R. Α. Nadkarni Exxon Chemical Company PARAMINS Technology Division Chemical Analytical Laboratory—Linden P. O. Box 536 Linden, ΝJ 07036

"The improvement of quality in products and the improvement of quality in ser­vice—these are national priorities as never before. "

— George Bush, 1990

Analyt ical chemis t ry has always been used to measure the quality of manufactured products, particularly in the chemical industry. In view of the ever - increas ing emphas i s on quality, analytical science has a piv­otal role to play in the crusade for

quality. In this REPORT, I will de­scribe the modern concepts of quali­ty, including the Japanese standards of quality and the teachings of major quality pioneers; methods for statis­tical quality control and assurance;

REPORT and, finally, total quality manage­ment practices used to run a success­ful business.

What is quality?

There are several ways to define "quality." Webster's Ninth New Colle­giate Dictionary defines it as "the de­gree of excellence or superiority in kind." Deming (1) defines it as a pre­

dictable degree of uniformity and dependability, attainable at low cost and suited to the market. J u r a n (2) defines it as fitness for use and con­formance to spec i f ica t ions , and Crosby (3) calls it conformance to requirements. Ishikawa (4) says it is t he development , design, and supply of a product and service that is economical, useful, and always satisfactory to the customer. Exxon Chemical Company's quality council has adopted the following defini­tion: "Quality is understanding the customer 's expectat ions, agreeing on performance and value require­ments, and providing products and services that meet the expectations 100% of the time." Quality can thus be summar ized as satisfying the customer's needs.

0003-2700/91 /0363-675A/$02.50/0 © 1991 American Chemical Society

ANALYTICAL CHEMISTRY, VOL. 63, NO. 13, JULY 1, 1991 · 675 A

The Quest for Quality in the Laboratory

REPORT

We need quality to stay competi­tive, to satisfy customer requi re­ments, and to improve our products and processes. I t also provides a measure of our efforts to achieve im­provement of a process or product, to achieve a system in control, to under­stand the variability in natural sys­tems, to reduce overall costs, and to guide efforts to increase productivity and innovation.

The Japanese quality culture

In recent years, Japanese goods have become synonymous with quality, du­rability, consistency, and reasonable prices. During postwar reconstruction, the Union of Japanese Science & En­gineering and the Japan Management Association appreciated the link be­tween quality and productivity. These organizations undertook the task of educating industry leaders in methods for quality improvement.

"Quality circles" appeared by 1960. The success of these small groups of workers in eliminating special cases of product variability, and in improving the system through changes in tools, design, scheduling, and other factors, convinced Japanese industry of the usefulness of quality circles. Soon thousands of these groups s tar ted working throughout the country. They continue to flourish (5).

The Japanese concept of "Kaizen" (6), or continuous improvement, op­poses the Western philosophy of rapid technological progress. Where­as Western innovation is often dra­

matic, Kaizen is often subtle, and its results are seldom immediately visi­ble. Kaizen is everybody's business, including top managers, supervisors, and workers, and the Japanese say that not a day should go by without some kind of improvement being made somewhere in the company. Table I contrasts Japanese Kaizen with Western innovation.

Pioneers of quality

In every process, companies must de­fine the characteristics that indicate quality so that they will know what to change and how to measure success. They must identify their most critical quality problems, and management must take the lead in solving them. They must achieve quality by under­standing and improving the system, and by preventing problems, rather than by reducing defects through in­spection and correction, and they must develop a statistical understand­ing of processes, and use statistics to solve problems.

The quality movement traces its origins to a group of people inspired by a common goal but having dis­tinctly different theories, percepts, and styles: Edward Deming, Joseph Juran, Philip Crosby, Bill Conway, Kaoru Ishikawa, and Genichi Tagu-chi. Despite their differences, each one believes (7) that every organiza­tion needs to have a commitment to quality, from top to bottom.

Deming and Ju ran are the most prominent among these quality pio­

neers. Deming defines management's five deadly diseases (1) as lack of constancy of purpose, emphasis on short-term profits, personnel perfor­mance evaluation, job hopping by management, and use of visible fig­ures by a management that has little unders t and ing of them. Deming's philosophy (8) is summarized in his famous "14 Points for Management" (see box on p. 677 A.)

