Using Statistical Process Control to analyse web activity

Evaluating web site activity is tricky business. Because web activity varies constantly, the challenge is to separate everyday random variation from the non-random changes due to marketing programs, web site design, or search engine rank.

Failure to recognise the sources of variation correctly can result in one of two errors:

1) Random variation in activity is ‘explained’ by a change in marketing or the web site when no “real” change in activity occurred.

2) A real change in activity is actually ignored.

Either of the two above errors wastes resources and business opportunities.

Enter Statistical Process Control. SPC charts were specifically invented to separate the two sources of variation. While control charts have been most commonly used in manufacturing processes, they ‘work’ with virtually any time series. Additionally, they are easy to understand and use. On a control chart, upper and lower control limits are automatically calculated to separate common variation from special variation. Points inside the control limits are due to common variation. Points outside the limits are due to special causes. To make the charts even more sensitive to small, sustained shifts in level, ‘pattern rules’ are also used. An example rule is, “Eight consecutive points above the centre line signals a special cause.”

If a shift in level is identified, a separate set of control limits can be applied to pre- and post-shift data.

As an example, web site traffic can be analysed by charting the weekly number of web sessions on a company web site. Every week, the count of sessions where three or more pages were hit is charted. Our SPC software product, NWA Quality Analyst, automatically extracts the data from a SQL Server database of web logs and charts the data. All the user has to do is enter the desired date range.

Such charts can help attribute variation in website traffic to the correct cause. SPC can help to…

1) Measure the effect of marketing campaigns and web adverts on web traffic

2) Illustrate the effect on web traffic of changes in web site navigation

3) Identify the most popular online articles

NorthWest Analytical Statistical Process Control software is supplied and supported in the UK and Ireland by Adept Scientific plc, Amor Way, Letchworth, Herts. SG6 1ZA; telephone +44 (0) 203 695 7810, fax +44 (0) 203 695 7819, email; or see Adept’s World Wide Web site Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

With offices in the UK, USA, Germany and throughout the Nordic region, Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

NWA Quality Analyst keeps Willamette in control

Northwest Analytical’s Quality Analyst allows Willamette’s Wilsonville Lab to quickly and easily analyse product data. Statistical quality control (SQC) is used throughout the company to assure customers that specifications are being met, to make operations more efficient, and even to reduce waste. The key to achieving these benefits with SQC is reducing variation, both in the manufacturing process and in the finished product.

Willamette’s West Coast Development Lab in Wilsonville, Oregon, is a key player in the company’s quality program. The lab performs testing and analysis for two paper mills, eight corrugating plants, one finishing plant, and two bag plants for the company’s Western Region. In addition to testing finished paper and products at the lab, the staff also does in-plant quality audits, serves as a consulting group for process capability analysis and improvements, performs field evaluations at Willamette customer sites, and does annual supplier certifications. Because the lab is not connected with any one Willamette plant, it generally is viewed by both customers and the plants as a neutral authority.

The Wilsonville Lab’s success is due to its rigorous adherence to testing standards and effective analysis. In addition to gauges and testing machines, the lab relies on NWA Quality Analyst (QA), an off-line statistical software package made by Northwest Analytical, Inc. The software enables Willamette to quickly and easily analyse data, as well as produce high-quality charts that enable both company employees and customers to see and interpret test results. Many of the plants also use this same software for statistical process control.

To produce reliable and convincing analysis, the lab must control variation in its own methods, ensuring gauges are correct and maintaining standard environmental conditions. The goal is to spot any problems early and correct them before they create a shutdown situation. To achieve this, the lab collects temperature and humidity data three times each day and analyses it with NWA Quality Analyst, which automatically produces x-bar and range control charts. The control charts graphically show the extent to which key variables are in or out of statistical control. With Quality Analyst, the lab technicians do not have to calculate the upper and lower control limits themselves; the software does this automatically based on the data entered. Lab technicians also look for trends, so where the measurements start trending toward either of the limits, the technicians can clearly see something is starting to go wrong and can make changes before the situation becomes serious.

The Wilsonville Lab’s use of statistical methods plays an important role in reassuring Willamette’s customers that the containers they purchase meet specifications. The lab tests the paper and containers. After collecting the data, the lab uses NWA Quality Analyst to perform a variety of statistical analyses to make sure that the product is being made to internal and/or customer specifications. Once assured the measurements being taken are in control, the technicians can perform a process capability analysis to see whether the product meets specs. The technicians also may perform Pareto analysis, which charts defect rates and shows types of defects by frequency of occurrence or cost. When the results of these tests reveal the product is not meeting specifications or there is a trend towards unacceptable variability, the lab teams up with plant supervisors and operators to solve the problem. Any product that does not meet specifications is scrapped. In-lab testing also plays a vital role in determining whether new product or customer specifications can be met. Before mass production begins, the lab acts as Willamette’s own supplier certification tester, making sure that quality is maintained before it ever becomes an issue for the customer.

While Willamette uses in-lab analysis to detect whether finished product meets specifications, the lab also uses statistical methods to detect process variation that eventually leads to non-conforming product. Several times a year, Wilsonville Lab technicians visit and audit manufacturing facilities. Measuring key process variables, they analyse the data on-site, work with the operators to adjust the process, and then regraph to see whether variation is reduced and/or output is improved. Audits also provide plant management with information needed to make larger process changes.

Plant managers support the lab’s work because it helps them improve customer relationships. Their analysis and reporting allow the plants to show their customers they are capable of meeting specifications. Also, it helps the plants to be more cost-effective and efficient by improving processes and reducing waste.

Occasionally the lab also goes to customer facilities to do analysis. If a customer is having a problem with a corrugated product, the lab can help determine whether the problem is with the product, environmental conditions, or the client’s process. Then the Willamette team works with the customer to make improvements. Maintaining good relationships with customers is the key to success, and Willamette’s West Coast Development Lab plays an important role in that relationship. The lab’s stringent testing and analysis, presented in NWA QA charts, provides convincing proof that customer specifications are being met. The lab also provides important independent feedback to Willamette’s manufacturing facilities, helping them to improve processes, meet specifications and reduce waste. In an industry that is being driven by customer change, Willamette’s philosophy is to get ahead of that change, continuously improving the company’s ability to meet tight specifications before customers ask for them.

Quality Analyst software is supplied and supported in the UK and Ireland by Adept Scientific plc, Amor Way, Letchworth, Herts. SG6 1ZA; telephone +44 (0) 203 695 7810, fax +44 (0) 203 695 7819, email; or see Adept’s World Wide Web site Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

With offices in the UK, USA, Germany and throughout the Nordic region, Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

SQC Software in the Food Processing Industries

Statistical quality control is a necessary part of modern food processing. The software chosen to satisfy basic food processing SQC needs will determine whether SQC is an awkward, intrusive task or a smoothly operating part of the process. It must not only collect quality data and produce control charts, but also provide those additional capabilities which make it the core of a well run and effective quality system.

