An Examination of Variability and Its Basic Properties for a Factory

Abstract

Variability is a key performance index of a factory. In order to characterize variability of a factory, definitions of bottleneck, utilization, and variability of a single machine are reexamined and clarified. The clarification leads to the introduction of a detail expression for the relationship between cycle time and work-in-progress.In order to quantify variability for factories, the author uses a single machine system to gauge the behaviors, and subsequently derives an explicit expression for the variability, of a simple factory, making use of analogy and the clarified definitions. The obtained results can be applied to many subjects in the field of manufacturing management, such as factory performance analysis, capacity planning, and cycle time reduction.With the derived results, properties of variability for a simple factory in the aspects of utilization versus throughput bottlenecks and nonthroughput bottlenecks, gap effects, and bounds on variability, are examined in detail to shed light on the insights of the stochastic behaviors of a complex factory.

Scheduling for an automated guided vehicle in flexible machine systems

“…All these will complicate the model and are left for future research. The transportation delay caused by AGV will introduce addition interruptions to a station , Wu et al 2011a, and affect the length of service time (Wu and Hui 2008), which will inevitably increase the variability of a manufacturing system (Wu 2005). The impact of transportation delay on queue time can be modeled as resource contention (Wu 2014a).…”

The Determination and Indetermination of Service Times in Manufacturing Systems

“…It brings the investigation back to how to define service time for individual lots, apart from its statistical means. The importance of this lies in a practical need to gauge performance of production lines using the notion of variability (12), which is defined by the variance of service times for individual lots.…”

“…The normalized cycle-time, defined as -ratio of cycle time divided by raw process time,‖ has its expected mean given by Two confusions often arise in the literature. One is in (11), queueing time is approximated by service times, not by EPT [12]. The second is raw process time ̅̅̅̅̅̅ was incorrectly used in (13) in place of the mean service time ̅̅̅̅̅̅̅̅ [14]- [17].…”

A literature review on variability in semiconductor manufacturing: The next forward leap to Industry 4.0

“…As utilization, as well as variability, also creates higher cycle times, one way to visualize the intrinsic variability of a manufacturing system is through "operational curves" or "cycle time throughput curves". These curves are found in 20 of the papers that we reviewed, and used in 12 of them to explain the impact of variability (Brown et al 2010, Delp et al 2006, Etman et al 2011, Ignizio 2011, Kim et al 2014, Martin 1999, Robinson et al 2003, Schoemig 1999, Shanthikumar et al 2007, Tirkel 2013, Wu 2005, and Zisgen et al 2008. These curves represent the Xfactor (the ratio between cycle time and raw processing time) as a function of the utilization of available capacity at different variability levels (as illustrated in Figure 1.A).…”