This blog post outlines the most common challenges we see across logistics businesses, and how AI, when implemented correctly, can solve them and improve performance.
Shipping and transport operations rely heavily on schedules that are constantly changing: vessels arrive early or late, berths become unavailable, and labour constraints shift. Most scheduling decisions are made reactively, and data is often locked in spreadsheets or disconnected systems.
By feeding historical arrival times, port congestion data, weather forecasts and vessel characteristics into a machine learning model, we can predict optimal port call windows. These insights can be integrated directly into a custom dispatch interface that suggests schedules or flags risk points, enabling faster, smarter operational decisions.
We use low-code tools (like OutSystems) to build a custom port call or dispatch planning system, then layer AI services (e.g. Azure Machine Learning or AWS Forecast) to deliver real-time insights. This approach gives operations teams visibility and control without overhauling every legacy system.
Bills of lading, customs declarations, inspection reports, chain of responsibility paperwork. Logistics is drowning in documentation. Much of it is still prepared manually, reviewed manually and stored in fragmented systems, introducing delays and compliance risk.
Natural Language Processing (NLP) can automatically extract and structure data from PDFs, emails, and scanned forms. AI models can also validate document fields against business rules, flag missing information or inconsistencies, and even pre-fill documentation based on shipment data.
We integrate OCR and NLP services (like Azure Form Recogniser or Amazon Textract) directly into custom document management workflows. This means documents can be automatically ingested, processed, validated and stored inside a secure, auditable system tailored to your compliance needs.
Overestimating demand leads to wasted fuel, labour and warehouse space. Underestimating demand causes missed opportunities, delays and unhappy clients. Most forecasting still relies on spreadsheets or historic averages, not dynamic inputs.
AI models can analyse historical booking data, seasonal trends, customer patterns, weather forecasts and economic indicators to create accurate, continuously updated forecasts. This allows operators to better allocate fleets, staff and stock in advance, rather than scrambling in response.
We create a forecasting model using your historical operations data (securely stored on Azure or a platform of your choice), then deploy the model through a simple dashboard or integrate it with your transport management system. Built using low-code, it can be adapted as your business grows or shifts.
Most logistics organisations still struggle to get a real-time view of where goods are, where delays are likely, or which teams are under capacity. Systems are disconnected, and data is buried in CRMs, ERPs, spreadsheets or even email chains.
AI can help by consolidating data from multiple systems and applying anomaly detection to flag issues before they escalate, such as delivery delays, route deviations or stock inconsistencies.
We start by identifying your key systems (TMS, WMS, CRM) and use APIs or middleware to connect them. Then, we apply AI anomaly detection to identify deviations which are flagged through custom dashboards built using low-code, mobile-ready interfaces. This turns raw data into useful alerts and decision support.
The logistics industry faces a growing labour gap, especially in customer-facing and admin-heavy roles. At the same time, customer expectations are higher than ever as they want visibility, instant updates and a seamless experience.
AI chatbots and virtual assistants can handle shipment tracking, ETA updates, booking changes and basic enquiries. Internally, AI can manage task routing, approvals and status updates, reducing pressure on overstretched teams.
Using Microsoft Power Platform or OutSystems, we build chatbots and customer portals connected to your backend systems. We integrate AI services for language understanding and sentiment analysis, so the bots aren’t just robotic scripts - they’re responsive, context-aware tools that scale with your business.
Transport and logistics companies are under pressure to cut emissions and improve reporting. But without accurate data and intelligent systems, measuring and managing environmental performance is difficult.
AI can model carbon emissions across routes, modes and assets, and suggest operational changes to reduce impact. This includes route optimisation, idle time reduction, and intelligent load balancing to make every trip more efficient.
We connect your fleet data and GPS systems to an emissions calculation engine, then surface insights via dashboards. We can also embed these metrics into your planning and compliance workflows so that sustainability becomes part of how you work, not an afterthought.
We’ve built smart systems for ports, shipping agencies and transport businesses across Australia and we’d be happy to show you how. Contact the team today for more information.
You can also download the checklist to see how your current processes stack up and spot the areas where AI can have the biggest impact.
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When you’re delivering software for government, there are no shortcuts. Security isn’t a feature. It’s a non-negotiable. At Kiandra, we work with government departments where privacy, compliance, and performance must co-exist – from health records to social services.
The logistics and transport industry is under more pressure than ever: rising costs, tighter delivery windows, and growing compliance demands – all while customer expectations keep climbing.
Whether you’re curious about custom software or have a specific problem to solve – we’re here to answer your questions. Fill in the following form, and we’ll be in touch soon.