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Introduction – One of six sigma's key objectives is to reduce variation, and comprehending these variations can be aided by using Multi-Vari charts. Leonard Seder first described Multi-Vari charts in 1950. Later, it was widely used to comprehend stock market fluctuations. Then, Dorian Shainin began utilising it as a root cause analysis tool, which he referred to as Red X. Definition – Multi-Vari charts analyse various sources of variation or classes of variation, according to their definition. It is useful for early root cause analysis and helps us focus on the inputs that are producing issues. These are sometimes referred to as multiattribute utility theory charts, and they assist people in comparing numerous options by outlining the advantages and disadvantages of each other. This is just an another way to narrow down our inputs by analysing the process Xs or the inputs. Especially in the early stages of data analysis, use a Multi-Vari chart to visually portray Analysis of Variance (ANOVA) data to analyse data, understand potential relationships, and root causes for variation. Multivariate graphs are particularly helpful for comprehending interactions. Sources of Variation as we have in ANOVA: Interaction Within Variables – This source let us understand the reasons for variation within a batch. This is also called positional variation. For instance, it is comparing within the variables if we observe variation within a batch on a given day. Interaction Between Variables – This source explains the causes of the change between batches. Additionally known as cyclical variation. For instance, if we are seeing variation between Batch 1 and Batch 2, it is comparing between the variables. Time-to-time Interaction – This provides the reasons between weeks or days or any time frame we want to analyse. This is also called temporal or shift-to-shift variation. For instance, if we are looking to find our variation across the Day 1 and Day 2, then the time factor comes into picture and it is Time-to-Time variation. A Multi-Vari chart is a two-axis plot. These graphs are used to examine a process's consistency or stability. Time is represented on the chart's horizontal, or X-axis while the process output or reaction measurement is depicted on the vertical, or Y-axis. The multiple measurements of each unit are plotted together. Consecutive measurements are plotted from left to right over time. A break in the horizontal groupings indicates a break in time during the sampling process. Advantages – Ø It gives us access to several sources of variation, such as within, between, and over time, and is comparable to the reproducibility and repeatability of ANOVA. Ø Despite the need for additional statistical analysis, it points us in the right path for determining the reasons of variations. Ø This is easily illustrable without the aid of any graphic programme. Disadvantages – Ø As was already said, Multi-Vari only provides preliminary sources of variations. Ø Given that it is merely a graphical tool, a thorough interpretation is not feasible. ANOVA can be used to undertake statical procedures and uncover the underlying causes of sources of variation. Ø With discrete data, it is ineffective. To measure the variances, we can only use continuously data. Let's look at one of the industry examples of Quality scores broken down by week that was taken from various clients below. Interpretation – · Client 5: The Multi-Variance chart shows that Client-5 demonstrated strong Quality performance over the course of all four weeks, with very little variations in their weekly results. · Client 2: The Multi-Variance chart shows that Client 2 has higher weekly variations and is also going lower than 90%. · Client 1 is performing similarly to Clients 2 and 4 in terms of quality, however there is more variance between the weeks. · Client 3: When compared to other clients, this client's quality is inconsistent and shows significant fluctuations.