Every modern business is a nest of interconnected software applications. As we look to improve performance and efficiency we often end up integrating large new software. This is a story of how I did this for one customer.
The customer had controlled their fleet of delivery riders through a routing algorithm that determined the most efficient delivery route, paper printouts of the route and reporting through WhatsApp. Whilst this had worked when their delivery operations were only a dozen riders it was not scalable. There were increasing problems in managing riders and controlling performance.
The solution was to find some software that would track the riders and provide a system that allowed easy operational control and management reporting.
There were a number of software options on the market and these were assessed against a dozen key performance criteria. Having selected a preferred solution the next step was to run trials to ensure that we had a reliable and scalable system in place.
The issue was then that the delivery riders disliked being tracked and we initially had only 20% compliance as we sought to run old and new systems in parallel. The main issues from the riders point of view were battery life, data consumption and phone compatibility.
After two weeks of trials we were confident that the new system would work, however we were very doubtful about workforce adoption. Then we had a system failure with the old process and it was a case of use the new system or cancel all the orders for the day and do no delivery.
Everything worked perfectly. Even better we were now able to measure individual rider performance and customer satisfaction in a way that had been impossible before.
The new system allowed us to reduce the delivery window from one hour to twenty minutes which significantly increased customer satisfaction. It also allowed us to improve our on time accuracy from 78% within a 60 minute delivery window to 94% within 20 minutes.
It was also instrumental in enabling us to clearly identify high performing riders and optimise the routing algorithm which increase delivery capacity by 8% by the same workforce in the same time frame.
For more case studies read here.