October 09, 2023 | Supply Chain Software
Data is essential to make smart supply chain decisions. But that's not enough.
Despite having access to vast amounts of data, organizations often struggle to make sense of it due to challenges such as data volume, reliance on IT for data access, data residing in isolated silos and limited data analysis capabilities. This difficulty in extracting meaningful insights from data can lead to slow decision-making and missed opportunities.
Complex supply chains and the never-normal supply chain landscape of today call for greater efficiency. As such, supply chain and procurement leaders are keeping AI at the forefront of their operations. AI-powered supply chains boost resilience, profitability and mitigate risks.
AI in analytics similarly improves supply chain performance. Businesses that neglect to leverage AI in their supply chain analytics face multiple problems like: inefficient demand forecasting, suboptimal inventory management, error-prone manual data processing, ineffective supplier management, limited supply chain visibility and inefficient logistics routing, among others.
These challenges can impede operational efficiency, decision-making capabilities and overall competitiveness.
AI algorithms can analyze historical sales data, market trends, and external factors like weather or economic indicators to predict demand. This enables businesses to optimize their inventory levels, reduce carrying costs, and minimize the risk of stockouts or overstocking.
AI-powered algorithms can determine the most efficient routes for transporting goods, taking into account traffic, weather and delivery windows. This reduces transportation costs, improves on-time deliveries and reduces the company’s carbon footprint.
AI can assess supplier performance by analyzing various data points, including delivery times, product quality, and pricing trends. This helps businesses identify potential issues with suppliers and make data-driven decisions to enhance supplier relationships.
AI-driven robots and drones can automate tasks in warehouses, such as picking, packing, and inventory tracking, leading to faster order fulfillment and reduced labor costs.
AI can assess and predict risks in the supply chain, such as natural disasters, geopolitical issues, or disruptions in the transportation network. This allows companies to develop contingency plans and mitigate potential disruptions.
Predictive analytics leverages the power of statistical modeling and regression analysis to identify and understand trends from historical data to predict future trends. This helps supply chain companies predict the most likely outcome and its business implications.
Descriptive analytics is a form of data mining that involves the analysis of large datasets to identify patterns and generate summaries that allow users to gain insight into a given situation. This type of analytics utilizes historical data to uncover trends and draw conclusions that can be used to inform decision-making.
Prescriptive analytics is a powerful tool for supply chain operations, allowing for the exploration of how specific changes will affect outcomes. Through this, potential improvements can be identified and recommended, providing a valuable resource for optimizing supply chain operations. This includes collaborating with logistic partners to reduce time and effort for maximum business value.
In the context of advanced analytics in supply chain management, cognitive analytics plays a vital role. It is best utilized for enhancing customer experience and relationships. The feedback data received through AI-driven systems is analyzed and executed in reports and dashboards to answer complex questions. This application of AI in the supply chain can automatically allow businesses to pursue breakthrough ideas and provide better customer needs and demands.
Consider using best-in-class AI-powered software solutions. These tools streamline data analytics by connecting, cleaning and analyzing both internal and external data sources. They provide robust AI capabilities for extracting valuable insights and integrating them into your business intelligence applications. When choosing AI for supply chain analytics, focus on solutions tailored for actionable insights, emphasizing speed and user-friendliness to accelerate workflow. Look for low-code/no-code features to reduce IT dependence and ensure seamless integration with other systems. Prioritize AI-driven data cleansing and normalization for improved data quality and adaptability in a changing business landscape.
It is also a good idea to partner with supply chain and procurement experts with a proven track record in AI and supply chain analytics. These experts can assist you in evaluating your unique requirements, crafting a customized strategy and pinpointing the AI solutions best suited to address your specific supply chain challenges. Their insights and guidance can be instrumental in steering your AI integration toward success.
Incorporating AI into supply chain analytics is essential for modern businesses. It offers numerous advantages, including improved forecasting, efficient inventory management, optimized logistics and enhanced supplier relationships. Deploying AI-powered supply chain analytics technology and collaborating with experts is key. Embracing AI in supply chain analytics is a key step toward greater efficiency, competitiveness and profitability in the never-normal supply landscape.
Learn more about GEP’s power low-code AI-based analytics solution for procurement and supply chains