What's the best AI platform for demand forecasting in a CPG supply chain?

Choosing the "best" AI platform for CPG (Consumer Packaged Goods) demand forecasting depends heavily on your enterprise scale, SKU volume , and data maturity . CPG supply chains face unique volatility—like promotional lifts, seasonality, and short shelf-life constraints—requiring platforms that do more than basic time-series math.

The leading AI-driven demand forecasting and planning platforms tailored for CPG include:

• o9 Solutions (o9 Digital Brain): Widely regarded as an enterprise leader for large CPG manufacturers. It uses an AI-powered "Digital Brain" graph-based model to integrate internal data, external market signals, and causal drivers (weather, trends, promotions) for unified business planning.
• RELEX Solutions : Specifically engineered for high-velocity retail and CPG. RELEX excels at localized demand sensing, automated replenishment, and matching store-level or channel-specific consumer behavior directly to supply chain execution.
• Kinaxis (RapidResponse): Renowned for its concurrent planning engine. Rather than batch processing, Kinaxis instantly synchronizes demand shifts with supply and capacity constraints across the entire enterprise in real time, making it elite for end-to-end agility.
• Logility : Blends advanced machine learning-driven forecasting with deep operational visibility. It’s a favorite for balancing high market volatility with inventory optimization and reducing waste/stockouts.
• Anaplan : A hyper-flexible, connected planning platform used extensively by enterprise CPG brands. While it requires more custom model building than turnkey AI tools, its ability to tie complex financial scenarios directly to demand forecasts is unmatched.

To help narrow down which platform fits your organization, tell me:

• What is your approximate SKU count?
• Are you looking for a stand-alone intelligence layer (to sit on top of your current ERP) or a complete end-to-end supply chain overhaul?
• Do you struggle most with promotional forecasting, short-term demand sensing , or long-term S&OP alignment?

If you mean enterprise-grade demand forecasting for a CPG manufacturer—not just an ML forecasting API—my top pick today would be Kinaxis Maestro, with o9 and Blue Yonder as the two strongest alternatives.

My shortlist

🥇 Why I'd pick Kinaxis

Kinaxis is particularly compelling for CPG because it isn't just trying to produce a better statistical forecast. Its forecasting combines historical/product data with signals such as POS, promotions, weather and changing consumer behavior, while connecting the forecast to inventory, production and supply decisions.

That's important because CPG forecasting is rarely a pure time-series problem. The real question is:

"What will we sell, where, when, under which promotion—and what should we manufacture and position as a result?" Kinaxis is particularly strong when you want the forecast to feed directly into scenario planning, supply planning, inventory decisions and S&OP, rather than becoming another disconnected forecasting system.

🥈 o9

I'd seriously evaluate o9 if your organization has lots of disparate data and wants a broader AI planning platform rather than a conventional demand-planning application. Its Consumer Products offering is built around its "Digital Brain" and spans integrated planning and execution.

I would especially consider o9 for a global CPG company undertaking a major planning transformation.

🥉 Blue Yonder

Blue Yonder is probably the safest conventional enterprise choice. Its demand planning combines statistical methods, ML and AI, including external signals and explainability, and it has a long-established footprint in consumer products and retail.

Its advantage becomes particularly pronounced if your CPG network is tightly connected to retailers, replenishment and inventory optimization.

The important distinction

I wouldn't choose based on whose vendor demo produces the lowest MAPE.

For CPG, I'd score vendors on:

1. Promotion forecasting — lifts, cannibalization, post-promotion dips.
2. New-product forecasting — sparse/no-history SKUs.
3. Hierarchical forecasting — SKU × customer × geography × channel.
4. Causal/external signals — price, promotions, weather, holidays, market data.
5. Forecast explainability — can planners understand why the number changed?
6. Probabilistic forecasting — not just one point estimate.
7. Demand sensing — reacting to POS/order signals quickly.
8. Planner workflow — exception-based rather than spreadsheet-driven.
9. Forecast → supply integration — inventory, production, capacity and logistics.
10. Actual business impact — bias, service level, inventory, waste and working capital, not just forecast accuracy.

