5 CHAPTER FIVE: CLUSTER DYNAMICS, RESOURCE ACCESSIBILITY AND THE
5.4 Fruit Processing Businesses’ Perceived Growth Output Performance in the Cluster
This section examines the performance of fruit processing businesses in the cluster by focussing on two variables: the processing businesses’ owners’ view of their growth and the
Figure 5.3 The supply chain structure and actors in the palm and pineapple clusters
Palm cluster
Medium scale processors Large scale farms/plantations Small and medium scale farms
Small scale processors
Large Scale processors
Sub-regional distributors
Strong ties
Out-grower schemes
Horizontal business relationship International market
Vertical business relationship Farm/plantation ownership
Pineapple cluster
Medium scale processors Large scale farms/plantations Small and medium scale farms
Small scale processors
Large Scale processors
Strong ties
Out-grower schemes
Horizontal business relationship International market
Vertical business relationship Farm/plantation ownership
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analysis of their output performance levels. This section addresses the research question, How do processing businesses perceive their level of output performance in the clusters?
5.4.1 Fruit processing businesses’ perceived level of growth
The majority of fruit processing business in both palm (84.2%) and pineapple (58.8%) clusters claimed that their businesses were growing. In contrast, more than one third of the respondents (35.3%) from the pineapple processing businesses cluster indicated that their output levels had remained the same as in previous years. This is significantly higher than that of the palm cluster, which stands at 14.6%. Nonetheless, the findings appear to show no significant difference between the performance of businesses in both clusters (p>0.01). This means that processing businesses in the two clusters experienced growth compared to the previous year’s performance (see Table 5.2 below).
Table 5.2 Perceived level of growth of fruit processing business relative to previous year
Performance Palm Pineapple Total
2
x
PGrowing 84.2% 58.8% 79.8%
5.90 0.06
Staying the same 14.6% 35.3% 18.2%
Shrinking 1.2% 5.9% 2.0%
Total 100.0% 100.0% 100.0%
These quantitative findings are corroborated by the participant interviews, in which most processors interviewed, indicated that their businesses have been growing. Fruit processing businesses interviewed said they had undergone some form of improvement in their operation in areas such as steam boiling and extraction machinery as a result of growing demand for their products (in-depth discussion on innovation is provided in Chapter six). The interview extracts from the manager of a medium-scale palm processing business and a large-scale pineapple processing enterprise capture their responses on growth:
... you see, demand for our products is growing [...] Initially everything was manual but gradually we are trying to add some little bit of automation to it [...] So it has supported our expansion in our outputs over the years, particularly our pineapples, even though we are introducing new products (Pineapple case 1; Nsawam, 2014)
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...our management have invested a lot into our operation so, like the new machines you saw when you were entering, we have been working hard towards continuously increasing our output and trying to meet [demand] (Palm case 4; Kwaebibirem, 2014)
5.4.2 Output performance of fruit processing businesses Total outputs for processing businesses in 2013
In order to test these ‘perceptions’, the researcher asked fruit processors to provide an estimate of their total output in the year before and the year of the survey. The findings from the combined data for the two clusters show that the majority (54.5%) of the processing businesses each produced between 500 and 1000 tons of output in 2013. Most of this individual output was less than 2000 tonnes. Ninety-five percent of the palm processors and 76% of the pineapple processors produced less than 2000 tonnes each. Even though a relatively significant number (23.5%) of processing business in the pineapple cluster produced above 2000 tons compared to those of the palm cluster, the difference is not statistically significant (p=n.s.) (see Table 5.3).
Table 5.3: Total output of processing businesses in 2013
Output volumes Palm processors Pineapple processors Total 2
x
P <500 tns 8.5% 11.8% 9.1% 7.44 0.12 500-999 tns 57.3% 41.2% 54.5% 1000-1499 tns 25.6% 17.6% 24.2% 1500-1999 tns 3.7% 5.9% 4% >= 2000 tns 4.9% 23.5% 8.2% Total 100.0% 100.0% 100.0%The findings are not surprising as the scale of operation in the combined data on the two clusters is significantly influenced by the operational scale of businesses in the palm cluster. Over 80% of palm processing businesses are classified as small scale (65.8% of the businesses produce below 1000 tons). The observations from the field show that these businesses apply basic oil palm extraction technology which sometimes may involve manually operating the extractors and so they are unable to produce on a large scale.
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Total outputs for fruit processing businesses in 2014
There were differences in the outputs from these processing businesses in 2014 (Table 5.4). The findings show that the combined total output produced in two clusters shrank (50.5%) in 2014 for processing businesses in the output range between 500 and 1000 tons even though it accounted for half of the total outputs for that year. The reduction in the total combined output was reflected in a significantly higher percentage of processing businesses in the pineapple cluster (35.3%) producing above 2000 tons as compared to a constant rate (4.9%) of output by processing businesses in the palm cluster in 2014.
Table 5.4: Total output of fruit processing businesses in 2014
Output volumes Palm Processors Pineapple processors Total 2
x
P <500 tns 1.2% 11.8% 3.0% 21.50 0.00** 500-999 tns 53.7% 35.3% 50.5% 1000-1499 tns 30.4% 17.6% 28.3% 1500-1999 tns 9.8% 0.0% 8.1% >= 2000 tns 4.9% 35.3% 10.1% Total 100.0% 100.0% 100.0%Note: **p significant at 0.01 level of significance
As a result, there exists a relationship between processing business cluster and productivity (p<0.01). Specifically, processing businesses in the pineapple cluster were more likely to produce on a larger scale than those in the palm cluster. This could partly be due to rising demand for output, as was explained by some large-scale pineapple processing businesses. In fact, three of the businesses interviewed, pineapple cases 1, 2 and 3, explained that the demand for processed fruit had risen. The situation meant that, on some occasions, Pineapple case 1 had to import raw materials from abroad to meet their clients’ demands. This is to ensure that they are able to keep doing business with them. The situation is explained by the operations manager below:
...we fly in the mango from Brazil to Ghana and then process it in Ghana and export it to Europe. In fact, which company can do that? It’s very expensive. In that period we don’t make any profit but that’s part of the commitment and trust building. We don’t want them to have their shelves empty. We are committed to them and they are committed to us (Pineapple case 1; Nsawam, 2014)
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The results from this section show that processing businesses in the two clusters produced more output in the present year than in the previous one. These findings on the increased output in the two clusters are critical to the discussions on spatial organisation since these processing businesses have different factors affecting their operations and differences in their supply chain structures and yet they appear to have experienced increases in output. Figure 5.4 below summarises the findings in this chapter.
Figure 5.4 A model of the nature of business cluster’s dynamics and operations