Client Stories

Transformation in the Real World.

Every engagement is designed to address client-specific challenges and market position. All solutions require attention to data architecture, AI design and industry business context.

Case Study 01

Retail: Predictive Demand and Merchandising Planning

Historical and disparate data stored in XLS was used to forecast customer demand.

Demand intelligence platform consolidated sales, inventory, promotion and market data. Predictive models generated forecasts, trend signals, and demand alerts.

Merchandising team improved forecast accuracy, response to market changes, and reduced inventory levels and stock-outs.

AI-enabled solution is estimated to reduce forecast errors 20–50% and lowers product unavailability by up to 65%.

Case Study 02

Retail: End-to-End Warehouse & Inventory Intelligence

Warehouse operations could not identify and track critical stock across multi-locations. Warehouses were reacting to stock-out escalations.

Supply-chain intelligence platform unified data across all warehouses to provide real-time visibility of inventory levels, product movement, and stock risk indicators.

Inventory control and operational efficiency improved. Stock-outs and supply chain disruptions were reduced.

AI-enabled solution is estimated to cut inventory costs 10–15% and reduce stockouts by 20%.

Case Study 03

Logistics : AI-driven Supply Chain - Just-in-Time Automotive

Logistics team was required to coordinate between production schedules, inventory availability, transport and assembly-line operations

Data platform integrated planning, warehousing, transport and procurement into a single intelligent supply-chain solution.

AI-driven solution enabled delivery of the right part to the right assembly line at the correct time and supported the organisation's lean manufacturing goals.

AI-enabled solution is estimated to cut transport costs 8–12% and improve on-time delivery.

Case Study 04

FMCG Manufacturing: Demand Forecasting & Product Mix Optimisation

Large dairy manufacturer lacked visibility across demand, production, finance, and supply chain — planning ran on disconnected systems and gut-instinct forecasting.

AI-driven platform was deployed to unify sales, finance, manufacturing, and supply chain data into one planning model, enabling demand forecasting, scenario modelling, and product mix optimisation across cost, capacity, and margin.

Comparable deployments point to 5–10% lower inventory costs, 20–30% fewer stock-outs, and 2–5% revenue uplift.

Case Study 05

Energy: AI-Assisted SAP Data Cleanse & Migration

Low data quality was impacting the ERP modernisation program schedule.

AI-assisted mapping, cleansing, scripting and agile testing enabled cleanse and upgrade of legacy SAP within 6 months.

SAP data cleanse and migration was delivered 50% faster than traditional approaches.

Case Study 06

Logistics: AI-Driven Supply Chain Mission-Critical Operations

Regulatory guidelines and KPIs meant strict controls to avoid under-delivery of pharmaceutical products to hospitals, while inventory costs and waste on perishables remained a top financial priority.

An ontology-driven Operational Decision System was deployed using Cauldrn's AI Cloud Foundry, unifying data from seven systems and recommending order quantities, safety stock levels, and suppliers.

Regional, manual planning was replaced with one connected platform and a shared national view.

Comparable AI-driven pharma programs identify 20–30% lower inventory levels, up to 80% fewer critical stockouts, and 5–8% better fill rates.

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