The Tutorials contain an explanation of SPC concepts in simple English with a guide to use the simulators, encouraging a hands-on approach in learning concepts. Dr. Deming’s Experiments SPC Wizard contains simulations of Dr. Deming’s Red Beads Experiment and Funnel Experiment showing how easy it is to come to the wrong conclusions about people and processes by looking at just raw data. This is a must.
DataLyzer’s own Tennis Ball Launcher and Bead box simulation programs generate ‘variable’ and ‘attribute’ type data that is required to recreate scenarios: Eg To produce data that shows special cause of variation.
The game simulates a process that is not initially in a state of statistical control. The student has to apply the SPC techniques to improve the process. The game is won when a required capability is achieved.
Provides facility to drag and drop data, select any section of the data, include/exclude data points with any combination of attributes, with instantaneous creation/refresh of control charts, histograms, Pareto’s and Scattergrams.
Data can be analyzed on the fly from DataLyzer, Microsoft Excel, Access, SQL Server, Oracle Server or any ODBC compliant database without the need to import the data. Data can be combined from multiple data sources offering a very flexible solution to find causes of variation.
Various analysis like Control chart analysis, capability analysis, multiple correlational analysis, Pareto Analysis, Multi-Vari analysis with F and t-test, frequency analysis can be performed in a quick an easy way with various filters and selection.
For more information please download the brochure
How to integrate FMEA, Control Planning, SPC and CAPAPDFThis white paper shows how you can really implement a working quality system where the techniques come together. |
SPC and SQC integratedPDFThis white paper explains how you can combine SPC and SQC in an organization to get the best of both worlds. |
Integrating SPC and TPMPDFThis white paper shows how you can integrate the two continuous improvement methods. |
APQP: Ballooning Control Plan SPCPDFThis white paper shows how you can integrate the design results with the quality planning and the quality execution process. |
Process Capability IndicesPDFThere is a lot of confusion about the use of Cp, Cpk, Pp, and Ppk. This white paper tries to remove some of the confusion and shows the power of using the indices Cp, Cpk, and Ppk simultaneously. |
Managing Control LimitsPDFThis white paper describes how to set control limits during the implementation of SPC to make sure the organization can handle the number of out of controls. |
Recalculating the Control LimitsPDFThis white paper recommends the use of two new indices to allow a quick and effective comparison between limits set historically and the current process. |
Using Control Charts for Quality ImprovementPDFThis white paper shows how SPC (Control Charts) can be used as a tool to support process improvement. |
Importance of the Range ChartPDFDuring SPC implementation, often too much emphasis is put on the use of the Average chart. This white paper explains mistakes made in this area and gives recommendations on how to prevent these mistakes. |
Parallel Processes and SPCPDFThis white paper describes which issues are important when you implement SPC in processes where you have parallel subprocesses like multiple cavities, multiple lanes, etc. |
Tracking and TracingPDFIn this white paper, it is explained how you can implement full tracking and tracing and how you can use DataLyzer to quickly analyze causes of variation. |
Reject reduction by analysis of process dataPDFIn this white paper, we show how advanced analysis can be used to analyze data gathered by DataLyzer. |
CMM Data acquisition and Chart creationPDFIn this white paper, we show how a CMM can be integrated into the SPC DataLyzer network. |
SPC in Crystalline PV module manufacturingPDFIn this white paper, we explain how you can benefit from implementing DataLyzer Spectrum. Please send us a mail if you like to receive this white paper. |
How to integrate FMEA, Control Planning, SPC and CAPAPDFThis white paper shows how you can really implement a working quality system where the techniques come together. |
SPC and SQC integratedPDFThis white paper explains how you can combine SPC and SQC in an organization to get the best of both worlds. |
Integrating SPC and TPMPDFThis white paper shows how you can integrate the two continuous improvement methods. |
APQP: Ballooning Control Plan SPCPDFThis white paper shows how you can integrate the design results with the quality planning and the quality execution process. |
Process Capability IndicesPDFThere is a lot of confusion about the use of Cp, Cpk, Pp, and Ppk. This white paper tries to remove some of the confusion and shows the power of using the indices Cp, Cpk, and Ppk simultaneously. |
Managing Control LimitsPDFThis white paper describes how to set control limits during the implementation of SPC to make sure the organization can handle the number of out of controls. |
Recalculating the Control LimitsPDFThis white paper recommends the use of two new indices to allow a quick and effective comparison between limits set historically and the current process. |
Using Control Charts for Quality ImprovementPDFThis white paper shows how SPC (Control Charts) can be used as a tool to support process improvement. |
Importance of the Range ChartPDFDuring SPC implementation, often too much emphasis is put on the use of the Average chart. This white paper explains mistakes made in this area and gives recommendations on how to prevent these mistakes. |
Parallel Processes and SPCPDFThis white paper describes which issues are important when you implement SPC in processes where you have parallel subprocesses like multiple cavities, multiple lanes, etc. |
Tracking and TracingPDFIn this white paper, it is explained how you can implement full tracking and tracing and how you can use DataLyzer to quickly analyze causes of variation. |
Reject reduction by analysis of process dataPDFIn this white paper, we show how advanced analysis can be used to analyze data gathered by DataLyzer. |
CMM Data acquisition and Chart creationPDFIn this white paper, we show how a CMM can be integrated into the SPC DataLyzer network. |
SPC in Crystalline PV module manufacturingPDFIn this white paper, we explain how you can benefit from implementing DataLyzer Spectrum. Please send us a mail if you like to receive this white paper. |
English 
Danish
Dutch
German
Polish
Portuguese
Spanish