Organisations are still lagging behind when it comes to equipping their teams and supply chain.
With the ever-growing focus on digital transformation, the sector is eagerly anticipating the next wave of solutions that promise to solve common procurement problems.
Recently, we explored RapidRatings’ observations on how, even three years on, COVID’s impact is still being felt by enterprises – costing as much as $228m each year. So, how can such disruption be mitigated?
Arkestro’s recent take on making supply chains more predictable observes how companies can protect themselves, while also achieving far greater efficiency. Whilst these insights were applied to automotive purchasing, its premise can be applied to nearly all sourcing teams, as it outlines how AI can add value to the purchasing process, split into two key parts.
Streamlining POs
- Using data to ‘de-silo’ procurement verification processes
- RPA and ML – predictive procurement orchestration = streamlining purchasing-related workflows from repetitive tasks
- Connecting enough data to incorporate real-time monitoring, to simulate routine purchases
“Over time, procurement supply chains have constantly been reactive. And reactive means: What do I need to do right now to solve the immediate problem?” Melissa Drew, Associate Partner, Finance & Supply Chain Transformation at IBM
Predicting trustworthy suppliers
- Enterprises that are not used to diversifying their supply options open themselves up to massive disruption in their operations
- At the heart of supply chain disruption, suppliers and their reliability have always been taken for granted
- ‘COVID supply chain problems are forcing automotive and heavy industry purchasers to reinforce the reliability of their suppliers with actionable intelligence’
- Such a crisis has put the need for data and AI-enhanced systems into clearer perspective
“When you talk about the role of machine learning and AI, it’s really about creating the benefits of optionality for your purchasing organization without the administrative process and overhead costs associated with managing that complexity.” Edmund Zagorin, Chief Strategy Officer & Founder, Arkestro
Key takeaways
As more organisations work on their digital transformation journey, it’s important to understand that a lot of quality data is needed to train AI models, along with investment to aid their supplier diversity efforts and operations.
This investment promises to reduce time spent on oversight and verification processes within supplier prediction models, providing category managers with enough transparency to predict why one supplier may be more reliable than another, thus enabling competitive advantage for their organisation.
- Invest in AI solutions
- Trust AI
- Build this trust – it doesn’t happen overnight, and the model needs to be fed data to make the right decisions
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