AI in Procurement: Streamlining Purchasing Processes

The Best AI Guide for Procurement Executives

AI in Procurement has proven to be a powerful ally in purchase approval workflows. The main objective is to reduce the daily back and forth of emails, WhatsApp messages, and spreadsheets between purchasing teams and suppliers, simplifying and speeding up the process. One of the most effective ways to implement AI in workflows is by creating no-code apps specifically designed to solve the team’s specific problem. These apps can automate tasks, optimize communication, and expedite approvals, benefiting both the purchasing team and the suppliers.

Simplifying Purchase Approval Workflows

Automating purchase approval workflows through artificial intelligence brings numerous benefits to companies. By creating a custom app for the purchasing team, it is possible to simplify the entire process from purchase request to final approval. These apps can be developed intuitively, making them easy for employees to use and reducing the learning curve.

Furthermore, AI can be programmed to identify patterns and provide intelligent recommendations. For example, the app can analyze previous purchase histories and suggest suppliers or products based on predefined criteria. This saves time and effort while ensuring that purchases are made based on solid data.

Streamlining Supplier Registration and Evaluation

Another area where artificial intelligence can be applied in approval workflows and supplier management is in the registration and evaluation of suppliers. Many companies deal with a large number of suppliers, and having an efficient process to manage this information is essential.

With the use of AI, supplier registration can be automated, eliminating the need to manually fill out extensive forms. Additionally, AI can analyze data and assess the reliability and quality of suppliers based on predefined criteria. This helps the purchasing team select the best suppliers quickly and accurately.

Dashboards for Purchase Savings Tracking

Tracking and measuring the savings generated by purchases is crucial for any purchasing team. Dashboards powered by artificial intelligence provide real-time information on purchasing performance and the financial impact of decisions made.

These dashboards allow users to visualize data such as the total amount saved, the percentage of savings compared to the budget, and the most impactful product categories, among others. This information helps the purchasing team identify improvement opportunities and make data-driven decisions.

Exploring Different Types of AI in Procurement

From a Procurement standpoint, any software solution that incorporates self-learning capabilities and intelligent algorithms can be classified as AI. This guide presents various examples in subsequent chapters and provides commonly accepted definitions.

Artificial Intelligence (AI): Algorithms that demonstrate any form of “smart” behavior.

Machine Learning (ML): Algorithms designed to identify patterns and utilize them for predictive or decision-making purposes.

Natural Language Processing (NLP): Algorithms capable of interpreting, transforming, and generating human language.

Robotic Process Automation (RPA): Algorithms that mimic human actions to streamline repetitive and simple tasks. RPA is generally not classified as a form of AI.

All forms of AI involve algorithms, which consist of rules specifying how to solve specific problems. While anyone proficient in mathematics can calculate algorithms, they serve as the foundation of most computer software. Although the inner workings of algorithms in software remain hidden to the human eye, experts can program and reprogram them to address important problems within software environments.

While Robotic Process Automation (RPA) presents numerous opportunities to enhance Procurement process efficiency, it should not be considered AI. To simplify matters, think of RPA as a software robot imitating human behavior, whereas AI represents a simulation of human intelligence.

The Role of AI in Procurement: Unlocking Potential

AI excels at solving complex problems that involve large volumes of data, provided there are clearly defined measures of success. A study conducted by Harvard Business Review and Deloitte examined the key areas where business executives anticipate the most significant benefits from AI implementation. While each organization faces its unique challenges and opportunities, the following areas highlight how AI can bring value to Procurement:

1. Making better decisions: Artificial intelligence can offer timely analytics and data-driven insights to enhance sourcing decisions.
   
2. Identifying new opportunities: By sifting through vast amounts of data, AI can uncover previously unrecognized cost savings or revenue-generating prospects.
   
3. Improving operations: AI has the potential to streamline and align internal business operations, even within large organizations with multiple business units or geographical locations.
   
4. Automating manual tasks: AI can automate time-consuming activities, such as monthly processes or Procurement performance reporting.
   
5. Freeing up time: By handling routine tasks, AI allows Procurement resources to focus on more creative or strategic responsibilities, such as managing key supplier relationships.
   
