The semiconductor industry’s complex global supply chains make it vulnerable to upheaval. In 2020, it was the pandemic. Today, tariffs, export controls and geopolitical tensions are fueling new turmoil. International trade policies change daily. Manufacturers can’t rely on luck to weather the storm; they must build operational agility for both the current challenges and inevitable future disruptions.
This agility requires high-quality, real-time data, strong analytics and AI. With a robust data infrastructure, chipmakers can produce more responsive pricing strategies, risk-adjusted contracts and visibility into downstream channel activity. Now is the right time to invest in tools that improve revenue resilience.
Quality data: The foundation of agility
Data access is not the primary challenge — data quality is. Most manufacturers collect information from across their supply chain, but Model N’s 2025 State of Revenue Report found more than 90% of surveyed executives have concerns about its quality.
Good data is fundamental for day-to-day operations; Errors lead to revenue leakage. For example, outdated product attribute data might prompt a manufacturer to apply the wrong pricing tier to a high-value component, resulting in undercharging.
Optimizing revenue processes begins by integrating, standardizing and enriching revenue data. This starts with aggregating data from across the organization and storing it in a centralized environment. Given the volume and diversity of information, traditional data warehouses fall short. Moving to a data lake architecture enables companies to handle structured and unstructured data.
Once data is centralized, it must be standardized. Without it, teams and systems must interpret and reconcile inconsistent definitions. For instance, a distributor’s book cost might be labeled one way in the ERP system, called something else in the CRM, and formatted differently in the revenue management platform. The disparity often leads to mistakes, such as incorrect incentives, inappropriate discount approvals and miscalculated margins.
Manufacturers must adopt a canonical data model to create consistent definitions for operational elements so that data can be accurately shared and interpreted across systems.
Standardized data paves the way for AI to clean, enrich, update and validate the information. AI tools detect inconsistencies, highlight discrepancies and automatically populate missing details to enhance data quality.
By adopting these practices, manufacturers have the quality data necessary to effectively manage revenue processes and extract strategic insights from analytics and AI. These capabilities support chipmakers’ supply chain and revenue management agility.
Improved supply chain visibility
With the speed at which policies are changing, manufacturers must have clear, real-time visibility into their entire product journey to manage tariffs, export controls, denied entity lists and other regulatory requirements. Combining quality internal data with external regulatory intelligence enables companies to quickly identify and rectify areas of noncompliance and build contingencies in anticipation of new policies. AI analyzes distributor behavior, detects deviations in expected sales patterns and surfaces subtle risks that manual processes might overlook.
Transparency also exposes supply chain vulnerabilities, such as an overreliance on a single supplier or geographic region. AI scenario modeling tools assess how shifting policies and disruptions could impact revenue. With this insight, manufacturers can adjust sourcing strategies and diversify their partners to mitigate revenue and compliance risks.
Flexible contracts and optimized pricing
Traditional multi-year, fixed-rate contracts become financial liabilities with the constantly fluctuating trade policies. Adopting contingency-based agreements offers a more adaptive approach. These contracts factor in pricing adjustments and performance incentives based on variables, such as market conditions, regulatory changes, supply chain disruptions and customer demand. This format enables manufacturers to protect against market volatility and share risk with their customers.
Accurate, timely data provides the objective metrics needed to define events that trigger adjustments, while AI and analytics monitor market activity, validate performance criteria and apply pricing or rebate changes automatically. These technologies make it easier for finance departments to develop and manage more complex contracts.
These same tools also power dynamic pricing strategies. Analyzing contract performance, channel behavior, material costs and demand patterns enables manufacturers to build precise customer segments and tailor pricing and incentive structures accordingly. As conditions shift, chipmakers can quickly refine their strategies to preserve margins and capitalize on market opportunities.
AI-powered predictive modeling further enables agility by allowing manufacturers to simulate how changes in trade policy, raw material costs, demand forecasts, pricing strategy and other variables could impact sales and revenue. These insights inform both contract structure and pricing decisions and support proactive adjustments as conditions warrant.
The current geopolitical tension isn’t the first crisis to rock semiconductor manufacturing, and it certainly won’t be the last. Chipmakers must be ready to respond to problems they can’t predict. Quality data, AI and analytics power this resilience. By surfacing revenue risks sooner and identifying new market opportunities, companies can make faster, more confident decisions that protect margins, improve compliance and maintain stability.
About the Author Ankur Gupta is Senior Director of product management for Model N High Tech products. Ankur is a seasoned product management leader, with total 20 years of experience in driving growth of enterprise SaaS based Revenue Management products at Model N (10 years) and Cloud Management product suite at Oracle (6 years). In his current role at Model N, he focusses on strategy, planning and execution of High-Tech product line.
This article was originally published on EMSNow