Whereas Deming demands a revo­lution in the way managers think, Ju ran tries to make quality a disci­pline of management. Juran's quality theory (2, 9) is that quality planning is analogous to financial planning, budgeting, and cost reduction. His 10 steps for quality improvement are summarized in the box on p. 677 A.

Statistical quality control

Statistical quality control (SQC) is essential to improving the quality of p r o d u c t s and p rocesses . W a l t e r Shewhart is considered the father of modern SQC methods. Working in the 1920s at Western Electric, he constructed the first control chart in 1924 and published a classic work on SQC in 1931 (10).

Among the areas to which SQC techniques can be applied are manu­facturing, purchasing, market ing , analytical science, and customer ser­vices. The seven basic tools of SQC are the flow chart, the cause and ef­fect diagram, the Pareto diagram, the histogram, the correlation chart, the run chart, and the control chart (Figure 1).

Flow chart. A flow chart is a pic­ture of the activities that take place in a process and is used to make interdependencies apparent. A flow chart should describe the process and identify each function, and it should be kept simple and current to main­tain its utility. When constructing a flow chart, it is essential to deter­mine boundaries and level of detail and to involve all appropriate people.

Cause-e f fec t diagram. C a u s e -effect diagrams are also called fish­bone d i a g r a m s because of t h e i r shape, or Ishikawa diagrams after their inventor. The major causes of a problem generally are designated as 4 Ps—policies, procedures, people, and plant, or 4 Ms—materials, ma­chines , methods , and manpower . From these major groups, branches are drawn listing the components of variation within each group. A cause can fall under more than one major area. To construct a cause-effect di­agram, identify the effect and enter it in the box, write four major causes in boxes and connect them with lines

Table 1. Features of Kaizen and Western innovation9

Feature Kaizen Western Innovation

Effect Long-term, long-lasting, Short-term and dramatic

Pace Time frame

Small steps Continuous and

incremental

Big steps Intermittent and

nonincremental Change Involvement Approach

Gradual and constant All personnel Collectivism, team

efforts

Abrupt and volatile A few champions Rugged individualism,

individual ideas and efforts

Mode

Spark

Maintenance and improvement

Conventional know-how and state-of-the-art

Scrapping and rebuilding

Technology breakthrough

Needs

Effort orientation Evaluation criteria

Little investment, but great efforts to maintain

People Process and effort for

better results

Large investment, but little effort to maintain

Technology Results for profits

Advantage Works well in slow growth economy

Better suited for fast growth economy

"Adapted with permission from Reference 6.

676 A · ANALYTICAL CHEMISTRY, VOL. 63, NO. 13, JULY 1, 1991

and arrows, and write minor causes on the chart around the major causes to which they relate.

In building a cause-effect dia­gram, Pareto diagrams and brain­storming are useful. Cause-effect diagrams provide visual documenta­tion of potential causes, which can be updated as learning occurs. Creating a cause-effect diagram is usually an educational process for all involved.

P a r e t o d i a g r a m . P a r e t o d ia­grams, named after Count Vilfredo Pareto, an I ta l ian economist who made extensive studies on unequal distribution of wealth, are a special form of vertical bar graph in which data classifications are arranged in descending order from left to right. Pareto diagrams are used to focus on problems in priority order.

For construction, data are collected for appropriate causes or problems, a bar graph is set up with classifica­tion along the horizontal axis, and the bars are arranged from most to least frequent. Greater focus can be achieved by performing Pareto anal­ysis on the largest bar of a previous Pareto chart. Repeating this macro -

to-micro Pa re to analys is several times can often identify the part of the process that requires a detailed examination.

H i s t o g r a m . A h i s t o g r a m is a graph that shows the distribution of occurrences. Products are usually de­signed to be produced to a specified value, and a histogram depicts how the actual measurements of different units of the product vary from this desired value. The frequency of oc­currence of any given measurement is represented by the height of the vertical columns on the graph. Ideal­ly a histogram should have a normal distribution or a bell-shaped curve.

Histograms are easy to plot and understand, and they concisely and effectively summarize the data. They aid in spotting outliers and problems by providing simple estimates of pro­cess d is t r ibut ion , percent wi th in specifications, and other information. Different people may plot the same data in different fashions, however, and histograms do not provide so­phisticated numerical details.

