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Labor Productivity Benchmark: Methodology and Excerpt
Audience: designed for productivity managers, HR directors, strategic planners
Author:Philip M. Parker, Professor, INSEAD
Price: $210
Pages: 173 pp
Published:
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Table of Contents
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Methodology/Excerpt INTRODUCTION & METHODOLOGY WHAT DOES THIS REPORT COVER? Are the combined human resources at American Quantum Cycles, Inc. productive? There is no absolute answer to this question. This report considers the extent to which the company's labor deployment indicators differ from global benchmarks. In this report we consider forecasts of differences between labor ratios and the resulting return on this human investment compared to global benchmarks; the estimation of such differences is commonly called a "gap analysis." What is the ratio of short-term and long-term assets to employee? What are typical capital-labor ratios? How different are these ratios to companies serving the same link in the value chain? What are the average sales and net profits per employee compared to global benchmarks? These and over 50 other indicators of labor productivity and utilization are considered in this report. The report does so by going beyond traditional analyses by considering companies competing in the same or similar industrial classification at a global level. The goal of this report is to save the reader time. It is designed to assist consultants, human resource managers, strategic planners, and corporate officers in gauging estimates of a company’s human resource indicators compared to firms competing or participating in the same economic sector, at the global level. This report is not about whether a particular company or industry has performed well or poorly in the past or will do so in the future. With the globalization of markets, greater foreign competition, and the reduction of entry barriers, it becomes all the more important to benchmark a company’s human resource indicators against other firms on a worldwide basis. Doing so, however, is not an obvious task. First, one needs to find firms competing in the same sector, but not necessarily competing directly with the company in local markets. These firms should not be perceived, therefore, to be direct competitors to the company in question, but simply those that have been classified by various sources (e.g. EDGAR or similar foreign filings), as competing to serve customers in the same link of the value chain, or broad industrial classification, as identified by SIC, NAICS or similar codes. Second, given the international nature of the task, one needs to control for exchange rate volatility. Finally, one needs use comparable financial standards. METHODOLOGY This report analyzes deviations between American Quantum Cycles, Inc. and international labor-productivity and utilization benchmarks. Based on the methodology described below, the following chapters report a labor-ratio analysis of American Quantum Cycles, Inc. vis-à-vis global benchmarks. In contrast to this report, most productivity and utilization studies focus on benchmarking against domestic ratios, often published by government agencies or commercial sources (e.g. Value Line, Dun and Bradstreet, and Standard & Poor’s). In their discussion of financial statement analysis and ratios, Skim and Siegel note that such comparisons (p. 43-44) : … allows you to answer the question, “How does a business fare in the industry?” You must compare the company’s ratios to… industry norms. In this report, I calculate an industry labor-productivity and/or labor-utilization norms by looking at firms at the global level, as opposed to a local level. In what follows, I will describe the seven-stage methodology used in performing this analysis. Each stage should be seen as a working assumption behind the numbers presented in later chapters. Stage 1. Industry Classification. This stage begins by classifying the company into an industry. For this, I have relied on a combination of the North American Industry Classification System (NAICS pronounced “Nakes”), a relatively new system for classifying business establishments, and the older Standard Industrial Classification (SIC) system. Adopted in 1997, NAICS codes are the new industry classification codes used by statistical agencies of the United States. NAICS was developed jointly by the U.S., Canada, and Mexico to provide comparability in statistics about business activity across North America. After 60 years of service, the outdated SIC system was retired on October 1, 2000, leaving only the NAICS codes for official use. The NAICS classification system adds some 350 new industries and represents a revision to over 60% of the previous SIC industries. Despite its official retirement, the SIC system is still commonly used (and often reported in firm’s financial statements). For most companies in the world, classification within either the new NAICS or older SIC systems is a rather straight forward exercise. For some, however, it can be problematic. This is true for several reasons. The first being that the SIC or NAICS classification systems are rather broad for many product and industry categories (a firm’s products or services may be only a minor aspect of the classification’s definition). The second is that some firms’ activities span multiple codes. Finally, it is possible that a firm is classified by one source using its SIC code, and by another using its NAICS code, and by a third using both. Furthermore, some sources do not report either code, but instead use qualitative statements of the firm’s activities. Nevertheless, if one wishes to pursue a labor productivity and utilization analysis, some classification needs to take place which selects a peer group. In making this classification, one can rely on a number of sources. In some countries, firms must “self” classify in official periodic reports (e.g. annular reports, 10Ks, etc.) to public authorities (such as the Securities and Exchange Commission). These reports are then open for public scrutiny (e.g. EDGAR filings). In other cases, commercial data vendors or private research firms provide SIC/NAICS codes for specific companies. These include:
