Most CPG enterprises have cracked the code to solve the recurring supply chain challenges such as demand forecasting, inventory and planning optimization, and order management. But is your supply chain equipped to tackle last-mile disruptions, exceptions management, fragmented planning, reporting, and dashboard saturation? The root cause may not be poor investment. It could be structural bottlenecks such as fragmented data, limited visibility, isolated insights, and legacy solutions built for more stable environments. As volatility increases and channels evolve rapidly, these constraints lead to outdated forecasts, inefficient inventory decisions, and reactive operations.
In this scenario, finding the right enterprise partner who can simplify analytical consumption and ensure supply chain connectedness using AI becomes critical to complete your supply chain picture. When embedded into existing supply chain frameworks, they enable real-time sensing, adaptive planning, and data-driven decision-making at scale.
Modernizing CPG Supply Chains for a Volatile World
At MathCo, we help CPG leaders modernize supply chains through enterprise-grade AI and analytics. We augment existing systems to improve speed, precision, and coordination across demand, inventory, logistics, and fulfillment. This shift enables enterprises to move from reactive execution to predictive, continuously learning supply chain ecosystems built to adapt in near real time.
Through AI- and data-engineering-driven capabilities, we help organizations evolve toward resilient, intelligent, and future-ready supply chains, without large-scale system replacements, unlocking measurable value across even the most complex CPG environments.
Collaborative Demand Planning: Breaking Functional Silos
Traditional demand planning often operates in isolation, relying heavily on cross-functional data and inputs. In a market shaped by frequent demand inflections, this approach quickly becomes obsolete. The result? Forecast bias, excess inventory, or missed revenue opportunities.
Our approach integrates inputs from sales, marketing, trade promotions, and external signals into a unified, AI-driven planning framework. Machine learning models continuously learn from deviations, while planners focus on exception management rather than spreadsheet reconciliation.
Success Delivered
A leading CPG F&B enterprise’s supply chain planning was affected owing to fragmented and inaccurate demand signals from over 100 distribution centers spread across the globe. Their complex workflows posed a challenge to standard forecasting solutions, leading to inefficiencies in supply and demand planning.
MathCo developed a customized collaborative planning tool on NucliOS. The tool could analyze the workflows and sparse data and integrate effortlessly into the client’s existing ecosystem. This unlocked $32M savings through excess inventory reduction under a year of deployment and reduced out-of-stock scenarios by ~17%.
Explore the full story here.
Stock Transfer Order and Last-Minute Order Management with Intelligent Response
Last-minute order changes are inevitable, owing to promotions, demand spikes, or supply disruptions. Legacy order management systems, built on rigid rules and delayed data updates, force planners into manual interventions and downstream firefighting. This leads to service-level erosion, expedited freight costs, and misaligned inventory allocations.
Modern, AI-enabled order management introduces real-time visibility into demand signals, inventory positions, and capacity constraints. Intelligent prioritization and scenario-based recommendations allow organizations to dynamically reallocate inventory, reroute orders, and fulfil demands. Additionally, our simulation models consider various constraints such as labor and order to solve multiple existing scenarios, all the while equipping them to plan and solve for the future ones.
Success Delivered
Frequent last-minute orders posed a critical challenge to managing stock transfer orders for a leading CPG enterprise. Heavily manual processes mandated human intervention at every step – from approval to stock analysis, leading to inventory imbalances, stockouts, higher logistics costs, and incomplete orders.
MathCo’s AI-powered replenishment system modernized the client’s legacy processes, empowering warehouse executives with next-gen tools that would transform warehouse ops and fulfill last-minute demands. Our solution reduced stockout incidents by ~30% and reduced annual lost revenue from $18M to $7.8M.
Learn more about our solution here.
New Product Demand Forecasting: Limited Time, Maximum Sales
New product introductions are increasingly frequent and shorter in lifecycle, yet legacy forecasting models lack the ability to reliably predict demand in the absence of historical data. As a result, launches are often accompanied by stock-outs, overstocks, or delayed course correction.
