Lima connects enterprise data across 40+ source types, applies supply chain domain intelligence through a trained semantic layer, and delivers autonomous decisions — from demand sensing to last-mile delivery. One platform. Every function.
Supply chain directors, planners, procurement leads, and finance teams ask questions in plain English. ChatLima translates intent into federated queries across every connected source system, enforces role-based and attribute-based access controls automatically, and returns structured, cited answers — with the underlying SQL visible on demand.
Cognitive Pipeline — When a user asks a question, ChatLima's NLP layer identifies entities (customers, OTIF, time period), maps them against Lima's supply chain ontology, and resolves semantic relationships across systems. "Customer" in SAP, "account" in CRM, and "ship-to" in WMS resolve to the same entity — automatically, based on the metadata dictionary of registered data sources.
Contextual Intelligence — The cognitive layer is trained on supply chain terminology specific to each industry vertical. It understands the difference between "lead time" in procurement and "lead time" in logistics, and queries the correct data accordingly. As new data sources are registered, the knowledge index updates automatically — expanding ChatLima's queryable domain without manual retraining.
Function Calling — Beyond data retrieval, ChatLima routes questions to Lima's function handler. Pre-built functions (days_inventory_outstanding, inventory_turns, backorder_rate) deliver consistent, auditable calculations. What-if functions integrate with Kinaxis, Blue Yonder, and o9 — a planner asking "What if we lose Supplier X?" triggers project_sdp_plan(), shortage analysis, and finished goods impact mapping through a single conversational query.
Bi-directional Execution — Accepted recommendations write back to source systems. An approved reallocation, an expedited PO, a rerouting decision — ChatLima executes within configurable autonomy boundaries. Every action is logged, auditable, and reversible.
Lima sits as the data intelligence layer between source systems and Advanced Planning. It handles integration, quality enforcement, MDM, and transformation — so the APS focuses exclusively on planning. Kinaxis-recommended best practice, proven at 8 enterprise deployments.
Part Masters · BOMs · Inventory
WIP · Sales Orders · POs
Historical · Calendars · Routing
Full UI visibility for IT, Data Owners & Process Owners
Spark-powered · 14GB in 30min · Elastic architecture
RR Parts · Sites · BOMs · PartSource
Demand · Supply · Constraints
Planned Orders · Pegging · WIP
90% of Kinaxis customers say data integration complexity was underestimated, leading to budget overruns and delayed go-lives. 80% have no optimal planning data management framework tribal knowledge lives in spreadsheets and emails. With legacy methods, up to 70% of APS implementation cost goes to data integration and transformation, and forcing that work into the planning system degrades its performance and creates a brittle architecture.
Lima inverts this as an intermediate intelligence layer. Source decoupling means upgrading SAP, migrating a WMS, or adding a new ERP division doesn't touch the APS integration — Lima absorbs the change. Integrated planning MDM captures Type 1 data (master), Type 2 (transaction), and critically Type 3 (planning parameters, hierarchies, tribal knowledge) — the category where most implementations stumble. Performance isolation on Spark-powered architecture processes 14 GB in 30 minutes with 32 cores; planning system resources stay reserved for planning workloads.
8 successful E2E deployments across enterprises from $5B to $75B revenue, leveraging 20 years of Kinaxis deployment experience and 24 years of supply chain solution delivery. Pre-built adapters, RR data model framework, and pre-configured transformations accelerate deployment from ~40 weeks to ~10 weeks per module.
Centralized governance of customer, product, supplier, and location master data — with golden record management, AI-powered classification, cross-system synchronization, and quality enforcement on the same federated architecture.
Clean data in → Reliable decisions out · Every intelligence layer depends on governed master data
100% UI-driven · Peta-scale · GDPR-compliant · SSO/LDAP · Column-level security
AI-Powered Stewardship — When the same component supplier is coded as three different entities across SAP, the procurement platform, and the quality system, Lima's ML-based matching engine resolves them through name resolution, pattern discovery, similarity measurement, and attribute matching with configurable confidence thresholds and human review for edge cases. AI-enabled supplier categorization classifies records automatically across the ontology.