It is interesting to note that commercial vendors often report different qualitative descriptions and industrial classifications from one to another. These descriptions and classifications may also be different from those reported by the firm itself. Anyone hoping to perform a benchmarking study, therefore, has to make a judgment call across these various sources in order to determine a reasonable classification. In this report, I have decided a meta-analytic process, by combining various sources (including linking a classification’s keywords to qualitative descriptions of the firm’s product line). In cases of inconsistency, the most recent or globally comparable available is chosen. Again, the overall goal is to classify firms, which either produce similar products, offer similar services, or are in the same stage of the value chain for a particular industrial classification. In the case of this report, the SIC code selected is: 37 which is defined as “Transportation Equipment Manufacturing”. This classification should be seen as a working assumption. In order to obtain a more detailed discussion of this classification, the reader is referred to the web sites developed by the U.S. Census Bureau: http://www.census.gov/epcd/www/naics.html. Basic definitions and descriptions are provided at: http://www.census.gov/epcd/www/drnaics.htm#q1. A full correspondence table between SIC and NAICS codes, and detailed definitions are given at http://www.census.gov/epcd/www/naicstab.htm. Stage 2. Firm-level Data Collection. A global search is conducted across over 20,000 companies in over 40 major economies for those that (1) may be included in the classification from Stage 1, and (2) report financials (balance sheet and income statements), and (3) report an estimate of employment levels. It should be noted that the public-domain financials can be either historic or projections. It should also be noted that even historic figures can be modified in the future and often represent “estimates”. Stage 3. Standardization. Once collected, public-domain financial figures of firms identified in Stage 2 are standardize into comparable categories (assets, liabilities, and income). From there, we eliminate all currency effects by standardizing within each category (creating ratios). In order to maintain comparability over time and across companies and countries, we use a common currency (the US dollar) and relate each measure to a “per employee basis”. Ratios are projected using raw financial statistics and, as ratios, are therefore comparable. Stage 4. Filtering. Not all the firms selected in Stage 2 or the ratios calculated in Stage 3 are used for the global benchmarks, as a number of companies are purposely dropped from the analysis, even though they may fall within the classification. This is justified by the “outlier” phenomenon that plagues such analysis. The problem lies in that any given company in the benchmarking pool may be facing some exceptional event or may be organized in an exceptional way so as to make its ratios vastly different from others in the same classification. By including such firms, the global benchmarks can be overly skewed. In many countries, firms are organized into holding groups. These groups nominally have very few employees (e.g. 4 to 25 employees), but have extremely large assets, liabilities, or revenues. As such, the inclusion or exclusion of firms having this form of management can affect the productivity and/or utilization ratios and benchmarks reported. Likewise, some firms have no net sales, no assets, no liabilities, or ratios. Others have ratios that appear implausible for a normal or viable company. In order to not allow these firms to affect the global benchmarks, I have attempted to choose only those firms with reasonable financials. Finally, in some countries, detailed financials are not available or are not comparable to either the company in question or the global norm (e.g. various forms of depreciation per employee). In this case, only those which exist and are comparable are reported. The details, therefore, that comprise a given productivity or utilization ratio or set of ratios may not be reported. This may lead to the addition of several ratios, not summing to the whole. Stage 5. Calculation of Global Norms. Once the filtering process has eliminated outliers, a final list of companies included in the global labor ratio averages is compiled. In this report, the following companies are included (country of headquarters in parentheses, exchanges, and ticker symbols); again this list should be seen as a working assumption:
