In manufacturing, the cost of invisibility is staggering. 94% of companies report that their revenue was negatively affected by supply-chain disruptions, yet 57% of supply chain professionals cite insufficient visibility as the biggest challenge facing their operations. The paradox? Manufacturers are drowning in data while starving for insight.
Your supply chain planner receives an urgent Slack message: "Customer ABC's order is delayed, when will it ship?". She opens the ERP. Then the MES. Then the TMS. Fifteen minutes and three spreadsheets later, she's still piecing together whether the delay stems from a supplier shipment, a production bottleneck, or a logistics issue.
In 2025, 57% of supply chain professionals cite insufficient visibility as their biggest operational challenge, a problem that cascades into missed deliveries, excess inventory, and painfully slow reaction times when disruptions hit.
This underscores the urgent need for enhanced data-sharing, real-time monitoring, and integrated visibility tools to improve supply chain performance.
The cost is staggering. 94% of companies report that revenue was negatively affected by supply chain disruptions, and every blind spot makes shocks worse. Yet the gap between leaders and laggards keeps widening. Companies operating in real-time enjoy 97% greater profits and 62% faster growth than their industry peers.
Peter Weill, the chairman of MIT’s Center for Information Systems Research (CISR), places significant emphasis on integrating generative AI and other technological solutions for businesses aiming to succeed in the digital age. He highlighted the impressive outcomes of this approach, noting that the companies he studied, which operate in real-time, experience 97% greater profits and 62% faster growth than their industry peers. He pointed out that the key factor contributing to this success is the elimination of inefficiencies, such as frequently revisiting decisions and relying on legacy systems. These challenges are commonly encountered by companies that do not operate in real-time.
How to use the power of AI for inventory optimization (having the right inventory at the right time), increasing workflow efficiency, and overall customer experience? The answer is the unified business context that makes supply chain data actually usable.
Manufacturing supply chains generate mountains of data. ERP systems track inventory, MES platforms monitor production, TMS solutions manage logistics, supplier portals document procurement, and WMS track warehouse operations. Each system is sophisticated. Each captures critical information.
But they don't speak the same language.
Your ERP calls it "Material Number." Your MES calls it "Item Code." Your supplier portal calls it "SKU." Even worse, your "customer ID" in the CRM doesn't automatically connect to the "ship-to party" in logistics or "account number" in finance. When your supply chain director asks, "Which customers are impacted by the supplier delay?" - you can't answer without manual data archaeology.
Organizations are implementing cloud-based digital technologies to create a unified data model across the entire supply chain, including manufacturing data, planning master data, supplier master data and customer data. Without this unified semantic layer, teams can't connect a supplier delay to a work-center schedule or trace how a customer allocation rule affects a specific shipment.
Technology is only as good as the data behind it and the processes behind it. Companies investing millions in AI-powered supply chain optimization discover their initiatives stall because the underlying data lacks the business context AI systems need.
The traditional answer is to hire data engineers to manually document everything. That means map every field, define every metric, and catalog every relationship. Six months and $500K later, you have a data dictionary that's already outdated because your business processes changed, you onboarded a new supplier system, or you acquired another company.
Manual metadata creation simply cannot keep pace with modern supply chain complexity. By the time you finish documenting your current state, it's no longer current.
Meanwhile, your team still can't answer basic questions like:
Leading manufacturers are taking a different approach in 2025: automated semantic layer generation that discovers business context from how data is actually used, without months of manual documentation.
Here's how it works:
AI analyzes your existing supply chain data (ERP, MES, TMS, WMS, supplier portals, IoT sensors) to automatically identify entities (SKUs, purchase orders, batches, shipments, plants, vendors) and propose relationships between them. The system recognizes that "Material" in SAP, "Item" in your MES, and "SKU" in your WMS all refer to the same thing.
Instead of static documentation, you get a continuously updated business vocabulary. When subject matter experts validate proposed definitions through simple workflows, the system learns and improves. Definitions evolve as your operations change, no manual maintenance required.
Trace from forecast → demand plan → work order → production batch → shipment with complete lineage showing where every KPI comes from and what changed. When a supplier lead time shifts, instantly see which SKUs, plants, and customer orders are at risk.
