Written by Cem Dilmegani
“The article was originally published on AIMultiple”
Artificial intelligence is transforming all industries and logistics is one of them. Logistics is the management of the flow of products between different locations. A global network of suppliers and customers complicates logistics operations and logistics companies contain both easy to automate tasks and complex processes that can benefit from AI/machine learning algorithms.
What does AI mean for logistics companies?
The technology offers a wide range of capabilities to logistics companies from autonomous machines to predictive analytics. According to Mckinsey research “AI adoption advances, but foundational barriers remain,” the logistics industry has adopted AI mostly for 4 business functions which are service operations, product and service development, marketing and sales, and supply chain management. These four business units cover 87% of AI adoption in logistics. Mckinsey estimates that logistics companies will generate $1.3-$2 trillion per year in economic value by adopting AI into their processes.
What are the applications of AI in logistics?
Logistics requires significant planning that requires coordinating suppliers, customers, and different units within the company. Machine learning solutions can facilitate planning activities as they are good at dealing with scenario analysis and numerical analytics, both of which are crucial for planning.
AI capabilities enable organizations to use real-time data in their forecasting efforts. Therefore, AI-powered demand forecasting methods reduce error rates significantly compared to traditional forecasting methods such as ARIMA, AutoRegressive Integrated Moving Average, and exponential smoothing methods.
With improved accuracy in demand prediction,
- manufacturers can better optimize the number of dispatched vehicles to local warehouses and reduce operational costs since they improve their manpower planning
- local warehouses/ retailers can reduce the holding costs (opportunity cost of holding the item instead of investing the money elsewhere)
- customers are less likely to experience stockouts that reduce customer satisfaction
Artificial intelligence help businesses analyze demand in real-time so that organizations update their supply planning parameters dynamically to optimize supply chain flow. With dynamic supply planning, businesses use fewer resources since dynamic planning minimizes waste.
According to the 2020 MHI Annual Industry Report, only 12% of businesses are using AI technology in their warehouses, but it is expected to reach 60+% in 6 years.
Warehouse robots are another AI technology that is invested heavily to enhance businesses’ supply chain management. The warehouse robotics market was valued at USD 2.28 billion in 2016 and is expected to grow at a CAGR of 11.8% between 2017 and 2022.
For example, the retail giant Amazon has acquired Kiva Systems in 2012 and changed its name to Amazon Robotics in 2015. Today, Amazon has 200,000 robots working in their warehouses. In 26 of Amazon’s 175 fulfillment centers, robots helping humans for picking, sorting, transporting, and stowing packages.
Damage Detection/Visual Inspection
Damaged products can lead to unsatisfied customers and churn. Computer vision technology enables businesses to identify damages. Businesses can determine the damage depth, the type of damage, and take action to reduce further damage.
Predictive maintenance is predicting potential machine failures in the factory by analyzing real-time data collected from IoT sensors in machines. Machine learning-powered analytics tools enhance predictive analytics and identify patterns in sensor data so that technicians can take action before the failure occurs.
Autonomous things are devices that work without human interaction with the help of AI. Autonomous things include self-driving vehicles, drones, and robotics. We should expect to see more autonomous devices in the logistics industry due to the industry’s suitability for AI.
Self-driving cars have the potential to transform logistics by decreasing heavy dependence on human drivers. Technologies such as platooning support drivers’ health and safety while reducing carbon emission and fuel usage of vehicles. Tesla, Google, and Mercedes Benz are investing heavily in the concept of autonomous vehicles, it is only a matter of time before autonomous trucks are seen on roads around the world. However, according to BCG estimations, only around 10 % of light trucks will drive autonomously by 2030.
For the logistics of products, delivery drones are useful machines when businesses deliver products to places where ground transfer is not possible, safe, reliable, or sustainable. Especially in the healthcare industry where pharmaceutical products have a short shelf life span, delivery drones can help businesses reduce wastage costs and prevent investments for costly storage facilities.
Dynamic pricing is real-time pricing where the price of a product responds to changes in demand, supply, competition price, subsidiary product prices. Pricing software mostly uses machine learning algorithms to analyze customers’ historical data in real-team so that it can respond to demand fluctuations faster with adjusting prices.
Route optimization/Freight management
AI models help businesses to analyze existing routing, track route optimization. Route optimization uses shortest path algorithms in graph analytics discipline to identify the most efficient route for logistics trucks.
Therefore, the business will be able to reduce shipping costs and speed up the shipping process. For example, Valerann‘s Smart Road System is an AI web-based traffic management platform that delivers information about road conditions to autonomous vehicles and users.
Every business unit has back-office tasks and logistics are no different. For example, there are numerous logistics-related forms like a bill of lading from which structured data needs to be manually extracted. Most businesses do this manually.
Automating Manual Office Tasks
Hyperautomation, also referred to as intelligent business process automation, means using a combination of AI, robotic process automation (RPA), process mining, and other technologies to automate processes in an end-to-end manner. With these technologies, businesses can automate several back-office tasks such as
- Scheduling and tracking: AI systems can schedule transportation, organize pipelines for cargo, assign and manage various employees to particular stations, and track packages in the warehouse.
- Report generation: Logistics companies can use RPA tools to auto-generate regular reports that are required to inform managers and ensure everyone in the company is aligned. RPA solutions can easily auto-generate reports, analyze their contents and based on the contents, email them to relevant stakeholders.
- Invoice/bill of lading/rate sheet processing: These documents help communication between the buyers, suppliers, and logistics service providers. Document automation technologies can be used to increase the efficiency of processing these documents by automating data input, error reconciliation, and document processing.
- Email processing: Based on contents in auto-generated reports, RPA bots can analyze the content and sends emails to relevant stakeholders.
For more RPA and hyperautomation use cases for businesses’ back-office tasks, feel free to read our articles:
- 60+ RPA applications
- 10+ Hyperautomation applications
- Supply chain automation
Customer Service Chatbot
Customer service plays an important role in logistics companies since customers will contact companies for any issue they experience in delivery. Customer service chatbots are capable of handling low-to-medium call center tasks such as:
- requesting a delivery
- amending an order
- tracking shipment
- responding to a FAQ
Chatbots are also valuable tech to analyze customer experience, chatbot analytics metrics enable businesses to understand their customers better so that they can enhance the customer journey they deliver.
Cem is a high-tech industry analyst and he served as a tech consultant at McKinsey and as a tech entrepreneur at Hypatos, the document hyperautomation company. AIMultiple.com provides 1M enterprises with transparent, data-driven insights on enterprise technology.