Functional periodicity as a concept for the (re-)design to agility of production systems

Abstract

The complexity of a production system is caused by two factors: by a time-independent poor design that causes low efficiency (system design), and by a timedependent reduction of system performance due to system deterioration or to market or technology changes (system dynamics). To optimize the efficiency and changeability of a production system, both factors must be considered. Starting from complexity theory, a procedure is presented in this paper that helps not only to design production systems with low or zero time-independent complexity (focus: flexibility and efficiency), but also to prevent the unpredictable influences of the time-dependent combinatorial complexity by transforming it into a periodic review and adaptation of the system's volume and variant capabilities (focus: agility). With the help of a practical industrial example the validity of the approach is illustrated.

Design of a Scalable Modular Production System for a Two-Stage Food Service Franchise System

“…All of the concepts have basic properties in common, including autonomy, distribution, decentralization, and flexibility, adaptability, and agility (Ryu, 2003). They are conceptualized to continuously adapt their organizational structures to changing environments by means of self-organizing mechanisms, considering that a production system is an organic complex consisting of diverse autonomous units, i.e., a fractal, a cell or a holon (Shin, 2009 (Spath and Scholz, 2007), mostly referring to the same or at least a very similar idea of a manufacturing system that shifts quickly between product models ideally in fast response to customer demand (Matt, 2010). Finally, some recent research has investigated aspects of crossenterprise production and supply chain design focusing, however, mainly on aspects of process module based modeling of process chains (Aurich et al, 2008) or collaboration based aspects with a special focus given to social and interpersonal factors (Jaehne et al, 2009).…”

“…Diverse authors also provide a comprehensive comparison between distributed manufacturing approaches such as bionic, fractal and holonic manufacturing systems, in terms of design, features and performance (Tharumarajah et al, 1996;Sitorus et al, 2006) and use them also in the context of network approaches (Montreuil et al, 2000;Frayret et al, 2001;Hongzhao et al, 2005). Some other research has extended the focus from level of the organizational structure towards a more comprehensive understanding of a production system's dynamics: diverse authors promote the concept of agility (Yusuf et al, 1999;Dove, 2006;Matt, 2010;Vinodh, 2011), changeability Heger, 2003, Wiendahl et al, 2007;Park and Choi, 2008;AlGeddawy and ElMaraghy, 2009) or mutability (Spath and Scholz, 2007), mostly referring to the same or at least a very similar idea of a manufacturing system that shifts quickly between product models ideally in fast response to customer demand (Matt, 2010). Finally, some recent research has investigated aspects of crossenterprise production and supply chain design focusing, however, mainly on aspects of process module based modeling of process chains (Aurich et al, 2008) or collaboration based aspects with a special focus given to social and interpersonal factors (Jaehne et al, 2009).…”

Axiomatic Design of a Framework for the Comprehensive Optimization of Patient Flows in Hospitals

“…According to this definition, the complexity of a system is determined by two factors [ 24 ]: by a time-independent poor design, which results in a systemic low efficiency (system design), and by a time-dependent decrease in system performance due to environmental changes or system deterioration (system dynamics).…”

“…This so-called time-dependent system complexity has its origins in the unpredictability of future events that might change the current system. According to Suh [ 23 ], the time-dependent complexity can be only controlled by introducing a functional periodicity [ 24 ]. In practical terms, this means to regularly “reset” the system, for example by the implementation of a Continuous Improvement Process or Kaizen [ 25 ].…”

Design of a scalable assembly system for product variety: a case study

“…Combinatorial complexity can be reduced through re‐initialization of the system by introducing a regular time interval triggering the reset of the originally defined FRs (Suh, 2005). The length of the regular time interval depends on industry or even company specific factors and has to be defined for the single case (Matt, 2010).…”