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Information-Driven Supply Chain for Manufacturing: Siemens, Alstom & Aberdeen Research
The Manufacturing Data Problem and Why 83% of Firms Are Still Struggling
Product quality, service excellence, and supply chain efficiency are the defining competitive battlegrounds for global manufacturers today. The pressure to produce products that meet constantly evolving customer demands — while remaining serviceable, compliant, and cost-efficient across complex global operations — has never been greater.
The challenge is not a lack of data. Manufacturers generate enormous volumes of it: maintenance contracts, technician documentation, engineering drawings, parts catalogues, supplier records, quality reports, CAD files, ERP outputs, and more. The problem is that this data is fragmented across dozens of disconnected systems, making it nearly impossible for operators, engineers, and supply chain teams to find what they need when they need it.
Aberdeen’s research makes this gap starkly visible: only 17% of manufacturing firms are fully satisfied with their ability to use their data to optimize the supply chain, deliver service excellence, and maximize customer experiences. That means 83% of manufacturers are leaving measurable value on the table, not because the data doesn’t exist, but because it isn’t accessible.
How Leading Manufacturers Are Closing the Gap
In this on-demand webinar, Omer Minkara, VP & Principal Analyst at Aberdeen, and Scott Parker, Director of Product Marketing at Sinequa, share how leading global manufacturers — including Siemens and Alstom — are using AI-powered enterprise search to build information-driven supply chains that connect their data, empower their teams, and drive measurable operational improvement.
The session covers four critical areas:
- The ROI of a single view of all product content — what becomes possible when engineers, technicians, and supply chain managers can access all relevant product documentation, maintenance records, and supplier data from one intelligent interface, rather than navigating ten disconnected systems
- Building blocks to modernize manufacturing operations — the technology architecture, data integration approach, and organizational steps required to move from fragmented, siloed information to a unified, AI-powered knowledge layer across the manufacturing value chain
- Technology tools that minimize complexity and enable growth — how enterprise AI search platforms reduce the cognitive load on frontline workers, accelerate onboarding, improve first-time fix rates for field service teams, and support faster, more confident decision-making across procurement, engineering, and operations
- Real-world success stories from Siemens and Alstom — concrete examples of how two of the world’s most complex industrial manufacturers applied information-driven approaches to reduce operational friction, improve supply chain visibility, and deliver better outcomes for their customers
Why AI-Powered Search Is Becoming Essential for Global Manufacturing
The most successful manufacturers of the next decade will be those that treat their data as a strategic asset — not a byproduct of operations. As product complexity grows, supply chains extend across more geographies and partners, and customer expectations for service quality increase, the ability to instantly surface the right information at the right moment becomes a direct driver of profitability.
Sinequa’s enterprise AI search platform connects to the full manufacturing data ecosystem — ERP systems, PDM/PLM platforms, MES, maintenance management systems, supplier portals, quality databases, and technical documentation repositories — indexing all content into a single, intelligent, searchable layer. Engineers find the right version of the right document in seconds. Technicians access step-by-step maintenance procedures in the field without calling back to headquarters. Procurement teams surface supplier performance data alongside contract terms and alternative sourcing options in one query.
The result is a manufacturing organization that operates with the full power of its institutional knowledge — regardless of how large, distributed, or complex that organization has become.
Frequently Asked Questions
An information-driven supply chain is one where every participant — engineers, procurement teams, technicians, logistics coordinators, and operations managers — has immediate, reliable access to the information they need to make good decisions. It means connecting data from ERP systems, supplier records, maintenance documentation, quality databases, and engineering repositories into a unified, searchable knowledge layer, so that supply chain decisions are based on complete, current information rather than whatever happens to be accessible in a given system at a given moment.
AI-powered search eliminates the time and friction of navigating disconnected systems by enabling workers to search across all relevant data sources simultaneously using natural language. A procurement manager can query supplier performance data, contract terms, and alternative sourcing options in a single search. A field technician can instantly retrieve the correct maintenance procedure for a specific part number. An engineer can find the latest version of a technical drawing without logging into three separate systems. Each of these improvements compounds into significant operational efficiency gains at scale.
Aberdeen’s research found that only 17% of manufacturing firms are fully satisfied with their ability to use their available data — including maintenance contracts, technician documents, and PDFs — to optimize the supply chain, deliver service excellence, and maximize customer experiences. This means the vast majority of manufacturers are operating with significant knowledge gaps despite having the underlying data available within their organizations.
Both Siemens and Alstom — two of the world’s most complex global industrial manufacturers — have applied AI-powered enterprise search to connect their distributed data ecosystems and improve operational performance. Their experiences, covered in this webinar, demonstrate how organizations operating at massive scale can reduce friction in supply chain workflows, improve service delivery, and equip their workforces with faster, more reliable access to the technical knowledge they need.
Sinequa connects to the full range of manufacturing data sources, including ERP and MES systems, PDM and PLM platforms, maintenance management systems (CMMS), technical documentation repositories, supplier portals, quality and compliance databases, CAD file metadata, and unstructured content such as PDFs, Word documents, emails, and scanned records. All content is indexed into a unified layer that supports natural language search, multilingual queries, and AI-powered relevance ranking.
The ROI of enterprise AI search in manufacturing comes from multiple value streams: reduced time searching for information (typically 35–40% improvement), faster first-time fix rates for field service teams, fewer errors from working with outdated documentation, accelerated onboarding for new engineers and technicians, reduced supply chain risk through better supplier data visibility, and improved product quality through faster access to quality history and engineering knowledge. Establishing a single, reliable view of all product content is consistently identified by Aberdeen’s research as one of the highest-ROI investments available to manufacturing operations leaders.
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