J.B. Akarakiri and  M.O. Ilori
Technology Planning and Development Unit, Obafemi Awolowo University, Ile-Ife
O.J. Ojo
Department of Civil Engineering, Osun State University, Osogbo.

The study examined the pattern of location of sawmill firms in southwestern Nigeria.  This was with a view to determining the advantages derived from firms that are within clusters.  The study covered sawmilling industry in southwestern Nigeria.  Twenty percent (20%) of the firms was selected in each of the states making a total of two hundred and sixty-four (264) firms.  The research instrument used was questionnaire.  It elicited information on the issues such as pattern of location of firms and clustering advantages.  The data collected were analysed using descriptive and inferential statistics.  The result showed that 90% of the sawmill firms were within clusters.  Also the clustering advantages examined in the industry were mainly joint provision of electricity, joint procurement of inputs such as trucks, tractors and sawdoctoring equipment, joint marketing and conversion rate control and credit provision through co-operative societies.  The study concluded that the clustering advantages found in the study area had increased the technological development of the industry by jointly provide electric poles and cables, transformers, trucks, tractors, sawdoctoring equipment among others.   These had made sawmill industry functional.

Keywords:  Enhancing, technology development, clustering, sawmill,  industry.


The basic idea behind the establishment of production firms in the country was to accelerate the technological development of Nigeria and position it as an industrialized nation.  Unfortunately, most of the firms established did not survive the administration that established them before they began to experience teething problems, which later snowballed into chronic diseases. An industry is a place where economic production activity usually manufacturing is being carried out.  It may be referred to as a number of firms producing broadly similar products.  Udu and Agu (2001), Khanna (2004) classify industry into three:
(a)        firms producing similar products; however, the products of each firm may be differentiated, for example, furniture industry.
(b)        firms producing different parts of a final commodity; for example, automobile industry.
(c)        a manufacturing unit that is involved in  the transformation or processing of material inputs into new products.  For example, cement industry which combined the mixture of limestone and chalk together to produce cement (Ewekoro Cement Industry in Ogun State of Nigeria).

An industrial cluster is a dense sectoral and geographical concentration of firms comprising manufacturers, suppliers, users and traders (Oyelaran-Oyeyinka, 2006).  Rojas (2007) also defines cluster as a group of firms whose linkages mutually reinforce and enhance their competitive advantage.  So members of a cluster could be competitors that produce similar products or customers that patronize similar producers, partners, suppliers among others.

In southwestern Nigeria, most of the sawmill firms are in clusters.  The only major raw material for the production of sawn timber in the sawmill is the sawlog.  Timber is one of the most important natural resources which is extracted from the forests as sawlogs which are used in various ways.  After a tree has been felled and cut into sawlogs with the aid of chainsaw machine, the logs have to be transported by truck, rail or water to the sawmill after which the sawlogs will be converted into the required standard marketable sizes.  Technology is very important for this transformation.  Ilori (2006), describes technology as systematic knowledge for the manufacture of a product, for the application of a process or for rendering of a service, including any integrally associated managerial and marketing techniques.  So industry operates effectively with the acquisition of  technology.

The analysis of the agglomeration of firms and the importance of geography to economic development formed part of economic theory beginning with the classical economists and continuing into the 21st century.  Many economists have used some theories to explain the locational patterns of the various factories and spatial pattern of industries still remains as subject of concern to the various economists.  Prominent among these earliest economists were Alfred Weber, Stord Palander, Edgar Hoover, August Losch, Melvin Greenhurt among others (Richard et al., 1974).  Marshall and Weber represent early classical exponents of issues pertaining to industrial agglomeration.  Today Weber’s pioneering effort in location theory has influence on modern cluster theory.

Statement of the problem
Most of the inputs required for timber production are very expensive for individual sawmill firms to procure.  The present economic situation in the country which is characterized by devaluation of naira has made importation of sawmilling machines very difficult.  Industrialists are unable to import machines and equipment into the country.

In addition, most of the sawmill firms had failed to function at an optimal level due to intermittent supply of electricity.  However, the problem of power supply in the country is not peculiar to the sawmill industry in the country; other sectors such as textile mills and the remaining manufacturing companies are equally facing the same problem.  Provision of electricity for high productivity of sawmill operation requires a transformer.  An isolated firm may find it difficult to do it alone.

Consequently, the clustered sawmill firms have resorted to various combination of efforts to keep the industry going.  Therefore, the paper is designed to examine the clustering advantages of the firms and the sustainability of the industry through such efforts.

Research questions
(i)         What is the pattern of location of your sawmill firm?
(ii)        What are the advantages of belonging to a cluster?

Study area
The study was carried out in southwestern region of Nigeria, comprising Lagos, Ogun, Oyo, Osun, Ondo and Ekiti states.  The whole of the area is located within the region known as lowland humid tropical rainforest (Fig. 1).  Dada et al., (2006).

