Supply chains are drowning in process and data debt, killing profitability

Compounding liabilities are crushing supply chain and procurement teams. Two debts stood out in our 2025 Pulse Study: data debt and process debt (see Exhibit 1). Given the strategic importance of these two functions, these two debts are creating strategic bottlenecks. They erode resilience, hamstring agility, and impact cost of goods sold (COGS) and selling, general and administrative (SG&A) expenses, all while tariff volatility and competitive pressure require peak performance. More investment in technology and people won’t create a solution when workflows are mired in decades-old mentalities.

Exhibit 1: Supply chain and procurement’s greatest challenges

Source: HFS 2025 Pulse, N=41 supply chain and procurement organizations

Process debt: Execution’s proverbial boat anchor

Legacy workflows, fragmented organizational orchestration, and manual workarounds litter supply chain and procurement organizations’ processes, making process debt the greatest concern. Despite significant investments in SaaS technology, processes can’t operate across the dozens of applications that organizations and their supply base use. As a result, supply chain and procurement organizations cited (see Exhibit 2) scalability (17%), reduced profitability (12%), and slower speed to market (11%) as the three most significant challenges. For example, a leading Asia-based electronics manufacturer struggled to scale new product introduction because its procurement workflows were locked in region-specific portals. During the holiday season, several mid-size retailers with their antiquated (and manual) vendor onboarding methods struggled to ship products to consumers on time, eventually losing a percentage of their booked sales—a common example of slower time-to-market owing to process inefficiencies.

Exhibit 2: Impacts of process debt on organizations

Source: HFS 2025 Pulse, N=41 supply chain and procurement organizations

Data debt: The silent killer of intelligence

Data debt, the second most outstanding debt, has always been a challenge for supply chain and procurement teams swimming in PDF contracts, poorly constructed purchase orders (POs), and master data difficulties in supply chain operations. Years of underfunded governance and vendor sprawl have resulted in systems that don’t talk and data that doesn’t align. It’s no wonder organizations feel like they are making decisions on shaky foundations. When asked how data debt impacts them, three of the four leading impacts were variations on decision-making challenges (see Exhibit 3): higher cost from redundant or siloed data (12%), missed innovation opportunities (11%), ineffective analytics (11%), and inaccurate decision-making (11%). Teams spend days of manpower reconciling basic spend, supplier, and supply chain data across numerous platforms before they can even start savings initiatives.

In 2023, disconnected systems across engineering, procurement, and supplier quality of an automotive manufacturer led to missed opportunities, such as overlooking a lightweight materials proposal that could have improved fuel efficiency because it was buried in an isolated R&D database. Analytics efforts were hamstrung as teams spent weeks manually aligning supplier, cost, and defect data from various platforms, delaying critical sourcing decisions. In one instance, leadership approved a new supplier contract based on outdated pricing data, unaware of recent delivery delays flagged in a separate logistics system. Compounding the issue, redundant data entry and lack of integration across functions resulted in duplicate orders, increased inventory elevated costs, and missed consolidation savings.

Exhibit 3: Impacts of data woes on organizations

Source: HFS 2025 Pulse, N=41 supply chain and procurement organizations

Investments focus on process gaps

Supply chain and procurement teams are investing significantly in workflow and orchestration capabilities to reduce accumulated debts. As shown in Exhibit 4, supply chain and procurement teams are prioritizing investments in machine learning (83%), agentic solutions (77%), and robotic process automation (RPA) workflows (73%) to tame process complexity and make data actionable. A compelling example comes from a global beverage manufacturer piloting a machine-learning model to predict out-of-stock scenarios at retail locations. The model generates proactive recommendations for production adjustments, enhancing forecast accuracy well beyond the existing baseline. While initial results were promising, scaling the solution required integration into legacy planning systems and retraining demand planners—demonstrating the depth of process and organizational change required. In another case, computer vision-based agentic systems were being tested on the manufacturing lines of a global toy manufacturer to spot product defects in real time, a task traditionally dependent on manual inspection. These use cases highlight how organizations are embracing intelligent automation to address both process and data debt, making workflows smarter and more responsive.

This said, company leadership averaged a 28.3% investment of agentic solutions to replace services, supply chain and procurement areas averaged 23.3% and 30%, respectively—neither were major leaders in transforming their organizations.

Exhibit 4: Machine learning followed by agentic and process automation are expected to attract substantial investments from the supply chain and procurement functions in 2025

Source: HFS 2025 Pulse, N=41 supply chain and procurement organizations

Debt repayment recommendations for supply chain and procurement teams

We make the following four recommendations:

• Reengineer for flow, not functions, so that functional siloes are eliminated between supply chain and procurement and their various stakeholders. Stakeholders should be at the center of flows, not afterthoughts.
• Establish a procurement and supply chain data fabric that connects enterprise resource planning (ERP), contract lifecycle management (CLM), materials requirements planning (MRP), and master supplier and finance data.
• Map data blind spots and audit where critical decisions rely on incomplete, inconsistent, or unavailable data. Focus on investing in data quality in the areas that impact decision-making most.
• Augment analysts through agentic and generative AI capabilities rather than focusing on functional efficiency. The best return on investment (ROI) will come from driving business results and profitability, not reducing department costs.

The Bottom Line: Repay the debts or remain in the muck.

The message from our latest data is blunt: process and data debts remain supply chain and procurement’s greatest operational liabilities. More investment in technology and people won’t create a solution when workflows are mired in decades old mentalities and data remains inaccessible. During a time when profitability and supply chain resilience are at its greatest demand, you must invest to clear these debts, or fall behind.