DataLyzer
Whitepapers
DataLyzer provides informational whitepapers to promote or highlight the features of a solution, product or service.
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, OEE and TPMIntegrating SPC and OEE in one improvement approach and using an integrated software solution has advantages: – Productivity and quality will be equally important, and the company will truly benefit if both are improved. – The methodology for continuous improvement will be accepted faster when both methods are integrated- and supported by one approach and an integrated software solution. – When companies use both methods, time required for training, system support and system maintenance is reduced. This white paper explains the differences between the techniques and shows the advantages of integrating the continuous improvement methods. |
Big data and Statistical Process ControlThis white paper shows how statistical process control should be applied in combination with big data. |
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. |
APQP: Ballooning Control Plan SPCThis white paper shows how you can integrate the design results with the quality planning and the quality execution process. |
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 Business Case for APQP Implementation in the Aerospace IndustryThe Aerospace industry is rapidly adopting a Zero Defects approach throughout its supply chain. Achieving Zero Defects cannot be done successfully with current quality systems. New approaches, like those developed in automotive and semiconductor manufacturing will need to be adopted. |
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 show how Big data, OEE and SPC are seamlessly integrated in DataLyzer. |
Optimizing automated SPC data collectionIn this whitepaper we show how DataLyzer supports and automated data collection requirement. |
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. |
The (R)evolution of SPCBecause of more and more automatic import of data the role of the operators is changing in a way it is not desired. In this white paper, we will explain how you can make sure the operator keeps playing they key central role in the Statistical Process Control process. |
Cost of qualityThis white paper aims to provide an overview into the Cost of Quality and to discuss ways to control the cost of quality. |
SPC in Crystalline PV module manufacturingIn 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. |
Implementing OEE using DataLyzer Spectrum PDFThis white paper shows how you can start with OEE implementation and how you can use the DataLyzer SPC module to record the data, perform the calculations and create reports. |
Big data, OEE and SPCIn this whitepaper we show how Big data, OEE and SPC are seamlessly integrated in DdataLyzer. |
Integrating SPC, OEE and TPMIntegrating SPC and OEE in one improvement approach and using an integrated software solution has advantages: This white paper explains the differences between the techniques and shows the advantages of integrating the continuous improvement methods. |
Introduction to Risk AnalysisIn the concept version of ISO 9000:2015 risk analysis will be a requirement. This white paper describes an introduction to risk analysis and FMEA, the history of FMEA, how to get started with FMEA and the layout for IATF 16949. |
Managing FMEA’sWhat is required to make your FMEA program a success? What do you need to prepare before starting with FMEA’s and what do you need to do per individual FMEA. |
FMEA ClassificationHow are is priority for improvement established and how are classification symbols and special characteristics used in FMEA? Different methods will be discussed for prioritizing: Risk Priority Matrix, RPN, Action Priority, Classification Symbols. |
Is Excel the right tool for FMEA?It seems at first but there are so many specific requirements which are very complicated to implement in Excel, that Excel is hardly the right tool. |
FMEA and HACCP: A comparisonIn industries, FMEA is used. In Food industry HACCP is used. What are the similarities and differences and what would be the advantages for the Food industries to apply FMEA. |
FMEA and Cost EffectivenessWhat problems do we fix, how much do they cost us? What are the current costs of customer returns? Does the FMEA affect the bottom line or the top line? How can FMEA generate new business? |
Capturing Process Knowledge using Reference FMEAsHow can knowledge be stored in FMEA’s and reused with new product and process development. |
Improve Reference FMEAsTo get the maximum result applying reference FMEAs you need an change the method normally used. |
Zero defects AS13100Aerospace Standard AS13100: A Sprint Toward Competitive High Ground |
DataLyzer FMEA in different industriesThis whitepaper shows how DataLyzer FMEA can be used throughout numerous different industries. It also highlights several features that have been added to improve the functionality of DataLyzer FMEA. |
Reverse FMEA, a practical approachThis whitepaper shows how reverse FMEA can be implemented in a practical way using the DataLyzer software suite. |
RM13004, a practical approachThis whitepaper shows how reverse FMEA can be implemented in a practical way using the DataLyzer software suite. |
Calibration and MSA – A practical approach to implementation –An important step which is often overlooked is the measurement systems analysis (MSA) step. In principle it is a very easy and logical step. Skipping this step can result in costly mistakes and loss of time spent on root cause analysis. In this document DataLyzer will give you some brief introduction in calibration and MSA and provide some guidelines how you can easily implement calibration and measurement systems analysis. |
Calibration and Control ChartsMost companies are aware of the power of control charts for process improvement. In most quality requirements, companies are required to perform calibration and even statistical process control. In the laboratory guidelines for determining calibration intervals ILAC-G24, it is even given as one of the possible methods to establish the calibration interval but there is hardly any information available how control charts can be used in improving the calibration process. In this whitepaper we will explain how calibration and SPC can be integrated. |
Attribute MSA studyMSA studies are well known nowadays in industry. But when we talk about MSA studies we are mostly referring to Gage R&R studies. |