- Chief Operating Officer (COO)
- Chief Technology Officer
- Head of Digital Transformation
- VP Supply Chain
- Head of Sustainability
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Business Challenge
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DOT Solution
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Measurable Outcome
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Intelligent Data Foundation
Autonomous Operations
Autonomous Operations
Assurance & Trust
- Data Liquidity Score: 38% → 91% in 10 weeks
- Unplanned downtime: −47%
- Forecast accuracy: 61% → 89%
- ESG reporting: 22 days → 3 days per quarter
Manufacturing — FAQ
DOT’s Data Liquidity Audit is an AI-readiness assessment of your entire data estate, not an ERP integration project. Where conventional integration connects systems to each other, DOT’s architecture design focuses on making data consumable by AI models — a distinct and more demanding objective. Our approach consistently delivers AI-ready outcomes that integration projects alone cannot achieve, because the underlying data quality and structural consistency requirements are materially higher.
For most manufacturing environments, DOT delivers a working predictive maintenance proof of concept within six weeks under the Rapid AI Pilot programme. Full production deployment across multiple assets, integrated with existing CMMS or ERP platforms, typically requires an additional eight to twelve weeks. The timeline is principally determined by data availability and the complexity of the sensor infrastructure in place.
Scope 3 emissions — encompassing supply chain, logistics, and product end-of-life impacts — represent the most data-intensive component of CSRD compliance for manufacturers. DOT’s GreenOps Intelligence ingests data from supplier questionnaires, logistics platforms, and procurement systems to calculate scope 3 emissions automatically, applying GHG Protocol methodology. The output is a continuously updated, audit-ready emissions register that satisfies CSRD evidence requirements.
Yes. DOT’s Assurance & Trust practice includes a dedicated OT/IoT security service covering ICS and SCADA system assessment, IT/OT network segmentation, remote access security architecture, and anomaly detection for industrial control systems. These environments require security expertise that is materially different from conventional IT security — DOT maintains this capability specifically for manufacturing and industrial clients.
Safety-critical AI deployments — including quality inspection, predictive maintenance, and production control — are governed by DOT’s AI Ethics Framework, which establishes explicit human-in-the-loop protocols, model confidence thresholds for autonomous action, mandatory escalation pathways, and comprehensive audit logging. No AI Agent deployed by DOT operates on safety-critical decisions without defined human oversight mechanisms in place and documented accordingly.