How Behavior Affects Our Attitude Toward Performance Metrics and Can Have Unintended Consequences on Business Results

As supply chain professionals, many of us operate amidst an abundance of performance metrics. More mature organizations often establish a hierarchy of these metrics to visualize their interrelationships and ensure that actions taken align with desired outcomes. For those interested, Gartner and APICS have published extensively on supply chain metrics and their relationship to key business results. However, while much attention is given to convincing stakeholders, freeing resources, and deploying dashboards to track metrics, often less thought is given to the selection of the metrics themselves. Research shows that the choice of metric types can significantly influence the quality of project decisions and the motivational impact on individuals involved.

The core of choosing effective performance metrics lies in understanding the distinction between linear and non-linear metrics. Humans often struggle to assess changes accurately in non-linear metrics. Take, for example, internet speed: if a small satellite office considers upgrading from 5 Mbps to either 25 Mbps, costing an additional 60 dollars or to 100 Mbps costing an additional 120 dollars, the latter might seem like a better deal at first glance—four times the speed for only twice the cost. However, this can be misleading because upgrading to 25 Mbps will reduce download time for 1 GB by 21 minutes, whereas 100 Mbps saves only an additional 4 minutes. Are those extra 4 minutes worth the 60 dollars additional cost? This example highlights how easily we can fall into “fast thinking” traps when dealing with non-linear metrics.

In supply chain management, we often interchangeably use linear and non-linear metrics without much consideration. For example, inventory can be measured in days of supply (time-based) or inventory turns per year (rate-based). Warehouse picking can be measured by picking time (e.g., 30 seconds per unit) or picking rate (e.g., 120 units per hour). Similarly, reliability can be measured by mean time between failures or failure rate. Many of our commonly used metrics can be expressed in either form.

Psychologists, notably Daniel Kahneman and Amos Tversky, have extensively researched human behavior and decision-making. Kahneman’s book Thinking, Fast and Slow (2011) describes two modes of thinking: System 1, which is fast, intuitive, and automatic, and System 2, which is slow, analytical, and deliberate. System 1 handles quick, heuristic-based decisions, while System 2 tackles complex problem-solving. When faced with seemingly simple decisions involving non-linear metrics, our brains often default to System 1, which can lead to flawed judgments.

In 2015, Tobias Stangl and Ulrich Thonemann from the University of Cologne’s Department of Supply Chain Management studied the effects of using different but equivalent metrics. When university students were presented with an inventory decision, 89% made optimal choices using the days of supply metric, whereas only 42% did so when using the inventory turn rate metric. Repeating this test with senior supply chain leaders at various conferences gave slightly better but similar results. The researchers concluded that inventory optimization decisions are more accurately evaluated using the days of supply metric than the inventory turn rate metric, with a clear advantage for the former in improvement project selection decisions.

Simplification is a powerful principle in supply chain management, and using more straightforward, unambiguous metrics supports this approach. Should we, therefore, translate all non-linear metrics into their linear equivalents? Not necessarily. Stangl and Thonemann also demonstrated that rate-based metrics can encourage greater effort toward metric optimization. Their findings suggest that the performance metric used influences the motivation and effort of individuals. Because non-linear metrics like inventory turn rate amplify the perceived impact of efforts on inventory reduction, individuals may invest more effort and achieve lower inventory levels using this metric compared to days of supply.

These results have significant managerial implications. If a non-linear metric like inventory turn rate is used, individuals may over-invest in improving categories with already low inventory and under-invest in those with higher inventory. This issue can be mitigated by using linear metrics. However, a metric that enhances perceived value, such as a non-linear metric, can motivate continuous improvement in settings where existing levels are already low. In essence one could say: improve decision-making with linear metrics, but drive motivation by rallying teams with non-linear metrics.

To read the full research by Stangl and Thoneman, visit https://ssrn.com/abstract=2642314.