Artificial Intelligence in Procurement: Learning on a particular Project

Procurement spends a lot of time analyzing order, performance and market data, especially as its operational tasks are increasingly receding into the background as a result of digitization. If artificial intelligence comes into play alongside classical data evaluation, further potentials and possibilities arise. Anyone who wants to use these methods should define concrete fields of application and gain experience in initial projects.

What are the Benefits of AI in Procurement?

The question that arises first: What are the benefits of artificial intelligence in procurement? What potentials are we talking about? And what are the challenges? In fact, the potential for designing effective processes for procurement using AI is great.

  • Speed: The frequency of global transaction processes is constantly increasing, and suppliers are increasingly demanding fast order processing.
  • Accuracy: Decision processes are more targeted because recommendations for action are based on large amounts of data.
  • Costs: AI applications perform partial steps in the procurement process, which further reduces process costs.

Combination of Man and Machine

Tools that seamlessly complement the existing system and process landscape and become helpers in everyday procurement via the AI are useful. The goal is a meaningful combination of man and machine, which complements each other and is being further developed constantly.
Artificial intelligence is always in demand when it comes to sifting through large amounts of data and making suggestions for optimizations. It is undisputed that AI can make procurement more accurate, faster and more effective in many areas. Here are a few examples

  • Requirements, prices, risks: Predictive analytics applications create demand analyses, forecast price developments and procurement risks.
  • AI-optimized RPA: Robotic Process Automation (RPA) and AI optimize and automate operational procurement processes.
  • Savings: AI tools determine savings potentials on basis of articles, product groups and suppliers.
  • Security of supply: KI determines recommendations for optimal order times and safety stocks.

Many procurement departments lack the knowledge and internal resources to use AI. Artificial intelligence does not exist as a patent solution. It only helps if algorithms are tailored to the specific problems of a company.

This is how you proceed

The biggest challenges in AI projects are data volume and data quality. It is not without reason that data analysts spend up to 80 percent of their time on data preparation in order to create a reliable basis for AI applications.
To build relevant procurement knowledge and gain valuable experience, we recommend the following approach:

  • Interdisciplinary work: Search in interdisciplinary teams for concrete fields of application for artificial intelligence.
  • Stay realistic: Communicate expectations and potentials realistically.
  • Limit application: Start with a defined application case. Define the goals clearly and realistically.
  • Start pilot: Start with a pilot project. Gain experience and plan further projects on this basis.
  • Check data quality: Analyze the quality of the required data.
  • Clean up data: Carry out the necessary clean-up steps. Check whether AI methods can already be used for this. You can learn from patterns in faulty data structures and perform the cleanup on this basis.

CONCLUSION: With the experience from clearly limited projects and a clean data preparation you lay the foundation for the future. The investment is worth it. The methods offer great potential for increasing the value contribution of procurement in the company.
The amc Group has been dealing with methods of artificial intelligence for procurement for a long time. Talk to us! We show you potentials for your company and support you with the implementation of projects.

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