Supply Chain Digital Twins: Turning Roadblocks into Opportunities

Did you know companies lose $184 million annually due to global supply chain disruptions? 

Traditional supply chain management is finding it difficult to keep up with the increasing complexity, shifting market conditions, and increased customer demands.

This is where supply chain digital twin technology steps in. 

Studies show that businesses that use digital twin technology reduce inefficiencies by 25%. By developing a real-time, end-to-end supply chain simulation, businesses can forecast risks, optimize digital twin stock, and make data-driven decisions.

The challenge: Cybersecurity threats, system complexity, and problems with data integration account for 50% of digital twin project failures. 

So, what is preventing companies from growing?

This blog will discuss the main barriers to maintaining a digital twin of a supply chain and how to get beyond them for an effective supply chain.

What is a Supply Chain Digital Twin?

• A supply chain digital twin is an electronic duplicate of its data, procedures, and assets.

• It creates a real-time simulation that aids firms in supply chain analysis, forecasting, and optimization by fusing digital twins and IoT sensors, AI, and big data analytics.

• Offering end-to-end supply chain visibility enables companies to estimate demand, optimize inventory, and streamline logistics.

• By 2025, Gartner predicts that half of major international corporations will employ digital twins for business to enhance supply chain operations, increasing resilience and productivity.

Supply Chain Digital Twin Examples

Here are some of the digital twin examples in the supply chain. 

• Retail: The use case of digital twins in Walmart is an example of how to forecast deficits, optimize restocking cycles, and track stock levels in real-time.

• Manufacturing:  Siemens uses digital twins to detect delays, simulate stages of production, and increase production efficiency, 

• Logistics and Transportation: FedEx and UPS use digital twins for fleet management, forecasting demands, and optimizing routes. 

Suggested read: Which industry uses digital twins?

Digital twins in supply chain management also come up with some challenges, even though there are various benefits to digital twins.

1. Data Integration and Accuracy Issues

Imagine attempting to integrate data from numerous sources, such as logistics platforms, ERP systems, inventory databases, and IoT sensors, only to discover that they don't match. 

One of the most difficult challenges in operating a supply chain digital twin is ensuring smooth data integration across these various sources.

• The Challenge: Digital twin models depend on real-time data streams, but faulty simulations can result from inconsistent, missing, or outdated data. 

Supply chain networks produce massive volumes of data, and it can be difficult to coordinate this data among many stakeholders.

• The Impact: Poor data quality can lead to incorrect demand forecasting, ineffective inventory tracking, and untrustworthy decision-making. 

The digital twin stock can alter reality without precise, organized data, which could cause supply chain interruptions and operational errors.

2. Lack of Standardization Across Systems

Suppose many supply chain partners, each with their own systems and data formats, speak distinct languages. 

The absence of uniform digital twin standards causes significant obstacles to end-to-end supply chain management.

• The challenge: There isn't a single, widely-used framework for integrating digital twins, so businesses are forced to rely on outdated systems, disjointed technology, and disparate information formats. 

Consequently, companies find developing an organized supply chain digital twin difficult.

• The Impact: In the absence of defined data-sharing protocols, real-time insights become disorganized. This results in a reduction in the efficacy of demand planning, logistics improvement, and predictive analytics. 

This may result in delays, mistakes, and inefficiencies in supply chain activities.

3. High Implementation and Maintenance Costs

Purchasing a supply chain digital twin seems like a game-changer until the expenses start piling up. For businesses thinking about digital twin implementation, the expense of hardware, software, AI-driven analytics, and cloud infrastructure might be a significant barrier.

• The Challenge: Implementing digital twins necessitates a sizable financial outlay for cloud computing, cybersecurity standards, IoT sensors, and AI-driven analytics. 

Financial expenses are increased by continuing maintenance and updates even after implementation.

• The Impact: The high initial and recurring expenses may prevent businesses from fully implementing digital twin technology. 

Businesses may find it difficult to defend costs without a clear ROI plan, which would discourage them from growing.

4. Scalability and Performance Issues

Digital twin applications must keep up with the expansion of supply chains, yet scaling a digital twin is challenging. 

High computing power, quick data processing, and smooth integration are necessary for managing a large-scale, real-time integration of digital twins in the supply chain.