The successful implementation of Statistical Quality Control (SQC) begins with the selection of the tools and methods best suited to the company’s quality goals. Because manual charting can be burdensome and time-consuming, PC-based SQC using specialised software is preferable for routine charting and essential for process improvement studies.

Numerous PC-based SQC software packages are readily available. Most, however, were created for discrete manufacturing such as auto parts machining, and consequently are limited in their application for other manufacturers. Food processors evaluating SQC software need to be aware of these shortcomings when making their selection. They need to ask themselves:

Q: Can the software handle both process and laboratory data? Will I be able to select one package to meet the needs of all users?
Q: Can descriptive, measurement, and defect data be viewed in and analysed from the same data file?
Q: Can routine charting tasks be automated to reduce training time?
Q: Is unattended operation possible?
Q: Can charts be configured to precisely meet internal QC needs and still meet customer and regulatory reporting requirements?
Q: Can the software easily collect process data?
Q: Can it accept instrument data?
Q: Can it share or exchange data with corporate or plantwide information systems?
Q: Are the software developers knowledgeable about the issues and special requirements of the food processing industry?

Northwest Analytical, Inc. (NWA) made the needs of the food processing industry a special focus. Because NWA’s development staff understands the needs and challenges faced in implementing SQC in the industry, NWA Quality Analyst is now a world leader in SQC software for food processors. Today, NWA Quality Analyst is used by small independents as well as major multinationals. Their applications range from internal QC and process improvement to vendor certification and regulatory compliance.

Our feature case study looks at a large regional food processor that uses NWA Quality Analyst to monitor quality in their dill pickle packing line. Finished jars of pickles are pulled from the production line for routine data collection and charting. Samples are then drained, weighed, and inspected for defects. Description variables and data as shown below are entered into a Quality Analyst data file. The description variables are used to label routine SQC charts and provide easy reference points for later process improvement studies.

Description variables are as follows: sampling date, stock pickle size being packed and Lot code. Measurement variables are: weight and drained weight of pickles. Defects and counts include: number of pickles per jar,  nubs, crooks, misshapen, broken, mechanical damage, rot, shrivelled, dirty, scarred, incorrect sizing, and hollow.

Note that all the above information is collected at the same time and entered into a single Quality Analyst data set. Charting can then be launched from the data entry screen with push-button ease. In fact, those abilities were significant factors in the processor’s selection of NWA Quality Analyst.

NWA Quality Analyst lets you enter any combination of variable and attribute data into a single file. Charts can be launched from the data editor with a single mouse click.

The full value of Quality Analyst became apparent when the company considered alternate solutions to a potential supply shortage. The company’s SOP (Standard Operating Procedure) for its 46-ounce (filled weight) jar required a “3A” pickle size (1-1/8 in. to 1-1/4 in.) When supplies ran low, the limitation forced a choice between buying more expensive 3A pickles on the open market or changing the SOP to allow use of another stock size, “3B,” 1-1/4 in. to 1-3/8 in. If the weight specification could be maintained, the alternate size would be acceptable. To find out, the company made a trial run using 3Bs. Once again, all data needed to analyse 3A and 3B stock could be entered into a single data file.

The apparent success or failure of using 3B stock would be indicated in a process capability histogram, a chart showing the distribution of pickle weights and their relationship to specifications. First, however, the weights must be analysed using a control chart to verify the packing process was in statistical control.

Labelling regulations allow up to 20 percent variation from the target. The Cpk index, a commonly used numeric representation of the capability of a process, shows both stocks meet production requirements. However, process capability doesn’t always tell the whole story. Another view of the data suggests further analysis is in order. NWA Quality Analyst allows users to easily examine their processes from a variety of perspectives. A routine review of defects using Pareto analysis finds that the defect “broken” had increased during the test run.

For further analysis, the lab produces a p-chart (percent defective SQC chart) and finds two points above the upper control limit. Pattern rule violations, shown on the chart by asterisks, provide further warning. The operator then clicks on each suspect data point to “drill down” for more information. The results pointed to the 3B stock. Using Quality Analyst’s unique Data Filter, separate p-charts for each stock type quickly confirm 3B stock as the source of the unacceptable levels of breakage.

A p-chart (percent defective) revealed significant control problems indicated by out-of-control points and many pattern-rule violations. “Drill down” detail on the out-of-control points identified the offending samples. Shifts in the control limit are automatic adjustments due to changes in the sample size.

Quality Analyst’s Data Filter and multiple chart display demonstrate the contrast in breakage between pickle stocks. Although the 3B stock has a significantly higher breakage rate, it is still in perfect statistical control.

Further study reveals that 3B pickles frequently must be forced into the jar, causing breakage. However, the p-chart shows the process itself to be in statistical control – breakage is a natural part of the process. The processors conclude that while the 3B stock could be used to remain in label weight compliance, breakage may be excessive.

The SQC analysis leads the processors to three key conclusions about their process: 1) They can maintain statistical control and process capability while using either or both pickle stocks. 2) Excessive broken pickles result when using the larger 3B stock. 3) SQC analysis of broken pickles for the 3B stock shows it to be in perfect statistical control; this means the higher breakage rate is characteristic of the process and not due to any “special cause.”

By having a clear understanding of their packing process, the company recognises three distinct choices: 1) Live with the breakage and risk customer displeasure. 2) Continue to study the process to determine if the process can be modified to reduce 3B breakage in a cost-effective manner. 3) Meet shortages by continuing to purchasing 3A stock on the open market.

Quality Analyst software is supplied and supported in the UK and Ireland by Adept Scientific plc, Amor Way, Letchworth, Herts. SG6 1ZA; telephone +44 (0) 203 695 7810, fax +44 (0) 203 695 7819, email; or see Adept’s World Wide Web site Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

With offices in the UK, USA, Germany and throughout the Nordic region, Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

SPC in Electronics Manufacturing

The need for Statistical Quality Control (SQC) in the manufacture of electronic components is well accepted. Not only does SQC allow firms to comply with vendor-certification programs, it is essential for maximising yields, minimising rework, and increasing profits. The following text describes the application of NWA Quality Analyst to quality control in the assembly of electronic components. The examples used here are composites of several NWA Quality Analyst users in the manufacture of electronic components.

SPC Charting with NWA Software

Many electronics-assembly operations use process-capability analysis to select, optimise, and certify production equipment. Once the system is established, variable and defect tracking monitors the daily production. If control charting reveals serious problems, an engineering study can reveal the sources of the problems.

Manually produced control charts can track manufacturing processes acceptably, but collecting and charting the data disrupts the production process while putting extra burdens on assembly and inspection staff. Also, manual collection and charting do not directly integrate into vendor and in-house reporting systems; data retrieval over any length of time is at best awkward.