An alternative approach to a clas­sical histogram is a stem-and-leaf

Deming's 14 points for management8

1. Create constancy of purpose toward improvement of product and service, with a plan to become competitive and stay in business.

2. Adopt the philosophy that we are in a new economic age. We can no longer live with commonly accepted levels of delays, mistakes, defective ma­terials, and defective workmanship.

3. Cease dependence on mass inspections; instead, build in the quality through statistical evidence.

4. End the practice of awarding business on the basis of price; instead, de­pend on measure of quality along with price.

5. Improve constantly and forever every activity in the company, to improve quality and productivity, and thus constantly decrease costs.

6. Institute modern methods of training on the job.

7. Institute modern methods of supervision of production workers.

8. Drive out fear, so that everyone may work effectively for the company.

9. Break down barriers between departments, between, for example, re­search and design and sales and production. All must work as a team.

10. Eliminate numerical goals, posters, and slogans for the work force.

11. Eliminate work standards that prescribe numerical quotas.

12. Remove barriers that stand between the hourly worker and his right to pride of workmanship.

13. Institute a vigorous program of education and retraining.

14. Create a structure in top management that will push everyone to im­prove by changing as described by the above 13 points.

"Adapted with permission from References 1 and 8.

plot, in which data are grouped into contiguous subsets. Stem-and-leaf diagrams are easy to plot by hand, and the raw data are always avail­able, whereas in a histogram they are usually lost.

Correlat ion chart . Also known as a scatter diagram, a correlation chart is used to determine whether two measurements agree with each other, or whether a relationship ex­ists between two sets of data. The x-and >>-axes represent the measure­ment values of the two variables.

A correlation can originate from a cause-effect relationship, a relation­ship between two causes, or a rela­tionship between one cause and two o the r causes . The d i rec t ion and tightness of the cluster indicate the strength of the relationship between two variables. The closer this cluster is to a straight line, the stronger the relationship between the variables.

Run chart. A run chart, or trend chart, is a graphic record of quality characteristics measured over time and is the simplest quality control tool to construct and use. The points are plotted on the graph in the order in which they become available.

C o n t r o l c h a r t . Like t h e r u n chart, a control chart is used to study variation in a repetitive process. It is

Juran's 10 steps for quality improvement0

1. Build awareness of the need and opportunity for improvement.

2. Set goals for improvement.

3. Organize to reach the goals (establish a quality council, iden­tify problems, select projects, ap­point teams, designate facilita­tors).

4. Provide training.

5. Carry out projects to solve problems.

6. Report progress.

7. Give recognition.

8. Communicate results.

9. Keep score.

10. Maintain momentum by making annual improvement part of the regular systems and pro­cesses of the company.

"Adapted with permission from Reference 2.

ANALYTICAL CHEMISTRY, VOL. 63, NO. 13, JULY 1, 1991 · 677 A

REPORT

simply a run chart with statistically determined upper and lower control limit (UCL and LCL, respectively) lines drawn on either side of the pro­cess average.

A control chart is used for continu­ously improving and t ightening a process. When the data measuring a process reflect stable conditions, the process is considered "in control"; when the data fluctuate widely and unpredictably, the process is consid­ered "out of control." Because a data point rarely falls outside the control limits in a process that is in statisti­cal control, such an outlier generates immediate suspicion that something has gone wrong. "Control" in this sense does not necessarily mean that the product will meet the specifica­tions; it only means that the process is consistent (sometimes, consistently bad). The different types of control charts are discussed in detail below.

Quality assurance and quality control Although these two terms are used interchangeably, they entail subtle differences. Taylor (11) defines quali­ty assurance (QA) as a system of ac­tivities whose purpose is to provide, with a stated level of confidence, the producers and users of a product or service the assurance that it meets defined standards of quality. Quality control (QC) is the overall system of activities designed to control the quality of a product or service so that it meets the needs of users.

Inspection can play an important role in QC. Inspection steps during sampling, calibration, and measure­ment can detect defects, malfunc­tions, or other problems that could jeopardize the analytical process.

Variance, accuracy, bias, and precision. Measurement is perhaps the most important tool in the quali­ty process. It is often said that you can't control what you can't measure and you can't improve what you can't control. Measurements help to verify cause-and-effect relationships, to de­termine the effect of changes in an operation, and to allow a before-and-after comparison.

