Supply chain analytics: Trends, benchmarks, and business impact

From the November 2025 edition of Supply Chain Management Review.

November 2025

The November 2025 issue of Supply Chain Management Review explores the topics of global supply chain resilience, innovation leadership, and data-driven transformation. Highlights include strategies for building…

Browse this issue archive.

Access your online digital edition.

Download a PDF file of the November 2025 issue.

Supply chains generate massive amounts of data, and there is no shortage of tools designed to harness that data for better organizational decision-making. Many companies are seizing the opportunity to identify patterns and enhance product quality and delivery. Analytics empowers organizations to achieve these goals and much more.

In the summer of 2025, APQC conducted research on supply chain analytics and data management that included 315 participants from across the globe. The project looked at the current state of organizational commitments to supply chain analytics and the impact analytics has had on businesses.

Organizational commitment to analytics

Organizations signal their commitment to analytics by assigning ownership for it, investing in it, and developing their analytical capabilities.

Ownership. APQC investigated how organizations are managing the primary responsibility for conducting supply chain analytics. The largest segment (36%) report relying on an internal, centralized analytics team, while 27% employ a centralized core team supported by decentralized staff within business units. Additionally, 29% utilize a blend of internal and external resources.

Related infographic, click here

The optimal model for managing supply chain analytics varies based on an organization’s structure, staff expertise, and specific analytics requirements. Regardless of the approach, the fact that leadership is making deliberate, strategic choices underscores a clear recognition of the value that analytics brings to the enterprise.

Investment. The level of financial investments that organizations are making in supply chain analytics also signals an increased commitment to it. Over the last three years, 86% of organizations have increased their financial investment in supply chain analytics. Further, 35% of organizations within that group have significantly increased such investments.

Maturity. Organizations’ maturity with regard to supply chain information availability and analysis capabilities also reveals the increased commitment organizations have made to supply chain analytics. As shown in Figure 1, more than half (55%) have the highest level of maturity, meaning they have real-time accessibility to internal and external data plus a culture that incorporates data-driven decision-making. With integrated real-time supply chain data available, users can access, manipulate, and visualize data on demand.

 

For organizations in the middle of the maturity scale (21%), available data is limited to internal operational data and possibly some external data. What is concerning is that these organizations focus their decision-making on analyzing past actions and consequences. Although there is value in considering past situations, the supply chain landscape has shifted significantly in recent years, with more changes on the horizon. Considering only the past puts an organization at a disadvantage when navigating these future landscape changes.

Use of analytics in supply chain processes

In its research, APQC also looked at the types of analytics that supply chain organizations use. It included the following five categories based on how data is used.

1. Descriptive analytics: Used to describe trends and relationships, such as supply chain performance or a warehouse’s inventory levels.
2. Diagnostic analytics: Used to diagnose supply chain problems, such as delayed shipments or missed sales targets.
3. Predictive analytics: Used to forecast future supply chain events.
4. Prescriptive analytics: Used to provide recommendations, such as optimizing inventory levels.
5. Cognitive analytics: Used to improve supply chain decision-making by applying machine learning.

As shown in Figure 2, the percentage of organizations that use these different types of analytics varies based on the function within the supply chain. Organizations are incorporating all types of analytics in supply chain planning, sourcing and procurement, and logistics and warehousing. Given that not all research participants conduct manufacturing, it was not included in Figure 2. However, for those organizations with manufacturing functions, all types of analytics are in use with predictive analytics slightly ahead and cognitive analytics use lagging.

 

Predictive, prescriptive, and cognitive analytics are more mature types of analytics as their use goes beyond simply describing trends to forecast events, provide recommendations, or improve supply chain decision-making. Overall, supply chains use these more mature forms of analytics less often than descriptive types of analytics.

Companies use analytics for a variety of activities depending on the area of the supply chain. Figure 3 shows the two most common activities for each area. Many organizations use analytics to optimize some aspect of their supply chain activities, whether it is inventory, cost, or order paths.

