(b) The term quality has a long history dating back to Aristotle’s days, while the term engineering level came into being (mainly in the Russian literature) in the last 30 – 35 years. This brings up the natural question: why use a new term if we have a long-established synonymous term?
(c) It is common knowledge that the quality of a finished product is defined by three factors: the quality of its design, the quality of its raw materials and semi-finished products, and the quality of its manufacture (that is, the extent to which its design parameters are met in manufacture).Sometimes the term engineering level refers to what is termed design quality in qualimetry.
Then the question arises: why introduce a new term, engineering level, if we can do with the good old term, quality (or more precisely, design quality)?
For these reasons, in the science of qualimetry (and in this ABC) the term engineering level is not used.
The term technical excellence is an absolute synonym of engineering level. Therefore, all that was said above regarding engineering level applies to technical excellence.
The term utility describes a property that characterises the aggregate of quantity and quality of an object (see, e.g., [1]).
For example, the utility of two houses is greater than that of one of exactly the same quality. However, utility and quality means the same thing when applied to one unit of quantity of an object. That is to say, we can assume that quality is the utility of one unit of quantity of an object. Since the quantitative estimation toolbox is better designed for quality than for utility in what follows we will use mainly the term quality, that is to say, consider mainly objects whose number is equal to one unit.
The term value is synonymous with utility but its use is normally restricted to the philosophical literature. All that we have said above about utility holds for value.
Concept of use value. If as shown above, quality is the utility of an object unit (that is, a property inherent in the object),use value is the object possessing this property, i.e. utility. As applied to an object whose quantity equals unity, use value is the object possessing this property whose quantity equals unity (see [1]). As the subject matter of this ABC is the quality of an object (e.g., the quality of life) and not its quantity, hereafter the concept of use value will not be generally used and our exposition will be in relation to the concept of quality.
The term efficiency has many different interpretations. With regard to the most commonly used one it is very close to integral quality. However, because of its ambiguity we will use it instead the term integral quality. On the other hand, since most of the statements relating to the concept of quality remain in force and applicable to the concept of integral quality, the latter will be used hereafter only in specified cases.
We introduce some more concepts related to the concept of quality.
Property / quality / integral quality index. Is a quantitative characteristic of a property / quality / integral quality.
Index value. Is a specific numeric value that an index can take. For example, the values for the property index “room temperature” can be 20° С or 22° С. Here the numerals 20 or 22 are the values of the property index. Similarly the term index value can be illustrated (this time in dimensionless units) with reference to quality. Let the quality index be expressed by the symbol Кк. Then in the expression Кк = 0.68 the numeral 0.68 is the value of Кк.
Where quality is analysed in general terms (i.e., not in a numeric but in an alphabetic form) the value of the index is expressed not by a numeral but by a lowercase letter (as opposed to the index itself, which is always denoted by a capital letter). For example, the expression KК= k1K reads as follows: the quality index KK has the value k1K. This applies to a quality index but also to a property index, an integral property index, etc.; to any index at all.
After we have clarified the meanings of the basic concepts related to the term quality we can analyse concepts related to the term control, which is in practice often linked with quality (e.g., in phrases like “product quality control”).
1.1.2. The Term Control and Its Difference from Other Similar Terms
Let us denote a given time point byt1and a time point in the future by t2 (obviously, t2>t1). Let us denote by ΔT the time elapsed from t1 to t2: ΔT= t2 – t1.
Let us define our terms:
Pre-settime ΔTSET: a time period ΔT, the value of which is pre-set by a human controller.
Indefinite period of time ΔTi: a time period ΔTi the value of which is not pre-set/defined by human controller.
Let us introduce some terms:
Object state: the state of an object at an instant defined by its quality whose index has the value kK.
Given object state: the state of an object at a given (initial) instantt1at which the value of its quality index is k1K.
Future object state: the state of an object at a future instant t2at which its quality index will be k2K.
Quality variation: a value given by the expression ΔKK = k2K– k1K.
Pre-set quality variation ΔKKPRE: a quality variationΔKK the value of which is given in advance by a human controller.
Indefinite quality variation ΔKK?: a quality variation ΔKK the value of which is not given by a human controller.
