Variation – Why Do We Measure?

Measurements may be influenced by the equipment used to make them or may be influenced by the process of making the measurements. The Measurement System includes the equipment and the process used in making the measurements. Therefore, we should perform a Measurement System Analysis (MSA) / Measurement System Evaluation (MSE) on a periodic basis (worst case it should be performed annually). In conjunction with an MSA / MSE we should capture, characterize, and analyze a sample set of manufacturing test data on a recurring basis, based on product volume, to ensure the continuing health of the product, process, and system. This ensures the product, process, and system remains in control during process changes, engineering change implementation, part changes, etc. throughout the product life cycle.

“We don’t know what we don’t know. If we cannot express what we know in numbers, we don’t know much about it. If we don’t know much about it, we cannot control it. If we cannot control it, we are at the mercy of chance.” – Mikel Harry

“If we cannot express what we know in terms of numbers then our knowledge is of a meager and unsatisfactory kind.” – Lord Kelvin

Variation exists in everything. However,
"A fault in the interpretation of observations, seen everywhere, is to suppose that every event (defect, mistake, accident) is attributable to someone (usually the nearest at hand), or is related to some special event. The fact is that most troubles with service and production lie in the system. Sometimes the fault is indeed local, attributable to someone on the job or not on the job when he should be. We speak of faults of the system as common causes of trouble, and faults from fleeting events as special causes." - W. Edwards Deming

To be competitive we must remember that statistical techniques have played an important role in maintaining competitive position for the manufacturing industry. In order to maintain a competitive position, companies must manufacture products with almost perfect consistency and repeatability. This requires an ability to measure the variability of highly complex manufacturing processes. Capturing, characterizing, and analyzing the data on a repetitive basis will enable companies to maintain control the product, process, and system.

There are a couple of methods in which we evaluate the data on a recurring basis. The first is using an MSA / MSE which may be performed semi-annually or annually and the other is Statistical Process Control (SPC) / Numerical Evaluation of Metrics (NEM) to be performed on a recurring basis, depending on product volume. We will discuss the application of these processes individually starting with the Measurement System Analysis. We will discuss the Statistical Process Control or Numerical Evaluation of Metrics in our next article.

Objectives of a Measurement System

• Understand the data required and analysis techniques of MSA


• Use Explicore, TestSoft’s Lean Six Sigma Tool (Data Analysis Scorecard Utility), to quickly identify the significantly few parameters in need of correction or improvement.


• Review Control Chart Methods and Data.


• Understand Common and Special Cause Variation (including the application of Western Electric Rules).


• Understand How to Use Information on the Variation Attributable to the Measurement System.

We can use an MSA which is a specially designed experiment that seeks to identify the components of variation in the measurement. Just as processes that produce a product may vary, the process of obtaining measurements and data may have variation and produce defects. An MSA evaluates the test method, measuring instruments, and the entire process of obtaining measurements to ensure the integrity of data used for analysis (usually quality analysis) and to understand the implications of measurement error for decisions made about a product or process. MSA is an important element of Six Sigma methodology and of other quality management systems. MSA analyzes the collection of equipment, operations, procedures, software and personnel that affects the assignment of a number to a measurement characteristic. Processing the data through TestSoft’s product, Explicore, will save valuable resource time and money to capture, characterize, and perform the initial analysis of the data.

An MSA considers the following:

• Select the correct measurement and approach.


• Assess the measuring device.


• Assess procedures & operators.


• Assess any measurement interactions.


• Calculate the measurement uncertainty of individual measurement devices and/or measurement systems.


• MSA identifies and quantifies the different sources of variation that affect a measurement system.


• Measurement Error: Variation in measurements can be attributed to variation in the item being measured or to the measurement system itself.


• The variation in the measurement system itself is measurement error.

Common tools and techniques of Measurement Systems Analysis include: calibration studies, fixed effect Analysis of Variation (ANOVA), components of variance, Attribute Gage Study, Gage R&R, ANOVA Gage R&R, Destructive Testing Analysis and others. The tool selected is usually determined by characteristics of the measurement system itself.

MSA Elements

• The objective of an MSA/MSE is to learn as much as possible about the measurement process in a short amount of time (e.g., potential study).


• The strategy is to include equipment, operators, parts, etc. that will usually be elements of the measurement process.


