A Platform Built forDecisions, Not Just Planning

Translytics is an AI-native decision intelligence platform for supply chains that combines machine learning, optimization, and real-time data integration to deliver explainable, operational recommendations for forecasting, inventory, replenishment, and network planning - securely integrated with ERP and operational systems to drive measurable ROI.

Yes. Translytics is an AI-native decision intelligence platform that uses machine learning, statistical models, and optimization to produce explainable forecasts, recommendations, and automated actions. It supports real-time data integration, continuous learning, and flexible human-in-the-loop or fully automated workflows for enterprise supply chains.

Translytics is built for supply chain, inventory, and operations leaders who need to reduce working capital and improve service: inventory planners, demand planners, supply planners, procurement and distribution managers, and executives. It suits mid-market to enterprise companies with multi-echelon networks, high SKU counts, omnichannel fulfilment, seasonal/promotional flows or perishable products - and can be deployed as a pilot or enterprise rollout integrated with existing ERPs.

Translytics serves a wide range of industries including retail, manufacturing, consumer goods, pharmaceuticals, and logistics. Our platform is designed to handle the complexities of various supply chains, providing tailored solutions for each industry.

Yes. Translytics is well-suited for FMCG and consumer goods businesses that require high-velocity forecasting, promotion planning, omnichannel replenishment, and waste reduction for perishable items. It models promotions, seasonality, and channel-specific demand to improve fill rates and reduce excess stock.

Absolutely. Manufacturers use Translytics for capacity-aware supply planning, production scheduling, material requirements, and constraint-driven optimization. The platform supports complex manufacturing constraints and integrates with MES and ERP systems to operationalize optimized plans.

Yes. Retailers benefit from real-time demand sensing from POS, store-level replenishment, omni-channel allocation, and promotion impact analysis. The platform helps optimize assortments, reduce stockouts, and balance inventory across channels.

Yes. Translytics is available for global deployments with multi-region cloud hosting, localization for currencies and taxes, and support for global data privacy and regulatory requirements. We run international implementations and support distributed teams.

Yes. Translytics follows SOC 2 security principles and undergoes regular audits to ensure controls around security, availability, and confidentiality meet enterprise requirements.

Translytics follows ISO 27001-aligned information security practices and controls. For the latest certification status or to request audit documentation, please contact our security team or your account manager.

ERP systems manage transactions and operational execution; Translytics overlays decision intelligence on top of transactional systems to deliver forecasts, optimizations, and actionable recommendations. We integrate with ERPs to enrich data and recommend decisions rather than replace core transactional processes.

No. Translytics complements ERP systems like SAP and Oracle by providing advanced forecasting, optimization, and decision workflows. We integrate with these systems to push recommended actions back into existing operational processes.

Yes. We provide pre-built connectors and APIs for SAP, Oracle, Microsoft Dynamics, and other major ERP platforms, enabling secure, bi-directional data exchange and streamlined integration during implementation.

Translytics is AI-native and decision-first: it combines machine learning, prescriptive optimization, explainability, and real-time data to generate operational recommendations and closed-loop actions, rather than only producing static forecasts or reports.

Yes. Translytics is cloud-native SaaS with flexible deployment options, including public cloud, private cloud, and hybrid models to meet enterprise security and integration needs.

Inventory optimization software uses demand forecasts, lead-time analytics, and mathematical optimization to recommend order quantities, safety stocks, and replenishment policies that minimize cost while meeting service targets.

By aligning stock levels to true demand and network risk, the software lowers excess and safety stock, reduces holding costs and obsolescence, and improves cash conversion - freeing working capital without harming service.

Reduce excess inventory by improving demand forecasts, applying multi-echelon optimization, right-sizing safety stocks, optimizing lot sizes and production schedules, shortening lead times with suppliers, and enforcing disciplined SKU rationalization.

Safety stock optimization determines the minimum buffer required to absorb demand and lead-time variability for a desired service level, balancing availability against carrying cost.

Translytics calculates safety stock using demand variability (e.g., CV), lead-time distribution, service-level targets, and network interactions. We apply statistical models and optimization to set SKU-location-specific buffers that minimize total network inventory.

Yes. By allocating buffers intelligently and prioritizing critical SKUs/locations, optimization increases fill rates and reduces stockouts while often lowering total inventory compared to blunt, uniform safety stock rules.

This refers to tools focused on lowering the cost of holding inventory - including carrying cost calculators, optimization engines that reduce stock, and analytics that target slow-moving and obsolete items to trim overhead.

