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Lean Six Sigma Basics
Published in James William Martin, Lean Six Sigma for the Office, 2021
Figure 3.6 shows how the bottleneck concept applies in an office. In the first scenario, a bottleneck feeds a non-bottleneck. In the second scenario the sequence is reversed. In either scenario, the production rate of all resources or operations must be subordinated to the bottleneck’s demand and is 10 units per day. Another important concept is that a bottleneck must be activated and utilized to meet the takt time. In situations where a bottleneck cannot meet its demand, then process improvements should first focus on increasing the bottleneck’s capacity as opposed to other operations. Increasing capacity elsewhere does increase the process throughput rate. If process improvements cannot provide sufficient capacity for the bottleneck to meet the takt time, then it could be utilized over multiple shifts or additional workers, and equipment could be added to support it. If a bottleneck gains capacity than another operation called a capacity-constrained resource may become the new bottleneck. A VFM is very useful to help understand operational capacity and locate the bottle neck and capacity-constrained operations.
EPMS for Business Process Analysis
Published in Vivek Kale, Enterprise Process Management Systems, 2018
The advantages of simulation include: A simulation can help in understanding how the system operates.Bottleneck analysis can be performed, indicating where work-in-process, information, materials, and so on are being excessively delayed.What-if questions can be answered, which is useful in the design of new systems.New policies, operating procedures, decision rules, information flows, organizational procedures and so on can be explored without disrupting the ongoing operations of the real system.Time can be compressed or expanded, allowing for a speed-up or slowdown of the phenomena under investigation.
Creating Standard Work
Published in Charles Protzman, Fred Whiton, Joyce Kerpchar, Christopher R. Lewandowski, Steve Stenberg, Patrick Grounds, James Bond, The Lean Practitioner’s Field Book, 2018
Charles Protzman, Fred Whiton, Joyce Kerpchar, Christopher R. Lewandowski, Steve Stenberg, Patrick Grounds, James Bond
Remember that any excess WIP or idle time is a sign of a problem within a Lean system. A bottleneck is the constraint (where the capacity of the machine cannot meet the demand) in any series of operations in a process. Per the theory of constraints, which is explained in detail in a book entitled The Goal,22 the cycle time will always be equal to the slowest machine or slowest person in the process. We refer to these constraints as “Herbies.” In theory, a person (Herbie) should never be a constraint as we can always add people to a line but we cannot always add or immediately speed up machines. In essence, only machines should be bottlenecks in a process. This is a good and bad news story. The bad news is we have a constraint or bottleneck. The good news is, once we have our analysis data, we not only know we have a bottleneck (constraint), but we can predict when the backup will start, how long it will last, and how many parts will queue up.
Data-driven machine criticality assessment – maintenance decision support for increased productivity
Published in Production Planning & Control, 2022
Maheshwaran Gopalakrishnan, Mukund Subramaniyan, Anders Skoogh
Even though Roser, Nakano, and Tanaka (2001) developed and tested this method on a discrete event simulation environment, this method needs to be adapted to the real-time data which is collected from the shop floor to detect the bottlenecks. Subramaniyan et al. (2018) proposed a manufacturing execution system (MES) based data-driven algorithm which converts the real-time data of the machines into active states and statistically detects the group of bottlenecks. More information on the details of the algorithm are available in Subramaniyan et al. (2018). Furthermore, the algorithm can also give diagnostic insights into the bottlenecks in terms of different components of active states. This will help to understand the nature of bottlenecks in the production system. For example, the bottleneck could be a cycle time bottleneck, downtime bottleneck or setup time bottleneck (Chiang, Kuo, and Meerkov 1998). Understanding the nature of the bottleneck will help in framing specific strategies to manage the bottlenecks and reduce its effect on the desired throughput.
Integrated analysis of productivity and machine condition degradation: Performance evaluation and bottleneck identification
Published in IISE Transactions, 2019
In addition, due to limited resources, such as budget, space, and technicians, it is important to identify the machines or states that are most critical to system performance, and to guide maintenance and system improvement activities. To this end, bottleneck analysis can serve as an effective tool. For instance, if a machine is in a state that has a significant influence on the overall performance of the system, most likely this machine is in an unhealthy state. Thus, to improve the system performance and minimize the chance of encountering catastrophic failures, maintenance and other improvement activities need to be scheduled before its health condition further deteriorates (Fitouhi et al., 2017). Moreover, since for most factories, limited resources can be devoted to system improvement activities, bottleneck analysis enables finding the most critical component and maximizing the reward of investments. Therefore, systemic and theoretic analysis of system bottlenecks with machine condition degradation is of significant importance and needs to be conducted rigorously.
A resource-constrained, multi-unit hospital model for operational strategies evaluation under routine and surge demand scenarios
Published in IISE Transactions on Healthcare Systems Engineering, 2019
Mersedeh TariVerdi, Elise Miller-Hooks, Thomas Kirsch, Scott Levin
Section 2.1 takes a demand-perspective. This section describes the queueing network model from the perspective of services offered. The nodes of the queueing network include, by design, locations of potential bottlenecks in critical services and their supporting units. For example, bottlenecks commonly arise in the ED, ORs and IGWs (Lakshmi and Appa Iyer, 2013). These bottlenecks can be due to excess demand in relation to unit capacity in terms of service rates. Backups due to bottlenecks in one unit can also cascade to other units, as a backup in one service can affect the ability to start another service. For example, backups arising in IGWs can slow down the transfer rates of patients out of the ED, preventing new patients from entering the ED. Links of the queueing network tie these services together to replicate typical or anticipated patient care paths. A comprehensive model of hospital operations across its many units, thus, is required to adequately capture these backups or bottlenecks as is necessary for understanding hospital performance.