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CHARACTERIZATION OF THE INDUSTRIAL STRUCTURE OF THE DISTRICT OF SANTA MARTA – COLOMBIA Gerardo Angulo-Cuentas Maryuris Charris-Polo Jaime Camacho-Pico Abstract This work, continues a series of studies on industrial dynamics of the district of Santa Marta - Colombia, assigning priority to the characterize the sector companies, how they are registered in the Chamber of Commerce, their age, their economic size, how they are in the market, its response to the demands for innovation and the compliance all quality standards. In this way, we reached to background of each of the companies to observe the behavior of their competitiveness and productivity. Finally, there are some conclusions and recommendations that will help bridge the gaps that the firms have. Keywords: productivity, competitiveness, industrial structure, manufacturing companies. 1. INTRODUCTION The manufacturing sector is considered, theoretically, as the main component of value added of the economy and employment generation. This sector in Santa Marta lacks a strategic vision that is necessary to raise awareness about the industrial orientation of the district, more so when it considers that the political openings that give rise to free trade, involve district and department as key areas of benefit. Rómulo Lander (2005) refers to the importance of the manufacturing sector, ensuring that the use of surpluses generated by multiple income within a State should be directed to the rescue of industries and infrastructure construction that will recreate the conditions for the foundation and development of manufacturing industries. These investments by the state will generate an immediate demand for human resources, which in many cases will have to retrain to be inserted in the contingent work and who then, once inserted, invigorate demand rapidly domestic goods and services. To the extent that such investments are multiplying; Both the industrial sector such as manufacturing, will begin to increase their relative importance in GDP. It notes that the National Bureau of Statistics (DANE) at the time of the Annual Manufacturing Survey (EAM) does not consider the department or the district in the sample, so that basic information on growth and evolution of this sector is unknown. Then it becomes necessary to determine the levels of growth of industrial sector in the district, as well as meet the technological infrastructure of
enterprises, and if they are available for association. This knowledge base permits serious financial planning of industrial development for the city. 2. OBJECTIVES •Identify existing infrastructure of hard and soft technologies of the business located at the district to determine what impact they generate. •Meet the profile of needs of the business district to implement strategies that lead to meet the needs. •Establish a baseline for evaluating, monitoring and tracking the performance of the industry in the district. •Categorize and group companies in the district for designing development policies. 3. METHODOLOGY The methodology includes the following phases: 1 Definition of study variables 2: Retrospective 3: Survey information 4: Information processing 5: Final Document Preparation 4. ANALYSIS OF RESULTS 4.1.
Overview of the business district of Santa Marta
4.1.1. Age of business
Figure 1. Pareto diagram for the Age of Companies
The 25.86% of firms are aged between two and six years, and 27.59% are firms aged between seven and eleven. This means that 53.45% of companies are aged between two and eleven years, and if they added those with one year old or less it can note that 57% of all companies surveyed are eleven years old or less. It confirms that most companies are new in the market; it can think that there is a hostile business environment where companies die at an early age. 4.1.2. Productive Sector By grouping of industrial enterprises in Santa Marta, you can find a greater concentration on the number of productive units in the sector "manufacturing of clothing," which represents 45%. Following this are the sectors "food, drinks" with 19% and "manufacture fabricated metal products" with 18% of the total surveyed. Concerning the other branches, companies are participating in the following way: "wood processing and wood product manufacturing and cork, except furniture" with 7%, 4% is part of the sector "publishing, printing and playback of recordings, a 2% correspond to the branch "manufacture of other non-metallic mineral products, and another 2% to the" manufacture of basic metals. " The sectors' production of paper, cardboard and paper products and cardboard "," Manufacture of chemicals and chemical products "and" manufacture of rubber and plastic, each part with 1%.
Figure 2. Productive Sector id according to ISIC Rev. 3
4.1.3. Legal Form An important aspect is the legal form that listed companies registered with the Chamber of Commerce. The chart shows that 74% of businesses are sole proprietorships, i.e. they are small units with little employment generation, for unskilled labor because their production methods are handmade. About 17% are
Limited Liability Companies; Corporations 4%, 2% is owned by Limited Partnership by Shares, a 1% Done Societies and finally a 2% representing other legal forms.
