Procuring a higher education in AI
Procuring a higher education in AI
CLIENT STORY

Procuring a higher education in AI

KPMG helped a world-renowned university advance spend management with leading data governance and analytical practices.

Client
A national research university
Sector
Education
Project
Procurement innovation
  • Client challenge
  • Benefits to client
  • Approach
  • KPMG insights

Client challenge

This national research university’s procurement department supports the school’s research, teaching, and practice missions by managing more than $2 billion a year in annual purchases. The department prides itself on providing accurate, timely, and trustworthy financial analysis and insight to decision makers at the university. The procurement team asked KPMG to help it go even further, and use leading machine learning and Artificial Intelligence (AI) approaches to better understand spending patterns.

KPMG helped build a new data classification framework and a purpose-built analytic model. The new solutions now support “a single version of the truth” and light up spending patterns by category, supplier, or time period. With our assistance, the university’s procurement department is advancing its own analytics agenda while contributing to the financial health of a world-renowned university.

Benefits to client

The university’s procurement department has deployed machine learning and AI to better leverage collective purchasing power, reduce the overall cost of goods and services, and facilitate acquisition. Specific benefits include:

  • spend data taxonomy in place to generate timely and integrated spend reporting and support strategic decision-making by university leaders
  • machine learning analytics in place to evolve Procurement’s data models as category, supplier, and market conditions shift
  • ability to continuously identify category opportunities from spend analyses established
  • ability to deliver continued strategic value to the university strengthened, in an era of steady disruption
  • the procurement team’s own data science practices advanced, along with its status as a best-in-class organization enhanced.

Approach

KPMG helped the university’s procurement department apply leading data governance and analytics practices to its spend management function. The KPMG team:

  • used proprietary KPMG algorithms to design, test, and deploy a purpose-built analytics model for spend data, combining self-refining machine learning with human adjustments
  • developed a spend data taxonomy to support accurate and timely spend insight and analysis
  • cleansed, normalized, and categorized data for more than two million transactions that account for more than $2 billion in annual spend, as the foundation for upgraded data standards
  • established the ability to identify category opportunities from rolling spend analyses
  • helped structure a managed-services partnership with its chosen analytics solution provider
  • trained procurement administrators to use the new spend data taxonomy and supporting analytics.

KPMG insights

Organizations of all types face strategy and tactical procurement challenges

As advisors on the journey with our clients, we must address both the strategic value issues all procurement leaders face, and tactical challenges such as spend analytics and third-party partnerships.

Advanced analytic and AI capabilities are a “must have” for organizations today

As analytics and AI become “must have” capabilities, organizations need to integrate the right combination of technology, organizational capital, and data strategy to advance their own analytics and AI agendas.

Commercial-sector innovation can easily be adapted for not-for-profit entities

Delivering measurable impact for mission-driven organizations, as with for profit companies, connects meaningful performance metrics tailored to the vision, objectives, and the needs of their stakeholders.

For this client, KPMG used proprietary algorithms to design, test and deploy a purpose-built analytics model for spend data, combining self-refining machine learning with human adjustments.

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