How Digital Twins Add Value in Supply Chains & Required Talent

rjmaguire
Nov 10
4 min read

A digital twin is a virtual replica of a physical system – in the supply-chain domain that could mean a warehouse, a production network, a transport fleet, or even the entire end-to-end supply chain. The twin is fed by real-time and historical data (via sensors, IoT, ERP systems etc.) and allows simulation, monitoring, scenario-planning and decision-support.

In effect it lets companies ask “what if this supplier fails?”, OR

“what if this warehouse goes offline?”, OR

“what if demand spikes by X%?” and test responses in a risk-free virtual setting before they affect the real world.

Value Adding in Supply Chains

Here are several key value levers:

Improved visibility & connectivity
Digital twins bring together data from across functions and nodes – procurement, production, transport, warehousing – thereby giving a holistic view of the chain. For example, companies have used twins to connect supplier status, inventory, and transport flows to see real-time performance.
Scenario-planning & risk mitigation
Because the twin simulates operations, companies can test disruptions, shocks, or strategic changes (e.g., new supplier, change in inventory policy) before implementing them. One case: a steel manufacturer used a Value-Chain Digital Twin to evaluate interdependencies of demand, supply and production volatility across 50 assets, 300 warehouses and 20,000+ SKUs, enabling them to anticipate risks up to 12 weeks ahead, reduce inventory by ~15 % and improve EBITDA by ~2 percentage points.
Optimisation of cost, service & resilience trade-offs
Using digital twins helps companies manage the classic trade-off: cost efficiency vs service vs resilience. The twin enables modelling to optimise buffer sizes, inventory levels, supplier portfolios and network flows. For example, procurement organisations using twins saw 3-6 % cost reductions even in mature environments.
Faster decision-making & operational agility
Because the twin layer turns what-if and “data to insight” into faster decisions, operational cycle times can drop, responsiveness improve, and systems become more agile. For example, procurement/planning activities that took days were reduced significantly in companies using digital twins.
Better support for digitalisation, IoT and sustainability initiatives
Digital twins are a key enabler of Industry 4.0-type supply-chain transformation. They link sensor/IoT data, analytics and digital platforms into meaningful applications. Also, they help with sustainability and decarbonisation modelling (e.g., simulating carbon footprints, alternate routing or production).

80 percent People 20 percent technology

Are hiring companies able to identify and attract the right talent to run software such as Digital Twin inside their supply chain?

Strategic Visionaries

Who they are: Senior leaders, transformation directors, or supply chain heads who can see the big picture.Why they matter:

Digital twins are not “tech for tech’s sake” — they’re tools to enable decision-making, cost optimization, and resilience.
These leaders define what the twin is for: is it improving service levels? Predicting risk? Cutting emissions?
They ensure the twin is tied to business KPIs, not just IT experiments.

Ideal roles:

Chief Supply Chain Officer
Head of Digital Transformation
Strategy / Operations Excellence Leaders

Systems Thinkers

Who they are: People who can see interdependencies across procurement, production, logistics, and finance.Why they matter:

A digital twin models the entire system, so it needs people who think holistically, not in silos.
They connect how a change in one node (like supplier lead time) ripples across others (like warehouse safety stock).

Ideal roles:

Supply Chain Network Planners
Business Process Architects
Value Chain Consultants

Data Architects & Engineers

Who they are: The bridge between the physical world and the digital model.Why they matter:

A twin is only as good as its data.
These professionals integrate data from sensors, ERP, MES, transport systems, and cloud platforms.
They ensure data quality, structure, and real-time flow.

Ideal roles:

Data Engineer
Cloud Architect
IoT Specialist

Data Scientists & Modelers

Who they are: The analytical minds who turn raw data into predictive insights.Why they matter:

They build the algorithms that simulate scenarios and forecast performance.
They connect the digital twin’s “what-if” capabilities to actual business levers.

Ideal roles:

Data Scientist
Simulation Engineer
AI/ML Engineer

Domain Experts & Operators

Who they are: People who understand how the physical process works — from factory floors to freight networks.Why they matter:

Digital twins must reflect reality — so domain experts validate assumptions, logic, and outcomes.
They identify where the model might diverge from real-world behaviour.

Ideal roles:

Production or Operations Managers
Supply Chain Planners
Warehouse / Logistics Managers

Change Managers & Communicators

Who they are: The human glue between tech and business teams.Why they matter:

Digital twin projects fail most often not due to tech, but due to adoption resistance.
These professionals drive user training, stakeholder buy-in, and behavioural change.

Ideal roles:

Change Management Lead
Digital Adoption Specialist
Learning & Development Partner

UX/UI Designers & Storytellers

Who they are: Those who design dashboards, visualizations, and intuitive interfaces.Why they matter:

The power of a twin lies in its ability to communicate insight clearly.
They make complex simulations easy for non-technical users to interpret and act on.

Ideal roles: