How did a global airline optimize cargo delays by 12%

A global airline approached us in 2015 to identify the reasons behind their cargo deliveries. We worked with the airline for three months and could save the airline more than $2mn. Read on how…

PROBLEM

A global airline wanted to identify the factors driving delay in cargo delivery.

Specifically, the time from when the flight lands to when the cargo reaches the warehouse is the bottleneck. This needed to be optimized.

APPROACH

Gramener built a model that identified the drivers of delay and created a what-if model that showed the impact of changing the underlying drivers.

This was presented as an interactive visual dashboard.

OUTCOME

The number of trained staff and number of forklifts (among others) emerged as the biggest drivers.

Visualizing the impact of changing staffing levels and forklifts, airports re-structured their budgets and met all SLAs.

Play with the visualization below to understand how this global airline actually found the root cause of the problem in an intuitive manner and took the corrective actions immediately. The savings is to the tune of $2mn per year.

The case study below is only for representation purpose but the concept will be clear if you can play around with it.

Index:

Delay filter: This is the cargo delay in hours. (worst: 2hr, best: 1hr). Drag the slider to filter it.

Trained Staff: Number of trained staff available at the site. Drag it to adjust.

At a top level, looks like the night shift has the best performance while the morning shift has the worst. Now let us figure out why the morning shift has the worst performance. To know when it is the worst, simply click on Morning.

When you click on Morning, you can easily see that the delay is the maximum on Fridays (darkest red).

Why does Friday have the worst performance? Again simply click on Friday.

To de-select any particular level, simply double click.

The analysis is so intuitive, presents new questions & new insights for you take immediate action.

The global airline in this case could quickly identify the root cause and take corrective action within a week, saving over $2mn over a year.

Do let us know in the comments what you think? Did you find this analysis interesting?