2023, perfect year for skiing!(?) 

Punctual planning is sometimes hard, whether it’s about preventive maintenance or a skiing trip. In addition to process implementation, measurement and control are at least as important. 

2023 has started, a good opportunity to look ahead. Especially to the skiing season! Soon, I’m preparing for a holiday to Austria. But the weather outlook is something else this time. Based on the past few weeks, I might have to make myself ready for some surfing lessons… Strong winds howling through the European Alps resulting in heavy rain instead of snow. With the worldwide environmental developments, we can call ourselves lucky in the Netherlands if we can still enjoy our skiing trips for the years to come.

Therefore, planning a skiing holiday is quite difficult. Do you plan to go in the beginning, middle or end of the season? Austria, France or Italy? Do you book early to secure your accommodation or later when snow is guaranteed? Many questions and I see a number of similarities in problems I encounter with punctual availability of spare parts. Specifically, with preventive maintenance planning. 

Importance of punctual planning of preventive maintenance 
When future material demand can be planned ahead, punctual and accurate planning results in great added value for efficient inventory management. The sooner demand on material level is defined, the earlier inventory planning can be adjusted accordingly. This translates to a decreased stock value, with an increased material availability.  

At various companies, I notice an increased awareness regarding this subject. However, unfortunately I still see examples of process implementations, where the expected benefits are not reached. 

Planning processes do not always reach their potential value 
An example is an implementation of a preventive maintenance plan controlled by sensors. Promising an increase in efficiency for preventive maintenance of assets regarding planning and performance. This is implemented by equipping sensors to assets to measure movement or the state of an object. Then, preventive work orders can be generated automatically based on asset conditions. Potentially, this results in decreased manual activities and an improvement of the reliability of planned demand. The reason is that in such a situation, planning doesn’t need to  be based on a periodic timeframe, but can be based on running hours or component wear. 

However, during development of a spare parts management dashboard, a substantial negative trend became apparent on delivering and closing of these work orders. The appointed cause after research with all stakeholders involved? Due to an incorrect configuration, work orders were generated on the same day as the targeted finish date. This didn’t correspond to the process design, which required work orders to be created weeks ahead of their required target date. Instead of punctual preventive planning, this resulted in an entirely unpredictable demand pattern. Of course, with many negative consequences for tactical inventory planning: time of the inventory planner is consumed by firefighting availability issues, diminishing focus on efficient management of spare parts.

The numbers tell the tale

For all process improvements it’s crucial to incorporate measurement and monitoring in the process design, in addition to its establishment and implementation. Not merely at a high level, such as measurement of fulfillment of work orders on their due date, but on specific process characteristics as well.

For example, the average processing time between  work order creation date and target finish date. And especially don’t forget to agree on an accompanying target. This ensures that the intended benefits and potential for improvement are realized.

My take-away from this project? The skiing trip of 2024 will be planned ahead well in advance, but I’ll definitely book a high-altitude region. Then at least I am better prepared for the following season!

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Misha Grift
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