Every process or system is beset with variation and noise, and the only way to control and reduce varia­tion is by identifying its cause, estimating its extent, and inter­preting its meaning (12). Variability arises because no two things are exactly alike, either in nature or in laboratory measurements.

Variance is expressed as the coeffi­cient of variation (CV) or relative standard deviation (RSD), which is

equal to S/X, where S is the standard deviation and X is the average value. There are two causes of variance: special or assignable causes such as those result ing from, for example, differences between workers , ma­chines, materials, and methods, and the common or chance causes that u n d e r l i e t h e a s s i g n a b l e c a u s e s . When assignable causes are present, variations in data do not follow ex­pected pat terns and the process is said to be out of statistical control.

Variance in a process can be mea­sured through the accuracy, bias, and precision of the measurements. Accu­racy is the extent to which the aver­age of several measurements on a product agrees with the "true" value for that product. Bias is the system­atic error either inherent in a meth­od or caused by some artifact of the measurement system. Bias can be positive or negative, and several

kinds can coexist concurrently, so that only net bias can be evaluated. Precision is the ability of an instru­ment or method to reproduce its own measurement.

The American Society for Testing Mater ia l s (ASTM) recognizes two levels of precision (13): repeatability, the random error associated with a single test operator in a given labora­tory with the same apparatus under cons tan t opera t ing conditions on identical test material, and reproduc­ibility, the random error associated with test operators in different labo­ratories using different apparatuses to analyze identical tes t material . Generally, reproducibility is two to three times larger than repeatability.

People often equate good precision with accuracy, but this should not be done. A result can be consistent, but consistently biased or erroneous. This is illustrated in the well-known

Flow chart Cause-effect diagram

Effect

Causes

Pareto diagram

Category

Histogram

*M ffil· Measurement

Correlation chart

Variable Β

Run chart

Time

Control chart

UCL

X

LCL

Figure 1. Seven statistical quality control tools.

678 A · ANALYTICAL CHEMISTRY, VOL. 63, NO. 13, JULY 1, 1991

bull's eye analogy in Figure 2 (14). In medieval t imes everyone believed t h a t the sun and s t a r s revolved around the earth. The precision of public opinion was superb, but the accuracy was terrible.

Control charts . The hear t of a quality control or quality assurance program is the control char t . I ts overwhelming advantage over nu­merical da ta is t ha t a picture is worth a thousand words.

However, control charts cannot tell the cause of the upset, only that there is an upset; it is up to the operator to investigate, identify, and eliminate the cause(s) and bring the process into a state of statistical control. A control chart must be viewed as a living docu­ment; any indicated problem must be fixed rather than not fixed and the chart merely filed away. For a labora­tory, the only thing worse than not having one plot a control chart is to

Precise, contains bias

Precise, no bias

ignore its message. A minimum of 12 and an optimum

of 30 data points should be collected before plotting a control chart. Data must be plotted in the exact sequence in which they are collected, and the upper and lower control limits must be statistically calculated from these data. A control chart should be re­vised only when the existing limits are no longer appropriate.

When interpreting a control chart, remember that the points that fluc­tuate within the control limits are a natural reflection of the inherent vari­ability or noise of the process. If the process is in control, leave it alone!

There are several statistical rules for identifying out-of-control results (15, 16). For example, a single point outside the control limits; a run of 8, 9, or more points in a row above or below the center line; 6 consecutive points increasing or decreasing; 14

Not precise, contains bias

Not precise, no bias

points in a row alternating up and down; 2 out of 3, or 4 out of 5 succes­sive points in the area near the outer control limit; a sawtoothed pattern; or sudden shifts in data can all indi­cate the need for appropriate action.

There are different kinds of control charts based on the type of data be­ing measured (Table II). Most com­monly used in the analytical labora­tory are individual , average, and range charts. The average (X) and the range (R) charts are almost al­ways plotted together; X is the aver­age of several observations, and R is the absolute difference between the highest and lowest values within a subgroup.

In the individual (I) chart, individ­ual values are plotted in the form of an average control chart for the sub­group size η = 1. This control chart is widely used in the chemical industry because of the cost of testing, test turnaround time, and time interval between independent samples. The individual chart is often the practical choice because only single observa­tions are available.