Object quality control: the transfer of an object from a given state k1K to a future state k2K at ΔKKPRE with in ΔTPRE (To rephrase it, to control the quality of an object is to ensure in the object a pre-set quality variation ΔKKPRE with in a pre-set time ΔTPRE).
It follows from this definition that if any of these conditions were not met (e.g., indefinite timeΔT? instead of pre-set time ΔTPRE or in definite quality variation ΔKK? instead of pre-set quality variation ΔKKPRE is used) it would be improper to refer to it as quality control. In actual fact a different process is in progress. Table 1 shows different processes and their relation to the quality control process.
Table 1. Kinds of processes related to variation in the quality of objects. NOTE: Lines 10 and 11 represent situations, in which quality control in the ordinary sense is indeed exercised
Table 1 lists twelve situations differing in their combinations of ΔKK (quality variation) and ΔT (time variation). Each has an associated process type related to quality variation, from total uncertainty to quality control, which may vary within pre-set limits within a pre-set time.
Regrettably, in practice the term quality control is frequently applied to processes that can at best be described as quality improvement (see, e.g., line 4 above).In these processes (which in most cases concern industrial products) the value of an object’s property index could be improved by so many per cent within a pre-set time; e.g., the life of a component part could be increased by 30%.It is then concluded that the quality of the object improved by the selfsame 30% supposedly as a result of quality control.
There are two principal fallacies here. One is that the magnitude of increase in the value of the quality index was determined incorrectly, taking no account of the fact that an improvement in the value of a property of an object by α% almost always leads to an improvement in its quality index by β% (with α<β).
The second fallacy is neglect the following: a quality improvement in one property of an object will result in an improved quality index of the object to the extent that none of its other property indices has deteriorated. Yet, this is a fairly common occurrence. Let us suppose that in the above case a 30% increase in the life of a component part is often accompanied by an increase in its mass. This leads to a deterioration of its “product mass” property by so many percent. Unless we make a qualimetric calculation we cannot say a priori whether – and by how many per cent – the quality of the product deteriorated or improved. (Proofs of both these assertions are to be found in books on theoretical qualimetry; see, e.g., [2]).
Therefore, it often happens in practice that the term quality control is applied to processes which, in control theoretic terms, cannot be considered quality control and, not infrequently, cannot be even called quality improvement because in reality they only ensure some indefinite quality variation (see lines 2 and 5 in Table 1 above).
The grey background in Table 1 is used to highlight two lines, 10 and 11, which represent the criteria to be met if we are to have a real quality control process. Line 10 describes the conditions under which, as common sense tells us, quality control is really achievable. That is to say, it is about a quality improvement is achievable to a pre-set extent within a pre-set time.
The case introduced by line 11 also belongs to control processes, though it is less apparent in the usual sense. Its only difference from case 10 is that the latter achieves a quality improvement (accordingly, ΔK
K
K
The process described in line 12 is also related to quality control is totally unobvious to common sense. In pure theory, however, one can imagine a situation where the goal is not to increase but to decrease the quality of a product within pre-set limits and within a pre-set time, e.g., in order to cut production costs so as to boost demand. Since this is more academic than a real-life situation the respective line (12) in Table 1 was not highlighted with grey.
The foregoing interpretation of quality and quality control suggests that if we are to control quality we must be able to calculate the values of ΔKK. To do it we must, in turn, be able to quantify or estimate quality using its index KK. Consequently, we need a tool for the quantification of quality, which is provided by qualimetry.
There were also other factors, which made the appearance of qualimetry necessary, even inevitable. They will be discussed in the section that follows.
1.1.3. The Origin, Growth and Future of Qualimetry
1.1.3.1. The Reasons Behind the Rise of Qualimetry as a Science
Qualimetry is a consequence of knowledge quantification
The term qualimetry (from the Latin quale, “of what kind”, and the Greek μετρεω, “to measure”) was initially applied to a scientific discipline studying the methodology and problems of quantitative assessment of the quality of various objects, mainly of industrial products [3]. By 1970 enough experience had accumulated to permit a thorough investigation of qualimetry, its subject matter and its relations with various scientific fields. At the same time there was a growing awareness of the need to expand the scope of qualimetry from product quality (which was the focus of some researchers) to the quality of objects of whatever nature, including socio-economic objects such as the quality of life.
