How long before AI shows results in supply chain?
Implementing AI in supply chain operations can take several weeks to show tangible results, as it requires setting up infrastructure, connecting data sources, and defining key performance indicators, but once done, it can lead to significant improvements in inventory management and customer satisfaction.
I still remember the meeting where our team decided to implement AsscherAi to improve our supply chain operations. We were all excited about the potential benefits, but we had no idea how long it would take to see tangible results. Looking back, I can see that the first fortnight was spent on connecting our systems and data sources to the AsscherAi platform, which was a necessary step but not exactly what we had in mind when we thought about AI transforming our supply chain.
Setting the foundation
The initial period is crucial in setting up the infrastructure for AI to work its magic. Our team had to work closely with the AsscherAi team to ensure that all our data sources were connected and formatted correctly. This involved mapping our data structures, defining the key performance indicators, and configuring the platform to meet our specific needs. It was a tedious process, but we knew it was essential to get it right from the start.
Most people get wrong about this phase is that they think it's just a matter of flipping a switch and letting the AI do its thing. But the reality is that AI needs high-quality data to produce meaningful insights, and that requires a significant amount of groundwork. I've seen many organisations rush through this phase, only to end up with subpar results and a lot of frustration.
The first question worth answering
Once the foundation was set, we started to think about the first question we wanted to answer using AsscherAi. For us, it was about understanding the inventory levels across our different warehouses. We wanted to know which products were overstocked, which ones were understocked, and how we could optimise our inventory management to reduce costs and improve customer satisfaction. It was a simple yet powerful question that would have a direct impact on our operations.
Our category manager was able to use AsscherAi to compare the inventory levels of different products and identify areas for improvement. She was able to see that we had an overstock of certain products in one warehouse, while another warehouse was running low on the same products. This insight allowed us to make informed decisions about inventory transfers and optimise our stock levels.
Measurable change
As we started to use AsscherAi to answer more questions and gain insights into our supply chain, we began to see measurable changes. Our lead times started to decrease, our inventory costs started to drop, and our customer satisfaction ratings started to improve. It was a leading measure that indicated we were on the right track. We were able to track these metrics through our operations dashboard, which gave us a clear view of our performance.
What's important to note is that these changes didn't happen overnight. It took several weeks of using AsscherAi and refining our processes before we started to see significant improvements. But once we did, it was clear that the AI was having a tangible impact on our supply chain.
Common pitfalls
One thing that can make the AI timeline longer than expected is the lack of clear goals and objectives. If an organisation doesn't have a clear understanding of what it wants to achieve with AI, it can lead to a lot of wasted time and resources. Another common pitfall is the failure to engage with the AsscherAi team and provide them with the necessary support and feedback. This can lead to a slow and painful implementation process.
For example, if an organisation is not willing to provide the necessary data and context, the AI will not be able to produce meaningful insights. It's like trying to build a house without a foundation - it's not going to stand for long. That's why it's essential to have a clear plan and a strong partnership with the AsscherAi team to ensure a successful implementation.
When AI is not the answer
While AI can be a powerful tool for transforming supply chains, there are times when it's not the right choice. If an organisation is struggling with basic operational issues, such as inaccurate data or inefficient processes, AI is not going to magically fix these problems. In fact, it may even exacerbate them. That's why it's essential to get the basics right before implementing AI.
If you're considering implementing AI in your supply chain, I encourage you to reach out to our team to discuss your options and determine whether AsscherAi is right for you. You can contact us through our contact page to learn more about how we can help you achieve your goals.
Frequently asked questions
How long does it take to implement AI in supply chain operations?
Several weeks are needed to set up infrastructure and connect data sources before AI can produce meaningful insights.
What is the first step in implementing AI in supply chain operations?
Setting up the infrastructure and connecting data sources to the AI platform is crucial for producing meaningful insights.
How can AI improve inventory management in supply chain operations?
AI can help identify areas for improvement by analysing inventory levels and providing insights on optimising stock levels.
What are common pitfalls to avoid when implementing AI in supply chain operations?
Lack of clear goals and objectives, and failure to engage with the AI team and provide necessary support and feedback, can lead to a slow and painful implementation process.