Golden Record Management — Consolidation logic merges records from multiple operational systems into a single authoritative golden record. Survivorship rules determine which source wins per attribute — freshest address from CRM, validated tax ID from compliance, most complete financials from ERP. Conflict resolution workflows route discrepancies to domain stewards with full context and audit trail.
Branch & Version Control — Test master data changes in isolation before committing to production. Create a branch, modify hierarchies or mapping rules, evaluate downstream impact through what-if analysis, and either merge to production or discard — without touching live operations. Critical for planning-sensitive master data where a misclassified product hierarchy cascades through demand planning, supply allocation, and financial reporting.
The Downstream Impact — Every intelligence layer depends on this one. ChatLima returns accurate answers because the ontology resolves entities through governed master data. Demand forecasting produces reliable projections because product hierarchies are clean and consistent. Supplier risk scoring delivers actionable signals because vendor records are deduplicated and harmonized. The platform handles hundreds of millions of records on distributed big-data architecture (Spark, HDFS, Kubernetes) with no performance degradation.
Lima's BPM module orchestrates cross-functional, multi-enterprise collaboration workflows directly on federated data. SmartApps — intelligent process applications — are configured and published on the fly, turning data insights into coordinated action across teams, organizations, and partner ecosystems.
Processes span across organizations & teams · Contextual collaboration tied to data, not email threads
SmartApps — Intelligent Process Applications — Lima's BPM module enables business teams to configure and publish collaborative process applications on the fly — directly on federated data, with no external development. Define business process mapping flows to orchestrate solution-centric team collaboration in multi-enterprise scenarios. Supply chain control towers, S&OP workflows, supplier collaboration portals, what-if scenario workbenches, and survey-driven feedback loops are all built as SmartApps through the same drag-and-drop, no-code framework.
Contextual Collaboration — Traditional collaboration tools disconnect discussions from the data they're about. Lima ties collaboration directly to the operational context — projects, contracts, exception records, planning decisions. Workflow and communication all take place within the same platform, on the same data. Collaboration types include approvals, assignments, escalations, broadcasts, notifications, FYI, blog/comments with sentiment sharing, multi-party signatures, and handshakes — all configurable per process.
Multi-Level Approval Hierarchies — Define functional hierarchies with approval, denial, and flow-up rules at each level. Rules are expressed using variables and calculations based on screen, app, global, or system variables — so approval routing adapts dynamically to the data being processed, not just a static org chart. Each level has its own UI screen, time limits, escalation paths, and delegation options. Series and parallel approval flows are supported, with automatic escalation when SLA windows breach.
Data-Driven SmartApps — In data-driven SmartApps, the starting point is existing operational data. The engine identifies records matching defined criteria, attaches users and app flow information, caches record lists per user, and launches isolated process instances with unique IDs — each tracked from start to finish with full audit logging. This means an exception detected in the Control Tower can automatically spawn a SmartApp workflow with the right data, right users, and right approval chain — without manual configuration for each instance.
Performance Measurement & Governance — Built-in KPI tracking measures process performance — cycle times, approval rates, escalation frequency, SLA compliance. Process flows span across organizations and teams with full role-based access control, multi-device support, SSO integration, and PowerBI-compatible reporting and analytics. Every action, approval, denial, and escalation is logged centrally, providing complete process auditability for compliance and continuous improvement.
Lima's Agentic AI platform deploys autonomous and hybrid decision agents across end-to-end supply chain functions. Each follows the four-stage intelligence spectrum — Descriptive → Pre-emptive → Prescriptive → Directive — with configurable autonomy boundaries defining where the system acts independently and where humans stay in the loop.
