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JMP

last updated:Apr 20, 2026

SAS Institute Japan JMP Japan Division
SAS Institute Japan JMP Japan Division
  • Official site

Transforming “time spent on data analysis” into “time that creates value”

JMP is a statistical analysis software used by researchers and engineers around the world. It is designed to efficiently connect data and insights obtained in the field to results that support decision‑making. One of its key features is the ability to perform a wide range of statistical analyses—such as advanced Design of Experiments (DOE), quality control, and multivariate analysis—using only mouse operations. Data managed in Excel and other tools can also be smoothly reshaped into analysis‑ready formats, enabling fast analysis without requiring complex programming. In JMP, graphs, statistical results, and data are displayed interactively and in sync, allowing users to deepen their understanding through exploration and iteration. Hypotheses and insights held by experienced engineers can be statistically validated, visualized, and organized in a form that is easy to share within teams. These visualizations support aligned discussions among stakeholders as well as evidence‑based reporting and explanation. Leveraging JMP not only reduces the burden of analysis and data preparation but also contributes to process improvement and faster development cycles. By connecting the expertise of seasoned engineers with statistical analysis, JMP aims to bring greater confidence to decisions made on the front lines.

    Production Management SystemProcess Control SystemBusiness Intelligence and Data Analysis
JMP

JMP

JMP Jpeg.jpg
JMP Jpeg.jpg
  • Related Link - https://www.jmp.com/ja_jp/home.html

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■ Improving Operational Efficiency Through a Consistent Analysis Workflow JMP supports the entire process—from data import and preprocessing to statistical analysis, visualization, and report creation. By streamlining and standardizing analytical tasks, it helps reduce the workload associated with data analysis operations. ■ Intuitive, Flow‑Preserving Interactions and Predictive Insights Analysis results and graphs are dynamically linked, allowing users to instantly identify the corresponding source data simply by clicking an outlier on a graph. By analyzing manufacturing data and combining the expertise of seasoned engineers with JMP’s statistical outputs, JMP supports the development of strategies to improve yield. ■ Optimization Through Design of Experiments (DOE) JMP enables the creation of efficient experimental designs for evaluating numerous factors—such as materials, temperature, and pressure. It also considers interactions that are often overlooked in one‑factor‑at‑a‑time studies. JMP further supports prediction of how characteristics change under different conditions and visualization of the “design space,” which identifies the range of conditions that meet target specifications. ■ Comprehensive Technical Support and Learning Resources In addition to Japanese‑language online manuals and help features, JMP provides technical support from Japan‑based staff. For beginners, monthly online hands‑on seminars are offered. A variety of free seminars, videos, and practical learning materials are also available.

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P3

Delivery Time

P2

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Applications/Examples of results

■ Research & Development / New Product Development JMP can be used for tasks such as evaluating material formulations and identifying factors that influence product characteristics. Through Design of Experiments (DOE) and visualization of analytical results, it supports shorter development cycles and cost reduction. ■ Quality Control & Process Improvement By using control charts, calculating process capability indices, and checking for outliers, JMP helps identify trends in quality variation and sources of fluctuation. It enables data‑driven investigation of conditions and patterns that may lead to quality issues, supporting yield improvement and quality stabilization efforts. ■ Equipment and Process Optimization Using data accumulated on the manufacturing floor, JMP organizes and visualizes factors related to quality and cost. It statistically captures the relationship between operating conditions and outcomes, supporting stable operation and the review of operating parameters. ■ Regular Reporting & Team Information Sharing Analysis results can be clearly summarized in graphs and tables for internal reports and presentations. Explanations grounded in objective data help facilitate reporting to management and smooth technical discussions across departments. 【User Case Studies】 Success stories from a wide range of manufacturing industries—including chemicals, machinery, automotive, pharmaceuticals, and semiconductors—are available on our website. User stories: https://www.jmp.com/ja/customer-stories/customer-listing/all

catalog(24)

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JMP

JMP

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How to create a "Sankey diagram" using the graph builder in the statistical analysis software JMP.

How to create a "Sankey diagram" using the graph builder in the statistical analysis software JMP.

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Five reasons to create a "tree map" with the statistical analysis software JMP.

Five reasons to create a "tree map" with the statistical analysis software JMP.

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Covering Space with Limited Trials Using Statistical Analysis Software JMP: Practical Implementation of Space Filling Plans

Covering Space with Limited Trials Using Statistical Analysis Software JMP: Practical Implementation of Space Filling Plans

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Factor settings to keep within specification limits using the statistical analysis software JMP - An approach through "simulation experiments."

Factor settings to keep within specification limits using the statistical analysis software JMP - An approach through "simulation experiments."