Spreadsheet-based charting improves on manual methods, but requires considerable up-front configuration and macro building. Maintaining macros requires an ongoing input of skilled labour and time. Adapting to changes in charting methods or requirements is expensive and time-consuming.

The alternative is to use SQC software such as NWA Quality Analyst and its companion plant-floor data-collection software, NWA Quality Monitor. For considerably less investment of time and money, the plant can collect data from the assembly operation and produce accurate control charts while minimising training time and interference with production.

Variable Measurement with X-Bar/Range Charting

In the following example, a Printed Circuit Board (PCB) manufacturer tracks solder height with NWA Quality Analyst. Keeping solder height within control limits is critical in PCB manufacturing because solder height determines how well components will sit on the board, and therefore, how well they will operate as designed. Also, if the boards are stored or stacked, the solder height determines whether copper in the components will migrate, causing the boards to become defective.

Inadequate solder height can indicate many problems including wear and tear on the solder stencil, a calibration problem with the machine applying the solder, inadequate stencil cleaning, or a raw material problem. Any of these problems can lead to higher production costs due to waste or rework.

In this example, the specification for the solder height is 0.25-0.55 mm. Periodically, samples of five units made at the same time are measured for solder height. This data is analysed with NWA Quality Analyst using X-Bar/ Range Charting to determine whether the height variation indicates an out-of-control condition. The Quality Control staff member knows that a certain amount of variation is natural to the process, but an out-of-control condition could indicate a problem that, if left unresolved, could become very costly to the plant downstream. Data from a full day’s run is charted in an X-bar/Range chart.

Inadequate solder height can occur as a result of a range of external causes; the problem might be with the raw material, with the machine applying the solder, or even with the measuring device itself. Upon investigating the records, the process engineers found that the inadequate solder heights all came from lots using raw materials from a single vendor. Further analysis of lots from that vendor revealed the vendor’s inability to produce consistent material. As a result, the manufacturer dropped the vendor as a supplier.

With the out-of-control condition removed, the process engineers analysed the process further. The process was run and charted for another day using only raw materials from reliable vendors. The process was in control but suffered from excessive variation and was not capable of producing output well within specifications. Such a situation indicates significant opportunities for process improvement because the in-control condition indicates that the source of the excessive variation is the process itself. In this particular case, the process engineering team discovered that the machine needed maintenance and recalibration. This was done and the results for the next day’s production indicated a process in control and capable of producing output well within specifications.

Defect Tracking with Pareto Diagrams

After the board has been assembled, the PCB manufacturer uses Pareto Analysis to examine the relative contribution of different defects that lead to PCB rejects. One advantage of SQC software is that the user can easily examine the data from different perspectives with minimal time and effort. With NWA Quality Analyst, the user also has complete control over labelling, individual defect percent, cumulative frequency, and so on.

Pareto analysis offers an excellent example of how NWA Quality Analyst delivers improved capability for considerably less effort. In our example of the PCB manufacturer, the plant uses Pareto analysis to track defects arising from the following solder-related attributes:

Insufficient Solder
Excess Solder
Cold Solder
Solder splash/ball
Flags (peaks)
Open joints
OtherMiscellaneous solder problems

Pareto diagrams are commonly used to rank the relative frequency of different categories of defects. With a Pareto diagram, the quality control staff can assess the relative contribution of different defects to PCB rejects and assign priorities for addressing their causes. The Pareto diagram is fundamental to defect tracking and analysis. NWA Quality Analyst lets you rank the charted defects by occurrences or relative cost. It is often useful to rerank defects by categories other than frequency of occurrence. One alternative ranking is cost. Reranking requires only a few mouse clicks in NWA Quality Analyst. The same data used to generate Pareto diagrams can also be plotted as percent defective control charts. NWA Quality Analyst’s Chart Group feature lets users quickly display multiple charts for process comparison, simplifying process analysis and general quality reporting.

Application to Electronics Component Manufacturing

Although our example has highlighted PCB manufacturing, NWA Quality Analyst is used in a wide variety of electronics manufacturing. Since the electronics industry is highly competitive, engineers must continually change and improve their processes to reduce variability that could lead to waste and rework. NWA Quality Analyst is a very easy-to-use tool that allows them to identify where improvements could reduce costs.

NWA Quality Analyst is a tool for variable and attribute analysis. The critical measures vary from manufacturer to manufacturer, but setting up the files within NWA Quality Analyst is easy because the software is configurable and has a spreadsheet-like look. The NWA Quality Analyst Data Editor simplifies data setup and handling. All routine SQC charting functions are just a mouse click away.

Integration with Manufacturing Information Systems

Many electronics operations maintain production records in some type of manufacturing information system (for example, ERP, MRPII, MES, and so on). NWA Quality Analyst works well with these and has been integrated successfully with many database systems.

This success results from three NWA Quality Analyst capabilities:

1) Straightforward data transfer:

NWA Quality Analyst can readily accept data from external databases, spreadsheets, and manufacturing information systems. Data can be read in as delimited ASCII data files or as an ODBC (Microsoft’s Open Database Connectivity standard) source. Once the ODBC source has been defined for a data set, its use is transparent to NWA Quality Analyst operation. The connection is automatic, and the user is always working with the most current data.

2) Automated charting functions:

In addition to ready data transfer by data file or ODBC, NWA Quality Analyst has an internal scripting system that allows the user to define standard sets of charting and reporting operations, then initiate a defined operation with a single mouse click. The package and these scripts can also be called by other software such as MES.

3) Parameterised charting and reporting properties:

Quality Analyst charts and reports can be easily configured to conform to virtually any analytical or reporting standards set by customers, regulatory agencies, or in-house quality-management staff. These parameters can be set using dialogue boxes in NWA Quality Analyst or transferred from the manufacturing information system.

Quality Analyst software is supplied and supported in the UK and Ireland by Adept Scientific plc, Amor Way, Letchworth, Herts. SG6 1ZA; telephone +44 (0) 203 695 7810, fax +44 (0) 203 695 7819, email; or see Adept’s World Wide Web site Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

With offices in the UK, USA, Germany and throughout the Nordic region, Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

Extend Quality Control into the Supply Chain

Article from Control magazine, December 21, 2001
By Rich Merritt, Senior Technical Editor

Sharing QC data with suppliers and vendors can reduce surprises and provide valuable feedback for process improvements.

What a revolutionary idea! Acquire quality control (QC) data while you are making the product, and then pass that data to your customer as part of your service. If you make a commodity product, it would differentiate you from your competition.

Better yet, take a lesson from the auto industry and require companies supplying you with feedstock to provide QC data on incoming materials. This would eliminate all the cumbersome lab testing and online sampling you do now, because you would already know the composition of the feedstock, and you could feed it into your process control system ahead of time.

