Dynamic flexibility metrics for capability and capacity

Abstract

In production environments, such as Flexible Manufacturing Systems (FMSs), the schedule can be disturbed by the occurrence of unplanned events. Machines stop for major failures, maintenance, tool changes due to wear, or tool reassignments. The rescheduling process, however, can be costly. In this study, a dynamic measure of flexibility which helps to determine an appropriate time for rescheduling an FMS has been defined and investigated. Flexibility is defined as a function of Capability and Capacity. Accordingly, two metrics have been developed to monitor the capability and capacity efficiency of each machine in the system for responding to the dynamic system status. The value of each metric falls between 0 and 1 at all times. Higher values in the capability metric mean better machine selection and part distribution strategies among the machines. Higher values for the capacity metric mean higher machine utilization in the production plan. Based on the interaction between the metrics and their respective behavior in the system, four states have been identified and characterized. Simulations of various scenarios can be used to demonstrate the use of these metrics for monitoring FMS operations and determining appropriate times for rescheduling and tool reassignment.

A review of machine efficiency in mass customization

“…Bullwhip effect can cause panic leading the supplier to quote higher lead time than reality that in turn can cause customers to order more in advance leading to chaos in supply chain. Hassanzadeh and Speredelozzi (2007) showed that due date change (both delay and advance) can cause capacity issues. To meet due dates, there is a need to release jobs into the production system earlier than necessary, leading to high WIP levels and longer wait times according to Pahl et al (2005).…”

“…Sofianopoulou's (2006) simulated manufacturing cell efficiency using a number for operators allocated or hired as one of the efficiency-influencing parameters. Dal et al (2000), Hassanzadeh and Speredelozzi (2007), Hill et al (1992) and Bamber et al (2003) described operator absenteeism as a major cause for OEE loss. Parkin and Cutri (1993) modeled direct operator supervision in computer-integrated manufacturing in a reduced downtime system.…”

“…Incorrectly timed new product, an unforecasted expedite request or lack of coordination with suppliers can trigger this. Hassanzadeh and Speredelozzi (2007) used material arrival delay as a cause along with material shortage for unutilized capacity. Segal and Kalir (2003) described low WIP and no WIP as important reasons for tool (machine) idleness in semi-conductor high volume manufacturing.…”

“…Gindy and Saad (1998) explained how well a manufacturing system needs to cope with sporadic disturbances and events under tight due date targets. Schedules get disturbed by disturbances according to Hassanzadeh and Speredelozzi (2007). Mohring (2014) explained about micro-level disturbances causing the signal-to-noise ratio that impact the sensor information while trying to control parameters in machining in the effort for performance improvement.…”

Flexibility in Organic Systems - Remarks on Mechanisms for Adapting System Goals at Runtime

“…spondingly, it is measured according to the aspects that are specific to the particular (application) domain. For instance, various metrics are proposed to measure the flexibility of manufacturing systems (Benjaafar and Ramakrishnan, 1996;Hassanzadeh and Maier-Speredelozzi, 2007;Shuiabi et al, 2005), programming paradigms, architecture styles, and design patterns (Eden and Mens, 2006) or different reconfigurable hardware architectures (Compton, 2004). There is no common definition, but the meaning of the term as "adapting the system appropriately when the goal is changed" is commonly agreed.…”