Figure 3 Percentage of Companies in Santa Marta with their legal form
4.1.4. Information Technology Companies’ computer equipment described as follows: a 1.04% use Pentium I, i.e. they are working with computers for 15 years five times its life expectancy. It can be inferred that the productivity of the firm is far from its necessary in full knowledge society. The 4.17% has Pentium II computers that are older than 11 years, almost four times its life and it puts firms in almost the same conditions as the company that works with Pentium I. 9.38% have a Pentium III computers that are older than 9 years, three times life.
Figure 4 Description of computer equipment
4.1.5. Sales Within of all companies surveyed in the city only 68 of them provided information about their annual sales. Thus, 34% of companies sold between 11 (U$ 5.500) and 50 (U$ 25.000) million pesos for 2008. This leads to the conclusion that the majority of companies do not generate significant value. 19.05% generated income between 101 (U$ 50.500) and 500 million (U$ 250.000), a 13.10% gain between 51 (U$ 25.500) and 100 million (U$ 50.000), 9.52% between 0 and 10 million, 2.38% between 501 and 1000 million.
Figure 5 Pareto sales in million pesos 2008
4.1.6. Human Resources Companies were consulted about the benefits they give to their staff. Transport subsidy obtained the highest percentage 45.74%, followed by 41.49% of them corresponding to dining; 4.26% external training.
Figure 6 Benefits those companies provide.
Figure 7, we can see that the 16.67% of companies testify that staff working in them have been trained in technical schools, institutes 2.94%, 10.78% in universities, a 20.59% corresponds to say that his staff has received training in higher education, and those whose staff says he learned his work as an autodidact represent the vast majority, with 47.06%..
Figure 7 Education of companies’ staff.
4.1.7. Projects Companies were asked about projects will be implemented during the years 2009 and / or 2010. The percentage distribution shows that 57% of companies answered yes. Those companies that advance projects were asked the exact number of these and replied as follows: 43% ahead of a project, 11% 2 projects, 2% out three and 1% ahead of four or more projects.
Figure 8 Percentage of companies that are conducting a project evaluation.
The following chart shows that 79% of companies responded that their projects are not export-oriented; the others firms (21%) responded that theirs projects are export-oriented, 19% of them will only develop a project to export and the rest will do two projects.
Figure 9 Companies whose projects evaluated are export-oriented
4.2.
Multivariate Analysis
According to Peña (2002), multivariate analysis of data, comprising the statistical study of several variables measured in elements of a population in order to: • Summarize data using a small set of new variables, constructed as the original transformation with minimal information loss. • Finding groups in data • Classify new observations in groups defined. • Relate two sets of variables. There are many multivariate analysis techniques; we have used the factorial analysis of correspondence to establish relations of classes on a heterogeneous collection of individuals. The main advantage of this tool is to characterize groups in terms of the variables by a simultaneous representation of them (Amar, Angulo et al, 2004). 35 variables were selected from the survey (see Table 1) for analyzing the productivity and competitiveness of enterprises. The variables are binary, ie, compliance with the observed condition is qualified with one (1) and the lack of it is scored with a zero (0). The exception is the firm age. This method explains the data variability in p minus one (p - 1) dimensions when performing the analysis in the space of individuals and n minus one (n - 1) dimensions when performed in the space of variables. The analysis of the results (Table 2) shows that the three-dimensional dataset represented explains 39.81% of the variability with minimal information loss.
