Consulting in Statistic and Data Analytics
Consulting in statistic and data analytics
We are experts in state-of-the-art multivariate analysis, machine learning, predictive modelling, business intelligence and statistical analyzes using powerful software.
Training in data analytics
Industrial statistics
- Acceptance sampling
- Measurement systems analysis (Gage R&R)
- Capability analysis
- Decomposition of variation (Multi-Vari Chart)
- Graphical Analysis: histogram, boxplot, Normal quantile plot, etc.
- Statistical process control (control charts)
- Statistical process adjustment
- Statistical tolerencing
- Change point analysis
- Monitoring of calibration
- Means comparisons (ANOVA)
- Time series analysis
- Power spectrum
- Correlation
- Linear and logistic regression
- Multivariate analyses (PCA, PLS, OPLS)
- Design and analysis of experiments (DOE)
- Sample size calculation
- Process optimization
Predictive modelling
- Discrete events simulation models
- Monte Carlo simulations
- Classification and regression trees
- Machine learning (neural networks, random forests, etc.)
- Automated analyses using VBA and R language
Performance indicators
- Implementation of key performance indicators (KPI)
- Statistical monitoring of indicators
- Evaluation of customers’ satisfaction
- Statistical analysis of surveys
- Building of centralized databases (Access, SQL, Excel)
- Automation of reporting in Excel VBA, R or Power BI
Statistical and analytical expertise
Measurement Systems Analysis
To diagnose the quality of a measurement system and to identify its sources of variation.
Statistical Process Control (SPC)
To determine an implementation approach for SPC adapted to your company and choose the type of control chart that will meet your needs and be best adapted to your data.
Design of Experiments (DOE)
To evaluate which factors contribute most to the variation of your process by an optimal and economic planning of the data collection procedure and a statistical analysis of the results carried out by experts.
Statistical Modeling and Multivariable Analysis
To obtain the maximum information from your data by applying multivariable statistical analysis. This analysis will allow you to identify variables having a strong link with a quality parameter and\or to build a model aiming at forecasting future results.
Problem Solving Techniques
To resolve important problems by using a structured method and tools adapted to the situation.
Statistical Tolerancing
Establish specifications for your process parameters in order to obtain the desired quality on finished products.
Measurement Systems Analysis
To obtain the maximum information from your data by applying multivariable statistical analysis or machine learning algorithms. This analysis will allow you to identify variables having a strong link with a quality parameter and\or to build a model aiming at forecasting future results.
Performance indicators – Decision helping tools for managers
Choosing the appropriate performance indicator and implementing an efficient monitoring technique in order to quickly understand the situation.
Measurement of Customers Satisfaction
To elaborate the approach that will allow you to measure customer satisfaction, and determine the critical elements affecting it. We do not limit ourselves to the measurement of satisfaction; we make sure actions will occur as a result.
Acceptance sampling
Determine sample size and decision rules to ensure batch quality control.
Statistical Programming
Some data science tasks require advanced and recent algorithms. Some repetitive analyzes and reports have to be generated frequently. Statistical programming using languages such a R or Python can address these needs.
Support on statistical software
Différence offers support to users of the following statistics softwares:
- JMP
- Simca
- Minitab
- Power BI
- R programming
JMP Partner
We are proud to be an official JMP partner, helping users get the most out of their investment in JMP products. JMP statistical software is made by SAS. It is visual and interactive, which gives users the ability to explore data dynamically and make statistical discoveries. Since 1989, scientists, engineers and other data explorers have been using JMP software to make their worlds better.