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Evaluation of Similarity of Dissolution Curves: Similarity Evaluation of Dissolution Behavior Using f2 and Confidence Intervals - Practical Example Using Statistical Analysis Software JMP

Evaluation of Similarity of Dissolution Curves: Similarity Evaluation of Dissolution Behavior Using f2 and Confidence Intervals - Practical Example Using Statistical Analysis Software JMP

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How to compare multiple process parameters in a single graph - Utilizing "multiple Y-axes" - Practical example using the statistical analysis software JMP.

How to compare multiple process parameters in a single graph - Utilizing "multiple Y-axes" - Practical example using the statistical analysis software JMP.

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Forecasting the number of births in 2026 through 'time series analysis' using the statistical analysis software JMP - Which prefecture is expected to see an increase?

Forecasting the number of births in 2026 through 'time series analysis' using the statistical analysis software JMP - Which prefecture is expected to see an increase?

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Investigating the conditions to make a "PET bottle lantern" the brightest using the experimental design method (DOE) in the statistical analysis software JMP.

Investigating the conditions to make a "PET bottle lantern" the brightest using the experimental design method (DOE) in the statistical analysis software JMP.

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Examples of Robust Engineering Using the Statistical Analysis Software JMP

Examples of Robust Engineering Using the Statistical Analysis Software JMP

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Deeply explore important factors intuitively with the volcano plot of the statistical analysis software JMP.

Deeply explore important factors intuitively with the volcano plot of the statistical analysis software JMP.

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How to create a "Dumbbell Chart" in the statistical analysis software JMP - for comparing changes or differences between two values.

How to create a "Dumbbell Chart" in the statistical analysis software JMP - for comparing changes or differences between two values.

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Case Study of Defect Classification in Wafer Maps Using Cluster Analysis Considering Spatial Features - To Detect Scratches

Case Study of Defect Classification in Wafer Maps Using Cluster Analysis Considering Spatial Features - To Detect Scratches

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Practical Example of Method Development Based on AQbD Using Statistical Analysis Software JMP: Optimization of HPLC Conditions Using Design of Experiments (DOE)

Practical Example of Method Development Based on AQbD Using Statistical Analysis Software JMP: Optimization of HPLC Conditions Using Design of Experiments (DOE)

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How to Utilize the By Variable Feature to Streamline Stratified Analysis (Practical Example Using the Statistical Analysis Software JMP)

How to Utilize the By Variable Feature to Streamline Stratified Analysis (Practical Example Using the Statistical Analysis Software JMP)

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A study on the deliciousness of caramel popcorn using the "MaxDiff plan" in the statistical analysis software JMP.

A study on the deliciousness of caramel popcorn using the "MaxDiff plan" in the statistical analysis software JMP.

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Consideration of pharmacokinetic models when varying covariates: Practical example using statistical analysis software JMP.

Consideration of pharmacokinetic models when varying covariates: Practical example using statistical analysis software JMP.

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Calculation of "Indoor Reproduction Accuracy" and "Parallel Accuracy" Using a Mixed Effects Model - Evaluation of Accuracy in Pharmaceutical Development - Practical Example of Statistical Analysis Software JMP

Calculation of "Indoor Reproduction Accuracy" and "Parallel Accuracy" Using a Mixed Effects Model - Evaluation of Accuracy in Pharmaceutical Development - Practical Example of Statistical Analysis Software JMP

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Detection of Recent Temperature Rise Using the 'Shift Graph' in 'Process Screening' - Practical Example of Statistical Analysis Software JMP

Detection of Recent Temperature Rise Using the 'Shift Graph' in 'Process Screening' - Practical Example of Statistical Analysis Software JMP

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In the end, what can be done with the "Python integration" of the statistical analysis software JMP?

In the end, what can be done with the "Python integration" of the statistical analysis software JMP?

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Three steps to efficiently investigate missing data using the statistical analysis software JMP.

Three steps to efficiently investigate missing data using the statistical analysis software JMP.

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Utilizing "Model-Based Multivariate Control Charts" to explore process anomalies using the statistical analysis software JMP.

Utilizing "Model-Based Multivariate Control Charts" to explore process anomalies using the statistical analysis software JMP.

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Optimization to Avoid Unreasonable Factor Settings in the Statistical Analysis Software JMP: Regarding the Suppression of Profile Extrapolation

Optimization to Avoid Unreasonable Factor Settings in the Statistical Analysis Software JMP: Regarding the Suppression of Profile Extrapolation

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A New Perspective on Disparity Analysis: Interpreting the Bias in MLB Home Run Counts Using the Gini Coefficient ‗ Practical Example of Statistical Analysis Software JMP

A New Perspective on Disparity Analysis: Interpreting the Bias in MLB Home Run Counts Using the Gini Coefficient ‗ Practical Example of Statistical Analysis Software JMP

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Management Chart Builder

Nagoya Seminar Announcement! Utilizing Data for Quality Improvement with the Statistical Analysis Software JMP (10/30)

