Today, every business organization demands seamless functioning in order to survive and upscale. When organizations employ AI in their supply chain, then there are a lot of things that can be enhanced such as visibility and tracking. This assists in minimizing the cost, enhancing on-time production, and conducing to customers’ satisfaction.
By using AI, businesses can change how they run their supply chains. They can track things better, spend less money, work more efficiently, and make customers.
AI can look at lots of data, find patterns, and make predictions. This helps companies run their supply chain operations better.
What is AI in the Supply Chain?
We have seen artificial intelligence (AI) streamlining business operations in various industries and supply chain is no exception. It can help businesses to process huge data amounts and predict trends so they can manage their inventory accordingly. Not only that, analyzing data can also help find out any possible supply chain disruptions.
As a result, they can come up with relevant strategies that can allow them to deal with any unexpected changes accordingly. According to Statista, an estimated 38% of business executives will consider AI to be a critical element in supply chain and manufacturing businesses worldwide in 2022 and 2025.
This shows an increase in the number from 2022 as only 11% of business executives thought AI was critical in the supply chain. While it is an estimation, the growing incorporation of AI in the supply chain due to the pandemic and rising costs shows how this projection might just be true.
How is AI Used in the Supply Chain and What are Its Top 5 Benefits?
AI simplifies supply chains by automating inventory management, optimizing delivery routes, and predicting demand patterns. It also enhances real-time tracking and decision-making, these advancements boost efficiency, reduce costs, and rationalize operations.
Let’s take a look at the top 5 examples of AI in supply chains and its respective benefits;
1. Predictive Analytics
If you find yourself unable to predict what customers will want in the future or where you could not analyze old data right or if you’re struggling with the issue of managing inventory then relax! AI can assist by factoring historical data and other related and seasonal factors, to be able to predict demand appropriately.
These will help you work on your inventory and shipping strategies. Most importantly, it will help you handle risks that might arise from issues of stockout situations, overstocking, and even wrong forecasts.
2. Reduced Operating Costs
There are countless ways businesses can save money using AI. With precise demand forecasting from predictive analysis, AI reduces the over-inventory and storage costs. It combines work and also condenses activities through various tasks done manually. Automated tasks render the immense necessity of human resources to minimize labor costs.
This is also a factor in spending less on fuel because, with the help of AI, you will have better routes. With better tracking and real-time data-driven decision-making, you will have fewer mistakes in orders and fewer returns as well – it only amounts to more savings!
Analyzing a lot of data certainly tells companies what their customers are likely to purchase next, and how they can manage stock about as tightly as it gets — generating big savings across the board and nothing short of happy customers all around.
3. Real-time Visibility
With AI giving real-time insights into how your supply chain runs, you can control inventory better, track deliveries more easily, and fix problems quickly.
Plus, it helps improve routes for faster deliveries! Real-time updates make your business stand out by keeping everyone informed and making customers happy.
4. Optimized Decision-Making
Artificial Intelligence gives businesses statistical-based insights that help them make good decisions fast – this cuts risks and improves results!
By using super smart algorithms to study huge amounts of data quicker than humans can, AI finds patterns and makes smart decisions fast. With AI’s help, businesses make choices using data instead of guesses – meaning fewer mistakes and better supply chain work.
5. Enhanced Customer Experience
Chatbots powered by artificial intelligence have significantly improved customer experience by providing instant access to key information. When integrated with cloud-based supply chain platforms, these chatbots can access real-time data, enabling even faster and more reliable service. Supply chain operators can acquire instant responses regarding the status of shipments and address inventory issues immediately and accurately.
This accuracy and rapid access to data enable businesses to meet their clients’ needs more efficiently through timely order fulfillment and minimize the chance of stockouts or overstocks, ultimately leading to quicker deliveries, fewer delays, and enhanced customer experience overall.
What are Some Challenges of AI in the Supply Chain?
The benefits mind sound appalling, but AI implementation requires careful planning. Business owners can expect to see the following challenges when incorporating and introducing AI into their supply chain systems.
1. Training Costs and Downtime
Any new technological advancements mean that the company needs to train its employees. It is important to do so since employees do not know how to use the technology which makes them inept to make the most out of it. Training comes at a cost and it also means downtime since companies need time to prepare and schedule accordingly to limit disruptions.
2. Costs for Implementing New Setups
Apart from training costs, there are other costs associated with the implementation of AI in the supply chain. Take, for instance, the cost of acquiring the software and the expertise needed to run it. If companies plan on building their own software, it would still require a good amount of money.
