3 Hidden Challenges Facing Today's Digital Supply Chains

3 Hidden Challenges Facing Today's Digital Supply Chains

Digital technology is transforming how businesses manage inventory, coordinate shipments, monitor deliveries, and respond to unexpected disruptions. Real-time tracking, artificial intelligence (AI), and connected logistics platforms are giving organizations new ways to improve visibility and make better operational decisions.

Yet adopting more technology does not automatically create a more efficient supply chain.

Businesses can invest in advanced software and still struggle with delayed shipment updates, inconsistent inventory records, disconnected systems, and limited information about deliveries. These problems can affect everything from warehouse planning to customer communication, making it difficult for organizations to achieve the results they expected from digital transformation.

Research from PwC illustrates this challenge. Its 2025 Digital Trends in Operations Survey found that 92% of surveyed operations and supply chain leaders said their technology investments had not fully delivered the expected results. Integration complexity was one of the most frequently cited obstacles, identified by 47% of respondents whose investments had fallen short of expectations.

The findings highlight an important distinction: having digital tools is not the same as having a well-connected, data-driven operation.

For businesses managing increasingly complex supply chains, three challenges deserve particular attention: disconnected systems, limited shipment visibility, and poor data quality. Understanding how these problems develop—and how they affect one another—can help organizations make more informed technology decisions.

1. Disconnected Systems Make Integration Difficult

Modern supply chains rarely operate through a single platform. A business might use a warehouse management system to track inventory, a transportation management system to coordinate shipments, an enterprise resource planning platform to manage business operations, and separate tools for customer communication.

Each system serves a specific purpose. The difficulty begins when these platforms cannot exchange information reliably.

For example, imagine an online retailer that uses one system to process orders and another to coordinate deliveries. An order may leave the warehouse, but its shipment status might not appear immediately in the customer service platform.

The warehouse team knows the package has been dispatched, while customer service representatives may still see an outdated status. If a customer contacts the business for an update, employees may need to check multiple systems or contact the carrier before providing an answer.

One delayed update might seem insignificant. Across hundreds or thousands of shipments, however, these information gaps can create substantial administrative work.

Why Supply Chain Integration Becomes Complicated

Integration challenges become more difficult when businesses work with multiple carriers, suppliers, warehouses, and logistics partners.

Different organizations may use different software, shipment identifiers, data formats, and communication processes. Some systems provide frequent automated updates, while others rely on less frequent updates or manual information sharing.

Without consistent integration, businesses can encounter several problems:

• Inconsistent shipment statuses: Different platforms may display different information about the same delivery.

• Repeated manual work: Employees may need to enter or transfer information between systems.

• Delayed decisions: Teams may not receive the information they need when they need it.

• Limited coordination: Departments and external partners may work from different versions of the same information.

PwC's 2025 Digital Trends in Operations Survey identified integration complexity as a contributing factor for 47% of respondents whose technology investments had not fully delivered the expected results. The same survey identified data issues as another major obstacle, reported by 44% of this group.

These findings suggest that adding more technology without addressing existing integration problems can leave important operational challenges unresolved.

How Businesses Can Improve Integration

The first step is to understand how information currently moves through the organization.

Businesses should identify which platforms hold important shipment, inventory, and order information, where updates are delayed, and which processes depend on employees transferring data manually.

They can then prioritize integrations based on operational importance rather than attempting to connect every system at once.

For instance, connecting a transportation management system with a customer service platform may allow delivery-status changes to appear automatically, reducing the need for employees to check multiple sources.

Standardized data formats, documented integration requirements, and clear rules for handling conflicting updates can further improve consistency.

Businesses should also establish a reliable source of truth for each important data type. If two systems disagree about a shipment's status, employees need a defined process for determining which information is authoritative.

The goal is not necessarily to replace existing technology. It is to ensure that the systems already in use work together in a predictable and dependable way.

2. Limited Visibility Makes Supply Chain Disruptions Harder to Manage

A supply chain involves much more than moving a package from one location to another. Depending on the business, a shipment may pass through suppliers, manufacturing facilities, distribution centers, customs procedures, transportation networks, and final delivery operations.

Every stage introduces the possibility of delays or unexpected changes.

A shipment might leave a warehouse on schedule but encounter a transportation delay. An international delivery might require additional customs processing. A supplier could miss a planned dispatch date, affecting inventory availability and subsequent deliveries.

When businesses cannot obtain timely information about these developments, they may struggle to respond before the disruption affects other operations.

This is where supply chain visibility becomes essential.

Supply chain visibility refers to the ability to access and understand information about goods as they move through the supply chain. Depending on the operation, this information may include shipment locations, tracking events, delivery estimates, inventory availability, and potential disruptions.

Good visibility helps organizations understand what is happening, identify exceptions, and determine which situations require attention.

However, visibility is not simply a matter of having access to a tracking page.