6. Capturing and utilizing scarce knowledge: AI assists Procurement organizations in harnessing relevant new data sources, including external sources like the Internet.
   
7. Identifying new suppliers or markets: Leveraging extensive external data, AI can aid in identifying new suppliers or market entry opportunities.
   
8. Optimizing supplier relationships: AI can enhance data-informed supplier relationship management.

Automation in Procurement: Streamlining Processes

Procurement automation involves automating various procurement processes to maximize efficiency and reduce cycle time. By automating repetitive, manual, and time-intensive tasks, automation empowers employees and accelerates the overall process. Here are six steps to automate your procurement cycle:

1. Mapping your current procurement process.
2. Auditing the current procurement process.
3. Identifying key areas for automation, focusing on labor-intensive and repetitive bottlenecks.
4. Selecting an analytics solution that supports automation.
5. Building automation workflows and defining approval points.
6. Measuring automation success and continuously improving the process.

Examples of AI in Procurement

While the adoption of AI in business applications is still in its early stages, numerous examples demonstrate its utilization within Procurement functions. Machine learning algorithms are widely employed in spend analysis to enhance and expedite various processes, including automatic spend classification and vendor matching. Here are some examples:

Machine learning spend classification:

• Supervised Learning in Spend Classification: Algorithms are trained by humans to identify patterns in spend, eliminating the tedious task of repetitive classification for new spend.
  
• Unsupervised Learning in Vendor Matching: Algorithms are programmed to detect new and interesting patterns in vendor relationships without human intervention. For instance, if you have DHL, DHL Freight, Deutschland DHL, and DHL Express in your data, machine learning algorithms can consolidate them as DHL for improved visibility and data coherence.
  
• Classification Reinforcement Learning: Human reviewers assess spend classification actions taken by algorithms and provide rewards or penalties based on the outcomes.

However, it’s important to recognize that achieving 100% automation is not always realistic when building a business case for AI. While around 80% of a process, such as spend classification, can be automated, the remaining 20% may still require human involvement. Applying the 80/20 rule helps in estimating the time required for an AI-driven process and assessing its potential for improving existing timelines.

Harnessing Supplier or Market Data

Techniques like natural language processing can be utilized to search for and capture data on suppliers or specific markets. For instance, tracking social media channels can provide insights into suppliers’ risk positions. AI can significantly enhance predictions, such as price forecasting, maintenance needs, and stock market trends.

AI enables organizations to leverage new sources of data, including market indices, company credit ratings, and publicly available information about suppliers. By sifting through vast amounts of external data, AI-powered methodologies identify opportunities, provide benchmarks, and offer recommendations for improving performance.

Consider benchmarking your performance against others as an example. Currently, you primarily rely on internal and static historical data to benchmark your performance. However, incorporating external data, such as market reports and stock prices, introduces a whole new level of insight.

Anomaly Detection

Imagine receiving automated notifications on anomalies, new opportunities, and recommended actions directly through your procurement dashboards. As AI processes an ever-increasing amount of data, it stays updated on the latest developments and changes in the operating environment.

This enables instant and accurate identification of anomalies and changes, allowing immediate notification to the team. AI can also provide suggestions for appropriate actions and showcase simulations for different scenarios and potential opportunities based on the data it accesses. Consequently, procurement practitioners can stay more informed and take timely action.

Furthermore, AI-based recommendations rely on real data rather than human hypotheses or guesswork. This provides procurement leaders with confidence in making data-driven decisions, removing uncertainty and leading to better outcomes.

AI best practices in procurement

To successfully integrate AI into procurement operations, consider the following best practices:

1. Start with practical problems: Instead of seeking miraculous solutions to transform procurement, focus on incorporating AI into existing processes that are challenging but mundane. Improve areas such as spend analysis or contract management to experience immediate value.
   
2. Capture comprehensive procurement data: Collect as much relevant data as possible, even if its quality may not be perfect. Assume that AI technologies can interpret and enhance data quality over time. The key is to provide more data for AI to learn from, resulting in better outcomes.
   
3. Clearly define procurement challenges: AI and machine learning excel in narrow use-cases. Identify routine tasks that consume significant procurement team time but offer clear performance outcomes. Utilize machine learning for tasks such as categorizing procurement costs based on invoice line items, while reserving complex supplier negotiations for human expertise.
   
