Luigi D’Amico, senior sales executive, and Julian Hanmer, Senior Consultant, Advisory and Design at iOCO, believe digital technologies offer the solution to the key transport and logistics challenges facing South Africa today. With fuel costs rising and the risks associated with transporting goods increasing, traditional approaches to logistics and planning are no longer enough, they say.
Trade is an extremely import part of the South African economy, with imports and exports equating to around 60% of South Africa's Gross Domestic Product (GDP) per quarter. Unfortunately, South Africa's transport infrastructure, particularly our rail and logistics systems, are under severe strain. As a result, the National Logistics Crisis Committee (NLCC) has been formed in order to “implement urgent interventions”.
Many of these interventions will require physical infrastructure management, but many of the country’s logistics challenges can be solved through the use of AI. “We’ve practically exhausted traditional route planning,” says Hanmer. “Organisations now need more advanced solutions to help them be more agile in their planning. For example, how to plan for issues such as an accident closing a freeway, or a hijacking. AI could do that for them, and digital twins can be used to support the ‘what if’ scenario planning. We see organisations with valuable goods using digital twins and 3D visualisation to identify the optimal procedures and routes should something happen along the route. The route planners run the models before sending the vehicles out. Digital twins are happening a lot in logistics now.”
D'Amico highlights another use case for AI in combating crime in a local logistics company: “To be sure drivers aren’t being threatened, or involved in plans to hijack a vehicle, AI analyses regular calls with the drivers to pick up signs of anxiety or deception in the driver’s responses. If a driver shows signs of stress, or answers differently from their usual pattern, an alert is triggered and additional security measures are allocated to the vehicle.”
Hanmer says another threat that increases risk and costs is internal and external fraud syndicates. “There is room for more fraud prevention when it comes to invoicing and tracking payments to suppliers. The delivery note still exists in our world, and we need to get rid of all the paperwork. That process from dispatch door to client’s receiving needs to be improved, digitised and automated for efficiency and fraud prevention,” he says.
They note that IoT devices like sensors and RFID tags are being used to track the location and condition of shipments in real-time, enabling better inventory management, route optimisation, and enhanced supply chain visibility. Blockchain technology to improve transparency and security in supply chains, providing an immutable ledger for tracking the movement of goods.
Reinventing warehouses – the hub of the logistics chain
Warehouses are the heart of logistics operations, and often the point at which delays occur, says Hanmer. “An issue we see with a lot of manufacturing and logistics companies is a lack of effective demand forecasting. This is traditionally done using Excel spreadsheets and building predictive models. If forecasts are flawed, they sit with the holding costs of excess raw materials, can’t meet spikes in retail demand, or plan loads inefficiently.”
Hanmer says: “There are a fair number of warehousing operations in South Africa where the stock or racks aren’t barcoded, which makes stock counting and inventory management very difficult.”
Says D’Amico: “Even the most experienced people can only do so much and process only so much information. They become a bottleneck in the processes. With AI-enabled predictive analytics, organisations can do more effective forecasting and scenario planning to meet changing production and retail demands and can plan loads better.”
Hanmer adds that bringing in external data such as traffic, weather patterns and spikes in congestion due to load shedding, could significantly improve digitise scenario planning.
Robotics and automation in warehouses and distribution centres also enhance efficiency and reduce labour costs, they say, while data analytics delivers insights organisations can use to proactively address inefficiencies.
“For example, they might incentivise a client who purchases a half a truck load of goods, to rather purchase a full truck load of goods at a slightly discounted rate to reduce the transport cost and empty loads,” D’Amico says. “Another way to reduce costs and improve efficiencies is to connect logistics and partner systems to pre-notify the destination so that they have loading bays available when the truck arrives. This eliminates congestion and waiting times. The broader ecosystem needs a ML and AI enabled logistics system upstream and downstream to support efficiency.”
Data to drive last mile efficiency
The last mile, accounting for a growing proportion of the cost involved in transportation, presents a wealth of opportunities for improvement, they note. Fraud, crime, and ineffective picking, packing, warehousing and scheduling can add significant time and cost to processes.
Hanmer notes that a common issue adding to last mile costs is single purpose vehicles making long trips and returning empty. “For example, a container truck delivering a shipping container from City Deep to Rustenburg will go back empty. The same applies to a specialised vehicle such as a refrigerated milk truck – you can’t do a return load because the vehicle must come back empty and be cleaned. Where vehicles aren’t multipurpose vehicles, the last mile is costly. You may need to try to find similar clients who can use the vehicle for the return trip so that trucks are better utilised. There are brokerage platforms for load matching, but this environment is not scaled or advanced at this stage.”
D’Amico says: “To reduce costs and improve efficiencies, organisations need to bring the entire fleet, logistics and supply chain online, and bring all the data together, to get visibility across the environment and support informed forecasting decisions with big data analytics. From there, they should integrate with internal systems, upstream and downstream partners and customers too. With all this near real time data coming in, they can improve efficiency, reduce costs, improve customer service and profits, and reduce risk, leakages and theft.”