Based on this list, the ratios discussed in Stage 3 are calculated for every firm, and then averaged to create global labor benchmarks. No weighting is used in this average. Stage 6. Projection of Deviations. The goal of this report is not to present the raw ratios or averages, but to present the difference between the company’s estimated labor ratio and the projected global average for that same labor ratio. Furthermore, it can be insightful to know the location of each ratio within the distribution of the companies used in Stage 5. These deviations, in fact, can be seen as projections or likely scenarios for the future. This is often true for two reasons. First, while a company’s financials and employment levels change from year to year, its ratios are often stable. This is especially true for the global benchmarks which represent averages across many companies. From a purely Bayesian sense, the difference between the company’s recent ratios and the benchmarks are a reasonable prior for future deviations. This is true, even if the entire industry is hit by an external or exogenous shock, such as an oil crisis or economic slowdown. In other words, I assume that the structure of the variance in the industry’s labor productivity and utilization remains stable. Second, many of the data are based on preliminary reports that might be changed in future filings. As forecasts, therefore, the numbers derived from these are also forecasts of past and future performance (with associated uncertainties). The calculation of the difference between a company's ratios and the global benchmarks is meant to yield roughly approximate forecasts, or "useful measures". Again, the forecasts are based on the assumption of relative stability. This assumption has proven extremely robust in previous applications of this methodology (i.e. today’s weather is a good predictor of tomorrow’s weather, but not the weather three years from now). Stage 7. Projection of Ranks and Percentiles. Based on the calculation of deviations, relative ranks and percentiles are calculated across the firms used in the global benchmarks. The percentile estimates the percent of firms within the same sector of the global economy that have values of the labor ratio lower than the firm in question. It is important to note that a percentile being high (or low) does not mean good (or bad) past, present or future performance. The reader must draw this conclusion on his own. The estimates provided were created to provide managerial insight, and not a recommendation with respect to any valuation of the company, its employees or its management. I graphically report, for each part of the financial statement, the larger structural differences between the firm and the global benchmarks, and provide a summary table of ranks and percentiles. A deviation from the global norm need not be a bad sign. Rather, it is simply a substantial difference that might merit further attention or perhaps signal a firm’s relative strength or weakness for the coming fiscal year. LIMITATIONS Shim and Siegal (p. 60) stress that “while ratio analysis is an effective tool for assessing a company’s condition, its limitations must be recognized.” In particular, they find that (p. 59) “no single ratio or group of ratios is adequate for assessing all aspects of a company’s financial condition.” The authors note the following limitations associated with ratio analyses which apply to the global benchmarking and labor ratios presented here (p.60):
Again, all figures reported here are estimates, so due caution is required. The above caveats, and the fact that statements made in this report are forward-looking, requires that this point be emphasized. A number of intervening factors can have material effect on the ratios and variances forecasted. These include changes in a company's management style, exchange rate volatility, changes in accounting standards, the lack of oversight or comparability in accounting standards, changes in economic conditions, changes in competition, changes in the global economy, changes in source data quality, and similar factors. Please refer to Chapter 5 for further caveats. EXTENSIONS A useful extension of the analysis presented here is to consider only a small subset of firms that compete more directly with the company in question, or its immediate suppliers or buyers (in the case of value chain analysis). Choosing this set of competitors or value-chain players may, however, be a very subjective task. While one can rely on public statements made from the firm itself about who it believes its competitors, buyers, or suppliers to be, an external analyst may have a very different opinion. ICON Group is able to generate additional reports that can benchmark any given company, including the one profiled here, against any combination of firms drawn from a pool of over 20,000 domestic and international companies. Should the reader wish a similar report to the one presented here, but for a very particular definition of company and benchmarks, please contact us via email with your request, at Skim and Siegel (2000), Financial Management published by Barron’s Educational Series, Inc. (BARON’S BUSINESS LIBRARY Series), ISBN: 0-7641-1402-6.
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. Please indicate the "target firm", and the "benchmark firms" that should be included in the pool to comprise the average benchmarks. ICON Group will quickly report back the feasibility of such a study. The turnaround for these requests is often only a few days, and the price of these reports is comparable to this report.