Process business intelligence, part 1 – Happy Customer | QPR

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19 October 2015

Process business intelligence, part 1 – Happy Customer

In many of our recent customer interactions and projects it has become evident that most of the leading companies of their respective industry have some process KPIs in use. The most commonly used KPI especially in the manufacturing companies is definitely OTIF (On Time In Full). Some call it delivery accuracy or something similar but the criteria is more or less the same; Did the customer get what they ordered and when it was promised? It is no surprise to me that most companies want to measure this as it is hard to think of anything more important from the customer point of view than a vendor performing as they have promised. 

What has really surprised us when digging this topic deeper are the fundamental problems that customers are experiencing in relation to the usage of OTIF as a process KPI. Way too many organizations who have implemented OTIF reporting are not really utilizing this information properly and operations are not managed accordingly. Many executives have admitted that their OTIF KPI doesn’t play as a significant role in their everyday business as it deserves. Why is that? Number one issue seems to be the reliability of the report. The results are often not generally accepted and discussion is not focusing on corrective actions and improvements but arguing the data reliability and correctness of the results. This is waste of executives’ time and often results in a typical situation that yes, we do measure OTIF but we don’t really use the information. This is a shame but luckily it is something that can be fixed. 

A way to cut off suspicions related to reported results is to provide executives with full traceability to the details from which the reported KPI is calculated from. Another important factor is to keep the calculation logic relevant but simple. We have experienced that both challenges are tackled with process mining. With QPR process business intelligence solution based on process mining technology we use transactional event data to visualize individual process cases and calculate process KPIs like OTIF. We provide full drill down capability from for instance OTIF KPI to the list of individual orders that didn’t fulfill the OTIF criteria. No datamarts or cubes are used between the transactional data and the management report hiding calculation and data transformation rules. There is no need to suspect or argue the results as the facts on lowest level of detail are available and calculation rules are transparent. Any KPI with unexpected results can be broken down to order line level to see what went wrong. 

Another important benefit from process mining is the ability to do the measurement correctly not only for management reporting but also for process improvement. With process mining technology OTIF is calculated on case level based on number of order lines that were delivered On Time In Full. This is the right way to do it to reveal process capability. Basing the calculation on delivered tons or euros puts weight on big orders. For a well-functioning process the size of the order is an irrelevant factor. Both small and big orders are delivered as promised. By weighting big orders in calculations many unsuccessful small orders can be hidden behind the averages. This will not provide truthful and accurate information of the process performance. 

We strongly believe that OTIF is a highly important process KPI for almost any company. It can be considered as the corner stone of customer satisfaction. It is a fair demand from the customer to get what they ordered at the time it was promised. If this fundamental criteria is not there the rest doesn’t really matter. Another thing that is always visible to customers and comes together with the delivery is the invoice. Invoicing accuracy is an example of another good process KPI measuring the outcome from customer point of view. Yet, based on our experience, this is not very widely used. Delivering what was ordered when it was promised and invoicing accordingly is likely to result as a happy customer who is likely to use your services again. 

We have now covered the happy customer dimension of QPR process business intelligence solution. The other equally important dimension is the internal happy flow. Process mining is hugely beneficial when measuring the happy customer. When measuring happy flow process mining is the only hope that customers have. Accurately and easily measuring process KPIs like First Time Right or different business rule violations is at least extremely difficult if not even impossible using traditional business intelligence solutions. Let’s come back to this other dimension of the process business intelligence next time.


Matti Erkheikki's picture
Matti Erkheikki
SVP, Process mining & strategy management business since January 2017