Online retail is becoming increasingly popular in Germany (during the Corona year 2021 alone, gross sales of e-commerce goods rose by a huge 19%) and, as a result, parcel delivery companies have to handle an constantly increasing volume of mail and parcels, including across national borders. Our client has turned to modern paretos technology and, as a result, is strategically focusing on data-informed, predictive operations that give the company an invaluable competitive edge in this dynamic environment.
Tobias S. explored the potential of Big Data analytics for our client’s business operations in his role as Senior Manager Product and today it is hard to imagine the internal forecasting workflow without predictions delivered by paretos. The automated analytics process provided by our platform generates quantifiable business impact:
Background
This client, one of Europe’s largest parcel delivery companies, oversees a fleet of over 6,000 delivery vehicles, 10,000 parcel stores and 100+ depots in Germany alone, whilst delivering to over 50,000 customers. If you factor in circumstances like weather conditions, detours, accidents or driver habits you are facing an immense logistical challenge.
Challenge
The biggest challenge for our client is to be able to forecast the volume of their expected daily shipments as accurately as possible. However, the number of shipments fluctuates frequently and, sometimes, quite significantly. There are also other external logistical uncertainty factors, such as weather or traffic conditions, which make it difficult to plan staff deployment and organize the vehicle fleet for various post codes and depots.
Until now, our client had responded manually to these variables in planning requirements. They were even unaware of which external factors actually mattered and the company did not have analytics tools that were easy to use. This meant that insights into upcoming staff and fleet requirements were quite unreliable and this lead to increased costs and understaffing or shortages in staff planning and vehicle fleets.
"We have entered a completely new chapter of planning and forecasting with paretos. We now have dynamic, tailored and highly precise forecasts of parcel quantities with the potential to add even more parameters or use-cases in the future. This gives us the chance to plan and balance our resources and suppliers much more efficiently – which is a core element of our success."
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Solution
It became clear to our client that AI-based technology was the key to unlocking the solution needed to manage their complex, dynamic planning processes. After some test runs with other approaches for a solution, they opted for the integration of our paretos platform in order to optimize the continuously changing planning requirements in a flexible, demand-oriented and transparent way.
Our Dynamic Resource Planning method automatically explores the various uncertainties based on historical data and relates them to weather conditions, holidays and driver data. This allows the company to make predictions up to three days in advance, guaranteeing unprecedented precision.
Using paretos’ API, both internal and external data sources are brought together, providing the company with a reliable picture of future deployment planning requirements. Indeed, this applies to every single one of their 100+ depots managed throughout Germany. Available data sources can easily be connected and the results are automatically transmitted to the client’s systems.
Result
The growing amount of available data and the automated forecasting enables the company to create more reliable deployment schedules – a key component in the company’s value chain to sustainably save operating costs and also increase customer satisfaction thanks to on-time deliveries.
After just five months use of the paretos platform and our dynamic resource planning use case, our client was able to increase their forecast accuracy by more than 10% to a previously unattainable almost 100%. Thanks to the automated data analysis, they achieved more than 95% accuracy on the first day and more than 93% accuracy on the second day. Even for day three, which is the day of highest uncertainty, paretos was able to achieve a precision of over 90%. These results were also the catalyst for a very pleasing side effect: because they are able to deploy their fleet and personnel more efficiently, our client also helps protect the environment with their paretos solution because of the considerably lower generation of CO2.
