Supply chain management should be consistent and good in any industry. The system of procurement, operations management, logistics, and marketing channels that enable raw materials to be transformed into completed goods and shipped to their final consumers is known as the supply chain management system. As the whole system together makes a supply chain then you can understand why management of supply chain is crucial for an organization. The global supply chain management market size was valued at USD 23.58 billion in 2023. The market is projected to grow from USD 26.25 billion in 2024 to USD 63.77 billion by 2032, exhibiting a CAGR of 11.7% during the forecast period. In this article, we will see the role of AI predictive analytics in enhancing supply chain management. We will also go through some additional details such as benefits, and challenges, and lastly conclude the whole discussion.
Instead of all supply chain management techniques, there are still many things that could affect the supply chain. In big organizations, daily there is a vast amount of data collected that cannot be analyzed to extract information. Since every organization has its data analytics still it is not enough to properly manage all supply chain stages and data. There are aspects like forecasting, market trends, quality, maintenance, errors, etc that can affect the efficiency and productivity of an organization if not managed properly.
To make things good in supply chain management, there is a tool or technology that is AI predictive analytics. This is one of the best technologies that can enhance the supply chain management inside an organization. AI Predictive analytics forecasts future trends and events using machine learning and statistical algorithms. By examining past data, businesses may predict risks and disruptions and take proactive steps to reduce them. Anticipate probable delays and disruptions from suppliers. This overall can enhance supply chain management.
As you know, artificial intelligence is the best technology that has been developed in the past decades. The global Predictive AI Market is expected to reach a valuation of USD 108.0 billion by 2033, with a steady Compound Annual Growth Rate (CAGR) of 21.9% over the next ten years. In 2023, the net revenue generated by the market was nearly USD 14.9 billion, projected to reach USD 18.2 billion in 2024. These are impressive numbers and also assure the bright future of AI and AI predictive analytics.
According to a recent survey by Gartner, organizations that adopt AI and predictive analytics in their supply chain operations can expect to achieve an average of 20% reduction in supply chain costs and a 10% increase in revenue by 2023. Additionally, statistics indicate that over 79% of companies with high-performing supply chains achieve above-average revenue growth within their respective industries.
What is predictive analytics in supply chain management?
AI- predictive analytics is advanced analytics that uses historical data along with statistical modeling, data mining, and machine learning to forecast future events. To detect trends in this data and identify possibilities and hazards, businesses use predictive analytics. This can be very useful in supply chain management as it enables the business to predict market trends, forecasting, data analysis, and reporting at a very fast pace. Manual analysis of data and making predictions can be a very long process whereas with AI predictive analytics, this all can sum up in no time.
How predictive analytics can enhance supply chain management?
Procurement and supplier management procedures can enhance AI predictive analytics. Predictive analytics algorithms can find high-performing suppliers, get better terms, and make the best sourcing choices by examining supplier performance data, market trends, and other pertinent information. You can also get proper insights to track the performance of your supply chain, aided by IoT solutions, which help strengthen the weak points. This increases the efficiency and productivity of the company. There are many other benefits that we will see in the upcoming paragraph.
Benefits of predictive analytics in supply chain management
AI predictive analytics offers a lot of benefits for supply chain management here are a few key benefits mentioned below
1) Better inventory optimization
Businesses can improve inventory level optimization through the use of AI predictive analytics, which involves pattern recognition, demand data analysis, and consideration of seasonality, promotions, and other pertinent aspects. This lowers carrying costs, prevents overstocking or understocking, and enhances cash flow. Overall, inventory management can be enhanced with this technology that overall makes a positive impact on supply chain management.
2) Forecasting and planning
Forecasting is a very important aspect as this enables companies to make decisions quickly. To provide precise demand estimates, artificial intelligence (AI) systems examine past sales data, industry trends, and outside variables. This helps businesses to better satisfy customers by limiting stockouts and decreasing excess inventory by enabling them to match their production, procurement, and inventory management procedures with projected demand.
3) Enhance supply chain visibility
AI predictive analytics-powered solutions give businesses real-time visibility throughout the supply chain, empowering them to anticipate and prevent possible interruptions before they happen. Supply chain bottlenecks, delays, or disruptions can be predicted by predictive analytics models, which enable efficient risk management and backup plans. This overall enhances the supply chain visibility for a supply chain.
4) Prediction of customer behavior and their experience
Knowing customer behavior is important in a business as this can help you to better understand customers and their needs. Without any proper insights, this is not possible to understand your customer better. Businesses can optimize their marketing and sales efforts by using AI-powered predictive analytics to anticipate their customers’ behavior. Customized services and recommendations, also improve client experiences.
5) Security for supply chain
Security is also important as a business contains a lot of confidential data related to the organization and customers. This data can be sensitive in various manners and can disrupt the supply chain management if handed over to the wrong hands. Where AI predictive analytics can predict any cyberattack in the initial phase. This helps organizations to take prevention before too much damage. This also helps to enhance the supply chain management.
Challenges with AI predictive analytics
We have seen a lot of benefits of AI predictive analytics for supply chain management but there are a few challenges too. Here are a few challenges mentioned below.
- Overfitting and outdated predictions can be issued with predictive AI analytics. This is very necessary to update the AI algorithm with time otherwise this can be a challenge for organizations.
- Initial implementation cost takes a lot of capital for AI predictive analytics so this could be a challenge for many organizations too.
- Bias characteristics of AI could be a challenge as the precision made by AI predictive analysis could be biased for the organization’s regulations.
Final words
It’s now very important to integrate AI predictive analytics in supply chain management to stay ahead of the competition in this fast-advancing world. Organizations can attain previously unheard-of levels of productivity, cost savings, and customer happiness by utilizing these technologies. The way supply chain management can be enhanced by using AI and predictive analytics. Which show the potential to completely transform how companies run. Businesses that don’t adopt these technologies run the danger of staying behind their rivals. Now is the moment for businesses to use AI predictive analytics in supply chain management to create a more robust and efficient ecosystem.
