Benefits DataLyzer® SPC software
DataLyzer® SPC software has been designed with the many functions of manufacturing businesses in mind. DataLyzer® provides customers with the following benefits:
Download the brochure for more information here.
DataLyzer® Qualis 4.0 is developed in the latest technology and can be installed as a desk top application or can be used browser based. This advanced technology allows you to run the software in your preferred environment.
DataLyzer® Qualis 4.0 can be configured to your specific needs. Some examples of preference options are:
Create variable and attribute control charts of all types with dozens of presentation and calculation preferences, reasonable limits, math functions that automatically derive subgroup data from previous data, attach work instructions and predefined out-of-control causes and actions, and freeform notes.
Charts can be set up with multiple traceability parameters. These parameters can be configured in multiple ways, such as: predefined list, values based on calendar, or even validation of data from third party software like SAP, or requiring password support as in FDA regulations. Optional traceability parameters examples are: operator name, shift name, batchnr, temperature and machine names.
One of the most popular features of DataLyzer® is the flexibility in data collection. Data can be collected manually or semi automatically using gage connections. The manual option allows data to be collected by part or by characteristic. Charts can be ordered or chained in a sequence and traceability parameters can be carried forward. Specific part-based charts can be sent to specific lines depending on the production setup. DataLyzer offers powerful solutions to integrate data entry in any user environment. It means customers can develop their own user interface or DataLyzer can develop specific user interfaces.
DataLyzer® Import Services can be automatically imported from databases, MES systems, OPC servers, Excel spread sheets, cameras, CMMs, text files, etc. Standard connectors are available to import data. The import can be configured based on import rules and can include email alerts, etc. It is even possible to automatically create the control charts based on the import. DataLyzer has an option to integrate with a historian were you can drill down to the second if required. DataLyzer allows to incorporate your own Python script providing more flexibility in the import solution. Attached brochure provides more detail.
A real-time dashboard can be configured for a single line, an entire plant or even multiple plants. Over 20 SPC statuses can be displayed for each chart, with ability to drill down to specific parts ad processes, control charts, histogram and subgroup data and traceability parameters. A special dashboard designer and dashboard viewer offers the option to create any report you like.
A standard set of 33 reports including control charts, histograms, bat charts, and Pareto charts that can be generated and filtered by date and traceability parameters. User-defined reports can be created using a vast set of statistics. These user-defined reports can also be configured to run at predefined time periods. In addition we supply webbased reports and a dashboard module where any kind of dashboard can be created. See the brochure of the dashboard module for more details.
The DataLyzer® COA module allows users to create their own certificates of analysis or First Article Inspection Reports (FAIR) or TCD based on templates. The certificates can be designed using Excel templates and choosing the data to be inserted at placeholders from the Spectrum system. The COA/FAIR module can be used in case the standard reports from DataLyzer are not sufficient. For more information download the COA brochure.
For industries working with moulds like injection moulding or glass manufacturing DataLyzer offers a mould management module. With the module you can manage blockages and validations in production and you will have full tracking and tracing of the cavity numbers per position per configuration. The mould management module fully integrates with SPC so this will facilitate data entry of measurements but also blockages are recorded in attribute charts with causes of blockages and follow up actions providing a full system to analyze root causes of blockages. You can download the mould management brochure here.
The movie gives you a complete overview of the DataLyzer Qualis 4.0 SPC software.
NOTE: The movie shows the older screens. We are in the process of updating the movie to the latest screens.
The movie gives you a clear understanding of how our DataLyzer SPC solution can be used for an Automotive company.
The movie shows how DataLyzer is implemented in more than 80 Coca Cola plants world-wide.