More complex control charts in­c l u d e c u m u l a t i v e s u m m a t i o n (CUSUM) and exponentially weight­ed moving average (EWMA) charts. A new chart that combines the ad­vantages of many of the traditional charts has been in use for three years in several Exxon Chemical and Re­finery laboratories worldwide. The control/performance (C/P) chart was developed by Gerald Shea of Exxon (17), and is more popularly called the "Swiss army knife" of control charts because it provides almost all the in­formation that can be derived from average, range, CUSUM, and indi­vidual charts with run rules as well as the s u m m a r y h i s tog ram. The chart limits are calculated from 2 0 -30 data points as well as their mean value and the standard deviation by determining the average range be­tween the measurements. The con­trol limits are drawn as the mean ± 1σ, 2σ, or 3σ as bias, warning, and action lines, respectively. Individual points are plotted on the I chart and on the histogram, and a 20% trend line is drawn through the points, which makes it equivalent to an EWMA plot. The 20% t rend line smooths the data to show either a constant average or a shift in the av­erage.

When interpreting the C/P chart, the following questions are consid­ered: Is a reading beyond the action or warning line? Is the trend line be­yond or approaching the bias lines? Does the histogram display the cor-

Figure 2. Illustration of the fact that precision and accuracy are not always equivalent.

(Adapted with permission from Reference 14.)

ANALYTICAL CHEMISTRY, VOL. 63, NO. 13, JULY 1, 1991 · 679 A

REPORT

rect bell shape? Figure 3 shows a typical C/P chart

with data obtained for the sulfated ash test (ASTM D874) on a gasoline additive. Colored sigma zones make the changes readily visible, with col­ors similar to traffic lights. The bias lines (1σ) are green; da ta within these lines are under control. The warning lines (2σ) are yellow; data within these lines are OK, but should be watched carefully so that any out-of-control tendencies will be spotted as soon as they develop. The action lines (3σ) are red; data within these lines are out of statistical control,

Table II. Types of control charts

and corrective action must be taken to bring the analyses under control.

Total quality management The keystone of total quality man­agement (TQM) is the concept of a customer and a supplier working to­gether for mutual advantage. Audit certification by an independent body is an effective means by which an or­ganization can demonstrate to i ts customers that it is operating under a quality system.

Audits compare actual practice with some concept of good practice. Most quality audits are used by com-

Type of data Subgroup size* Chart to use

Classification Variable Percent defective Count Variable Incidences per unit Count Constant Number of incidences Measurement 1 I, C/P Measurement Constant (>1) X, R Measurement Variable X,R

"The subgroup is a small group of measurements, usually four or five, taken at nearly the same time.

panies to evaluate their own quality activities as well as those of their vendors, licensees, and agents. Regu­latory agencies also use quality au­dits to judge the quality activities of the organizations they are assigned to regulate. For example, a number of automotive manufacturers have instituted TQM audits of petrochem­ical producers to ensure consistent quality.

The International Standards Or­ganization (ISO) is a Geneva-based body consisting of member bodies from 90 countries, including ANSI (American National Standards Insti­tute) from the United States, AFNOR (Association Française de Normalisa­tion) from France, BSI (British Stand­ards Inst i tut ion) from the United Kingdom, and DIN (Deutsche Insti­tu t fur Normung) from Germany. ISO issued its standards for TQM in 1986, based on existing standards in the Uni ted Kingdom, the Uni ted States, and the Far East. Sixteen western nations have accepted these ISO standards in toto, changing only the designation numbers.

The ISO individual standards look for documented quality in all aspects of business, including purchasing,

