Supply chain optimization is an urgent priority for businesses worldwide. AI has emerged as an ideal solution, but there are many possibilities under this umbrella to choose from. One of the most beneficial is AI in procurement.
Procurement is the side of supply chain management that deals with finding suppliers and buying materials, parts, or products from them. It is an essential but often complicated step, with much room for human error, delays, high costs and regulatory issues. AI can help overcome those challenges.
Applications of AI in Procurement
Like AI in supply chains as a whole, AI tools have many possible applications within procurement. Here are a few of the most significant.
Supplier Risk Assessment
One of the most impactful uses for AI is analyzing suppliers to determine their risk levels. A stunning 89% of companies today have experienced a supplier risk event in the past five years, leaving them vulnerable to delays or other disruptions. AI – which can spot trends in data more accurately than humans – can show which suppliers offer the lowest risk, helping companies avoid these situations.
Spend Analysis
Similarly, AI tools can review financial data from current operations and compare them to other available suppliers. This analysis can reveal if businesses are spending more than necessary and highlight more cost-effective alternatives. The most affordable strategy will likely change over time, too, so automation is crucial to account for these shifts quickly.
Inventory Management
Effective procurement must be timely on top of being accurate. Consequently, inventory management is another ideal use case for AI in procurement. AI can analyze real-time data about stock levels and shipping times to determine when companies must order additional supplies. Machine learning can also recognize seasonal trends to predict demand shifts, and change orders accordingly to prevent stock-outs or surpluses.
Contract Management
AI can also help supply chain managers draft, edit and pull important information from contracts. These tasks are error-prone and time-consuming when manual, but natural language processing tools address both concerns. Businesses have been able to shorten contract signing times from 21 days to just five through automation.
Best Practices for Procurement Professionals
As these use cases demonstrate, applying AI to procurement has many advantages. However, businesses must approach this technology carefully to experience these benefits fully. Here is how they can capitalize on procurement AI‘s potential.
Create a Clear AI Vision
The first step to successful AI implementation in procurement is like any AI application. Organizations must identify a specific use case and set of goals for the technology.
AI projects are more likely to fail from non-technical issues than technological ones. Unrealistic expectations, misunderstanding AI, lack of guidance and adopting AI without a value-based use case are among the most common drivers of failure. In light of these risks, supply chain managers must stress the AI development and integration planning phase.
Creating a clear, actionable AI vision starts with identifying company-specific needs. Once businesses know where to improve, they can see how AI can help them meet these goals. Setting expectations and specific use cases from the beginning will inform more cost-effective and relevant investments.
Collect and Clean the Necessary Data
Next, supply chain managers must review the scope and quality of their data. Poor-quality or insufficient data will produce unreliable AI outputs, leading procurement professionals to make costly mistakes.
What data to collect depends on the organization’s specific use case. Inventory management AI needs up-to-date information about stock levels, so businesses should implement IoT warehouse tracking solutions. By contrast, a contract management solution needs access to past and current contracts, and a spend analysis model requires real-time and historical financial data.
Procurement departments must also ensure this data is clean. Organizing and removing errors from the records will ensure AI tools are as accurate as possible. The best way to do this is through automated data cleansing tools, as they are faster and less error prone than manual alternatives.
Start Small
AI in procurement is also most cost effective when organizations start with smaller, more targeted applications. These solutions can be complex and expensive, so managing multiple at once without experience can quickly lead to confusion and high costs.
If supply chain organizations hope to achieve better returns on their AI investments, they should begin by automating smaller, less complex tasks. After they are sure of these projects’ success, they can move on to larger, more complicated ones. This gradual approach spreads resources more evenly and helps manage the AI learning curve.
Starting small and growing from there allows procurement professionals to learn more about AI and how to implement it effectively. That way, by the time they address more sensitive issues, they have the experience and knowledge to do so effectively.
Foster AI Skills from Within
As part of this growth, procurement professionals should realize they are not AI experts. They must also recognize the need for AI-related skills for effective implementation. However, the best solution to this gap is to grow talent from within instead of relying on outside sources.
Technology skills gaps exist across all applications, but 63% of U.S. companies say their largest shortages are in AI and machine learning. Because this issue is so pervasive, attracting and retaining outside AI talent will be difficult. The more reliable alternative is to upskill existing employees.
Supply chain leaders should offer AI training programs to their employees and incentivize participation. That way, they can acquire AI-skilled workers who are already familiar with the company’s specific workflows and goals without relying on more expensive hiring strategies.
Secure AI Tools
AI in procurement must also focus on security. Machine learning‘s massive data requirements can introduce new cybersecurity concerns as companies store more information in centralized databases. Procurement teams must bolster their security infrastructure as they adopt AI to ensure it does not become riskier than beneficial.
Access to training data – as well as to the AI model itself – should be minimal. Only those who need it to perform their jobs correctly should be able to access it. Implementing tighter verification controls like multi-factor authentication and biometrics is also necessary. Companies should lock AI data behind high encryption standards and use automated monitoring tools to watch for breaches.
Businesses should also consider AI‘s privacy and ethical concerns. That means restricting what AI can access and control, and reviewing models regularly to ensure they are reliable and fair. Humans should always have the final say in any AI-influenced decision.
AI in Procurement Can Revolutionize Supply Chains
If organizations learn how to manage it properly, AI can transform supply chain management. It will become faster, more cost-effective, safer and more flexible.
These benefits will ripple throughout the entire supply chain. As AI revolutionizes procurement, downstream manufacturers and end customers will reap the benefits of lower prices and higher availability.
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