SolvedBy.Ai helps CPG organisations manage demand volatility, promotional complexity, inventory risk, and margin pressure by connecting forecasting directly to inventory, pricing, production, labour, and capital decisions. This allows plans to reflect how markets actually behave, not how they behaved last year.
Traditional CPG forecasts rely heavily on historical averages and manual adjustments. SolvedBy.Ai models demand at product, customer, channel, and geography level, incorporating promotions, pricing, seasonality, weather, macroeconomic factors, and competitive signals to produce forward-looking forecasts that are more responsive to change.
Yes. SolvedBy.Ai can combine sell-in, sell-out, shipment, and consumption signals, allowing CPG teams to understand true demand rather than relying on orders alone. This improves forecast credibility across commercial, supply chain, and finance teams.
SolvedBy.Ai provides a single, coherent demand signal that feeds S&OP and IBP processes. Scenarios can be tested before decisions are locked in, reducing late changes and improving alignment between commercial plans, supply capacity, inventory, and financial targets.
AI Inventory Optimisation balances service levels against working capital by positioning stock where demand is most likely to occur. This reduces excess inventory, write-offs, and obsolescence while protecting availability for key customers and channels.
By reducing overproduction, excess safety stock, and misallocated inventory, CPG organisations can release cash tied up in stock. This improves capital efficiency without increasing service risk.
The solution models how demand responds to price changes, promotions, and trade activity. This helps CPG teams design pricing and promotion strategies that drive volume where it is profitable, rather than relying on blanket discounts that erode margin.
Yes. By forecasting uplift and cannibalisation effects, SolvedBy.Ai helps CPG organisations evaluate which promotions are likely to deliver incremental volume and which simply shift demand. This improves ROI on trade spend.
AI Resource Allocation evaluates competing investment options — such as brand spend, capacity investment, or market expansion- based on expected demand and return. This helps leadership allocate capital and budget to the highest-impact opportunities.
In CPG manufacturing, EBITDA is primarily driven by how accurately demand is translated into production volumes, inventory positions, and plant utilisation. SolvedBy.Ai improves demand accuracy at SKU, customer, and time-period level, reducing overproduction, write-offs, and excess finished goods while lowering expediting and changeover costs caused by late plan changes.
More stable demand signals improve line scheduling, reduce overtime and downtime, and allow inventory and working capital to be held at levels aligned to real consumption rather than safety buffers. Pricing and promotional decisions are also informed by forward demand visibility, protecting margin during volume volatility. These effects are measured directly against cost, margin, and cash metrics that flow into EBITDA.
The solution identifies patterns that indicate potential equipment failure before it happens. This reduces unplanned downtime, stabilises production schedules, protects service levels, and avoids costly emergency maintenance.
Yes. More accurate demand forecasts and improved asset reliability lead to fewer last-minute plan changes, smoother production runs, and better utilisation of manufacturing capacity.
Labour demand is forecast based on expected production volumes, throughput, and operational activity. This helps plants and distribution centres plan staffing levels accurately, reducing overtime and labour inefficiency.
AI Staff Scheduling converts labour demand into workable schedules that respect contracts, skills, and operational constraints. This improves workforce utilisation while reducing scheduling rework and compliance risk.
No. SolvedBy.Ai is technology-agnostic and integrates with existing ERP, IBP, APS, and BI systems. It enhances decision quality without requiring system replacement.
Each model is built and calibrated at the level CPG organisations operate, by brand, SKU, customer, channel, and region. There are no generic templates applied across the business.
The models continuously learn from new data and external signals, allowing forecasts and plans to adjust as conditions change rather than waiting for monthly or quarterly reforecast cycles.
Commercial, supply chain, finance, operations, and executive teams all use SolvedBy.Ai outputs, each through a lens relevant to their decisions, from promotions and inventory to capacity and capital allocation.
Performance is measured using forecast accuracy, inventory turns, service levels, margin outcomes, working capital impact, and EBITDA contribution. Value is tracked transparently.
CPG organisations typically receive a minimum 10:1 return on investment, driven by improved demand accuracy, more stable production plans, and better inventory positioning. The largest sources of value usually come from reduced excess and obsolete stock, lower write-offs and waste, fewer last-minute production changes, and reduced overtime and expediting costs.
During the Proof of Concept, SolvedBy.Ai combines the organisation’s internal demand, production, and inventory data with large, deep sets of external demand drivers such as promotions, seasonality, market signals, and external drivers to test whether materially better demand signals can be generated. ROI is quantified against real cost, margin, and cash outcomes, and scaling only occurs where value is demonstrably proven.
SolvedBy.Ai operates under ISO 27001, ISO 42001 and Cyber Essentials Plus certifications. Models are explainable, auditable, and governed, ensuring responsible, transparent use of AI in critical planning decisions.
CPG organisations retain full ownership of their data and outputs. SolvedBy.Ai acts as a trusted partner, not a data owner.
Building an in-house data science team is rarely enough to achieve world-class forecasting performance. Forecast accuracy at scale requires deep specialist expertise, large and continuously expanding libraries of models, access to extensive external demand drivers, and constant benchmarking and improvement as patterns change.
SolvedBy.Ai exists solely to be the best in the world at forecasting operational demand. Our teams of PhDs and applied scientists continuously test, tune, and replace models across industries, geographies, channels, and time horizons, using far broader data and learning loops than any single organisation can sustain internally.
This gives customers access to forecasting accuracy and depth that would take years to replicate in-house, without the cost, talent risk, or ongoing maintenance burden. Crucially, our focus does not move on once models are built. Staying at the frontier of forecasting performance is our core business.
