Metrics are used to identify, deane, measure, and control performance gaps f or i mprovement as w ell as to d escribe о perational p erformance. They should be aligned with higher-level metrics in ways discussed in the previous section and linked to others as needed to measure performance. An example is organizational or corporate warranty expense. It should be clearly deemed, measured, and calculated by location. Warranty is an aggregated expense rolled up from lower levels and locations in the organization to ensure linkage as shown in Figure 1.2. This ensures alignment of warranty expenses to one organizational number. A lignment i s also
Strategically aligning metrics.
important to ensure resource allocations are made to those areas where warranty expense is highest. Once reduced, these will provide the most productivity opportunity and ROE.
Warranty expense as shown in Figure 1.2 is linearly additive because its unit of measure is defined in monetary units (i.e., dollars). At an operational level, there are other metrics that must be measured and improved to reduce warranty expense. These are at a project level and they need to be correlated with higher-level metrics, either financial or operational. A second important consideration is the ease of data collection, analysis, and presentation. This is an important consideration for displaying metrics using automated and visual displays to show their status. As an example, for operations centers, process status is displayed using visual display boards. These show the current transaction volume, waiting time, and other measures for managing the operations center.
It is important that metrics be actionable by the people responsible for them. If a material planner is assigned the responsibility for controlling inventory, this person must also be able to see product lead times, demand, inventory status, and other relevant information to determine where inventory should be placed to meet service targets. Because lead times are set by other organizations and demand variation cannot be controlled by material planners, the inventory investment targets must be clearly defined so they coincide within the expected range of lead time and demand variation. Once the inventory investment metric is defined, reasonable, and actionable, it is very difficult to manipulate or to distort it in practice.
Metrics are classified for their organizational impact and specific format. These include the dimensions of time, cost, and quality. Within these dimensions, teams create metrics to measure, analyze, control, and continuously improve process performance. Service centers may use the average time to answer a call, and production may use lead time. Table 1.2 shows a partial listing of common metrics used to measure and control processes across a global supply chain. These can be classified into the dimensions of time, cost, and quality. They are all also linear or volume adjustable.
Metrics can also be classified into the four categories of business, financial, operational, and compensating. Business metrics are used to linearly deploy metrics throughout an organization. They are aggregated at an organizational level, a local business unit level, or a local process level. Business metrics are typically measured on a percentage-of-total basis.
Commonly Used Metrics
Examples include percent recordable accidents, percent of equipment uptime, percent forecasting accuracy, percent on-time delivery, warranty cost as a percent of sales, and scrap and rework as a percent of cost. Financial metrics are directly correlated to business metrics and measure financial performance and productivity. Referencing Figure 1.2, the financial metric warranty expense is correlated to the business metric warranty expense as a percent of sales. Figure 1.2 also shows warranty expense on a percent basis at several organizational levels and its monetary impact.
At a local process level, a third metric type—operational—is defined based on the project type. Operational metrics are used to measure, analyze, improve, and control operations within a process. As an example, warranty expense at a facility level could be caused by three different types of defects. These defects might include a dimensioning problem, an offcolor product, or damaged packaging. The corresponding operational metrics would be defined in units of inches, color coordinate using an instrument to measure color, and the tear strength of the packaging. These operational metrics must align with and be translatable into the financial metric of warranty expense and the business metric of percent of total sales.
The fourth metric is a compensating measure because it is used to balance the impact of the other metrics. As an example, reducing process lead time to lower inventory investment requires a compensating metric such as a customer service target so that, when inventory investment is lowered at a constant sales level, customers still receive products. Reducing internal scrap and rework without increasing external warranty expense is another example of a compensating metric.
Metrics definitions are important because they require resources to develop, deploy, and use every day. Important decisions are made based on metrics. Good metrics are differentiated by a focus on both customer value and actions that improve productivity. Good metrics help speed product introductions, improve on-time delivery, optimize capacity, foster continuous process improvement, increase productivity, and align operational performance with strategic goals. These attributes enhance relative competitiveness.
Table 1.3 lists some competitive metrics that enable organizations and global supply chains to compete effectively. These are only a few examples, and different organizations may use others that better reflect their strategy. Net promoter score, which measures the likelihood customers will repurchase products and services, will be discussed in Chapter 3. Other metrics in Table 1.3 measure how well sales and market share are growing. Customer retention is useful for determining whether customers continue to purchase. This does not imply, however, that they are satisfied. There may not be competitive alternatives. Sales from new products and services are good predictors for future sales. Employee-related metrics are an important group for measuring employee satisfaction. The operations- related metrics measure lead times, yields, productivity, and various costs. To summarize, there are many potential metrics that organizations can use to measure their performance. But measurement is only the first step. Analysis of trends and other patterns in the metrics will yield insights for process improvements and creation of aligned projects. Organizations should use a minimum number of metrics to cost effectively focus on the important performance predictors.
Productivity at an organizational level is calculated as outputs divided by inputs. It is useful because it calculates throughput relative to the
Some Competitive Metrics
resources needed for the throughput. At an operational level, it is similarly calculated but at a cost-center level. Productivity also varies by industry and is used to estimate relative organizational competitiveness within an industry. At an individual organizational level, year-over-year productivity measures how well an organization performs. Table 1.4 shows relative organizational competitiveness. It is a qualitative representation which suggests that industries move from left to right relative to the removal of competitive barriers. The question for a specific organization is how to increase productivity to offset competitive pressures. These pressures may or may not be fair, depending on geographical, technical, political, and cultural barriers to market entry. The productivity numbers do not represent all industries and organizations. The thought is that organizations with low productivity are protected if isolated from competitors. If competition is high, then higher productivity will be needed to compete. Barriers to market entry (e.g., geographical, technical, political, and cultural barriers) can isolate an organization for a time. Productivity can
be set at convenient levels (e.g., low, medium, and high) using industry benchmarks of productivity performance data.
Productivity levels vary by industry and organization. The important concept is how well a specific organization compares to its competitors. But being competitive with current competitors may be enough if disruptions such as unexpected new market entrants, market collapse, technology advances, customer preference changes, or other conditions occur. This situation is in contrast to organizations that have comparatively low productivity levels that are noncompetitive. Organizations make choices when they create strategies, enable initiatives, design products and services, and create supporting processes. Making the correct choices reduces organizational friction and increases effectiveness, efficiency, and productivity. This increases competitiveness. In the absence of artificial competitive barriers to market entry, it will always be true that competitors have advantages. But these need to be offset by creating competitive solutions. In contrast, noncompetitive organizations will not be able to satisfy demands with lower costs, faster service, and higher quality and variety. The competitive landscape in the automotive and consumer electronic industries provide useful case studies for the importance of creating operational excellence.