In today’s connected, disruption-prone world, supply chains are only as strong as the data that powers them. The good news is that, when it comes to data and analytics, supply chains are experiencing strength and maturity.
Through a survey of 315 global supply chain organizations, APQC found that data and analytics maturity is growing across industries. Organizations that invest in structured data and governance for analytics are reporting benefits like higher productivity, lower costs, and more accurate forecasts.
The progress that many organizations have made does not mean it’s now time to run on autopilot. In the face of ongoing budgetary concerns and skills gaps, supply chain leaders need to continually make the business case for analytics and sound data management practices. Here’s a rundown of some key findings along with the opportunities and challenges that lie ahead for supply chain data and analytics.
Getting governance right
Organizations have several different options for how to structure data management. The largest share of respondents (40%) uses an internal centralized team for data governance. Another 28% uses a hybrid governance model that includes centralized oversight with decentralized execution across business units. This structure allows for both consistency and flexibility, ensuring that data quality, security, and accessibility are maintained across the enterprise.
APQC
Most organizations keep data management entirely in-house. Only 5% outsource all of their data management to an external firm, while 17% use a combination of internal and external resources.
Interestingly, almost half of surveyed organizations (48%) assign governance responsibilities to different teams by function or area. Less than one-third (32%) have a single team managing data on behalf of the entire enterprise. While organizations value centralization, it’s also clear that many still tailor governance to the unique needs of different supply chain functions.
APQC
Why good data management matters
When asked to identify the top benefits respondents have achieved through effective data management, the top responses included:
» improved productivity (63%)
» more accurate forecasts (50%)
» reduced costs (50%)
» improved decision making (48%)
» improved customer satisfaction (48%)
These supply chain data management outcomes aren’t just theoretical. Effective data management enables real-time tracking, supplier performance monitoring, demand forecasting, and optimized inventory and logistics. All of these benefits translate into tangible business value through waste reduction and cost savings.
From emerging technology to strategic lever
Data found a dramatic shift in how organizations view and use supply chain analytics in 2025 compared to 2019. For example, 91% of respondents today rate their analytics as effective or very effective, while only 66% said the same in 2019. Satisfaction with access to relevant supply chain data has also surged to 87% from 63% in 2019.
3 keys to future success
The maturity and progress that many organizations have achieved with supply chain analytics is a direct result of the hard work put in, sometimes over decades. When asked about the practices that helped these respondents to improve their supply chain analytics effectiveness, the top responses were:
» Having the right technology/tools/infrastructure (48% of respondents). Organizations with mature supply chain analytics make deliberate investments not just in a single tool or technology, but in the technology ecosystem as a whole. For example, it’s critical to integrate supply chain analytics systems with other enterprise technologies and to have standardized approaches for storing, cleaning, saving, and retrieving data.
» Regular access to high-quality data (47%). Analytics are only as good as the data that organizations feed into it. Pursuing data quality means working to ensure that data is accurate, complete, consistent across systems, unique (i.e., not duplicated elsewhere), and available at the time of need.
» A well-defined value proposition for analytics (46%). There are a wide variety of analytics tools available in the marketplace today, with vendors promising shiny new features and big results. Having a value proposition for analytics is key for building buy-in from leaders because it keeps the focus on business value, not chasing after shiny objects.
Barriers
Supply chain organizations have come a long way in their analytics maturity, but they are still confronting at least two significant barriers:
» Financial constraints (40% of respondents)
» Lack of skilled analytics talent (39%)
These challenges aren’t likely to go away any time soon. They are also interrelated. Financial constraints not only limit the technologies that supply chains use but also the ability to hire or develop supply chain talent to close skills gaps. These challenges make it critical to continually stay competitive by strengthening your cost-benefit analysis for the value of analytics and the need for analytics talent.
Key takeaways
As supply chains become more complex and the volume of data continues to grow, organizations that treat data as a core asset will be better positioned to adapt, compete, and thrive. While organizations are still confronting significant challenges, the progress they’ve made toward analytics maturity is already paying off through benefits like lower costs, more accurate forecasts, and more.
Whether you are just starting a supply chain analytics journey or working toward greater maturity, the following key takeaways from APQC research will help with next steps:
» Leading organizations provide centralized forms of governance over data while allowing some degree of flexibility in different business units or areas.
» Without action, skills gaps will only continue to grow as technology advances. Partner with HR or a learning and development function to make a plan for how to close and prevent skills gaps for the use of analytics technology.
» Keep your business case for analytics fresh. Continually gather data that speaks to the business value of analytics and qualitative success stories about how it’s helping people to work smarter and faster. Even if leaders today are convinced that analytics is valuable, organizational priorities can easily shift with changes in leadership or the broader business environment.