Of course these are great ideas. That’s why people in the pulp and paper industry, among others, have been doing it for years. If you haven’t heard of supply chain QC before, it’s because it’s not exactly widespread, and because today’s software technology is just now making it easy for everybody to pass feedstock data through the supply chain.

Following the Paper Trail

Control engineers in the pharmaceutical, chemical, nuclear, and wastewater industries are familiar with the paper trails required by various government agencies. Quality data is required in huge volumes for regulatory purposes, so companies grudgingly produce reams of printouts and enshrine billions of bytes of process trivia into databases that never see the light of day unless some quality problem comes up down the road.

Parameter Pipelines

Quality control (QC) information flows through a process from the supplier to the manufacturing plant to customers. For example, QC data is gathered during raw material manufacturing by a laboratory information management system (LIMS) and stored in a process information management system (PIMS) database. QC data can be accessed by customers via the Internet through a web server, or by direct transmission via a virtual private network (VPN) over the Internet.

When a shipment of, say, Pirelli’s Miracle Elixir turns out to be tainted, examining all its stored QC data reveals how the problem happened, so it can be fixed in the future, and to whom the product was sent, so it can be recalled in the present. While those are noble goals, it seems like all that data might be put to a more immediate use, such as ensuring better process control further down the supply chain.

They do it in the food business, because they have to. “Once a food product has been through a manufacturing process, little can be done to alter its quality,” says Pearl Adu-Amankwa of the Food Research Institute, Accra, Ghana. Therefore, careful quality control throughout the process ensures good results. “Raw material control and process control are interrelated. The factory must not be deprived of an essential raw material while it awaits quality control clearance. This means that the work of quality control must be integrated with the factory management plan,” he explains.

Adu-Amankwa went on to describe the meticulous way the food industry uses quality control measures to test raw materials, put data into the production process, and eventually produce food that’s fit for consumption. While the problems of food production are magnified by the perishable nature of its feedstocks, the lessons learned there apply everywhere.

Carol Jackson, director of corporate accounts at OSI Software, says the paper industry, like the food industry, has been doing this for years. “Paper producers send production and quality information to the converters of their product whether the converter is on-site or miles away,” explains Jackson. “Exchanging information happens quite often where the feed to the converter’s machinery requires both process and quality parameters for the proper setup and operation of the production line. They need to know the moisture and weight profile across the sheet of paper or board that they are cutting, coating, or gluing to maximise their productivity and minimise losses to scrap.”

The Pipeline Continues

At the manufacturing plant, feedstock arrives and is stored in raw material tanks. QC data about the feedstock arrives via the VPN. Operators examine QC information and use it to adjust the control system. QC data obtained during manufacturing is processed by the plant’s LIMS, put into a PIMS database, and made available to downstream customers via the Internet and web browsers. Other systems in the manufacturing plant make use of all the QC data, including ERP, material balance, control optimisation programs, and similar software.

Jackson described examples of P&P companies who have been sharing data like this for 10 or 12 years with OSI’s software products. One company made a me-too commodity product that suddenly became very successful when the company made basis weight and moisture data available to its customers online. With this information, corrugated box plants could set up their machinery to run more efficiently.

Other examples of supply chain quality control are out there but difficult to find; perhaps because it takes two to tango, sometimes three. “We purchase granulated blast furnace slag from the steel industry as a raw material to make blended hydraulic cements,” says Jeremy Baldridge, production manager at Lone Star Industries, New Orleans. “We require our slags to be water-cooled and have a certain particle size, and we have specific requirements on total sulfur content, total alkalis, and silica content, to name a few.”

The steel mills supply this information to Lone Star, which uses it to control the process. “The chemical makeup of the slag affects the rate of reaction during hydration,” says Baldridge. “We control the reactivity by controlling the particle size during finish milling.”

Lone Star, in turn, supplies all this data to its customers. “Our customers require full chemical and physical data such as particle size; seven, 14, and 28-day compressive strengths for fully hydrated product; and a variety of chemical species concentrations,” says Baldridge.

The logic in these examples is straightforward: If any company measures quality control parameters during or after its production processes for better control or for regulatory purposes, and stores all that QC data somewhere, it could easily make it available to its customers and claim a competitive advantage. On the other hand, since customers know the data is available, why don’t they demand that it be supplied with the product?

“In most cases, companies are not tackling the supply chain quality issue with as much vigor as they should,” admonishes Cliff Yee, president of Northwest Analytical. “The software tools are available to do this, and Wall Street analysts list supply chain quality problems as the number one threat to the value of the stocks of most global companies. So why aren’t companies making supply chain quality a huge priority?”

Checking Incoming Feedstock

“A manufacturer monitors incoming quality for two reasons,” explains Paul LeMert, director of business programs for Wonderware’s eManufacturing Systems Group. “One is to ensure that the supplier is providing material that meets the specification laid down in the purchase agreement and second, more importantly, to correlate the incoming quality attributes against performance during the manufacturing process.”

Automotive manufacturers figured the first part out years ago. They’ve also figured out how to get their suppliers to do all the quality control work for them at little or no cost. Russ Agrusa, president of Iconics, says that some auto companies demand suppliers provide quality data on 100% of the incoming parts.

One such supplier is Ordnance Engineering Associates (OEA), Denver, a supplier of airbag systems. It has to develop a database of information on every aspect of the testing and manufacturing process for every component, and then maintain the database for the lifetime of a vehicle.

“The automotive guys don’t check all the incoming parts,” says Agrusa. “They do spot checks and compare it to the supplier’s data. If you have a good record with them, they pass your parts easily. If you don’t have a good record, you get checked more often.” Agrusa says much of incoming QC checking is similar to the way airlines are double-checking passengers these days. “If you fit the profile for a troublemaker, you get checked,” he explains.

Of course, the automotive people are looking at discrete parts, which usually have a simple pass/fail quality check. What about feedstocks? The same pass/fail checks don’t apply as well. Nevertheless, the quality of incoming feedstock directly affects further manufacturing processes, and you can bet that suppliers will be judged by it.
Dale Evely, consulting engineer in I&C at Southern Co., Birmingham, Ala., requires QC information from suppliers. “Our feedstocks are primarily fuel of various types, such as coal, oil, and gas,” he says. “We periodically sample and analyse the fuel to make sure it meets our specifications.”

“Statistically measuring incoming materials gives a manufacturer good data on the capability of a supplier to consistently produce material to exact specifications,” says LeMert. He says it’s a good practice to compare and contrast materials from various suppliers and determine how they affect a given process. LeMert recommends that all process manufacturers make the following determinations based on incoming feedstock quality:

Q: How does my process respond to variations in each key material attribute?

Q: How do variations affect my cycle times/run rate?

Q: How do variations affect my downtime?