• A random selection of parts representing inherent process variation from production should be made.


• The parts should be labeled in a way to record measurements and remove possible operator bias (blind marking).


• Each part will then be measured multiple times (at least twice) by each operator using the same equipment. This can be for each set of equipment.

Measurements may be influenced by the equipment used to make them or may be influenced by the process of making the measurements. The Measurement System includes the equipment and the process used in making the measurements.

As part of ISO9000:2008, the MSA is defined as an experimental and mathematical method of determining how much the variation within the measurement process contributes to overall process variability. There are five parameters to investigate in an MSA: bias, linearity, stability, repeatability and reproducibility. A general rule for measurement system acceptability is: Under 10 percent error is acceptable; 10 percent to 30 percent error suggests that the system is acceptable depending on the importance of application, cost of measurement device, cost of repair, and other factors; and over 30 percent error is considered unacceptable, and you should improve the measurement system.

Also, we need to understand the Western Electric Rules when reviewing the graphical data. In Statistical Process Control, the Western Electric Rules are decision rules for detecting "out-of-control" or non-random conditions on control charts. Locations of the observations relative to the control chart control limits (typically at ±3 standard deviations) and centerline indicate whether the process in question should be investigated for assignable causes. Their purpose was to ensure that line workers and engineers interpret control charts in a uniform way.

In addition to percent error and the Western Electric Rules, you should also review graphical analysis over time to decide on the acceptability of a measurement system.

Basic Parameters of the MSA

Any measurement process for a system typically involves measurement precision as well as measurement accuracy of the system variables subject to the constraints of the system. Requirement for statistically analyzing a system would involve a process to determine the variations from the mean (central) location which is imperative to analyze the measurement accuracy taking into consideration factors of bias, stability and linearity.

The parameters of MSA can be described as follows:

Bias refers to a probability of presence of certain factors in a system which can influence deviation from the standards in the system. Bias can lead to sampling of a data which on analysis appear to be different from the actual or anticipated data set. In order to measure the process measurement bias, for determinate measurement a process called calibration is needed which is of higher level than measuring the data average. In case of indeterminate measurement process owing to constraints, normally the data average values are compared with the standard values. In other words the Bias Effects is an average of measurements that are different by a fixed amount. Bias effects include:

• Operator bias - different operators get detectably different averages for the same thing.

• Machine bias - different machines get detectably different averages for the same thing, etc.


• Others - day to day (environment), fixtures, customer and supplier (sites).

Stability refers to processes which are normally free from special cause variations. Analyzing a system for stability typical involve the standard statistical processes such as SPC (Statistical Process Control), scatter plots, ANOVA techniques and other standard deviation measurement tools. Determination of stability standards in a system requires data sampled to cover a wide range of possible variation factors and intensive piece meal statistical tests covering variations in human resources, tools, parts, time, space and location factors.

Linearity refers to different statistical results from measurements when subjected to different metric spaces. Linearity in a system is determined using higher levels of calibrations in measurement standards which often guided by inferences drawn from various interaction factors influencing a system. For instance, a non linearity in a system may result from equipment (or tools) not calibrated for various levels of operating range or poor design of system or any other system constraint.

Discrimination is the ability of the measurement system to adequately differentiate between values of a measured parameter.

Accuracy or instrument accuracy is the difference between the observed average value of measurements and the master value. The master value is an accepted, traceable reference standard (e.g., NIST).

Variable Measurement Systems: Repeatability, Reproducibility

For measurement systems that result in quantitative measurements such as weight, concentration, or strength, it is important to determine the magnitude of any error in the resulting measurements. If the error is large, it may be impossible to determine whether or not an individual sample is within spec. In addition, designed experiments rely on the ability to separate real effects of making changes from the background noise and could be sabotaged by an inadequate measurement system.

When quantifying measurement error, it is common to separate the error:

• Repeatability (or measurement precision – the error due to the instrument or measurement procedure) and


• Reproducibility (the difference in the average of the measurements made by different persons using the same or different instrument when measuring the identical characteristic).

Attribute Measurement Systems

When the results of a measurement system are PASS or FAIL rather than a quantitative value, special procedures are necessary. There are three procedures to deal with such systems: risk analysis method, signal theory method, and analytic method. In the risk analysis method, multiple appraisers measure samples with known characteristics. Statistics are calculated based on how often the appraisers correctly characterize each sample and how frequently they agree with themselves and each other.