CONTROL-PERFORMANCE CHART DFTFRMINATIOC

SULFATED ASH (SASH) SAMPLE

LUBRICANT A D D I T I V E

PfRIOD COVFRF.D

4 / 4 / 9 0 - 8 / 3 0 / 9 0 METHOD

D874

SAMPLE

LUBRICANT A D D I T I V E

PfRIOD COVFRF.D

4 / 4 / 9 0 - 8 / 3 0 / 9 0 METHOD

D874

SAMPLE

LUBRICANT A D D I T I V E

x = 7 . 0 8 σ . 0 . 1 1 4 UNIT OF MEASUREMENT

WT.%

SAMPLE

LUBRICANT A D D I T I V E

x = 7 . 0 8 σ . 0 . 1 1 4

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1 2 3 4 5 Β

ΞΞ 7 8 10 11 1 2 13

ΞΞ 14 1 5 6 17 1

READING 7 . 0 9 7 . 2 0 7 . 2 3 7 . 0 2 7 . 1 6 6 . 9 5 6 . 8 5 7 . 0 1 6 . 8 0 6 . 9 0 7 . 1 2 7 . 1 5 7 . 1 7 7 . 1 1 7 . 1 3 7 . 1 9 7 . 0 8 7 . 0 4 DATE 4 / 4 4 / 4 4 / 4 4 / 4 4 / 4 4 / 6 4 / 6 4 / 6 4 / 6 4 / 6 4 / 9 4 / 9 4 / 9 4 / 9 4 / 9 4 / 2 0 4 / 2 0 4 / 2 0 INITIALS CLG CLG CLG CLG CLG CLG CLG CLG CLG CLG CLG CLG CLG CLG CLG NEH NEH NEH RUN 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

READING 7 . 0 7 7 . 1 4 7 . 0 1 7 . 1 0 6 . 9 3 7 . 1 5 7 . 2 6 7 . 1 2 7 . 4 1 6 . 9 5 6 . 8 8 7 . 1 2 7 . 0 ) 7 . 1 4 6 . 9 8 6 . 9 8 7 . 1 4 7 . 1 0 DATE 4 / 2 6 4 / 2 6 5 / 1 0 5 / 1 6 5 / 1 7 5 / 2 2 5 / 2 5 6 / 1 4 6 / 2 5 7 / 2 5 8 / 2 R/7 8 / 8 8 / 1 3 8716 8 / 1 6 8 / 2 1 8 / 3 0 INITIALS CLG CLG CLG CLG CLG CLG CLG NEH NEH CLG CLG CLG NEH NEH CLG CLG NEH NEH

REMARKS:

Figure 3. Control/performance chart for the sulfated ash test (ASTM D874) on a gasoline additive.

680 A · ANALYTICAL CHEMISTRY, VOL. 63, NO. 13, JULY 1, 1991

designing, manufacturing, analyzing, testing, shipping, storage, after-sales service, document control, personnel t r a in ing , u s e of s t a t i s t i ca l tech­niques, and quality management. A properly documented system is the hear t of an ISO audit. ISO stresses taking timely and effective corrective action, so that nonconforming prod­ucts are not shipped to customers.

Commitment, active support, and involvement at all levels of manage­ment and staff are required for ISO accreditation. Several teams must generally work for several years to plan, identify, and allocate resources for improving key areas before an or­ganization feels confident that it can pass an ISO audit. ISO-authorized auditors (e.g., BSI in Europe or Underwr i te rs Laboratories in the United States) audit the plant inten­sively to verify that actual practice is consistent with documented stand­ards.

Obtaining ISO accreditation is not an end in itself, but only the begin­ning of a quality spiral with continu­ous improvement. Accreditation may be withdrawn if the system is found substandard in subsequent audits . The quality system is maintained by

regular internal audits and reviews, plus up to four unannounced external audi ts per year . ISO 9000 Series standards do not restrict change or innovation; rather, they ensure that change is controlled and properly documented.

The manpower involved in these efforts is significant. TQM activities r equ i re fu l l - t ime a t t en t i on from some personnel, and all personnel must devote some time to meeting the documented protocol.

The advantages of ISO certifica­tion, however, outweigh the efforts required to achieve it. It provides tangible evidence to customers and suppliers of an organization's com­mitment to quality. It also encour­ages customers to reduce their own materials testing, and it provides lev­erage on suppliers to improve their quality standards. The disciplines of ISO are designed to eliminate the likelihood of expensive errors and to provide the means for prompt and ef­fective corrective action. Economic benefits accrue from better use of raw mater ia ls , reduced inspection costs, continuous improvement, and elimination of waste. Quality is free once you have invested in doing it

Nadkarni's 10 standards for total quality management

1. Commitment. Believe in the new philosophy of quality and show constan­cy of purpose because it's the right thing to do; it is here to stay for the fore­seeable future.

2. Leadership. Show quality behavior by example. Leadership is for every­one, not just managers.

3. Customer orientation. Be nice to your customers; they are the reason you are here. Determine their needs and educate them to help them better understand their needs.

4. Teamwork. We are all in this together—subgroups, departments, suppli­ers, customers, chemists, engineers, service staff, and research staff. Build on each other's ideas and strengths.