These questions are important, LeMert says, when you have multiple suppliers and each supplies material within acceptable limits. What would be even better, of course, is if each of those suppliers provided QC information with each batch of feedstock. Presuming that you make the determinations listed above, you could automatically adjust your process to account for minor differences in feedstock.

“It is very common in the refining and chemicals industries for suppliers to provide a certificate of analysis or product quality,” says Jim Christian, principal consultant at Honeywell Industry Solutions. “Nearly all products in these industries must meet octane, vapor pressure, and many other specifications. Ethylene produced in one chemical plant must meet a purity spec to be used in another chemical plant, and so on.”

Christian says quality data typically characterises properties of the product, such as purity, viscosity, density, chemical composition, energy content, and so on. And, Christian says, refineries use this data to control processes. “Many refineries use incoming quality data in advanced process control. Advanced applications such as soft sensors and inferential calculations use it to improve their quality estimators. Feed quality information can also be used to automatically change production modes.”

Why aren’t more companies doing this? The excuse, one supposes, is that appropriate software has not been available until now.

Come and Get It

Customers of Koppers Industries can call up quality data over the Internet using a browser, put the data into charts and graphs, and manipulate the data any way they want using Northwest Analytical statistical software. When satisfied, they can output the data in tabular form, then cut and paste it into an Excel document for input into their process control system.

Regulated Solutions

“We’re drowning in data,” says Agrusa. “We have terabytes of data stored away describing manufacturing processes.” The problem, he says, is that little of it is regulated. “In the food and pharmaceutical industries, the FDA sets the rules. They say what data will be collected, how it will be collected, and how it will be presented. The rest of our industries are self-regulated.”

Jeffrey Johnson, senior project manager at Intellution, says FDA Regulation 21 CFR Part 11 is the critical rule for drug companies. “Pharmaceutical companies have historically created and retained batch records that document virtually every step of the production process,” he says. Although the various processes were thorough, they were far from foolproof, and the resulting paper documents were becoming unwieldy. Enter the FDA. “The FDA’s regulation mandates how companies are to create, store, and retrieve electronic records and the corresponding electronic signature,” explains Johnson.

But in addition to standardising the record-keeping aspect of quality data, 21 CFR 11 also makes life easier for quality people. “FDA-regulated businesses that adhere to 21 CFR 11 will avoid the sting of fines, penalties, and inspectional observations,” notes Johnson. In other words, supply your quality data correctly, and you don’t “fit the profile” anymore.

Alas, if you are not in a FDA-regulated industry, you don’t have 21 CFR 11 to guide or rule you. For years, companies who shared feedstock quality data almost always used the same software package, or they agreed on a format.

Dow Corning, Midland, Mich., uses the same software in 35 of its manufacturing plants worldwide. Using the same software makes it easier for the company to share quality data when the output of one process is used as the feedstock for another. The data is easily obtained because it is kept in the same database at each plant.
While the system has many other advantages, it greatly simplifies tasks for control engineers who are trying to use quality data for process control. One of Dow Corning’s research projects uses a combination of statistical and first-principle models to make correlations between process variables and product quality attributes. “This allows us to certify product quality by ensuring that our processes are well controlled, rather than by extensive laboratory quality testing,” says Barry MacGregor, company manager, manufacturing systems.

Using such data, Dow Corning was able to satisfy the needs of a customer that required extensive finished product quality assurance testing prior to shipment. MacGregor explains that this laboratory testing was costing his company about $500,000 per year. “We went to our customer with a proposal: If the product stays within certain statistical quality control parameters during manufacturing, then by definition the product should meet the customer’s specification,” says MacGregor. “We agreed to provide all the QC data needed to prove this by giving the customer access to data in the PI System.”

The customer agreed that the QC data was sufficient, and Dow Corning was able to greatly reduce its laboratory quality assurance testing.

Generating the Data

The easiest part of all is generating quality control data. Laboratory information management system (LIMS) software packages are available everywhere for extracting data from plant laboratories and making it available in a database or on the Internet. Statistical process control (SPC) and statistical quality control (SQC) software packages exist by the score. These examine the raw quality data from online analysers and instrumentation, grind it all up and present the results as control charts, histograms, X-bar charts, moving averages, and a host of other tools that make sense to quality control people.

We don’t want to get into the mechanisms for obtaining quality data, because that is an entire article unto itself. Suffice to say that virtually every process control system on the face of this Earth has the ability to load up an easily obtained software package that will capture this data for you and put it into whatever form you need to ship it on to your customer.

Likewise, every process control system has the ability to take quality data from another system in its own software family, plug it into its real-time control algorithms, and control the process using QC data from a feedstock supplier. That assumes, of course, that supplier and customer are running the same software.

The trick is to find a way to obtain the QC data you need in a standard form, so you can take quality data from anybody, plug it into your control system, and use it to run your plant. Similarly, you need a way to send quality data to your customer in a form they can use. And that’s the rub. There is no system available that will guarantee you can do that.

There is hope that such systems will be coming our way soon, based on OPC. “OPC is a key player in all this, because the OPC standards have created a standardized interface at the software level between applications,” says John Weber, president of Software Toolbox. “This makes it easier than ever before to get at the data.”

OLE for Process Control, or OPC, is a standard that makes it possible to connect software programs from different vendors. In a nutshell, instead of each software package requiring a custom driver to understand the output of another program, OPC programs conform to a standard way of defining attributes about data. This can include range information, data type, quality flags, date and time information (down to the millisecond), etc. Although nothing about Microsoft standards is as simple as it seems on the surface, OPC does make it a lot easier than previous methods for connecting, say, the output of a SPC/SQC program to the input of a process control system if both subscribe to OPC.

Another way is to make data available via the Internet or a private extranet. That way, the data is in XML, HTML, text, or some other universal format that can be manipulated easily and downloaded into a control system.

Koppers Industries, Pittsburgh, has such a system. Koppers makes carbon pitch, coal tar distillates, and phthalic anhydride and ships products to customers in rail cars. Customers typically ask for quality data on water content, density, softening point, flash/fire point, toluene insolubles, coking value, and viscosity. This data is derived from laboratory testing on the finished product and gathered by NWA Quality Analyst Web Server software from Northwest Analytical.

The Quality Analyst software creates a web page containing all the necessary data for each batch, and Koppers makes this available to its customers over a private extranet. Customers can browse the data using a standard commercial browser. Tushar Lovalekar in the IT department at Koppers says that customers can call up their batch and use NWA tools to analyse raw data and create control charts. “A customer can also create a tabular output on the screen, then cut and paste it into an Excel document,” says Lovalekar. “This lets them input it directly into their process control system.”
Since it takes several days for the rail cars to arrive, customers have adequate time to prepare. “Our customers find the forward view of what’s coming very helpful,” says Charles Kraynik, carbon materials product manager at Koppers. “It is most definitely a competitive advantage for us.”

