EVALUATION OF QUALITY OF DEFENCE INDUSTRY ENTERPRISES AS AN ELEMENT OF LOGISTICAL NETWORK

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1 EVALUATION OF QUALITY OF DEFENCE INDUSTRY ENTERPRISES AS AN ELEMENT OF LOGISTICAL NETWORK Marian BRZEZIŃSKI, Mariusz GONTARCZYK, Szymon MITKOW and Andrzej ŚWIDERSKI Abstract: The authors present in this paper the concept of interpretation of the quality of the technical system and one of the possible methods of evaluating quality of logistical systems. This evaluation has been carried out using numerical taxonomy method. The basic model of evaluation has been shown in the form of the proposed sequence of conduct and the exemplary results of the calculations in the form of a table have been presented. Keywords: logistics, systems evaluation, taxonomy 1. Introduction The problem of evaluating the quality of logistical systems and processes is a very important issue. It is hard to imagine a rational management of logistical processes in the Army and the national economy without its objective evaluations. The search for methods to evaluate quality of logistical systems is a very important problem, both theoretical and practical. The aim of this paper is to analyse the qualitative characteristics of the logistical system and to assess the quality of the logistical system of the defence industry company. In order to solve a research problem the study incorporates was both theoretical and empirical research methods: analysis of the research subject literature, comparison, analogy, generalization, interview, synthesis and reasoning, as well as mathematical methods. To evaluate the logistical system of the defence industry company, the numerical taxonomy method was used. 2. Nature of the quality of logistical systems Quality is a concept comprehended in the following categories: philosophical, psychological, sociological, technical, economic and marketing. In a general sense, it means the characteristics, sort, value of the given object (phenomenon), or an attribute or set of attributes relevant in terms of its structure, internal interaction and relationships with the environment. A meaningful definition is given in [1]: "Quality of service is the ability of the supplier to produce an activity, at the beginning intangible and requiring client s participation, in accordance with his expectations, at least at the level required by him". This definition shows that the customer can decide whether and to what extent the service corresponds to his expectations and meets his needs. It is him who defines demands (requirements, expectations), confirming the quality of the logistical system. 19

2 Quality is a set of characteristics which determine the assessment of a particular product 1. Quality is the degree of compliance with the requirements or approach perfection 2. Quality is the set of attributes making the object which it is, and not any other 3. From an economic point of view, the quality of products (...) is the intensity of the economic characteristics (...) determining the ability of the given goods to satisfy the needs of users 4. Currently, there are three areas identifying quality: - technical - based on meeting the technical parameters of the individual elements of the system, - economic - based on economic criteria, - comprehensive - based on considering not only technical and economic factors but also technological, operational, ergonomic ones, etc. The quality of logistical systems can be evaluated according to their hallmarks. Classification of these features may therefore be done with regard to: - object of evaluation, - subject of evaluation, - aspect of evaluation, - scope of evaluation, - way of expressing the evaluation result. As far as the object of evaluation (the one who formulates the problem) is concerned, one can distinguish, among the others, following features characterising the system: - features formulated by customers, - features formulated by competition, - features formulated by suppliers of goods and services, - official characteristics (e.g. of the legislation), - features formulated by the state administration and local governments. As far as the subject of the assessment (what is being evaluated) is concerned, one can distinguish, among the others, following characterising features: - characteristics used to evaluate the quality of the individual components of the system, - characteristics used to evaluate the whole of a system. As for the aspect of the evaluation being considered one can distinguish, among the others, the following characteristic features: technical characteristics, deficiency features, time -accuracy characteristics and quality costs characteristics. Regarding the scope of the assessment there can be following features distinguished: - comprehensive features (additive) - expressing as a whole the particular aspect of assessment, e.g. overall costs of quality, punctuality criteria, - partial characteristics (single- subject), which are used to evaluate the system components. In order to give an assessment of the quality of a product, service or process of their implementation, it is necessary first to establish a set of attributes (criteria), according to which the assessment is issued. Taking into account the ability to make measurements and the method of expressing the result of the evaluation, the features can be divided into 5 : 1 Dunaj B.(red.), Popularny Słownik Języka Polskiego, Wyd. Wilga, Warsaw 1999, p Kolman R., Ilościowe określanie jakości, PWE, Warsaw 1973, p. 22, Mały Słownik Języka Polskiego, PWN, Warsaw 1968, p.3. 4 There in, p.3. 5 Hamrol A., Zarządzanie jakością z przykładami, PWN, Warsaw 2008, p

