While several industries are still struggling to overcome the post-pandemic effects, there are a few industries that took the opportunity to adopt these modern technologies at a large scale.
One of them is the supply chain industry. Data from Statista shows that AI solutions in the supply chain market have resulted in better inventory management, smart manufacturing, dynamic logistic systems, and real-time delivery controls.
The main objective of using AI in supply chain and logistics is to increase efficiency and productivity. This introduction of AI in supply chain management has led to more sustainability, making every enterprise wonder if digital transformation at this scale can benefit their respective supply chain business.
A recent study conducted by McKinsey has determined that implementing AI-enabled supply-chain management has led to significant improvements. Specifically, adopters of this technology have seen a 15% reduction in logistics costs, a 35% reduction in inventory levels, and a 65% increase in service levels. This demonstrates the potential of AI-enabled supply-chain management to revolutionize the industry and its importance in the modern business landscape. This blog will help you understand what AI and data analytics in the supply chain can do for your business.
So, let’s move ahead and look at how AI in the supply chain impacts businesses. In addition to this, we will also take a look at how integrating AI development services for your enterprise will bring the workforce, machines, and software into action.
What are the benefits of AI in the supply chain?
Given the significant business value at stake, multiple supply chain vendors and distributors are now looking forward to the benefits of AI in supply chain planning. The new offshoots of the supply chain business include demand planning (revolutionized by AI ML in the supply chain), real-time inventory management (controlled by IoT and connected systems), and end-to-end dynamic margin optimization within the supply chain industry (driven by AI-based solutions).
At such times, choosing the right solution is critical. Therefore, to manage the complexity of the modern supply chain, your business needs to embrace these smartly designed solutions aligned with your everyday needs.
Why else do you need to invest in AI and analytics-based solutions? Find the primary reasons below:
1. Enhanced 360-degree responsiveness
The most sought-after use of AI in the supply chain is enhanced visibility and responsiveness. Artificial intelligence in the supply chain allows your business to gather relevant past and current data from multiple connected devices. This includes implementing the SRM software, CRM and ERP systems, and business intelligence solutions to existing data. This way, you can evaluate the performance on a broader scale. Similarly, supply chain data analysis also predicts and minimizes risks and negative impacts on the distribution channels.
2. Improved customer experience
This cannot be stressed enough that big data and artificial intelligence have taken the customer experience to a whole new level. These solutions allow the supply chain to create personalized products based on the current user demands. One widely used instance can be modern transport and logistics using voice-activated means of tracking shipments and orders. This goes both ways where even the customers can perform the voice-activated query search using Alexa or Google Assistant.
3. Increased fleet efficiency
Increased fleet efficiency is another use of AI in the supply chain that has made several organizations implement the use of this technology. The essential role of AI in supply chain management is on-time product delivery. The advanced AI-based GPS tools enable better navigation and optimization of the route for fleeting and transportation. These tools access the most effective route for product delivery by processing the driver, vehicle, and customer data through machine learning. Simultaneously, AI and machine learning in the supply chain will help you save both time and money for future shipments.
4. Competitive edge
Observing and acting upon market trends and patterns is the key to staying ahead in the supply chain business. AI in supply chain analytics can harness real-time data from external resources such as industrial production, weather, and employment history. With all the accumulated data, you can better gauge the market conditions and assess upcoming demands for stable growth.
You may also leverage the AI’s sensory competencies to reshape your product portfolio and capital expenditure. This is currently the preferable application of AI in supply chain management.
5. Simplified inventory management
Let’s not forget that well-organized inventory management is the foundation of the supply chain business. The analytics-based machine vision software can minimize the standard manual input and create accurate forecasts. The AI in the supply chain management system also interprets real-time machinery data that continuously monitors the inventory and stock in the warehouses.
6. Access to future insights
AI in the supply chain ecosystem makes way for a supreme level of agility for the business. The same goes for the use case of data science for supply chain forecast, where you can determine your client’s needs before they arise. This will help you take your supply chain enterprise to a whole new level and achieve the maximum customer satisfaction level.
The above benefits can help you understand the ever-expanding scope of AI in logistics and supply chains. Your decision to invest in AI in supply chain analytics solutions will be easy once you check out the use cases of these technologies in your business.
Understanding the future of AI in the supply chain: Best supply chain predictions for 2023 and beyond
According to a study from McKinsey, implementing AI and data analytics in logistics and supply chain management will enable industries to improve their logistics costs by 15%. In addition, they can also expect an improvement in inventory and service levels by 35% and 65%, respectively. Here are the few best supply chain predictions that will help you comprehend AI in supply chain trends effectively.
1. It is predicted that more than 75% of large enterprises will use intralogistics smart robots to manage their warehouse operations by the year 2026. These robots will further supplement the need of the human workforce while making sure that the implementation of processes is faster and less expensive.
2. According to a recent report, by the year 2026, more than 75% of vendors dealing with commercial supply chain management applications will be able to deliver results backed by advanced analytics and artificial intelligence (AI), and data science. Using analytics in operations supply chain management will further ensure an improved decision-making process for supply chain users. Many large supply chain vendors have already signed up for implementing AA and AI in their services, while small vendors are catching up shortly.
3. By 2025, more than 25% of supply chain vendors will witness the power of a microservices architecture while making way for true composability. Even though upgrading the SCE application requires them to invest a huge amount of money at the initial stages, the investment is worth it, considering they will be able to tackle the challenges associated with adoption at scale.
4. It is also predicted that not implementing AI in the supply chain will lead to significant value loss for more than 80% of the supply chain organization as they will not be able to merge their initiatives for digital supply chain twin and control towers. The latter is further responsible for handling delays in the shipments, thereby decreasing losses and increasing the average ROI.
Make your supply chain business future-ready
The benefits of artificial intelligence in supply chain management are indispensable. AI is a commonplace technology for the supply chain now that enterprises of every scale and size have adopted its extensive applications. Given the current scenario, every supply chain business model needs to be critically integrated with AI and analytics solutions for optimization.