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Improving Logistics Network Capacity Management with AI & Demand Forecasting


The state of logistics network operations has advanced dramatically over the past decade, mostly as a result of the implementation of Artificial Intelligence into capacity management. AI and advanced analytics are used across industries, and their technologies have an essential role in improving inefficient traditional processes. 

For example, in shipping, data is oftentimes recorded over paper, telephone, and email. These procedures are not only outdated and antiquated, but they are inconsistent – making it impossible to build a usable baseline for operations and thereby management of those operations.

Advanced analytics driven by AI has become a defining technology for not only large companies with a global reach but also small and mid-sized firms, enabling them to take advantage of the latest developments in machine learning. This is significant because it levels the playing field – allowing smaller companies to take on the big guys in a highly competitive market.

2020 has been a pivotal year for these firms, accelerating the adoption of a variety of new technologies in order to meet both the changes brought by a post-pandemic economy and the overall upward trend in total shipping volumes across industries. 

So exactly how does AI do it? Let’s take a look at some of the ways AI optimization and demand forecasting can improve network capacity management in 2021 and into the future.

Understanding Optimization

The central goal of any logistics firm is to find the most efficient method of managing their shipping capacity from the first mile to the last. 

Planning these logistics processes, whether through pen and paper as in the 20th century or via Excel spreadsheets as a contemporary solution, is a daunting task. From the warehouses and docks to the vehicles that transport goods across variable distances, the work of a logistics planner encompasses a large number of variables. Not to mention that the larger the company, the more these variables can multiply.

One key issue in network capacity management lies in the regular linehaul schedule. 

Typically, a company will maintain enough capacity to handle their lowest shipping volume, which they identify through historical data. For example, a given firm may know from experience that the winter months have the lowest volume—thus, they’ll keep enough capacity on hand to handle this amount. But demand volume can increase between 20-30% or more in months with higher activity, allowing companies to add additional capacity through their own stock or through subcontractors. 

While this flexibility is an asset for capacity management, the seasonal approach to volume forecasting is a must. Companies, big or small, suffer gaps in their capacity due to over or underestimating capacity week to week and day to day, overspending on unneeded cargo space, or losing revenue by underutilizing their fleet.

The ones that cannot adapt to this new world fail to compete at a competitive level and run the high risk of going out of business. 

Augmenting Intelligence

The main issue of network capacity management is aggravated by volume consolidation. Not every shipment goes from point A to point B—middle locations and cross docks can act as a stopping point for shipments, allowing for consolidation that improves delivery times, shipping volumes, and capacity utilization.

Ascertaining the best path between these middle points is nearly impossible for a human to do alone. 

The sheer amount of potential pathways increases exponentially with every potential stopping point. Yet with an algorithmic analysis through Transmetrics, logistics companies can not only identify the right stopping points but go further and also gain the best information to increase efficiency. 

A Transmetrics analysis does this through deriving useful insights by applying data evaluations, predictive analytics, forecasting, and optimization models. These insights, backed by data, are fine-tuned through machine learning. This can help organizations make the best possible decisions by anticipating and implementing necessary network changes ahead of the demand curve.

Some real-life examples of this applied technology include knowing what type of truck needs to be where, up to the very hour. Or, the best route for one shipment’s remaining capacity, with all stops considered.

Network Investment Planning

Logistics Network Capacity Management

In order to truly utilize the abilities of capacity management through AI, accurate capacity forecasting of KPIs is necessary. There are a variety of forecasting models available that can be used to predict network performance, but for the most part, they can be divided into two models: qualitative and quantitative. 

Qualitative methods are used when historical data is not available or when data is noisy. Quantitative methods, on the other hand, make forecasts based on mathematical models rather than subjective judgment. Choosing the right forecasting model depends on many factors like the context of the forecast, relevance, availability of historical data, and the desired level of accuracy.

Transmetrics’ linehaul planning algorithm helps to custom-design an accurate AI forecasting model that applies specifically to your logistics company. This application is essential to solving the problem of capacity utilization mentioned above, creating a standard of knowledge that translates across the entire company, informing all departments, and increasing efficiency overall.

What’s more, human operators can employ the information provided by Transmetrics to decide which routes are best suited for their current capacity, and use this data to forecast future volumes to best allocate resources.

Case Study: DPD

Logistics Network Capacity Management

Courier company, DPD, faced the common issue of forecasting demand by hiring more trucks during peak periods, specifically around holidays. As they say, better safe than sorry, but in this case, the overspending for trucks and drivers could be something to be sorry about.

By using Transmetrics’ AI, the company was able to receive better data about their loading capacity and improve network capacity management. This allowed them to improve forecasting their numbers, and decrease their deficits. 

These AI-powered changes resulted in a 7-9% total cost reduction, proving the essential power of augmented intelligence in logistics. Read the full case study for more information.

The Necessity Of Change

In 2018, 12.3% of road freight journeys in the EU were performed by empty vehicles. Each of these trips represents a loss of revenue for the companies involved, but it’s hardly the fault of the logistics planners at the helm. 

Network capacity management is a massive feat. When you consider all of the variables, variables that are constantly evolving and changing, one can realize the sheer amount of data that goes into these efforts. But success and efficiency are possible.

The first step is determining the right data. This is done through purpose-built algorithms in order to best forecast accurate numbers for your logistics company. This data then partners with human managers who from there can make better-informed decisions applied to multiple facets of your business. This includes but is not limited to; which routes can be utilized based on capacity, more accurate delivery times, and more precisely predicted future demand.

Through Transmetrics and our applied AI-powered linehaul planning, logistics firms gain a clearer picture of their demand and volume from week to week. This keeps their capacity in-line to optimize shipping needs, increases revenue due to more efficient operations and better capacity utilization, and finally significantly impacts efficiency outcomes across operations and seasons. To find out more about how Transmetrics can improve your company’s logistics operations, request a demo today.


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The Transmetrics project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 945610.