Accurate demand forecasting is the foundation of effective supply chain planning.
We combine:

• Holt-Winters exponential smoothing to capture seasonality and trends

• Neural networks to learn complex patterns in sales behaviour

• Human-in-loop forecast validation, ensuring no “black-box” decisions or hallucinated outputs

Key Capabilities

• Multi-year seasonality modelling

• Forecast adjustments based on marketing inputs

• Automated forecast ranges for uncertainty management

• Ability to integrate contract-based or customer-specific outlooks

• Forecasts that feed directly into value chain planning and scheduling

Our forecasting engine is deliberately designed to be transparent, auditable, and business-controlled—no uncontrolled generative AI beha

Traditional planning tools optimise cost or output.
eValueMaxAI optimises your entire enterprise value chain.

Our Value Chain Planner integrates:

• Demand forecasts

• Multi-plant production capabilities

• Machine group bottlenecks

• Bill of Materials and co-products

• Opening and desired closing stocks

• Raw materials, consumables, utilities, packing materials

• Labour, overheads, depreciation, and even interest cost

• Distribution network and depot structure

What Makes It “Value Chain” and Not Just Supply Chain?

Because we incorporate every cost element and optimise for PBT, not just volumes.

Technical Highlights

• Built on large-scale Mixed Integer Programming (MIP)

• Handles millions of variables and constraints

• Supports management overrides for marketing priorities or production feasibility

• Automatic allocation of fixed overheads based on production hours

• Generates feasible, profit-maximising production and procurement plans

This is enterprise-grade deterministic AI, not probabilistic guesswork.

Our production scheduling engine converts the monthly production plan into a weekly, machine-group-level execution schedule.

Incorporates Real Manufacturing Constraints

• Changeover costs and times

• Minimum run quantities

• Backordering penalties

• Customer-wise delivery priorities

• Machine-specific capabilities

• Lead times and required delivery dates

How It Works

• Monthly schedule created using multi-period MIP

• Every week, the scheduler re-optimises the remaining month

• Combines deterministic optimisation and combinatorial search for the best weekly plan

The output is both feasible and optimised, enabling manufacturing teams to operate with clarity and precision.

Material scheduling links procurement directly with production and demand realities.

Key Inputs

• Production schedules

• Opening stocks

• Reorder quantities & cycle times

• Lead times

• Desired closing stocks

• Safety stock levels

Material schedules ensure:

• Zero stockouts for critical materials

• Optimised inventory investment

• Minimal obsolescence & excess

• Integration with procurement & vendor management systems

For organisations with multiple depots, distribution is as important as manufacturing.

Our module generates optimised dispatch schedules based on:

• Forecasted depot requirements

• Actual and planned production

• Transportation constraints

• Lead time and availability windows

• Cost considerations & route optimisation

This ensures balanced inventories across depots, lower freight cost, and improved service levels.

✔ Enterprise-Grade AI, Not Hallucination-Driven

All optimisation is deterministic. Neural networks are human-verified. No generative randomness.

✔ End-to-End Integration

Demand → Production → Materials → Procurement → Distribution — unified in one engine.

✔ Real-World Feasibility

Every plan respects machine capacity, costs, BOM, stocking rules, and delivery commitments.

✔ Profit-Maximising

Direct optimisation of PBT using true cost structure—including depreciation and interest.

✔ Scalable Architecture

Designed for organisations with thousands of SKUs, multi-plant setups, and complex BOM structures.

eValueMaxAI is built with 25+ years of real manufacturing, finance, and ERP experience.
Our SCM modules are not academic models—they are battle-tested in real enterprises where mistakes cost crores.