LONDON, Dec. 18, 2016 /PRNewswire/ -- This report supplies a complete method for evaluating the performance of a sample of world producers of machinery for the ceramic industry and world producers of glazes and colours for the ceramic industry. It is the first part of a broader suite of products for analysing data provided by the ACIMAC Research Department which have 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 sector companies in Italy and in other countries or groups of countries where ceramic machinery manufacturers or ceramic glaze and colour producers 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 and the REST OF THE WORLD). This section examines the incidence of costs, outsourcing and manufacturing efficiency, investments and financial structure, as well as and 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 economic and financial variables (including indices and ratios) 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 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 similar characteristics.
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, production 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 the 6 most interesting variables.
In PART THREE the world ceramic machine manufacturers and color & glaze manufacturers are further classified according to a multidimensional ranking based on a system of 13 indicators (11 financial statement ratios, 2 rating company synthetic indicators). This will enable readers to make a direct comparison between a company's results and those of its competitors. This decision was based above all on the results of the cluster analysis showing which indicators can be considered robust for discriminating between levels of company performance. The general approach to construction of the multidimensional ranking proposed by the Acimac Research Department and the corresponding results were confirmed in the cluster analysis. It was also decided to use three-year averages for the chosen indicators so as to obtain a more structural or medium-term picture. In a sector subject to significant annual sales fluctuations, this makes it possible to provide more stable and accurate rankings for each individual company. The criterion for constructing the multidimensional ranking proposed by Acimac focused on 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 and Profit/Loss margin) and 3 indices for structure and financial solidity (Cash Flow margin, Equity ratio and Gearing). Although not directly related to economic performance, the dimensional indicators of operating turnover and number of employees were also included in the multidimensional ranking. The companies were first ranked on the basis of an index that takes account of the company's ranking for each chosen indicator. The final ranking was then calculated as an average of the rankings obtained for the 13 selected variables. It should be noted however that the chosen indicators assign greater weight to the profitability and efficiency of management processes than to financial strength and equity structure.
PART FOUR of this report, organised in an analogous way to PART ONE, is devoted to world producers of ceramic glazes and colours. For this group of companies the results were again analysed on the basis of two main geographical areas, ITALY and REST OF THE WORLD (given the very small number of non-Italian companies). This analysis examines the incidence of costs and profit margins at all levels of operation and the relevant trends, comparing the last few years of financial statement data and calculating the relevant variations. The two geographical regions are then compared and commented on. For this group of producers it was not considered appropriate to perform cluster analysis. The number of observed companies for which complete economic and financial data are available (40 companies) did not appear sufficiently large to ensure a reliable cluster analysis.
PART FIVE analyses the individual companies, each of which was examined and compared with the reference groups processed in previous part. The same analysis method was adopted as for the ceramic machinery manufacturers. The companies were analysed through a standard index-based financial statement analysis using balance sheet and income statement data. Lastly in PART SIX the group of ceramic glaze and colour producers underwent a final multidimensional ranking using the same method and criteria as those adopted for the ceramic machinery manufacturers. The Appendix to this report includes a detailed glossary listing the indices and ratios used.
The following criteria and guidelines were followed in the economic and financial analysis of the ceramic machinery 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). The balance sheet items were divided into Assets, Liabilities and Owner's Equity (or Solo Equity). The various items were then classified according to their liquidity as current or non-current. In particular, an asset is classified as current if it satisfies at least one of the following criteria: it is expected to be sold or destined for sale/consumption within the company's normal operating cycle; it is held primarily for the purpose of being traded; it is expected to be realized within the 12 months following the end of the reporting period; it is a cash or cash-equivalent. All other assets that do not satisfy the above-mentioned criteria are classified as non-current.
Likewise, a liability is classified as "current" if it satisfies at least one of the following criteria: it is expected to be settled within the company's normal operating cycle; it is held primarily for the purpose of being traded; it is due to be settled within the 12 months of the reporting period; the company does not have an unconditional right to defer settlement of the liability for al least 12 months after the reporting period. All other liabilities that do not satisfy the above-mentioned criteria are classified as non-current.
The income statement classifies items essentially by function. Given the significant variations in the minimum amount of information required by national laws and regulations, we are proposing a simplified income statement format to which a further list of important items is added. The income statement first provides the values of Operating Revenue, Sales and EBIT.
Next, a section on non-operating net revenues gives detailed figures for financial revenues and expenses and the corresponding profits/ losses (P/L). This makes it possible to identify the P/L before taxes generated by non-core operations together with corresponding operating profits or losses (P/L or net income per period). Further important items listed are: Material Costs, Costs of Employees, Depreciation & Amortization, Interest paid, Cash Flow, Added Value and EBITDA. More detailed definitions and descriptions of the calculated indices/ratios can be found in the index and ratio glossary in the Appendix.
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 scale and scope economies, but that this condition is neither necessary nor sufficient to ensure growth. We decided to use the statistical classification techniques to define the variables mostly describing differences and similarities between companies. We can then 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 on the whole sample of companies. The similarity (or diversity) is evaluated statistically by using the entire set of chosen variables. 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; 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 average financial statement ratios of the corresponding clusters.
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 mean values. The first benchmarking analysis level is presented in Part One of this report. It provides the lists of companies belonging to the various clusters along with summary tables showing the average characteristics of each cluster of companies. For the second benchmarking level, the reader can refer to these tables of cluster average values. Furthermore, each company (in the individual sheets in Part Two 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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