Evaluating supply chain analytics

The adoption and use of analytics for supply chain is only part of the story. Successful organizations must also have satisfaction and confidence in analytics’ capability to support organizational goals. Luckily, many companies seem to have both. In APQC’s research, a majority of survey respondents indicate that their organizations’ use of analytics to solve strategic supply chain challenges is effective (51%) or very effective (40%).

 

In addition, organizations have a high degree of confidence in the accuracy of their supply chain data (Figure 4). The largest group of organizations (51%) are very confident, and one-third are completely confident.

Similarly, most organizations have some degree of satisfaction with their ability to access relevant supply chain data. The largest group of organizations (46%) are satisfied, and almost as many (41%) are very satisfied (Figure 5).

 

Effectiveness of analytics. Organizations have enhanced the effectiveness of their supply chain analytics by focusing on enabling business drivers. Nearly half (48%) named having the right technology and tools in place as a driver of effectiveness. This is logical since technology is integral to conducting analysis.

Nearly as many (47%) named regular access to high-quality data as a driver. This data is the foundation for accurate insights. Aside from infrastructure-focused business drivers, having a well-defined value proposition for the use of analytics is also a top driver for organizations (46%). Being able to show how analytics can improve supply chain performance gains buy-in for the effort within the company and ensures that support continues.

 

Benefits of analytics. The benefits of analytics named by organizations in APQC’s research touch on key priorities for supply chains. The top benefit is cost savings (54%), followed by improved forecast accuracy (48%) and improved employee productivity (48%).

Any organization would welcome reduced costs, and analytics is providing many organizations with the opportunity to do so by improving speed and accuracy. Further, improved forecast accuracy provides a clearer picture of the future, even with the instability we have seen in recent years. Improved employee productivity means that staff spend their time on activities that directly benefit the organization, rather than on navigating inefficiencies.

Building capabilities

Organizations have embraced the use of analytics in supply chain. Leaders are taking this seriously, with many making improvements in terms of staffing, financial investments, and process maturity.

There is variation, however, in the degree to which organizations use analytics for the various parts of the supply chain. The most popular use case is for supply chain planning. Specifically, two-thirds of organizations use analytics to fulfill different segments of customer demand at the lowest total cost.

Even with the variation in use within the parts of the supply chain, most organizations view their analytics as effective. They also have high degrees of confidence in the accuracy of supply chain data and having the access needed for timely decision making.

Organizations benefit from the use of analytics through cost savings and improved forecast accuracy, which can also lead to reduced costs. Further, the use of analytics supports more efficient workloads for supply chain staff and increased productivity.

Improved forecast accuracy and increased employee productivity enhance the experience of the customer. A more accurate forecast helps the organization better plan for production and warehousing, resulting in more accurate inventory availability. Increased employee productivity means staff members can focus on the priority work
that benefits the customer receiving their product.

Organizations show no signs of slowing their investment in analytics capabilities. Given the clear and measurable benefits, it is expected that companies will continue to advance their analytics maturity and broaden the application of analytics across the supply chain.

About APQC

APQC (American Productivity & Quality Center) is the world’s foremost authority in benchmarking, best practices, process and performance improvement, and knowledge management (KM). With more than 1,000 member organizations worldwide, APQC provides the information, data, and insights organizations need to support decision-making and develop internal skills.

This content includes median values sourced from APQC’s Open Standards Benchmarking database. If you’re interested in having access to the 25th and 75th percentiles or additional metrics, including various peer group cuts, they are either available through a benchmark license or the Benchmarks on Demand tool depending on your organization’s membership type.

APQC’s Resource Library content leverages data from multiple sources. The Open Standards Benchmark repository is updated on a nightly cadence, whereas other data sources have differing schedules. To provide as much transparency as possible, APQC will always attempt to provide context for the data included in our content and leverage the most up-to-date data available at the time of publication.

Data in this content was accurate at the time of publication. For the most current data, visit apqc.org.

SC
MR