Manufacturing data consulting
Manufacturing analytics succeeds when it shows up on the plant floor and in the boardroom with the same numbers. DI Squared builds manufacturing data consulting engagements that integrate ERP, MES, quality, and sensor data into a model the plant manager and the CFO can both work from. We are vendor-neutral, fluent in SAP, Oracle, Microsoft Dynamics, and the modern data stack, and we have been doing this work since 2008.
Snowflake, Databricks, dbt, Fivetran fluent
HQ Atlanta, serving US, Canada, Europe
- Plant-floor analytics, OEE, supplier and quality, S&OP, and cost-to-serve.
- Typical stack: Snowflake or Databricks; dbt; Qlik, Power BI, or Tableau; IoT integration via your historian or cloud provider.
- We respect operations technology boundaries. OT does not change without OT engineering.
- Discover, Map, Navigate, Adjust applied across every engagement.
What manufacturing analytics actually requires
Manufacturing analytics is the integration problem more than the visualization problem. ERP holds the financial truth. MES and the historian hold the operational truth. The quality system, the supplier portal, and the maintenance system each hold partial views of the same equipment, the same product, and the same supplier relationship. Manufacturing data consulting, done well, accepts that those systems will not consolidate themselves and that the data engineering, governance, and modeling work has to happen with the same rigor we would apply to a financial close. The dashboards come last. The reconciliations come first.
- Manufacturing engagements delivered across discrete, process, and hybrid environments
- Integration experience across SAP, Oracle, Dynamics, JD Edwards, and Infor M3
- Common BI footprint: Qlik, Power BI, Tableau
Manufacturing analytics use cases we deliver
Manufacturing engagements cluster around a few patterns. Most clients are working on more than one at the same time.
OEE and plant-floor analytics.
Availability, performance, quality, and the underlying loss categories, by line, shift, product, and crew.
S&OP and demand planning analytics.
Forecast accuracy, bias, MAPE by SKU and family, and the data foundation that lets a planner trust the number.
Supplier and quality analytics.
PPM, on-time delivery, supplier scorecards, non-conformance trends, and corrective action tracking.
Maintenance and reliability analytics.
MTBF, MTTR, work order cycle time, and the leading indicators of equipment failure where the historian data supports it.
Cost-to-serve and product profitability.
Landed cost by SKU and channel, customer profitability, and margin leak analytics.
ESG and emissions reporting.
Scope 1 and Scope 2 emissions data, energy usage analytics, and the reporting needed for customer and regulatory disclosure.
IT, OT, and the line between them
Manufacturing analytics requires data that lives across the IT and OT boundary. We do not change OT systems. We integrate them. In practice that means working with your existing historian (PI, Wonderware, Ignition, AspenTech IP.21, or a cloud-native equivalent), reading data through a designated interface, landing it in a cloud data platform, and modeling it alongside ERP, MES, and quality data in a way that the plant engineering team has signed off on. The IT and OT teams are both in the room from the start. The architecture has to satisfy both.
Discover, Map, Navigate, Adjust in manufacturing
Discover.
We assess the ERP, MES, quality, maintenance, and historian footprint. We talk to plant leadership, supply chain, quality, and finance. We look at where the operational reports come from today, how often they reconcile to the financial close, and where the team has learned to work around the data.
Map.
We document a manufacturing-specific target state: data domains, ingestion pattern across IT and OT, warehouse model, BI footprint, governance approach, and the sequence of work. The document is one a plant manager, a CIO, and a CFO can all read.
Navigate.
We deliver in phases that put usable analytics in front of the plant within the first quarter wherever possible. Change management at the plant level is part of the work, not a hand-off at the end.
Adjust.
As product mix changes, capacity comes online, and supplier networks shift, we help the data and analytics keep pace. Manufacturing data platforms that ship and then stop evolving lose trust quickly.
Manufacturing technology stacks we work with
We are vendor-neutral, but fluent in the manufacturing platforms that run modern operations. Our clients commonly use SAP, Epicor, Infor, and Microsoft Dynamics 365 alongside MES, quality, warehouse, and shop-floor systems. We connect these data sources to create a trusted foundation for reporting, analytics, and AI.
A typical engagement may use Snowflake or Databricks as the data platform, dbt for transformation, and Fivetran or client-specific connectors to integrate ERP, MES, and operational data. For governance, we work with platforms such as Collibra, while Qlik, Power BI, and Tableau provide the analytics layer.
We are pragmatic about existing technology investments. If your organization has standardized on SAP and Power BI, or Epicor and Qlik, we build on that foundation rather than recommend starting over. Our focus is maximizing value from the tools you already own while preparing for future growth.
Manufacturing data consulting: common questions
Q: Do you work with both discrete and process manufacturing?
A: Yes. The data models differ (lot and batch versus serial and unit), the OEE conventions differ, and the regulatory perimeter often differs as well, but the engagement pattern is similar. We adapt the data model and KPI definitions to your specific manufacturing process, not the other way around.
Q: We have an existing historian and an existing ERP. Do we have to replace them?
A: No. Most of our manufacturing engagements integrate the existing historian and ERP rather than replace them. Replacement is a separate program with a separate business case. Analytics modernization can usually proceed without it.
Q: How do you handle the IT/OT boundary?
A: Carefully. We do not change OT systems or write to OT devices. We read historian data through a designated interface that the OT engineering team has approved, land it in the cloud data platform, and model it alongside IT data. The OT team is part of the design from the first conversation.
Q: How long does a manufacturing analytics engagement take?
A: A focused strategy engagement runs six to ten weeks. An implementation engagement that delivers OEE, supplier, and finance-grade plant analytics across a small portfolio of plants typically runs six to twelve months. Multi-site rollouts past that are scoped per-wave.
Related Case Studies
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Jacuzzi partnered with DI Squared to bring the “One Jacuzzi” vision to life. A moonshot ERP and CRM cloud migration, 50,000+ attachments processed inside a 48-hour backup window, and a master data cleanup that decommissioned tens of thousands of obsolete SKUs.
From "Wild West" reporting to a governed analytics foundation.
DI Squared took ownership of four critical use cases that a lower-cost consultancy could not complete, delivered the original project a week ahead of schedule, and has spent the last six-plus years building Dorman a governed Qlik and Power BI foundation that runs the business every day.
Modernizing Enterprise Analytics with Microsoft Fabric and Power BI
A global specialty materials provider partnered with DI Squared to modernize its analytics environment by migrating from a legacy BI platform to Microsoft Power BI and Fabric. Through a phased migration, modern governance, and hands-on training, the company established a scalable analytics platform that empowers both business users and IT.
Make plant data work for the plant and the boardroom
Talk with a DI Squared manufacturing strategist about your ERP, MES, and historian footprint, and the analytics roadmap your operations and finance teams both need.