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).

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.