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Multivariate Statistical Analysis on Logistics

Discuss about the Multivariate Analysis in the Logistics.

This research presents the most used techniques of multivariate statistical analysis. Its content integrates multivariate descriptive methods of data mining, and pattern recognition, with statistical inference procedures for vectors of variables. The first part explains the methods to describe data sets, where many variables are observed in each element and It can serve as a text for a four-month course oriented to applications in logistics, Engineering and transport. The second part explains the methods of construction of statistical models for multivariate data and can be used in a more advanced course for statistical forms, or experts in market research, quality methods, social or environmental researchers. In general, the presentation of the material always takes into account the explanations, but the topics are treated rigorously so that the text can be useful for different audiences. The exposed methods are illustrated with real examples, and several data banks have been prepared (Ala-Harja, H. and Helo, P., 2015).

The possibilities currently offered by Logistics have motivated that in many fields of research scientific data is available with large volumes of data, with the cost of processing the information that entails, in terms of time and material means.

The greater the volume of information available, the more likely it is that the data a common or very related content, that is, containing redundant information, negatively affecting the study of the phenomenon of interest. Therefore, it is necessary to have tools that simplify and improve decision-making processes, such as statistical techniques of data reduction (Brizzi and Heuret, 2018). Its main objective is to reduce the number of variables available to a smaller set of mutually incorrect factors, obtained as linear combinations of these variables, which explain a high percentage of the information contained in the original variables, in terms of variance, eliminating the redundancy existing in the data and with the least possible loss of information.

Business, which is one of the most important areas within the company, due to the consequences of all kinds that the crisis or disappearance of a company has on the set of agents that operate in an economy. In particular, we determine the economic-financial factors that allow characterizing the failure of business through its selection by the ACP and which may be the starting point for the development of prediction models of failure (Gutierrez and Martins, 2015). For this reason, in the study empirical that we carry out, we selected a sample that includes both companies failed as not unsuccessful, in order that in the same they consider companies that they present a differentiated behavior regarding the phenomenon of failure.

Application of Multivariate Statistical Techniques

Managing a complex economic entity involves optimizing the flow through it (converting resources into goods) at all stages of its movement. Hence the most important principle of effective management is the principle of global (in terms of full coverage of the controlled process) optimization. However, limiting the subject of logistics to only "logistics" operations and applying the criterion of a minimum of "logistics" costs makes it impossible to adopt globally optimal solutions(Li and Luo, 2016.

A possible reason for the technological emphasis of "logistics" optimization is the uncertainty of which type of costs - gross or medium - is subject to minimization. If it is a question of gross expenses, then, firstly, one must understand that they are generally not minimized, because as output increases, they always grow. Minimization of gross costs as a selection criterion is applicable only in the aspect of comparative effectiveness of alternative variants identical under all other conditions (and primarily in terms of output). But all the creative potential of logistics is precisely connected with the rejection of this identity. Secondly, the inclusion in the analysis of the parameters of time and place of delivery not only expands the space of permissible choice, but also raises the question of optimizing logistics(Gutierrez and Martins, 2015).

In logistics, a lot of solutions are developed based on knowledge, logic, intuition, and calculations can be simple or complex, depending on the "price of the issue." For example, a logistics company will refuse to inventory hundreds of penny goods in a warehouse, despite the requirements of an accountant. Without calculations it is clear that stopping shipments and diverting tens of employees from the work to recalculate cheap goods, the possible shortage of which is estimated by insignificant amounts, means losing revenue due to a stoppage of shipments(Brizzi and Heuret, 2018). Even arguing about it with an accountant is a waste of time. It is more reasonable to calm the accountant with a fictitious inventory sheet indicating how real those quantities are counted according to accounting. In fact, they do so in many warehouses. If penny goods are of particular importance (medicines in the hospital, components for assembly in the factory, etc.), then the inventory is done daily, and it is part of the technological process(Bouzon and Campos, 2016).

Logistics specialists need extensive knowledge, familiarity with the capabilities of various branches of science, information technologies and technologies of moving in the space of material objects and information, technologies of motivation and stimulation of people. Only thoroughly erudite specialists can confidently rely on their intuition and logic in solving logistics problems.

The Terms

None of the dozens of definitions of logistics encountered in the literature disclose its actual functions. The term "logistics" as applied to the sphere of production and circulation has been widely used since the 1970s. In the same sense, it is used now by the armed forces of some countries. In the economy, logistics was firstly called scientific and practical actions that optimize transportation processes. Later, it was called actions that optimized almost all the processes of production, distribution, transportation and sales(Rezende, M.,  and Reis, 2016)..

In view of the fact that for three decades neither scientists nor practitioners have developed a single definition for the term "logistics", this word has been replaced by many others - from the name of science to the name of individual processes on(Manly and Alberto, 2016).

