What AI Can Do For Supply Chain Challenges

The Pandemic has damaged our supply chain to the point where recovery will take many months. From sheet metal and computer chips to food ingredients, we are seeing the aftermath of a series of disruptions that have plagued factories and shipping centres.

While retailers remain upbeat that the holiday season saw an increase in sales, roughly half of them remain worried about supply chain disruptions having a negative impact on product inventory and timely deliveries.

This pandemic has taught us a lesson or two about technology and manufacturing. