Business metrics: A key to competitive advantage.

INTRODUCTION

Business metrics and performance measures serve as dashboard gauges that help in guiding the strategic direction of a firm (Rubin, 1991). The dashboard consists of appropriate gauges, metrics, which indicate the current performance, baselines, directional trends, and targets. These gauges indicate where a business is headed if the current strategies were to continue unchanged. Any change in the environment in which the firm is competing in will affect the performance of the firm. This lowered performance of the firm should then be captured as measurements by the gauges, metrics, as long as the proper metrics are being deployed.

These measures would be the pivotal sparks leading to changes in the firm's business strategies. Timely and appropriate steering of a firm's business strategies is a key matter for any firm in sustaining business success. Such tight management of an organization's strategies, however, is possible only by the knowledge and measurement of the appropriate metrics.

Different metrics serve different purposes. In general, there are business metrics for accountability purposes and others for organizational improvement purposes (Irwin, 1997). Some measures are used for the efficient strategic steering of a firm, while other measures are used for communicating the proper worth of a business to all interested parties. Business metrics serve the different interests of different stakeholders. Some business metrics are used as the basis for an organizational tune-up, quality improvement, or business process reengineering. In such cases, the stakeholders are generally internal to the firm. There are other business metrics that provide accountability measures used by shareholders, customers, vendors, or creditors in evaluating the general quality of a provider or estimating the future growth of a firm.

The employees within a firm are the ones who ultimately implement the business strategies. Proper individual performance measurements help define and promote desired behavior, activities, and attitudes within an organization (McChesney, 1996). The right behavior must be consistent with and in support of the strategies an organization has adopted to move it towards its preferred future. According to McChesney (1996), people do what is inspected and not what is expected, thus requiring the proper channeling of the various metrics to employees at all different levels, (Aggarwal, 2004).

Unfortunately, many people do not understand what the information presented by the business metrics means. Most employees are not mid-level and senior managers and therefore are unlikely to have a grasp of core financial concepts, performance improvement practices, and the tenets of operational excellence. To help in clarifying the proper usage of business metrics, this paper presents a survey of the application of business metrics in various business activities, highlighting the various constructs that guide the adaptability of the metric to the business application, ultimately providing managers and practitioners with guidelines for the selection of the proper metrics that serve the strategic direction of the firm. The paper presents some of the limitations of the traditional use of metrics, followed by some guidelines for constructing and selecting good business metrics. Following this, the paper presents examples of the applications of business metrics and their implications in the steering of corporate strategic directions. Finally, the paper concludes by highlighting the importance of business metrics and providing guidelines for managers and practitioners for selecting the proper business metrics to steer corporate strategic directions.

LIMITATION TRADITIONAL BUSINESS METRICS

Traditionally, businesses have used financial performance measures as their mainstay in (a) tracking their gains and losses, (b) formulating business strategies, (c) communicating market value to shareholders, and (d) analyzing competitors' strengths and weaknesses, Figure 1.

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While the traditional metrics have served adequately for the contexts they were designed for, they increasingly fall short of reflecting today's business environment. Examples of shortcomings of such metrics evaluated in light of today's powerful business drivers as provided by Campbell (1997) are:

* In software companies, the real assets include the people, software, and R&D. Traditional accounting methods do not record these "off-balance-sheet" assets. Hence, traditional metrics do not reflect these assets.

* In automotive companies, sales revenue and customer satisfaction are not always correlated. Within the industry, one of the most important metrics that signals future sales growth and market-share formation is "customers' intent to purchase." This is a non-traditional metric and is never revealed in traditional accounting information.

This metric is measured and reported by organizations such as J.D. Power based on customer surveys.

* In food companies, the metric "brand equity" reflects the degree to which a product has differentiated itself from the competitors' products. This again is a nontraditional metric. It is not reflected in traditional financial statements.

The notion that traditional financial metrics reflect less and less current strategies has been argued throughout the literature (Rangone,1997). A study conducted by Ernst & Young LLP's center for Business Innovation concludes that two-thirds of the allocation decisions financial analysts make are based on non-financial metrics. Specifically, the three non-financial metrics that matters the most are (a) quality of product, (b) quality of management, and (c) market position of firm. Calabro (1996), in discussing the results of this study, concludes that "the market evaluates a company based on the perception of its non-financials. To realize full value for your company, you have to communicate non-financial information." Unless a construct is defined and measured, it cannot be communicated.

   While manufacturing has changed dramatically during the past decades
   and customers are more demanding of delivery, innovation, quality,
   and service, the formula for measuring productivity has stayed more
   or less the same. As a real indicator of performance, though,
   productivity alone is not enough. Drickhamer (2004) argues that
   better quality, which is certainly better from the customer's point
   of view, won't show up in a standard productivity measure. The
   bottom line is that many of the business metrics being utilized
   today were designed for a different era when the business
   environment was very different. This has led to a dangerous misuse
   of metrics that could potentially result in disastrous consequences.

DESIGNING NEW METRICS

In coming up with new metrics, it is best to specify the properties desired in that metric and then define the metric. As an illustration, consider the link between strategy and performance of a business. The metrics in use will ultimately reflect the strength of the strategy performance linkage; however, different metrics will invariably reflect different levels of this linkage. For example, in studying the impact of total quality management (TQM) programs, different metrics may reflect different effects. A study conducted by Hendricks and Singhal (1997) that compares quality award winners with other control firms shows that as quality increases (a) operating-income increases, (b) sales-growth increases, but (c) costs do not decrease. Depending on which of the three metrics are being used, the impact of the TQM program could be measured differently. Thus, based on the metric being used, one could end up with a biased conclusion regarding the efficacy of that strategy.

There are several properties inherent in a good metric that has been identified throughout the literature. A list of these properties can be summarized as follows:

* Metrics, designed for purposes of accountability (Cooper, 1996), should be constantly reviewed based on the changing standards of accountability.

* Good performance metrics need to reflect progress against a plan (Fleisher and Mahaffy, 1997). This property allows a metric to go beyond being just a measure. Metrics with this property are vehicles for organizations to clarify, communicate, and manage strategy.

* Good metrics should closely reflect long-term organizational success and not just short-term financial gains. A survey of 420 practitioners (Dempsey, 1997) suggests that analysts go well beyond traditional financial measures and use a broad range of strategic leading indicators to assess long-term organizational success. Thus, it makes sense that organizations use the type of metrics analysts use to evaluate them.

* Any good metric should be part of an integrated performance measuring system (Ghalayini et al., 1997). Metrics, no matter how well defined they are, if interpreted in isolation, can lead to problems. It is best to construct metrics by fragmenting the measurement system.

* Good performance metrics should be properly aligned with business strategy (Stainer, 1996). It is not uncommon to find an organization redoing its business strategy but without concurrently redefining its metrics. As discussed earlier, changing business strategy, without updating the metric, can lead to serious problems in measuring the strategy--performance link.

Building on these properties, the next sections present several managerial applications for using business metrics and how the proper selection of business metrics can help steer the strategic direction of the company towards enhancing its competitive advantage.

MANAGERIAL IMPLICATIONS OF BUSINESS PERFORMANCE METRICS

Senior executives understand that their organization's measurement system strongly affects the behavior of managers and employees. Therefore, they need non-financial measures besides the traditional financial measures in their organization's measurement system. The inadequacy of traditional metrics is clear when it comes to measuring the performance of different organizational functions. For example, there is a need to develop non-financial measurements to evaluate the performance in functions like sales and marketing, manufacturing, information systems, purchasing, R&D, or any other organizational function. In this section, we will discuss, from a functional point of view, the managerial implications of utilizing inadequate traditional metrics. The discussions will suggest how new metrics are helpful in such managerial functions.

PRODUCT-LIFE-CYCLE IMPLICATIONS

A key aspect of the design of new business metrics is to take into account the product-lifecycle stage of products and organizations as a whole. It is becoming increasingly clear that firms utilize different business strategies for different stages of a product-life-cycle. Product-life-cycle management has become a mainstay of business strategy; so much so that fundamental organizational decisions such as resource allocation, shareholder value creation, and customer satisfaction hinge on product-life-cycle issues.

Product-life-cycles (PLC) are not only becoming shorter (Garud et al., 1995), but they also are increasingly skewed to the left (McGrath, 1997). Any change in a product-life-cycle, length or shape, calls for a corresponding adjustment in business strategies. If such PLC-based adjustments in strategies are not forthcoming, then the firm is in serious jeopardy of failing to sustain its business success. The big question is what those strategic adjustments should be. This is where business metrics play a critical role. An organization that is reading the contextually appropriate metrics will be in a sound position to make the right strategic modifications, thereby sustaining business success.

For instance, as product-life-cycles become more left-leaning, time-to-market becomes a more critical business metric (McGrath, 1997). Therefore, modified strategies that lead to a lowering of the time-to-market metric will payoff significantly for left leaning product-life-cycles. The key here is that only firms that deliberately monitor the time-to-market metric can leverage changes in their product-life-cycles. Other, less observant, firms may continue to place sole emphasis on more traditional metrics such as manufacturing costs, sales revenues, market shares, etc., leading to misdirected strategies.

According to Dellecave (1995), electronics manufacturers face product-life-cycles that are so short that the customers' requirements may have changed by the time a component moves out of a design process. He suggests that those manufacturers that continue to be successful have changed their strategies and are turning to distributed computing to provide the efficiency and flexibility needed to meet customer needs in the face of shrinking product-life-cycles. Similarly, Griffin (1997) suggests that companies are focusing more on shortening product development cycle-times in order to effectively deal with compressed product-life-cycles. Product development cycle-times are the metric suggested in this study.

The link between business strategies and organizational goals is ever-changing. Constant, close monitoring of this link, via the use of appropriate business metrics, is essential for sustained business success. Shrinking product-life-cycles have turned the spotlight more glaringly on the cost metric. Electronics companies have adapted to this shift by outsourcing key portions of their business functions (Levin, 1996). Using the proper metric, in this case, has resulted in a definite strategic modification, the sudden rise of outsourcing as a key strategy.

In a marketplace characterized by brand proliferation, condensed development cycle times and product-life-cycles, globalization and media fragmentation, and building brand loyalty is more important than ever but is getting tougher to achieve (Munger, 1996). The fact is, these market characteristics are fueled by powerful business trends such as the growing assertiveness of consumers and increasingly fierce competition and advances in manufacturing technologies (Kotha, 1996; LaBahn, 1996; Goldhar 1995). Strategies, suitably modified so that they lead to an increase in the brand proliferation metric or to a lowering of the development cycle-time metric, are the ways to build brand loyalty in this newer marketplace. Others (Hughes et al., 1996) suggest that in the contemporary marketplace competitors come and go, technological changes occur in an ever-increasing rate, customer wants and needs are constantly shifting and a product's life-cycle may be shorter than its development time. In order to remain viable in this fast paced environment, it is crucial that firms introduce new products frequently (Raman et al., 1995). In this situation, the proportion of a firm's revenue from new products is considered an important metric.

In the beverage industry, brand proliferation and shorter life cycles are typical (McSparran, 1995). Hence, a similar metric to the above would be functional. According to Taylor et al. (1996), banks are competing in a fast-changing environment where product-life-cycles are as short as 18-24 months and shrinking. Again, an appropriate metric would be like the one above. Firms in the electronics industry manage their supply chain in a manner that quickly pushes products out into a fast time-to-market environment (Taninecz, 1995). For electronics manufacturers faced with a shorter product-life-cycle, the performance of the new product development function can determine the firms' success. Loch et al. (1996) postulate that overall development productivity, measured by development expense intensity, is the clearest predictor of business success. The latter is a timely PLC-based business metric.

It is well known that product marketing strategies vary depending on the stage of the life-cycle the product is in. Moore (1997) extends this concept and suggests that at every stage of a technology-life-cycle there are key metrics that need to be leveraged and key anti-metrics that should be de-emphasized in forming strategies. This concept can be adapted to the product-life-cycle, as discussed below.

In the introductory stage of the product-life-cycle, the preferred marketing strategy is to seed the market by targeting the "innovator" and "opinion leader" type customers. The marketing mix strategies are aggressive and driven by key metrics, which are market acceptance of the product and customer satisfaction. The key anti-metric here, the one that could lead to a premature market failure, is product profits. In the growth stage of the product-life-cycle, the preferred marketing strategy is to focus on market penetration, with the goal of quickly establishing a dominating presence in a niche. The key anti-metrics in this case are segment-share and time-to-segment-dominance. The anti-metric to be actively avoided in this stage is total revenue. The third stage in a product-life-cycle is the maturity stage. The strategic focus here is on standardization and securing mass-segment customers. The metric driving the strategies is overall market-share and cost-minimization while the key anti-metric to be suppressed is customer satisfaction. The decline stage is considered the final one in a product-life-cycle. Marketing strategies that are usually advocated at this phase are segment-of-one marketing mass-customization. The metric driving these strategies is profits and the anti-metric to be wary of here is design changes.

SALES AND MARKETING IMPLICATIONS

In a rapidly changing marketplace, where customers expectations change quickly and their ways of doing business is increasingly fickle, companies need to correspondingly change their ways of doing business. If businesses do not react to changes in customers' decision process by making changes in their ways of doing business, then they will lose an increasingly larger share of their current customers (Colletti and Wood, 1996). This will happen even if that business is able to recruit new customers at a fast rate.

A well known adage in sales says that you formulate selling strategies as a mirror image of customers' buying strategies. Knowledge of how customers buy should determine how firms sell. Hence, observing and understanding customers' buying behavior is critical. The measures and metrics used by organizations in tracking this construct need to be in tune with current market dynamics and not reflect outdated situations and business contexts.

The "customer-churn" metric indicates changes in customers' ways of wanting to do business. Sales strategies that work well in recruiting new customers may not be effective in retaining current customers. A high customer churn rate may indicate the need for a change in the firm's marketing strategies to current customers.

   Bank of America has spent the past several years working on ways to
   make service and the overall branch experience better, including
   using the Six Sigma process to spot and reduce errors. Many of its
   branches show business-news programs on TV sets mounted
   above the teller windows, and some offer free coffee on rolling
   carts near teller lines (Boraks, 2004). But how will customers who
   normally avoid ATMs react to the teller terminals? This is a simple
   example of how improved marketing processes will be flying "blind"
   unless proper metrics are used to reveal its success.

Marketing executives typically track (a) market-shares, (b) sales volume, and (c) contribution margins. These traditional metrics would then serve as the basis for reformulating marketing strategies. Such strategic reformulations usually take the form of changing the marketing mix combinations. Changes in price levels, advertising, and promotions tactics are typical (Band, 1988). However, there is clearly a need to develop and use causal metrics that more accurately monitor the link between strategy and performance. Examples of causal metrics, in this case, would be (a) customer loyalty, (b) brand equity, and (c) customer satisfaction. Causal metrics, generally, are more timely and accurate indicators of customer dynamics. The challenge in implementing such metrics, however, is in defining operational measures for them.

MANUFACTURING IMPLICATIONS

Manufacturing strategies are directly influenced by drivers such as technology and by manufacturing goals. As manufacturing moves from a cost-cutting and efficiency mode to a growth and innovation mode, the metrics used to track manufacturing performance towards its goals should also change appropriately. Similarly, as manufacturing moves from satisfying the needs of mass markets to those of market segments to that of segment-of-one markets, the metrics used to track manufacturing performance towards its goals should also change appropriately.

Consider the effect on manufacturing of the technology explosion. Advances in manufacturing technologies, such as flexible manufacturing systems (FMS) and computer-integrated-manufacturing (CIM), have made it possible to mass-customize (Goldhar, 1995). Thanks to such drivers, manufacturers will have to simultaneously strive to manufacture innovative products at a low cost while maintaining high quality and providing outstanding customer service (Watchorn, 1991). Even if mass customization does not create as much impact as the mass production system in the previous industrial revolution, the principles behind it will certainly change the way business is conducted (Lau, 1995). Just as traditional manufacturing methods cannot take advantage of newer manufacturing drivers, traditional manufacturing metrics will not reflect appropriately the performance of newer manufacturing strategies. Thus, a critical element in taking advantage of newer manufacturing technologies is in the development of newer manufacturing metrics.

The danger in using an improper metric is that it will provide a false indication of performance. Consider the commonly used factory metric, efficiency ratio. The efficiency ratio compares direct labor hours to machine hours. An over-emphasis on this metric will lead to higher levels of inventory, which may not reflect customer demand. Hendricks et al. (1996) write that Caterpillar implemented new financial and non-financial business metrics to measure their factory performance. This move was a direct result of a change in their corporate strategies to move certain factories from being cost-centers to profit-centers.

The importance of developing metrics in conjunction with strategies cannot be overemphasized. Take the case of total quality management (TQM) principles in a manufacturing process. Chenhall's (1997) research, based on actual firm data, suggests that for TQM to enhance the profitability of companies, the TQM principles should be developed together with managerial performance evaluation systems employing appropriate manufacturing metrics. The research specifically revealed that, amongst similar firms that had implemented TQM, those that had designed appropriate business metrics also had the higher performance. Caldeira (1997) studies the best practices of quality award winning firms and concludes that, among others, such firms seem to place a premium on valid performance measures.

Sheridan (1997) echoes a growing view that manufacturers are moving from a cost-cutting phase to a growth phase. The notion that cost-cutting is not really a strategy for long-term prosperity is becoming reality. Manufacturers are beginning to act on the belief that long-term prosperity can be derived through growth and market expansion. Care should be taken that as manufacturers redefine their strategies, they also redefine the metrics. Otherwise, the traditional metrics will incorrectly measure the effect of the strategies, leading to a more serious problem of incorrect strategy formulation.

Applications of comprehensive Quality Function Deployment (QFD)--or QFD in the broad sense--to strategic management have been known for some time, and its results have been discussed within the international community of QFD specialists. It is therefore tempting to investigate the contribution of combinatory metrics to strategy deployment. Combinatory metrics are constructed upon the capability of QFD to evaluate the deployment topics' contribution to customers' needs (Fehlmann, 2003).

INFORMATION SYSTEMS (IS) IMPLICATIONS

The impact of information technology on businesses has been significant. However, the role of information technology, within businesses, is constantly undergoing evolutionary changes. In the past, investments in information systems were primarily justified by determining cost savings through labor displacement and increased productivity. In other words, investing in IS was taken to be, mainly, an investment in automation and efficiency. When five billion dollar Massachusetts Mutual installed a new life and health claims adjudication system, the department was able to process 10 percent to 12 percent more claims with 35 percent to 40 percent fewer staff members (Sullivan-Trainor, 1991), resulting in cost savings through increased productivity and labor displacement.

Not all IS investments are wise investments. Managers need to put a price tag on the benefits of information technology. Corcoran (1997) suggests that to successfully implement IS projects, managers must arm themselves with a set of appropriate business metrics.

Compared to the past, the current role of information technology within organizations is vastly different. It is a primary vehicle for redesigning the business process, not just a way to cut costs. Information technology has become so embedded in business functions, that it is extremely difficult to identify and measure the yield due to IS. To illustrate the complexity of measuring the impact of information technology, consider the research by Rai et al. (1996). The authors study the link between various business performance metrics and investments in information technology by using statistical analysis on 210 firms. They find that, using aggregate metrics, the IS budget is not related to firms' financial performance, but is positively related to firms' sales performance. Using intermediate metrics, such as asset turnover and labor productivity, Rai et al. (1996) find that the effect of IT investments on intermediate firm performance is mixed. They concluded that it is essential to use intermediate and aggregate metrics in measuring IT value.

A key element is the realization that the performance metrics used earlier for IS effectiveness will have no value in assessing today's expanded IS role. Just as information technology has changed and the role of IS within businesses have changed, it is crucial to constantly update the metrics being used. Metrics such as billing accuracy rates derived from the efforts of cross-functional teams are becoming more popular as measures of business performance. Increasingly, IS's fate is entwined with business units that are being judged according to those new business metrics (Fabris, 1996). New metrics often combine IS and business unit objectives in one measurement.

Different business organizations have a somewhat different take on what metrics to use in evaluating the impact of IS on a firm's performance. Return-on-investment (ROI) metrics and cost-benefit metrics are commonly used measures. These metrics, however, are appropriate for an earlier time period when IS played a narrow role in businesses. Additional metrics will need to be developed to measure, more accurately, the current role of IS. According to Laplante (1996), Ryder System Inc. utilizes a multifaceted scorecard that includes the traditional metrics along with customer-based metrics and competitor-based metrics. Similarly, ITT Corporation has come up with a performance measure that encourages innovation. In addition to traditional benchmarking and ROI analyses, the company tracks the percentage of time, effort, and budget that the IS group in each ITT business unit devotes to revenue-generating activities (LaPlante, 1996).