Description Age of business Sole Company Participation and partnership experience in the last 5 years Provision for the association Outdated technology Existence of computer equipment Internet Access Website Computer network Management Software Using spreadsheet and word processor Personnel training Training needs ARP Membership Staff involved in occupational health Waste Management Make fiscal contributions Availability of logistic resources Logistics Outsourcing Logistics management system manual Knowledge of industrial parks Provision of facilities in an industrial park Substitute providers identified Availability of a quality control system Make exports International Trade Marketing of products Product advertising Offer after-sales service Monitoring clients Ongoing formulation of projects Project formulation exporters Knowledge FOMIPYME Knowledge COLCIENCIAS Transactions with banks or other financial institution
Code 10 20 30 31 40 41 42 43 44 45 46 50 51 52 53 54 60 70 71 72 80 81 90 QC EX OE SP PU PV CRM PYT PYX FOM COL OB
Table 1 Key variables used in multivariate analysis
I
Eigenvalue
1 2 3
0,2083 0,1199 0,0672
Variance explained by Vi 20,97 12,07 6,77
Cumulative variance explained by Vi 20,97 33,05 39,81
Table 2 Eigenvalues of explanation of variability
In figures 10, 11, 12 six clusters can be differentiated: Group 1 (G1): Productive and Competitive Business. Companies S21, S72 and S84 are 3.57% of the units studied. The variables that determine the formation of the group are: No single person, the existence of computers, internet access, have a network of computers, software management, use of spreadsheet and word processing, availability of a quality control system, implementation of fiscal contributions, know the lines of funding of COLCIENCIAS and FOMIPYME;
perform operations with banks or other financial institution and concerned for association. Group 2 (G2): Companies directed towards acceptable levels of competitiveness and productivity. Companies S41, S68, S71, S73, S74 corresponding to 5.95% of the units studied. They have computer equipment, use of spreadsheet and word processor, ARP Membership, offering after-sales service and ongoing development projects. Also, are characterized by not having experienced association in the past five years. Group 3 (G3): Companies that wants to grow. Companies S19, S26, S33, S60 and S77 corresponding to 5.95% of the units studied. They have computer equipment, use of spreadsheet and word processing. There is not a system of quality control. Also, they share the following conditions: need of training, there is not computer networks, have a manual system of logistics management, have not associative experience over the past 5 years, do not make export-oriented projects and do not have staff dedicated to occupational health and do not manage their waste. Group 4 (G4): Companies with very low productivity. Formed by units not classified in another group, they are 82.14% of total. These enterprises typically do not export, are sole proprietorship, have no experience in partnering, and they do projects for export, have manual logistics management system, do operations abroad and have an average age of 11 years of operation. Group 5 (G5): A Company without desires for growth. Enterprise S31 information technology has not and therefore does not have internet access, or networks, or management software. Also, it has not developed quality management systems, not interested to partner, not to export, does not advertise its products, does not offer after-sales service and customer follow up, does not make projects, does not know financing lines offering and has no operations with banks or other financial institution. No shows desire to excel and not rely on the growth potential. Group 6 (G6): A Company with a tendency to disappear. Enterprise S67 is having its machinery and other outdated technologies. It compromised its stay in the market because it does not have: computer equipment, internet access, computer network, management software, does ongoing training of its staff and has no availability of logistics resources. It is concerned that membership in any ARP, which does not have staff dedicated to occupational health and does not make fiscal contributions.
Figure 10 Two-dimensional representation of the relative positions of companies in the space of variables
Figure 11 Three-dimensional representation of the relative positions of companies in the space of the variables
Figure 12 Three-dimensional representation of the relative positions of companies in the space of the variables
5. CONCLUSIONS AND RECOMMENDATIONS 1. 58% of companies in Santa Marta report using computers, but many of these information technologies are obsolete, so it is necessary to advance any of the following strategies: • •
Sensitize and train employers in using the latest computer technology in order to increase their levels of productivity and competitiveness. Disclosure of credit lines and financing funds provided by the government for the process of technological upgrading of small and medium enterprises (SENA, Fomipyme et al.)
These strategies should be implemented immediately to take advantage of revaluation of the peso against the dollar and the companies can buy computer technology with low prices. 2. In Santa Marta, firms interact in a hostile environment that causes them to die at an early age. It needs business incubators, support programs that provide growth opportunities or facilities for start-ups or old business who are in difficulties. 3. Companies do not intend to expand their capacity and could not do so if they would want because they do not have enough capital, these companies are
unaware of credit lines offering BANCOLDEX, FOMIPYME, SENA, FINDETER and Others. It is necessary to advance the spread of these lines of credit and / or financing as one of the advantages of partnership projects etc. 4. As a general conclusion, 82.14% of the companies that participated in the study are characterized as very low levels of productivity according to the conditions and variables selected for multivariate analysis. It is necessary that government agencies and unions respond expeditiously to the findings, conclusions and recommendations presented in this paper.
6. REFERENCES AMAR, Paola, ANGULO, Gerardo, et al (2004). Characterizing knowledge management in manufacturing department of the Atlantic by a multivariate analysis. Information Technology Management magazine. Ed 6 Vol 3. LANDER, Romulus (2005). Manufacturing Sector as an Engine for Development. Available in http://www.analitica.com/va/economia/opinion/5235059.asp. Accessed April 30, 2007.
