According to a recent study, logistics companies can generate economic value of $1.3-$2 trillion per year for the next two decades by adopting AI into their processes.
The successful implementation of AI has helped businesses improve logistics costs by 15%, inventory levels by 35%, and service levels by 65%.
Muteki Group have been transforming Logistics solutions for different markets applying wide expertise in Artificial Intelligence and Machine Learning, Deep Learning and Neural Networks, Computer Vision, Digital Signal Processing and Speech Recognition
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Logistics planning involves coordinating with suppliers, customers, and various units within the company, which requires significant planning. Machine learning solutions can facilitate planning activities, as they are adept at handling 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 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.
Supply planning involves analyzing demand in real-time and updating supply planning parameters dynamically to optimize the flow of the supply chain. By using artificial intelligence to achieve dynamic supply planning, businesses can minimize waste and use fewer resources.
Automated Warehousing refers to the use of robotics and AI to manage and optimize warehouse operations. The MHI annual industry report projects that by 2026 the adoption of AI-powered warehouse solutions by businesses will reach 60+% as compared to 2020
Warehouse robots are an AI technology that is being heavily invested in to enhance businesses' supply chain management. The warehouse robotics market was valued at USD 4.7 billion in 2021 and is expected to grow at a CAGR of 14% between 2021 and 2026.
For instance, the retail giant Amazon acquired Kiva Systems in 2012 and renamed it Amazon Robotics in 2015. Today, Amazon has 200,000 robots working in their warehouses. In 26 of Amazon's 175 fulfillment centers, robots assist humans in tasks such as picking, sorting, transporting, and stowing packages.
Damage detection/ Visual Inspection
Damaged products can lead to unsatisfied customers and increased turnover. However, Computer Vision technology can help businesses identify damages and ensure quality control in warehouse operations. With this technology, logistics managers can determine the size and type of damage and take necessary action to minimize further damage.
Predictive maintenance involves predicting potential machine failures in a 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.
Self-driving cars have the potential to revolutionize logistics by reducing the heavy reliance on human drivers. Recent surveys indicate that autonomous cars and trucks will be implemented in real-world scenarios in the near future. Technologies such as platooning support driver safety while decreasing carbon emissions and fuel usage. Companies such as Tesla, Google, and Mercedes Benz are investing heavily in the concept of autonomous vehicles, and it is only a matter of time before autonomous trucks are seen on roads worldwide. However, according to BCG estimations, only around 10% of light trucks will be autonomous by 2030.
Delivery drones are becoming an increasingly valuable tool for logistics, particularly when ground transport is not feasible, safe, reliable, or sustainable. They are particularly useful in the healthcare industry, where pharmaceutical products have a limited shelf life. By using delivery drones, businesses can reduce waste costs and avoid the need for expensive storage facilities.
Dynamic pricing refers to the practice of adjusting the price of a product or service in real-time based on various factors, including changes in demand, supply, competition, and related product prices. This pricing strategy relies heavily on machine learning algorithms and pricing software that analyze historical customer data in real-time, allowing businesses to quickly adjust their prices to respond to fluctuations in demand.
AI models assist businesses in analyzing their current routing and optimizing their routes. Route optimization employs shortest-path algorithms in the field of graph analytics to determine the most efficient route for logistics trucks. This allows businesses to reduce shipping costs and expedite the shipping process.
Route optimization tools can also help companies to reduce their carbon footprint.
Route optimization/ Freight management
Automating document processing
Automating invoice, bill of lading, and rate sheet documents can improve communication among buyers, suppliers, and logistics service providers. Document automation technology can increase processing efficiency by automating data input, error reconciliation, and document processing.
Hyperautomation, also known as intelligent business process automation, involves utilizing a combination of AI, robotic process automation (RPA), process mining, and other technologies to automate end-to-end processes. By using these technologies, businesses can automate various back-office tasks, including:
Scheduling and tracking: AI systems can schedule transportation, organize cargo pipelines, assign and manage employees to specific stations, and track packages in the warehouse.
Report generation: Logistics companies can use RPA tools to automatically generate regular reports that inform managers and ensure alignment across the company. RPA solutions can easily generate reports, analyze their content, and email them to relevant stakeholders.
Email processing: Based on the contents of auto-generated reports, RPA bots can analyze the content and send emails to relevant stakeholders.
Customer service chatbot
Customer service is a crucial aspect of logistics companies, as customers often reach out to companies regarding any issues they may face during the delivery process. Customer service chatbots are capable of handling low-to-medium call center tasks such as requesting a delivery, amending an order, tracking a shipment, and responding to frequently asked questions.
Chatbots are also valuable tools to analyze customer experience. Chatbot analytics metrics enable businesses to better understand their customers and improve the customer journey they provide.
Lead scoring refers to the process of enabling sales representatives to concentrate on the appropriate prospects. AI-powered tools can be employed to automatically assign scores to leads based on their profiles, behavior, and interests. AI-based lead scoring systems leverage machine learning algorithms to rapidly analyze data and accurately identify which leads are most likely to become paying customers.
Sales and marketing analytics
AI can provide more accurate sales and marketing analytics. Logistics service providers can use AI-powered tools to analyze customer behavior and employ predictive analytics to gain better insights into what their customers are likely to do next. AI-based systems can also be used to track changes in the market, allowing logistics service providers to stay ahead of the competition and make data-driven decisions that lead to greater efficiency.