Koppers got into this because a major customer called up and said they were buying feedstock from another company that made such information available, and they asked if Koppers could do it, too. “We looked at what they were doing, and decided that not only could we provide such data, we could do a much better job,” says Kraynik. Site analysis numbers indicate that some customers glance at the data occasionally, while others use the data extensively.

As NWA’s Yee points out, supplying data in this manner requires a forward-looking company. “The idea of ‘open kimono’ manufacturing, or allowing customers to see into the operation, is a scary idea. Eventually, we believe that companies will come to see such openness as a competitive advantage. The best manufacturers will be proud to show how well-run their processes are.”

I’ll Show You Mine

In many cases, marketing drives technology. So what might happen in the near future is that well-run companies could see sharing QC data as a competitive advantage. They would start marketing the fact that they show all their data, but their competitors (like you?) are not making quality data available, so they must have something to hide.

As we all know, there are some production processes out there that are better left under cover, because it’s a miracle that they last through the day or survive between OSHA and EPA inspections. Running SPC or QC on such a process would depress a regression analysis and put histograms into hysterics.

But if your plant is well controlled, and you are proud of the tight SPC and SQC numbers being produced, consider making it public. It could be a competitive advantage.

You may be driven to it anyway, so be prepared for the day that your marketing department comes calling and wants you to provide QC data to customers. Your response should be: “Send QC data? Sure, we can do that. I thought you’d never ask.”

Quality Analyst software is supplied and supported in the UK and Ireland by Adept Scientific plc, Amor Way, Letchworth, Herts. SG6 1ZA; telephone +44 (0) 203 695 7810, fax +44 (0) 203 695 7819, email; or see Adept’s World Wide Web site Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

With offices in the UK, USA, Germany and throughout the Nordic region, Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

Saint-Gobain Containers Achieves High Efficiency with NWA Quality Analyst Software

When glass-container company Saint-Gobain Containers (formerly Ball-Foster Glass Container) implemented a company-wide quality program across its 18 plants, the company standardised its quality data management with NWA Quality Analyst software from Northwest Analytical, Inc.

“Our quality has always been very good, but to be the best in this marketplace, we wanted even higher levels of efficiency, productivity and quality,” says Mike Puhl, corporate senior process engineer. “Standardising on NWA Quality Analyst has created efficiencies and easy comparisons.”

With 18 U.S. plants, Saint-Gobain makes between 1000-1500 different glass container products. Inside the plants, sand, soda ash and limestone are mixed with recycled glass and fed into a furnace, which melts the glass at 2700 degrees F over about 24 hours. After going through a temperature conditioning unit, the glass is cut into pieces that are the exact weight of the finished jar or bottle. The pieces are then dropped into a forming machine and then transferred onto a conveyor for hot-end coating. Then a cold-end coating is applied, and the finished bottles are inspected for multiple criteria such as thickness, cracks and bubbles.

During the process, NWA Quality Analyst is used to chart variables such as temperature in the hot-end coating hood, temperature of glass at cold-end coating, flow rates and furnace temperatures. If the control charts show data points trending outside of the upper and lower control limits, mid-course corrections can be made before defects are produced.

“We focus on key characteristics of large-volume products,” says Puhl. “That’s where the quality program can make the most difference.”

Because NWA QA is off-line software, St. Gobain also uses it to see the big picture in terms of how processes perform over time. The software makes it easy to compare data collected over days, weeks, months and even years, allowing them to spot large trends and unusual patterns that might otherwise be missed in the day-to-day views. They also use the software for Pareto analysis of customer complaints, sorting them by defect types to help prioritise quality efforts.

Creating Savings on Expensive Equipment

Saint-Gobain’s Pevely, MO, plant manufactures more than three million beer bottles per day. One way they use NWA Quality Analyst is to monitor and extend the life of bottle moulds.

Moulds break down over time and need retooling or replacing at a cost of between $50,000 to $60,000. Replacing a mould too early – before it is necessary – reduces capacity and increases costs. Replacing the mould too late causes product scrap and rejects.

By control charting container capacity measurements, the plant can determine more exactly when to make process adjustments to keep the product on target and extend mould life. “If a product is budgeted for four moulds in a year and we only use three, that $50,000 goes straight to the bottom line,” says Paul Delaney, plant quality control manager. “And if you consider that each machine produces 20 to 40 products, each with their own set of moulds, you can see there is great potential for savings.”

The plant also uses NWA Quality Analyst to track yields (how many bottles make it into final inspection from production). They monitor a production shop’s 30-day daily performance against its % pack (yield) target and review reasons for deviations. Using the software in their daily production meetings allows the whole team to review shop performance data, calculating and projecting control charts, histograms and statistics on a screen for everyone to see.

Delaney says pursuing SQC with the NWA software has many benefits:

  • It allows for comparisons between areas, shops, tools and products. He’s constructed customised templates for standardised reports.
  • It has removed the subjectivity from describing shop performance; there is more focus on numbers and trends.
  • Trends are visible; they are able to see problems developing earlier while there’s time to prevent losses.
  • Having clear data stops people from saying “we can’t do that” when the data shows they can. Conversely, the data also stops people from saying “we can do that” when the data shows they can’t.
  • The charts and graphs allow management to set goals based on data and tighten up performance. Goals are becoming more based on control and capability rather than on just meeting specifications.

For Delaney, a typical Saint-Gobain SQC software user, flexibility and ease-of-use are what matter most. “I just show people how to log on and navigate,” Delaney says. “With Quality Analyst they need almost no additional training, so we can get right to work.”

Quality Analyst software is supplied and supported in the UK and Ireland by Adept Scientific plc, Amor Way, Letchworth, Herts. SG6 1ZA; telephone +44 (0) 203 695 7810, fax +44 (0) 203 695 7819, email; or see Adept’s World Wide Web site Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

With offices in the UK, USA, Germany and throughout the Nordic region, Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

Iams premium pet food targets package fill weights with NWA Quality Analyst software

When it comes to feeding cats and dogs, The Iams Company is serious about quality.

The worldwide leader in premium pet foods uses statistical process control (SPC) and continuous process improvement at every stage of its production operation. “Our quality system is developed around the whole production cycle,” says Dave Bechtel, Iams director of worldwide quality. “It entails every phase of the manufacturing process: from development to procurement to manufacturing to shipping and distribution to customer follow-up.”

Running to goal at the Aurora Plant

The Iams processing plant in Aurora, Nebraska, is ground zero for the company’s quality and process improvement efforts. New SPC programs were first tested and implemented here, then rolled out to other plants in the enterprise.

A typical Iams facility, the Aurora plant produces 200,000 tons of premium dry cat and dog food each year. It employs 150 people in raw material receiving, milling, processing, packaging, warehousing and shipping.