   State agencies often find themselves caught between the need for
   technological innovation and the reality of large-scale technology
   failures, increasing the importance of supporting an information
   technology (IT) investment with a systematic and repeatable
   approach of analysis. Rivenbark et al. (2003) explore the
   application of business metrics in state government for
   selecting and evaluating IT initiatives.

PURCHASING IMPLICATIONS

As in other business functions, the drivers for purchasing have changed over the years. Purchasing is being influenced more by longer term strategic considerations rather than by short-term operational ones (De Rose, 1991). There are three specific drivers that currently have a dominant effect on the purchasing function, though they were not critical factors in the past. First, the increasingly common practice of outsourcing by companies has turned purchasing into a profit-and-loss center from a cost-center. Second, rapidly changing technology and intense competition are steadily shortening the product-life-cycles, which in turn impact the purchasing function. In fact, even within a product's life cycle, the various stages of the PLC affect the type of purchasing strategy employed by firms (Birou et al., 1997). Third, the pursuits of total-quality-management and just-in-time-management practices have created additional and newer demands on purchasing. The presence of these newer drivers call for a redesign of the metrics being used to measure the performance of purchasing.

The role of the purchasing function in best practice organizations is to add value to the business through the effective development and management of the supplier base. In many business organizations, the only metric used to measure purchasing performance is stock level (Parsons, 1997). This is the traditional metric for the purchasing function. However, a business with a lower reorder level (ROL) and lower reorder quantity (ROQ) will have, ceteris paribus, a lower stock level. This firm's purchasing function, based on the stock level metric, is operating very efficiently. However, this firm has significantly increased its order-processing expenses. They are able to lower the stock level by increasing the number of orders and their total costs have increased considerably. Such obviously sub-optimal decisions are the direct results of using poor metrics, as the buyer was only performing according to the way he was measured.

As Parsons (1997) says, if we want the purchasing department to achieve cost savings, then we need to measure that with the appropriate metrics. Developing the appropriate metrics for purchasing gets complicated as lower costs can be achieved through unintended means, i.e. through poorer quality, delivery, service or reliability. It is undoubtedly clear that the metrics used for measuring performance of the purchasing function are critical. Whenever there is a paradigm shift in the purchasing function, there should be a corresponding shift in the metrics being utilized.

RESEARCH AND DEVELOPMENT IMPLICATIONS

Given the tremendous pressure on businesses to improve performance, every function in these business organizations is being closely scrutinized for its value added. This emphasis on accountability has extended to the farthest reaches of firms, even to research and development. There is renewed interest in developing proper performance metrics. Measurements in science are in vogue. They are taking center stage because of the emphasis in industry, government, and academia on measuring the value of research--in particular basic research (Jacobs, 1997).

The transition for several corporate functions from being cost-centers to profit-and-loss centers has hit even R&D. The challenge for R&D organizations is to consistently contribute to the long-term generation of corporate wealth (Jaskolski, 1996). However, a critical step in transforming R&D into profit-and-loss centers is the development of proper performance metrics. If we cannot measure the performance of R&D accurately and reliably, then we cannot design strategies for improving its performance.

R&D directors continue to behave defensively when negotiating their budgets, while their colleagues who represent other business functions use more aggressive approaches. Developing R&D metrics that resemble the standard business metrics used in other functional areas could support a more offensive approach (Brockhoff and Chakrabarti, 1997).

There is yet to be a commonly accepted set of metrics for measuring R&D performance. Several years ago, Schainblatt (1982) published the results of a survey of 34 leading US companies. Results showed that there was limited use of R&D performance metrics because their managers were skeptical of their validity. An astonishing 60 percent of all companies surveyed had no activity at all in R&D performance measurement (Werner, 1997). Moser (1985) analyzed the frequency of use of commonly cited R&D effectiveness indicators. Like previous surveys, relatively little use of these methods was found. In 1993, Tipping published the results of a survey of the use of R&D metrics within 100 U.S. firms. Only half of the firms routinely used any systematic method. Interestingly, Tipping found that only those firms in which R&D and marketing departments were strongly interdependent carefully assessed their R&D performance. Also, all the firms used R&D metrics retrospectively, with no emphasis on using the measures to develop R&D strategies.

Werner (1997) compared the use of R&D metrics by U.S. firms and German firms. He found that most U.S. firms prefer output-based R&D metrics such as patent counts, rate-of-return, total quality management audits, and cost/time performance assessment. On the other hand, most German firms tended to use input-based performance metrics such as annual expense per R&D employee. Maximizing the results from each dollar spent on R&D is an increasingly important task in today's competitive global economy (Werner, 1997). Greater attention is therefore being focused on the measurement of R&D effectiveness through the development of appropriate metrics.

Others

In most areas, the use of proper business metrics to measure performance has become a high priority. Even areas that traditionally did not measure performance are beginning to realize the need for sound metrics. In the healthcare field, Palmer (1996) suggests that too few managed care organizations provide performance measures to physicians for their use in quality improvement. Developing such metrics and encouraging the use of them will result in significant improvements in performance. Wilensky (1997) finds that the privatization of healthcare is leading to the development of more and better quality metrics.

