Reducing Excess Inventory and Optimizing Working Capital in High-Tech Supply Chains Using Decision Intelligence

A Capital-Intensive Industry Facing a Costly Imbalance

High-tech manufacturers operate in one of the most capital-intensive environments in the global economy. With complex bills of materials, long lead times, and short product lifecycles, these supply chains are under constant pressure to balance availability with efficiency — and that balance is becoming harder to maintain.

To avoid stockouts and keep production flowing, many companies have leaned into inventory buffers. But over time, this safety-first approach has led to a buildup of excess stock across the network, tying up billions in working capital and eroding agility. What was meant to provide resilience is now introducing rigidity.

At the same time, volatility in demand, shifting product mixes, and upstream disruptions have also made it harder to predict what inventory will actually be needed, when, and where. Traditional planning systems — built around static forecasts and siloed data — simply can’t keep pace. The result is a growing mismatch between supply and demand, and a widening gap between financial goals and operational reality.

To move forward, high-tech supply chains need more than tighter forecasting. They need a smarter, faster way to match supply to real demand, one that continuously aligns inventory decisions with business outcomes. That’s where decision intelligence comes in.

The High Cost of Inventory Imbalance

Inventory has long served as a buffer against uncertainty — but in high tech, that buffer can quickly become a burden. With product lifecycles shrinking, component lead times stretching, and demand shifting faster than ever, even well-intentioned inventory strategies can lock up working capital, increase waste, and limit responsiveness.

Unlike consumer goods or durable products, high-tech devices often rely on thousands of interdependent components, sourced globally and subject to rapid obsolescence. A single delay or design change can ripple through the supply chain, leaving companies with the wrong inventory in the wrong place at the wrong time.

When inventory management breaks down, the consequences are operational, financial, and strategic:

• Excess stock ties up millions in working capital, limiting funds for innovation, growth, or investment.
• Aging inventory loses value rapidly, especially in semiconductors or advanced electronics.
• Costly write-offs and markdowns increase, especially when slow-moving items accumulate unnoticed.
• Planners operate reactively, without timely insight into demand, delays, or constraints.
• Storage and handling costs climb, as warehouses fill with unsold goods.

Despite these risks, many companies still depend on outdated forecasts, spreadsheets, and batch-based planning. These may have worked in more stable times, but fall short in today’s fast-moving environment. Without real-time visibility or dynamic modeling, planners must rely on static safety stock rules and coarse averages. Inventory becomes misaligned — too much of what isn’t needed, not enough of what is. Opportunities are lost when companies can’t respond quickly to changes in demand or supply.

The underlying issue isn’t a lack of data; it’s a lack of actionable intelligence. High-tech companies need a new way to manage inventory: one that understands complexity, responds in real time, and continuously optimizes for business outcomes.

How the Industry Is Responding: From Reactive Planning to Real-Time Optimization

To address the growing imbalance between inventory and working capital, high-tech companies are rethinking how decisions are made. Instead of relying on static forecasts or manual rebalancing, they’re turning to platforms that use real-time data, AI, and automation to drive more responsive, efficient supply chain performance. This evolution is transforming how inventory is managed. No longer constrained by fixed planning cycles or siloed systems, teams can now sense and respond to changes as they happen — whether it’s a sudden shift in customer demand, a supply delay upstream, or a misalignment in stock placement across the network.

These advanced solutions deliver critical capabilities that traditional tools lack:

• End-to-end visibility into inventory, orders, and constraints, updated continuously and accessible across functions.
• AI-powered analytics that detect patterns and recommend actions with speed and precision.
• Automated execution of routine decisions, minimizing manual effort and delay.
• Scenario simulation tools to evaluate trade-offs and choose the best path forward — before risks escalate.

By embedding this intelligence directly into daily operations, high-tech manufacturers can respond faster, manage complexity more effectively, and unlock capital trapped in excess stock. The shift is enabling companies to operate with greater clarity and control, making decisions that protect both financial performance and customer satisfaction. This is more than an efficiency play. It’s a strategic move toward a supply chain that is more agile, resilient, and aligned with the pace of high-tech innovation.