The production process begins with testing ingredients to make sure they conform to specifications. Then the ingredients are placed into bins before being put into mixers. From the mixer they are placed in an extruder and then cut into the sizes and shapes demanded for various products. After cutting, the pieces go into a dryer, where the moisture is reduced to specified levels. External fat and ingredients are then sprayed on and the pieces are cooled and packaged.

At each step, Iams uses SPC to determine whether the process is capable of meeting specifications (in statistical terms, this is expressed as Cpk). Once assured that it can, operators gather routine day-to-day data for trend analysis. From there, SPC efforts focus on establishing tighter and tighter controls to decrease variability.

“We know we’re making progress when line operators are no longer running to specifications, but to targets that were established by our on-site manufacturing process control teams,” says Clint Paisley, quality assurance manager at Iams Aurora.

Targeting package fill weights

A good example of how Iams uses SPC, is to more closely target the accuracy of package fill weights. As all packagers know, underfilling is not an option, both because of customer satisfaction and strict regulatory requirements. Overfilling is one way to make sure requirements are met, but is bad for the bottom line because it adds cost. The goal is to get as close as possible to the target fill weight without going under.

During each shift, an Iams operator measures fill weights and plots the data. At shift’s end the data is entered into a statistical software package, NWA Quality Analyst. The software analyzes the data and when a new shift starts, gives the incoming operator a statistical profile for the preceding shift: average package weight, the number of measurements below the lower control limit, and the number that exceed the upper control limit.

“We provide operators with the information and, if they see that a particular product is running outside of limits, the process needs to be evaluated,” says Paisley. “We look to run on both sides of the target with minimal variation and not under declared value weight.”

Years ago, before the statistical process control program was implemented, operators didn’t have the data to evaluate capability from one filler to another. “Without data, we couldn’t optimize each line for fill weights,” says Bechtel. “Now we’ve got it defined and have narrowed the targets substantially.”

Bechtel says that NWA Quality Analyst enables them to take data and – from a capability standpoint – determine what their systems are able to deliver. “For a long time we used a standard fill weight target,” he says. “Now, with NWA allowing us to talk specifically in terms of data instead of in generalities, we’ve been able to optimize fill weights, generating savings while staying fully compliant. And, as we get more involved, we’re tapping into NWA’s more technical capabilities such as t-tests and regression analysis. There’s room to grow as our quality program develops.”

Ease-of-use makes all the difference

Iams chose NWA Quality Analyst for SPC data analysis because it is exceptionally easy to use. “I’d been using Excel for SPC, but found it didn’t provide the desired statistics. With the amount of data generated, the files became much too large to handle,” says Paisley. “I wanted software that was easy enough that the operators would be willing to use it for decision-making. NWA has all the SPC tools I want and it has exactly the easy-to-use interface I need.”

Using the software, Iams operators can see how the process is performing by using I/R charts and histograms. The software makes it obvious when rule violations (instances in which something in the process is outside normal parameters) happen. When rule violations occur, operators and supervisors record the causes and corrective actions in log books. Paisley uses the information to track changes to control limits and machine maintenance.

“With NWA, people no longer have to depend on gut-feelings or guesses about how the process is performing,” says Paisley. “They have real data upon which to make analyses and then decisions. This leads to high consistency.”

NWA makes data accessible

Before Iams started using NWA software, most of the quality data was kept in Excel spreadsheets. “We had pages of data that was not being manipulated in a way that was helpful,” says Bechtel. “We were able to get averages, but weren’t able to talk much about variability and standard deviation. We didn’t have a glimpse of the whole situation. Now we’re applying analysis to many product characteristics, using NWA as an assessment tool.”

Paisley says the biggest value of NWA is its ease of use. “Other software programs just spit out stats that intimidate people,” he says. “We don’t need complex statistics. We need basic tools such as how to collect data, how to look at standard deviations and simple work instructions on situations that occur. Today, I have operators who can readily pull up histograms and use them to make the process better. NWA Quality Analyst is perfect for that.

NWA Quality Analyst is supplied and supported in the UK by Adept Scientific plc, Amor Way, Letchworth, Herts. SG6 1ZA; telephone +44 (0) 203 695 7810, fax +44 (0) 203 695 7819, email; or see Adept’s World Wide Web site Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

With offices in the UK, USA, Germany and throughout the Nordic region, Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

SPC Inspires Global Quality Culture for Multinational Giant

Mounting pressure for product consistency and quality and a mandate to reduce costs spurred container manufacturer Crown Cork and Seal Co. Inc. (Philadelphia, PA) to institute quality measures that could be applied across the board throughout its manufacturing sites around the world. The fully appreciate the size and importance of this task, consider the company’s business climate.

Crown Cork and Seal, one of the world’s largest suppliers of beverage containers, is an $8 billion manufacturing company with operations in 247 plants in 49 countries serving global customers like Coca-Cola Co. (Atlanta, GA) and Anheuser-Busch Cos. Inc. (St. Louis, MO). Customers expect a beverage container purchased in Taipei to be the same as one purchased in Chicago, Geneva, or Toronto. The challenges and costs of producing identical products in plants around the world are staggering and daunting.

With quality-control software from NorthWest Analytical (NWA), cans produced at Crown’s Cabreuva, Brazil plant will be exactly like the cans produced in Chicago, Geneva, or Toronto.

Add to this equation the fact that the container industry is competitive in terms of cost. As a high-volume, commodity-producing industry, profit margins are thin. Any increase in margin is important for the bottom line, and profitability depends on efficiency and waste reduction.

“Our industry is also competitive in terms of quality,” says David MacBurnie, vice president of world-class performance/total quality. “There is constant pressure to do more with less [for example, to make cans with thinner materials without compromising quality].” He says customers’ expectations continue to rise; they now require zero-defect products, low prices, and high quality. “This is challenging, because as you speed up equipment and reduce materials, you also increase process variability, which can lead to defects.”

A related aspect of this quality requirement—producing consistent cans around the world—places enormous pressure on the company to reduce variation across many manufacturing facilities. This requires that quality must be addressed on a company-wide basis, according to MacBurnie. Some of the most important employees in this effort are the line operators, who must continually make decisions about keeping the process in control. This requires training, not only to overcome fear and resistance to change but also to teach statistical concepts that can be applied to line operations.

“Our strategy for meeting these challenges is to implement a company-wide quality standardisation program, using the NWA Quality Analyst [for off-line statistical quality control] and NWA Quality Monitor [for on-line statistical process control] from Northwest Analytical.” Crown Cork and Seal has started creating a worldwide, multinational user group to solve statistical process control (SPC) problems, based on NWA’s two major software suites. This strategy can be applied up and down an entire supply chain.
The container company is using Northwest Analytical’s products to accomplish three goals, according to MacBurnie:

1) TO REDUCE PROCESS VARIATION: The Northwest Analytical software packages allow Crown to examine the process and distinguish between natural and assignable variation. Assignable variation means that the process is out of statistical control and needs to be fixed. Reducing that variation means that the company can produce containers with less waste and rework, which helps the bottom line. It also means that Crown can reliably meet customer specifications.

