Logistics as a Service, LaaS, does not emerge as just another software category, but as a response to a fundamental shift in how logistics actually operates.
By: Per Imer, CEO, Homerunner
Contains: 950 words
When complexity reaches a certain level, it is no longer systems that create stability, but people. LaaS is an attempt to make that stability structural.
For many years, the answer to growing logistics complexity was more software. More systems, more modules, and more integrations. Each new challenge was met with a new tool promising greater control. But there comes a point where adding more systems no longer creates clarity. Instead, it produces more interfaces, more interpretations, and more places where responsibility disappears.
Most companies operating across B2C, B2B, omnichannel, and inbound flows have already passed this point. Not as a strategic decision, but as a practical reality. Logistics works because people hold it together, not because systems do. This is not a question of digital maturity, but of architecture.
The traditional approach to logistics IT has been to divide reality into domains. B2C became one flow, B2B another. Inbound was handled separately. The warehouse became a closed universe, and transportation was optimized independently. Each domain received its own system and its own version of truth.
ERP knew one thing, WMS another, TMS a third. Integrations moved data between systems, but context was lost. What was transferred was status, not decision history. The result was a lack of shared context.
This architecture worked as long as flows were linear and predictable. Modern logistics no longer is.
B2C, B2B, and omnichannel are often treated as separate logistics problems. In reality, they are variations of the same decision backbone.
B2C is high volume with low variation. B2B is lower volume, but higher complexity and greater consequences when errors occur. Omnichannel is about simultaneity, where the same warehouse and the same products must support different promises at the same time.
In closed data architectures, organizations attempt to manage this through separate flows, rules, and system logics. This creates friction. In practice, all three disciplines require the same foundation. A shared decision context, real-time visibility across operations, and the ability to weigh consequences rather than simply optimize locally.
Inbound logistics, from containers to pallet freight, is often the most underestimated link in the supply chain. This is where uncertainty and variation originate. Containers do not arrive as planned. Goods are split, delayed, and reprioritized.
These events affect warehouse capacity, outbound planning, B2C delivery promises, and B2B projects. Yet inbound is often treated in isolation. In closed data architectures, inbound cannot communicate with outbound in real time. Decisions are made sequentially, and consequences are discovered too late. This is where complexity accumulates.
Omnichannel is often described as a technical setup where systems and channels must connect. In practice, omnichannel is about freedom.
The freedom to shop online, collect physically, and switch channels without having to explain yourself. This does not require more screens in the store, but fewer interruptions and logistics that work quietly in the background.
For relationships to return, technology must take up less space. Deliveries should happen without explanation, decisions should be made in the background, and complexity should be absorbed without landing on employees or customers.
When logistics functions without noise, something rare emerges in retail. Calm. Calm to be present, and to meet people rather than processes.
Open data does not mean that everything is public. It means that data is not locked inside domains. Events are stored as events. History is preserved. Decisions can be recalled. Inbound, warehouse, and outbound share the same timeline.
When a container is delayed, it is not just an inbound event. It is a decision that affects B2C orders, B2B deliveries, and omnichannel promises. Open data makes it possible to see these connections before consequences reach customers.
AI is not neutral. It amplifies the architecture in which it operates. In a closed data architecture, AI becomes local and reactive. It optimizes each domain separately. In an open data architecture, AI can trace decisions across domains, understand dependencies, and weigh consequences between B2C, B2B, and omnichannel.
That is the difference between automation and judgment at scale.
This is where Logistics as a Service becomes meaningful. LaaS is not another system, but a shared decision and accountability layer built on top of existing infrastructure. ERP, WMS, and TMS remain necessary, but they are no longer the center.
LaaS connects inbound and outbound, channels and flows, people and machines. It creates coherence, makes consequences visible, and places responsibility where decisions are made. Stability shifts from systems and features to transparency, connectedness, and clear ownership.
For many years, logistics has been managed through control, and that approach has taken us far. But the world has changed. Today, complexity does not require more control, but more coherence. Open data provides visibility. AI provides scale. Logistics as a Service provides structure for accountability.
For that reason, LaaS is not simply a new model. It is a new language for how we operate supply chains in a world that remains interconnected, no matter how much we try to divide it.