5. Communication. Appreciate other viewpoints. Communication is vital to all business, whether global or close to home.

6. Empowerment. As a boss, take the blame and pass on the credit. Give people power, responsibility, pride of workmanship, and recognition.

7. Statistical quality assurance. Employ statistical tools to identify causes and resolve problems. Quality assurance is an integral part of manufacturing and laboratory operations, not an added chore.

8. Training. Use continuing education for continuous improvement. Refresh what you know with further training.

9. Style. Believe in your own quality philosophy. Learn from quality pioneers, taking the best from each to suit your circumstances.

10. Pride of workmanship. Enjoy your work. However bleak things may look, you can hold your head high knowing that you did the job your way, that you did it the right way, and that you did it the quality way.

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ANALYTICAL CHEMISTRY, VOL. 63, NO. 13, JULY 1,1991 · 681 A Circle 32 for literature. Circle 33 for Sales Ren.

SmV/sec 10Pm GCE electrode Model 270 4 mM Norepinephrine

REPORT

right the first time (3). Although 16,000 European busi­

nesses have been ISO accredited, only 30 or so have been accredited in the United States. In view of the in­creasing unification of European economies, ISO 9000 accreditation will be an important requirement for doing business in the post-1992 Eu­ropean Economic Community. Some are even calling it a nonbenign trade barr ier in post-'92 Europe. Nor th American industries that ignore the ISO 9000 movement do so at peril of their future business in Europe.

There is no denying t h a t to ta l quality management can be a tough pill to swallow. But if American in­dustries and businesses are to sur­vive and prosper, this is a medicine they have to take. The essential in­gredients of TQM are shown in the box on p. 681 A. To compete in the global village of today and tomorrow, there is no alternative to quality. To­tal quality management is here to stay for the foreseeable future! To quote Edward Deming (i), "Charles Darwin's law of survival of the fit­test . . . holds in the free enterprise as well as in the natural selection. It is a cruel law, unrelenting. Actually,

the problem will solve itself. The only survivors will be companies with con­stancy of purpose for quality, produc­tivity, and service."

References (1) Deming, W. E. Out of the Crisis; MIT

Press: Cambridge, MA, 1989. (2) Juran, J. M. Quality Control Handbook;

McGraw Hill Co.: New York, 1974. (3) Crosby, P. B. Quality Is Free; New

American Library: New York, 1979. (4) Ishikawa, K. Quality Progress, Septem­

ber 1989, 70. (5) Kregoski, R.; Scott, B. Quality Circles;

Dartnell Press: Chicago, IL, 1982. (6) Imai, M. Kaizen; Random House: New

York, 1986. (7) Wood, R. C. Quality Review, Winter

1988 18 (8) Sch'erkenbach, W. W. The Deming Route

to Quality and Productivity; Leep Press Books: Washington, DC, 1988.

(9) Juran, J. M. Quality Progress, August 1986, 19.

(10) Shewhart, W. A. Economic Control of Quality of Manufactured Products; Van Nostrand: New York, 1931.

(11) Taylor, J. K. Quality Assurance of Chemical Measurements; Lewis Publish­ers: Chelsea, MI, 1988.

(12) Nelson, L. S. Presented at the MIT Conference on Managing Systems of People and Machines for Improved Qual­ity and Productivity, 1983, Cambridge, MA.

(13) American Society for Testing and Materials. Manual on Determining Preci­

sion Data for ASTM Methods of Petroleum Products and Lubricants; (RR-D-2-1007, Annual Book of ASTM Standards); ASTM: Philadelphia, PA, 1989: Vol. 5.03.

(14) ASTM Standardization News, January 1985, 45.

(15) Nelson, L. S. /. Qual. Technol. 1984, 16(4), 100.

(16) Nelson, L. S. / Qual. Technol. 1985, 17(2), 114.

(17) Shea, G. Presented at the American Society for Quality Control Annual Meeting, Oct. 1987, Atlantic City, NJ.

R.A. Nadkarni received his Ph.D. from the University of Bombay. Prior to joining Exxon, he was a research associate at the University of Kentucky and a research manager of the Materials Science Center analytical facility at Cornell University. He currently coordinates the analytical QC activity and long-range analytical di­rections of 20 laboratories worldwide.

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