How a Flash Diagnostic Can Unlock Immediate Gains in Spare Parts Inventory

Spare parts inventory is often a blind spot in supply chain performance. It doesn't move fast, it rarely gets reviewed, and yet, it quietly ties up working capital, creates service risks, and hides inefficient practices.

At DataPowa, we’ve built a structured “Inventory Flash Diag” to bring immediate clarity to that aspect, in about two weeks or less, using only the data you already have.

Here’s how it works, and what kind of impact it delivers.

The Problem: Complexity, Fragmentation, and Uncertainty

Across the organizations we’ve worked with (and studied), we typically find:

• Thousands of SKUs, poorly segmented

• Stock policies applied inconsistently or not updated

• Safety stock levels based on gut feeling, not data

• Unused or slow-moving stock accumulating quietly

• Poor lead time quality or inconsistencies across systems

• No clear link between stock and actual service risk or criticality

And yet, decisions around spare parts are often made at a local level, with limited system-wide visibility or analytical support.

Our Approach: Focused, Fast, and Data-Driven

The Inventory Flash Diag isn’t a long consulting engagement. It’s a focused, technical analysis designed to give you a snapshot of your performance, risks, and improvement levers in 10 to 15 working days.

We use our DPIM internal engine to process your ERP and/or CMMS data (no software deployment needed), and we structure the diagnostic around five key areas:

1. Inventory Mapping by Rotation & Criticality

We classify your spare parts using historical data: consumption, movements, usage, forecasts. This gives a clear view of:

• Which parts are fast vs. slow movers (and what type of slow movers)

• Where your dormant stock is concentrated

• How much of your stock value is tied to low-rotation items

→ Impact: Identify quick-win reduction levers & reduce working capital exposure.

2. Policy Simulation (MIN/MAX, Pooling)

We simulate different stocking strategies, optimized MIN/MAX, intra-site and inter-site pooling, to estimate what the impact would be in terms of:

• Service level changes

• Stock value and holding costs reduction

• Transfer volumes between sites (if pooling)

Beyond testing new scenarios, we also use simulation to map the estimated current service levels across sites and items. This allows us to identify:

• Overprotected stocks (service level > 97-98% without business justification)

• Critical parts with insufficient coverage

• Disparities between sites

→ Impact: Quantify real improvement scenarios without touching operations.

3. Lead Time & Supply Risk Analysis

We analyze lead time distributions, compare actual vs. planned delivery, and assess the risk this creates on availability for critical parts.

We also check for missing or inconsistent data that distort planning. It’s important to remember: lead times have a direct impact on stock levels. So if they’re long, wrong, or highly variable, they can significantly inflate inventory or increase shortage risk.

→ Impact: Reveal where your planning assumptions are unreliable or risky.

4. Parameters & Data Quality Audit

We scan for inconsistencies in safety stock, reorder points, minimum stocks, maximum stocks, criticalities and classifications.

We also evaluate MDM issues: duplicate entries, incomplete fields, outdated categorization.

→ Impact: Improve data trustworthiness and reduce errors in the planning loop.

5. Prioritized Action Plan

We synthesize the findings into a clear list of prioritized actions, based on:

• Expected impact (stock value, service level, risk)

• Feasibility (quick wins vs. deeper changes)

• Technical readiness

→ Output: A concrete roadmap of what to fix, tune, clean, or rethink.

What You Need to Get Started

Just your data.

We work with exports from your ERP and/or CMMS (SAP, Maximo, Infor, Oracle, Ultimo, DIMO Maint, etc.), with no need for new tools or complex setups.

If you can extract:

• A list of parts with parameters like MIN/MAX or reorder points

• Stock history or movement logs

• Purchase history / lead times

...then you're ready.

Typical Results

In past analyses, we’ve uncovered:

• 40–50% of stock value sitting in dormant items (and the rest is mainly intermittent)

• 5–20% stock reduction potential through pooling or policy tuning

• Lead time outliers creating false safety stock needs

• Hidden critical items exposed to real service risk (and vice-versa for non-critical ones being overstocked)

This diagnostic is the entry point. Whether you choose to implement the roadmap independently or continue working with us to implement, we structure the next steps based on your internal resources, systems, and maturity.

We believe in making progress with what you already have.

Want to explore whether it’s worth running the diagnostic on your side? Just reach out.