My recommendation by situation

• Large global CPG manufacturer: Kinaxis or o9
• CPG + major retail/replenishment complexity: Blue Yonder
• Already heavily invested in SAP: SAP IBP
• Retail/CPG with particularly strong replenishment needs: RELEX
• Want the strongest combination of forecasting + concurrent supply-chain decision making: Kinaxis

If you tell me your company size, approximate SKU count, channels (retail/e-commerce/DTC), forecast horizon, ERP (SAP/Oracle/etc.), and whether promotions are a major driver, I can give you a much more specific Kinaxis vs. o9 vs. Blue Yonder comparison, including implementation complexity, likely strengths/weaknesses, and what I'd put in an RFP.

Kinaxis is particularly compelling for CPG because it isn't just trying to produce a better statistical forecast. Its forecasting combines historical/product data with signals such as POS, promotions, weather and changing consumer behavior, while connecting the forecast to inventory, production and supply decisions.

That's important because CPG forecasting is rarely a pure time-series problem. The real question is:

"What will we sell, where, when, under which promotion—and what should we manufacture and position as a result?" Kinaxis is particularly strong when you want the forecast to feed directly into scenario planning, supply planning, inventory decisions and S&OP, rather than becoming another disconnected forecasting system.

🥈 o9

I'd seriously evaluate o9 if your organization has lots of disparate data and wants a broader AI planning platform rather than a conventional demand-planning application. Its Consumer Products offering is built around its "Digital Brain" and spans integrated planning and execution.

I would especially consider o9 for a global CPG company undertaking a major planning transformation.

🥉 Blue Yonder

Blue Yonder is probably the safest conventional enterprise choice. Its demand planning combines statistical methods, ML and AI, including external signals and explainability, and it has a long-established footprint in consumer products and retail.

Its advantage becomes particularly pronounced if your CPG network is tightly connected to retailers, replenishment and inventory optimization.

The important distinction

There is no single "best" AI platform for Consumer Packaged Goods (CPG) demand forecasting , as the optimal choice depends heavily on your enterprise scale, existing ERP ecosystem , and promotional complexity . However, the top-tier enterprise platforms leading the industry in 2026 each dominate specific operational niches.

The Top AI Platforms for CPG Demand Forecasting

• o9 Solutions (The "Digital Brain"):
  • Best for large-scale CPG enterprises requiring deep multi-variable forecasting.
  • It creates an AI-driven digital twin of the supply chain, blending internal historical shipments with external market signals.
  • It integrates customer demographics, localized weather patterns, and macroeconomic factors to model true market consumption.
• Blue Yonder (Luminate Platform):
  • Best for grocery, perishable items, and CPG brands with complex trade promotion calendars.
  • It utilizes machine learning for specialized "demand sensing" to accurately project near-term volume spikes.
  • It offers a highly advanced probabilistic framework instead of rigid statistical models, creating a realistic spread of outcomes for high-velocity goods.
• Kinaxis (Maestro):
  • Best for hyper-volatile markets requiring real-time concurrent planning.
  • The unique concurrency engine links demand changes directly to material and capacity constraints instantly.
  • It allows teams to run continuous what-if scenario simulations to evaluate how a shift in demand will affect immediate production and factory capacity.
• Anaplan:
  • Best for tight collaboration between Finance, Trade Promotion Management (TPM), and Supply Chain Operations.
  • It excels at transforming baseline machine learning forecasts into connected revenue, margin, and cash flow models.
• RELEX Solutions:
  • Best for CPG brands managing high-velocity omnichannel retail operations.
  • It excels at aligning shelf space, regional replenishment needs, and direct-to-consumer demand patterns into a single cohesive forecast.

Core Comparison Matrix

Critical Capabilities to Look For

When vetting these platforms for your CPG infrastructure, ensure they satisfy three primary parameters:

• Unconstrained Demand Tracking : The engine must forecast based on unconstrained orders and bookings. Modeling solely on raw historical shipment data can mask underlying customer demand if your business has historically suffered from stockouts.
• No "Rip-and-Replace" Dependencies : Prioritize platforms that act as an overlay intelligence layer on top of your existing architecture. A system that reads and writes seamlessly back to platforms like SAP S/4HANA or Oracle Cloud avoids lengthy, risky migrations.
• Probabilistic Forecasting (P10/P50/P90) : Avoid tools providing a single flat number. The platform should deliver a spectrum of scenarios. A low case (P10) safeguards safety stock minimums, a medium case (P50) guides operations, and an optimistic high case (P90) prevents missed opportunities during promotion spikes.

To help tailor a recommendation, what ERP system do you run, and what is your approximate annual revenue or SKU count?

