# Increasing Resilience of Production Systems by Integrated Design
## 2 Materials and Methods
### Risk Management and Resilience Concepts Transferred to Manufacturing Systems

Table 1: Risk categories and corresponding cause–effect relationships. Resilience as a feature of systems covers multiple aspects and dimensions related to the prevention and mitigation of risks. A conceptual distinction between \"engineering resilience\" (\"efficiency of function\") and \"ecosystem resilience\" (\"existence of function\") is traced back to [PERSON] (1996) [10]. The first represents stability in the sense of efficiency, continuity, and predictability in order to generate fail-safe (technical) designs and to always remain close to a targeted state of equilibrium as a given measured variable. In contrast to this, the second form describes resilience as persistence, change, and unpredictability, in which a system can flexibly adapt to new conditions. In this case, resilience is measured by the amount/extent of disturbance that can be absorbed prior to system change. Hence, ecosystem resilience often refers to the assessment of complex social and ecologic systems. [PERSON] et al. (2018) [11] mention that resilience as a term is used in many variations, depending on the scientific area. With a focus on urban environments and systems they developed a mathematical resilience framework for evaluating and comparing different prevention and reaction strategies for hazardous situations on a quantitative basis. The derived resilience cycle comprises the phases prepare, prevent, protect, respond, and recover and is based on a study published by the German Academy of Technological Sciences (acatech) [12]. Thus, this cycle expands the formerly established social resilience cycle, which comprised the four phases of preparedness, response, recovery, and mitigation, that was described by [PERSON] (2009) [13]. Figure 2 illustrates the described resilience types and cycle phases in a schematic manner, applied to the impact of hazards on production system performance. Figure 1: Concept of a situationally optimized production system for increased resilience. The illustrated time series helps to allocate terms and methods that aim to reduce the effect of internal and external disturbances on manufacturing system performance. In an exemplary scenario, the initial performance reduction--caused, e.g., by supplier breakdown--may lead to the decision of preparation strategies such as (1) the improved synchronization among supply chain partners. In case of assumed bottlenecks, prevention methods such as (2) the increase of incoming stocks may be performed to reduce the impact of interruptions in material supply. Protection methods (3) may comprise strategies of supply chain management and production scheduling in order to mitigate performance losses due to supply failures. As a response (4), product components are substituted and lead to the necessity of system reconfiguration due to adapted production processes. The corresponding recovery phase (5) essentially affects profitability and competitiveness. However, market demand for the fictitious product may steadily decrease, caused by changing customer demands and may finally lead to the transformation of an overall production system, which is illustrated by shifting from dark to lighter green. Based on this classification, the actions and reactions during dangerous impacts can be sorted along their time-related occurrence. However, in order to develop a structured portfolio of measures, the appropriate model as a guideline is required. The automation pyramid is a well-established model for factory automation [14]. In accordance with the recent trend of Industry 4.0, this model was extended to a three-dimensional expression, including more aspects than the level of automation. The resulting RAMI 4.0 (Figure 3) includes elements and IT components in a layer and life cycle model. It tries to decompose complex processes into modules and adds aspects of data privacy and IT security. RAMI 4.0 is intended to elaborate the understanding and discussions of all participants involved in Industry 4.0 [15].