• The Challenge: A digital twin in the supply chain works effectively when it is small. 

However, when companies grow, they find it challenging to manage large amounts of real-time data, which causes latency problems and performance snags.

• The Impact: A slow or ineffective digital twin might result in decision-making blind spots, erroneous projections, and delayed insights.

Companies lose competitiveness if the system cannot handle massive amounts of real-time data effectively.

5. Cybersecurity and Data Privacy Risks

Digital twins provide supply chain transparency, but they also provide opportunities for cyberattacks. As companies gather critical operational data, the risk of cyberattacks, malware, and data leaks rises.

• The challenge: Because a supply chain digital twin links numerous vendors, systems, and stakeholders, it is susceptible to cyberattacks. 

Concerns over digital twin security, like ransomware assaults, data theft, and illegal access, are rising.

• The Impact: A cyberattack may distort supply chain predictions, interfere with business as usual, and undermine consumer confidence. 

Attackers could undermine supply chain effectiveness if they obtain a company's digital twin stock and logistical data.

6. Resistance to Change and Lack of Skilled Workforce

One major obstacle to the acceptance of new technologies is humans. Employee resistance, a lack of digital skills, and an unwillingness to implement digital twin technology are problems that many firms face.

• The Challenge: Managing supply chain digital twins requires knowledge of IoT, data analytics, and integrating digital twins and AI. 

Nevertheless, many workers are not adequately trained to use and maximize digital twin platforms.

• The Impact: Employee resistance to digital twin software could result from inadequate training. 

This could lead to underutilized systems, implementation problems, and ineffective workflows.

Further read: Managing supply chain risks with Digital Twin.

1. Implementing Robust Data Governance Strategies

The Solution:

• Create unified data governance guidelines to guarantee uniformity among various sources.

• To eliminate discrepancies in digital twin stock, automate data validation and cleansing.

• Ensure that ERPs, warehouse management systems, and IoT devices synchronize data in real-time.

2. Standardizing Data and Interoperability

The Solution:

• Use middleware and established APIs to enable smooth data transfer between systems.

• Integrate cloud-based digital twin solutions with enterprise software that is already in place.

• Use consistent data protocols to guarantee uniformity among all supply chain digital twin components.

3. Connect With Expert Supply chain digital twin companies

The Solution:

• To access ready-to-deploy solutions, collaborate with leading digital twin companies such as Toobler.

• Use premade analytics models for logistics, optimization of inventories, and demand forecasting.

• Outsourcing the administration of digital twin infrastructure can lower maintenance expenses.

4. Ensuring Scalability with AI and Edge Computing

The Solution:

• Automate finding anomalies and data insights with AI-powered analytics.

• Reduce latency by implementing edge computing to process essential data closer to the source.

• Create scalable and modular digital twin networks that can expand to meet changing business requirements.

5. Strengthening Cybersecurity with Advanced Protection Mechanisms

The Solution:

• To protect operational data and digital twin stock, use end-to-end encryption.

• Limit unwanted access by implementing role-based access control, or RBAC.

• Perform penetration testing regularly to find weaknesses in digital twin supply chain systems.

6. Workforce Training and Change Management

The Solution:

• Offer practical training courses suited to various supply chain positions.

• Promote cooperation between supply chain management and IT teams to guarantee seamless adoption.

• Create an internal change management plan to increase ROI and user engagement.

Data integration problems, interoperability gaps, cybersecurity threats, high expenses, and employee resistance are just a few challenges of managing a supply chain digital twin. 

But as we've shown, businesses can overcome these challenges with strong data governance, AI-driven automation, scalable architecture, and knowledgeable collaborations with supply chain digital twin firms.

The overall picture, however, is that digital twin technology represents the future of supply chain management. 

The question is, are you prepared to act?

At Toobler, we specialize in developing digital twin solutions for intelligent supply chains that offer automation, real-time visibility, and predictive analytics. 

We have specially designed digital twins to assist companies with inventory optimization, demand forecasting, operational risk reduction, and end-to-end supply chain efficiency. 

We enable businesses to make more informed decisions for a robust and flexible supply chain by utilizing cutting-edge AI, IoT, and cloud integration.

So, connect with us to understand and get your business's best supply chain digital twin.