The Challenge
Demand volatility and channel complexity at global scale
Retail power shifts, raw material inflation, and digital channel proliferation are compounding complexity for every CPG brand.
Demand Forecasting Accuracy
SKU-level forecast errors of 30–50% drive billions in excess inventory and out-of-stocks globally. Seasonal products, new launches, and promotional lifts are particularly poorly forecast by legacy statistical methods.
Trade Promotion Waste
CPG companies spend 20–25% of revenue on trade promotions, with an estimated 70% generating negative or zero ROI. The lack of post-event analytics perpetuates ineffective promotion patterns.
Supply Chain Fragility
Single-source raw materials, concentrated manufacturing, and JIT inventory strategies create systemic fragility. The COVID disruptions exposed how few brands had real visibility below Tier 1 suppliers.
Retailer Data Leverage Asymmetry
Major retailers hold POS and loyalty data that gives them a systematic information advantage in negotiations. Brands that can't synthesize multi-retailer sell-through signals are operating blind.
Education AI Solutions
AI systems for demand, trade, supply chain, and brand
Demand Sensing & Forecasting
ML forecasting at SKU-retailer level that incorporates POS data, weather, promotions, social signals, and economic indicators, reducing forecast error by 30–40% vs. statistical baselines.
Trade Promotion Optimization
AI that models promotion ROI pre-event, recommends optimal promotional mechanics, and generates post-event attribution analytics, improving trade spend efficiency systematically.
Supply Chain Risk Monitoring
AI monitoring of Tier 2 and Tier 3 supplier risk, port congestion, weather events, and geopolitical signals, with automated alternative sourcing recommendations.
Retailer Intelligence
Aggregation and analysis of retailer POS feeds, shelf data, and competitive promotional activity, giving brand teams real-time visibility into sell-through dynamics across channels.
New Product Forecasting
AI that combines analogous product histories, market test signals, and distribution velocity data to generate launch forecasts for new SKUs with quantified uncertainty ranges.
Revenue Growth Management AI
ML systems that optimize pricing, pack architecture, and promotional strategy by consumer segment and channel, identifying revenue and margin expansion opportunities systematically.
End-to-End Implementation
End-to-end AI implementation for consumer goods
Solnix delivers the full lifecycle, from opportunity mapping through production deployment and continuous improvement. The same proven methodology powers every consumer goods engagement, tailored to your systems, data, and regulatory environment.
Discovery & AI Opportunity Mapping
We start by understanding your operations, data landscape, and goals, then map where AI delivers measurable value and where it does not. Every engagement begins with a prioritized opportunity backlog, not a technology pitch.
Data Foundation & Readiness
AI is only as good as the data behind it. We assess data quality, connect fragmented sources, and build the secure, governed pipelines that production AI depends on, with privacy and compliance designed in from the start.
Model & Agent Development
We build the models, retrieval systems, and AI agents tailored to your use cases, selecting the right approach (fine-tuning, RAG, multi-agent orchestration) for accuracy, cost, and latency, and validating against your real-world edge cases.
Integration & Workflow Embedding
AI only creates value when it lives inside the tools your teams already use. We embed models and agents into existing systems, surfaces, and workflows, so adoption is natural and human-in-the-loop controls stay in place.
Deployment, Security & Compliance
We deploy to production with the security, monitoring, and compliance controls enterprises require, including bias and fairness testing, audit logging, and the observability needed to operate AI responsibly at scale.
Optimization & Continuous Improvement
AI systems improve with use. We measure outcomes against the goals set in Phase 01, retrain and tune from live feedback, and expand to the next set of use cases, turning a single deployment into a compounding capability.
Methodology
How Solnix Builds for Consumer Goods
01, Data Landscape Assessment
01, Data Landscape Assessment
We audit your existing demand, supply chain, and trade data infrastructure, assessing data quality, granularity, and integration gaps before designing AI solutions.
02, Retailer Data Integration
02, Retailer Data Integration
We integrate with Walmart Retail Link, Target Partners Online, 1WorldSync, and syndicated data providers (IRI, Nielsen). Integration with your EDI and ERP systems is handled in the initial scoping phase.
03, Forecast Model Development
03, Forecast Model Development
Demand models are built on your category-specific data and validated against held-out periods before deployment. We establish the business rules and exception management processes alongside the model.
04, Commercial Team Workflow Integration
04, Commercial Team Workflow Integration
AI outputs are delivered in the tools your commercial team uses. Excel, Tableau, or custom dashboards, with narrative summaries that enable action without requiring data science fluency.
05, S&OP Integration
05, S&OP Integration
Demand AI is integrated into your S&OP process, providing consensus forecast inputs, uncertainty ranges, and risk scenarios that improve the quality of supply planning decisions.
FAQ
Questions from SVPs of Supply Chain, RGM leaders, and CDOs
Get Started
Better Forecasts. Smarter Trade. Resilient Supply.
Solnix builds consumer goods AI that gives brand and supply chain teams the systematic intelligence to win shelf space and protect margin.
Request a Consumer Goods AI Assessment →