What Are the Advantages of Using AI in Supply Chains?
When artificial intelligence (AI) and machine learning (ML) tools are integrated, the performance of an organization’s supply chain changes dramatically. In this blog, we tell you about artificial intelligence in supply chain management.
The supply chain transforms from inelastic to responsive
Instead of creating predictions based on prior trends, it operates on real-time data. The supply chain abandons inefficient cost management strategies in favour of dynamic should-cost models. Furthermore, its lack of inventory control allows for end-to-end visibility into inventory demand and supply, decreasing waste, lowering business risk, and increasing working capital.
AI-Led Supply Chain Transformation Happens in Three Ways:
Demand Sensing for Accurate Forecasting
Organizations frequently underestimate as well as exaggerate demand. This is because standard supply chain management software forecasting methods rely on historical sales data to predict future demand. This method diminishes forecast accuracy since past patterns rarely match what is happening right now. The methods do not account for the impact of external factors that have influenced demand in the past and may do so in the future. Previous statistics, for example, could not have forecast the demand disruption caused by the pandemic.
By combining internal and external data coming in from all sides into a data lake, AI-powered supply chain management software puts the past behind. The real-time data is quickly accessed from here to optimize operations. Organizations collect real-time data, select vendors and suppliers that support demand planning, and refill resources more quickly by integrating warehouse management systems with other supply chain systems.
Dynamic Should-Cost Models Save Money
It is critical for supply chain managers to understand how a tiny adjustment in the cost structure can have a cascading effect on the ultimate costs of the product or service when sourcing. As a result, precision is essential for estimating predicted pricing based on cost drivers such as raw materials, energy usage, transportation, packaging, storage, byproduct credits, fixed costs, overheads, and profits.
Should-cost models, on the other hand, can be off the mark if the underlying cost structure is inelastic, which is frequently the case. The idea is to make should-cost models dynamic and fully automated so that they can adjust in real-time to changing market indices.
With an AI-based engine that analyses existing pricing, analyses trends and market indices, and compares should-cost to actuals, forecasting becomes more precise. The models provide insights into cost evolution as well as cost variation alerts.
Supply Chain Softwares
Organizations can also use supply chain software to create models of varied scales, ranging from basic products to systems with thousands of moving parts. When fundamental prices change, organizations will have an advantage when negotiating with suppliers and understanding the impact on their supply chains.
They will be able to scale resources and efficiently manage risk. And, of course, bring in more savings. Viewing hierarchical, multi-layered should-cost models that optimize to real-time price swings is a revelation for category managers and sourcing experts.
Inventory and warehouse management in real-time
Inventory and warehouse management systems are riddled with inefficiencies today, because of silos of data, a lack of real-time information, and slow, manual operations. There is either an overabundance or underabundance. The system is not adaptable enough to respond to external events that have an impact on inventory decisions. There is a lack of supply chain insight across facilities, vendors, and ERPs, resulting in suboptimal decisions and unnecessary costs.
All of this may be avoided if the business uses the cloud to break down these barriers, combines the inventory management system and the warehouse management system, automates replenishments, and uses predictive inventory alerts to change the supply network.
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