Assessing a Measurement Error

This can be evaluated by comparing the width of the control limits (average chart) with the spread of the plotted points (product variation).

• If all of the points fall within the control limits of the average chart, the measurement variation will over-shadow any product variation. Thus, any improvements to the production process may be undetectable due to extreme measurement error.


• If less than half of the average points are outside of the control limits, the measurement system may be inadequate for detecting product variation.


• If less than one fourth of the average points are inside the control limits, the measurement system is capable of assessing product parameters.

In Summary

• Pick the measurement system to be evaluated


• Map the process


• Conduct data collection rigorously and use Explicore to quickly identify where to put resources to fix problem areas


• Think about the measurement process when drawing conclusions from the MSA/MSE using TestSoft’s product, Explicore. Remember, Explicore identifies the problems quickly up-front and gives a company the ability to correct problems before they become very costly to fix.


• Pay attention to the likely cause of measurement variation


• Implement the countermeasures


• Hold the gains: Control and improve the Measurement System


• Continually capture, characterize, and analyze the Product, Process, and System health to ensure the gains remain in control. The frequency of the evaluation depends on product volume.

A Lean Six Sigma Strategy

Six Sigma (6σ) is a business management strategy originally developed by Motorola. As of 2009, it enjoys widespread application in many sectors of industry, although its application is not without controversy. Six Sigma seeks to improve the quality of process outputs by identifying and removing the causes of defects (errors) and variability in manufacturing and business processes. It uses a set of quality management methods, including statistical methods, and creates a special infrastructure of people within the organization (Black Belts, Green Belts, etc.) who are experts in these methods. Each Six Sigma project carried out within an organization follows a defined sequence of steps and has quantified targets. These targets can be financial (cost reduction or profit increase) or whatever is critical to the customer of that process (yield, cycle time, safety, delivery, etc.). The Six Sigma methodologies include, but are not limited to: DMAIC, RDMAIC, and DMADV. We will explore the DMAIC Six Sigma methodology.

No matter how you approach deploying improvement teams in your organization, they will all need to know what is expected of them. That is where having a standard improvement model such as DMAIC (Define-Measure-Analyze-Improve-Control) is extremely helpful. It provides teams with a roadmap. DMAIC is a structured, disciplined, rigorous approach to process improvement consisting of the five phases, where each phase is linked logically to the previous phase as well as to the next phase:

• Define the problem, the voice of the customer, and the project goals, specifically.
• Measure key aspects of the current process and collect relevant data.
• Analyze the data to investigate and verify cause-and-effect relationships. Determine what the relationships are, and attempt to ensure that all factors have been considered. Seek out root cause of the defect under investigation.
• Improve or optimize the current process based upon data analysis using techniques such as design of experiments, poka yoke or mistake proofing, and standard work to create a new, future state process. Set up pilot runs to establish process capability.
• Control the future state process to ensure that any deviations from target are corrected before they result in defects. Control systems are implemented such as statistical process control, production boards, and visual workplaces and the process is continuously monitored.

There are many resources that describe the DMAIC process. Our purpose here is to focus on special considerations for using the Lean Six Sigma DMAIC process in a manufacturing environment, including TestSoft’s scorecard utility, Explicore, that is particularly helpful to root out areas in need of improvement or correction.

The root of both Lean and Six Sigma reach back to the time when the greatest pressure for quality and speed were on manufacturing. Lean rose as a method for optimizing automotive manufacturing; Six Sigma evolved as a quality initiative to eliminate defects by reducing variation in processes in the semiconductor industry. It is not surprising that the earliest adopters of Lean Six Sigma arose in the service support functions of manufacturing organizations like GE Capital, Caterpillar Finance, and Lockheed Martin.

A Key Concept: In short, what sets Lean Six Sigma apart from its individual components is the recognition that you cannot do "just quality" or "just speed," you need the balanced process that can help an organization to focus on improving product, process, system, and service quality as defined by the customer within a set time limit.

