Manufacturing
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Manufacturing

Manufacturing

Operational inefficiencies, downtime, and supply chain opacity impact margins. We enable predictive maintenance, production optimisation, and end-to-end supply chain visibility.

25%Higher forecast accuracy

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Manufacturing industry
Our Expertise

What we do in Manufacturing

Demand planning

Forecast demand with precision using ML-driven models that account for seasonality, promotions, and market shifts reducing stock-outs and overstock across production lines.

Transportation and logistics

Optimise routing, carrier selection, and delivery scheduling with data models that cut logistics costs and improve on-time delivery performance.

Predictive maintenance

Identify equipment failure before it happens using sensor data and ML models, reducing unplanned downtime and extending asset lifespans.

CRM Analytics

Understand distributor and dealer behaviour, improve retention, and identify cross-sell opportunities using customer relationship analytics tailored for manufacturing networks.

Supply chain overhead

Surface hidden overhead drivers across procurement, warehousing, and fulfilment enabling cost reduction without disrupting operational continuity.

Pricing strategy

Build dynamic pricing models that respond to input costs, competitor moves, and demand signals to protect margins and improve revenue realisation.

Smart factory

Connect IoT data streams with advanced analytics to monitor production efficiency in real time, identify bottlenecks, and enable closed-loop process improvements.

Parts calibration

Optimise spare parts inventory and calibration schedules using historical maintenance data, ensuring parts availability without excessive stockholding costs.

Our Work

Case studies in Manufacturing

Case StudyPredictive Maintenance

Risk assessment models that turn hidden inventory losses into smarter supply chain decisions

Most consumer goods manufacturers struggle with fragmented supply chain visibility, leading to inventory write offs and avoidable overhead losses. Without predictive risk assessment, the drivers of obsolescence remain hidden and inventory decisions stay reactive. A global beer manufacturer faced this challenge while managing large scale operations across multiple regions with rising annual obsolescence losses. ZDS built driver and risk assessment models to identify root causes, predict obsolescence risk, and enable smarter inventory planning decisions.

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Let's build something
remarkable.

Tell us about your data challenge and we'll come back with a clear, actionable plan — no jargon, no fluff, just a path forward.