The Balanced Scorecard (BSC) has emerged as an important strategic management system. By incorporating the perspectives of customers, internal business processes, and learning and growth, it enables users to identify and measure factors critical to an organization's efforts to become more flexible and responsive to customer needs. Whether the BSC is used as a performance measurement or strategic management system, a company must address how targets and goals--the metrics used in the scorecard process--are established. According to Weinstein and Astellano (2004), while benchmarking best practices of other organizations and using stretch targets are methods discussed in the scorecard literature, these approaches may undermine the credibility and usefulness of the BSC. Such measurements are simply arbitrary goals and targets. It is critical that the right metrics are used in any BSC analysis. The call for using a balanced scorecard reporting by healthcare providers is not unusual (Forgione, 1997). Such a report will include both financial and quality performance metrics. Such measures may be used internally for quality improvement purposes or externally for marketing to the purchasing public.

   A similar approach has also been frequently proposed for the field
   of auditing (Applegate, 1997). The idea is to develop and use
   performance metrics that focus not only on expenditure of resources
   and time by audit project but also on audit quality and customer
   value. Traditional business metrics used in auditing include audit
   cycle time and hours-per-project. These types of metrics are useful
   in assuming the audit project population is homogenous. If, however,
   the nature and scope of the audits differ markedly from project
   to project, then using the cycle time metric will be unreliable.
   Even if cycle time variance were predictable enough to track
   meaningful trends, the potential of any management action to reduce
   cycle time would still remain unknown in terms of audit quality and
   customer value (Applegate, 1997). Thus, there is a critical need
   for refining the performance measurement process by developing
   newer metrics.

Accountability in organizations in a knowledge-based economy should move decision authority from a centralized vertical command and control structure to distributed decision-making at the right level. In reinventing corporate governance, executive accountability and pay for performance, the board and the CEO should ensure that the level of work and related accountabilities of the CEO are matched to the business strategy. They should also ensure that the accountability and decision authority structure is appropriately cascaded into the organization and that performance measurement and the requisite leadership capabilities are aligned at each work level (Van Clieaf, 2003). The transparency of corporate governance processes is possible only with improved business metrics designed specifically for that purpose.

CONCLUSION

The theme of this article is to highlight the importance of using timely and current business metrics. Changes in product-life-cycles and different stages within a product-life-cycle call for changes in the business metrics used. Poorly conceived or inappropriate metrics result in an organizational insensitivity to changes in firm performance. As Lewis (1996) puts it, regarding business metrics, "use it well and performance will improve. Use it poorly and only the measure will improve." The gauges may show higher customer satisfaction, but the profits may not have increased.

Actively managing customers can improve profitability, and a firm's success depends on treating these customers as key assets rather than just as a pool of transactions that follow from business activities. Customer performance metrics provide objective criteria that everyone can view, understand, and act on in ways that are consistent with overall strategies. Wyner (2004) states that they provide the vehicle for determining how well the business is working. While some argue that there is a particular way to implement a metrics plan, no single metric or approach will be appropriate for every company.

While strategy formulation and strategy implementation are essential to achieving superior organizational performance, an increasing number of authors have concluded that implementing strategy is where companies succeed or fail. Organizations that excel at execution know how to create value for customers and shareholders. Spanyl (2003) suggests that companies that aspire to superior, sustainable performance cannot afford to be diverted from its strategic goals--and that requires operating discipline.

A firm can only manage what it measures. But, by the same token, a firm should also measure only what it wants to manage (Brancato, 1997). A business organization that continually monitors and effectively manages the use of proper business metrics will have discovered a fundamentally unique source of competitive advantage.

REFERENCES

Aggarwal, Raj. (2004, June 1). The soft side of performance. Intelligent Enterprise, 7, (9), 6.

Allen, J.R. (1996, August). The use of performance measurement in government. Government Finance Review, 12 (4), 11-15.

Applegate, D.B., Bergman, L.G., and Didis, S.K. (1997, April). Measuring success. The Internal Auditor, 54 (2), 62-67.

Bacidore,J.M., Boquist,J.A., and Thakor, A.V. (1997. May/June). The search for the best financial measure. Financial Analysts Journal, 53 (3), 11-25.

Band, W.A. (1988, February). Use the right measures to track marketing performance. Sales & Marketing Management, 29 (2), 30-31.

Birou, L., Fawcett, S.E., and Magnan, G.M. (1997, winter). Integrating product life cycle and purchasing strategies. International Journal of Purchasing and Materials Management, 33, (1), 23-32.

Blankenship, D. (1996, August). Understanding selling patterns. Apparel Industry Magazine, 57, (8), 74-77.

Boraks, David. (2004, February 19). B of A says teller line devices are doing well in test. American Banker, 169 (33), 2.

Brancato, C.K. (1997, May).Implementing strategic-performance measures. Across the Board, 34 (5), 55-56.

Klaus, K., Brockhoff, Alok., and Chakrabarti, K. (1997, Sept/Oct.). Research Technology Management, 40 (5), 5, 37.

Calabro, L. (1996, December). What analysts really want. CFO, 12 (12), 15-16.

Caldeira, E. (1997, October). Follow these 6 quality methods for award-winning performance, Professional Builder, 2-5.

Campbell, A. (1997, summer). Keeping the engine humming. The Business Quarterly, 61 (4), 40-46.

Chenhall, R.H. (1997, June). Reliance on manufacturing performance measures, total quality management and organizational performance. Management Accounting Research, 8, (2), 187-206.

