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Business area digitalisation analysis

Digitalisation of courier and delivery services

How to better manage the last mile, routes, courier work, recipient choices and unit delivery cost

Digital maturity

Typical digital maturity

Shows the level of technological and process digitalisation at which companies in the sector or business area typically operate today.

A typical market situation is assessed, not the most advanced companies.

The assessment consists of five equally weighted dimensions:

Core system usage
Whether ERP, CRM, WMS, MES, customer portals or other operationally important systems are widespread in companies.
Process digitalisation
How many core processes run in systems and how many are still managed manually.
Systems integration
Whether core systems exchange data between themselves or whether employees transfer information manually.
Data quality and readiness
Whether core data is structured, up-to-date, consistent and suitable for automation and analytics.
Advanced data use
Whether real-time analytics, forecasting, automated alerts, optimisation models or AI are used.

The final score is the average of the five dimensions.

1–5 scale

  • 1 very low maturity
  • 2 low maturity
  • 3 medium maturity
  • 4 high maturity
  • 5 very high maturity

A low maturity score does not necessarily indicate low potential. On the contrary, low maturity and a high level of manual work may indicate significant untapped digitalisation value.

High
Skaitmenizacijos potencialas

Digitalisation potential

Shows how much significant business value a typical sector or business area company can create by systematically digitalising core processes.

The rating is calculated on a 100-point scale across five dimensions:

Process frequency and scale 20 %
An assessment of how frequently the digitalised processes recur and what proportion of operations they represent.
Manual work intensity 20 %
An assessment of the extent to which processes depend on email, telephone, Excel, paper documents and repeated data entry.
Impact on revenue and costs 25 %
An assessment of the potential effect on sales, margin, customer retention, administrative costs, errors, downtime or inventory.
Growth and scale potential 20 %
An assessment of whether digitalisation would enable operational capacity to be increased without expanding headcount and costs at the same rate.
Impact on decisions and risk 15 %
An assessment of the potential effect on data reliability, decision-making speed, customer experience, and the reduction of errors and operational risk.

The final score is calculated according to the assessments and weights of all dimensions.

100-point scale

  • 0–20 very low potential
  • 21–40 low potential
  • 41–60 moderate potential
  • 61–80 high potential
  • 81–100 very high potential

A high score does not mean the solution will be easy to implement. It indicates the size of the potential value, not the implementation complexity.

84/100
Biggest challenge
Address and delivery instruction quality is insufficient
Biggest opportunity
Dynamically Managed Last Mile

The greatest result is created not merely by the routing algorithm, but by the entire last mile process from address quality to successful delivery.

How courier and delivery services work

The business area encompasses parcel delivery to homes, business addresses, parcel lockers and collection points. Economics are driven by route density, courier productivity, recipient availability, first-attempt success rate and network throughput.

Very high operational frequency

Thousands of small events must be processed in real time.

Low margin per delivery

An additional kilometre or attempt quickly eliminates profitability.

The recipient actively influences the process

A change in time or location can recalculate the route.

Pronounced seasonal peaks

Systems and the network must withstand much higher short-term volumes.

Market and technology context

Dynamic routing, recipient self-service, parcel locker networks and increasingly accurate estimated time of arrival are becoming baseline last-mile elements. Advanced models only work with reliable address and actual execution data.

  • Recipient control expectationRecipients expect to change delivery time or location and receive an accurate estimated time of arrival.
  • Labour and transport cost pressureCourier time and mileage constitute a significant portion of delivery cost.
  • Expansion of parcel locker and collection point networksRequires management of occupancy, redirection and different delivery models.
  • Large seasonal peaksPlanning and operations systems must rapidly adapt to flow surges.

Typical value chain

01

Receiving shipment order

Sender, recipient, address, service and delivery condition data are received.

02

Address verification and sorting

Addresses are validated, shipments are allocated to terminals and routes.

03

Route and capacity planning

Shipments are grouped by location, time windows, couriers and transport.

04

Courier execution

Managed route, navigation, statuses, exceptions and proof of delivery.

05

Recipient self-service

The recipient receives an estimated time of arrival and can change delivery time, location or collection method.

06

Cost and quality analysis

Kilometres, attempts, courier performance, agreed service level KPIs and claims are evaluated.

Digital maturity model

0

Delivery progress depends on manual coordination

Addresses, routes, courier status and recipient changes are managed separately, with exceptions resolved by phone calls.

1

Digital parcel accounting and static routes

Parcels are registered in the system, but address quality, recipient self-service and actual exceptions are weakly linked.

2

Courier app and separate recipient channels

The courier registers status digitally, but route recalculation and recipient changes are not seamless.

3

Integrated last-mile execution

Shipment, address, route, courier, recipient choices and proof of delivery are managed in one chain.

4

Real-time last mile management Typical current situationSiektina

Routes, parcel locker capacity and recipient choices are adjusted based on actual events and flow forecasts.

5

Predictive and adaptive delivery network

Models predict failed delivery risk, network load and recommend route and capacity changes.

Key finding

Last mile costs are most often increased not merely by route length, but by poor addresses, late recipient changes and fragmented courier workflow.

Shipment data, address validation, route, recipient self-service and proof of delivery must operate as a single workflow.

It is best to first select one city or delivery model and connect address preparation, dynamic routing, courier application and recipient choices.

Related topics

Last mile managementCourier appRecipient self-service
Next step

An assessment of where the last mile loses the most kilometres and time

A review of address quality, route planning, courier operations, recipient options, parcel locker network and actual delivery cost.

Discuss the first digitalisation phase