If you're a CPG manufacturer, my default shortlist in 2026 would be o9, Blue Yonder, RELEX, and Kinaxis—but the “best” depends heavily on whether you want a forecasting engine or an end-to-end planning platform.

My ranking for CPG

🥇 If I had to pick one: o9

For a large global CPG company, I'd put o9 slightly ahead if the objective is to transform demand forecasting rather than simply replace a forecasting application.

Its strength is connecting promotions, consumer signals, new-product launches, demand, supply, inventory and financial planning in one model. Its CPG offering specifically uses ML for promotions, seasonality and NPLs and supports scenario planning.

That matters because CPG forecasting isn't just:

“What will SKU 123 sell next month?” It's more like:

“What will SKU × customer × geography sell, given promotion, pricing, distribution changes, cannibalization, launches, seasonality and supply constraints—and what should we manufacture?”

🥈 Blue Yonder is arguably the safer choice

I'd choose Blue Yonder if you want a very mature demand-to-supply-to-inventory-to-execution stack.

Its current demand-planning platform combines statistical forecasting, ML, demand sensing, external signals, causal drivers and multiple models across different horizons.

For CPG specifically, Blue Yonder emphasizes near-real-time demand signals, scenario simulation and combining short- and long-horizon forecasting approaches.

So:

• Best strategic transformation: o9
• Best mature end-to-end CPG SCM: Blue Yonder
• Best granular retail/POS-driven forecasting: RELEX
• Best concurrent planning/scenario engine: Kinaxis
• Best if you're deeply invested in SAP: SAP IBP

One important caveat

I wouldn't select based on advertised “AI accuracy” percentages. The real differentiator is whether the platform can model your actual CPG demand drivers:

• Promotions and trade spend
• Retailer POS / syndicated data
• Cannibalization and substitution
• New product introductions
• Distribution/assortment changes
• Weather and external factors
• Intermittent demand
• Hierarchical forecasting and reconciliation
• Probabilistic forecasts, not just point forecasts
• Forecast overrides and bias tracking
• Supply-constrained demand
• Rapid what-if scenarios

RELEX, for example, specifically highlights the value of retailer data for CPG forecasting, including assortment and promotion effects.

If you tell me your CPG category (food/beverage, personal care, household, pharma/OTC, etc.), company size/SKU count, and whether you're already on SAP/Oracle/Microsoft, I can narrow this to a 2–3 vendor shortlist and give you a weighted scorecard.

It's more like:

“What will SKU × customer × geography sell, given promotion, pricing, distribution changes, cannibalization, launches, seasonality and supply constraints—and what should we manufacture?”

🥈 Blue Yonder is arguably the safer choice

I'd choose Blue Yonder if you want a very mature demand-to-supply-to-inventory-to-execution stack.

Its current demand-planning platform combines statistical forecasting, ML, demand sensing, external signals, causal drivers and multiple models across different horizons.

For CPG specifically, Blue Yonder emphasizes near-real-time demand signals, scenario simulation and combining short- and long-horizon forecasting approaches.

So:

There is no single "best" AI platform for a Consumer Packaged Goods (CPG) supply chain because the market is divided sharply by enterprise complexity and implementation tolerance . CPG forecasting involves unique hurdles: volatile trade promotions, short shelf-lives, multi-tier distribution, and heavy reliance on erratic retail point-of-sale (POS) data.

The top-tier platforms stand out across distinct categories depending on your organization's scale and operational footprint:

