When discussing business metrics and KPIs, there is a critical aspect that people often overlook – variability. While averages and totals are important, they only tell part of the story. Understanding the spread of your data through measures like standard deviation can provide crucial insights that might otherwise be missed.
Why Variability Matters
Risk Assessment
Variability is a key indicator of risk. High variability in production output, customer demand, or quality metrics can signal potential issues that need addressing.
Process Stability
Low variability often indicates a stable, well-controlled process. High variability might suggest inconsistencies that need investigation.
Customer Satisfaction
Consistent experiences typically lead to higher customer satisfaction. High variability in service times or product quality can lead to unpredictable customer experiences.
Resource Planning
Understanding the variability in demand or production can help in more accurate resource allocation and inventory management.
Realistic Goal Setting
Knowing the typical range of performance helps in setting achievable yet challenging targets.
The Pitfalls of Ignoring Variability
False Sense of Security
Relying solely on averages can mask underlying issues. For example, a manufacturing line might have an acceptable average output, but high variability could indicate frequent breakdowns offset by periods of overproduction.
Missed Opportunities
Low variability in certain metrics might indicate areas where processes are well-optimised, providing valuable lessons for other parts of the business.
Ineffective Interventions
Without understanding variability, you might implement changes that address symptoms rather than root causes.
Misallocation of Resources
You might over-invest in areas with naturally high variability while neglecting areas where reduced variability could yield significant benefits.
Incorporating Variability into Your Metrics
Use Range and Standard Deviation
Alongside averages, report the range (minimum to maximum) and standard deviation of key metrics.
Visualise Distribution
Use histograms or box plots to visually represent the spread of your data.
Track Trends in Variability
Monitor how variability changes over time. A reduction in variability often indicates process improvement.
Set Variability Targets
For critical processes, set targets and control limits, not just for averages, but for acceptable ranges or standard deviations.
Investigate Outliers
Extreme values can provide valuable insights into both problems and exceptional performance.
Real-World Example
Consider a food manufacturing company tracking order fulfilment times:
- Average fulfilment time: 2 days
- Standard deviation: 0.5 days
This tells us that about 68% of orders are fulfilled between 1.5 and 2.5 days, and 95% between 1 and 3 days (assuming a normal distribution).
Now imagine two scenarios with the same average but different standard deviations:
- Scenario A: Average 2 days, Standard deviation 0.2 days
- Scenario B: Average 2 days, Standard deviation 1 day
In Scenario A, the process is much more consistent, with most orders fulfilled between 1.8 and 2.2 days. In Scenario B, there’s much more variability, with a significant number of orders taking less than 1 day or more than 3 days.
The implications for customer satisfaction, resource planning, and process improvement are vastly different in these two scenarios, despite the same average.
Conclusion
While averages provide a quick snapshot, incorporating measures of variability like standard deviation into your KPIs offers a more nuanced and actionable view of your business performance. By paying attention to the spread of your data, not just its central tendency, you can uncover hidden risks, identify improvement opportunities, and make more informed decisions.
If you would like support in optimising your business operations and to learn how using standard deviation can help you plan better, then contact our team of experts today.