What leaders see
Work keeps moving, but only because people fill the gaps.
Teams rely on manual checks, side files, rekeying, status meetings, and individual knowledge to keep the process alive.
Services
Data Analytics · Inventory
Inventory Optimization Analytics
Carry less inventory and stock out less -- at the same time. Most operators solve stockouts by adding more safety stock, and solve excess inventory by running promotions. Neither addresses the root cause. Analytics-driven inventory optimization calculates the right stock levels for every SKU at every location based on actual demand variability and service level targets.
01
The Problem
Inventory Levels Set by Gut Feel and Fear of Stockouts
The problem is not one broken tool. It is an operating gap between who owns the work, which record can be trusted, and how exceptions move through the business.
What is actually happening
The workflow has no clean source of truth.
Records, rules, approvals, and handoffs are split across systems, so each step introduces delay or reconciliation.
What gets worse
Automation amplifies the weak spots.
The faster the business moves, the more bad data, exception work, and decision ambiguity compound across the operation.
02
What Changes
What Inventory Optimization Analytics includes.
Most operators solve stockouts by adding more safety stock, and solve excess inventory by running promotions. Neither addresses the root cause. Analytics-driven inventory optimization calculates the right stock levels for every SKU at every location based on actual demand variability and service level targets.
01
Safety Stock Optimization
Calculate optimal safety stock for every SKU based on demand variability, lead time variability, and target service level. Replace blanket formulas with item-specific calculations that balance cost and availability.
02
Reorder Point Calculation
Dynamic reorder points that update as demand patterns and lead times change. No more static reorder points set during ERP implementation that nobody has reviewed since.
03
ABC/XYZ Classification
Multi-dimensional inventory classification by revenue impact (ABC) and demand predictability (XYZ). Different inventory policies for different segments -- high-value/predictable items managed differently than low-value/erratic ones.
04
Excess & Obsolete Analysis
Identify slow-moving, excess, and obsolete inventory with aging analysis, usage trend tracking, and disposition recommendations. Quantify the carrying cost of dead stock.
05
Multi-Location Optimization
Optimize inventory placement across warehouses and distribution points. Balance stock where it's needed based on demand geography, not just where it's convenient to store.
06
Service Level Modeling
Model the trade-off between inventory investment and service level. Show leadership exactly what it costs to go from 95% to 98% fill rate -- and where the diminishing returns start.
03
How It Fits Your Operations
How Inventory Optimization Analytics fits your operation.
Operations layerWhich manual workflow is costing the most time, rework, or decision delay.
Governance dependencyThe workflow needs clear ownership, trusted data, and exception rules before automation is worth building.
Operating data involved
orders
approvals
documents
exceptions
reporting handoffs
What Launchpad defines before engineering begins
- What the business needs to change and why.
- Which systems, records, risks, and readiness gaps shape the work.
- What should be built, how it fits the architecture, and in what order.
Related Foundations
What to evaluate next.
Follow the dependencies behind this service instead of treating it as an isolated project.
01
Data analytics and business intelligence
Connect governed data to the operational measures and decisions leaders need to trust.
Explore next step02
API and integration engineering
Move approved records between ERP, cloud applications, portals, reporting, automation, and AI.
Explore next step03
AI automation and agents
Apply AI after the data foundation, permissions, workflow, and review boundaries are ready.
Explore next step04
Launchpad readiness and Roadmap
Assess readiness, dependencies, risk, architecture, and implementation order before engineering begins.
Explore next stepLaunchpad Before Engineering
Decide what to build and in what order.
Launchpad assesses the business and turns discovery into priorities, risks, readiness, architecture, and an implementation Roadmap. Metrotechs then engineers and supports the approved solution.
Metrotechs designs, builds, integrates, and supports the approved solution.
Launchpad keeps priorities, risks, owners, decisions, and delivery governance connected.
04
Delivery sequence
How Metrotechs delivers Inventory Optimization Analytics.
Most operators solve stockouts by adding more safety stock, and solve excess inventory by running promotions. Neither addresses the root cause. Analytics-driven inventory.
01
Inventory Data Analysis
Analyze current inventory levels, demand patterns, lead times, and service level performance across all SKUs and locations. Identify where investment is misallocated.
02
Policy Design
Design inventory policies by segment -- safety stock formulas, reorder points, review frequencies, and replenishment methods. Align with operations on service level targets.
03
Optimization Modeling
Run optimization models to calculate target inventory levels. Compare current vs. optimized inventory investment and projected service level impact.
04
Implementation & Monitoring
Update ERP planning parameters with optimized values. Deploy monitoring dashboards tracking inventory turns, service levels, and excess stock. Monthly reviews to maintain optimization.
05
FAQ
Questions that usually decide the scope.
Straight answers to what operators ask before committing budget to this work.
Typical results: 15-30% reduction in total inventory investment while maintaining or improving service levels. The biggest wins come from right-sizing safety stock on high-value items and eliminating excess on slow-movers.