3 - measurable - can be measured and expressed in an appropriate unit of measurement; - not-measurable - can be described only in words, in the scale: - two-stage (two-state) - there are only two states of the attribute, - multi-stage (multi-state) - there are more than two states. The measurable features are also called quantities and not-measurable qualities, attributes 6. In assessing the quality of logistical systems one needs to bear in mind that the characteristic features do not need to be fixed and may vary. The variability can be inspired by: - change of the customer s requirements, - changes on the market and the competitors, - changes in the legislation, - technological and scientific progress, etc. Proper selection of the characteristic features of logistical systems is an essential element of quality assessment (Figure 1). The most common irregularities in assessing the quality of logistical systems include, among the others, the following: - improper selection of characteristic features, as a result, what is being assessed is not the problem formulated to be tackled, - lack of parameterization of the evaluation, which prevents its objectification. During the quality assessment by the customer, one can assume the following objectives: - client evaluates functioning of the logistical system from a personal point of view, - client perceives all elements of the system as a whole, - weaknesses of the system functioning adversely affect its assessment, - to the customers, the internal organization of the system is of no importance. Fig.1. Action in the process of assessing the quality of the system Source: Hamrol A., Zarządzanie jakością z przykładami, PWN, Warsaw 2008, p Taxonomy method for evaluation of quality of logistical systems The numerical taxonomy method of is designed to compare the systems characterized by an identical or similar functional purpose. The systems are being compared using the same characteristics that can be described quantitatively [2, 3, 4]. This method, as the criterion of comparison of the systems, adopts the state of quality, which is a function of the essential features of the compared systems. The components of the quality status determine appropriate relationships of domination. The absoluteness of the comprehensive evaluation indicator measurement should be interpreted as a deviation of the systems state of quality from the beginning of a numerical scale. In contrast, the relativity of measure indicates the degree of deviation 6 There in, p

4 from the object quality indicator regarded as a model system. The quality indicator of the model system is a certain number indicating the optimum (extreme) - desired value of the characteristics. Hence, determination of a comprehensive indicator takes place by aggregating characteristics using model or non-model method. The model method involves analytical interdependencies determining the distance of the tested system from the reference system. In turn the non-model method the aggregation of characteristics involves averaging normalized values of characteristics describing a given system. The algorithm of proceeding in the numerical taxonomy method is shown in Figure 2. Identification of the system constraints is to determine the boundaries of the values of the attributes characterising the system in which it will operate. They will be qualitative constraints, quantitative, spatial, temporal, information, economic ones and others. There are internal and external constraints. The internal constraints are a function of the potential of the logistical system. In contrast, external constraints, independent of the system tested, the result from the dynamics of environmental change. As a result of the analysis of requirements and system constraints there will be a sub-set of admissible variants created, in which there will be variants possible to be applied and a sub-set of variants impossible to apply. 22 Figure 2. Algorithm of proceeding using numerical taxonomy method for testing the systems. Source: Brzeziński M., Systemy logistyczne, WAT, Warsaw 2007, p In the next step, it is necessary to specify the set of all characteristics of the systems studied, and then select a sub-set of features relevant to evaluated systems, which represent evaluation criteria. Each system compared can be described by any number of features. The choice of features has the greatest impact on the outcome of a comparative assessment, and thus on the accuracy of the decisions made.

