Leading Procurement Teams Prioritising Data Driven Decision
According to recent findings from McKinsey & Company, companies with leading procurement functions are prioritising data-driven decision-making, further outperforming their peers to achieve EBITDA margins at least 5% points higher.
Mauro Erriquez, a Senior Partner at McKinsey & Company Operations Practice and the global leader of the firm’s product development and procurement efforts. Based in Frankfurt, he advises clients on value creation, strategy and growth with a focus on the materials, automotive and machinery industries.
Since taking over the role of global leader, Mauro has shaped the vision for the procurement function for the future and holds a crucial part in developing and nurturing the next generation of procurement professionals.
Mauro spoke to Procurement Magazine about how leaders are implementing data-driven decision making and the challenges presented when building strong data systems.
How can procurement leaders effectively implement data-driven decision-making strategies to achieve the 5% higher EBITDA margins mentioned by McKinsey & Company?
When we launched our first procurement benchmarking survey nearly two decades ago, we uncovered a clear link between greater procurement maturity and higher business profitability.
An important long-term finding from our benchmarking is that there is no shortcut to procurement excellence. High-performing procurement functions don’t excel on just one or two maturity dimensions, they excel on many of them — and digital makes the difference. Top performers have maturity scores at least 40% higher than average players in strategy, digital and data and analytics.
Today’s digitised, connected organisations must tap into this data and develop new tools to make faster, better sourcing decisions. Mastering the data will empower procurement teams to achieve strategic objectives that go far beyond traditional cost, quality and delivery metrics.
What specific digital tools (AI, data analysis, automation) are top-performing procurement teams using to improve their understanding of data and predict risks in the supply chain?
Better data is essential to support decision-making across the sourcing life cycle, from the development of category strategies and the assessment of potential suppliers to the execution of negotiations and ongoing supplier performance management. Done well, that can increase the pipeline of value creation initiatives by up to 200%.
With the application of AI and gen AI technologies, category management can be automated and accelerated in multiple ways. For example, spend categorisation algorithms can create cleaned spend cubes seamlessly. Or demand forecasts and optimisation will see a step change in accuracy, making sourcing, demand and supply chain control as well as optimisation much more relevant.
Procurement teams will also be able to combine internal data with external market reports and databases, including machine learning algorithms to uncover patterns and trends in commodity prices. CPOs and category managers will rely on such predictions to stay at the forefront of industry profitability with real-time transparency on price volatility exposure.
They will be able to dynamically compute the should-cost of their most volatile commodities and negotiate with suppliers based on facts. They will be able to assess the impact of any variation in input prices on the product margins and analyse multiple scenarios to define the right actions to protect those margins.
How can procurement teams balance the implementation of new technologies with the necessary changes to people and processes to ensure successful data-driven transformations? What are the main challenges procurement leaders might face when building strong data systems and how can they overcome these obstacles?
CPOs expect data, analytics and Gen AI to play a core role in every business decision by 2030, but procurement leaders admit that their data infrastructure is not ready to support this ambition. From our research, we know only a fifth of respondents from our survey feel their data infrastructure maturity is low, with less than 70% of spend data stored in one place.
Scaling a data analytics transformation is what differentiates frontrunners from the rest of the pack. Companies that have achieved this tend to get a few vital things right.
They focus on a small number of high-value digital and AI use cases. They build and, crucially, own a robust data infrastructure to support those use cases. In addition, they spend as much time thinking about people and processes as on technology: adapting their core business processes and operating model, upskilling and reskilling their people as well as steering change across the organisation.
How can procurement leaders in materials, automotive and machinery industries adapt your insights to their specific sector challenges for 2025?
Across industries, we see the conversation around digital and Gen AI evolve from the potential toward how organisations can capture the value of these technologies and navigate its profound impact.
Finding where to focus energies and scale will be important in 2025 and beyond. For that, organisations need to rewire across six core dimensions: strategy, talent, operating model, technology, data and AI and adoption, scaling and sustainability. Beside digital and AI, geopolitical developments are top of mind for business leaders across countries and sectors.
Tariff readiness is a new boardroom agenda item and another demonstration of the strategic role of procurement in an organisationās success. There is no single template for this, as each business has its own vulnerabilities and opportunities.
However, there are common themes, for example, firms can examine the complexity and geography of each step in their manufacturing processāmapping goodsā countries of origin and total landed costsāto estimate the potential effects of tariffs. Scenario planning can cover potential impacts on both production and delivery, such as disruption of critical inputs, changes in cost and whether manufacturers can absorb added costs.
Mapping out supply chains, assessing the feasibility and costs of switching suppliers can help inform leadersā decisions on tariff responses. And here, data, digital and AI will play a pivotal role in navigating these ever-changing developments.
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