AI In Supply Chain & Logistics: Delivering Augmentation And Automation
Artificial Intelligence or AI, is gradually picking up pace in supply chain, global logistics, and transportation. The evolution of technologies like AI, machine learning, etc possesses the potential to bring in disruption and innovation in these industries.
Supply chain and logistics are critical in every industry and are progressively gaining focus from various AI startups and vendors, given the amount of data generated by logistics, supply chain, and transportation. Artificial Intelligence provides various techniques that analyze vast amounts of data collected from the supply chain and logistics, and get actionable insights that can initiate processes and complex functions. Irrespective of the techniques and tools leveraged, the two main functionalities that must be met by any AI technology are
- Augmentation: Augmentation, basically, reduces the errors caused due to human bias. It assists humans with their daily tasks, without having complete control of the outcome. Virtual assistants are the best examples of augmentation.
- Automation: Automation refers to the use of machines to perform tasks, without the need for any human intervention. Robots, performing crucial steps in a manufacturing plant, are the best example of automation.
Prerequisites for AI in Supply Chain & Logistics
Many companies attempt to implement AI into their supply chain and logistics operations but fail. For the successful implementation of AI to offer optimal value, it is important to meet a few prerequisites, which include:
- Real-time data access: Access to real-time data enables supply chains to optimize their decision-making capacity. AI, when fed with real-time local data as well as outside data, will eliminate the problem of stale data and will be able to forecast the demand and supply. Real-time access to the data outside the enterprise should be made available to any AI, deep learning, or machine learning algorithms.
- Support for the enterprise-level primary goal: In order to derive the optimum value from the optimized service levels and costs, the AI solutions must support and focus on the global customer-driven goals. The algorithms should support the primary goal of providing service to the customer at the lowest possible cost.
- Continual and self-learning decision process: With real-time data from various internal and external sources, there arises the issue of variability and latency as the data keeps continuously changing. The constant change in data requires replanning and changing execution plans which will increase the overall costs. Therefore, AI solutions must be continuously learning and self-monitoring to provide actionable data efficiently.
- Scalable and autonomous AI systems: With big data from within and outside the enterprise, the AI systems are required not only to make smart and fast decisions on a large scale but to execute them too.
- User-friendly AI systems: The AI system must give users the visibility to decision criteria, communicate, and enable them to understand the issues that the system cannot solve. Human interventions should only be allowed on an exception basis and/or allow the user to add more information that the AI system may not know.
AI influencing Supply Chain
Enhancing the efficiency of the supply chain is very crucial for many companies. Effective supply chain optimization is one of the toughest tasks and AI helps in improving it.
- AI provides the intelligence that helps in reducing operating costs and managing inventory. AI, with machine learning, are being leveraged to extract insights on warehouse management, logistics, and supply chain management. For instance, Image recognition technology identifies damage and can take photos of the cargo by using special cameras.
- The insights provided by AI boost productivity. AI, along with reinforcement learning, supervised learning, and unsupervised learning identifies the factors and issues affecting the performance of the supply chain.
- Large volumes of data can be analyzed with AI technologies that measure and track all the factors responsible for accurate and continuous demand forecasting based on various factors like weather, real-time sales, etc.
- AI enhances customer experience. It enhances the relationship of the customer with the provider with the help of chatbots.
- AI-powered solutions help enhance prove the factory scheduling and production planning as they can analyze different issues and optimize them.
AI influencing Logistics
AI is taking a pivotal role in the Logistics industry with the Big Data-driven applications streamlining the logistics on a large scale. Artificial Intelligence helps the logistics industry in-
- Enhancing the proactiveness of companies and the efficiency of network planning and predicting the demand. Knowing what the markets demand can help swiftly draft measures to meet the demand and bring down the operational costs.
- Tracking, locating, and move inventory within the warehouse with the help of robots. Deep learning algorithms are built to drive these robots to make autonomous decisions regarding the various processes that are performed within the warehouse.
- Optimizing the future performance, forecasting the accurate outcomes, supply chain transparency, route optimization, etc with the help of Big Data. Machine learning algorithms help analyze the data, enhance its quality, and identify issues to attain transparency and obtain business benefits.
- Increase efficiency and safety with the help of driverless/autonomous vehicles.
Do you want to enhance your supply chain or logistics process leveraging AI & Data Intelligence? Our on-demand platform, Qualetics can help you power your processes with AI & Data Intelligence quickly without the barriers of resources, time, and money. Book A Free Demo here to see how Qualetics can help your business.
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