Lean Six Sigma is a business improvement methodology that maximizes shareholder value by achieving the fastest rate of improvement in customer satisfaction, cost, quality, process speed, and invested capital. The fusion of Lean and Six Sigma improvement methods is required because:

• Lean cannot bring a process under statistical control
• Six Sigma alone cannot dramatically improve process speed or reduce invested capital
• Both enable the reduction of the cost of complexity

Ironically, Six Sigma and Lean have often been regarded as rival initiatives. Lean enthusiasts note that Six Sigma pays little attention to anything related to speed and flow, while Six Sigma supporters point out that Lean fails to address key concepts like customer needs and variation. Both sides are right. Yet these arguments are more often used to advocate choosing one over the other, rather than to support the more logical conclusion that we blend Lean and Six Sigma since they complement one another. Here is a quick overview:

Six Sigma:

• Emphasizes the need to recognize opportunities and eliminate defects as defined by customers
• Recognizes that variation hinders our ability to reliably deliver high quality services
• Requires data driven decisions and incorporates a comprehensive set of quality tools under a powerful framework for effective problem solving
• Provides a highly prescriptive cultural infrastructure effective in obtaining sustainable results
• When implemented correctly, promises and delivers $500,000+ of improved operating profit per Black Belt per year (a hard dollar figure many companies consistently achieve)

Lean:

• Focuses on maximizing process velocity
• Provides tools for analyzing process flow and delay times at each activity in a process
• Centers on the separation of "value-added" from "non-value-added" work with tools to eliminate the root causes of non-valued activities and their cost
  
  The 8 types of waste / non-value added work
• Wasted human talent – Damage to people
• Defects – "Stuff" that’s not right & needs fixing
• Overproduction – "Stuff" too much/too early
• Transportation – Moving people & "Stuff"
• Waiting Time – People waiting for "Stuff" to arrive
• Inventory - "Stuff" waiting to be worked
• Motion – Unnecessary human movement
• Processing Waste – "Stuff" we have to do that doesn’t add value to the product or service we are supposed to be producing.
• Provides a means for quantifying and eliminating the cost of complexity

The two methodologies interact and reinforce one another, such that percentage gains in Return on Investment Capital (ROIC%) are much faster if Lean and Six Sigma are implemented together.

In short, what sets Lean Six Sigma apart from its individual components is the recognition that you cannot do "just quality" or "just speed," you need a balanced process that can help an organization focus on improving service quality, as defined by the customer within a set time limit.

Within the individual phases of a DMAIC or DMADV project, Six Sigma utilizes many established quality-management tools that are also used outside of Six Sigma. This is where TestSoft’s product, Explicore, is able to help. Explicore is a Lean Six Sigma data analysis scorecard utility that combines the use of Lean and Six Sigma.

Let’s define what Explicore, a Data Analysis Scorecard Utility, does for the organization. Explicore is a patented software solution that has been created and refined since 1998. It enables companies to test the robustness of their manufacturing and design processes. It takes all parameters related to a product, process, or system and within minutes Explicore identifies the parameters in need of correction or improvement. The output of Explicore is a statistically based report that identifies the Key Process Indicators (KPIs) so a company can quickly identify where to put their resources to correct problem areas.

Explicore will aid in tying Lean and Six Sigma together through the use of the tool. From a Six Sigma perspective, Explicore identifies which parameters require improvement or correction. From a Lean perspective, it will identify the defects quickly and will help in the identification of waste. For example, a company started shipping a product after a design change was installed. The product went through production without too much of an issue. However, several customers complained that the product did not function properly and the product was sent back to the factory. Production was stopped and we started the investigation of the customer complaints. We deployed Explicore and discovered there was a number of design and test related issues. Our discovery found three major issues with the design and several measurement problems in test. We were able to re-design the product, correct the test problems, and made improvement adjustments in production. The results were significant. The mean time between failures increased from ten hours to more than ten thousand hours, the first pass yield improved from sixty seven percent (67%) to ninety three percent (93%), and the warranty issues decreased from $9,300,000 to $600,000 year-over-year. The production line went through the lean process and reduced cycle-time from 48 hours to 18 hours.

Intuitively, we know that Explicore saves a company resource time, cost, and improve product reliability. We realize that a customer will achieve quick results with TestSoft’s balanced approach which helps protect your investment. We believe Explicore is an excellent Lean Six Sigma tool that should be in your toolkit.

