3 Ways AI Helps in Supply Chain Transformation (By Making Supply Chain Software Smarter)
Benefits of Using AI in Supply Chains?
- Embed artificial intelligence (AI) and machine learning (ML) tools and the way an organization’s supply chain performs changes completely.
- From being inelastic, the supply chain becomes responsive.
- Instead of making predictions by analyzing past patterns, it acts basis real-time data.
- The supply chain leaves behind unoptimized cost management methods and moves to dynamic should-cost models.
- And its lack of inventory control gives way to end-to-end visibility into inventory demand and supply, reducing wastage, minimizing business risk and increasing working capital.
AI-led supply chain transformation takes place in 3 ways:
1. Accurate forecasting with demand sensing
It’s common for organizations to underestimate as well as overestimate demand. This is because forecasting methods in traditional supply chain management software rely on historical sales data to guess future demand. This approach reduces prediction accuracy since past patterns never really match what’s happening right now.
The methods do not measure the effect of external factors that either impacted demand in the past and can do so in the future. For example, past data could not have predicted the demand disruption brought about by the pandemic.
AI-power supply chain management software leaves the past behind, by unifying internal and external data pouring in from all sides into a data lake. From here, the real-time data is quickly accessed to optimize operations.
By integrating warehouse management system with other supply chain systems, organizations collect real-time data, pick vendors and suppliers that support demand planning and replenish materials faster.
In fact, the amount of data the supply chain software analyzes can be expanded exponentially for better and better demand sensing.
Also, no more just macro-level forecasts. Supply chain managers can model demand at the granular level and detect and resolve issues they never knew existed.
- More revenue
- Lower costs
- Shorter lead times
- Better inventory management and improved working capital
2. Savings with dynamic should-cost models
When sourcing, it’s vital for supply chain managers to understand how a small change in the cost structure can have a cascading effect on the final prices of the product or service.
Hence, accuracy is crucial in estimating expected prices based on cost drivers such as raw materials, energy usage, transportation, packaging, storage, byproduct credits, fixed costs, overheads and profits.
But should-cost models can go off mark if the underlying cost structure is inelastic, which is often the case.
The solution lies in making should-cost models dynamic and fully automated to respond to changing market indices in the real time.
There is more accurate forecasting with an AI-based engine that analyzes existing prices, assesses trends and market indices and compares should-cost against actuals. The models provide cost evolution insights and cost variation alerts.
Organizations can also build models of varying scales -- from simple products to systems with thousands of moving parts – in the supply chain software.
What’s the outcome?
- Organizations will be at an advantage while negotiating with suppliers and understanding the impact on their supply chains when underlying costs change.
- They will be able to scale resources and manage risk effectively. And yes, bring in more savings.
This is a revelation for category managers and sourcing experts: to view structured, multi-layered should-cost models optimizing to real-time price movements.
3. Real-time inventory and warehouse management
Siloed data, lack of real-time information and slow, manual processes — inventory and warehouse management systems are rife with inefficiencies today. There’s either overstocking or understocking. The system is not flexible enough to respond external events that impact inventory-related decisions. There is disintegrated supply chain visibility across plants, vendors and ERPs, leading to suboptimal decisions and costs that could have been avoided.
All this can be avoided if the organization breaks down these barriers with cloud, if it unifies the inventory management system and the warehouse management system into one, if it automates replenishments, if it uses predictive inventory alerts to adjust the supply network.
An AI-powered and cloud-native supply chain management software gives this unified view into real-time inventory across all locations, suppliers, vendor-managed inventory as well as inventory sitting across different ERPs. Inventory mangers know in real time which material is lying where and for how many days. They have a snapshot into which material can face a shortfall based on shipments data and resolve the issue there itself.
This real-time and synchronized inventory visibility to the buying organization and the vendors reduces operational costs and avoids scenarios where a given material is not available at a given location.
The software also improves warehouse management system by supporting transactions between warehouses on a day-to-day basis. Since real-time visibility is critical for procurement decisions, the software syncs with real-time demand and supply information.
The software integrates with warehouse suites and different ERPs so that managers can print barcodes and RFID labels for efficient and connected warehouse transactions. This means more accuracy in tracking inventory and seamless execution of tasks such as picking, packing, shipping, receiving, put-away, cycle-counting and physical counting. Users can walk with the device, scan an item, look at the availability, click and initiate all the relevant warehouse transactions.
- Lower costs and risks
- More operational efficiency
- Inventory and warehouse management truly optimized for any real-world scenario