Coit, C.I., and Karr, J. (1996, Sept/Oct). Performance measurement: Miles traveled, miles to go. Banking Strategies, 72 (5), 68-73.

Colletti, J.A. and Wood, W. (1996, June). Hold on! Across the Board, 33 (6), 27-33.

Cooper, C. (1996, September).Yes Virginia, there is a way to measure employee performance. Incentive, 170 (9), 14.

Cooper, R.G., and Kleinschmidt, E.J. (1996, July/August). Winning businesses in product development: The critical success factors. Research Technology Management, 39 (4), 18-34.

Corcoran, C.T. (1997, May 26). IS managers need to put a price tag on productivity. InfoWorld, 19 (21), 65-70.

Dellecave, T. Jr. (1995. September 18). InformationWeek 500: Charged with change. Information Week, 545, 100-102.

Dempsey,S.J., Gatti,J.F., and Grinnell, D.J. (1997, summer). The use of strategic performance variables as leading indicators in financial analysts' forecasts. Journal of Financial Statement Analysis, 2 (4), 61-79.

De Rose, L.J. (1991, May). Meet today's buying influences with value selling. Industrial Marketing Management, 20 (2), 87-90.

Drickhamer, David. (2004. August). YOU GET WHAT YOU MEASURE. Industry Week, 253 (8), 24.

Drucker, Peter F. (1995, Jan/Feb). The information executives truly need. Harvard Business Review.

Elliott, R.K., and Pallias, D.M. (1997, July). First: Know your market. Journal of Accountancy, 184, (1), 56-63.

Erasmus, M. (1997, May/June). Firmwide risk management - The final frontier. The Bankers Magazine, 180 (3), 39-43.

Fabris, P. (1996, November). Measure of distinction. CIO 10, (4), 68-72.

Fehlmann, Thomas. (2003). Strategic management by business metrics: An application of combinatory metrics. The International Journal of Quality & Reliability Management, 20 (1), 12,134.

Fleisher, C.S., and Mahaffy, D. (1997, summer). A balanced scorecard approach to public relations management. Public Relations Review, 23 (2), 117-118.

Forgione, D.A. (1997, fall). Health care financial and quality measures: International call for a 'balanced scorecard' approach. Journal of Health Care Finance, 24 (1), 55-58.

Garud, R., and Kumaraswamy, A. (1995, summer). Technological and organizational designs for realizing economies of substitution. Strategic Management Journal, 16, Special Issue, 93-109.

Ghalayini, A.M., Noble, J.S., and Crowe, T.J. (1997, February 14). An integrated dynamic performance measurement system for improving manufacturing competitiveness. International Journal of Production Economics, 48 (3), 207-225.

Grant, P. (1997, March). How financial targets determine your strategy. Global Finance, 11 (3), 30-35.

Griffin, A. (1997, February). The effect of project and process characteristics on product development cycle time. Journal of Marketing Research, 34 (1), 24-35.

Gutgeld, Y., and Beyer, Damon. (1995). Are you going out of fashion? The McKinsey Quarterly, 3, 54-61.

Hendricks, J.A., Defreitas, D.G., and Walker, D.K. (1996, December). Changing performance measures at Caterpillar. Management Accounting, 78 (6), 18-25.

Hendricks, K.B., and Singhal, V.R. (1997, September). Does implementing an effective TQM program actually improve operating performance? Empirical evidence from firms that have won quality awards. Management Science, 43 (9), 1258-1274.

Hughes, G.D., and Chafin, D.C. (1996, March). Turning new product development into a continuous learning process. The Journal of Product Innovation Management, 13 (2), 89-103.

Press, Irwin. (1997, October 27). Guidelines needed for health care performance measures. Marketing News, 31 (22), 19-21.

Jacobs, M. (1997, September 29). Measure for R&D measure. Chemical & Engineering News, 75 (39), 3.

Jaskolski, S.V. (1996, Nov/Dec). New role for R&D: The challenge of growth. Research Technology Management, 39 (6), 13-18.

Kaplan, Robert, S. (1992).The balanced scorecard. Harvard Business Review. Jan, Feb.

Kotha, S. (1996). Mass-customization: A strategy for knowledge creation and organizational learning. International Journal of Technology Management, 11 (7, 8), 846-858.

Kumar,K., Subramanian,R., and Yauger, C. (1997, summer). Performance-oriented: Toward a successful strategy. Marketing Health Services, 17 (2), 10-21.

LaBahn, D.W., Ali, A., and Krapfel, R. (1996, June). New product development cycle time: The influence of project and process factors in small manufacturing companies. Journal of Business Research, 36 (2), 179-188.

LaPlante, A. (1996, June 24). Rethinking the numbers. Computerworld, 30 (26), 78-79.

Lau, R.S.M. (1995, Sep / Oct). Mass customization: The next industrial revolution. Industrial Management, 37 (5), 18-22.

Levin, R. (1996, September 9). IT is the key change component. Informationweek, 596, 114-116.

Lewis, B. (1996, February 19). Measure for measure: For better performance, call it what it is. InfoWorld, 18 (8), 65-68.

Loch, C., Stein, L., and Terwiesch, C. (1996, January). Measuring development performance in the electronics industry. The Journal of Product Innovation Management, 13 (1), 3-20.

Lowenstein, R. (1997, February 13). Intrinsic value: Rethinking the latest economic elixir. Wall Street Journal, C1.

