loom speed, task time, break frequency, and worker experience. 2. Since the user requests to use EXCEL for data analysis, the first step is to organize the dataset in EXCEL with columns representing each factor and the fabric output (meters per day). 3. Next, perform descriptive statistics to understand the distribution of fabric output and each factor. 4. Use pivot tables or filters to compare fabric output across categorical variables like shift, machine type, fabric type, automation level, and operator skill. 5. For continuous variables like loom speed, task time, break frequency, and worker experience, create scatter plots and calculate correlation coefficients to assess relationships with fabric output. 6. Conduct regression analysis (multiple linear regression) in EXCEL to quantify the impact of each factor on fabric output, identifying significant predictors. 7. Interpret the regression coefficients to understand how changes in each factor affect fabric output, e.g., higher loom speed increases output, more breaks decrease output. 8. Use the analysis results to recommend process optimizations such as increasing automation, training operators, optimizing shift schedules, and minimizing breaks to maximize fabric output. Final answer: The analysis approach involves organizing data, performing descriptive and inferential statistics, and applying regression modeling in EXCEL to understand and optimize fabric output based on the given factors.