Business area digitalisation analysis

Warehousing and order fulfilment digitalisation

How to better manage inventory, receiving, locations, picking, returns, automation and agreed order deadlines

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.

medium
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.

89/100
Biggest challenge
Inventory quantity and location insufficiently accurate
Biggest opportunity
Reliable and optimised order fulfilment

The greatest result is created not by standalone automated equipment, but by a reliable WMS process that precisely manages physical product movement.

How warehousing and order fulfilment works

The business area encompasses owned and 3PL warehouses, distribution centres, e-commerce fulfilment and various inventory holding models. Core value is created by accurately and quickly converting an order into a dispatched shipment.

High frequency of physical operations

Every movement must be recorded on time and in the correct location.

Labour productivity is determined by route and task sequence

Unnecessary steps quickly increase the cost per line.

Inventory error affects all channels

Inaccurate stock levels lead to cancelled orders, urgent searches and customer dissatisfaction.

Automation requires disciplined data

Robots and conveyors cannot compensate for inaccurate product, location and task data.

Market and technology context

Robotics, goods-to-person, computer vision and warehouse scenario simulation expand automation opportunities. However, return on investment depends on warehouse management system data, process discipline and integrations.

  • Shorter order fulfilment timesCustomers expect same-day or next-day dispatch.
  • Labour cost and workforce shortage pressureThe value of task optimisation, scanning and automated equipment is growing.
  • Higher SKU and smaller order countsE-commerce increases picking complexity.
  • Maturity of automation solutionsRobots, conveyors and automated storage are becoming more accessible, but require robust warehouse management system tasks.

Typical process chain

01

Goods receipt

Quantities, packaging, batches, damage and order compliance are verified.

02

Putaway and location management

Products are assigned to the appropriate location based on size, turnover and storage rules.

03

Replenishment and inventory control

Picking zones are replenished, cycle counts are performed.

04

Picking and packing

Tasks are grouped, products are picked, checked and packed.

05

Dispatch and carrier handover

Shipments are assigned to a route, labelled and handed over to the carrier.

06

Returns and quality management

Returned products are checked and restored to the appropriate stock status.

Digital maturity model

0

Warehouse movements managed by paper and employee memory

Actual product location is verified manually, whilst tasks and exceptions are recorded separately.

1

Accounting system warehouse module

Stock balances are recorded in the system, but physical locations and employee actions are not always confirmed by scanning.

2

Individual warehouse processes are scanned

Receiving or picking is digitalised, but replenishment, exceptions, returns and automated equipment are not seamless.

3

Integrated warehouse execution Typical current situation

Items, locations, tasks, workers and key exceptions are managed in the warehouse management system in real time.

4

Data-optimised warehouse Siektina

Tasks, layout, labour capacity and equipment are planned based on actual flows and deadlines.

5

Partially autonomous warehouse execution

Automated equipment and forecasting models dynamically assign tasks according to safety, quality and capacity rules.

Key finding

Warehouse process optimisation does not start with robots, but with a reliable data model for product, location, order and task.

If product movements are recorded late, WMS cannot reliably plan replenishment, picking, labour or automated equipment.

It is best to first select one complete order flow from receipt or order entry to dispatch and integrate scanning, tasks, exceptions and KPIs.

Related topics

Warehouse management systemPicking optimisationWarehouse automation
Next step

Let us assess where the warehouse loses most time and throughput

We will review receiving, locations, picking, exceptions, returns, employee work and automation opportunities and help select the first stage.