Your supply chain AI systems (demand forecasting, inventory optimization, supplier risk prediction) access not just raw data but the business meaning behind it. This makes AI outputs accurate, explainable, and trustworthy. By using AI, you can obtain answers significantly faster.
For Supply Chain Planners: Answer "what-if" questions in minutes, not days. Understand true end-to-end lead times across suppliers, production, and logistics with confidence.
For Procurement Teams: Unified supplier performance visibility across quality, delivery, and cost. All connected to downstream production and customer impact.
For Manufacturing Operations: Real-time root cause analysis when KPIs deviate. Instantly understand whether the issue stems from material shortages, equipment problems, or planning assumptions.
For Finance & Leadership: Trusted metrics everyone agrees on. No more reconciling conflicting reports or debating whose numbers are "right."
Supply chain visibility requires two layers working together:
Snowflake, Databricks, PostgreSQL, or SharePoint consolidates data from your ERP, MES, TMS, WMS, supplier portals, and IoT sensors. This brings data together in one place where analytics and AI can access it.
Dawiso sits above your data warehouse, adding business meaning... unified definitions, explicit relationships, data lineage, and quality indicators. This makes integrated data understandable and trustworthy.
Integration alone leaves you with consolidated confusion. Even when all supply chain data lives in Snowflake, "Material_Number" from SAP, "Item_Code" from MES, and "SKU_ID" from WMS are still disconnected. Finance and operations calculate "on-time delivery" differently. Nobody knows which "inventory" definition to trust.
Dawiso adds the missing layer. Automatically discovering that these disparate fields refer to the same entities, maintaining unified business definitions, and providing context to both human planners and AI agents through Model Context Protocol (MCP).
Most organizations invest heavily in Layer 1 but struggle with Layer 2. They consolidate terabytes of data, then discover teams still can't answer "Which customers are impacted by this supplier delay?" because the business context, and the MCP connection that makes it accessible to AI, is missing.
Dawiso's AI Context Layer sits above your unified data platform (Snowflake, Databricks, or your enterprise data warehouse), adding business context to your integrated supply chain data:
Dawiso analyzes the consolidated data in your data platform (from ERP, MES, WMS, TMS, planning tools, supplier systems, and customer portals) to automatically generate semantic relationships. The platform understands that "customer" in CRM relates to "ship-to location" in logistics and "sold-to party" in finance, creating unified business entities from fragmented technical tables.
Business definitions for supply chain metrics (on-time delivery, inventory turns, supplier lead time), product hierarchies, supplier taxonomies, and operational KPIs are automatically generated and continuously updated as operations evolve. Always reflecting your current business reality, not outdated documentation.
See how data flows through your supply chain systems, and which transformations occur in your data platform, and how business logic is applied. All expressed in plain business language accessible to supply chain planners, not just data engineers.
Through Model Context Protocol (MCP) integration, your supply chain AI systems access rich business context alongside data from your unified platform, enabling accurate natural language queries like "Show me all SKUs at risk from the Port of LA closure" that understand your specific business rules.
Dawiso doesn't replace your data warehouse or lake. It enhances it by adding the semantic intelligence that makes integrated data truly valuable.
Learn more about developing reliable AI models using data on our Context Layer for AI page.
That's the path from "we think there might be a problem" to real-time decisions that measurably improve margins and growth.
Supply chain visibility isn't about collecting more data. You already have that. It's about connecting data across fragmented systems and adding unified business context that makes your existing data intelligible, trustworthy, and actionable.
Your data lives in ERP, MES, TMS, WMS, supplier portals, and IoT sensors. The challenge is twofold: bringing that data together in a unified layer, then ensuring everyone, humans and AI, understands what it means.
Organizations with good supply chain transparency and resilient, diversified supply chains will be better positioned to adapt to emerging challenges. In an era of tariff volatility, labor shortages, and rising customer expectations, visibility transitions from an operational nice-to-have to a competitive necessity.
The manufacturers winning in 2025 aren't those with the most sophisticated systems. They're those who've solved the context problem. Ready to join them?
Keep reading and take a deeper dive into our most recent content on metadata management and beyond:
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