Supply chain analytics software we provide

Supply chain analytics software that explains why the number moved

Your dashboards show that on-time delivery dropped. They don’t show which lane, carrier, or supplier caused it. We build analytics software that uses AI to trace each change to its root cause and flag the next one early.

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Why more dashboards haven’t made decisions faster

Most supply chain teams have plenty of reports. The trouble is that each one pulls from a different system and uses different definitions. Meetings spend more time agreeing on numbers than acting on them.

Svitla builds supply chain analytics software on one governed data model. Orders, shipments, inventory, suppliers, and costs connect at the level where problems start. Every KPI has one definition, and users drill down from the whole network to a single shipment.

Who we serve

Supply chain executives

See cost to serve, service levels, and risk across the network, and test decisions before you make them.

Planners and buyers

Find which items, suppliers, and locations drive misses, and act on the exceptions that matter most.

Logistics and transportation managers

Track carrier performance, freight spend, and lane reliability from one source.

Challenges we address

In Gartner’s 2023 supply chain analytics survey, 33% of leaders cited lack of trust in data as a top hurdle to adopting analytics. When nobody trusts the numbers, every report turns into a debate.

Which numbers are right?

Challenge: Each team reports on-time delivery, fill rate, and inventory differently.
How Svitla solves it: One semantic layer defines every KPI once, with lineage back to the source systems.

Why did cost per order go up?

Challenge: Reports show the total but not the drivers behind it.
How Svitla solves it: Driver trees and root cause analysis break each change down by lane, carrier, product, and customer.

What happens if this supplier fails?

Challenge: Supplier risk lives in spreadsheets and in buyers’ heads.
How Svitla solves it: Risk scoring and scenario models show exposure by item, plant, and customer.

Will this shipment arrive late?

Challenge: Tracking data sits in carrier portals and email threads.
How Svitla solves it: Shipment events from carriers and visibility providers feed one view with predicted arrival times.

Our process at Svitla

STEP 1

Which decisions matter most?

We start with the decisions your team makes every week and the questions behind them. Each one becomes a requirement.

STEP 2

Where does the data live?

We map source systems, data owners, and quality gaps. Where we can, we fix problems at the source.

STEP 3

What does each KPI mean?

We agree on definitions with operations and finance, then build each one once in the shared data model.

STEP 4

Is it working?

We track usage, data freshness, and the decisions made with the new tools. Then we add analysis where users ask for it.

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Unified supply chain data model

Connects orders, shipments, inventory, suppliers, and costs.

Keeps one definition for every KPI.

Tracks data lineage back to each source.

Monitors data freshness and quality on every load.

Performance dashboards

Covers OTIF, fill rate, inventory turns, and cost to serve.

Drills down from network to region, site, and shipment.

Sends alerts when KPIs cross set thresholds.

Offers role-based views for executives and operators.

Root cause and driver analysis

Breaks each KPI change down by driver.

Compares periods, regions, and partners.

Highlights the biggest contributors first.

Links every finding to the underlying records.

Scenario and what-if modeling

Models changes in demand, suppliers, and routes.

Estimates cost and service impact before you commit.

Saves scenarios for review and comparison.

Supports network design and sourcing decisions.

Supplier and logistics performance

Scores suppliers on delivery, quality, and lead time.

Tracks carrier on-time rates and freight spend by lane.

Flags partners whose performance is slipping.

Shares scorecards with suppliers and carriers.

Is custom supply chain analytics software the best option for your needs?

Answer a few simple questions to find out whether you should choose a custom solution or a pre-built supply chain analytics platform.

1 / 7

Do different teams report different numbers for the same KPI?

Do analysts spend more time collecting data than analyzing it?

Do you pull data from more than five systems for supply chain reporting?

Has a BI tool or packaged analytics product failed to answer your key questions?

Would anomaly alerts or predicted arrival times change how your team works?

Do you plan to add sites, partners, or regions in the next two years?

Important integrations for supply chain analytics software

ERP

Orders, purchasing, costs, and financial results.

WMS

Inventory, labor, and warehouse performance data.

TMS

Shipments, carriers, rates, and freight spend.

Demand planning tools

Forecasts and forecast accuracy by item and location.

Supplier portals and EDI

Confirmations, ASNs, and delivery performance.

Visibility and telematics providers

Real-time shipment events and locations.

CRM and order management

Customer orders, service levels, and returns.

External data

Weather, port congestion, and commodity prices where they help explain performance.

Faster decisions

Less time spent reconciling numbers.

Meetings focus on actions, not definitions.

Lower logistics cost

Lane and carrier analysis finds savings.

Cost-to-serve data informs pricing and service terms.

Better service levels

Root causes of misses get found and fixed.

Late shipments get flagged early.