How to integrate FMEA, Control Planning, SPC and CAPAThis white paper shows how you can really implement a working quality system where the techniques come together. |
SPC and SQC integratedThis white paper explains how you can combine SPC and SQC in an organization to get the best of both worlds. |
Integrating SPC and TPMThis white paper shows how you can integrate the two continuous improvement methods. |
APQP: Ballooning, Control Plan, SPCThis white paper shows how you can integrate the design results with the quality planning and the quality execution process. |
Process Capability IndicesThere 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 LimitsThis 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 LimitsThis 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 ImprovementThis white paper shows how SPC (Control Charts) can be used as a tool to support process improvement. |
Importance of the Range ChartDuring 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 SPCThis 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 TracingIn 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 dataIn this white paper, we show how advanced analysis can be used to analyze data gathered by DataLyzer. |
CMM Data acquisition and Chart creationIn this white paper, we show how a CMM can be integrated into the SPC DataLyzer network. |
Defects prevention for the Aerospace industryIn this white paper, we shed some light how the Zero Defects approach is used more and more in the Aerospace supply chain and how DataLyzer can support with this implementation |
SPC software and Industry 4.0More and more companies are looking for an SPC software tool which fits in their Industry 4.0 approach. This whitepaper describes the criteria to take into account, when you are looking for an SPC software solution |
Big data, OEE and SPCIn this whitepaper we demonstrate a powerful real life example how to integrate Big data, OEE and SPC. |
Automatic SPC importIn this whitepaper we show the challenges with automatic import of data into an SPC software solution and these challenges are solved in DataLyzer. |
Operators and automated SPCIn real-time SPC systems we see more and more automated import. For example CMM data, PLC data, camera data. To get maximum results from your SPC implementation it is critical you keep your operators involved. In this whitepaper we show how you can keep operators involved when data is completely imported automatically. |
Short Run SPCIn this whitepaper we explain Short Run SPC and show how this is integrated in DataLyzer Qualis 4.0. |
SPC 4.0In this whitepaper we show that applying SPC in modern industry requires a different approach how to set control limits. 3 sigma limits should not be applied in most situations |
Integrating AI and Machine Learning in Process ControlArtificial Intelligence and Machine Learning (Further abbreviated as AI) are buzzwords that are raising a lot of interest in manufacturing. Applying AI is not easy and should not be taken lightly. In this whitepaper, we will explain how you can take the first steps in AI and what the prerequisites to applying AI are. |
SPC in Crystalline PV module manufacturingIn this white paper, we explain how you can benefit from implementing DataLyzer Qualis 4.0. Please send us a mail if you like to receive this white paper. |
For manufacturing company managers and executives, access to process and production data is key to implementing continuous improvement programs and decision makings. But they invariably face challenges with data management and data aggregation for the following reasons:
A solution that meets the following requirements can be a valuable tool for managers and executives:
The DataLyzer Import Service is designed to make data collection a smooth and uncomplicated task. See the whitepapers for more information.
How to integrate FMEA, Control Planning, SPC and CAPAThis white paper shows how you can really implement a working quality system where the techniques come together. |
SPC and SQC integratedThis white paper explains how you can combine SPC and SQC in an organization to get the best of both worlds. |
Integrating SPC and TPMThis white paper shows how you can integrate the two continuous improvement methods. |
APQP: Ballooning, Control Plan, SPCThis white paper shows how you can integrate the design results with the quality planning and the quality execution process. |
Process Capability IndicesThere 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 LimitsThis 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 LimitsThis 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 ImprovementThis white paper shows how SPC (Control Charts) can be used as a tool to support process improvement. |
Importance of the Range ChartDuring 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 SPCThis 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 TracingIn 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 dataIn this white paper, we show how advanced analysis can be used to analyze data gathered by DataLyzer. |
CMM Data acquisition and Chart creationIn this white paper, we show how a CMM can be integrated into the SPC DataLyzer network. |
Defects prevention for the Aerospace industryIn this white paper, we shed some light how the Zero Defects approach is used more and more in the Aerospace supply chain and how DataLyzer can support with this implementation |
SPC software and Industry 4.0More and more companies are looking for an SPC software tool which fits in their Industry 4.0 approach. This whitepaper describes the criteria to take into account, when you are looking for an SPC software solution |
Big data, OEE and SPCIn this whitepaper we demonstrate a powerful real life example how to integrate Big data, OEE and SPC. |
Automatic SPC importIn this whitepaper we show the challenges with automatic import of data into an SPC software solution and these challenges are solved in DataLyzer. |
Operators and automated SPCIn real-time SPC systems we see more and more automated import. For example CMM data, PLC data, camera data. To get maximum results from your SPC implementation it is critical you keep your operators involved. In this whitepaper we show how you can keep operators involved when data is completely imported automatically. |
Short Run SPCIn this whitepaper we explain Short Run SPC and show how this is integrated in DataLyzer Qualis 4.0. |
SPC 4.0In this whitepaper we show that applying SPC in modern industry requires a different approach how to set control limits. 3 sigma limits should not be applied in most situations |
Integrating AI and Machine Learning in Process ControlArtificial Intelligence and Machine Learning (Further abbreviated as AI) are buzzwords that are raising a lot of interest in manufacturing. Applying AI is not easy and should not be taken lightly. In this whitepaper, we will explain how you can take the first steps in AI and what the prerequisites to applying AI are. |
SPC in Crystalline PV module manufacturingIn this white paper, we explain how you can benefit from implementing DataLyzer Qualis 4.0. Please send us a mail if you like to receive this white paper. |
For manufacturing company managers and executives, access to process and production data is key to implementing continuous improvement programs and decision makings. But they invariably face challenges with data management and data aggregation for the following reasons:
A solution that meets the following requirements can be a valuable tool for managers and executives:
The DataLyzer Import Service is designed to make data collection a smooth and uncomplicated task. See the whitepapers for more information.
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