Artificial intelligence (AI), detailed process data and digital twins will remain high on the agenda of logistics managers in 2026. But what tangible benefits do they bring to day-to-day operations? Particularly in bulky goods logistics, where a two-man handling team cannot simply be redeployed at short notice like a parcel delivery driver, different standards apply when assessing how and where AI can add value.
Without Good Process Data, AI Remains Theoretical
On the last mile in bulky goods logistics, delivery promises, assembly services, time slots and end-customer expectations all come together. A single incorrect or incomplete data point in the planning process can be enough to disrupt an entire delivery route.
Planning models that rely primarily on fixed averages and historical experience can only reflect the actual dynamics of operations to a limited extent. AI can help bridge this gap, provided that it has access to high-quality process data and is deployed specifically where it can support operational decision-making.
This requires processes to be consistently digitalised and data to be captured across the entire supply chain, ideally from the moment an order is received through to handover of the product at its final point of use.
Reducing the Burden on Dispatch Teams: Better Forecasts Through Better Data
One of the first areas where AI can make a significant difference is forecast quality. AI-powered forecasting systems combine historical order data, seasonal patterns, traffic volumes and regional characteristics, continuously analysing this information.
This enables more accurate forecasts of shipment volumes during peak periods, required vehicle and staffing capacities, expected delivery times and regional capacity utilisation.
For two-man handling, this means that capacities can be adjusted at an early stage instead of relying on average values. Hermes Einrichtungs Service (HES) also incorporates this type of data into its operational planning, helping the company manage capacity realistically before pressure builds rather than reacting once it has already arisen.
Digital Twins: Testing Decisions Before They Go Live
Alongside AI-powered forecasts, digital twins are becoming increasingly important. These virtual representations of real-world processes, vehicles and routes are connected to the actual system and continuously supplied with current data.
In bulky goods logistics, digital twins can be used to:
- simulate different route scenarios,
- evaluate alternative routes as well as depot or vehicle utilisation,
- analyse potential bottlenecks, and
- assess new processes before they are introduced.
This means decisions can be tested before they are implemented in live operations. This is particularly valuable in areas where mistakes are costly, such as designing new capacity models for peak periods or introducing new service types for deliveries.
Intelligent Slot Planning for Time Windows Customers Can Rely On
An HES study found that 84% of end customers want to choose their own delivery day and time window. Meeting this expectation while planning economically viable routes is one of the central challenges of the last mile.
Intelligent slot planning takes into account vehicle capacities, team availability and stop durations for different service types, as well as regional characteristics. For many years, dispatchers have manually balanced these variables and translated them into economically viable route structures.
AI accelerates this process, giving employees more time to focus on cases that require human judgement. However, this only works if the system understands the relevant differences between individual stops. For example, a combined stop that includes the removal of an old appliance requires more time than a straightforward delivery. Failing to account for this leads to unrealistic route planning.
HES uses AI-powered systems to automate delivery notification processes and proactively inform end customers about delivery dates and any changes. This makes appointment coordination more efficient while providing better support for dispatch operations.
Exception Alerts: Identify Problems Early Instead of Reacting Too Late
In bulky goods logistics, disruptions can trigger a chain reaction. A vehicle breakdown affects an entire route with multiple end customers whose appointments are coordinated with one another and who have organised their day around the agreed delivery time.
AI-powered exception alerts can identify deviations at an early stage, using data such as traffic information, vehicle telematics or unexpected increases in stop duration. This gives dispatch teams the time they need to reorganise routes.
- If a route is identified as being at risk at 9 a.m., there are often still several options available: replacement capacity may be available, customers can still be contacted and appointments can be rescheduled.
- If the same route does not escalate until 2 p.m., the situation becomes considerably more difficult for the remaining stops. Capacity may already be fully allocated, end customers may be waiting at home, and a tradesperson booked for 3 p.m. may no longer be able to cancel at such short notice.
HES uses digital control systems that highlight deviations in real time. The AI platform Hermine also supports customer communication: its outbound voicebot informs end customers about upcoming deliveries and automatically collects their feedback.
Hermine already handles a significant share of HES’s approximately 1.8 million annual delivery-notification contacts, relieving employees of repetitive standard tasks.
How More Realistic ETAs Can Replace Blanket Buffers
One of the most common weaknesses in route planning is the ETA, or Estimated Time of Arrival. ETAs are often based on average values that take neither current traffic conditions nor the complexity of individual stops into account.
AI-powered ETA calculations combine historical stop durations, current traffic data and order-specific factors, such as whether an assembly service is required, an old appliance needs to be removed, or a delivery involves carrying an item up a staircase in a building without a lift.
The more information a system has gathered from previous routes and can incorporate into its calculations, the more accurate its forecasts can become. This creates a learning effect that improves with every additional order.
Especially when delivering bulky products, where recipients may need to take a day off work or arrange for someone to assist them, a reliable ETA is an important part of the customer journey.
What AI Cannot Replace
An unexpected construction site or individual circumstances at the delivery location cannot be fully automated. AI delivers the greatest value when it supports human decision-making.
It can analyse data within seconds, provide relevant information, identify developments at an early stage and create a solid basis for informed operational decisions.
Yet despite advances in technology, the last mile remains a people-driven business. In two-man handling, what happens during those final minutes at the point of use ultimately remains in human hands.
Conclusion: Day-to-Day Operations Are the Real Benchmark
AI in the supply chain will ultimately be judged by whether it makes a tangible difference in day-to-day operations. Better forecasts, digital twins, intelligent slot planning, exception alerts and realistic ETAs can all help make the complexity of the last mile in bulky goods logistics more manageable, benefiting retailers, manufacturers and end customers alike.
HES therefore uses digital data and intelligent planning processes to manage routes even more efficiently and continuously improve the quality of its last-mile services.