McChesney, H. (1996, November). Change your performance measurements or there may be no future. Hospital Material Management Quarterly, 18 (2), 86-90.

McGrath, M.E. (1997, January). Forget what you learned. Electronic Business Today, 23 (1), 20-21.

McNerney, D.J. (1996, September). The link to customer satisfaction. HR Focus, 73 (9), 1-4.

McSparran, K. (1995, February). Managing the product portfolio. Beverage World, 114 (1585), 101-103.

Moore, G. (1997, June,). The Metric System. Upside, 9(6), 60-63.

Moore, N.G. (1996, November). Measuring Corporate IQ. Chief Executive, 118, 36-42.

Moser, Martin R. (1985). Measuring performance in R&D settings. Research Management, 28 (5), 31-33.

Munger, S. (1996, December). Leveraging new technology to build brand loyalty. Direct Marketing, 59 (8), 58-62.

Norton, David, P. (1992). The balanced scorecard. Harvard Business Review, Jan, Feb.

Nuelle, F. (1996, Jan/Feb). The two faces of EVA. Chief Executive, 110, 38-39.

O'Hanlon, J., and Peasnell, K. (1996, February). Measure for measure? Accountancy, 117 (9), 44-46.

Oliver, J. (1996, November). Which numbers count. Management Today, 106-109.

Owen, A. (1997, July / August,). A measure of their worth. CMA, 71(6), 12- 16.

Palmer, R.H. (1996, June 19). Quality health care. Journal of the American Medical Association, 275 (23), 1851-1852.

Parsons, W. (1997, July 17). Flushed with success. Supply Management, 2 (15), 40-43.

Peck, F. (1997, April). Retailing logistics - All change at the checkout. Logistics Focus, 5 (3), 14-15.

Prabhaker, P.R., Goldhar, J.D., and Lei, D. (1995). Marketing implications of newer manufacturing technologies. The Journal of Business & Industrial Marketing, 10 (2), 48-58.

Rai, A., Patnayakuni,R., and Patnayakuni, N. (1996, August). Refocus where and how IT value is realized: An empirical investigation. Omega, 24 (4), 399-406.

Raman, N., and Chhajed, D. (1995, June). Simultaneous determination of product attributes and prices and production processes in product-line design. Journal of Operations Management, 12 (3, 4), 187- 204.

Rangone, A. (1997, June). Linking organizational effectiveness, key success factors and performance measures: An analytical framework. Management Accounting Research, 8 (2), 207-219.

Rubin, H. (1991, January). Measurement: Shifting the focus to business value. Capacity Management Review, 19(1), 1-4.

Russell, W.G., and Sacchi, G.F. (1997, summer). Business-oriented environmental performance metrics: Building consensus for environmental management systems. Environmental Quality Management, 6 (4), 11-19.

Schainblatt, Alfred H. (1982). How companies measure the productivity of engineers and scientists. Research Management, 25 (3), 10-18.

Schwartz, S. (1997, April). Transcending financial measures. Insurance & Technology, 22 (4), 52-53.

Sheridan, J.H. (1997). Going for growth. Industry Week, 246 (11), 32-40.

Spanyl, Andrew. (2003, March). Strategic achievement. Industrial Engineer, 35 (3), 40.

Stainer, A. (1996, October). Effective and meaningful performance measurement. Management Services, 40 (10), 38-40.

Sullivan-Trainor, M.L. (1991, May 13). Is technology worth it? Computerworld, 25 (19), 81-88.

Taninecz, G. (1995, May). Faster in, faster out. Industry Week, 244 (10), 27-29.

Taylor, D., Mehta, B., and Wurster, T. (1996). Searching for retail strategies. Bank Management, 72 (1), 22-31.

Thompson, T. (1997, April). Measuring performance: How do you stack up? Oil & Gas Investor, 17 (4), 59-66.

Tipping, James W. (1993). Doing a lot more with a lot less. Research Technology Management, 36 (5), 13-14.

Van Clieaf, Mark. (2003, December). Board's due diligence. Ca Magazine, 136 (10), 46-48.

Vesey, J. (1991, May). The new competitors: They think in terms of 'speed-to-market'. The Academy of Management Executive, 5 (2), 23-33.

Watchorn, W.E. (1991, spring). Manufacturing at the crossroads. The Business Quarterly, 55 (4), 105-110.

Weinstein, Larry., and Castellano, Joseph. (2004, April). Scorecard support. CMA Management, 78 (2), 18.

Werner, B.M., and Souder, W.E. (1997, May / June). Measuring R&D performance--U.S. and German practices. Research Technology Management, 40 (3), 28-36.

Wilensky, G.R. (1997, May / June). Promoting quality: A public policy view. Health Affairs, 16 (3), 77-82.

William C., Rivenbark., Kevin M., and Schelin, Shannon H. (2003, winter). Analyzing information technology investments in state government. Social Science Computer Review, 21(4), 497.

Wyner, Gordon (2004, Jan/Feb). The right side of metrics. Marketing Management, 13 (1).

Mohamed Askar ([e-mail]) is an associate professor of management in the Brennan School of Management at Dominican University.

Syed Imam ([e-mail]) is a consultant based in Cairo, Egypt.

Paul R. Prabhaker ([e-mail]) is an associate dean in the College of Business at Northern Illinois University.