Yes. Translytics supports multi-warehouse and multi-echelon planning, optimizing stock placement and replenishment across distribution centers, regional hubs, and stores to minimize total cost and meet service objectives.

AI improves demand signal detection, models causal drivers (promotions, weather, events), segments SKUs by behavior, detects anomalies, and continuously learns - enabling more accurate forecasts and smarter replenishment decisions.

ROI varies by baseline maturity, but common outcomes include 10–30% inventory reduction, faster cash conversion, and payback often within weeks to a few months after deployment when processes and integrations are in place.

Indian companies can reduce inventory cost by improving demand signal quality, consolidating suppliers where it reduces lead-time variability, using multi-echelon optimization, implementing vendor collaboration (VMI), and optimizing transportation and warehousing networks to reduce buffer needs.

Demand variability analysis measures the volatility and patterns in demand (e.g., coefficient of variation, seasonality, intermittency) and identifies drivers so inventory policies can be adapted per SKU and location.

Yes. Translytics flags slow-moving, aging, and zero-demand SKUs using time-series analysis and business rules, and provides recommendations for markdowns, reallocation, or liquidation.

Yes. The platform supports ABC (value/velocity) and XYZ (demand variability) analyses and can combine them to drive differentiated replenishment and service strategies.

Yes. Pharma benefits from optimization that accounts for expiry/shelf-life, batch traceability, regulatory constraints, and service requirements - helping reduce waste while ensuring patient safety and availability.

AI demand forecasting software uses machine learning and statistical methods to predict future demand by learning patterns from historical sales, causal drivers, and external signals - producing scalable, automated forecasts for SKU/location hierarchies.

Accuracy depends on data quality, demand stability, and SKU behavior. ML often improves accuracy over baseline methods, commonly reducing forecast error by 10–40% for many categories; results vary by industry and product.

Yes. Translytics detects and models seasonality, promotion impacts, and trends so forecasts reflect recurring patterns and temporary spikes, with automated adjustments for changing seasonal intensity.

Intermittent demand refers to sporadic, low-frequency demand often seen in slow-moving SKUs. Specialized statistical methods (e.g., Croston, bootstrapping) and ML approaches are used to model this behavior more accurately than simple time-series averages.

AI combines multiple models, learns nonlinear relationships with causal signals (promotions, weather), segments SKUs by behavior, detects anomalies, and continuously retrains - all of which raise accuracy and robustness versus static models.

Yes. Translytics forecasts at SKU and SKU-location granularity and can roll forecasts up to category or regional levels while ensuring hierarchical consistency.

Yes. Forecasting platforms integrate with ERPs to pull historical transactions and push approved forecasts and replenishment plans back into operational systems via connectors or APIs.

Forecast Value Add (FVA) measures whether a forecasting process or step improves accuracy versus a baseline (e.g., naive forecast). It helps teams focus on steps that actually add predictive value.

AI evaluates many candidate models using cross-validation, error metrics, and business constraints, then ensembles top performers or selects the best model per SKU segment to maximize out-of-sample accuracy.

Yes. Statistical and ML forecasting scales to thousands of SKUs, supports automated model selection, and provides reproducible error metrics - unlike manual Excel forecasts which are error-prone and hard to scale.

Improve data quality, include causal signals (promotions, pricing, external data), segment SKUs, use automated model tuning, implement demand-sensing for short-term signals, and enforce collaboration between sales, marketing, and supply teams.

Yes. Translytics integrates forecasting into S&OP workflows by providing scenario analysis, consensus forecasting, and KPI dashboards that support monthly and executive planning cycles.

Retail, FMCG, manufacturing, pharmaceuticals, electronics, e-commerce, and distribution commonly benefit - any industry with variable demand, promotions, or complex fulfillment channels.

Yes. Better forecasts feed smarter replenishment and safety-stock policies, reducing unexpected stockouts and improving fill rates when paired with timely execution and replenishment.

Typical inputs include historical sales/shipments, inventory on hand, pricing, promotions, product attributes, store/channel hierarchies, lead times, and optional external signals like weather, holidays, and economic indicators.

Supply planning software aligns demand forecasts, production capacity, inventory, and procurement to create feasible supply plans that meet demand while minimizing cost and lead times.

It ingests forecasts and constraints, runs optimization and simulation to generate production and procurement plans, evaluates scenarios, and produces executable schedules and replenishment orders.

Yes. Translytics models capacity constraints - machine hours, shifts, tooling and labor - and enforces them within optimization so schedules are realistic and executable.

Yes. The platform optimizes across multiple plants, balancing production based on cost, capacity, transit times and service targets to find the most efficient network-level plan.