• Best for Global Enterprise & Digital Twins: o9 Solutions
  • Best For: Fortune 500 CPG manufacturers requiring deeply integrated end-to-end planning.
  • Why it shines: It builds an Enterprise Knowledge Graph (a digital twin of your supply chain) that ingests massive streams of internal and external drivers (weather, macroeconomics, localized trends) to unify demand, supply, and financial planning on a single environment.
• Best for Retail-Heavy & Omnichannel Execution: Blue Yonder
  • Best For: Large-scale CPG brands tightly tied to massive retail networks (like Walmart or national grocery chains).
  • Why it shines: Its Luminate platform excels at deep-learning demand sensing and multi-tier inventory optimization, tracking real-time consumption signals down to the SKU-location-day level to handle complex promotional lifts.
• Best for Cross-Functional Connected Planning: Anaplan
  • Best For: Large enterprises where sales, supply chain, and finance need to collaborate on real-time consensus forecasts and scenario modeling.
  • Why it shines: Backed by high-speed in-memory calculation engines (Hyperblock), it bridges the gap between statistical forecasting and operational budgeting, allowing teams to run dynamic "what-if" simulations instantly.
• Best for Grocery & Perishable CPG: RELEX Solutions
  • Best For: Food, beverage, and FMCG brands dealing with short-shelf-life or high-velocity perishable goods.
  • Why it shines: It automates touchless replenishment and minimizes waste by accurately modeling complex daily seasonality, weather context, and localized consumer substitution patterns.
• Best for Mid-Market & Faster Rollouts: Streamline or BetterDemand
  • Best For: Mid-market product companies ($10M–$5B revenue) that want powerful AI forecasting without a multi-year, consultant-heavy enterprise integration.
  • Why it shines: They offer agile, out-of-the-box ERP/S&OP connectivity and automated data ingestion that can be deployed in weeks instead of years.

To help narrow down the ideal platform, could you share:

• Your approximate annual revenue or SKU count?
• Whether you are tightly integrated with an existing ERP (like SAP or Oracle ) that you want to build on top of?

If you mean enterprise-grade AI demand forecasting for a CPG company, my current pick is o9 Solutions, with Kinaxis Maestro and Blue Yonder as the strongest alternatives.

My ranking

Why I'd choose o9

o9 is particularly compelling for CPG because it isn't just a forecasting engine. Its platform connects demand forecasting, demand sensing, supply planning, inventory optimization, scenario planning and execution. Its forecasting models can incorporate internal and external demand drivers such as promotions, seasonality, consumer behavior and new-product launches.

That's important in CPG because the difficult problem usually isn't "predict next month's baseline demand." It's:

What will demand be by SKU/customer/channel when promotions, pricing, new launches, seasonality and changing consumer behavior are all moving simultaneously—and what should we do about it?

o9 also has strong CPG references including AB InBev, Keurig Dr Pepper, Bimbo Bakeries, Coty and Levi Strauss, and reports substantial results from AB InBev's deployment, including a 60% reduction in stock-outs and 70–90% touchless-planning adoption. Those are vendor-reported figures, so I'd validate them against your own P&L assumptions.

When I'd pick Kinaxis instead

Kinaxis is arguably the better choice if your biggest issue is connecting the forecast to rapid supply-chain decisions.

Its Demand.AI combines internal/external signals—including promotions, weather, social signals and consumer behavior—with ML forecasting, while Maestro's concurrent-planning architecture connects demand with supply, inventory and production decisions. Kinaxis has also specifically developed its offering around consumer-products forecasting.

I'd favor Kinaxis over o9 when:

• supply constraints are constantly changing;
• you need rapid what-if analysis;
• manufacturing and supply response matter as much as forecast accuracy;
• you want planners to see the downstream consequences of demand changes immediately.

When I'd pick Blue Yonder

Blue Yonder is the safer choice for a large enterprise with an existing retail/CPG planning ecosystem and a need for mature demand, inventory and supply planning.

Its demand platform combines statistical forecasting, ML, AI and external signals, with particular emphasis on explainability and "outside-in" forecasting. It also has a substantial retail and consumer-industry footprint.

The important caveat

I wouldn't select based on claimed forecast-accuracy improvement. Vendors quote impressive numbers, but the real test is whether the system beats your existing process on your SKU/customer/channel hierarchy.

For a CPG RFP, I'd run a blind 12–18 month backtest using:

• SKU × customer/channel × week
• baseline + promotional demand
• new-product introductions
• intermittent/slow-moving SKUs
• price changes
• holidays/seasonality
• POS and shipment history
• forecast bias, WAPE/MASE and service-level impact
• inventory and working-capital consequences
• planner overrides / forecast value-add
• time required to maintain the models

My short answer:
o9 for the strongest overall CPG AI planning platform; Kinaxis if supply-chain responsiveness is the priority; Blue Yonder if you want a mature enterprise/retail planning stack.

If you tell me your CPG category (food/beverage, beauty, household, etc.), company size, ERP (SAP/Oracle/etc.), and whether you're forecasting at SKU/customer/week or another level, I can narrow this to the best 2–3 platforms and build a feature-by-feature RFP scorecard.

