5 Reasons Inventory Optimization Underperforms and How to Unlock Full Potential

Inventory Optimization (IO) is one of the most powerful capabilities within SAP Integrated Business Planning (IBP). When used correctly, it reduces carrying costs, prevents stockouts, and determines where and how much inventory to carry across your network. It replaces intuition with data science and turns inventory from a cost center into a strategic advantage by enabling companies to  leverage Integrated Business Planning (IBP) as a data-driven approach to planning.

Yet many companies struggle to unlock its full value or believing it did not meet expectations because outputs feel confusing or counterintuitive. Trust erodes quickly, and it can appear as though IO has failed—even though the underlying issues may stem from how it is used. Overcoming these early challenges often requires proper technical infrastructure—advanced SAP planning tools can strengthen inventory optimization capabilities and accelerate the path to measurable results.

IO is designed to deliver sound answers, but its effectiveness depends entirely on the information and conditions it relies on. When data, assumptions, or processes fall short, results naturally do too. There may be the perception that the tool is flawed when the underlying challenges sit elsewhere:

1. Teams do not always trust the algorithm

IO operates more like an autonomous agent. You cannot see its internal logic, and its outputs can challenge assumptions. When recommendations contradict tribal knowledge, planners might feel the need to push back. Without transparency around how inputs drive outputs, adoption may lag. Trust must be built deliberately: teams need to understand why the results look the way they do, not just what they are.

This tension between intuition and mathematical guidance surfaces across supply chain planning—from network optimization to balancing availability and efficiency in inventory management. In each case, the challenge remains the same: harmonizing analytical rigor with practical decision‑making to drive outcomes that teams understand and trust.

2. Data is not clean or consistent

IO is only as reliable as the data beneath it. Incomplete master data, inconsistent attributes, or noisy history will distort results and undermine credibility. When outputs seem off, users quickly conclude that the system itself is flawed. Successful IO depends on disciplined data hygiene and continuous maintenance—not a one-time cleanup.

Data challenges often stem from disconnected systems and siloed insights, a theme explored in how unified data platforms enable more resilient manufacturing, which emphasizes the importance of unified and reliable data foundations.

3. Business realities are not reflected

IO produces mathematically correct answers, but it cannot account for real-world nuance on its own. It does not know about supplier instability, labor constraints, or executive commitments to key customers. When results are technically “right” but operationally unrealistic, trust decreases. IO must be interpreted in context and paired with business judgement rather than treated as an isolated calculation.

4. Teams are not aligned across business functions

Inventory optimization doesn't happen in isolation. Decisions about inventory reverberate across procurement, planning, finance, operations, and customer service—which means success demands cross-functional alignment from day one. When IO becomes an IT-owned initiative locked in a silo, it loses the collaborative momentum needed to drive real business value.

The tool can only deliver meaningful insights when every stakeholder clearly understands their role, shares ownership of the outcomes, and actively participates in the decision-making process. Without that unified commitment, IO recommendations will miss the mark on how your business actually operates—and the investment won't deliver the returns you expect.

5. IO is expected to solve upstream planning problems

Inventory optimization does exactly what its name suggests—it streamlines inventory management. It won't fix insufficient demand planning, correct inaccurate lead times, or patch broken replenishment processes. When foundational planning practices are weak, IO acts as a diagnostic tool, surfacing deeper systemic issues rather than masking them. That's valuable feedback, but it can make outputs appear inconsistent or misaligned if you're expecting IO to compensate for upstream gaps.

Set clear expectations from the start: IO is a powerful component within your broader planning ecosystem, designed to work in concert with strong foundational processes—not replace them. Position it as part of an integrated strategy, and you'll unlock its full potential to drive measurable improvement.

How to get IO right

Some organizations walk away from IO before realizing its full potential. Others transform it into a strategic capability that delivers value year after year. The differentiator isn't the platform—it's the implementation strategy."

Start small and expand slowly

A phased rollout is the most effective path. Starting with a limited scope lets you validate your assumptions, pressure-test data, and build trust before scaling. Early wins help teams gain confidence in the logic and understand how their decisions affect the outputs. A gradual approach creates stronger adoption and smoother expansion.

Treat your data as a strategic asset

Clean, structured, reliable data is the foundation of IO. Companies that excel with IO invest early in data quality and establish clear ownership for ongoing maintenance. By treating data as a continuous responsibility—not a one-off task—you preserve the integrity of your IO results over time.

Invest in training and change management

Training and education is essential. Even though IO’s logic isn’t directly visible, planners must understand the principles behind it. When users know why the tool recommends certain values, they are far more likely to trust, adopt, and defend the output. Successful companies dedicate time to change management, open conversations, and iterative learning so the organization grows comfortable with the model and its behavior.

Create cross-functional alignment

Inventory affects every part of the business. The most successful IO implementations have finance, operations, procurement, planning, and IT aligned around shared metrics and common goals. When teams collaborate, IO becomes a strategic capability rather than a tool planners use in isolation. Alignment ensures that decisions about service levels, safety stock, and constraints reflect the needs of the entire organization.

View IO as an ongoing capability

IO is not something you turn on and leave alone. It requires regular review and refinement. Assumptions change, service levels evolve, and business priorities shift. Companies that treat IO as an evolving capability frequently revisit parameters, evaluate performance, and adapt the model over time. This iterative mindset ensures that IO continues to deliver value as the business grows or changes.

Make IO the cornerstone of your supply chain

If you need guidance on where to begin or how to maximize your SAP solutions, a partner like Argano can help. By working alongside your teams, validating inputs, and contextualizing outputs, we help you rebuild trust in the solution and unlock the benefits it was designed to deliver.

With the right approach, IO can become the cornerstone of your supply chain strategy and a driver of long-term success. For practical next steps, Argano’s Inventory Optimization Decision Guide offers a clear, structured way to assess your readiness and path forward. Contact us today to get started.