5 The essential features characterizing the systems can be measured directly or indirectly. The measured values are real figures with denomination, i.e. measurable in the physical sense. The indirectly measurable trait is a one of the values contained in the set of integers. The verbal description is, in this case, the output form of expression of the value of a feature, then assigning to it the numerical attributes. The set of essential features should only contain only ones that are of sufficient variation of the value while transiting from variant to variant. Thus, the condition of sufficient variability is another formal postulate of the selection of relevant features. The essential parameters are characterized by varying direction of affecting the comprehensive system quality indicator. This impact can be positive, negative or neutral. From this point of view the characteristics can be divided into stimulants, destimulants and nominants. As a stimulant we will call such diagnostic variable, whose increase must be associated with an increase, and a decrease with a decrease in the assessment of the phenomenon. In contrast, the destimulant will be such a diagnostic variable whose increase should be associated with the decline, while the decline with the increase in the assessment of a complex phenomenon. The nominant, in turn, is such a variable that has a specified, the most advantageous (from the point of view of assessing a complex phenomenon) value called the nominal value. Nominant takes on values lesser or greater than the nominal value, respectively, with a decrease in the assessment of a complex phenomenon. Encountered in practice are the situations where the nominal values form specified numerical interval. Any deviations of the nominants from the normal level are negative phenomenon from the perspective of the efficiency indicator examined. The nominants can be easily converted into destimulants by setting the absolute deviation of a given value from the level considered to be the nominal one. Establishing a set of characteristics relevant to the systems being compared, as well as their classification is the basis for implementing later stages of the method. In order to establish a uniform denominationless grading scale of the systems features one conducts their normalization. It is based on converting absolute values of the characteristics into relative values. The normalization is carried out according to the equation: C in = C in C i S i for i = 1,2,, I and n = 1,2,, N (1) Where: C in normalized value of the feature, Cin the value of the i-th features of the process of the number n, Ci average value of the i-th characteristics of the calculated from the equation: C i = 1 N C N in n=1 (2) The standard deviation Si of the i-th characteristics is calculated from the equation: S i = 1 N N n=1 (C in C i ) 2 (3) where: l- number of features by which we evaluate the system; N- number of system Then it is necessary to make a choice of, so called model (reference) system, i.e. an abstract system established by the collection of the best values of the characteristics - Coi from the set of all the features of the systems. C oi = min C in when C in is n destimulant max C in when C in is n stimulant (4) 23

6 After determining the reference system it is necessary to calculate dispersion between of the standardised values of features and model characteristics according to the equation: δ in = C oi C in 2 for i = 1,2,, I and n = 1,2,, N (5) Determining a comprehensive efficiency indicator based on the specified features of the system requires establishing relative weights of the individual characteristics. Determining relatively objective weights is important for the final result. Determining the weight values can be determined using the preferences of experts or statistically. Taking into account the weighting factors, one can calculate the "distance" between the characteristics don of the system under consideration and a model solution from the equation: d on = I i α i δ ni (6) where i weight coefficients for the characteristics of i number. Aggregation of the system characteristics is an operation allowing to obtain a comprehensive assessment segregating systems compared. Aggregation can be made by the model method or non-model method. The model method uses analytical interdependencies to aggregate attributes determining the distance of the tested system from, so-called, model system. The model system may be an ideal system, or so called anti-model, which is potentially the worst system. However, in the non-model method the operation of aggregating characteristics of the system is based on averaging the normalized values of characteristics describing the given system. Aggregation of the parameters can be done from the analytical inter-dependences determining averages: arithmetic, geometric and harmonic mean. In order to establish a uniform, denominationless scale of assessments, the standardization of them is carried out. The calculations made using one of the methods - the model or non-model one, of the comprehensive systems evaluation indicators - are subject to the normalization to the interval [0,1]. For this purpose, the average value and the variance, needs to be determined in a set of distances, from the equation: d o = 1 N N n=1 d on (7) D o 2 = 1 N N n=1 (d on d o ) 2 (8) Then the limit value is determined in the form: 24 d o = d o + 3 D o 2 (9) The comprehensive system evaluation is determined from the equation: χ = 1 d on d o (10) As mentioned earlier, comprehensive system evaluation indicators are numbers from the interval [0,1]. The numerical taxonomy method may be used e.g. for comparison of logistical systems, selection of logistical concept according to the criteria adopted by the evaluator. It allows a quantitative assessment of systems based on an identical set of measurable and immeasurable qualities. The greater the number from the interval [0,1], the higher the quality of the logistical system. Numerical taxonomy method was used to assess the quality of four logistical systems, which are characterized by fourteen following attributes: 1 - indicator of ensuring the needs of the operating system by the logistical system [%];