2) TO DETERMINE WHETHER THE PROCESS IS CAPABLE OF MEETING CUSTOMER SPECIFICATIONS: These specifications are set by customers and Crown Cork and Seal engineers. Neither group has complete knowledge of what the manufacturing lines can meet. So when new specs are required, Crown engineers use the software to determine whether those specs can be met with existing configurations, staff, and equipment.

3) TO REPORT TO CUSTOMERS: Crown’s customers demand proof that the company is meeting specs and employs a comprehensive SPC/SQC program. The software allows Crown to provide its customers with charts that clearly show how Crown is meeting specs and quality goals.

Standardising on NWA’s products throughout the Crown organisation allows the company to meet several critical goals, the first of which is the achievement of economies of scale. “Standardising on NWA software allows us to develop one quality training program that we can implement with all our plants,” MacBurnie explains. “Our trainers can go from plant to plant without having to relearn or adapt the training program to fit different software. In addition, their ability to teach SPC is enhanced because they know NWA inside and out. It allows them to focus on helping people to use the software more effectively.”

“In addition, a shared language about SPC helps form a problem-solving user base among plants,” MacBurnie says. As more people learn the software and apply it to Crown operations, these users can talk with each other about what they are doing. This internal network is helping people implement more changes at the plant level. Fellow users at other plants face similar problems, so their insights are more helpful than advice from corporate headquarters would be. “This is incredibly important when the plants are essentially making the same products and need to use their resources wisely,” MacBurnie says.

Given the same platform of quality programs, Crown employees can move from plant to plant without having to learn new procedures. Everything is the same: the software, the charts, and the specifications. Also, with this common language and procedures about SPC, corporate-level management can more easily compare results across all facilities.

In the area of cost control, not only does the NWA software reduce waste, but it also cuts costs by providing one SPC training program and materials used in all of the Crown facilities. “This program offers enormous potential in terms of cost savings,” says Bob Truitt, executive vice president of operations for Crown Cork and Seal’s North America Metals Division. “It allows Crown to create centralised quality expertise and enables us to reap benefits more quickly from our SPC program.”

Reductions in the time required to produce line information on charts have also saved money. Before implementing the software, Crown engineers could slash the time it takes to produce setup evaluations for quality purposes whenever there was a changeover on a given line. “Prior to the charting techniques of the NWA software, the collection of data and charting manually would take up to eight hours before we could get a line in operation,” explains Jim Arends, senior manager of SPC at Crown. “That time has been reduced drastically to around 30 minutes, and we do these evaluations fairly frequently in some plants.”

Implementation of the NWA software throughout the Crown organisation is an ongoing activity, Arends says. The software is operating in some plants, about 50 so far, throughout the U.S. and Canada, and installations are planned for Crown plants in South America, the Caribbean, and Mexico. Crown’s European operations will also eventually share in the benefits of the NWA software.

QC Package Features Ease of Use

By implementing Northwest Analytical’s NWA Quality Analyst and NWA Quality Monitor in a company-wide quality-standardisation program, Crown Cork and Seal gains significant benefits, including ease of use, technical stringency, and software adaptability. Training the line operators is a big challenge for the company, but the software is easy to learn and use; it also makes the statistical calculations automatic. Operators can now learn to read a control chart, and they can know whether their process is out of control and whether they should adjust it or leave it alone. And they can do that without learning complex math.

By providing technical stringency, the software allows Crown to do all levels of analysis, from simple control charts to advanced statistics. Thus, the software meets the needs of the line operators as well as the manufacturing engineers and quality managers. The software is also very flexible and easy to customise to meet the company’s needs.

Taken from an article written by James F. Manji

NWA Quality Analyst and NWA Quality Monitor are supplied and supported in the UK by Adept Scientific plc, Amor Way, Letchworth, Herts. SG6 1ZA; telephone +44 (0) 203 695 7810, fax +44 (0) 203 695 7819, email; or see Adept’s World Wide Web site Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

With offices in the UK, USA, Germany and throughout the Nordic region, Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

New Quality Analyst 5.2 Now Shipping

Adept Scientific plc announces that the eagerly-awaited new release of NWA Quality Analyst, version 5.2, is now shipping from their Letchworth, Herts warehouse. Northwest Analytical’s Quality Analyst is the world’s leading Windows software for Statistical Process Control (SPC) charting and analysis, helping users to maintain quality, consistency and cost-efficiency by identifying variations in a production line or other process. Quality Analyst 5.2 introduces many new and enhanced features, including features to assist with 21CFR Part 11 compliance.

This brand-new version of NWA Quality Analyst conforms to the most stringent technical requirements and easily integrates into manufacturing data systems in any industry or business, from parts manufacture to chemicals to food processing. Its powerful SPC charting and analysis capabilities make it ideal for vendor certification, process improvement and regulatory compliance.

Quality Analyst Version 5.2 includes the following major enhancements:

  • User-modification protection for files. Quality Analyst now uses operating system permissions to protect data and header files from even temporary modification, thereby ensuring the integrity of your data. This means that Quality Analyst helps you meet the requirements of the US Food & Drug Administration’s 21CFR Part 11 ruling, widely accepted as an international standard on safeguarding data sets in regulated environments.
  • De-normalise Function for Database Connectivity. This function allows users to read from ‘normalized’ database tables that currently require complex SQL queries. This enables highly effective connectivity, making it easier for users to connect to the dataset they are interested in.
  • Exception Reporting. Another new function allows users to create multiple exception report definitions for each dataset. The reports will list specification, control limit, and/or rule violations for selected variables, and will display the associated SPC chart at the click of a button, making it easier to see the results the user is most interested in.
  • Long Variable Names. To enable database connection where the database field names are long, NWA Quality Analyst now allows variable names with up to 32 characters.
  • Choice of period (.) or comma (,) as decimal. By allowing users this choice, the software offers a more international approach.

NWA Quality Analyst is supplied and supported in the UK by Adept Scientific plc, Amor Way, Letchworth, Herts. SG6 1ZA; telephone +44 (0) 203 695 7810, fax +44 (0) 203 695 7819, email; or see Adept’s World Wide Web site Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

With offices in the UK, USA, Germany and throughout the Nordic region, Adept Scientific is one of the world’s leading suppliers of software and hardware products for research, scientific, engineering and technical applications on desktop computers.

For the time being we are unable to offer the following product ranges although we are currently working hard to increase the number of products we can offer in the future. Please contact us to talk about alternative products that we may be able to offer you.