A box plot is a graph that shows the frequency of numeric data values. It can be drawn either horizontally or vertically. It is referred to as a Box-and-Whisker Plot as it displays the data in a box-and-whiskers format. Box plots are widely used in statistics, scientific research, higher education, process improvement, and in social and human sciences.
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A histogram is a graph which shows the frequency of continuous data values. It is a type of bar chart that can be drawn either vertically or horizontally. Histograms are widely used in statistics, process improvement, scientific research, economics, and in social and human sciences.
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Graphing the data can be utilized for both historical data already available and when analyzing the data resulting from live data collection activities. Of course, you need to pick the right graphical tool as there are a lot of different ways to plot your data. A number of commonly used graphical tools will be covered here. However, note that if one graph fails to reveal anything useful, try another one.
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Continuous distributions describe variables that take values from a continuous range and can be measured with any degree of accuracy. The commonest and the most useful continuous distribution is the normal distribution. The Normal Distribution is a symmetrical probability distribution where most results are located in the middle and few are spread on both sides. It has the shape of a bell and can entirely be described by its mean and standard deviation. Read more »
Most improvement projects and scientific research studies are conducted with sample data rather than with data from an entire population. A Probability Distribution is a way to shape the sample data to make predictions and draw conclusions about an entire population. It refers to the frequency at which some events or experiments occur. It helps finding all the possible values a random variable can take between the minimum and maximum statistically possible values. Read more »
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces. It involves describing, summarizing and organizing the data so it can be easily understood. Graphical displays are often used along with the quantitative measures to enable clarity of communication. Descriptive statistics helps exploring and making conclusions about the data in order to make more rational decisions. Read more »
Value is one of the most important concepts within Lean thinking and one of the most valuable outcomes Lean provides. Value Analysis focuses on what adds value to business processes as perceived by the customer. A process that does not add value to the product or service should be redesigned or eliminated altogether. Problem solving and continuous improvement efforts often focus on those aspects of processes that add no value to the customer and are therefore wasteful. Read more »
In a Lean culture, Waste is anything that doesn’t add value from the customer’s perspective. It includes activities and resources beyond what is needed to meet customer requirements. Waste Analysis involves identifying, quantifying, eliminating and preventing waste in manufacturing, service and office environments. Many Lean tools and techniques focus on continually identifying and eliminating these wastes to bring efficiency and effectiveness to existing processes, and this is one of the core principles of Lean thinking. Read more »
A Value Stream Map (VSM) is a high-level visual representation of a business process. It helps to understand the flow of value in the process as perceived by the customer. Its primary goal is to identify and eliminate waste (Muda) and make the process as close to lean as possible. Although it is often associated with manufacturing, it can also be applied in product development and service-related industries such as healthcare, hospitality and logistics.
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Histograms and box plots are graphical representations for the frequency of numeric data values. They aim to describe the data and explore the central tendency and variability before using advanced statistical analysis techniques. In this article, we will further discuss the similarities and differences between these two tools.
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An ideal process must produce without defects and without rework. To expose these unnecessary and costly inefficiencies, you should have appropriate performance metrics to measure process yield, or otherwise, the true process yield might be underestimated. Process yield measures should be able to expose even the smallest inefficiencies in a process, which will enable operations to understand their true process yield in order to set realistic improvement targets.
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The Kano Model is a framework that is used to analyze customer needs and how those needs change as time goes on. It helps categorizing and prioritizing the different features of a product or service based on their impact to customer satisfaction. Those categories are then considered when analyzing potential opportunities for improvement. The Kano Model is often used to better understand how a product (or service) fits customer needs by mapping the existing or proposed features into the Kano categories.
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