Advanced forecasting approaches leverage analog products, market attributes, early sales signals, and external data to generate probabilistic demand forecasts for new launches. AI-driven learning loops refine predictions as data emerges, enabling faster stabilization. Enterprises that fail to modernize this capability face higher launch risk and slower time to value from innovation investments.
Success Delivered
A leading fashion apparel brand relied on traditional methods to forecast demand for their limited-edition SKUs. As a result, there was a significant decline in post-launch sales, affecting their revenue and inventory alignment. Key issues included slow response to market signals and poor lifecycle visibility, leading to suboptimal promotional planning and inventory inefficiencies.
MathCo developed a SKU-level demand forecasting model to tackle this challenge, enabling real-time insights, improved forecast accuracy, and agile supply chain decisions. This helped in achieving 25 – 30% improvement in new launch forecast accuracy and prevented over $5M in missed sales opportunities.
Explore the complete story here.
Executive Reporting for CXOs: Always One-Step Ahead
Legacy executive reporting focuses on historical KPIs, often delivered weeks after events have occurred. While useful for retrospection, these reports offer limited guidance on what actions to take next.
In 2026, CXO reporting is forward-looking and prescriptive. AI-driven data stories synthesize end-to-end supply chain signals into clear narratives, highlighting risks, trade-offs, and recommended actions. This shift enables leadership teams to proactively steer outcomes rather than react to missed targets. Organizations that rely on static, backward-looking reports risk slower decisions and misaligned strategic responses.
Success Delivered
Disparate market signals, lagging demand insights, and fragmented executive reporting limited leadership’s ability to proactively steer supply chain performance, capital allocation, and risk posture. MathCo’s CXO Data Stories delivered always-on, AI-powered executive narratives that continuously synthesize market intelligence, competitive signals, and internal performance data—surfacing what is changing, why it matters, and where leadership attention is required. With one-click deep dives and data-backed recommendations, CXOs could stay ahead of market shifts and align faster on enterprise-level actions. This improved enterprise decision quality and alignment—unlocking 5–7% working capital efficiency while strengthening supply resilience and service outcomes across priority markets.
MathCo – The Critical Differentiator
Most enterprises might instinctively choose off-the-shelf tools, considering the initial low setup charges. Yet the reality is that the recurring subscription fees, rigid feature roadmaps, and ongoing customization quickly inflate the total cost of ownership. More critically, these tools frequently require enterprises to re-engineer established workflows to fit the technology. This causes disruption, adoption friction, and limited long-term flexibility.
MathCo takes a fundamentally different approach. Rather than forcing standardized solutions into complex, context-rich supply chain environments, we design contextualized solutions that align with an organization’s data maturity, operating model, and decision cadence. This ensures analytics and AI are embedded naturally into existing processes, enabling faster adoption and sustained impact.
A key differentiator is 100% IP ownership. Unlike proprietary platforms that lock enterprises into vendor dependency, we ensure clients retain full ownership of the solutions built. Our approach provides transparency, control, and the freedom to evolve capabilities as business needs change. This helps enterprises save ~$5M licensing fee every year.
MathCo also accelerates value realization through a library of proven accelerators built from deep CPG supply chain experience. These reusable frameworks, models, and data assets reduce implementation timelines while remaining fully adaptable to enterprise-specific requirements.
Solve Your Supply Chain Challenges with MathCo
To navigate supply disruptions, CPG enterprises need to move on or modernize their rigid and backward-looking systems to ones that are built to adapt to market volatility today. By augmenting existing operations with contextualized AI and advanced analytics, MathCo helps enterprises build intelligent, adaptive supply chains that deliver sustained resilience, faster decisions, and measurable business impact, transforming the supply chain into a strategic advantage. Keen to learn more about the challenges that we solve for enterprises and help them win every share, shelf, and margin? Explore here.