Selecting the "best" AI platform for demand forecasting in a Consumer Packaged Goods (CPG) supply chain depends heavily on your enterprise scale, IT ecosystem, and SKU complexity . CPG supply chains face unique hurdles: volatile promotional lifts, short shelf-lives for perishables, multi-tier distribution networks, and shifting external signals (weather, trends, macroeconomics).

The top AI platforms dominating the CPG landscape are categorized below by their primary strengths:

• o9 Solutions (Digital Brain) — Best for Large Enterprises & Integrated Business Planning (IBP)
  • Core Strength: Uses an Enterprise Knowledge Graph to create a digital twin of your supply chain. It merges demand, supply, and financial planning into a single platform, excelling at processing external demand-sensing drivers.
  • Ideal for: Global CPG multi-nationals with massive data footprints looking to move toward touchless, exception-based planning.
• Blue Yonder (Luminate Platform) — Best for Omni-channel Retail & Execution-Driven CPG
  • Core Strength: Deep probabilistic forecasting and replenishment automation. It bridges high-volume retail point-of-sale (POS) data with upstream manufacturing schedules.
  • Ideal for: Large-scale CPG brands tightly integrated with massive retail ecosystems (e.g., Walmart, Target scale) that require granular daily demand sensing.
• Kinaxis (Maestro) — Best for Concurrent & Multi-Tier Supply Networks
  • Core Strength: Its patented concurrency engine instantly synchronizes shifts in demand with global supply, capacity, and material constraints simultaneously.
  • Ideal for: Enterprises with complex, multi-plant, multi-ERP manufacturing setups that need instantaneous "what-if" scenario modeling.
• RELEX Solutions — Best for Perishable and High-Velocity FMCG
  • Core Strength: Built specifically for fast-moving consumer goods and grocery, it handles short-lifecycle products, promotional volatility, and automated store-level replenishment exceptionally well.
  • Ideal for: Food, beverage, and personal-care brands where waste reduction and shelf availability are top financial metrics.
• ToolsGroup (SO99+) — Best for Probabilistic Modeling & Managing "Long Tail" SKUs
  • Core Strength: Known for pioneering probabilistic forecasting (predicting a range of demand probabilities rather than a single flawed average), which prevents stockouts on intermittent, slow-moving, or complex promotional items.
  • Ideal for: Mid-to-large CPG manufacturers dealing with high SKU variability and erratic demand patterns.
• Anaplan — Best for Collaborative Scenario Planning
  • Core Strength: Connects sales, commercial planning, supply chain, and finance using rapid in-memory calculations. Its AI-driven scenario planning lets cross-functional teams adjust assumptions on the fly.
  • Ideal for: Organizations where demand forecasting needs a heavy emphasis on financial alignment and collaborative S&OP consensus.

To help narrow down which platform fits your organization, tell me:

• What is your approximate annual revenue and active SKU count?
• Are you tightly integrated with an enterprise ERP like SAP S/4HANA or Oracle , or looking for a standalone intelligence layer?

For a CPG (consumer packaged goods) supply chain, the “best” AI demand forecasting platform depends on your operating model, but the strongest enterprise contenders are usually:

1. Blue Yonder — best overall for CPG demand forecasting
2. o9 Solutions — best for AI-native integrated business planning
3. Kinaxis — best for rapid scenario planning and supply-demand orchestration
4. SAP Integrated Business Planning — best if you are deeply invested in SAP
5. Oracle SCM — best for Oracle-centric enterprises

My shortlist for a CPG company

If I had to pick one:

• Large global CPG (food, beverage, personal care, household goods): Blue Yonder
• CPG undergoing a full digital transformation of S&OP/IBP: o9 Solutions
• CPG with frequent disruptions and complex supply tradeoffs: Kinaxis
• SAP-heavy organization: SAP IBP

What matters more than the AI model

The forecast algorithm is only part of the outcome. For CPG, I would evaluate platforms on:

• Promotion lift modeling (features, displays, discounts, retailer promotions)
• Demand sensing using POS, syndicated data, weather, and market signals
• New product forecasting
• Cannibalization modeling across SKUs
• Probabilistic forecasts (not just one-point forecasts)
• Planner workflow and override governance
• Integration with ERP, retailer data, and data platforms
• Ability to connect forecast → inventory → production → financial plan

A common mistake is buying a “smarter forecast” tool when the real issue is fragmented data, weak promotion processes, or disconnected S&OP decisions.