Practitioners need to know in order to drop a lot of incorrect or meaningless versions of the application of the word "logistics", that logistics is not a routine execution of the movement of goods. Logistics is the creative organization of all processes that ensure the movement of the flows of materials, people and information in the most optimal ways. And creativity includes both logic, intuition, and calculations, and knowledge. Development of logistics Practices are ahead of science. If theorists still defend the thesis on "old logistics", then the practitioners are simply forced to master "new logistics ".

"Old Logistics" includes: - management of the logistics channel; - management of personnel engaged in logistics activities; - cooperation with logistics service providers; - Integration of the elements of the physical distribution function. "New logistics" includes all the elements of the "old logistics" and, in addition: - mastering of major technical achievements in supply practice; - cooperation with the heads of all functional services, consumers and suppliers; - selection and development of the best systems for performing work, often changing elements inside and outside the company. "The old logistics" is focused on reducing costs. "New logistics" - to increase profits even at the cost of increasing costs. "Old Logistics" has limited rights in interfering with the activities of units. "New logistics" has no limitations and can reorganize the activities of any unit. "Old Logistics" encourages the creative reorganization of the familiar elements. "New logistics" allows you to creatively approach unfamiliar spheres of activity. Logistic systems Types of systems For the introduction of modern logistics methods, the following developments are required: - the ideology and concept of the distribution system; - technical assignments for design, programming, document circulation, instructional and educational materials(Varmuza, K. and Filzmoser, P., 2016). Consumers benefit from such organization of the supplier that will ensure the receipt of the goods at the right time, in the right place, in the required quantity and condition at the lowest cost. Therefore, the logistics of the promotion of goods through the distribution channels has received the greatest development in recent years as a promising means of increasing competitiveness. Traditional means - improving the quality and differentiation of goods and services, reducing prices and activating advertising - do not allow people to stay ahead of the competition for a long time - and new products, new services and new methods of work are almost immediately copied by competitors. Only the use of logistics, the possibilities of which have just begun to be used, can provide a significant increase in profitability of activities. Expenditures on the distribution of goods reach 25% of gross income, and investments in commodity stocks often exceed 40%. Due to new technologies for managing the distribution and movement of goods, it is possible to significantly reduce the costs of distribution and maintenance of commodity stocks, making these processes more efficient. Thus, to improve the quality of customer service, that, in the distribution process.

Importance of Multivariate Analysis on the Company

Finally we must not forget a very important consideration in the same way that we determine SWOT analysis (Strengths, Opportunities, Weaknesses and Threats) for our company in general, we must do it for each particular logistic service, based on all the data collected and the characteristics and existing possibilities at the moment. We will take advantage of the business Opportunities that the environment provides, we will minimize our exposure to the existing Threats (or we will try to change a Threat turning it into an Opportunity), we will strengthen and support ourselves in our Strengths, also reducing our internal Weaknesses (Bouzon and Campos, 2016).

  1. The logistic service does not have clients yet: if there has been any technological innovation without its potential customers being sufficiently informed about its advantages, or ready for its use. Should be informed about them.
  2. It does not cover a real need: it is more an infatuation of its managers, who believe that it will be a success, than a satisfaction of a certain need.
  3. Sales of the logistic service are not as expected because there is not so much demand: a very common error when solid market studies are lacking, and more work is done by the managers' intuition than by contrasted data.
  4. Customers do not perceive the difference between our logistic service, and that of the competition: either because of a wrong communication strategy, or because of an absence of differential factors, the truth is that our logistic service ends up being "one more" of the already existing ones in the market.

Bad distribution: choice of the wrong channel, lack of supply at points of sale, lack of coordination with the start of the advertising campaign, etc.

  1. Problems with the price: the customer does not respond well when comparing the quality of the logistic service, with its price. Do you consider it very expensive for what it is? Or perhaps you suspect its quality, seeing a cheap price? Over time, it shows that it is not profitable: multiple causes, but among them figure the use of very low prices in the intention to penetrate quickly, or to wage constant wars of prices with the most solvent and powerful competitors, which end up bleeding the company(Marcoulides and Hershberger, 2014).
  2. Problems with the packaging: either because it is not recyclable, or its size or shape makes it impractical.
  3. Problems with the brand: it may be due to the fact that it is a name that is difficult to retain, or slightly suggestive.
  4. Design or logistic service failures: the logistic service has defects that become evident at certain times, or under certain conditions of use.
  5. Perpetuate the same design and features even if time passes and trends evolve: staying with the original development without innovating or constantly improving, makes us very vulnerable to competition. Unless our logistic service becomes the undisputed leader, we will also appreciate a decrease in sales volume.
  6. Powerful competitors: When the market has competitors positioned as undisputed referents, with large logistic service and distribution chains, highly motivated sales teams and a considerable market share, facing them with a logistic service whose advantages can easily be overcome is directly exposed to failure. Other times, the error lies in not anticipating the strategies of the competition, acting reactively (and very late, when they already launched a better offer) instead of doing it proactively.
  7. Wrong advertising campaign: it is not located in the usual means of our client-goal, or because it does not highlight the differential advantages of the logistic service, it does not help a clear positioning, or because it fails in its attempt to strengthen the brand
  8. Force of sale with little training, or little motivated: any of these causes will cause that the sales do not reach the expected level here are many methods of multidimensional data analysis, but they are disjointed and, as a rule, irreducible to a single whole.

