Financial Statement Analysis - World Ceramic Tile Manufacturers
LONDON, Dec. 19, 2016 /PRNewswire/ -- This report suggests and applies a complete method for evaluating the performance of a sample of world ceramic tile manufacturers. It is the second informative publication following the World Production and Consumption Of Ceramic Tiles and has the aim of providing entrepreneurs, managers of Italian and non-Italian companies, scholars and sector analysts with a tool for greater understanding of the main characteristics and trends in the sector and its key players and competitors. It is also a powerful integrated tool containing organised data that will help entrepreneurs and managers draw up the best manufacturing and commercial strategies at a company or group level. It will enable readers to evaluate the economic performance of tiles production companies in Italy and in other countries or groups of countries where ceramic tile manufacturers have a significant presence.
The Report is organised as follows:
PART ONE serves as an introduction and consists of three separate sections.
The first section briefly discusses the methodological and statistical aspects of the survey.
The second section provides an initial analysis of the results on a geographical basis. At this stage the analysis is performed by grouping companies together by individual countries (if the number of companies is sufficiently large) or by larger geographical regions if the number of companies is too small (aggregate average data are provided for ITALY, SPAIN, ASIA). This section examines the incidence of costs, outsourcing and manufacturing efficiency, investments and financial structure, as well as profit margins trends at all levels of operation. The last few years of financial statement data and the relevant variations are also calculated and compared. Finally the main geographical regions are then compared and commented on.
The third section provides an analysis of companies grouped into homogeneous groups or clusters of profitability performance based on a cluster analysis approach. The aim is to interpret economic and financial data and performance indicators independently of prior, geographical, sectoral or dimensional classifications, thereby allowing a more consistent comparison to be made with companies with similar results and levels of performance regardless of their size and nationality. In other words, direct comparisons can be made between companies' management and business models. This section also examines the most important variables for differentiating companies into clusters, namely the indicators that more than others determine whether a company is to be placed in a strongly performing cluster (corresponding to a successful business model) or one experiencing structural, economic or management difficulties. Lastly, the characteristics of the various business models are compared and a list is provided of Italian and non-Italian companies with characteristics that make them consistent with those models.
PART TWO analyses the individual companies, each of which is examined and compared with the reference groups described in Part One. In this section the companies are analysed through a standard index-based financial statement analysis using balance sheet and income statement data. In particular it focuses on the following:
1. structure and recent trends in the economic and financial results of the various management areas (e.g. production, its costs and the inventory cycle; personnel management; financial management; asset management, etc.);
2. structure of debt and equity capital;
3. main financial indices and economic ratios;
4. the added value creation process, including its implications in terms of costs and plant capacity utilisation and make-or-buy tradeoff corporate decisions (which in turn determine the company's degree of vertical integration);
5. other profitability margins at various levels in the company's chain of value;
6. alert and financial vulnerability indices;
7. ratings (from various sources) of individual companies, each placed within the context of sector averages;
8. graphical representation (using RADAR diagrams) of the degree of similarity between each company and the cluster it has been assigned to during the cluster and benchmarking analysis. The radar diagrams show the key characteristics of the companies and clusters based on just 6 variables.
In PART THREE the world ceramic tile manufacturers are further classified according to a multidimensional ranking based on a system of 13 indicators (11 financial statement ratios and 2 rating company synthetic indicators). This enables readers to make a direct comparison between a company's results and those of its competitors. The criteria for construction of the multidimensional ranking proposed by Acimac gave preference to the choice of 4 synthetic indices for company profitability (ROI, ROE, ROS and ROA), 4 indices for economic/productive and management efficiency (Added Value margin, EBITDA margin, EBIT margin, Profit/Loss margin) and 3 indices for structure and financial solidity (Cash Flow margin, Equity ratio, Gearing). The companies are first ranked on the basis of a multidimensional index that takes account of the company's ranking for each chosen indicator. The final ranking is then calculated as an average of the other 13 rankings. It should be noted however that the chosen indicators implicitly assign greater weight to the profitability and efficiency of management processes than to financial strength and equity structure. The report also includes a detailed glossary of the indices and ratios used.