Why Knowing a Shipment's Status Is Not Always Enough

Consider an e-commerce business that promises customers delivery within a particular time window.

A shipment's latest tracking event indicates that it has reached a regional distribution facility. However, the business has no clear information about whether the package has been assigned to a delivery route or whether an exception will affect the expected arrival date.

The tracking information is useful, but it does not necessarily give the business everything it needs to manage the situation.

Employees may need to investigate further, contact the carrier, and update the customer once more information becomes available.

This illustrates the difference between tracking an individual shipment and having visibility that supports operational decisions.

Effective visibility should help businesses answer practical questions:

• Which shipments have not received an expected tracking update?

• Which deliveries have encountered an exception?

• Which delayed shipments could affect important customer commitments?

• Which issues require immediate investigation?

• Which customers need an updated delivery estimate?

The answers depend on the available data, the capabilities of the systems being used, and the organization's operational requirements.

The Role of Artificial Intelligence in Supply Chain Visibility

Artificial intelligence is increasingly being explored as a way to identify patterns, analyze operational information, and anticipate potential disruptions.

For example, an AI-based system might analyze historical delivery performance, shipment milestones, and available transportation information to identify deliveries that appear more likely to be delayed.

Such predictions can help teams prioritize investigations instead of treating every shipment as equally urgent.

PwC's 2025 Digital Trends in Operations Survey found that 53% of respondents were using AI in some areas to anticipate and mitigate supply chain disruptions. Another 31% were testing or piloting AI for these purposes.

These findings demonstrate growing interest in using AI to support disruption management.

However, predictive systems have limitations. An AI model cannot reliably identify every disruption, and its predictions depend on the relevance, accuracy, and availability of the information it receives.

A business should therefore treat AI-generated predictions as decision-support information rather than guaranteed outcomes.

How Businesses Can Strengthen Visibility

Organizations can begin by defining the shipment events and exceptions that matter most to their operations.

For example, a company might prioritize shipments with no recent tracking updates, deliveries that have missed expected milestones, or consignments affected by a reported exception.

It can then establish clear procedures for investigating these situations and communicating updates to the appropriate teams.

Connecting carrier tracking information with internal order and customer service systems can also reduce the time employees spend searching for information.

Another important step is measuring visibility performance. Businesses can monitor how quickly shipment exceptions are identified, how long investigations take, and how frequently customers receive accurate delivery updates.

These measures help organizations determine whether their visibility improvements are producing meaningful operational benefits.

Ultimately, the value of supply chain visibility lies not just in knowing where goods are, but in using reliable information to make timely decisions.

3. Poor Data Quality Undermines Digital Transformation

Connected systems and advanced analytics are only as useful as the information supporting them.

Supply chains depend on accurate records of shipment destinations, inventory levels, order details, tracking events, supplier information, and estimated delivery times.

When these records are incomplete, outdated, duplicated, or inconsistent, operational decisions can become unreliable.

The consequences are not always obvious at first. A small data-entry mistake may affect a single shipment, while a recurring problem with data collection can create inconsistencies across multiple systems and departments.

How Poor Data Quality Affects Daily Operations

Consider a shipment with an incorrect delivery address.

A tracking platform may display the available shipment events correctly, but accurate tracking information cannot compensate for an incorrect destination recorded in the order.

Similarly, a warehouse system may report that an item is available when the physical inventory has already been allocated to another order. The business could promise a product it cannot dispatch as planned.

These examples illustrate an important point: a system can function as designed while still producing an unsatisfactory outcome because the underlying information is wrong.

Poor data quality can create several operational difficulties:

• Employees spend time correcting records and investigating discrepancies.

• Customer service teams may provide inconsistent information.

• Inventory planning can become less reliable.

• Automated workflows may process incorrect information across connected platforms.

• Performance reports may fail to reflect actual operational conditions.

As businesses introduce more automation, data quality becomes even more important. Incorrect information can move between systems quickly, potentially spreading errors rather than eliminating them.

What Industry Research Reveals

PwC's 2026 Digital Trends in Operations Survey examined 767 operations and supply chain leaders at US companies.

The research found that 87% of respondents said poor data quality had affected their organization's ability to achieve value from digital initiatives.

This finding reinforces the importance of treating data quality as an operational priority rather than a problem that can be left entirely to technical teams.

The survey also highlights a broader challenge: organizations may recognize the potential of digital technology without consistently establishing the data foundations needed to make the most of it.

How Businesses Can Improve Data Quality

Improving data quality starts with identifying where errors originate.

Businesses should examine how shipment details, inventory records, addresses, and tracking events are collected and updated. They should also determine whether multiple systems maintain conflicting versions of the same information.

From there, organizations can introduce practical controls.

Standardize data entry. Use consistent formats for addresses, shipment identifiers, dates, and other important fields. Clear rules reduce ambiguity when information moves between systems.