4. Embrace experimentation: Although AI holds the potential to improve procurement performance, uncertainties remain. Be open to experimentation by involving emerging AI technology specialists and providing them with training samples of your data. Allow room for learning from mistakes and focus on expected business benefits. Keep in mind that technology evolves rapidly, making today’s failed experiments tomorrow’s possibilities.
   
5. Foster human + machine collaboration: Successful AI implementations in procurement require active guidance and support from procurement experts. Plan for collaboration between humans and machines, where the expertise of your procurement team is augmented, not replaced, by artificial intelligence. Embrace change to make the best use of both human and machine intelligence.

The Future of Procurement with AI

Predicting the exact state of procurement in the next 10-20 years is impossible, but some conclusions can be drawn regarding the future possibilities for AI and procurement. Analysts widely agree that the current applications of AI will continue to evolve. Here are some potential scenarios:

1. Total process automation: Operational procurement tasks, including routine processes, approvals, compliance, and reporting, may no longer require human involvement.
   
2. Automated value creation: Machines may autonomously make decisions and take action on savings and value generation opportunities, reducing the need for human input.
   
3. Full spend transparency: All procurement-related spend could be effectively leveraged and readily available whenever necessary, ensuring accuracy and reliability.
   
4. Agile supplier ecosystems: Managing strategic supplier relationships will take on an entirely new dimension as data seamlessly flows between partner systems. AI will provide recommendations and take actions based on data across the ecosystem, not limited to a single player.

While these scenarios are hypothetical, they represent potential outcomes of current AI applications. The future of procurement relies on its ability to deliver tangible business value. The transformation of procurement aims to maximize return on investment (ROI) through cost savings, efficiency gains, collaboration, innovation, sustainability, and financial success.

Procurement ROI is measured by comparing the procurement function’s costs with the savings it generates. These savings can enable investments, research and development, improved customer experiences, sales enablement, sustainable offerings, and more. AI can significantly amplify the impact of procurement in achieving these goals.

FAQs (Frequently Asked Questions)

1. How can artificial intelligence help reduce the back and forth of emails, WhatsApp messages, and spreadsheets between purchasing teams and suppliers?

Artificial intelligence can automate tasks, simplify processes, and expedite approvals, reducing the need for constant communication through emails, WhatsApp messages, and spreadsheets. By creating custom no-code apps designed to solve the purchasing team’s problem, communication can be streamlined, and approval workflows can be simplified.

2. What are the benefits of automating purchase approval workflows?

Automating purchase approval workflows brings several benefits, such as reducing human errors, increasing efficiency, speeding up approvals, and improving traceability of requests. Furthermore, automation allows employees to focus on more strategic tasks rather than wasting time on manual and repetitive processes.

3. How can artificial intelligence simplify supplier registration and evaluation?

Artificial intelligence can simplify supplier registration by automating the process, eliminating the need for manually filling out extensive forms. Additionally, AI can analyze data and evaluate the reliability and quality of suppliers based on predefined criteria, making the evaluation process faster and more accurate.

4. What are purchase savings tracking dashboards?

Purchase savings tracking dashboards are tools that provide real-time information on purchasing performance and the financial impact of decisions made. Powered by artificial intelligence, these dashboards display data such as the total amount saved, the percentage of savings compared to the budget, and other relevant metrics.

5. How can dashboards help the purchasing team?

Dashboards help the purchasing team gain a clear understanding of purchasing performance and identify improvement opportunities. Based on the data provided by the dashboards, data-driven decisions can be made, optimizing purchasing processes and reducing costs.

6. Is it possible to customize AI apps to meet the specific needs of each purchasing team?

Yes, AI apps can be customized to meet the specific needs of each purchasing team. With no-code development tools, it is possible to create tailor-made apps with functionalities and workflows adapted to the demands of each team. This allows for greater flexibility and efficiency in using AI in purchase approval workflows and supplier management.

Conclusion

Artificial intelligence has revolutionized purchase approval workflows and supplier management, providing greater agility, efficiency, and cost reduction for purchasing teams. By adopting this technology and creating custom apps, companies can streamline processes, automate tasks, and optimize communication between purchasing teams and suppliers.