What is On Time In Full (OTIF)? How to calculate OTIF as a KPI?
Do you know how often your customers receive what they order from your organization at the time they expect to have it? You can find out by calculating the "On Time In Full" KPI.
OTIF is applicable in almost all industries, such as manufacturing and services, in both B2B and B2C businesses. OTIF is typically related to the Order-to-Cash process and customer satisfaction. The OTIF calculation often varies depending on the organization’s goals. However, there are some common characteristics.
The OTIF definition consists of two parts - on time and in full. It refers to the KPI measuring the efficiency and accuracy of delivery or logistics in the supply chain.
The most common way to define in full is that the customer gets exactly the amount they have ordered.
Now that we have defined the meaning, let's proceed to the OTIF calculation!
On Time In Full = (Cases matching the criteria) / (Total number of cases)
On Time = (Delivery time) - (Confirmed delivery time)
In Full = (Delivered amount) - (Confirmed amount)
Having calculated the OTIF KPI, you may want to consider various ways to improve it as well as increasing delivery accuracy and customer satisfaction. Did you know that many companies measure OTIF, but they don't use the information to improve their OTIF KPI because they doubt the reliability of the report?
With the help of process mining, you can calculate OTIF KPI accurately and automatically. You can then rely on the results and drive the process improvement in your organization to deliver business value.
The OTIF calculation using process mining is based on transactional event data extracted from your IT systems, thus it is more likely to be free of human errors and assumptions. Process mining calculates OTIF on a case level based on the number of order lines that were delivered on time and in full.
This is the correct way to measure and reveal the process capability, not only for management reporting but also for process improvement.
It is because a calculation based on delivery in quantity or monetary value only puts the focus on big orders, overlooking unsuccessful small orders that are hidden behind the average. OTIF should measure a well-functioning process in terms of both small and big orders and whether the orders are delivered as promised. This provides the most truthful and accurate information on the process performance of your organization.
Not only does process mining calculate OTIF accurately with simple and relevant logic, it also provides full transparency with drill-down capability into the KPI details. As process mining does not use data marts or cubes between the transactional data and the management report, the calculation and data transformation rules are transparent.
Any KPI with unexpected results can be broken down to the order line level to see what went wrong. The calculation transparency and the availability of facts on the lowest level of detail leave no room for argument or doubt of the results.
A 3-step Solution to Improve OTIF Enabled by QPR ProcessAnalyzer
You can gain a better understanding of why the OTIF KPI fails by conducting process analyses. Some analysis examples are: requested delivery vs. outbound delivery, delivered once vs. delivered several times.
To locate root causes for deliveries that take longer than average, run a duration analysis in QPR ProcessAnalyzer, and select the relevant cases for influence analysis.
To locate root causes for deliveries that delivered more than once, select the looping events for influence analysis.
As you identify changes and bottlenecks for improvement, your OTIF KPI should look better than before the changes. To take it further, you can work on reducing the variations and improving the conformance. As compliance improves, some of your sub-processes should be ready and beneficial for automation. With the help of automation, you can easily reduce human errors and improve the KPI.
Learn more about improving on-time delivery and logistics and supply chain processes: Logistics with Process Mining