The Flexible Job Shop Scheduling Problem (FJSSP) is one of the most studied problems in the literature thanks to its practical relevance. Many industrial production scheduling problems can be seen as a FJSSP with different constraints and objectives. The studied problem arises in a textile factory, precisely in the sewing process. Multiple real-world constraints are tackled. Individual skills of operators and different types of setup times are considered: machine-change setup time for operators, colour-change setup times and configuration-change setup times. The objective is to minimize the total tardiness. To the best of the authors' knowledge, this is the first study that tackles this variant of the problem. This paper presents a preliminary study to validate the approach and evaluate the tractability of the industrial problem. A mixed-integer linear programming (MILP) model is proposed and solved using a commercial solver. Real-world data are used to generate more than 1000 problems that are clustered in 4 groups based on the number of jobs to be scheduled, the number of operators and flexibility of their skills. The results show that the proposed MILP model can reach optimality for the small size instances but it is intractable for most large-sized instances. Moreover, the results highlight how diversifying the operators' skills can help find better solutions.