Continuously monitors master data completeness across CM partners. When a finished good is missing yield data from a CM line, the agent detects the gap, assesses MRP impact, and triggers an exception workflow. Maintains historical data quality scoring to enable data SLA negotiations.
Calculates excess exposure at every inventory node by combining live stock (WMS), demand (APS), and lifecycle signals. Scores SKUs on a liability risk matrix (high-value/low-demand) and tiers recommendations by financial impact for reallocation or returns.
Touchless forecasting with continuous self-correction. Compares projections against actuals and adjusts bias. Tests alternative ML ensembles and statistical models when errors exceed thresholds, selecting the lowest-error approach per SKU-channel-geography.
Runs multi-factor allocation across live inventory and inbound pipelines when supply falls short. Uses what-if functions to model constrained scenarios, prioritizing by customer tier, margin, and contractual penalties with automatic write-back.
Every use case follows the same intelligence progression — systematic escalation from monitoring to autonomous action with configurable human involvement at every decision point.
Cognitive model evaluates conditions continuously against live data. Rules expressed in natural language through the SCM ontology.
"Monitor inventory health across regional DCs. Alert if any drops below 2 weeks coverage."
Alerts trigger when approaching thresholds — before problems cascade. Entity relationships surface related impacts automatically.
"DC-West approaching threshold. Breach projected in 4 days. Related customer commitments identified."
Resolve framework recommends actions with financial context. Within confidence limits, execute automatically. Outside limits, present options.
"Recommend expedited transfer from DC-Central. Alternative: DC-North. Cost: $45K vs $62K."
Execute agreed resolution through bi-directional APIs. Synchronize across ERP and APS. Every resolution feeds back into the learning loop.
"Transfer initiated. ERP updated. Customer commitments reconfirmed. Resolution logged."
Beyond the core capabilities above, Lima's intelligence layer powers operational use cases across every supply chain function — each drawing on the same federated data, ontology, and agent framework.
Unifies portfolio review, demand, supply, reconciliation, and management review into a single intelligence-driven workflow. AI-driven demand sensing flows directly into supply feasibility. Scenario planning auto-generates alternatives prioritizing cost, margin, and key customers.
Autonomous transportation network selection — choosing between hub-and-spoke and direct shipment, consolidating orders for container utilization, and rerouting in-transit goods based on evolving demand. Heuristics-based allocation with lead time comparison.
Visibility into storage, inbound, and outbound projections against constraints. Weekly-to-daily conversion using lane-level trends. Recommendations tiered: short-term (relax soft constraints), mid-term (flexible capacity), and long-term (redesign and CAPEX).
Early warning systems detect patterns correlating with future disruptions — financial health, geopolitical signals, and delivery deterioration. Risk scoring across configurable dimensions generates mitigation recommendations before disruptions materialize.
Unifies inventory, production, logistics capacity, and customer commitments into a single service layer. OTIF analysis runs continuously identifying root causes. Customer prioritization considers value segmentation and account strategy.
Cost-to-serve analytics across customer segments. Working capital optimization tracking inventory turns and cash conversion. Budget variance analysis identifying areas requiring immediate attention and ROI measurement of supply chain investments.
40+ connectors, zero-copy access. Trino-powered federation pushes predicates into each source. 80 GB/day processed, 0 bytes moved. Traditional ETL equivalent: eliminated.
Industry 4.0 semantic layer. Entity resolution across system boundaries. Business hierarchies, relationships, and context mapped natively. Enables cross-system queries and intelligence.
NL to SQL translation, conversational follow-up, schema-aware suggestions, access-controlled results. Powers both autonomous agents and interactive access for every stakeholder.
Rapid solution development on the same fabric. Configurable BPM workflows, multi-enterprise collaboration, Spark-powered data science pipelines, and 150+ transformation operators. ML models publish as platform-wide services.
Every use case runs on a single platform — one data fabric, one semantic layer, one intelligence engine. Request a demonstration configured for your supply chain operations.