METHODOLOGICAL NOTES
The following criteria and guidelines were followed in the economic and financial analysis of the ceramic tile manufacturing sector. To create a systematic, readable and comparable framework within which to interpret the collected economic information, the available data were organised according to the new International Financial Reporting Standards (IFRS) or a mixture of IFRS and the older International Accounting Standard (IFRS/IAS).
CLUSTER AND BENCHMARKING ANALYSIS
The purpose of cluster analysis is to select and group together similar companies taken from a seemingly heterogeneous set without using the usual size classifications (classes of employees, classes of turnover). Instead, the similarities or differences are determined on a multidimensional level, allowing them to emerge from a broad set of indicators and economic variables examined using statistical methods. The basic hypothesis is that company size may be a condition for profitability growth, for example by leveraging economies of scale and variety, but that this condition is neither necessary nor sufficient to ensure growth. An initial subdivision was therefore performed based directly on companies' main strategic and operational goal, namely profitability. Regardless of size classes, significant differences in company operating model can be observed if we classify companies according to profitability (independently of whether this profitability was achieved by a large company or a smaller company). After completing this basic classification, we can proceed with cluster analysis by examining the degree of similarity of companies over a wide range of variables describing their structure, management and results.
In other words, by performing this kind of analysis it is possible to establish which companies within a sample can be considered similar amongst themselves and dissimilar from others in terms of a large number of variables. These similarities or dissimilarities between companies can then be used to perform the benchmarking process, which involves identifying the business models that represent the best practices in terms of structure, management and performance. This way we can identify the strategic and managerial levers that can be used to approach, match or even exceed these best practices. In order for this strategic and managerial benchmarking process to be more meaningful, companies must be compared with others that are similar to them in terms of structure, management and market. In terms of procedure, the cluster analysis was performed by initially dividing up the sample of 289 companies according to classes of ROI, as this financial index was identified as the most important indicator of company profitability.
The following ROI classes were used (in decreasing order):
class 1: ROI above 15% (class AA)
class 2: ROI between 7% and 15% (class A)
class 3: ROI between 3.5% and 7% (class AB)
class 4: ROI between 0% and 3.5% (class B)
class 5: ROI below 0% (negative) (class C)
Within each ROI class, a Two Step Clustering method was then used based on the SPSS Statistics Package. The output of this analysis enabled us to determine the size of the groups that are formed based on the following 7 variables:
AddedValue Margin (on sales, %);
Equity Ratio %;
EBITDA Margin (on sales, %);
Return On Assets (ROA, %);
Cash flow/Operating Revenue;
Return On Investments (ROI, %);
Return On Sales (ROS, %).
Each company classified in a given group has statistically similar characteristics to the other companies in the group but is different from the companies in other groups. This similarity (or dissimilarity) is evaluated statistically by using the entire set of chosen variables. However, by using cluster analysis it is possible to determine which of the chosen variables are the ones that are most important in determining similarities and differences (in technical terms, this involves identifying the best cluster predictors and the degree of importance of the predictor in distributing the companies among the various clusters).
Finally, after forming clusters of relatively homogeneous companies, it is possible to perform benchmarking analysis on two different levels: a more general and descriptive level (performed and presented by the Acimac Research Department MECS) which involves making comparisons between homogeneous clusters within each profitability class; a more analytical level (which is left to the reader) in which each company can be compared with those that are similar to it in order to identify (and compare) group best practices. However, the two benchmarking levels share the same methodology for comparing and identifying best practices: the financial statement ratios of the various clusters are compared, while those of individual companies are compared with the median financial statement ratios of the corresponding clusters.
Median values are used to identify the characteristics of the most representative companies in terms of frequency of economic and financial indicators and ratios. This makes it possible to determine whether the company's characteristics are better or worse than the majority of the companies in its cluster, or in other words relative to the "typical" cluster companies. Similarly, the various clusters are also compared using median values. The first benchmarking analysis level is presented in Part Three of this report. It provides the lists of companies belonging to the various clusters along with summary tables showing the median characteristics of each cluster of companies, divided up by class of ROI. For the second benchmarking level, the reader can refer to these tables of cluster median values. Furthermore, each company (in the individual sheets in Part Four of this report) is provided with a Radar chart showing a visual representation of the main differences within the cluster it has been assigned to.
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