Supply chain managers are looking for digital transformation solutions as technology plays an increasingly significant role in the efficiency of business operations. The use of AI-driven technologies can help to meet the challenges arising from increased global digitalization. Therefore, AI has already become a key tool for enterprises looking to improve their supply chain management processes.
However, organizations must address some challenges before fully implementing AI, such as privacy concerns around customer data, lack of trust between organizations, difficulty integrating different systems, and more.
This blog post will explore how AI can be used as a digital transformation solution to improve efficiency and mitigate supply chain challenges faced by enterprises today.
AI and the changing world of supply chain management
Over the last decade, supply chains have grown increasingly difficult to manage. The complexity of product portfolios has increased, with longer and more interconnected physical flows reflecting that. Market volatility, exacerbated by the COVID-19 epidemic, has increased the need for agility and adaptability. And a greater focus on supply chain sustainability forces companies and stakeholders to consider building resilient supply chains.
Artificial intelligence (AI) is expected to be a powerful instrument for companies to tackle the difficulties outlined above.
How AI helps with tackling challenges that come with digital transformation of supply chains
Following are the three ways in which AI-led digital transformation for supply chains takes place –
1. Demand Sensing Concerning Trend Forecasting
Organizations often underestimate and overestimate demand. This is because traditional supply chain management software relies on past sales data to predict future demand, which is inaccurate. After all, external factors like the pandemic have proven to impact demand.
AI-powered SCM softwaresmake sense of the real-time data from all sides and use it to optimize operations. Not just that, supply chain managers can model and forecast demand at a granular level to find and address issues that were earlier not detected.
For instance, warehouse management systems provide real-time data, choose vendors and suppliers who support demand planning, and replenish resources faster. As a result, companies benefit from better inventory management, more revenue, lower costs, shorter lead times, and better working capital.
2. Dynamic Should-Cost Models and Real-Time Pricing
When sourcing, it is crucial for supply chain managers to understand how a small change in the cost structure can affect the final prices of products. Thus, it is vital to accurately estimate anticipated prices based on cost drivers like raw materials, energy usage, packaging, transportation, storage, fixed costs, overheads, and profits.
Should-cost models may go wrong if the underlying cost structure is inelastic, which is frequently the case. To respond to changing market indices in real-time, should-cost models must be dynamic and fully automated.
More precise forecasting is possible when an AI-based engine analyzes current rates, predict trends and market indices, and compares should-cost to actuals. The models provide cost evolution insights and cost variation alerts.
In supply chain analytics, modeling can be done at various levels: from basic items to complex systems with hundreds of moving components.
As a result, companies can better understand the impact of change in underlying costs on their supply chain, scale resources, and manage risks effectively.
3. Real-Time Inventory and Warehouse Management
A cloud-native and AI-powered supply chain management solution gives a unified view of real-time inventory across all locations, suppliers, vendor-managed inventory, and inventory sitting across multiple ERPs. Inventory managers can see where the product is located and how long it will remain there in real-time. They have a window into which material may fall short according to the shipment data and resolve the issues there and then.
This real-time and synchronous visibility for materials to purchasers and suppliers reduces operational expenditures while avoiding situations where a certain material is unavailable at a certain place.
Barcodes and RFID labels can be printed via the software, which works with warehouse suites and other ERPs to enable managers to create barcodes and RFID labels for efficient and connected warehouse transactions.
All of this means better inventory control and smoother execution of activities like picking, packing, shipping, receiving, putting away, cycle counting, and physical counting. Users can walk around with the device while scanning an item, view the status, click to start all necessary warehouse operations, and do it all at once.
All of the above reduces costs and risk, brings in more operational efficiency, and makes supply chain management truly capable of handling any real-world scenario.
Summary
AI-powered digital transformation solutions in supply chain management can help with demand sensing, cost modeling, and optimizing operations for increased profitability. Furthermore, real-time inventory and warehouse management make it easy to track materials instantly across all locations while ensuring smooth transactions through barcodes, RFID labels, and other tracking tools.
Its ability to analyze huge volumes of data, understand relationships, provide visibility into operations, and support better decision-making makes AI the digital transformation tool of choice for forward-thinking organizations.
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