The power of good maintenance data and having more time for tacos!
Are you aware about the importance of the information needed before, while, and after taking any decision? Read in this blog post why you should perform a health check on your maintenance data.
From what shirt to wear to deciding if it is going to be chicken or beef tacos for lunch today, we take decisions very frequently in a day. Daily decision-making is an intuitive process that we tend to underestimate, because we become unaware of the huge amounts of information that our brain de-codifies each millisecond, in order to allow a fast process. This seamless process of de-codifying information sets and parameters is of utmost importance for our correct function in daily life, as it allows our brains to focus on the decisions we take and on what comes next. With this natural approach, we have more time for focusing on other more important decisions, or for decisions that lack a reference and need our special attention. It even lets us enjoy apparent banalities when -through this seamless process- we free more time slots in our daily schedule (for me, this could mean having more time for enjoying delicious Mexican tacos).
The usual roots of bad quality data and the key factors for a data health check
Let us, extrapolate the process of seamlessly analyzing big bunches of data to our professional context and let us focus for this, in maintenance data. Are we seamlessly de-codifying and analyzing information in order to take quicker and failsafe decisions in our daily operations? Even further: are we aware of the quality, completeness and consistency of the information that we are using? We think not. At least, not at the degree that you could wish for.
While supporting one of our airline customers in Mexico, we developed a systematic health check of data quality, as well as of all systems and tools used to capture the data, to store it and to make it flow from one digitalized process function to another. All this, considering the big picture of the airline in terms of single processes within the value chain, control metrics and specific KPI`s.
We have not only performed this health check for our customer in Mexico, but also for other airlines, to a point, where we can start establishing some fundamental conclusions about structural data health according to our observations:
- 1. Mind the QUALITY of your data: we have concluded that the decision-making processes in general, are still very painful, due to the lack of consistent and trustfully data to base any decision or any support model upon. From big strategic decisions to daily operational questions, the lack of data and/or the low quality of data does not allow expedite decision-making process at all levels. As an example, we tend to find a high percentage of improvisation in Maintenance Control Centers (MCCs) when deciding about unplanned maintenance tasks of single tailsigns. A well-structured set of information (for example about failure frequencies, downtimes of repairable items in relation to flight hours/cycles, as well as failure-related repair or preventive tasks and required resources, among many others) could allow trustworthy forecasting models. Such models could be crucial for focusing the tasks of an MCC to allocate resources accordingly. This resources and required activities could be identified beforehand, in order to be prepared to tackle unplanned events or even to prevent this events of happening in the first place, instead of trying to identify what resources are needed to face said unplanned scenario without any warning (aka. Firefighting).
- 2. Tear down your SILOS: we have observed that key processes within the value chain of the maintenance operations and back-office are not in harmony and/or aligned with one another. This means that important outputs of one process are not synchronized with the required inputs of other processes. This usually materializes due to cultural and systematic elements within an organization. On the cultural side, the focus of a process’ or department’s goals is all too often defined single-handedly and without alignment towards the bigger picture. On the systematic side, the focus of certain functions are parametrized to deliver specific information or metrics that again, do not meet the needs of the complete value chain on a bigger scale. Take the supply chain as an example and try to align the goal of maintaining the lowest inventory in the warehouses, while considering unexpected repairs and pushing the purchasing department to “lower” logistical costs through “longer” lead times. It is not that easy, but it all begins by tearing down the silos and transparently communicating about the needs and requirements of each of the involved parts. This process will let you with a set of aligned information that will be key for quicker and safer decisions in your daily operations.
- 3. Mind the HUMAN FACTORS: again a corporate cultural trait: people that are involved with feeding data to the systems will do the minimum effort to do so. Big amounts of important and decision-relevant data get lost in the process of feeding information to digitalized functions. The impacts of this are evident, as stated in point 1) and 2), but the root causes are more complicated and broad. Is it because of the bad interfaces, lack of training in the IT tools, lack of knowledge or even that there is no “win-win” vision when it comes to understand the importance of having a specific set of information? If your planning department wants to ensure better forecasts that will allow better material planning and resource allocation -even during unexpected scenarios-, you will need your mechanics and your engineers to feed real data in the correct form. Remember the “Garbage-in / Garbage-out” principle!
Why should I worry about performing a health check on my maintenance data?
Now that you know some of the key aspects that may give indications about the general health of your data, you may be expecting to see tangible use/business cases to support such a health check. There are only good news:
- Be more failsafe: the impacts of a bad decision, due to lack or inconsistency of data could range from a longer time at the office all the way to a couple of thousands of any monetary unit. Being on the safe side and making more failsafe decisions will add tangible value to your maintenance organization and to your airline
- Be better prepared: having good and healthy data opens your range of opportunities in many ways. One of them -one of the most evident- is better planning. Maintenance planning is the backbone of a good maintenance organization, but there is absolutely no possibility to have a good maintenance planning organization and process, without healthy and qualitative data. You will find your business case everywhere, if you focus on good planning data: from improving your forecasts for better resource allocation (e.g. less extra hours in the hangar) all the way through better material planning (e.g. just-in-time deliveries and adequate stock levels) to better reaction during unplanned events.
- Broaden your options: could you imagine letting an algorithm to take over some of your key functions? This is very possible, if you focus on maintaining your data healthy. There are many use cases in which an algorithm will take better decisions than a human, for example when letting the machine learn about historical schedules of your fleet, to better allocate maintenance slots or when forecasting the probability of a failure, among many other examples. Gaining valuable time for focusing on tasks that require the human experience and emotions will be a game changer in the near future. Be open and ready for this!
Overall, healthy data within healthy and aligned processes in your maintenance organization will positively affect the bottom line of your airline! Believe me when I say that you will have more time for you, for your team and for your work-life balance (as well as for those tacos).
Please, do not hesitate to contact me if you have comments, questions or interest in getting our tailor-made data health assessment. I will be more than happy to support you!