When the term (and the respective concept) was first used it seemed unexpected, almost fortuitous; some still regard it so.
However, it would be wrong to speak of the fortuity of qualimetry. On the contrary, its appearance should be seen as one of the many perfectly natural signs of the general broadening of the scope of quantification and the use of quantitative methods in scientific and, generally, cognitive activities at large.
The universal and imperative nature of this tendency to expand the use of quantification as a major tool of cognition was succinctly stated by Galileo, who said “Measure what is measurable, and make measurable what is not so.” The Russian Mathematician D. B. Yudin expressed nowadays essentially the same idea: “Quality is a yet unknown quantity”.
Many great minds were aware of the important influence that mathematics, as a general framework of quantification techniques, has exerted on the development of science.
K. Marx was of the opinion that a subject could be called a science if it had a mathematical foundation. A century before him, I. Kant wrote in his Metaphysical Foundations of Natural Science, “I maintain, however, that in every special doctrine of nature only so much science proper can be found as there is mathematics in it”. Three centuries before Kant, Leonardo made a similar statement: “No human investigation can be called real science if it cannot be demonstrated mathematically”. Five centuries before Leonardo, in the 9
th
Quantification is steadily broadening its scope of application, as evidenced by the growth of scientific disciplines or technical problem solving techniques that include the Greek μετρεω in their name. Here are a few examples:
Absorptiometry; autometry; autorefractometry; adaptometry; axiometry; actinometry; algometry; amperometry; angiostereometry; anthropometry; astrocalorimetry; astrometry; astrophotometry; audiometry; acidimetry; batimetry; biometry; bibliometry; veloergometry; visometry; viscosimetry; gigrometry; hygrometry; hydrometry; glucometry; gravimetry; gradiometry; densitometry; didactometry; dilatometry; dynamometry; dielectrometry; dosimetry; dopleometry; isometrym impedancemetry; inclinometry; interferometry; cliometrics; calipometry; calorimetry; chelatometry; conductometry; craniometry; coulometry; lipometry; luxmetry; mediometry; mercurimetry; morphometry; scientometrics; nitritometry; optometry; ordometry; oscillometry; optometry; perimetry; pirometry; pH-metry; planimetry; polarimetry; psychometrics; potentiometry; pulseoxymetry; radiometry; radiothermometry; redoxmetry; roentgenometry; refractometry; sensitometry; sociometry; spectrometry; spectroradiometry; spectropolariometry; spectrophotometry; spirometry; spiroergometry; stabilometry; stereometry; sphincterometry; tacheometry; tensometry; technometry; tonometry; turbidemetry; uroflowmetry; fluorimetry; photogrammetry; photocolorimetry; photometry; chronometry; equilibriometry; econometrics; exponometry; electrometry; echobiometry. Qualimetry is also a member of this steadily expanding family. (It would be wrong, however, to believe that every discipline using quantification has metry / metrics in its name.)
Qualimetry: A Tool for Enhancing the Efficiency of Any Kind of Work
What happened for the qualimetry to appear in the 1960s?
Modern management science has formulated five necessary and sufficient conditions for the success of any work, which can be represented by a “condition tree” (Figure 1).
Figure 1. Necessary and sufficient conditions for the success of any work
Four of these conditions, TO KNOW, TO BE ABLE, TO MANAGE, and TO MOTIVATE, are relatively easy to meet technically; regulatory documents for respective calculations are already in place. For example, every productive industry uses its own rate setter’s handbook (or a similar document), which is used to calculate the workforce and the time and tools needed to perform a piece of work (TO MANAGE condition). Other documents, like wage rate books, specify the requirements to be met in selecting the workforce to do some work successfully (TO BE ABLE condition).It is relatively easy to secure the TO KNOW condition: you only need to set the work executors a task. Finally, to meet the TO MOTIVATE condition all businesspersons or managers have a broad range of stimulatory actions they can use on their subordinates: material or moral; positive (“carrot”) or negative (“stick”); individualized or team-directed; one-off or time-phased, etc.
The TO EVALUATE condition is a very different case. What we evaluate is work. Any work (and its output) is characterized by three parameters: quantity, cost and quality. Arguably, the numerical evaluation of the quantity and cost parameters does not present any essential difficulties to most occupations in the real sector.