The rapid development of AI in supply chain in the last decade has made moving packages from one end of the globe to the other more seamless than ever. From virtual assistants, robotics, analytics, and autonomous vehicles, AI technology has provided sophisticated solutions to supply chain and logistics operations around the globe.
With the volume of data entering the supply chain every day, AI computing technology has the potential to shift business trends to encourage revenue. Specifically, AI can contribute to supply chain and operations with machine learning, language processing, and deep learning. AI in supply chain and operations can efficiently handle data for analysis and automated functions.
AI significantly reduces money and time spent on tedious processes with automation. AI automatically gathers and analyzes complex information to aid efficient decision-making. In particular, AI can help in demand forecasting, warehousing, and logistics route optimization:
These technologies ultimately reduce shipping costs and drive up revenue.
According to a report, AI combines the powerful capabilities of three technologies to enhance the supply chain through sophisticated analysis. These include unsupervised, reinforcement, and supervised learning technologies to identify factors and issues that affect supply chain performance.
AI can analyze data to improve supplier-related decision-making and relationship management. Some of the most significant data AI handles to improve decision-making include audits, delivery performance, credit scoring, and evaluations.
In recent years, AI has become a vital tool for predictive demand and network planning. This technology involves using data to make decisions based on anticipating events, avoiding risks, and creating solutions. An example is how freight forwarders use AI to predict shipment delays with operational variables, sales, and other factors.
With AI, companies can optimize processes by identifying supply chain constraints and bottlenecks. Once AI predicts supply chain constraints, specified operations will automatically take place to optimize workflow and keep production seamless.
AI technology can also be used to improve safety and efficiency in the logistics sector. This includes road haulage which involves lane assist, highway autopilot, and assisted braking. An example is a development of driving systems to improve road formation to lower fuel consumption.
AI holds an overwhelming potential to optimize supply chain management and logistics operations, but how exactly can you start adopting AI?
Before you can find AI solutions, you need to identify the specific supply chain issue you want to address. By identifying specific problems, you can focus on finding the right AI to get the best results. Otherwise, you are applying ill-fitting solutions to a wide range of processes down your supply chain.
Before your AI can provide you accurate solutions, you need to give it something to work with –– granular data. Data granularity is simply how detailed your data set is. You’ll likely be gathering data from different systems ––CRM, MRP, ERP, among others. You need to ensure that these data collection and storage systems are set up to collect highly detailed information, down to years worth of daily transactions.
Adopting AI into your supply chain and logistics operations requires years of in-depth knowledge before you can effectively apply its capabilities. Partnering up with AI specialists will open a new door to companies that have yet to expand their potential with data-driven solutions. Take due diligence to find a partner that can actively address your business goals and needs.
While GoShip does not operate using AI at this time, our freight shipping services are automated and perfect for the shipper who is looking for fast and reliable access to carriers. GoShip.com will help you find flexible solutions for better logistics management and business growth. Sources