Baseline Measurement or Base lining as it is shortly called is the process of establishing the starting point of any process/metric, from which the improvement or impact of any change measure is calculated. It is used to gauge how effective an improvement or change initiative is.
Now, let us look at where and how a baseline measurement is used.
Where do we use Baseline Measurement?
- In Improvement initiatives like Six Sigma, LEAN, KAIZEN, etc.
- In Change initiatives like automation, Business Process Reengineering, Mergers & Acquisitions etc.
- In medical field for measuring treatment effectiveness
- Product improvement and modification
- Software version changes
The above are just a few of the many places where baselining of data is done. There are a lot more applications of Baseline measurement.
How to do a Baseline Measurement?
- The first thing to look at is scope of the initiative: the departments /teams it is going to cover, the product lines, the scenarios being considered etc.
- The next thing is to set the objective/Goal of the initiative & its unit of measurement. For example, if a medical research team is going to measure the impact of a medicine that reduces fever, the goal should be the normal body temperature.
- The next step is to collect historical data of the measure. The best way is to collect the past data. In cases where all of the data cannot be collected, an appropriate sampling method can be applied. Care should be taken to ensure the sample is a representative of the original data lying behind. Some scenarios require ‘Surveying’ customers to collect data. Some other scenarios require data of competitive product or industry average. Employing and involving experts who can judge the right approach for data collection is the key to success of this activity.
- Estimate the baseline with appropriate statistical method. Sometimes it would be simply enough to plot a line graph and then arrive at the baseline. Some methods simply require averaging of the past data. Some complex scenarios require cleansing of the data for abnormal scenarios or advanced statistical techniques. User should adapt an appropriate method for arriving at the baseline.
Once the baseline value or range is obtained, the project can be kicked-off and the result can be monitored against the baseline measurement.