Turning Intelligence into Action with Aera Decision Cloud

For high-tech companies working to reduce excess inventory and improve working capital, Aera Decision Cloud offers a transformative path forward. Unlike traditional systems that rely on static planning rules or delayed analytics, Aera embeds decision intelligence directly into supply chain operations, enabling real-time visibility, predictive insights, and autonomous execution at scale.

At the heart of the platform are prebuilt, domain-specific Aera Skills — AI-driven capabilities that continuously learn from data, evaluate trade-offs, and take targeted actions. These skills are purpose-built to manage the complexity and speed of high-tech operations, where long lead times, fluctuating demand, and rapidly aging components make conventional inventory approaches unsustainable.

Three skills in particular deliver critical impact for high-tech supply chains:

Together, these skills transform inventory management from a reactive process into a proactive, intelligent system. Instead of waiting for issues to emerge, companies can sense imbalances early, model corrective actions instantly, and execute the best response — automatically or with guided human oversight.

The impact is both operational and financial:

• Working capital is unlocked, as excess stock is reduced and turnover improves.
• Obsolescence is prevented, thanks to early risk visibility and timely intervention.
• Cash flow is strengthened, through leaner inventories and smarter utilization.
• Service levels are maintained or improved, without costly safety buffers.

For high-tech manufacturers navigating long pipelines and tight financial constraints, Aera Decision Cloud provides a new way to run — faster, leaner, and smarter. By replacing fragmented decision-making with decision intelligence, Aera helps strike the right balance between efficiency and resilience.

Real Results: Proactively Managing Obsolescence Risk Across a Complex Product Portfolio

A global technology hardware company, known for its highly configurable products and broad component base, faced a recurring challenge in managing inventory obsolescence. The company’s legacy processes were manual, reactive, and narrowly focused on unique parts nearing end-of-life. High-value commodities used across multiple product lines were often overlooked until it was too late, exposing the business to mounting financial risk.

To address the issue, the company partnered with Aera to implement the Excess, Obsolescence, and Material Aging Risk Mitigation Skill. This Aera Skill was designed to automate risk detection across the full inventory spectrum, from niche, configure-to-order (CTO) components to widely used commodity parts, providing early warnings and recommending targeted actions well before end-of-life.

The solution aggregated data from across the enterprise, including SAP material master records, production plans, demand forecasts, sales orders, revenue data, and supplier inputs. It applied AI and machine learning models to assess risk exposure, detect aging inventory, and generate proactive recommendations to mitigate financial impact.

With continuous monitoring in place, the company gained real-time visibility into excess and obsolescence risk — not just at the part level, but mapped across product families and business units. When risk was detected, the skill triggered recommendations tailored to business priorities, including:

• Demand shaping actions like pricing updates, configuration changes, and e-commerce defaults.
• Supply-side actions such as modifying commitments or reallocating inventory.
• Business outcome-based trade-offs, weighing margin, revenue, and customer experience.

Once approved, these actions were executed through connected systems, and the models continued to learn from real-world outcomes — improving future recommendations and reinforcing accountability across teams.

The impact was clear:

• Expanded visibility into obsolescence risk, across all parts and commodities.
• Early detection of $100 million in exposure, enabling timely intervention.
• Elimination of reactive workflows, reducing manual burden and improving coordination.
• Smarter, faster decisions, supported by clear ownership and AI-driven insight.

By automating a once-manual process and embedding intelligence into operations, the company gained the ability to manage obsolescence proactively — preserving working capital, improving supply chain discipline, and aligning operational actions with business goals.

Building a Leaner, Smarter Supply Chain with Decision Intelligence

For high-tech manufacturers, managing inventory isn’t just about operational efficiency; it’s about financial performance, risk mitigation, and long-term agility. But in an environment defined by volatility, complex product configurations, and constant innovation, traditional methods simply can’t keep up. Excess stock accumulates. Capital gets trapped. And the window to act closes quickly.

That’s why decision intelligence is essential. With the Aera Skills embedded in Aera Decision Cloud, companies gain the power to continuously monitor, analyze, and act on inventory signals as they emerge — all while aligning decisions with business goals. These skills allow manufacturers to balance supply and demand in real time, reduce exposure to aging stock, and sense demand to avoid overproduction.

The result is a smarter supply chain — one that’s leaner, more responsive, and more capital-efficient. High-tech organizations can unlock working capital, reduce waste, and ensure that critical components are always where they’re needed most.