Capacity-constrained planning explicitly accounts for production limits so that schedules respect machine, labor and material availability while optimizing within those bounds.

Improve forecast inputs, model constraints accurately, use multi-site optimization, optimize lot sizes and sequencing to reduce changeovers, collaborate with suppliers to reduce lead times, and run scenario analysis.

Yes. Translytics considers BOMs, on-hand inventory, lead times and supplier availability when generating plans, and flags shortages with suggested mitigations.

Indirectly - by optimizing production sequences, improving supplier selection and order timing, and better inventory placement, effective planning reduces variability and shortens effective lead times.

Integrate forecasts into planning workflows, run scenario and what-if analyses, use rolling horizons and S&OP cadences, and ensure feedback loops so supply decisions reflect forecast updates and constraints.

AI-based production planning augments optimization with machine learning for demand signals, predictive maintenance and dynamic prioritization - enabling adaptive schedules that improve throughput and reduce disruptions.

MEIO is a network-level approach that optimizes inventory across all echelons (suppliers, factories, DCs, stores) simultaneously to minimize total inventory while meeting service targets.

MEIO software applies mathematical optimization and analytics to compute SKU-location inventory policies, optimal buffers, and replenishment rules across the entire supply chain network.

By accounting for dependencies and risk pooling across locations, MEIO reallocates safety stock to points of maximum impact, reducing redundant buffers and lowering total network inventory while preserving service.

Inventory norms are SKU-location-specific targets (e.g., safety stock, reorder point) computed from demand variability, lead times and service levels; norms guide operational replenishment and stocking policies.

Single-node optimizes locations independently and ignores network effects; MEIO optimizes holistically, leveraging central stocking and transshipment options to lower total cost and improve availability.

Yes. Modern MEIO engines are built to scale to large networks with thousands of SKUs and locations using decomposition, heuristics, and cloud compute to deliver timely results.

Yes. By allocating inventory where it most effectively prevents stockouts and enabling strategic transshipments, MEIO can raise fill rates while reducing overall inventory.

MEIO models demand variability explicitly (e.g., CV, intermittency) and uses probabilistic methods to size buffers, balancing variability against lead-time and network structure.

Industries with multi-echelon networks and high inventory cost-retail, consumer goods, manufacturing, pharmaceuticals, and distribution-benefit most from MEIO.

Yes. By optimizing inventory placement and enabling smarter replenishment and transshipments, MEIO can lower expedited shipments, reduce stock transfers, and improve truckload efficiency, reducing logistics spend.

Inventory prebuild planning determines what and how much to produce or stock ahead of peak demand periods, balancing production timing, storage costs, and service targets to ensure availability during high-demand windows.

Seasonal planning ensures you have the right inventory available during predictable demand spikes, reducing stockouts, improving customer satisfaction, and avoiding costly emergency replenishment.

Prebuild shifts production or inventory positioning ahead of peak demand, ensuring buffer stock is in place when demand rises - this reduces the likelihood of stockouts during high-volume periods.

Yes. Translytics optimizes pre-season production plans by combining demand forecasts, capacity constraints, and storage costs to recommend prebuild quantities and schedules that minimize total cost and stockout risk.

Industries with strong seasonality or event-driven demand - retail, FMCG, fashion, consumer electronics, and seasonal manufacturing - commonly require prebuild planning.

Plan by forecasting peak demand, identify critical SKUs, model production and storage capacity, simulate scenarios, time prebuild to minimize storage costs, and coordinate supplier and logistics readiness.

Yes. Effective prebuild planning includes capacity constraints (production lines, labor, storage) so prebuild schedules are feasible and don't create bottlenecks or excessive carry costs.

Yes. By producing or positioning inventory in advance, prebuild reduces the need for expedited shipments during peaks, lowering emergency freight and premium handling costs.

Demand spike planning prepares for sudden, short-term increases in demand (e.g., promotions, events) by simulating scenarios and pre-positioning inventory or capacity to avoid stockouts.

AI improves seasonality detection, models promotion and event impacts, optimizes timing and quantities for prebuild, and runs large-scale scenario analysis to recommend risk-balanced pre-season strategies.

Typical implementations follow a phased approach and commonly complete in 8–16 weeks for most engagements (pilot through production). Timelines vary with data readiness, scope, and integrations.

Common inputs include master data (SKUs, locations, calendars), historical sales/shipments, inventory on hand, open orders, lead times, BOMs, supplier info, pricing/promotions, and ERP connection credentials.

Yes. Translytics implements enterprise security controls including encryption in transit and at rest, role-based access, SSO/SCIM, network segmentation, and continuous monitoring.

