How parcel data analytics will change the work of distribution professionals

Digitalisation and data analytics will change the CEP industry. A big part of the change comes from providing distribution centre professionals with valuable insights that transform their ability to work efficiently. 

By Per Engelbrechtsen


As digitalisation continues to gain traction in the CEP industry, data analytics will change how distribution centres function.

So far, most of the attention surrounding digitalisation has focused on what we may call the macro-level benefits: the ways in which distribution centres and the entire CEP industry can benefit from implementing data analytics and other digital tools; improved transparency in track and trace services; maximum utilisation of machine capacity; or simulation of future production aided by a digital twin. Just to name a few.

Broad operational improvements and how they affect the final bottom line of the company have been the main perspective in digitalisation. In this article, however, we are going to approach the subject from a slightly different angle: we’ll talk about the people involved!

The human workforce of digitalisation

Digitalisation, including data analytics is only a game changer to the extent that it can help CEP distribution centres – and their staff in particular – work more efficiently towards their specific goals.

Read our introduction to digitalisation for parcel distributors.

Focus should ideally be to help staff at distribution centres in their roles as maintenance, operations or management professionals to:

  • Reduce costs per item
  • Reduce risk
  • Increase overall capacity

How digitalisation helps distribution centre professionals

In the following sections, we take a closer look at how digitalisation helps three different roles among distribution centre professionals:

By loading the video, you agree to Vimeo’s privacy policy.
Learn more

1. Maintenance

Typically, efforts to maintain a sortation system are structured as what we may call routine or time-based preventive maintenance.

Maintenance operators haven’t exactly been waiting for a machine to stop working before stepping in. But they have been almost completely reliant on their calendar.

“We usually take a look at the sorter installation around this time of the year” has been the modus operandi. Without having any concrete evidence of how the specific installation is performing at the time. So if, let’s say, the performance had been significantly better if upkeep took place more frequently, no one would have discovered.

It is important to note that none of this is the fault of maintenance operators. Without any hard statistical facts to rely on, it only makes sense to plan maintenance around whatever available structure you may have. A calendar for example – or simply routine.

Data analytics on the maintenance level

Digitalisation can make a significant difference in how maintenance is conducted.

When distribution centres begin to detect and analyse data from their sortation systems, maintenance operators enter an entirely new realm of insights. The system will begin to connect data to previously unexplained incidents. It will learn that when an installation begins to show certain data, it means that maintenance has to step in to perform a certain task.

Maintenance operators can practise predictive maintenance as opposed to time-based preventive maintenance.

Instead of checking in on installations because it is that time of the year, maintenance operators can base their efforts on real-time performance. A part of the system may go through a performance dip of five percent and no one would be able to tell just from looking at it or its output. With data analytics, no one will have to, because the system will tell them.

The result of data-driven asset management is a sortation system that functions as close to maximum capacity as possible by reducing unwanted stops.

Get the full overview: Read the Parcel distributors’ guide to digitalisation.

2. Operational

Data analytics not only improve the maintenance of a sortation system and gear the system to function at its highest capacity. The process of working with data enables the system to be operated in a manner that takes full advantage of the designed capacity.

For example, traditional B2C e-commerce distribution centres may assign specific destinations to specific postal codes in ascending or descending order. A basic flow and simple to manage, but also rather inflexible if, for example, there are no parcels for one particular destination. Why would you allocate a chute to a destination with no parcels while other destinations may be overloaded and end up causing bottlenecks?
Clearly, this is no way to achieve the highest possible utilisation of the system.

The reason why distribution centres often settle for somewhat underperforming flows is the enormous complexity of the operation. Take a vast parcel mix and combine it with a large and chaotic sortation hub and what you have is an extremely difficult equation for the operators to solve. And almost as soon as the production is over, a new window begins. Nonetheless, they have to solve it in real time to achieve optimal utilisation of the sortation system.

No wonder why familiar strategies such as pairing chutes with postal codes have a strong appeal in scenarios where modern, digital resources aren’t available.  This is where digitalisation and data analytics in particular can lend a huge helping hand to operators.

Decision science on the operational level

Decision science is a type of artificial intelligence that helps operators achieve the optimal utilisation of a sortation system.

By analysing data, the algorithms identify the best use of chute destinations in relation to parcel mix, for example. Or the best allocation of operators. Or any other sortation related conundrum.

And it is real-time analysis, so operators always have an efficient and data-based strategy at their disposal to navigate the complex balancing act between parcel mix and sortation capacity.

The result is that operators won’t have to rely on simplified strategies such as pairing postal codes and chutes. Data will reveal how the system is utilised most efficiently – and propose a decision to the operators.

Decision science is just one part of it though. Through the study of algorithms and statistical models, machine learning warns operators of under-utilisation in parts of the system or workforce or upcoming incidents such as bottlenecks.

The achieved outcome is a sortation system working close to maximum capacity.

3. Management

By comparing data from previous parcel productions and forecasting future productions, management can settle on the best use of resources.

Distribution centres can learn more efficiently from previous experience to improve future operations – e.g. in terms of hiring and allocating operators and resources.

Forecasting and other strategic challenges

By every indication, e-commerce is only going to increase. There is also a growing number of peak shopping holidays and special occasions that present their own unique challenges to distribution centres.

A growing, increasingly complex and more varied parcel mix combined with numerous peak seasons results in a more difficult future to navigate in. At the same time, competition in the CEP industry – and consumer expectations – will give rise to more demanding delivery services in intervals of six or twelve hours for example.

In the future, management at distribution centres will need to become better at learning from experience and planning future production. This requires a detailed and accurate assessment of past and future production.

Thankfully, this is exactly what data analytics provide.

Different visualisations of data for different roles

Going forward, all areas of the distribution centre organisation will benefit from data. But not necessarily the same data or with the same perspective on data.

Systems providers work with each area of the organisation specifically. The objective is to identify the data that is most relevant to each staff function and visualise the data in the exact way that creates the most value for each level of the organisation.

  • Maintenance needs data to keep the sortation system in peak condition and avoid breakdowns.
  • Operational needs data to make adjustments in real time.
  • Management needs data to support long-term decision making.

Systems providers will help identify the relevant data and support each role within the organisation to perform more efficiently.

That is the potential of digitalisation and data analytics in terms of roles in a CEP distribution centre. Do you want to know more about how your distribution centre can benefit from digitalisation? Read our e-book about digitalisation for parcel distributors.




Subscribe to our newsletter