Lower risk exposure

Supplier risks surface before they hit production.

Scenarios are ready for likely disruptions.

Decision and KPI discovery

We interview executives, planners, logistics managers, and finance. We list the decisions they make and the KPIs those decisions need. Each KPI gets an owner and a draft definition.
Source system and data quality assessment

We profile data in the ERP, WMS, TMS, and partner feeds. We measure completeness, timeliness, and consistency. Gaps get fixed at the source or scheduled for later phases.
Data architecture

We design the data platform, pipelines, and refresh schedule. Each source runs in batch or streaming mode, depending on how fast decisions need it. Access rules follow user roles.
Semantic layer and KPI build

We build the shared data model and define every KPI. Operations and finance sign off on each one. Every KPI traces back to its source records.
Dashboards and analysis tools

We build role-based dashboards, drill-downs, and alerts. Users test them on real decisions before release. Screens show fewer charts and clearer next steps.
AI model development

We train anomaly detection, arrival time prediction, and risk scoring on your history. We measure accuracy before release. Every prediction shows the factors behind it.
Adoption and ongoing support

We train users by role and track how they use the tools. New sources and KPIs join as needs grow. We monitor pipelines and models and retrain them on a set schedule.

How much does supply chain analytics software cost?

A focused analytics layer over a few core systems, with governed KPIs and dashboards, typically starts around $50,000. A network-wide solution with many data sources, real-time visibility, scenario modeling, and AI predictions can reach $400,000 or more. The number and quality of data sources drive most of the cost.

Want to understand the cost of your supply chain analytics software?

Calculate the cost

Check our client success cases

An Award-Winning Digital Transformation For A Shipping Specialist

Project Type AU Solution, Cloud Solutions, Front-end, Web Development

Industry Logistics & Transportation

Svitla Systems replaced MONSON's aging SaaS with MB2, a fully custom in-house shipping agency application built on the OutSystems low-code platform. Featuring an intelligent rules engine, automated documents, and multi-port estimators, it saves staff roughly two hours a day and won the 2022 OutSystems Innovation Award.

Complete Platform Overhaul In Just Six Months

Project Type AU Solution, Web Development

Industry Logistics & Transportation

Svitla Systems re-platformed GS1 Australia's MyGS1 member portal onto OutSystems in just six months, redesigning the UX for its 25,000 users and integrating external systems and a common data service. The overhaul modernized legacy architecture and now lets product and marketing teams make content updates without developer support.

Platform That Combines Online Ordering With Brand And Business-Building Information

Project Type AU Solution, Cloud Solutions, Web Development

Industry E-commerce & Retail, Logistics & Transportation

Svitla Systems built a secure, multi-device platform for one of North America's largest FMCG distributors that combines online ordering with brand- and business-building content. Serving 30,000+ retailers, it auto-generates orders, applies dynamic pricing, automates returns, and turns ordering behavior into insights that drive revenue.

Consulting

We assess your decisions, KPIs, data sources, and reporting tools. You get a prioritized roadmap and a data quality report.

Engineering

We design, build, and test your analytics solution, from pipelines to dashboards. Our teams cover data engineering, BI development, and machine learning.

Modernization

We audit your legacy reports, spreadsheets, and data warehouse, then plan the migration. We add what the old setup lacked, such as governed KPIs or anomaly alerts. Cutover happens report by report, with old and new numbers reconciled before retirement.

Data pipeline and partner integration engineering

We build and maintain feeds from carriers, suppliers, visibility providers, and external data sources. When a partner changes a format, we update the pipeline without breaking reports.

AI model retraining and support

Supply chain patterns shift with new lanes, partners, and markets. We monitor model accuracy and retrain on your latest data.

FAQ

Why build custom analytics when we already have a BI tool?

A BI tool draws charts from the data it receives. Most of the work is the data underneath: pipelines, definitions, and quality checks across many systems. We often build on your existing BI tool and focus custom work where it falls short.

How do you get operations and finance to agree on KPI definitions?

We run definition workshops with both teams and document each KPI with an owner. We test disputed definitions on real data until both sides accept the result. Later changes follow a simple approval process.

How do you build trust in an AI anomaly alert?

Each alert shows what changed, by how much, and which records drove it. Users mark alerts as useful or not. We use that feedback to tune thresholds and cut noise.

What happens when a source system sends bad data?

Quality checks run on every load. We hold back bad records and flag them to the data owner. Dashboards show when data was last refreshed, so users know what they’re looking at.

Can we share analytics with suppliers and carriers?

Yes. Partner portals show each partner only its own scorecards and data. Access is controlled by role and partner.

What would your team decide if it trusted every number?

Tell us which supply chain decisions take too long today. We’ll show you where custom analytics could speed them up first.