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Financial Statement Analysis European Packaging machinery Companies - over 25 mln euro Turnover

Financial Statement Analysis European Packaging machinery Companies - over 25 mln euro Turnover

  • June 2016
  • 379 pages
  • ID: 3915709
  • Format: PDF
  • By UCIMA

Summary

Table of Contents

This report supplies a complete method for evaluating the performance of a sample of European producers of machinery for the packaging industry. Moreover a complete cluster and benchmarking analysis is supplied here. This is the first part of a broader suite of products for analysing data provided by the UCIMA Research Department: the other products are the international sales trends and the analyses tailored to customers’ needs ( specific customers/suppliers/competitors’ portfolio analyses requested on-demand). These products have in common 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. 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 packaging machinery manufacturers have a significant presence.

The rest of 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 (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, GERMANY, and the REST OF EUROPEAN COUNTRIES). 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 compared and commented on.

The third section provides an analysis of companies grouped into homogeneous groups or clusters of 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:

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.); structure of debt and equity capital; main financial indices and economic ratios; 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); other profitability margins at various levels in the company’s chain of value; alert and financial vulnerability indices; ratings (from various sources) of individual companies, each placed within the context of sector averages; 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 main European ceramic machinery manufacturers are further classified according to a multidimensional ranking based on a system of 13 indicators (11 financial statement ratios and 2 rating agencies’ synthetic indicators). This will enable readers to make a direct comparison between a company’s results and those of its competitors. In order to allow for a better comparison, 2 company size indicators are also presented. 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 UCIMA 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 general approach to construction of the multidimensional ranking proposed by the UCIMA Research Department and the corresponding results were largely confirmed by the cluster analysis. The final ranking proposed by UCIMA is as shown below: 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 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. The Appendix to this report includes a detailed glossary listing the indices and ratios used.

METHODOLOGICL NOTES

The following criteria and guidelines were followed for the economic and financial analysis of the sectors of European producers of machinery for the packaging industry: To create a legible and comparable systematic framework for the economic information collected, the data where organised – when directly available – according to the new schema proposed by the 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 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 twelve months of the reporting period; the company does not have an unconditional right to defer settlement of the liability for at least twelve 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.

TREATMENT OF ANOMALOUS CASES AND SAMPLE SIZE

To ensure the representativeness of the groups of companies (Italy, Germany, other European countries) on which cluster analysis was performed, all the financial statement data, indices and ratios displaying anomalous values (outliers) were excluded from the calculation. It should be noted, however, that eliminating individual outlier values from the calculation of the averages did not result in the remaining company descriptor values being excluded from the cluster analysis. The individual sheets for each company complete with all the data provided are in any case published in Part Two of this report. The company outliers were excluded and the average values calculated using trimmed means instead of simple arithmetic means. The reason for this is that a simple mean is overly sensitive to extreme cases (too high or too low) and is consequently unsuitable as a synthetic and robust measure of a given distribution of values.Moreover, medians are indipendent from outliers effects but to represent the average firms they are not the best choice when discontinuous distributions of data, as it's often the case in financial statements data, are present. Instead, by using a trimmed mean with a threshold set discretionally at 20% (as in this case), the calculation of mean values is limited to the 80% of observations located in the central portion of the distribution.

This makes it possible to exclude the effect of anomalous cases on the representative means of sectoral or geographical aggregates. It is important to note that this procedure of excluding outliers is performed on each individual variable, so the aggregate in question (Italy, Germany, and so on) contains data solely for companies contributing non-anomalous values to that aggregate. For example, if in the calculation of the Italy aggregate the Italian company Alfa displays an anomalous value for Added value margin, that figure is excluded from the calculation of average Added value margin for Italy (whereas all the other companies with non-anomalous values of Value added margin are included). However, the financial statement data for the company Alfa will continue to be included in the calculation of the average data of the Italy aggregate for all other variables in which it displays non-anomalous data. Applying this calculation method effectively excludes the residual part of the frequency distribution of individual variables containing the 10% of values that are excessively low and the 10% of values that are excessively high. A further synthetic index calculation was performed to determine the threeyear averages of the financial statement ratios for each company and for each aggregate.

This provides a succinct measurement of the individual company values used by the UCIMA Research Department to construct the multidimensional company rankings (Part Three for the main European producers of packaging machinery) and in the cluster and benchmarking analysis. However, the choice of three-year averages, which constist in three-year moving averages (each year the oldest observation is eliminated and the most recent one added) have considerable significance in describing more structural trends. By damping cyclical fluctuations in financial statement variables and indicators, they are more effective at representing structural trends without being overly dependent on the last year of data (e.g. a significant reduction in profitability in the last year may be due to a cyclical but transitory increase in costs of service and should be considered a characteristic of a structural trend if and only if the fall in profitability is confirmed for a number of consecutive years). However, the information expressed in the form of three-year averages is not given in the data sheets for the individual companies as it was decided to give preference to the more recent data which more accurately reflects the current economic situation.


The companies included in the UCIMA “European sample of main packaging machinery producers” have the following geographical distribution:

ITALY: 72
GERMANY: 24
OTHER EUROPEAN COUNTRIES: 39
TOTAL COMPANIES ANALYSED: 135

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