If you're not surprised that artificial intelligence (AI) can do these days, you might be thinking, “I didn't know that AI could handle it,” but you might already be late. Fortunately, there is no shortage of opportunities to explore how AI can support the supply chain.
Today's supply chains are entering a new era of AI and advanced technology. Many industries compete first, but supply chains often fall behind. This is not because leaders lack interest, but because building the right foundation is complicated and underrated.
AI stands in the supply chain today
Some early adopters have already seen the results with tools such as:
- Optical character recognition, which reads spreadsheets and PDFs, sends data directly to the order management system.
- An automation platform that creates, routes and ships purchase orders once the planner signs off.
- Inventory planning system for vendor-managed or offsite inventory. Minimize manual monitoring and expensive telemetry.
- A demand planning engine that takes into account external variables such as weather and key events to provide smarter forecasts.
- Automation of warehouses to improve space utilization and reduce dependence on physical labor.
At its heart, AI allows you to train your system to make decisions based on defined rules and learning patterns. I don't do AI think It itself applies structured logic along the training.
How can you prepare?
Even if AI feels like years of being away for business, investment in the right foundations often creates a tech-centric culture, often with immediate benefits, especially for teams using Enterprise Resource Planning (ERP) or Material Requirements Planning (MRP) systems.
There are four ways to get started.
1. Start with a clear vision
Start by defining what your future looks like and decide how you will use the tool to support it. In today's digital environment, asking questions is more relevant than ever before, “Can I use technology to solve this?”
Whether the goal is to increase inventory density, reduce staffing without sacrificing throughput or reduce prediction accuracy, a well-defined problem statement is the first step towards meaningful transformation.
2. Prioritize people and processes
Before you automate, make sure your process is standardized.
For example, take a purchase order form. The system needs to check the pricing, find the right approver based on the order value, and know how to determine the expected receipt date. There is a lot of information needed throughout the process. And with that, you need to make a decision.
Automation and AI can be difficult to implement effectively when teams don't have clear workflows or decision-making processes in place.
3. Standardize and cleanse data
Technical systems rely on clean, consistent data. The system cannot automatically tell “something”, “some.thing”, “some_thing”, and “smthg” all the same thing. Training a system often starts with building a mapping table, but it is much more efficient to cleanse and normalize the source data from the start. This may involve shifting how data is collected and where it is housed.
Automated order entries that use AI to support automation are a popular goal, but for it to work, you need to know where the system finds information and how to interpret it. Some organizations resolve this by requiring customers to use portals or structured templates.
Advanced systems can analyze unstructured documents such as PDFs, but require training to identify fields such as company names, order quantities, and purchase orders.
4. Define decision rules
AI preparation means documenting the “IF/THN” logic that drives decisions. AI can handle countless variables, but you need clear rules. Map how your team makes decisions and even everyday operational choices that seem very basic. This step is important for successfully integrating AI into your business.
Don't overlook your foundation
All of these steps assume you have a core system like ERP and are placed immediately. If your supply chain does not have a cohesive platform to connect order entry, procurement, production tracking, costing and accounting, implementing that foundation is your number one priority.
Also, if you are using low-performance ERP, these strategies not only promote immediate improvement, but also set up for future success with AI and other advanced technologies.
Looking ahead
Don't wait for AI to fully mature and start preparing for your business. Building a clear vision, enhancing processes, data cleanup, and defining decision rules will prepare you to take advantage of the opportunities AI brings to your supply chain. In the meantime, you will be revealing the efficiency and cost savings that will benefit you long before your first AI project is published.