Beyond Failure Analysis

In any product development or manufacturing environment, failures are bound to occur. However, failures would be less frequent if the engineers were to characterize the product, process, and system before problems occurred. Our product, Explicore, is a data analysis scorecard utility that provides an automated data capture, characterization, and analysis on various types of data. As you will see, the product was extremely useful in resolving engineering issues.

In many cases, engineers may not always know the problems they are facing in manufacturing when failures occur. The engineers must perform painstaking analysis to get to root cause. Effective analysis is tedious and difficulty arises when engineers have to resolve these problems quickly. Here are three examples of failures that went drastically wrong.

Example 1: a company fielded a product with anticipation that the product would be highly successful. Unfortunately, the product operated less than 10 hours before it failed and many customers quickly returned the product. The engineers had to scramble to resolve the technical issues. They were not successful identifying the problems and did not know where to begin. The engineers felt considerable pain from management when they were unable to resolve the issues and the engineering groups started blaming each other. Engineering deployed Explicore to characterize and analyze all the parameters from functional test and Explicore immediately highlighted the problems; engineering corrected the design and the improved product was once again fielded. The cost of the product re-design was significant in terms of money, engineering resources, emotions, and customer loyalty. Based on the outcome of the product characterization and analysis, the organization predicted the mean time between failures (MTBF) to be 10,000 hours and later discovered the re-designed product exceeded this prediction. Customers were highly satisfied with the product, but it took a long time to re-gain the customer’s loyalty and trust. This characterization and analysis resulted in an annual warranty savings of nearly $9,000,000 year-over-year.

Example 2: a company completed a new design that was put into the initial production phase. The yield was an abysmal 10%. All the engineering groups assembled on the production floor to determine the root cause of the issues and were soon blaming each other for the poor yield. The customer was furious and informed the company to supply a down revision product until the issues were resolved and then the company would replace the down revision product with the new one. The engineering group ran the test data through Explicore which immediately characterized and analyzed all the parameters to determine what exactly was happening with the product. The top issues were uncovered quickly. The RF Circuits were found to be the major problem and design engineering redesigned the circuits. The updated design was put into a preliminary production run and the test data went back through Explicore and although there were minor issues with a few parameters, the product yield increased to 86%. The company sent the newly designed product to the customer and had to replace more than 500 units at a cost to the company of approximately $32,000,000. The company lost credibility with the customer and almost lost the account. It has taken a long time to re-gain the customer’s loyalty and trust.

Example 3: a company was to provide a combined technology product to a major customer. During the initial production run, the yield was a mere 15% and management and engineering attributed it to new processes being deployed in manufacturing. All the engineering groups were assembled to determine the problem and it became the ‘blame game’. It wasn’t a pretty sight with all the emotional upheaval. Engineering groups struggled to locate the root cause and were perplexed as to why the product did not work. Quality engineering used Explicore to characterize and analyze all the test data parameters to determine the problematic parameters. They uncovered design related issues as well as some potential test issues. The test engineers ran a Measurement System Evaluation (Gage R&R) on the test system and discovered there were issues with the test process (later discovery found issues with both the software and the hardware). Design had to scramble to get to the root cause of their issues and test engineering had to redo many tests. These efforts resulted in a 3 month delay in product launch. Finally, the yield increased to 92% with the redesign of the product and updates to test system. The company had the customer shut down and this delay cost the company thousands if not millions of dollars due to the delay – the penalty for being late. The estimated loss for the company was about $18,000,000 (in penalties and lost revenue). This little faux pas took the company a long time to re-gain the customer’s loyalty and trust.

If the engineers were to perform this characterization up-front in both design and production, there is a high likelihood that the amount of time spent on failures would have been minimized. The ideal situation is to generate a characterization baseline in the design process and manufacturing for the product, process, and system, which consists of capturing data, characterizing that data, and performing a preliminary analysis on the data. A baseline may be created manually which is extremely time consuming or automatically which literally only takes minutes to perform. Explicore is an excellent product to provide the automated data capture, characterization, and analysis. The product simplifies the process and provides a baseline characterization which leads to better visibility when a failure occurs. In addition, the engineers should periodically capture, characterize, and analyze their test data to continually monitor the health of the product, process, and system.

