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The Importance of Using the Right Data in Home Delivery

Tuesday 23 February 2021

Paul Dawsey's picture
By Paul Dawsey

The Importance of Using the Right Data in Home Delivery

The simplest of logistics setups is challenging enough, with so many things to take into account, including but not limited to vehicle capacities and constraints, driver hours, delivery time windows, traffic, legal and physical road constraints, wages, fuel costs, locations of delivery points… the list goes on and on.  A logistics manager trying to strike the right balance of meeting customer expectations, staying legal whilst turning in a profit is a valuable asset.

However, delivering to homes as opposed to business addresses typically throws up additional layers of complexity, which must be overcome. One way of overcoming them is by ensuring that the right data is used for the specific logistics home delivery setup.

 

Here are five examples of how the right data can help facilitate a smooth cost-effective operation, whilst using the wrong data can be frustrating, inefficient, and costly.

 

#1 - Residential

Firstly, residential. The spread of residential addresses typically occupies smaller footprints than businesses and the precise location is less easy to pin down. A warehouse, business park or office block may have its own postcode, whereas one residential or rural postcode has on average 17 addresses – the maximum being 100. Many companies try to deliver to a customers’ postcode, which is an area, not a precise point. This can cause confusion, unnecessary time spent by the driver finding the desired address, longer routes and result in unnecessary costs. If ‘premise level’ data is used to plan routes, they will be more precise, resulting is less frustration and lower costs. This is a detailed subject and CACI will be publishing a more detailed article shortly which takes a deeper dive into the science of ‘premise level geocoding’ – watch this space.

 

#2 - Building Type

Secondly, the drivers may need to spend a differing amount of time at each customer address. For example once parked, taking five bags of groceries to a customer that lives on a terrace of houses with parking outside the front door may take 3 minutes, as opposed 15 minutes to a customer that wants the same groceries but lives on the top of a 20 story block of flats. A plan that takes these nuances into account by using data on the type of building, for example, will be more efficient and cheaper to run than a plan that doesn’t.

 

#3 - Road Type

Thirdly, to get to customers’ homes, delivery vehicles are likely to need to travel on the most minor of roads, navigate narrow country lanes and travel to the ends of cul-de-sacs. This may sound obvious, but the level of mapping data needed to underpin such an operation cannot be taken for granted. Often mapping data doesn’t include these minor roads and drivers can find themselves high and dry, needing to ask a friendly local to find the right place.

 

#4 - Vehicle Type

Fourthly, home delivery vehicles are typically not cars and range from small vans up to medium sized trucks. All roads cannot accommodate all vehicles, as for example tall vehicles cannot go under low bridges, wide vehicles can’t get down narrow lanes, heavy trucks can’t go over weak bridges, and long vehicles need a large turning circle. There are also legal restrictions to consider, such as one-way streets, lanes reserved for public transport and banned right turns.

Many mapping solutions, including Google, are designed for cars and so this data doesn’t cater for any of these restrictions.

 

#5 - Road Speeds

Fifthly, commercial vehicles typically drive at different speeds to cars, road speeds are affected by the time of day and speeds differ depending on the location of the road (in fact every road has unique traffic patterns). Planning routes without an understanding of the speeds that can be expected for roads that are required to drive down, the vehicles being used, at the right time of day can result in vehicles getting snarled up a traffic jams and late customer deliveries at worst, but will almost definitely result in less efficient routes which will cost more money to fulfil.

 

For More Information

All companies have different requirements and require solutions that are right for their setup. CACI logistics has worked for over 30 years in this field, developing solutions to address all of these problems (and many more).

We love nothing more than discussing which data products are most suitable to resolve our customers’ problems. Please contact us for more information.

Five examples of how the right data can help facilitate a smooth cost-effective logistics operation, whilst using the wrong data can be frustrating, inefficient, and costly.

The Importance of Using the Right Data in Home Delivery