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Although quality data and process data are accumulated, there are challenges such as "missing signs of abnormalities" and "being unable to identify the cause when problems occur." In the seminar, we will cover practical methods for quality management and factor analysis, introducing a data-driven approach to problem-solving. We will explain analytical approaches that can be utilized in manufacturing sites, from quality monitoring to cause exploration during problem occurrences, with demonstrations included. 【Target Audience】 Quality management personnel in manufacturing Production technology and process improvement personnel Those who want to utilize process data and quality data Those who feel challenges in factor analysis when problems occur 【Overview】 1) Taking Quality Management to the Next Step - Utilizing JMP Beyond Control Charts Using quality management tasks as a subject, we will cover the flow from control charts and process capability analysis using JMP to cause exploration, as well as methods for sharing analysis results. 2) Data-Driven Factor Analysis and Quality Improvement An analytical approach to efficiently narrow down the causes of problems using process data and quality data. ▼ Details & Registration https://www.jmp.com/ja/events/seminars/discovery-seminar-series/2026/10-30

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Experimental plan

Utilization of Experimental Design with JMP, a Statistical Analysis Software for the Chemical Industry

  • Seminar・Event

In the chemical industry, research and development face complex challenges where many factors, such as the optimization of formulation ratios and examination of process conditions, affect properties. Additionally, due to constraints on raw material costs and evaluation labor, there is a need to efficiently gain insights with a limited number of experiments. This seminar will introduce practical applications of Design of Experiments (DOE) using JMP. In the first half, we will explain how to create and optimize experimental designs considering formulation factors and constraints. In the second half, we will introduce Deterministic Screening Designs (DSD) that can efficiently identify important factors with fewer experiments. 14:00 - 14:45 Practical Application of Experimental Design in the Chemical Industry - Experimental Design Considering Constraints and Formulations 14:45 - 15:30 Connecting Screening and Optimization - Practical Use of Deterministic Screening Designs ▼Details & Registration https://info.jmp.com/register?formId=d79d4aa3-0ca5-4858-80f4-fdc32193a6b9&campaignId=701WP00001PHptfYAD

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Bayesian optimization

Utilization of the experimental design features of the statistical analysis software JMP in the manufacturing industry.

  • Seminar・Event

JMP is a software that not only excels in statistical analysis and graph creation but also has robust features for experimental design. By utilizing experimental design, efficient data collection can be achieved while keeping time and costs down. In this seminar, we will focus on the features of experimental design, specifically the sample size explorer and Bayesian optimization, and present examples of their application in the manufacturing industry in a demonstration format. [Overview] First half: Utilization of t-tests in the manufacturing industry and simulation of required data numbers We will explain the overview and examples of t-tests, which are used for various purposes in the manufacturing industry, and introduce analysis examples using JMP. Additionally, we will demonstrate how to simulate the relationship between the required data numbers for t-tests and statistical power using the sample size explorer. Second half: Utilizing JMP Pro Bayesian optimization - Application to simulation experimental data The Bayesian optimization platform is originally designed for experimental data that includes measurement errors; however, there is also a significant demand to apply it to simulation experimental data that does not include errors, such as CAE. In this seminar, we will introduce approaches for utilizing Bayesian optimization with simulation experimental data.

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JMP is not just for analysis! Ways to utilize data processing, automation, and visualization.

  • Seminar・Event

"Data processing takes time," "I keep repeating the same tasks," "Creating graphs is surprisingly difficult." Do you have such concerns? As the importance of data utilization increases, a lot of time is spent on tasks such as data processing and graph creation. Especially when working with spreadsheet software, mistakes due to repetitive manual tasks are often a challenge. JMP not only offers advanced statistical analysis but also has a wealth of features for data preprocessing and visualization. In this seminar, we will introduce methods to streamline data operations, from data loading to processing, formatting, graph creation, and automation. 【Target Audience】 Anyone, regardless of industry, who regularly processes data or creates graphs using spreadsheet software Those who find it cumbersome to create reports or graphs on a monthly or weekly basis Individuals who want to streamline data preparation tasks and spend more time on analysis and reporting Those who want to learn how to utilize JMP not only for analysis but also for data preprocessing and visualization ▼Details & Registration https://www.jmp.com/ja/events/live-webinars/non-series/2026/10-08-data-processing

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SAS

SAS Institute Japan JMP Japan Division

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The JMP story goes back to 1989 when John Sall decided to combine statistical analysis capabilities with graphical visualizations to animate and visualize data. For more than 35 years now, John Sall has led JMP R&D, making each version of JMP more visual, more interactive, and more practical to help users understand their data. What started as a passion project has grown by leaps and bounds. It’s now a family of statistical software products designed with scientists and engineers in mind and used worldwide in nearly every industry.

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Utilization of JMP's experimental design features in the manufacturing industry.
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Utilization of Experimental Design with JMP, a Statistical Analysis Software for the Chemical Industry.
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