When demand exceeds supply, allocation decisions determine who gets product and who waits. Most manufacturers make these decisions in spreadsheets, email threads, or by whoever gets to the warehouse manager first. We replace that with rules-based allocation logic that\u2019s governed, auditable, and integrated with your ERP. Austin is where engineering culture meets business scale. Tesla Gigafactory, Samsung's $17B chip fab, a booming tech sector, and one of the fastest-growing business markets in the country. Austin businesses demand AI systems built to engineering standards — not chatbots, not SaaS features. Custom AI agents that operate inside complex business processes, trained on your data, and owned by you permanently.
Austin businesses are tech-native and engineering-literate. They recognize the difference between an AI chatbot bolted onto a SaaS platform and a custom AI system built for their specific operation.
Define allocation rules by customer tier, channel priority, order type, and product category. Rules execute automatically when inventory is received or demand changes \u2014 no manual intervention.
Allocate across warehouses based on proximity, stock levels, shipping cost, and fulfillment capacity. The engine considers all locations simultaneously, not one at a time.
Prioritize allocation by channel \u2014 contract orders before spot sales, key accounts before general distribution, backorders before new orders. Business priorities enforced systematically.
Real-time ATP that reflects current allocations, not just raw inventory counts. Sales and portal systems show what\u2019s actually available to promise, not what\u2019s in the warehouse.
Full visibility into what\u2019s allocated to whom, why, and when. Every allocation decision is logged with the rule that triggered it \u2014 no more "who took my inventory?" conversations.
Allocation engine reads inventory from and writes allocation commitments back to your ERP and WMS. Sales, operations, and warehouse all see the same allocated state.
Document current allocation practices, customer tiers, channel priorities, and pain points. Identify where allocation decisions are being made and what rules (if any) govern them.
Design the allocation rules hierarchy \u2014 tier priorities, channel rules, product-level overrides, and exception handling. Get business stakeholder sign-off on the priority logic before building.
Build the allocation engine and integrate with your ERP inventory module and WMS. Real-time inventory feeds trigger allocation recalculation as stock moves.
Test allocation scenarios against historical demand patterns. Validate that the rules produce the expected outcomes for normal, constrained, and peak-demand situations.
Deploy with allocation dashboards and audit reporting. Tune rules based on real-world results and evolving business priorities.
Inventory Allocation Engine for Austin technology & saas operations - configured around local workflows, data ownership, and implementation governance.
Inventory Allocation Engine for Austin clean energy operations - configured around local workflows, data ownership, and implementation governance.
Inventory Allocation Engine for Austin healthcare & life sciences operations - configured around local workflows, data ownership, and implementation governance.
Inventory Allocation Engine for Austin semiconductor & electronics operations - configured around local workflows, data ownership, and implementation governance.
Inventory Allocation Engine for Austin construction & development operations - configured around local workflows, data ownership, and implementation governance.
Inventory Allocation Engine for Austin distribution & logistics operations - configured around local workflows, data ownership, and implementation governance.
Yes. The engine handles mixed-mode allocation \u2014 reserving MTO production output for specific orders while allocating MTS inventory by tier and channel rules. Both modes coexist in the same rules framework.
The allocation rules execute in priority order \u2014 highest-tier customers and contractual commitments first, then down the priority stack. Shortfall is visible immediately so sales can manage customer expectations proactively.
It augments it. Most ERP allocation modules handle basic FIFO or manual allocation. This adds tiered priority logic, multi-warehouse optimization, and real-time ATP that the native module doesn\u2019t support.
Typical results: 30\u201350% reduction in allocation-related order changes, 15\u201325% improvement in on-time delivery for priority customers, and elimination of the manual spreadsheet allocation process entirely.
Most manufacturers are still running workflows that require a person to touch every exception, every order, every routing decision. AI agents eliminate that bottleneck — not by replacing your people, but by handling the work that was always below their pay grade.
Most manufacturers forecast demand with spreadsheets, gut feel, and last year's numbers adjusted by 5%. ML models trained on your actual order history, seasonality patterns, and market signals replace guesswork with predictions your planning team can act on.
Odoo Maintenance captures work orders, failure reasons, repair times, and equipment history. We build AI models on top of that data to identify failure patterns and recommend maintenance windows before breakdowns occur — no new hardware, no IoT infrastructure required.
Odoo Quality captures inspection results, non-conformances, scrap reasons, and lot traceability across every production order. We build AI models on top of that data to surface defect patterns, predict quality risk, and trigger alerts before scrap accumulates — no cameras, no hardware.
Most manufacturers price by cost-plus formula or by whatever the sales rep negotiated last time. AI pricing models factor in material costs, competitive positioning, customer segment, order size, inventory position, and market conditions — governed by business rules so every price stays within approved boundaries.
When an order hits your system, someone decides which warehouse ships it — usually based on habit, proximity, or whoever answered the phone. AI order routing makes that decision in real time, optimizing across inventory availability, shipping cost, delivery speed, and warehouse workload.
Manufacturers still process thousands of POs, invoices, RFQs, spec sheets, and BOLs manually — reading PDFs, retyping data into the ERP, and fixing the errors that come with it. Document intelligence extracts structured data from unstructured documents automatically, with validation rules that catch errors before they enter your systems.
Your dealers call or email to check stock before placing orders because they can't see what's available. We give them live ATP visibility across all your warehouses — available, allocated, in-transit, and expected replenishment dates — straight from your ERP and WMS.
We govern cloud migration in phases — every dependency mapped, every workload sequenced, every cutover window defined. Zero-downtime migration for manufacturers who can't afford an outage.
Most manufacturing AI projects die in the pilot phase. We deploy AI that integrates into your actual workflows -- demand forecasting, predictive maintenance, pricing optimization, and intelligent routing -- governed by operational data contracts.
Your demand planning process runs on last year\u2019s sales adjusted by a gut-feel percentage. ML models trained on your actual order history, seasonal patterns, and market signals produce forecasts that are measurably more accurate \u2014 and they improve automatically as more data accumulates.
Your legacy system holds critical data that modern applications need -- but it has no APIs, no webhooks, and no modern integration points. We build a REST/GraphQL API layer on top of your legacy system so new applications can access data without touching the core.
Metrotechs starts with the operating questions: which records are trusted, which workflows are manual, which systems own each decision, and where AI can safely improve throughput.
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