3. Security and Privacy Vulnerabilities
The growing concern regarding cyberattacks and hacks from customers makes it difficult to collect and use data. It requires organizations to provide a more concrete solution for businesses to focus on using techniques that can safeguard consumers’ privacy and data rights.
4. Sourcing the Right Data
The main idea of AI is that it relies on the data quantities received from various sources. And if the data contains prejudice and misinformation, then this will inevitably adversely affect the result and the analysis. Thus, the selection of the right data is vital.
5. Over-Reliance on AI
In many ways, AI can bring convenience to different aspects of business processes. However, it is something important to know and remember here that such a process should not eliminate human communication.
Some might expect that with AI in place human intelligence will be eliminated, but it doesn’t to be so. Supply chain risks should be mostly handled and managed through human interventions to achieve their risk management goals.
Preparing Your Supply Chain for AI Implementation
Any kind of integration of AI in the supply chain is not without its advantages and disadvantages. The best strategy for managing the challenges with the resulting increased implementation of benefits is to have a good implementation plan. The following are the steps you can follow to properly integrate AI into your supply chain applications.
Step 1: Set Objectives in the Initiation Phase
The first question that you must answer when tackling AI in the supply chain is to identify the issue in the supply chain that you wish to solve using AI implementation. It could be the bottlenecks and shared frustrations in managing inventory or in fine-tuning the entire value stream.
In either case, you have to seek out the problem so you know where you will be able to find the proper tool for the job. Organizations might be experiencing a loss because of inadequate inventory management while others may wish to capture missed sales because of poor prognostication. When you describe the problem, then you have a good notion of what you are intending to solve.
Step 2: Check the Quality of Data
As mentioned before, AI models work on data and the wrong data can lead to inaccurate analysis. Remember that the AI tool or model’s quality will depend on the data provided. So, invalid, incomplete, or inaccurate data can lead to more problems rather than solutions.
You might see 10% to 15% inefficiency in demand forecasting if the data has mislabeled product categories. Therefore, it is highly important to cleanse the data to improve the accuracy of AI’s predictive analytics in supply chains.
Step 3: Choose the Right KPIs
AI implementation doesn’t mean it’ll solve your problems overnight. It is a step-by-step process so you should narrow the issues identified in the first step to focus on specifics. That will allow you to set the right KPIs (Key Performance Indicators) going forward.
For instance, you can try to reduce inventory of items that don’t have a higher demand by 3% to improve margins by 3%. Either way, the goal of choosing the outcomes is to check if the AI implementation is helping you get the results you wanted.
Step 4: Select the Right AI Solution
Once you have a roadmap with problems and metrics, you should evaluate various AI systems to find the best fit for your specific needs. Consider factors such as scalability, flexibility, and integration capabilities.
For instance, you can find out how a particular tool can help you optimize your inventory so that you can reduce stocks of items that are not in high demand. You might also look for a tool that can evaluate data to provide a better impact on the bottom line for getting the desired ROI.
Step 5: Monitor and Optimize
Last but not least, continuously monitoring the performance of your AI solution and making necessary adjustments is vital. You need to check whether the problem identified in Step 1 was resolved with the implementation of the AI tool mentioned in Step 4 to achieve the KPIs set in Step 3.
Let’s simplify this for you. Did using the AI tools allow you to reduce overstocking of products that don’t see a high demand by 25% to enhance margins by 3% to 4%? This step sets the basis for learning and continuous improvement.
Key Considerations When Implementing the AI in Supply Chain
Even if you’re following this process, there are a few things to keep in mind when implementing AI in the supply chain. For instance, you need to check the data quality and ensure your data is clean, accurate, and structured to maximize the effectiveness of AI algorithms.
Furthermore, it is important to prepare your employees for the changes that AI implementation may bring. Additionally, regularly reviewing and refining your AI strategy to adapt to changing business conditions is vital.
Following these steps and addressing key considerations can help you effectively prepare your supply chain for AI implementation. You can reap the benefits of improved efficiency, cost savings, and enhanced decision-making.
Final Thoughts
In a nutshell, Artificial Intelligence has made far-reaching changes in supply chain management by providing predictive analytics, reduced operating costs, real-time visibility, optimized decision-making, and enhanced customer experience.
As the supply chain industry continues to evolve, we can get an idea of what the future of AI in the supply chain will be like. Embrace the power of AI to transform your supply chain operations with cutting-edge supply chain software development and stay ahead in the competitive market.
