The Role of Artificial Intelligence in Supply Chain and Logistics
In the era of globalization, Artificial Intelligence (AI) is revolutionizing many industries including supply chain and logistics. Supply chain and logistics encompass the flow of the products from one location to another using AI-based prompt decisions.
The invention of AI optimizes supply chain decisions and boosts the automation of logistics in warehouses. The fusion of AI with machine learning techniques increases the productivity of the stock by spending less time.
AI has taken over the time-consuming and error-prone human tasks. Artificial intelligence is helping the supply chain and logistics industries by automating the warehouse inventory management, forecasting the demand, process automation, monitoring the product quality, and quick data analysis.
Source: Ballooning-Potential economic-value creation from AI in the next 20 years $trn. (McKinsey.com)
The warehouses and transport operations are generating voluminous data. To gain full insight into the data, the application of analytical tools is necessary. By opting for Machine learning techniques, different patterns of the supply chain can be discovered to streamline the chain.
Various AI-based software are developed that provides valuable supply chain management decisions in a matter of seconds. Artificial Intelligence has tremendous benefits. AI models help businesses to analyze the fluctuation in the price and demand of the product.
Similarly, AI-based models are also used for the shortest path identification to reduce the shipment cost and speed up the shipping procedure. Another important role of AI in logistics is customer service. An AI-based chatbot is being used by companies to communicate with the customer and sort out their problems.
Artificial Intelligence is mimicking the human intelligent procedures through the creation of autonomous vehicles and collaborative robots in the warehouses that are used to fully automate the processes like inventory control, moving products in the warehouses, and the pick and pack process.
The invention of sensors, cameras, AI-based software can drive autonomous vehicles to carry out cognitive tasks more efficiently and error-free. These sensors and cameras are also used for the predictive maintenance of potential machine failures.
IoT-based sensors are used to gather predictive data about the technical fault in the machines allowing the technicians to take action before the failure occurs. Another use of AI in warehouses is the detection of damaged products through AI-based software. A damaged product can damage the business’s reputation. Companies can determine the type and depth of the damage to take further action.
Fortunately, AI has advanced many organizations to regulate their supply chain without the need for extra labor, physical space, and expertise. Artificial Intelligence is transforming the supply chain and logistic industries to work better, faster and smarter.
Companies can opt the AI technologies to minimize the risk, increase the quality of the product, fasten the shipment, improve customer service, and increase visibility across the end-to-end supply chain.
The AI-based procedure allows the companies to thrive in the market by building a more flexible and agile supply chain. However, supply chain leaders that haven’t adopt the AI techniques faced major pitfalls in supply chain management. (Source: McKinsey.com)
Source: McKinsey survey of global supply chain leaders, December 4-18, 2020, n=52. (McKiney.com)
Use Cases of AI in Logistics and Supply Chain
The power of Artificial intelligence fused with machine learning techniques improves the operational flow by optimizing the routes and speed up the delivery. However, AI-based software gathers information, analyzes it, and gives informed decisions efficiently.
Artificial intelligence brings a revolution in logistics industries by reducing operational redundancies, mitigating risks, predicting the customers’ behavior, enhancing fleet efficiency, and increase the supply chain. Some of the use cases of AI in the logistics and supply chain sector are discussed below:
Predicting Customer Behavior
For the past few years, AI and marketing are working in a close association. AI is revolutionizing the logistics industry and is strongly impacting many marketers and product sellers to look for innovative ways and strategies that boost up their product sales.
To analyze the online buying customer behavior about a product or service AI plays a great role. The new AI feature lets the customers check the new products and services and left prompt feedback. Analyzing the need of the customer and maintaining the customer satisfaction level is very important to maintain a good business reputation.
The advent of new AI technologies has a strong impact on consumer marketing. The data collected from data sources used to make strategic decisions based on Artificial Intelligence. AI target a certain set of audience to send customized messages that create a strong bond between the customer and a brand.
Similarly, chatbots are used to conduct an online conversation between a customer and seller to answer the inquiry of the customer about tracking shipment, order cancelation, and requesting a delivery. This can lead to an increase in sales and overall business profit.
Increasing Fleet Management
Artificial Intelligence is not just assisting consumers but is also used for fleet management. The fusion of advanced AI algorithms with strong mathematical models is used for accurate decision-making. One of the use cases of AI is the increase in fleet management.
The old linear models of fleet management (for instance to calculate the engine oil changing date from milage, checking fuel consumption, and weather forecast) is taken over by advanced models. AI models are getting smarter by taking large input data and give precise results in a matter of seconds.
Today AI-based decision-making is widely used for fleet management by taking several attributes of data like driving behavior, weather condition, fuel consumption, oil change dates, technical fault in the vehicle, and route optimization.
The logistics industries are using AI-based data mining tools to discover the relationship between different entities of data for a better understanding of the outcome.
For example, AI can help many fleet managers to choose the optimized routes for fast delivery and less fuel consumption. Similarly, AI is also used to analyze the driving performance by considering a series of events.
In short, AI-based decision-making software allows you to automate fleet management without putting in a lot of effort. It has been observed that by the end of 2027 the fully autonomous trucks will be used for the shipment. This will automate the processes in warehouses more quickly as picking and shipping will be possible 24/7 (Source: McKinsey)
Source: Route 2030: The fast track to the future of the commercial vehicle industry, September 2018. (McKinsey.com)
Ensuring Tracking Accuracy of Arriving and Departing Orders
The invention of AI is a turning point for many industries including logistics. Many companies were manually operating the arriving and departing orders in the warehouses which leads to a huge financial loss due to the poor positioning of the pallets. The older items were pushed backward on the arrival of new items which causes the expiration of the products due to non-utilization for a long time.
By the use of AI and machine learning algorithms, this problem is solved. As many companies are switching to advanced AI-based models that keeps a track of itemset in the inventory according to the date for accurate positioning of the pallets. The invention of autonomous robots and drones operated through dedicated AI-based software helps in taking charge of maintaining the inventory, to move the products quickly, and put the itemset at the correct place.
Revolutionizing Logistics Industry with Labeled Data
Artificial Intelligence with deep learning models has revolutionized the logistics industry making it more productive using different annotation techniques. Data labeling in supply chain and logistics are used in many areas that are listed below;
- The packed orders in the warehouses are labeled using advanced deep learning techniques to enable smart logistics applications.
- Various annotation techniques are used to label bar codes of the packages for pricing and inventory information.
- For a smart shelving system and product recognition in the warehouses, all items in the inventories are labeled using bounding box techniques or point clouds labeling.
Huge voluminous trained labeled data is required for the annotation of the items used in the supply chain and logistics sector. For that, Labellerr.com provides a simple, clear, and user-friendly UI to perform data or image labeling with a supersonic speed dealing in a variety of industries like Health, Logistics, Business, and E-Commerce. It provides a large set of labeled datasets that are best for the training of the AI and machine learning-based models.
Source: www. Labellerr.com