About 20% of the population was chosen in each of the states using multi-stage random sampling technique, making a total of 264 firms within clusters.  The research instrument used was questionnaire.  The questionnaire was designed to elicit information on pattern of location of firms and advantages of belonging to a cluster.  The questionnaire was subjected to content validity.  Out of 264 copies of questionnaire distributed only 222 copies were retrieved.  The data were analysed by using simple descriptive and inferential statistics.

The results are presented according to research questions.

Research question1
What is the pattern of location of your sawmill firm?

Table 1 shows that about 90.4% of the sawmill firms were within clusters while only 9.6% were outside clusters.  This reveals that most of the sawmill firms in the study area were within clusters.

Research question 2
What are the advantages of belonging to a cluster?

Table 2 shows the advantages of belonging to a cluster.  When rated on a likert scale of 1 being very low to 5 being very high, none of these advantages of belonging to a cluster was rated very high.  However, joint provision of electricity was rated highly (4.64), while joint procurement of inputs e.g. truck, tractor, sawdoctoring equipment (3.45), lobby governments for assistance to members who contravene government guidelines (3.41), joint marketing control (3.41), joint conversion rate control (3.34) and credit provision  through co-operative society (3.23) were rated moderately.  The remaining advantages, thrift (2.93), provision of good roads (1.15), provision of maintenance electrician (1.06), provision of vocational training (1.05) and provision of maintenance mechanics (1.04) were rated low and very low.

In the clustering system, all the firms invested in one or more transformer(s) which (steps) down electricity for their use.  An isolated firm may find it difficult to do it alone.  Most of the inputs such as trucks, tractors, sawfilling equipment among others were very expensive for individual sawmillers to procure.  Therefore they were able to purchase them through monthly association levies and their co-operative societies.  This agrees with the proposed project of Manufacturers Association of Nigeria (MAN) to have an independent power supply especially where there are clusters of manufacturers where the project can be sited so as to be producing electricity for the members (Akinpade, 2009).

Hiring of some of the inputs generates more funds for the association.  This corroborates the claim by Fisseha (1985) who reported that technological needs of “micro” and larger Forest-Based-Small Scale Enterprises (FBSSE) call for clustering of industry segments in order to solve the problems of needs, areas of weakness and strengths.  Sometimes the problem of production technology is simply the unavailability or shortage of simple tools and equipment used in existing production process and techniques.  Industrial clusters solve the problem through  related and supporting industries.  Also Tsournos and Hayness (2004) in Rojas (2007) states that concentration of several firms within an industry offers a pooled market of workers with industry specific skills.

When many firms within an industry locate within a concentrated area, it is advantageous for other firms to specialize in providing services to the concentrated industry and to locate near the concentrated industry, therefore woodworking machines fabricators should be allowed to locate their firms close to the clustered sawmill firms.  This may reduce the downtime of the machinery and equipment being used in the sawmill firms.

Akinpade, I. A. (2009):  “More Manufacturing Firms May Leave Nigeria.”  Tribune, Saturday March 28, 2009.  p. 10.

Fisseha, Y. (1985):  Part 1:  Review of Forest Based Small-Scale Processing Enterprises Report of a 1985 Pilot Survey.  Rome : FAO.  pp 7-9.

Ilori, M. O. (2006):  From Science to Technology and Innovation Management.  Inaugural Lecture Series 191, Obafemi Awolowo University, Ile-Ife, Nigeria. p. 2.

Oyelaran-Oyeyinka (2006):  Learning to Compete in  African Industry; Institutions and Technology in Development, England: Ashgate Publishing Ltd.  pp. 131-133.

Richard, A. J., William, T. N., and Roger, C. V. (1974):  Productions and Operations Management, Boston: Houghton Mifflin Company.  pp 159-161.

Udu, E. and Agu, C. A. (2001):  New System Economics; A Senior Secondary Course.  Onitsha: Africana Fep Publishers Ltd.  pp. 219-222.

Khanna, O. P. (2004):  Industrial Engineering and Management, New Delhi: Dahmpat Rai Publications (P) Ltd., pp 13.1-13.12.

Rojas, D. T. (2007):  “National Forest Economic Clusters: A New Model for Assessing National-Forest-Based Natural Resources Products and Services.  Pacific Northwest Research Station.  United States Department of Agriculture Forest Service.

Dada, O. A., Jibrin, G. M. and Ijeoma, A. (2006):  Macmillan Nigeria Secondary Atlas, Ibadan.  p. 19.

Table 1:  Pattern of location of sawmill firm



Within Cluster

Outside Cluster




























76 (9.6%)

*Source:  States’ Ministry of Agriculture (Forestry Dept.)

Table 2:  Advantages of belonging to a cluster of sawmilling industry


Mean Rating

Provision of electricity


Joint procurement of inputs e.g. truck, tractor, sawdoctoring equipment


Joint marketing control


Lobby governments for assistance to members who contravene government guidelines


Joint conversion rate control


Credit provision through co-operative society




Provision of good roads


Provision of maintenance electrician


Provision of vocational training


Provision of maintenance mechanics


Mean with the same letters along the same column are not significantly different at P<0.05

Key:    Very low         -           1
            Low                 -           2
            Moderate         -           3
            High                -           4
            Very High       -           5

Figure 1