STATISTICAL PROCESS CONTROL JMP JSL & SAS AUTOMATION

JMP JSL Statistical Automation

SAS designed JMP to perform dynamic visual data exploration and statistical discovery. Rothenberg Industries, LLC fully automates any JMP process, custom report generation, and SPC pipeline using JSL (JMP Scripting Language).

Automate Your JMP Routine Official JMP.com

Capabilities Matrix

What We Can Automate in JMP

From raw SQL queries to multi-variable ANOVA regressions, we build custom JSL scripts that eliminate repetitive manual data wrangling and produce publication-ready PDF reports.

  • Six Sigma Control Charts: IR, X-bar, & R charts with SPC flagging, capability analysis (Cp / Cpk) using custom or Shewhart spec limits.
  • Automated Data Ingestion: Direct ODBC import from SQL Server, Azure SQL, AWS, Access, Excel, CSV, & flat text files.
  • Dataset Manipulation: Automated merging, sorting, filtering, and custom formula applications across complex datasets.
  • Custom Report GUIs & High-DPI PDFs: Interactive graphical user interfaces that compile statistical tests, hide unnecessary UI chrome, and output high-DPI vector PDFs.
  • DOE & Measurement MSA: Design of Experiments (DOE) consultation, Gage R&R measurement systems analysis, ANOVA, and multi-variable regression.

Turnkey Automation Service

RAPID DELIVERY

We offer comprehensive services ranging from initial consulting conference calls to door-to-door turnkey solutions. Most custom applications can be developed to optimize your business efficiency in under two weeks.

JSL SCRIPTING ENGINE Compatible with JMP 12 through JMP 17+ Enterprise

Industrial Execution

JMP JSL Automation Case Studies

Real-world deployment examples of automated SPC analysis, custom GUI reporting, and database integration.

CASE STUDY 01 SPC & CAPABILITY ANALYSIS

JMP Automated SPC Report with Capability Analysis

A two-step statistical analysis process that generates control & spec limits from a user-defined dataset and then generates an SPC report from another dataset using reference limits generated by the first step.

The first script prompts the user for a lot range and calculates control and spec limits defined by ±3σ and 5σ respectively. The "CL/SL Generator" script delimits data by variable levels, family, or lot, saving these limits into a master table. The second "SPC Analysis" script imports reference CL/SL sets and compiles an X-bar Variability Chart, Moving Range Chart, and Levey Jennings SPC chart with WECO rule violation flagging.

View Detailed Case Study (PDF) *All demonstration data fabricated for confidentiality
JMP SPC Report Output
CONTROL LIMIT & SPEC LIMIT GENERATOR
SPC Cp Report 1
SPC / Cp Report: X-Bar & Moving Range Charts
SPC Cp Report 2
Process Capability Distribution & Normality Test
JMP GUI Animated Demo
OPERATOR INTERFACE: UPC, DATE & METRIC SELECTION
CASE STUDY 02 GUI & AUTOMATED PDF

JMP Report Generator with GUI and High-DPI PDF Output

Designed for an enterprise client to provide operators with a simple Graphical User Interface (GUI) to specify reporting parameters, import metrics from Microsoft SQL Server tables via ODBC, compile distribution and run charts, and export a formatted PDF report window automatically.

Upon running the script, the GUI displays interactive options allowing operators to toggle capability analysis (Cp / Cpk) and switch between calculated ±3σ / 5σ limits or live SQL master tables.

Generated JMP Report GIF
Automated JMP Report Window compilation and vector PDF export

Technical Foundations

Essential Statistical Review

Fundamental analytical statistical concepts for interpreting empirical process data and drawing valid inferences.

The Population and Sample

Inferential statistics makes generalizations from your sample data to a larger population based on probability theory. Descriptive statistics organize and summarize the main characteristics of your sample data.

Inferential Statistical Process
  1. Identify the target population or industrial application.
  2. Draw a statistically representative sample.
  3. Compute sample statistics to describe sample characteristics.
  4. Use sample information to make valid inferences about the population.
Population vs Sample Diagram

Central Limit Theorem

The Central Limit Theorem states that the distribution of the sum of random variables approaches a normal distribution as sample size increases, regardless of the underlying population distribution. A sample size of N = 25–30 is the standard industry threshold for population inference.

Confidence Intervals

A confidence interval provides a range of values associated with a specified confidence level (e.g. 95%) that contains the true population parameter. If sample mean x̄ = 10 kg and s = 1 kg, a 95% confidence interval is 10 kg ± 2σ.

Normal Distribution Confidence Intervals
Normal Distribution & Confidence Interval Boundaries

Hypothesis Testing & Types of Error

Statistical testing evaluates two competing hypotheses: the Null Hypothesis (H0) of no effect/correlation, and the Alternative Hypothesis (H1) of a true relationship.

Significance Level (α)

Type I error rate (α): Probability of rejecting the null hypothesis when it is true (false alarm).

Statistical Power (1 − β)

Type II error rate (β): Probability of failing to reject H0 when false. Power (1 − β) is the probability of correctly detecting a true effect.

Types of Error Decision Matrix
Decision Matrix: Type I (α) vs Type II (β) Error

Get Started

Automate Your JMP Statistical Workflows

Whether you need a quick consulting call or a complete door-to-door JSL script package, we deliver production-ready JMP statistical automation.

Request JMP Automation Quote