7 2 - duration of the implementation of the logistical system for the enterprise s needs [h]; 3 - deviation from the date of supply [h]; 4 - the number of customer complaints about the functioning of the logistical system [1 / year]; 5 - indicator of the transport means technical readiness [%]; 6 - indicator of the transport means utilization time [%]; 7 - indicator of the transport means mileage utilization [%]; 8 - indicator of damage to the goods in transit [%]; 9 - warehouse space utilization indicator [%]; 10 - indicator of damage to the goods during storage and loading work [%]; 11 - coefficient of the internal transport means utilization [%]; 12 - management span in the logistical system [Quantity]; 13 - logistical staff turnover [%]; 14 - logistical costs in relation to sales results [%]. The values of the parameters of characteristics and the results of logistical systems evaluation are shown in Table 1. The analysis conducted shows that the highest comprehensive evaluation index received system 4. (ᵡ4 = 0,357), slightly lower, system 1. (ᵡ1 = 0,348). The evaluation used parameters that have been quantified so that the assessment outcome was objective, to the highest degree, and independent of the evaluator. In contrast, the evaluator could influence the selection of features of the evaluated systems and the weight coefficients (weights adopted were i = 1). However, the algorithm of proceeding in the numerical taxonomy method allows a little subjectivity of proceeding. 4. Conclusion The issues raised in the article on the evaluation of the quality of logistical systems are not exhaustive of all relevant, mainly from the perspective of clients, issues in this area. Other methods and issues require separate consideration: improving the quality of logistical systems and the use of appropriate methods and tools in the area of quality Qualitology, described in the literature [5], mathematical modelling of quality assessment, enabling its parametric evaluation, the use of taxonomy method in the integrated logistics design process in the context of the weapons systems lifecycle management, including in the evaluation of hardware the preferences for requirements of the decision makers and the logistical systems end users. 25

8 Table 1. Logistical system quality evaluation Systems S j Features X i , Average value Ci 95,750 11,500 1,125 9,750 95,750 73,500 49,000 1,750 66,500 3,500 76,750 14,000 10,000 23,750 Standard deviation Si 1,281 0,854 1,252 3,010 1,675 5,105 1,708 0,826 9,375 0,854 11,856 5,292 1,414 4,492 Standardized feature Si* 1,757-0,586 0,195-1,366 0,586 1,757-0,586-1,757 1,498-0,899-0,100-0,499 0,083 1,744-0,581-1,246-0,448-1,641 0,746 1,343 1,273-0,490-1,077 0,294 Model set-feature Coi 1,757-1,757-0,899-1,246 1,343 1,273 1,757-0,908 1,227-1,757 1,18 1,134-1,414-1,948 0,000 5,486 5,746 1,767 3,206 0,000 1,371 5,863 0,102 1,371 5,578 0,000 2,000 Dispersions dni 5,486 12,343 0,000 8,943 8,905 3,108 12,343 0,000 7,111 5,486 2,846 0,143 0,000 2,438 1,371 0,638 0,442 0,356 5,525 5,486 1,466 3,686 12,343 0,000 5,143 8,000 9,752 0,000 0,160 0,000 0,000 0,959 0,000 0,000 0,000 0,000 0,178 3,571 2,000 Weights i Distance don do1 do2 do3 do4 0,586-1,757-0,586 1,757 6,119 8,168 6,938 6,037 Average value d o 6,815 Variance D o 2 0,734 Limit value do* 9,385 1,513-0,908 0,303-0,908 0,907-1,440-0,693 1,227-0,586 0,586 1,757-1,757-1,244-0,569 1,118 0,696 1,134 0,756-1,134-0,756 0,000-1,414 1,414 0,000 0,278-1,948-0,835 2,504 4,955 0,000 1,239 19,819 System evaluation ᵡn ᵡ1 ᵡ2 ᵡ3 ᵡ4 0,348 0,130 0,261 0,357

9 It must be stated unequivocally that the continuous monitoring of the quality characteristics of logistical systems listed in the article, is crucial in providing services at the level required by customers. References [1] BRUHN M.: Qualitätsmanagement für Dienstleistungen, Springer, Berlin 2003, s.31. [2] BRZEZIŃSKI M, FIGURSKI J., KOCHAŃSKI T., Jakość systemu logistycznego, Logistyka 2012 nr 4, płyta CD, s [3] Brzeziński M., Rozwój sieci logistycznych w wojsku, Biuletyn WAT 2010, nr 1, s [4] BRZEZIŃSKI M., Systemy logistyczne, WAT, Warszawa [5] DUNAJ B.(red.), Popularny Słownik Języka Polskiego, Wyd. Wilga, Warszawa 1999, s.196. [6] KOWALSKA NAPORA E., TKACZYK ST., Strategia zarządzania jakością, Wydawnictwo Difin, Warszawa [7] KOLMAN R., Ilościowe określanie jakości, PWE, Warszawa 1973, s.22, 37. [8] KOLMAN R., Kwalitologia, wiedza o różnych dziedzinach jakości, Wydawnictwo Placet, Warszawa