Updated 7/10/25
Today’s enterprise relies on data collection and analysis for an edge over their competition. Data analysis plays an important role in realizing the benefits of EDI, as it enables companies to gain a deeper understanding of their supply chain’s dynamics and complexities. Analytics can help improve decision-making, uncover inefficiencies and problems, and identify areas for optimization. However, there are a number of different issues that can adversely affect data analytics. In this blog, we’ll examine the top EDI and supply chain analytics problems as well as the challenges in supply chain management.
Table of Contents
- Key Issues in Supply Chain and Data Analytics
- Problems, Challenges and Insights in Supply Chain Management
- Major Risks Supply Chain Leaders Face
- Solving Supply Chain Challenges
Key Issues in Supply Chain and Data Analytics
There are several supply chain analytics challenges, including incomplete or inaccurate data, a lack of visibility into EDI transactions, inefficient processes, poor coordination between supply and demand, long lead times, high costs, stockouts and other disruptions. These issues must be addressed in order to optimize the performance of a company’s supply chain operations and EDI processes.
A range of supply chain analytics solutions and software can be used to address these challenges effectively. These can include predictive modeling, forecasting, optimization, data visualization, and simulation. For example, developing predictive models for demand or supply, using optimization techniques to streamline processes and reduce costs, implementing data visualizations to gain valuable insights into the entire supply chain, and simulating different scenarios to test various response plans. There are so many use cases for deploying a comprehensive EDI dashboard , between the pharma company who realized a 300% ROI based on the manual time it took their client services team to resolve customer order to cash issues to the ubiquitous food conglomerate that leveraged the Exceptions and Notifications feature to understand when ASNs are not delivered by the Expected Ship Date to either expedite shipments or divert supply from other parts of their network to keep product on the shelf.
Ultimately, the goal is to use data-driven insights to make more informed decisions about how to manage and improve the performance of the supply chain.
Problems, Challenges and Insights in Supply Chain Management
Supply chain management is a complex and dynamic process, involving many players, activities, and factors that must be coordinated in order to achieve optimal performance. However, the following factors pose challenges to effective management .
Globalization
One of the major challenges in supply chain management is dealing with the effects of globalization, which makes it difficult to coordinate supply and demand across different regions or countries.
Data analytics tools and techniques are often used to help address this by incorporating real-time data from different sources and using advanced analytics methods like predictive modeling and simulation to more accurately forecast demand, optimize processes and resource allocation, mitigate supply risks, and respond to disruptions.
Additionally, by leveraging big data technologies such as cloud computing, artificial intelligence (AI), machine learning, and the Internet of Things (IoT), it is possible to gain much greater visibility into the end-to-end supply chain, which can help further improve coordination and decision-making.
Fast-changing Markets
Staying current with fast-changing markets and trends is also a key challenge in supply chain management. This can be particularly difficult due to the high levels of uncertainty that are often present in these environments, as well as the many moving parts and stakeholders involved in managing a supply chain. To effectively address this challenge, it is important to have robust analytical tools and techniques in place, as well as an understanding of how to interpret the data that these tools generate.
Maintaining ongoing communication and collaboration among all supply chain stakeholders can also help to ensure decisions are being made based on the most current information available.
Quality and Compliance
Quality and compliance are often major challenges in supply chain management, as they can affect every aspect of a company’s operations, from change management, to production and distribution and beyond. Issues related to quality and compliance can arise due to poor product design or manufacturing processes, regulatory requirements that are not properly understood or followed, lack of oversight and accountability, or many other factors.
The effective use of data analytics tools can help organizations gain visibility into quality and compliance issues in their supply chains. These tools, such as vendor scorecards and order lifecycle reconciliation, can also help identify where areas need improvement. This ultimately helps companies achieve better overall performance, reduce costs, minimize risks, and improve customer satisfaction. One large Fortune 50 retailer reports, “Never before have we been able to implement a successful ASN Compliance Program.”
Major Risks Supply Chain Leaders Face
There are several risks that supply chain leaders face, including the risk of disruptions to the supply chain, the risk of poor coordination between different parts of the supply chain, suppliers going out of business, high costs, and the continued impacts of the COVID-19 pandemic.
These risks can have a major impact on a company’s bottom line. Therefore it is important for supply chain leaders to have a plan in place to mitigate these risks.
One way to do this is to use analytics to improve decision-making and reduce the likelihood of these risks occurring. Another way to mitigate these risks is to have contingency plans in place so that if one part of the supply chain is disrupted, the rest of the supply chain can continue to operate smoothly. Additionally, it is important for supply chain leaders to stay up to date on industry trends and best practices in order to effectively mitigate these risks and maintain an optimal supply chain performance.
Solving Supply Chain Challenges
Data is only as powerful as the insights it enables. Poor data or lack of alignment across supply chain processes can easily undermine even the most advanced systems. To realize the full benefits of EDI and analytics, organizations must address core supply chain problems in three critical areas. These include data quality, inventory optimization, and predictive analytics. When these foundations are strong, businesses are better positioned to make faster decisions, reduce risk, and drive measurable performance improvements.
Data Quality
High-quality, accurate data is the foundation of effective supply chain analytics. Unfortunately, many companies struggle with a data strategy that is incomplete, inaccurate, or inconsistently formatted across systems. In the context of EDI, this materializes as mismatched part numbers, missing shipment details, or discrepancies in invoice data. All of which lead to delays, chargebacks, or compliance issues.
Improving data quality starts with standardization, validation protocols, and tight integration across EDI systems and ERPs. With clean, consistent data, companies can trust their dashboards, uncover actionable trends, and eliminate the guesswork that leads to reactive decision-making. Data security is also an area that must be prioritized. It’s critical to ensure sensitive transactional information is protected while maintaining compliance with industry and trading partner requirements.
Inventory Optimization Tools
Poor inventory visibility and inaccurate demand forecasts are common pain points for supply chain leaders. And this often results in stockouts, overstocking, or excess carrying costs. Inventory optimization leverages analytics to strike the right balance between supply and demand, minimizing waste while maximizing customer satisfaction.
Integrated EDI solutions enable real-time visibility into order status, shipment updates, and supplier performance. Combined with inventory management tools and historical trend analysis, this visibility allows businesses to make more proactive decisions about reorder points, safety stock levels, and replenishment strategies. Inventory levels are optimized while reducing excess stock, minimizing carrying costs, and ensuring product availability to meet customer demand.
Predictive Analytics and Reporting
While traditional reporting tells you what happened, predictive analytics helps you anticipate what’s coming. By analyzing historical data, identifying patterns, and modeling future scenarios, supply chain leaders can use AI to better forecast demand and anticipate disruptions.
<p>Whether it’s predicting supplier delays or understanding seasonal sales fluctuations, predictive analytics transforms raw data into a strategic asset. GraceBlood’s clients, for example, use predictive tools to respond to real-time ASN gaps or reroute product based on dynamic lead time analysis. They are literally turning challenges into opportunities for agility and efficiency. Ultimately, you set yourself up for a more resilient supply chain.
Navigate Supply Chain Analytics Challenges with GraceBlood
While supply chain challenges will always exist, analytics can help identify and mitigate many of the risks associated with them. By improving risk management practices and making more informed decisions, supply chain professionals and data scientists can improve their bottom line and ensure that their business functions as efficiently as possible. What the top challenges for your supply chain analytics? Do you need a partner who truly understands data? Discover how GraceBlood can help you overcome your toughest supply chain challenges—connect with us today.
Learn how to thrive in a world full of supply chain challenges in our free ebook: Supply Chain Insights
