Product-Service System (PSS) has been perceived since the 90s as a concept supporting enterprises of various indus-tries in creating a competitive advantage and generating new value for customers by expanding the offer with addi-tional services related to the product. Product-Service System (PSS) draws attention to the life cycle of products and services and the circular economy, which supports sustainable development. All the time, practitioners and theorists report the need to develop new Product-Service System (PSS) for other industries. Until now, a number of practical and methodological aspects related to design remain unresolved. The paper presents issues related to the Product-Service System (PSS) and PSS design. A literature review and gaps in available methods are presented. A conceptual frame-work for Product-Service System (PSS) design that has been used in the logistics industry is presented. By referring to the design of a selected process from the logistics industry, it was presented how to analyze the process during design and what methods of design support to use. Reference is made to mathematical modeling based on the optimization function and computer modeling with the use of a simulation model. Attention was also paid to the importance of knowledge of the industry and having expert knowledge about the designed processes in the systems. It is also extremely important to have the appropriate data set for a given case. In addition to the general mathematical and computer model, reference was also made to a chosen element of Product-Service System (PSS). The mathematical and simulation model included in the study refer to the process of completing customer orders in a logistics company. It is one of the most laborious and time-consuming processes. The FlexSim simulation environment was used to perform the computer simulation. A total of 15 variants were considered, which differ in terms of the scope of services provided during the process. The scope of services significantly affects the cost, time and profit. The purpose of the constructed model is to find a variant for the adopted data in which the profits will be maximized while maintaining the constraints imposed on the system.