Validate information early. Check important fields when orders are created or shipment records are imported. Identifying an invalid address before dispatch is generally more useful than discovering it after a delivery attempt fails.

Define ownership. Establish which team or system is responsible for maintaining each important record. Clear responsibility makes it easier to investigate and correct recurring errors.

Monitor recurring problems. Track common data issues, identify their causes, and review whether corrective measures are working.

Automate carefully. Automated validation and data-transfer processes can reduce repetitive manual work, but they should include appropriate checks to prevent incorrect information from spreading.

Organizations do not necessarily need to make every data record perfect before improving their operations. They should prioritize the information that has the greatest effect on important decisions and continuously improve its accuracy.

Reliable data creates a stronger foundation for tracking, reporting, forecasting, and other digital capabilities.

How Businesses Can Prioritize These Three Challenges

Disconnected systems, limited visibility, and poor data quality often influence one another. An organization may struggle to provide accurate shipment updates because its platforms are not connected, while integration efforts may be complicated by inconsistent information.

Trying to solve every problem simultaneously can make digital transformation unnecessarily difficult.

A more practical approach is to prioritize improvements according to their operational impact.

Step 1: Identify the Biggest Information Gaps

Start by mapping how an order moves through the business, from processing and dispatch to transportation and delivery.

Identify where information becomes delayed, where employees must manually retrieve updates, and where different systems display conflicting records.

The purpose is to understand the actual problem before investing in another tool.

Step 2: Measure the Current Performance

Establish a baseline using relevant operational measures.

Depending on the business, these may include:

• The percentage of shipments with complete and timely tracking updates.

• The frequency of incorrect or incomplete shipment records.

• The time required to investigate delivery exceptions.

• The percentage of delivery estimates that remain accurate.

• The amount of manual work required to reconcile information across systems.

Not every business needs every measure. The right metrics depend on the organization's size, shipping model, and operational priorities.

Step 3: Address High-Impact Problems First

Suppose a retailer discovers that customer service employees regularly check three separate systems to confirm delivery status.

Connecting the relevant platforms may be a more immediate priority than introducing a sophisticated AI forecasting system.

Alternatively, a company experiencing repeated delivery failures caused by incorrect addresses may benefit more from improving data validation before investing in additional tracking features.

The most appropriate solution depends on the cause of the problem, not simply on which technology is newest.

Step 4: Test the Improvement Before Expanding It

Implement the proposed change in a manageable part of the operation.

Measure whether it reduces information gaps, improves response times, or lowers the frequency of errors. Collect feedback from the employees who use the system and investigate any new problems.

If the improvement produces measurable benefits, the organization can consider extending it to other processes.

Step 5: Review Results Regularly

Digital transformation is not a one-time project.

Systems change, shipping partners introduce new processes, and operational requirements evolve. Regular performance reviews help businesses identify emerging problems and determine whether existing improvements remain effective.

By connecting technology decisions to measurable business needs, organizations can avoid investing in capabilities that look promising but do not address their most important challenges.

What These Challenges Mean for the Future of Supply Chains

The future of supply chain management will involve increasingly connected systems, greater use of automation, and more sophisticated ways of analyzing operational information.

These developments offer opportunities to improve coordination, identify disruptions earlier, and make more informed decisions.

However, technological progress does not remove the need for sound operational processes.

Disconnected systems can prevent useful information from reaching the people who need it. Limited visibility can delay responses to unexpected events. Poor data quality can undermine both conventional reporting and advanced AI applications.

These challenges are interconnected, which means businesses need to consider how their systems, information, and workflows operate together.

Organizations that focus on practical improvements can build a stronger foundation for future innovation. This may involve simplifying existing processes, establishing reliable data standards, improving communication between partners, or integrating systems that currently operate in isolation.

It also requires realistic expectations. A new platform may improve one part of an operation without resolving every existing problem. Businesses should evaluate technology according to its measurable contribution to their specific goals.

For some organizations, the immediate priority will be improving shipment tracking consistency. For others, it may be reducing manual data entry, strengthening inventory accuracy, or identifying delivery exceptions more quickly.

The right starting point is the one that addresses a genuine operational need.

Final Thoughts

Digital transformation has the potential to improve supply chain performance, but its success depends on more than the number of technologies a business adopts.

Disconnected systems can create information gaps, limited visibility can make disruptions harder to manage, and poor data quality can weaken even well-designed digital processes.

Addressing these challenges requires a practical combination of system integration, reliable information, clear operational procedures, and ongoing performance measurement.

Businesses do not need to solve every problem at once. By identifying their most significant information gaps, prioritizing high-impact improvements, and measuring the results, they can make more informed decisions about where to invest their time and resources.

Artificial intelligence, automation, and connected tracking platforms can support these efforts, but they work best when built on dependable processes and information.

Ultimately, a more effective digital supply chain is not simply one that uses more technology. It is one that helps people access the right information, understand what is happening, and respond to operational challenges with greater confidence.