Key Characteristics of Supply Chain Analytics High Performers

Talent, technology and governance play critical roles in supply chain analytics excellence.

Increased disruptions call for new levels of supply chain analytics excellence

In a recent Gartner survey, 76% of supply chain executives indicated that compared to three years ago, their company is facing more frequent disruptions in its supply chain. With such a large majority of companies experiencing significant volatility, understanding and benchmarking the practices that align with analytics-driven improvement are critical to enabling supply chain analytics leaders to prepare.

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    Despite higher supply chain analytics spend, only a fraction of organizations see ROI

    Ninety-five percent of organizations have increased their spending in supply chain analytics, and 95% plan to increase investments in the next two years. Yet fewer than 25% have been able to reap high levels of analytics-driven improvements.

    Organizations that see major analytics-driven improvements share similar traits

    Organizations that have rated their analytics-driven improvement as a five or six across all functional and cross-functional use cases—where six represents the highest level of improvement and one indicates no improvement—are referred to as "high supply chain analytics performers" in comparison to their peers. This section highlights practices that correspond with the higher levels of analytics-driven improvement achieved by these organizations.

    Prioritize supply chain analytics technologies embedded in existing applications

    For both foundational analytics (e.g., reports, dashboards and visualizations) and advanced analytics (e.g., predictive analytics, machine learning and AI), the data shows that high performers use all technologies more broadly than their lower-performing peers. 

    The one significant exception: High performers rely less on stand-alone supply chain analytics platforms compared to organizations that achieve lower levels of improvements. This is counterintuitive, as one might expect that higher performers would use stand-alone, best-of-breed, advanced supply chain analytics platforms more than their counterparts. 

    There are many possible reasons for this distinction — including vendor incumbency, familiar user experience and prebuilt data integrations. But the key take-away is clear: Work with application vendors to extract more value from current technology solutions as the first option for supporting analytics.

    Make the case for two key roles — and house key analytics roles in supply chain

    Two key roles can make the difference between low and high supply chain analytics–driven performance. Supply chain analytics coaches serve as the connective tissue between creating and consuming analytics, supporting broader adoption across the organization. Data stewards work with data and IT groups to ensure the availability, reliability, accuracy and completeness of required data.

    In advanced supply chain analytics, higher-performing organizations are 20% more likely to rely on analytics coaches and 48% more likely to leverage data stewards. In foundational analytics, those numbers increase to 41% and 32%, respectively.

    In addition to prioritizing these two roles, high-performing organizations are also more inclined to house analytics leaders, analytics coaches and data steward roles in the supply chain function. A dedicated supply chain analytics team, embedded in supply chain, is usually more likely to have deeper supply chain domain expertise in addition to technical know-how. This positions the team to build and deploy solutions that meet unique supply chain needs.

    Manage analytics solutions throughout their life cycle

    Organizations that achieve higher levels of improvement through supply chain analytics are consistently stronger in managing analytics as assets. 

    Like physical asset managers at a factory or warehouse, analytics leaders invest in data foundation and analytics solutions. They track the returns on assets, assign resources to their ongoing maintenance and sunset the solutions at their end of life. 

    Notably, 91% of high performers report they know what analytics solutions are in active use (versus 83% of lower performers), and 87% report they conduct thorough due diligence on available supply chain analytics solutions before investing further (versus 78% of lower performers). 

    Supply chain analytics FAQs

    What are the key characteristics of high-performing supply chain analytics organizations?

    High-performing supply chain analytics organizations excel by leveraging talent, technology and governance. They prioritize embedding analytics technologies within existing applications, employ key roles like analytics coaches and data stewards, and manage analytics solutions throughout their life cycle to ensure continuous improvement and ROI.

    Why do high performers in supply chain analytics use fewer stand-alone platforms?

    High performers in supply chain analytics often rely less on stand-alone platforms because they extract more value from existing technologies integrated into their applications. This approach benefits from familiar user experiences, vendor incumbency and prebuilt data integrations, which can enhance efficiency and effectiveness.

    How do analytics coaches and data stewards contribute to supply chain analytics success?

    Analytics coaches facilitate the adoption of analytics across the organization by bridging the gap between data creation and consumption. Data stewards ensure data availability, reliability and accuracy. Together these roles help high-performing organizations achieve significant improvements by fostering a data-driven culture and maintaining high-quality data.

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