An important issue in performance measurement is how a company can use measures to gauge its supply chain‘s performance. To do this effectively, a target for each measure needs to be established, providing the framework for determining the answer to three questions that arise when evaluating a performance metric:
Has the metric improved from the last time it was reviewed?
By how much?
How close is the metric to where it should be?
In order for this evaluation to be meaningful, however, the direction of improvement needs to be established. Should the metric have gone up or gone down?
Frequently, in looking at productivity-related metrics an increase represents an improvement; similarly, for cost-related metrics a decrease represents an improvement. This is not always the case! For example, an increase in manufacturing productivity and a decrease in cost would normally be considered an improvement. It would not be an improvement if it caused degradation in customer service performance.
In a way similar to picking a set of balanced metrics, performance targets need to be jointly, not individually, developed. To achieve objectives some metrics may need to increase and others may need to decrease. Each metric in the set has to be viewed in conjunction with the others to determine its proper target. For example, in a situation where a company is trying to achieve same day delivery, delivery times should decrease, while warehouse handling and transportation costs might actually increase.
Thus, while there a variety of ways in which to set performance targets, they should always be jointly set in the context of strategic objectives. Generally, there are four methods that can be used to set performance targets, described in detail below:
- Historically based targets
- External benchmarks
- Internal benchmarks
- Theoretical targets
1.Historically Based Targets
Historically based target setting is the most frequently used among all the methods. In using this method, performance targets are based on historical baseline levels. For example, a company having an historical order fill rate of 90% might set a performance target at 95%, trying to improve by five percentage points.
This method is the most frequently used because it is the easiest to implement. Once the baseline metrics are established, the same procedures and systems that were used to establish the baseline numbers can also be used on an ongoing basis to measure changes in the metrics.
2. External Benchmarks
Using external benchmarks to help set performance targets is currently popular, but difficult to use in practice. In general, benchmarking has been in the business limelight for almost ten years, with companies looking outside their operations for best practices and performance comparisons. This method relies on collecting information on performance metrics of companies internal and external to one‘s industry.
Once external benchmarking metrics are collected, a company‘s internal metrics are generated and a gap analysis is done – typically looking at the best-in-class within their own industry as well as external to it. This is followed by more analysis to assess the degree to which the company can achieve these performance levels, including what business practice changes are necessary to close the gaps.
While appealing, the external benchmarking method has a major shortcoming to it, as to which set of companies are comparable. A substantial amount of analysis is required to ensure that external benchmarks are meaningful, especially when using data from companies that operate within different business environments (e.g., differing products or sales channels). This makes the use of external benchmarks difficult, since comparable external benchmarks may not be available or too controversial. On the other hand, external benchmarks, especially from one‘s competitors, may be extremely important towards keeping an organization‘s supply chain
3. Internal Benchmarks
Performance target setting using internal benchmarks is a common approach, since it requires only internal measures. Within this method, comparable functional departments, processes, and facilities within a company are measured in the same way. For example, there may be a set of metrics in use for all warehousing facilities, another set for all manufacturing plants, and another set for all customer service departments. Similar to the external benchmarking approach, ―best-in-class‖ functional organizations are identified and their benchmark metrics are used as the basis for establishing performance targets for other functional organizations. In contrast to external benchmarking, internal benchmarking data is easier to collect. The method is less controversial when comparing business operations since internal organizations usually operate in similar business environments.
While this internal benchmarking method is easier to implement, it too has some serious drawbacks to it. The major one involves stretching the organization to achieve better performance. That is, using a ―best-in-class‖ internal organization to set targets may limit the company‘s performance relative to its competitors.
4. Theoretical Targets
The use of theoretical target setting is a relatively new method advocated by some consultants. Under this method a company conducts an analysis to theoretically determine how its supply chain performance could be improved. It would then implement the business changes necessary to achieve these improvements and put a set of performance targets in place based on estimates made during the analysis.
This can be done with the use of supply chain optimization to help set theoretical performance targets. Using his approach, a company would first undergo an analysis to determine how it should optimize supply chain performance. It would then use the estimates made during the analysis to set its performance targets. For example, a company might determine that in order for it to maximize its long-term profits, it should increase on-time order due-date performance, while increasing its manufacturing costs and decreasing its air freight charges. The company would then use the results of the analysis to increase its performance targets for manufacturing
costs and on-time order due-date performance, while appropriately decreasing the target on its airfreight charges.
While conducting an optimization analysis is an intuitively appealing method for determining performance targets, it is not always the easiest to do. Another alternate approach involves the use of supply chain simulation analysis that includes conducting what-if studies on initiatives to improve performance. The results of these studies could then be used to set theoretical targets. For example, a ―what-if‖ study might be conducted to assess inventory reductions that might accrue from statistical safety stock setting. The study‘s estimated reductions would be used to reset performance targets for inventory turns.
Setting performance targets on a theoretical basis is most useful for insuring that a balanced set of metrics is developed. Often, only by doing a thorough analysis can one assess how an initiative would impact various aspects within a supply chain. In practice, a combination of the four performance target-setting methods described above should be used. No one method is practical for determining targets since one cannot always get a full set of comparable benchmarking information or conduct the extensive analyses needed to develop a full set of theoretical performance targets.