Engineers cannot learn from failures if they do not discuss and analyze them. However, the analysis needs to be thoughtful, fully discussed, and insightful. We know and understand that failure analysis is extremely important, but very tedious to carry out. Ask any engineer, examiner of an airliner crash, or a medical practitioner about failed systems. Unless deeper analysis of the nature of the error(s) is conducted, it is difficult to ascertain what needs to be corrected. Effective analysis of a failure is found in the meticulous and painstaking analysis that goes into understanding the problem. For example, hundreds of hours may go into gathering and analyzing data to sort out exactly what happened and what is learned from the analysis. When failure analysis consumes engineering, it is like a plague. It paralyzes the company, its people, and may stifle growth even if it is short in duration. Analysis can only be effective if the engineer speaks openly about what they know that enables a new understanding of what happened, especially when the engineer has characterized the product, process, and system. Typically engineers address only significant failures, rather than identifying and learning from all failures. We understand that failure analysis takes precious time and resources to address failures. We also realize that individuals will typically experience negative emotions when examining failures, so analysis of a failure requires openness, patience, and acceptance of ambiguity.

However, failure analysis is not enough. We need to consider doing something before the failures create larger problems. There are alternatives to preclude the need for failure analysis. One alternative is to use the automated process Explicore, a data analysis scorecard utility. Explicore is a utility that reviews all data for a product, process, and system, and statistically determines which parameter(s) need improvement. Here are some questions to consider. What would life be like if engineers were able to understand their product more fully before it is launched? Would it help people to glean a comprehensive understanding of the product, process, and system before a failure occurred? Would it be helpful if the product, process, and system were characterized (baselined) before failures were to occur? Would the company then be able to concentrate more on growth and profitability?

Failures would be less frequent if the engineers were able to understand the nature of the problems before they occurred. The best alternative is to characterize the entire data set early in the life cycle. This becomes a 3-pronged approach: identify areas of product improvement during the design and development phase, baseline the manufacturing process; and baseline the system. Note that this process may be applied during any portion of the life cycle to baseline and periodically monitor them to determine the health and integrity of a product, process, and system. The data provides in-depth information about product design, manufacturing, test, and service processes. It simply leads to improving the product and process development cycle, and the manufacturing cycle faster through this continuous improvement effort. This applies to the service industry as well – they will be better prepared when things go wrong.

This idea of generating a baseline and periodically monitoring the product, process, and system ensures that continuous improvement will ultimately decrease the overall cost of product ownership (design, manufacturing, service, and warranty cost). Explicore provides the automated method to generate a solid baseline for data capture, characterization and analysis, and this baseline will provide more insight when discussing, analyzing, and applying failure analysis. Management and engineering would minimize potential negative emotions since they will have a higher level of confidence in the product, process, and system of the product. In addition, a periodic analysis, based on product volume, will continually monitor the product, process, and system to validate their health, especially when any changes occur (i.e. design changes, process changes, or test data changes).

This recurring data capture, characterization, and analysis helps to reduce the cost of failure analysis if continuous improvement actions are implemented during the entire life cycle. Note that a product, process, or system does not need to fail to be improved. A product, process, or system that is within the specification may be in need of improvement if the data is skewed (where the predicted yield is low).

The benefit of going beyond failure analysis is clearly a cost-benefit decision of the company, but it becomes a pay-me-now or pay-me-later decision. The pay-me-now decision may cost a little more up-front, but the benefits of performing a characterization baseline far outweigh the cost of warranty, lost customer loyalty and trust, or simply the loss of a customer. The pay-me-later decision can be very costly since the product is already fielded with significant warranty implications attached to it. These implications include embarrassment, loss of customer loyalty, potentially the loss of a customer, and the expense of redesign and fielding product upgrades, etc.

In addition to the technical aspects of periodic data capture, characterization, and analysis, improving the product, process, and system has important organizational benefits. First, the data provides an opportunity for others not involved in the data process to learn from it. Second, others may bring new perspectives and insights that further improve the product, process, and/or system (this applies to service as well) which help to counteract self-serving biases that may alter perceptions of those directly involved. With failures, the tendency is for people to blame others or forces beyond their control. If this tendency goes unchecked, it reduces the ability for the engineers to effectively learn. Explicore helps to glean an in-depth understanding of the product, process, and system which minimizes the emotional impact on a company.

