Business area digitalisation analysis

Digitalisation of retail chain operations

How to connect location inventory, reservation, order collection, store employee tasks, pricing implementation and stock transfer

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.

84/100
Biggest challenge
System and actual location balance do not match
Biggest opportunity
Reliable foundation for location inventory and order fulfilment

The digital maturity of a retail network is demonstrated not by the number of central systems, but by the ability to reliably convert their decisions into actual action in every store.

Operating model

The business area includes companies managing multiple physical retail locations and using stores not only for sales but also for order collection, returns, picking and stock transfers.

Many locations and local exceptions

Stores differ by size, range, demand, number of employees, stock levels and fulfilment capabilities.

High volume of daily operations

Sales, receiving, replenishment, repricing, stocktaking, returns and customer service occur every day.

Multiple customer channels

Physical store, e-commerce, app, customer service and social channels must function as a single experience.

Promotions and pricing intensity

Prices and promotions may vary by time, segment, location, channel, loyalty status and stock availability.

Importance of inventory accuracy

Even a small difference between system and actual stock levels across many locations causes lost sales, incorrect promises and inefficient replenishment.

High employee turnover and training need

Stores employ many staff with varying levels of preparation, so processes and tools must be clear, mobile and easy to learn.

Market and technology context

Retail technology direction is moving towards unified customer and operational data, more precise product identification, real-time stock visibility and more advanced store automation. GS1 is developing 2D barcodes and GS1 Digital Link, RFID is being used for item-level visibility, and the EU digital product passport system will increase the demand for structured product data.

  • Unified channel experience expectationCustomers expect a consistent product, price, loyalty, order and return process regardless of the channel chosen.
  • Stock and availability pressureAccurate location-level stock becomes essential for both shelf replenishment and e-commerce order promising and fulfilment.
  • Employee productivity requirementRising labour costs and staff turnover increase the value of mobile task management, automated repricing, stocktaking and clear processes.
  • Data-driven personalisationLoyalty, sales and behavioural data enable offers and communications to be tailored to the customer, but require clear consent and data governance discipline.
  • New product identification and information formats2D codes and GS1 Digital Link can connect product identifiers with additional information, whilst digital product passports will increase the importance of product origin and sustainability data.
  • Loss and returns controlStock shortages, theft, spoilage, price discrepancies and returns have a direct impact on margin, requiring more detailed event analytics.

Typical process chain

01

Assortment and supplier management

Categories, products and suppliers are selected, assortment roles, purchasing terms and location coverage are defined.

02

Product, pricing and promotion preparation

Product cards, variants, prices, promotion rules, loyalty offers, labelling and publication to channels are managed.

03

Demand and stock planning

Demand is forecast by SKU and location, purchasing, distribution and replenishment decisions are made.

04

Goods receipt and distribution

Goods are received at the warehouse or store, quantities are verified, movement is recorded and distributed to locations.

05

Store operations

Replenishment, shelves, price labels, tasks, stocktakes, losses and staff response to exceptions are managed.

06

Customer selection and sale

The customer searches, compares, receives recommendations, checks availability, pays or places a digital order.

07

Order fulfilment coordination

Fulfilment location is selected, goods are reserved, picked, handed over for collection or delivery and statuses are updated.

08

Returns and customer service

The purchase is verified, the item is accepted, the return is calculated, the balance is updated and the reason is analysed.

09

Performance and customer analysis

Sales, margin, availability, promotions, customer value, labour productivity, returns, losses and channel profitability are evaluated.

Digital maturity model

0

Locally and manually managed retail

Stores rely on local processes, paper-based tasks and delayed reports, whilst channels operate separately.

1

Core retail systems

POS, ERP and basic stock accounting are in place, but product data, promotions and location processes are integrated to a limited extent.

2

Digital channels and separate automation

E-commerce, loyalty, WMS or mobile tools are operational, but there is no unified view of stock, orders and customers.

3

Integrated omnichannel retail Typical current situation

Products, prices, customers, stock and orders are synchronised, and stores can fulfil digital orders and returns.

4

Data-driven network operation Siektina

Forecasting, replenishment, store tasks, personalisation and profitability are managed according to unified near-real-time data.

5

Predictive and partly autonomous retail

AI and optimisation systems continuously recommend or execute inventory, pricing, task, customer and loss control decisions within established limits.

Key finding

The digitalisation problem of a retail network most often arises between the central system and the specific store. The stock level, price or order status visible in the system does not necessarily match what a staff member can actually fulfil at the location.

Therefore, the first priority is not another customer channel. Value is created by one reliable scenario in which availability, reservation, staff task, customer notification and actual collection form a seamless process.

Only once this foundation has been stabilised is it worthwhile to expand pricing implementation, replenishment, transfers, returns and more advanced store automation scenarios.

Related topics

Omnichannel commerceOrder management systemProduct information managementInventory management integrationStore staff appCustomer data platformDemand forecastingRFID inventory managementRetail analytics
Next step

Connect the central system with real store operations

The analysis will review location inventory, reservation, collection, pricing implementation, replenishment and staff task processes to help select one measurable pilot.