Artificial intelligence (AI) has the potential to transform all industries, including logistics. In logistics, the flow of products between different locations, as well as the global network of suppliers and customers can be very complicated to manage. AI resolves this by offering a wide range of optimization solutions to aid logistic companies, such as predictive analytics and autonomous machines to carry out manual tasks.
Adopting AI will generate $1.3-$2 trillion per year in economic value
According to a research by McKinsey, 87% of AI adoption in the logistics industry mainly serves 4 functions: service operations, product and service development, marketing and sales, and supply chain management. The research predicts that logistics companies adopting AI into their processes will generate $1.3-$2 trillion per year in economic value.
AI offers a number of solutions to various optimization problems in the logistics industry. Below are just a few examples:
1.) Vehicle routing problem
AI solutions geared towards Vehicle Routing Problems (VRPs) are just one of the ways that help logistics companies make significant savings on operations costs. The goal of VRP is to find the best routes for multiple vehicles visiting a set of locations. Vehicle routing is a key class of complex logistics management problems that a company needs to solve if they want to cut costs and maximize their resources.
Planning routes presents several uncertainties — changing demands, traffic, and weather conditions to name a few, – which can lead to higher costs if not appropriately managed. VRP can also help a logistics company by resolving some of the most expensive first mile and last mile service problems (FM/LM). Through vehicle routing optimization, specifically-designed routes are then assigned for vehicles so they can efficiently depart from a given number of different depots, travel through several locations to deliver some service, and return to a set location.
Rosebay, an IT incubation company with offices in Malaysia, Indonesia and UK, works with several companies and customers in the logistics industry, including Post Office Indonesia (POS Indonesia) in helping their organization optimize their VRPs through more data-driven approaches.
Rosebay helped POS Indonesia gain a total of 22%-71% savings
For POS Indonesia, Rosebay helped provide a comprehensive vehicle routing optimization solution, from individual single-vehicle routes to countrywide multi-channel end-to-end logistics optimization. In a few months, Rosebay helped POS Indonesia gain a total of 22%-71% savings in terms of time, distance and fuel expenses after implementing a three-phase roadmap.
Perceiver AI helped one company save 40% on their fuel costs, even after they engaged with another AI company. What can Perceiver AI do for your company? Learn more!
2.) Automating manual office tasks
Hyperautomation, also known as intelligent business process automation, combines AI, robotic process automation (RPA), and analytical disciplines such as process mining to automate processes in a simpler, end-to-end manner. With hyperautomated technologies, businesses can now efficiently perform several back-office tasks such as:
Scheduling and tracking: AI systems can design schedules for transportation, organize pipelines for cargo, assign employees to specific stations, check and report shipment status, and track packages in the warehouse.
Report generation: RPA tools are also helpful in auto-generating regular reports that are required to provide insight for managers and ensure that every employee is aligned with company standards and regulations. These reports can be easily auto-generated and emailed to relevant stakeholders. RPA bots can also analyze the contents of auto-generated reports they create before sending them to the recipients.
Invoice/bill of lading/rate sheet processing: These documents act as a form of communication between buyers, suppliers and logistics vendors. Through the use of document automation technologies, processing such documents can be done more accurately and efficiently through automated data input, error reconciliation and document processing.
Order processing: RPA bots can also be used for order processing. They can automatically record and enter data, create customer profiles, process payments, place orders and control order updates. British AI company Shipamax, for example, offers RPA solutions that can extract data from a company’s own emails with customers and vendors, including attachments of various formats, and prepares it for further analysis.
3.) Autonomous Things
Autonomous Things (AuT), or the Internet of Autonomous Things (IoAT), are machines or programs that work on specific tasks autonomously without human interaction. These include robots, vehicles, drones, autonomous smart home devices and autonomous software. These machines are enhanced with sensors, AI and analytical capabilities so that machines make data based decisions and autonomously complete tasks.
There is currently a labor shortage in the U.S. and the U.K, especially with truck drivers since the workforce is aging. According to truckinginfo.com, the US market saw a shortage of around 50,000 drivers in 2016. The average age of a truck driver is 49, and many of them are nearing retirement. Long-haul trucking is an integral part of the logistics market, and shortage of truckers can be a problem.
Logistics company DHL has begun testing their driverless trucks with the goal of significantly cutting delivery time and operational costs. In late 2019, they tested one of their driverless trucks to autonomously deliver butter from California to Pennsylvania, which is about over 4,500 km apart. They did this in 3 days in inclement weather; a normal trip would have taken about 5-9 days.
4.) Predictive Maintenance
Predictive maintenance (PdM) is done to prevent potential problems rather than conducting maintenance on a fixed schedule or when an issue arises. Predictive maintenance is better than condition-based approaches because it eliminates the need for spending too much money on frequent and unnecessary maintenance. Predictive maintenance and analytics can also anticipate the need for replacements of specific machines. Hence, reducing the risk of technical downtime.
Predictive maintenance resulted in a 50% reduction in downtime due to equipment failures
In a study conducted by McKinsey in 2015, predictive maintenance resulted in a 50% reduction in downtime due to equipment failures. Downtimes can be extremely costly, especially for port operators. According to Predikto CEO Mario Monta, ports experience 800 to a thousand hours a year in downtime due to crane malfunctions.
The same study also showed that through predictive maintenance, there was a 10-25% reduction in worker injuries. Through sensor data incorporated in analytic systems, industries can avoid injuries in operations.
The study also suggested that predictive maintenance is good for the environment. Machines that remain useful for longer periods and continually increase their efficiency through advanced analytics also means less use and wasting of natural resources. Predictive maintenance is just one of the few ways that shows us that using AI in logistics will not just help companies with their bottom line, it will also cultivate a culture of social responsibility in the industry.
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