The other types of distributions (nonlinear, nonparametric, robust, neural, etc.) empirical statistics do not know or almost does not know. What is the solution in this case? The laws of some distributions are replaced by others, a leapfrog is introduced or, worse, the question of the hypothesis is simply ignored. Of course, this is an extreme case. Specialists, of course, usually turn to so-called combinatorial methods or artificially fit the observed data, cutting off those data sets that do not fit into their schemes, qualifying such data as abnormal, random or degenerate. This sometimes gives good results, but in most cases is far from a constructive solution. Combinatorics, like the substitution of the so-called abnormal distributions and non-linear connections by normal distributions and linear connections, in essence, does not give anything: multidimensional analysis is left without clear theoretical grounds, and the results obtained are of the necessary meaningful meaning. It is clear that in the presence of different types of distributions and tasks to appeal to the same procedures for their identification and solution is impossible in principle. At the occurring imitations here, one should not pay attention at all (Syazwan and Bakar Abdul Hamid, 2014). In essence, this means a transition from multidimensional methods of solving corresponding classes of problems to one-dimensional methods (Varmuza, K. and Filzmoser, P., 2016).


In addition to methods of correlation study of connections, methods of component, factor, discriminant and cluster analysis, modeling and comparison of data that represent the subject of this training manual are of particular importance (Y?ld?r?m, G. and Tokal?o?lu, ?., 2016). To solve problems of this kind with the help of methods presented in the manual, the author the present work. it not only encourages, but also methodically, step by step shows how it should be done, that it deserves approval and, in my opinion, can cause genuine interest and benefit both students and many researchers, as well as all those involved application and development of fundamental methods of modern applied statistics.


Ala-Harja, H. and Helo, P., 2015. Reprint of “Green supply chain decisions–Case-based performance analysis from the food industry”. Transportation Research Part E: Logistics and Transportation Review, 74, pp.11-21.

Brizzi, S., Sandri, L., Funiciello, F., Corbi, F., Piromallo, C. and Heuret, A., 2018. Multivariate statistical analysis to investigate the subduction zone parameters favoring the occurrence of giant megathrust earthquakes. Tectonophysics, 728, pp.92-103.

Bouzon, M., Govindan, K., Rodriguez, C.M.T. and Campos, L.M., 2016. Identification and analysis of reverse logistics barriers using fuzzy Delphi method and AHP. Resources, Conservation and Recycling, 108, pp.182-197.

Gutierrez, D.M., Scavarda, L.F., Fiorencio, L. and Martins, R.A., 2015. Evolution of the performance measurement system in the Logistics Department of a broadcasting company: An action research. International Journal of Production Economics, 160, pp.1-12.

Li, C.Z., Hong, J., Xue, F., Shen, G.Q., Xu, X. and Luo, L., 2016. SWOT analysis and Internet of Things-enabled platform for prefabrication housing production in Hong Kong. Habitat International, 57, pp.74-87.

Marcoulides, G.A. and Hershberger, S.L., 2014. Multivariate statistical methods: A first course. Psychology Press.

Manly, B.F. and Alberto, J.A.N., 2016. Multivariate statistical methods: a primer. CRC Press.

Mertler, C.A. and Reinhart, R.V., 2016. Advanced and multivariate statistical methods: Practical application and interpretation. Taylor & Francis.

Rezende, M., Loguercio, A.D., Kossatz, S. and Reis, A., 2016. Predictive factors on the efficacy and risk/intensity of tooth sensitivity of dental bleaching: A multi regression and logistic analysis. Journal of dentistry, 45, pp.1-6.

Stadtler, H., 2015. Supply chain management: An overview. In Supply chain management and advanced planning (pp. 3-28). Springer, Berlin, Heidelberg.

Syazwan Ab Talib, M. and Bakar Abdul Hamid, A., 2014. Halal logistics in Malaysia: a SWOT analysis. Journal of Islamic Marketing, 5(3), pp.322-343.

Varmuza, K. and Filzmoser, P., 2016. Introduction to multivariate statistical analysis in chemometrics. CRC press.

Y?ld?r?m, G. and Tokal?o?lu, ?., 2016. Erratum to “Heavy metal speciation in various grain sizes of industrially contaminated street dust using multivariate statistical analysis”[Ecotoxicol. Environ. Saf. 124 (2016) 369–376]. Ecotoxicology and Environmental Safety, (128), p.266.

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