Logistics Analytics & BI Solutions for Transportation and Ops Teams
TwinCore builds logistics analytics and BI solutions that turn data from your TMS, WMS, ERP, carriers/3PLs, telematics, and finance into a single operating model, with role-based dashboards and reliable reporting that teams can actually use to run the business.
This is built for one practical outcome: faster answers to operational and financial questions, consistent definitions across teams, and analytics that stays stable when systems, partners, and volumes change.
Most logistics teams do not suffer from a lack of data. They suffer from a lack of alignment.
Reports take too long to prepare, and the numbers do not match across teams. Dashboards exist, but people do not trust them. “Why did the cost increase?” or “Why did service drop?” turns into a manual investigation across multiple systems, spreadsheets, and email threads.
Over time, the symptoms become predictable: definitions drift, data quality issues spread, and every serious question requires a custom export and a reconciliation exercise.
Logistics BI is not a set of charts. It is a management layer built on four elements: integrated data, a consistent model, shared metric definitions, and visualization that supports daily decisions.
A good setup makes it easy to answer the questions that actually matter in operations and finance: what changed, where it happened, who it affects, what caused it, and what should happen next.
We connect the sources that define daily logistics reality: TMS/WMS/ERP, carriers and 3PLs, telematics, and finance. We normalize formats and build stable pipelines so analytics does not break every time a source changes.
We build an analytics storage layer that supports the relationships logistics teams need to drill into details: order → shipment → stops/milestones → carrier → invoices and accessories. This makes analysis traceable and defensible instead of “best effort.”
Most analytics problems start when the same metric means different things to different teams. “On-time,” “delivered,” and “cost per shipment” must have one definition across Ops and Finance.
We formalize metrics, formulas, and counting rules so dashboards stay consistent and self-service does not create five competing versions of the truth.
Dashboards are designed around how people work: what changed, where the issue is, how to filter, and how to drill down to root causes and supporting detail. Ops, Finance, and Management see different views, but built on the same data model.
In practice, logistics dashboards need real-time visibility and alerting for operational deviations, not only monthly summaries.
We set up scheduled reporting in formats teams actually use (PDF/CSV/Excel) with templates and predictable delivery, suitable for leadership and internal reviews, and stable enough to audit.
These categories align with common search intent (Logistics Analytics Software, Transportation BI, Supply Chain Dashboards) and with how teams naturally split questions.
A transportation view focused on execution: shipment progress, problem lanes, carrier performance comparisons, delay reasons, and reliability trends by direction and customer.
This is the “what broke today and where” layer, with the ability to drill into shipments, milestones, and exception patterns.
A cost view that makes accessorials, surcharges, invoice variance, and margin leakage visible in a way Finance can trust and Ops can act on.
This is where teams finally stop arguing about totals and start seeing which customers, lanes, carriers, or service patterns consistently create cost risk.
When the problem is wider than TMS, we connect transportation with warehouse and inventory signals to show end-to-end impact on availability and order fulfillment.
This is typically where leadership wants one stable picture instead of “transport says one thing, warehouse says another.”
We support two valid paths, depending on how analytics is used and who needs access.
If you want a familiar ecosystem and fast adoption, we deliver dashboards in Power BI. If you need analytics embedded inside your workflow systems or portals, with more controlled roles, client access patterns, or multi-tenant requirements, we build embedded/custom BI as part of your product experience.
Embedded analytics, by definition, is analytics placed directly inside applications or portals rather than living as a separate BI destination.
Dashboards do not survive on visualization alone. They survive on managed data.
We implement normalization, deduplication, consistent status language, and controls around source changes so teams can trust the outputs and changes do not silently break reporting.
We deliver this in phases: first a reliable data foundation, then MVP dashboards, then reporting and expansion across teams or regions.
A typical rollout looks like this:
Logistics analytics becomes valuable when it produces consistent answers under real operating pressure: different systems, different partners, changing volumes, and a continuous stream of exceptions.
TwinCore builds BI as a managed operating layer, not as a visualization layer. Data integration, a stable model, shared metric definitions, access control, and post-release support are treated as first-class parts of the solution, because that is what determines whether the dashboards remain usable six months later.
We implement the data warehouse/lakehouse and ETL/ELT pipelines that fit your stack, then deliver dashboards in Power BI or embed analytics into your systems where workflow integration matters. Monitoring, logging, and access control are included so the platform stays supportable.
Deployment can be cloud or on-prem, depending on operational and security constraints.
Fixed Price works best for well-defined scope and clear delivery milestones.
Time & Material fits discovery-heavy work where requirements evolve after data validation.
Dedicated Team is the right model when analytics becomes an ongoing capability and needs continuous expansion and support.
TwinCore has elevated the client's customers to the next level of supply chain management. The team is highly cost-efficient from a project management standpoint, and internal stakeholders are particularly impressed with the service provider's team dynamic.
TwinCore delivered a fully functional solution on time, meeting expectations. The highly organized team employed a DevOps approach, swiftly responded to needs and concerns, and led a productive, enjoyable workflow. Their receptiveness to client requests and feedback stood out.
Thanks to TwinCore’s work, the client has gained a user-friendly, stable, and scalable SaaS platform. The team manages the engagement well by being reliable and punctual; they deliver tasks on time. Their resources are also highly flexible, resulting in a truly seamless engagement with the client.
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Yes. Many implementations begin with one high-impact area. Transportation BI is often chosen when teams need faster operational visibility and exception patterns. Cost analytics is a common starting point when margin control, invoice variance, and accessorial spend are the priority. The solution is structured so the initial scope can expand without redoing the foundation.
It depends on the questions you need answered first. If operational visibility and service reliability are the priority, TMS is typically the first source because it defines execution, milestones, and carrier performance. If margin transparency and billing accuracy are the priority, finance/ERP and invoicing data need to be included early. In many cases, we connect a minimal set from both sides to avoid building dashboards that look correct but cannot be reconciled financially.
We treat metric alignment as part of the build, not as a documentation exercise. Definitions are agreed through working sessions using real examples: which events count, when a shipment is considered “on time,” how cost is allocated, and how exceptions are handled. These definitions are implemented in a shared semantic layer and validated against historical periods so both teams can reconcile outcomes and trust the numbers.
Yes. Power BI is a common first step when speed of adoption matters and teams want a familiar environment. When analytics needs to live inside operational tools or client portals, the same underlying model and pipelines can support embedded BI. The key is designing the warehouse and metric layer so the visualization layer can change without rebuilding the foundation.
We implement validation checks, monitoring, and controlled transformations in the pipelines. Data quality rules detect missing feeds, schema changes, duplicates, and unexpected value patterns. When source systems change, the pipeline behavior is observable and failures are surfaced early. We also version critical transformations and metric logic so changes can be reviewed and rolled out in a controlled way.
Both models are supported. The choice depends on security requirements, data residency, and how your source systems are hosted. The architecture is designed so cloud or on-prem deployment does not limit pipeline reliability, access control, or reporting.
Support includes ongoing monitoring, incident response for data failures, and controlled updates as sources evolve. This covers pipeline health, scheduled reporting, access control, and changes to metric logic when business rules shift. The objective is stability: dashboards and reports should continue to be trusted month after month, not require constant manual intervention.
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