## 4 Simulation Models and Sustainability: Characterization
Tactical models are addressed to time frames from few months to two years, and the operational modeling deals with the weekly, daily or hourly operations. In context of manufacturing and supply chains, the strategic modeling spans the organizational design, facility location, product development, supplier selection, etc. The tactical modeling includes problems such as warehousing, maintenance, and stowage handling. Operational modeling deals with scheduling, lot size assessment, and workforce planning, among others. In manufacturing, various simulation tools have been employed with the majority of the research works and applications using the following simulation tools:  
* Discrete Event Simulation (DES);
* Agent-Based Simulation (ABS);
* Systems Dynamic models (SD);
* Hybrid models. Figure 9 indicates the growth of research in DES, ABS and SD simulation models in manufacturing and SCs as gleaned from the data from 2000-2021 on the SCOPUS database. The DES found great use early on, while SD and ABS are slowly catching up. As can be gleaned from Figure 9, DES is the most used simulation method in the published research works [70]. Comparing the DES with the SD, the DES is applied in the cases where queuing of some sort is being used, whereas SD is used in the systems which are dynamic and thus exhibit a \"flow\". The DES thus deals with the microscopic level such that they take into account the smaller details of the system. The consequence  
Figure 9: Yearly research output across the three simulation techniques. The figure shows the research output in case of the three simulation techniques in the field of manufacturing and supply chains as gathered from the SCOPUS database. Although DES far outnumbers the other two initially, there has been increasing research in SD. of this is that there is a greater requirement of data in the case of DES and it should be of high quality [71]. Furthermore, when the DES is applied in the case of a production system, there is a requirement that there is a constant updating of the data as well [72]. SD employs a system framework while using flows, stocks, and feedback loops to model the behavior of the system through time [73]. The SD, on the contrary, has a macroscopic perspective and takes into account a wider perspective. SD and DES also deal differently with randomness. DES is stochastic and, therefore, for each given run gives a different result, whereas the SD is deterministic and gives the same results for each run. The process of model building has been compared in the case of SD and DES [74]. The modeling in DES on average takes more time than the SD and, therefore, it is assumed that DES is more complex than the SD modeling. DES allows investigators to model the progression of a given system, e.g., the advancement of a product through an assembly line--and is frequently used to depict a system's business operations. When the temporal fluctuation of the states of system elements is an important source of variability in the system's outputs, this technique is particularly well suited. Although SD and DES may generate identical findings, the method used may have an impact on the system's limits and how an investigator addresses a given issue [75]. Other comparisons cited in the literature [76] are:  
* Animations are fundamental to DES whereas model descriptions are more important to SDs;
* SD models are more representative of the problems than DES models;
* SD aids in conceptual learning more than DES;
* Results of DES are more difficult to interpret than that of SD. Recently, a good deal of research has been conducted by taking up the hybrid approach wherein both SD and DES are used together. Some of the instances in case of manufacturing include use cases, including performance analysis [77], re-configurable manufacturing [78], value chain environment [79], etc.