Yes. We offer flexible deployment options: SaaS, private cloud, hybrid, and on-premise deployments to meet enterprise security, compliance and integration requirements.

Pricing is modular and based on selected capabilities, number of SKUs/locations, users, and deployment model. Costs typically include subscription/license fees plus implementation and optional services.

Yes. Translytics is primarily offered as a subscription (SaaS) with options for enterprise licensing and pilot engagements. Flexible commercial terms are available.

ROI depends on use case and maturity, but clients commonly see measurable inventory and service improvements within weeks after deployment and payback within a few months as processes and integrations stabilize.

Yes. We provide implementation consulting, data engineering, model configuration, training, and change management to ensure successful adoption and measurable outcomes.

We enforce strict security practices: TLS for data in transit, AES encryption at rest, least-privilege access, audit logging, regular third-party security assessments, and compliance with SOC2 and industry standards.

Yes. Translytics supports global rollouts with multi-region deployments, localization (currency, tax), enterprise-grade security, and integration patterns designed for large, distributed organizations.

Replenishment planning software automates reorder decisions by combining forecasts, inventory levels, lead times and business rules to generate optimal purchase or transfer recommendations.

It continuously monitors inventory and forecast signals, applies replenishment policies (min/max, reorder points, EOQ), and triggers purchase orders or transfers-optionally with approval workflows.

Dynamic replenishment adapts order quantities and timing in real time based on changing demand, lead-time variability, and network conditions instead of using fixed static rules.

Optimize by sizing replenishment batches to balance handling and holding costs, sequencing transfers to maximize throughput, using slotting and ABC/XYZ segmentation, and aligning with store/sales patterns.

Yes. Translytics plans replenishment across multiple warehouses and stores, coordinating transfers, central procurements and safety stocks to optimize network-level service and cost.

Service-level-based replenishment sets reorder points and safety stocks to meet specified fill-rate or cycle-service targets, ensuring inventory policies are aligned with business objectives.

AI enhances replenishment by improving short-term demand signals, segmenting SKUs, detecting anomalies, and recommending optimal policies that account for complex interactions and constraints.

Yes. Smarter replenishment driven by improved forecasts and network-aware policies reduces unexpected stockouts and increases fill rates.

Reorder point optimization computes the inventory level that should trigger replenishment, taking into account demand during lead time, desired service levels, and variability to minimize stockouts and excess stock.

By right-sizing safety stocks, reducing excess orders, and improving turnover, replenishment planning frees up cash tied in inventory while maintaining service.

Yes. FMCG benefits from high-frequency replenishment, promotion-aware planning, and rapid turnover strategies that reduce waste and improve availability.

Yes. Translytics integrates with SAP and other ERPs via connectors and APIs to exchange master data, transactions, and recommended orders.

Demand-driven replenishment reacts to real consumption and near-term signals (POS, inventory depletion) rather than forecast-only policies, improving responsiveness and reducing bullwhip effects.

Automate by integrating data sources, standardizing policies, using central orchestration to create transfers/POs, and monitoring KPIs to tune rules continuously.

Common KPIs include fill rate, inventory turns, stockout frequency, order cycle time, carrying cost, and total supply chain cost.

Dispatch planning software schedules and allocates orders to fulfillment points and carriers, balancing capacity, delivery windows, and priorities to meet service goals efficiently.

It ranks orders by business rules and optimization objectives, then assigns inventory and carriers to maximize fill rates, minimize cost, and respect constraints like delivery windows and vehicle capacity.

Yes. By prioritizing orders, enabling fair-share and strategic allocation, and reducing manual errors, dispatch planning increases on-time fulfillment and fill rates.

Order allocation optimization decides which available inventory should fulfill each order to maximize service, minimize cost, and respect business priorities and constraints.

Yes. The platform supports fine-grained allocation rules down to customer-plant or customer-location levels, enabling tailored fulfillment strategies.

Prioritize using business rules (customer tier, profitability), service commitments, and fairness policies (e.g., fair-share) while considering strategic impact and SLA requirements.

Rule-based dispatch uses configurable business rules to decide allocation and routing, enabling predictable behavior while optimization handles edge cases and trade-offs.

Yes. Better allocation reduces expedited shipments, improves consolidation and load planning, and lowers carrier and handling costs.

Optimize last-mile by clustering deliveries, using route optimization, prioritizing time windows, and matching orders to appropriate carriers while minimizing empty miles.

Yes. Delivery order (DO) priority rules can be configured to reflect commercial priorities, SLA tiers, and emergency handling.