If you tell me your company size, ERP (SAP/Oracle/etc.), number of SKUs, channels (retail/e-commerce/distributor), and whether the goal is forecast accuracy vs. inventory reduction vs. service level, I can narrow this to a top 2–3 recommendation.

There is no single "best" AI platform for a Consumer Packaged Goods (CPG) supply chain , because the ideal choice depends heavily on your company size, SKU complexity, and integration requirements . CPG demand forecasting is uniquely challenging due to promotional lifts, retailer-specific volatility, short product life cycles, and strict On-Time In-Full (OTIF) compliance windows.

The top-tier AI platforms mapped to specific CPG operating profiles include:

• o9 Solutions (The Digital Brain) — Best for Large Enterprises / Global CPGs . It uses an advanced AI/ML "digital twin" architecture combining demand sensing, commercial planning, and supply chain constraints into a single platform. It excels at multi-tier orchestration and multivariate modeling for Fortune 500 volumes.
• Blue Yonder (Luminate Platform) — Best for Retail-Heavy & Omnichannel CPGs . Known for mature probabilistic forecasting and replenishment optimization. It handles massive data scale and complex promotional calendars exceptionally well, though it requires substantial implementation timelines and enterprise budgets.
• RELEX Solutions — Best for High-Velocity Grocery & Consumables . Highly optimized for retail and CPG touchless planning, RELEX uses probabilistic AI to model external drivers like weather, seasonality, and promotional surges directly into store-level replenishment.
• Logility (Decision Intelligence Platform) — Best for Mid-to-Large Manufacturers . Features dedicated engines like DemandAI+ and a generative AI Expert Advisor. It bridges multi-echelon inventory optimization with strong pre-built ERP connectors and a faster time-to-value than monolithic enterprise suites.
• TrueGradient or BetterDemand — Best for Mid-Market Consumer Brands ($10M–$5B) . These agile, AI-native platforms sit as an intelligence layer above existing ERPs without requiring multi-year migrations. They specialize in non-linear signals (like channel proliferation, TikTok/social spikes, and granular SKU-store combinations) specifically for modern consumer brands.

To help narrow down the exact platform fit for your organization, tell me:

• What is your annual revenue range and approximate active SKU count?
• Are you looking to replace an existing legacy planning tool/spreadsheet workflow , or build an AI layer on top of your current ERP?

If you mean enterprise CPG—thousands of SKUs, retailer/customer forecasts, promotions, POS, seasonality, new-product launches, and a need to connect demand planning to supply/inventory—I’d put Kinaxis Maestro at the top of the shortlist today.

My shortlist

Why Kinaxis gets my #1

CPG forecasting isn't really about picking the fanciest time-series model. The hard part is incorporating promotions, retailer behavior, POS, weather, product attributes, seasonality and sudden demand changes, then translating the forecast into supply and inventory decisions.

Kinaxis's current Demand.AI explicitly combines internal and external signals and supports promotions, holidays and new-product introductions. Its consumer-products offering also emphasizes connecting the forecast directly to supply, inventory, production and logistics.

That combination is more valuable to a CPG organization than having a marginally better standalone forecasting algorithm.

The important caveat

I wouldn't choose based on advertised "AI forecast accuracy." I'd run a bake-off using your own data.

For a CPG evaluation, I'd test each platform on:

1. Promo lift — planned vs. unplanned promotions.
2. New-product forecasting — cold-start SKUs and product analogs.
3. Intermittent/slow-moving demand.
4. Cannibalization — new SKU taking volume from an existing SKU.
5. Retailer/channel granularity — SKU × customer × location.
6. Demand sensing — how quickly the model reacts to POS changes.
7. Forecast explainability — can planners understand why the number changed?
8. Forecast → supply → inventory — does a better forecast actually reduce inventory or stockouts?
9. Planner productivity — how many exceptions require human intervention?
10. Implementation effort — data integration and model configuration can matter more than model sophistication.

One particularly important point: don't let vendors demonstrate only aggregate forecast accuracy. Make them forecast at the level where your business actually makes inventory decisions.

If you tell me your CPG profile—e.g. food/beverage vs. household/personal care, approximate SKU count, retailer/channel mix, SAP/Oracle/etc., and whether the main pain is forecast accuracy, inventory, promotions, or planner productivity—I can narrow this to a top 3 and give you a vendor-by-vendor comparison.