Business outcome
Use this capability when the framework shows that people cannot trust the records, measures, or exceptions needed to make decisions across demand, supply, production, inventory, fulfillment, delivery, or service.
Services
Data & Analytics · Data Analytics · Inventory
Inventory Optimization Analytics
Set inventory policy from demand, lead time, variability, and service requirements.
Manufacturers need inventory decisions that account for demand variability, supplier lead time, production constraints, order policy, location, and customer commitments. We build analytics that make those assumptions visible and support controlled stocking decisions.
Launchpad assesses the operating need and creates the Roadmap. We engineer this capability when the approved plan calls for it.
A strong fit when
- Safety stock set by a blanket formula or "whatever the buyer thinks is enough" -- not by demand variability analysis
- Reorder points that haven't been updated since the items were set up in the ERP
- ABC classification done once and never maintained -- C items getting the same attention as A items
- Excess inventory and stockouts happening simultaneously because different SKUs have different demand patterns
Why this service exists
Connect the technology decision to the work the manufacturing business must control.
System responsibility
Data engineering turns records from ERP and connected systems into governed information for shared visibility, reporting, reconciliation, automation, and accountable action.
Ownership and control
The manufacturer must own the definitions, source authority, access, lineage, quality rules, reconciliations, and decision context behind every important measure.
01
The Problem
Inventory policy is being set by habit instead of measurable operating conditions.
Analytics problems begin when a dashboard is built before records, definitions, timing, lineage, and accountability have been reconciled across the operation.
What leaders see
Reports disagree about current conditions.
Teams debate demand, workload, capacity, inventory, service, delivery, and margin instead of acting on them.
What is actually happening
Measures inherit unresolved record conflicts.
Sources, identifiers, definitions, refresh timing, adjustments, and ownership differ across systems and teams.
What gets worse
Faster reporting accelerates the wrong answer.
More dashboards spread inconsistent measures and make exception ownership harder to establish.
02
What changes
Make the operating responsibility visible and governable.
Manufacturers need inventory decisions that account for demand variability, supplier lead time, production constraints, order policy, location, and customer commitments. We build analytics.
01
Operating outcome
Set inventory policy from demand, lead time, variability, and service requirements.
02
Operating records to reconcile
demand and orders, products, services, and inventory, work and capacity
03
Decision and exception path
Which decision the view supports, which records establish it, and how disagreement or missing data becomes visible.
04
Ownership and continuity
Measures need defined ownership, timing, lineage, reconciliation, and access before dashboards can become a basis for operating decisions.
03
Architecture
Build the service around the business record and decision.
Which decision the view supports, which records establish it, and how disagreement or missing data becomes visible.
01Source record
02Governed connection
03Validation
04Business system
05Accountable owner
Operating records to reconcile
demand and ordersproducts, services, and inventorywork and capacityquality and customer servicedelivery and financial results
04
Engineering scope
What Metrotechs engineers for Inventory Optimization Analytics.
The exact scope follows the approved business objective, source records, dependencies, controls, and delivery sequence.
01
Safety Stock Optimization
Calculate proposed safety stock for the items in scope using demand variability, lead-time variability, and service policy. Replace blanket formulas with item-specific evidence that balances 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 policy. Show leadership how different targets affect working capital, availability, and risk by item and location.
05
Delivery sequence
From operating reality to a solution the business can own.
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.
Policy Design
Design inventory policies by segment -- safety stock formulas, reorder points, review frequencies, and replenishment methods. Align with operations on service level targets.
Optimization Modeling
Run optimization models to calculate target inventory levels. Compare current vs. optimized inventory investment and projected service level impact.
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.
Related services and systems
Continue through the connected operating environment.
Use these connected services and references to understand the records, workflows, and systems surrounding this work.
06
FAQ
Questions to answer before implementation begins.
Clear answers for manufacturing leaders evaluating the work, operating responsibility, and delivery path.
Results depend on current policy, demand variability, lead-time reliability, data quality, constraints, and service commitments. We establish a baseline and model proposed policy changes before updating planning parameters.