NFF (No Fault Found) is an often-used KPI to which may get a whole new meaning with the introduction of predictive maintenance. Let’s go back to the origin of this KPI. Some two decades ago it gained in attention as companies increasingly focused on customer satisfaction, people found out that many so-called ‘bad parts’ that were injected in the reverse supply chain tested perfectly and were therefore flagged NFF. There has been an ongoing struggle between the field organisations’ and the reverse supply chain’s goals. Field service did all it could to increase the number of interventions per engineer per day, even at the expense of removing too many parts, under the motto that time is more expensive than parts. Removed parts then get injected in the reverse supply chain where they’re typically get sent to a hub to be checked and subsequently repaired. To the reverse logistics/repair organisation, NFF create ‘unnecessary’ activity – parts needing to be checked without any demonstrable problem being uncovered. These parts then need to be re-qualified: documented, repackaged,… Therefore, NFF is really bad to the observed performance of that organisation.
Back to predictive analytics: whereas CBM (Condition Based Maintenance) will order for a maintenance activity based on the condition of the equipment/part – and therefore do so when there’s demonstrable cause for concern – predictive maintenance will ideally generate a warning with longer lead time. Often before any signs of wear/tear become apparent! Provided certain conditions are met (see previous posts on criticality/accuracy/coverage/effort), parts will be therefore be removed which technically will be NFF! Because the removal of these parts will prevent a much costlier event this is not a problem per se, but it will require rethinking not only internal and external processes but also KPI’s! If we want adoption of these predictive approaches to maintenance and operations, KPI’s need to be rethought to reflect the optimised nature of these actions. We can’t allow anybody to be penalised for applying the optimal approach!
Predictive (or by extension, prescriptive) maintenance has huge potential for cost savings and as we’ve seen before (see previous blog entries), these savings should be looked at from a holistic point of view. Some costs may actually go up in order to bring down overall costs. Introducing such methodologies therefore also demand a lot of attention to process changes and to how people’s performance is measured. The good news is that introduction of predictive maintenance can be gradual; i.e. start with those areas that offer high confidence in the predictions and high return. Nothing helps adoption better than proven use cases!