Lastly, the value of learning from utilizing the data may prevent simple mistakes from being overlooked prior to launching a product or service. Many scientific discoveries have resulted from attentiveness to detail including the simple mistakes that are often overlooked. With attention to minor details that Explicore brings to the table, the fielded product, service, or process will have minimized the possibility for failure. Ultimately, if engineering were to use Explicore earlier in the life cycle and learn from it, then they may be able to preclude failures that could otherwise occur without the data characterization process being invoked.

Please visit TestSoft’s website www.testsoftinc.com to learn more about Explicore. We would like to show you first-hand why TestSoft stands above the competition. It would be our pleasure to demonstrate TestSoft’s capabilities and show you how the unique features of Explicore – a Data Analysis Scorecard Utility would benefit you.

Automating a Manual Process

Given the competitive nature of corporate business, companies constantly seek the most effective way to improve productivity and maximize their monetary gain. Consequently, companies are demanding more control over their processes. The need to get products to market on-time is crucial and trying to get this accomplished with limited time and resources makes it imperative to automate processes whenever and wherever you can. One of the biggest challenges for businesses today is the fact that it takes too long to get data analysis performed manually. Monitoring and validating the product, process, and system health is extremely important if you want to keep your products flowing into their respective markets. This is difficult when data capture, characterization, and analysis methods are processed manually. The manual methods take too long to determine the root cause of a problem and can be prone to error.


TestSoft’s software product, Explicore, serves as the bridge that joins technology with business, streamlines workforce effort, reduces task time, minimizes process variation, and provides more predictable results. Explicore in conjunction with TestSoft’s patented process effectively and efficiently captures, characterizes, and analyzes all data automatically enhancing accountability. The product uncovers significant defects that cause rework, manufacturing timing, and/or delays in development. It is vitally important to remember that data parameters do not need to fail to require improvement.


Explicore has the ability to harvest vast amounts of data automatically (in minutes) to yield immediate results. It rapidly determines which parameters within your data set require improvement as well as effectively and consistently evaluating your product, process, and system health. Evaluations should be conducted periodically and based on the volume of output for a given product thereby continuously validating the integrity of product, process, and system. Explicore will also help you improve the window of opportunity for product release from design – it helps to establish the product health initially. Using TestSoft’s Explicore will make your business more robust and profitable.


Essentially, as it relates to business performance, Explicore identifies the problems that prevent products from being delivered to their respective markets on-time. TestSoft’s patented process and product will allow you to visually view process gains as well as eliminate costly and error-prone manual methods. People and systems, as process participants, can now perform to unprecedented levels of control and predictability using Explicore. Sustainable improvements that previously took an extended period of time to accomplish are possible more efficiently and effectively through the integration of Explicore and Six Sigma. The results of aligning Explicore and Six Sigma are a significant indicator of what businesses could accomplish to contend as a legitimate competitor in their industry.


A leading Fortune 100 firm committed to improving their business by instituting Six Sigma process efforts in specific departments across the company. They expanded Six Sigma training and used the methodology to reduce errors in existing processes. As a result, the company increased efficiency, reduced costs and created revenue-producing opportunities. Now, in a strategic effort to further optimize the business processes by aligning them with enterprise objectives and integrating them with other processes across the company, they needed more than just Six Sigma. The company needed a tool that would augment the principles of Six Sigma to provide an enterprise view of processes and more direct control and manipulation over improvements to those processes. They turned to Explicore.


Explicore will help to dynamically align processes across an enterprise while using technologies to provide visibility and management at any point in the product or process lifecycle. Imagine the possibilities to significantly reduce product, process, and system down-time by rapidly identifying problematic areas. Explicore roots out problems on an average of 2,000 times faster than manual methods. For example, we worked on a 3300+ parameter product manually that took 12 weeks to complete. Running the same product through Explicore took less than 5 minutes to capture, characterize, and analyze the data. In another example, we saved an $88,000,000 company $8,700,000 in warranty cost since we were able to identify root cause efficiently and effectively saving precious resources months of analysis.


TestSoft’s ‘hassle-free’ solution helps reduce the total cost of product ownership, protects your resource investment, and Explicore will give you peace of mind. Together we can make it happen.