Bottleneck

Abstract

Bottleneck gets considered as a critical challenge in manufacturing industries that should get addressed so as to increase productivity. It is thus essential for manufacturing industries to use bottlenecks detection method to maintain a better production rate. This paper comprehensively explores the challenge of a bottleneck in manufacturing industries, the available bottleneck detection methods and the benefits observed in the use of the methods. The paper also makes recommendations for the use of bottleneck detection methods.

Literature Review

Bottleneck gets considered as a critical challenge in manufacturing industries that should get addressed so as to increase productivity. The bottleneck is a primary factor that may result in decreased company productivity. Various scholars have defined bottleneck using different definitions as outlined below. A bottleneck is a resource in which demand temporarily exceeds capacity. It also gets defined as a resource with more demand request compared to the available capacity. It also gets viewed as a resource in which there is a maximum work-in-process (WIP) inventory waiting in the queue. It also gets seen as a resource for maximum long-run utilization. It also gets defined as a resource with the smallest isolated production rate among those in the system. It also gets defined as the resource that comprises of the minimum combined total time spent in inactive states. It also refers to the resource that runs out of capacity first hence limiting the system throughput. It also refers to the resource that strongly impedes the performance of a system. Some of the definitions above do not explain the relationship between the meaning and the implicit reason the resource gets referred to as a bottleneck. Others describe the relationship between the total performance and the changes in the specified bottleneck resource (Betterton & Silver, 2012). According to Betterton & Silver (2012), the bottleneck gets defined as the resource with the strongest impact on the performance of a system. Simply it is the resource with the largest influence on a system performance due to a given differential increment of change.

According to Li, Chang, Ni, Xiao & Biller (2007), the existing work in bottleneck detection gets categorized into two parts namely analytical methods and the simulation-based methods. There is a restriction of most of the bottleneck studies using the analytical approaches to long-term steady state bottleneck detection due to their statistical and probability distribution assumptions for machine performance. The simulation-based methods get characterized by various drawbacks such as system specific knowledge, long development time, relatively low flexibility to layout changes and the potential misinterpretations of the simulation results. The use of real-time data analysis offers sustainable benefits or opportunities that do not get sometimes recognized during the long-term analysis. It is thus critical to make real-time decisions based on bottleneck identification and mitigation in all practical solutions.

Bottleneck detection methods

There are eight methodologies applied in detecting bottlenecks as described below;

a)      Utilization Method: Also known as the effective process time methodology. Utilization of a resource refers to the long-term fraction of time in which the resource does not idle due to lack of work. It is the ratio of the rate in which items arrive to get processed to the efficient production rate. The effective production rate is the average maximum rate in which the resource works while putting into consideration the impact of downtime on all resources. 

b)      Active period method: It measures the duration of the periods that a station stays active without interruption and also makes the calculation of the average active time for each station. The machine that records the longest average active period gets viewed as the bottleneck.

c)      Inactive period method: In this method, the station in which the minimum combined total time spent in inactive states gets viewed as the bottleneck.

d)     Arrow method: Its name gets derived from the practice of drawing arrows pointing left or right indicating the stations that have a higher blocking and starving as compared to adjacent stations.

e)      Turning point method: The turning point refers to the station in which the trend of blockage and starvation moves from a higher blockage than starvation to greater starvation than blockage. The sum of the total blockage and starvation time of a turning point station is smaller than that of its two neighboring stations. Simply meaning the turning point has a higher percentage of operating time plus downtime than its adjacent stations.

f)       Average Waiting Time Method: In this method the bottleneck gets viewed as the station where work waits longest as measured by the average time a job spends in the queue.

g)      Longest waiting time method: The bottleneck in this method gets viewed as the station where work waits longest as measured by the maximum time a job spends in a queue.

h)      Longest Queue method: The bottleneck in this method refers to the station having the greatest number of waiting for jobs in queue for the largest proportion of the entire line processing period.

i)        Overall Throughput Effectiveness (OTE) method: This method incorporates all forms of station delay and downtime as well as cumulative yield loss, and the bottleneck gets viewed as the station having the smallest Overall Throughput Effectiveness.

Advantages of using bottleneck detection methods

An effective bottleneck detection method allows for fast and correct identification of the bottleneck locations. This aspect can result in an increase in the system throughput, an improvement in the operation management of utilizing finite manufacturing resources, and reducing the total cost of production (Li, Chang, Xiao, & Biller, 2007). The discrete event simulation may get used in understanding complex layouts as well as dynamic performance.

Conclusion

Bottleneck detection methods play a significant role in ensuring high productivity in manufacturing companies. It is the responsibility of the individual organization to determine the suitable bottleneck detection method that adequately suits their company goals.

Recommendations

The bottleneck control efforts get based on the recent system performance; hence further studies should get undertaken on the effect of the recent changes in the system’s future performance to enable forecasting of new bottleneck locations. Further work is required to characterize the serial lines where bottleneck detection fails, or in circumstances where detection methods do not locate multiple bottlenecks.

References

Betterton, C. E., & Silver, S. J. (2012). Detecting bottlenecks in serial production lines–a focus on inter-departure time variance. International Journal of Production Research, 50(15), 4158-4174.

Li, L., Chang, Q., Ni, J., & Biller, S. (2009). Real-time production improvement through bottleneck control. International Journal of Production Research, 47(21), 6145-6158.

Li, L., Chang, Q., Ni, J., Xiao, G., & Biller, S. (2007, July). Bottleneck detection of manufacturing systems using the data-driven method. In Assembly and Manufacturing, 2007. ISAM'07. IEEE International Symposium on (pp. 76-81). IEEE.

Sherry Roberts is the author of this paper. A senior editor at MeldaResearch.Com in customized term papers if you need a similar paper you can place your order for research paper custom.