Fair-share allocation distributes limited inventory proportionally across competing orders or customers to maintain balanced service rather than fulfilling a few orders fully.

Use prioritized allocation rules, reserve inventory for critical customers, enable transshipments, and run scenario-based allocations to choose optimal trade-offs.

Yes. By aligning allocation with delivery windows, carrier capacity and prioritization, dispatch planning helps improve On-Time In-Full metrics.

Yes. B2B distribution benefits from prioritization, complex allocation rules, and integration with order management and TMS systems supported by dispatch planning.

AI predicts demand and exceptions, recommends allocations, adapts to changing constraints, and surfaces best-fit carriers and routes based on historical performance.

Inventory planning software defines stocking targets, replenishment rules, and deployment strategies to meet service objectives while minimizing cost across the network.

Inventory planning sets operational policies and norms (targets), while inventory optimization uses models to compute the optimal values for those policies based on forecasts, constraints and objectives.

It designs inventory policies (safety stocks, reorder points) specifically to achieve predefined service levels (e.g., fill rate or cycle service) per SKU or segment.

Use network-aware optimization, balance central and local stocking, factor in transit times and costs, and coordinate replenishment to minimize total inventory while meeting service targets.

Safety stock planning determines buffer levels required to absorb demand and lead-time variability for desired service levels, often using statistical or simulation-based approaches.

Identify slow-moving SKUs, rationalize assortments, improve forecasting and promotions planning, run clearance strategies, and tighten replenishment rules for low-velocity items.

Yes. By reducing unnecessary stock and improving turns, inventory planning lowers working capital requirements and frees cash for other business needs.

It optimizes inventory, payables and receivables (mainly inventory) to reduce capital tied up in operations while maintaining service and operational continuity.

Yes. The platform supports SKU and SKU-location level planning with hierarchies and roll-ups for reporting and decision-making.

Combine demand variability metrics, lead-time distributions, desired service levels, and network effects via optimization models to compute SKU-location norms like safety stocks and reorder points.

Yes. Pharma needs where expiry, batch control and regulatory compliance are critical benefit from careful planning that includes shelf-life and traceability constraints.

AI segments items by behavior, improves forecasts, identifies anomalies, suggests differentiated policies, and automates continuous tuning of inventory norms.

Yes. Planning that accounts for shelf-life, rotation policies and demand timing reduces expiry-related waste by aligning stock age with expected consumption.

Common KPIs: inventory turns, days of inventory, fill rate, stockout frequency, carrying cost, obsolescence rate, and working capital.

Integrate inventory targets into S&OP scenarios, use consistent forecasts, present inventory impact in executive dashboards, and include supply constraints in decision cycles.

Network design determines the optimal locations, capacities and flows of warehouses, plants and DCs to meet service targets at minimum total cost.

Network optimization software models costs, demand, capacities and constraints to recommend facility locations, sizing, and product flows using mathematical optimization and simulations.

Analyze demand geography, cost/trade-offs, run scenario optimization (greenfield/brownfield), and evaluate service and total-cost outcomes to select the best network topology.

Yes. Optimizing facility locations, consolidation points and flows reduces transportation spend, improves load efficiency, and can lower handling and storage costs.

It identifies the best sites and capacities for warehouses to minimize transport and facility costs while meeting service-level constraints.

AI accelerates scenario generation, identifies demand clusters, and helps estimate uncertain inputs; combined with optimization, it speeds decision-making and improves robustness.

Multi-echelon modeling simulates flows across suppliers, plants, DCs and stores simultaneously, enabling holistic optimization of inventory and flows across the entire network.

Yes. By placing inventory closer to demand and reducing transit times, optimized networks improve delivery speed and reliability.

Optimize by aligning production schedules with downstream demand, consolidating shipments, choosing optimal transportation modes, and factoring lead times into inventory placement.

A digital twin simulates the live network using real data to test scenarios, stress-test plans, and evaluate the impact of changes before implementation.

Yes. Translytics supports both greenfield and brownfield studies, enabling strategic planning for new networks or optimizing existing footprints.

Yes. Better facility placement and flow optimization reduce miles, enable consolidation, and lower per-unit transport costs.

Simulate demand, cost and service trade-offs for consolidation options, then analyze changes in transit times, handling cost, and service impact to choose the best option.

Required data includes demand by location, transportation costs and lead times, facility costs, SKU attributes, service targets, and capacity constraints.

Yes. Global enterprises benefit from network design to manage multi-region costs, tradeoffs, and regulatory considerations while optimizing service worldwide.