Page 39 - Case Study Annual 2018
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many kilometres would be travelled, what for example restaurants, had steady complete
times could be expected on the routes, sales and generally took on similar days.
which deliveries would be late and pull driver They were able to offer this group a standing
compliance reports. order system based on how many days per
When the system was fi rst implemented, the week they could hold stock. There were
consultant asked what the order cut-off times also those within that group who needed
were. This had never been considered before top-ups depending on how busy they were
- orders were added on by the time the trucks that night, so WhatsApp order groups were
went out to save a second trip. That worked implemented for them with an early morning
when the business was still small, but as it grew, cut-off time.
late deliveries became a trademark. For the convenience and retail sector that
After a few weeks of constantly changing showed little ability to get the forecast right,
routes and late nights of planning due to ‘stop and drop’ routes were implemented,
new orders being added in all the time, the whereby a truck is sent to replenish based
company realised it needed to implement on the space it has available on the day. The
cut-off times for orders. The customers planners call the customer as the truck leaves
took a harsh view on this as their orders are the depot to ensure time is not wasted on
mostly guesswork based on predicted sales stops that don’t need stock.
for the night ahead, with many variables For the rest of the customers, a 2pm
determining whether those guesses would be cut-off time was implemented for next-day
accurate or not. delivery and 10am cut-off for same-day
To solve this, the planners and drivers emergency orders.
had to receive extra training on how to use
the system. Then company looked at the The result
customers and segmented them according The outcome so far has been positive with
to their delivery characteristics and priority happier customers, less late deliveries and
according to volume and days of sales. more logical routes that the company can
From that they were able to determine track live. Deliveries are more efficient, taking
which customers were best at forecasting less time and travelling fewer kilometres.
their needs, and which struggled the most Turnaround times are faster and improved
based on the number of returns with full efficiency has enabled the take-on of more
freezers. They found that most consumers, customers, enabling growth. •
The Logistics News Case Study Annual 2018 37
