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Process Optimization through Structured Problem Solving
Published in Natalie M. Scala, James P. Howard, Handbook of Military and Defense Operations Research, 2020
David M. Bernacki, Robert E. Hamm, Hung-da Wan
Organizations find themselves addressing problems that have supposedly been “solved” many times before because the problem-solving focused on the symptom(s) of a problem and not the root cause of the problem. In this case study, the team worked to identify the most probable root causes of the high cost of shelter lighting through a structured and thoughtful analysis of the value stream. The team is looking for the high cost drivers, i.e., the costliest steps in the aircraft shelter lighting process. It should be noted that there is seldom a single root cause of any performance gap. Teams will most likely identify several root causes and typically attempt to develop countermeasures for each one later in Step 5. The reality is that teams are seldom given the time and resources necessary to solve for every root cause. As a result, the facilitator will lead the team through an exercise to prioritize the root causes. The root causes that the team believes will provide the greatest improvement in the process will be solved first using the Pareto principle or what most of us know as the 80/20 rule. Simply stated the Pareto principle asserts that a team can achieve as much as an 80% improvement in the performance of a process by providing countermeasures for the top 20% of the root causes of poor performance (Breyfogle, 2003). It is only a rule of thumb, but application of the 80/20 rule can help teams determine which root causes to address with the time and resources available.
Project Systems Scheduling
Published in Adedeji B. Badiru, Project Management, 2019
For project control purposes, the Pareto principle states that 80% of the bottlenecks are caused by only 20% of the tasks. This principle is applicable to many management processes. For example, in cost analysis, one can infer that 80% of the total cost is associated with only 20% of cost items. Similarly, 20% of an automobile’s parts cause 80% of maintenance problems. In personnel management, about 20% of employees account for about 80% of absenteeism. For critical path analysis, 20% of the network activities will take up 80% of our control efforts. The ABC classification based on Pareto analysis divides items into three priority categories: A (most important), B (moderately important), and C (least important). Appropriate percentages (e.g., 20%, 25%, 55%) are assigned to the categories.
Graphical displays of data and descriptive statistics
Published in Andrew Metcalfe, David Green, Tony Greenfield, Mahayaudin Mansor, Andrew Smith, Jonathan Tuke, Statistics in Engineering, 2019
Andrew Metcalfe, David Green, Tony Greenfield, Mahayaudin Mansor, Andrew Smith, Jonathan Tuke
The Pareto principle is named after the economist Vilfredo Pareto who observed in his 1906 Manuale di Economia Politica that 80% of the land in Italy was owned by about 20% of the population. He found similar results for other countries. The business management consultant Joseph M. Juran suggested that similar results could be found in business: 80% of business from about 20% of customers; 80% of defective items caused by around 20% of possible faults. Rooney (2002) quotes Microsoft’s CEO as saying: “About 20 percent of the bugs causes 80 percent of all errors, and – this is stunning to me – 1 percent of bugs caused half of all errors.” The rule extends in a self-similar fashion so, 80% of the remaining 20% of errors would be prevented by fixing the most commonly reported 20% of the remaining 80% reported bugs. These percentages are just based on empirical observations and there is no compelling reason for such self-similarity in economic or industrial processes. Nevertheless, if all faults require similar resources to fix, it is sensible to attend to faults that cause the majority of defects first.
Tomato leaf disease identification by modified inception based sequential convolution neural networks
Published in The Imaging Science Journal, 2023
R. Dhanalakshmi, Balakrishnan K, Bam Bahadur Sinha, R. Gopalakrishnan
Table 5 shows the number of samples considered for the training and validation of the datasets. Four different training and test sample variants were tried to separate the images into different labels: 60:40, 70:30, 75:25, and 80:20. The dataset was randomly partitioned into two segments: (1) Training: 80% of the training dataset is used when the actual dataset is used to train the model (weights and distortions for neural networks). (2) Testing and validation: 10% of the samples in the dataset are used for the validation set, which provides a distortion-free evaluation of the model that fits the training dataset while the model's hyperparameters are 10% of the samples in the dataset are utilized in the testing set, which allows for an objective assessment of the final model that matches the training set. It is discovered that the Pareto principle (80:20) provides the highest accuracy for the suggested approach. Two facts are used to calculate this ratio. To begin, there is the sheer volume of information. Second, this is the problem statement for automating the process of locating a particular class inside a multiclass data set. Moreover, this ratio, when trained with very few hyperparameters is easy to validate and tune which reduces the size of the validation set.
Development of a structural framework to improve reconfigurable manufacturing system adoption in the manufacturing industry
Published in International Journal of Computer Integrated Manufacturing, 2023
Rajesh Pansare, Gunjan Yadav, Madhukar R Nagare
Pareto analysis is a statistical decision-making technique that is used to identify RMSEs that have a significant impact. It employs the Pareto principle, which states that 80% of problems are caused by 20% of the causes (the 80/20 rule). As shown in Figure 8, the authors created a Pareto chart in which the RMSEs that significantly contribute to driving power can be identified and analyzed. According to the graph, a total of 17 RMSE’s account for 80% of driving power. Many of the top-ranked RMSEs depend on these RMSEs to achieve their goals, so organizational leaders must take care of them to ensure successful RMS implementation. Each of these two RMSEs, research and NPD activities, as well as funds for implementation, contribute to a maximum of 6% of driving power, demonstrating that the majority of RMSEs depend on them. Also, Top management commitment and clear vision, as well as government policies and regulations, have all significantly contributed to driving power.
A Pareto investigation on critical barriers in green supply chain management
Published in International Journal of Management Science and Engineering Management, 2019
Jasneet Kaur, Ramneet Sidhu, Anjali Awasthi, Samir K. Srivastava
Pareto analysis was used in this study due to its ability to distinguish the critical barriers. Pareto principle, often termed as the 80/20 rule was introduced by Vilfredo Pareto who found that 80% of Italy’s wealth was distributed among 20% of the total population (Craft & Leake, 2002). Applying this analogy, the Pareto 80/20 rule can be used to separate between the ‘vital few’ from the ‘useful many.’ Pareto analysis has been applied in various areas namely enterprise resource planning, food safety assurance system (Fotopoulos, Kafetzopoulos, & Gotzamani, 2011), total quality management (Karuppusami & Gandhinathan, 2006), supply chain management (Karim, Marosszeky, & Davis, 2006), inventory/material management and quality management (Reid & Sanders, 2005, Swink, Melnyk, Hartley, & Cooper, 2017).