Case Study: Optimizing a $30B Product Portfolio to Fund Strategic Growth

When you’re managing 110 PC products with a fixed $200M R&D budget, “do more with less” isn’t a strategy. It’s a platitude. Dell’s President of Product Development faced a classic portfolio dilemma. He knew growth opportunities existed, but every dollar was already committed. The question wasn’t whether to invest in new avenues. It was how to free up the resources to do so without simply cutting budgets and hoping for the best.

The Challenge

Dell’s PC portfolio had grown to 110 products across a $30B revenue base over years of incremental additions. Each product had been launched with good intentions, each consumed R&D resources, but not all delivered proportional returns. The Product Development organization had approximately $200M in discretionary R&D spend, and that budget ceiling wasn’t moving. Meanwhile, market opportunities in emerging segments and technologies demanded investment.

The traditional approach had failed before. Asking leaders to voluntarily cut their products or applying across-the-board reductions led nowhere. Product managers are naturally protective of their portfolios, and without clear data on true ROI, every product seemed essential to someone. Previous attempts to rationalize the portfolio had stalled in endless debates about strategic importance, with no objective framework to guide decisions. What made this particularly difficult was that simply identifying low-revenue products wasn’t enough. The real question was whether removing a product would actually free up R&D capacity, both in dollars and engineering bandwidth.

My Approach

I built an activity-based costing model from the ground up to understand the true cost of each product in the portfolio. This started with detailed process mapping across the product development organization, followed by an employee activity survey that captured how engineering time was actually spent. By connecting this activity data to internal revenue figures, we created product-level and product-line-level returns on R&D investment. For the first time, we had an objective view of which products were generating returns and which were destroying value.

But data alone doesn’t drive change. I structured the decision-making process as a series of leadership workshops where we used the ROI analysis to identify candidates for retirement and investment. These weren’t PowerPoint presentations. They were working sessions where the President and 15+ product line leaders grappled with the trade-offs and collectively decided which products to remove from the portfolio. I simultaneously solicited ideas for new investments so the conversation wasn’t just about cuts, but about strategic reallocation.

The critical work happened in parallel with operational teams. I worked deeply with engineering and program management to understand what “savings” really meant. When you eliminate a product, you don’t automatically get back 100% of its cost. Some resources are shared, some activities have fixed costs, and some capabilities need to be maintained for related products. We mapped these interdependencies rigorously across the six-month project timeline to determine what dollars and bandwidth were truly accessible for redeployment. Finally, I created a reallocation strategy that leadership committed to deliver. Not a theoretical model, but an executable plan with specific savings targets tied to named owners.

Results & Impact

• Portfolio rationalization: Eliminated approximately 15 underperforming products while adding 10 new products in higher-return areas
• Market share growth: Delivered consistent share growth of more than 100 basis points for the next two product cycles following the portfolio shift
• Revenue and profitability performance: Achieved industry-leading revenue growth and profitability improvement in reallocated segments
• R&D efficiency: Freed up $30M in R&D capacity (15% of addressable spend) for strategic redeployment without increasing overall budget
• Organizational capability: Established repeatable framework for portfolio ROI analysis and resource allocation that leadership continued to use in subsequent planning cycles

Key Insight

The most difficult part of portfolio optimization isn’t the analysis. It’s translating detailed spend data into dollars or capacity that’s truly accessible. Leaders can easily agree that a low-performing product should be cut. What’s hard is committing to deliver specific savings when some costs are fixed, some resources are shared, and some capabilities need to be retained for related products.

This required very deep analysis of key activities and headcount drivers to understand what’s actually freed up when you remove a product or set of products. You can’t just multiply the product’s revenue percentage by the total R&D budget and call that the savings. You need to map the work, understand the dependencies, and identify what resources can genuinely be redeployed. This whole process took finesse and discussion with leaders to build consensus on what we could commit to deliver, but that commitment is what made the reallocation real rather than theoretical.