**2. Methods**

Significant progress in the paints and coatings R&D can be made by process re-engineering, not only by improving individual phases (e.g., lab testing). For radical improvement, it is necessary to use business process re-engineering (BPR) or business process managemen<sup>t</sup> (BPM) and innovative information and communication technologies (ICT), combined with a digitalization approach.

For re-engineering the paints and coatings development process, a process analysis has to be performed for which relevant and up-to-date data has to be obtained, process models developed and a "technological enabler" selected [16]. Finally, the renewed process has to be validated by the simulation.

Process execution data were obtained through targeted interviews and modeling of the development process. In order to obtain the throughput time of individual phases and process activities, a deconstruction was done for each individual activity; into waiting time, orientation time (preparation-finishing time), and processing time [23] (Figure 1). The deconstruction of time was simplified with regard to the structure of activity time, which is stated by Ljubiˇc [24]: processing time, preparation-finishing time, waiting time before and after activity, and transport time to the next activity (Figure 2). Time data can be measured in various time units, for example: seconds, minutes, hours, or days. Hours were used as the time unit in the present research.

**Figure 2.** The structure of activity throughput time.

Based on the obtained sample, individual expected times with calculations from the PERT method were predicted [25]: optimistic, most probable, and pessimistic time:

$$\mathbf{t}\_c = (\mathbf{t}\_o + 4\mathbf{t}\_m + \mathbf{t}\_p)/\boldsymbol{\theta} \tag{1}$$

te—expected time

to—optimistic time

tm—most probable time

tp—pessimistic time.

This is a development process with a relatively small number of repetitions, therefore possible errors in determining the actual values of time should not be ignored. However, this error was canceled out, since the same time values were taken into account in the proposal of the renewed process. Thus, the relationship with which the development process was supported with the latest information technology was justified, remains the same.

The business process simulation was conditioned by having process models in the appropriate repository. It is only possible to perform a simulation with a proper set of data. Architecture of integrated information systems (ARIS) methodology, more specifically an event-driven process chain model (EPC model type), was used for modeling because it presents a user perspective of the process [26–28]. This model is based on the logic that an event triggers an activity (task) or several activities. Consequently, the activity ends with a new event or several events.

Standard symbols [23] for business objects and relations were used (an example of a process section is given in Figure 3). The rules for using logical operators are also shown in the example.

**Figure 3.** Process example and description of symbols used in business process mapping with the event-driven process chain (EPC) model type.

Through the literature review a lot of recommendations for process performance efficiency assessment were encountered [19,29,30], which can be performed based on the operational and structural efficiency indicators. The first indicators are connected with time, costs, and/or quality [31,32], whereas structural efficiency indicators are connected with business process structural complexity [33–36].

For each performance dimension, it was possible to identify different key performance indicators. Due to the simulation, the focus was on the time dimension and the following indicators were identified [32,37]:


The simulation is generally an imitation of the operation of a real-world process or system. It is used to assist decision-making by providing a tool that allows the current behavior of a system to be analyzed and understood. It is also able to help predict the performance of that system under a number of scenarios determined by the decision-maker [38]. Simulation modeling has been used for many years in the manufacturing sector and has become a mainstream tool in business situations. This is partly because of the popularity of business process re-engineering (BPR) and other process-based improvement methods that use simulation to help analyze changes in process design [39].

Simulation, in general, covers a large area of interest (e.g., business system performance prediction, providing performance measures). Simulation can refer to a range of model types, from spreadsheet models (static models) to system dynamic and discrete event simulation (dynamic models) [39]:


In the validation and verification of the simulation model, the dynamic simulation was used. Also, the static simulation was used for the impact assessment of the proposed changes in the process implementation. For static simulation execution Excel spreadsheets were used and for dynamic simulation execution the Aris tool was used. Discrete event simulation (DES) in the Aris tool works by modeling individual events that occur using a time-based engine, taking into account resources, constraints, and interaction with other events. This technique can easily reflect the process rules, randomness, and variability that a ffect the behavior of real-life systems and complex operating environments [40]. The simulation execution steps are as follows:


A technical enabler for the proposed improvement of development process has already been developed and is used as an information tool of the fourth generation [41]. The tool is at the stage of prototype testing and, according to this research, is the only all-in-one tool that enables online, real-time searching for raw materials, virtual formulation of paints and coatings, and the creation of digital technical and safety data sheets. It enables the formation of paints and coatings formulations based on data about binders, pigments, additives, and solvents. The formulator uses materials data from the structured database, which is available in digital form in a cloud. The re-engineering point is that the formulator has instant and free access to a large number of raw materials and is guided by the platform to select only those that are functionally relevant, safe, environmentally acceptable, and a ffordable, even before the individual formulation is laboratory-tested. This method significantly reduces the number of unnecessary laboratory tests and consequently significantly reduces the paints and coatings development throughput time. The advantage of the reengineering is that the data for the product are already generated, available, and ready to use for the preparation of necessary documentation (i.e., safety data sheets, technical data sheets, and hazard labels).
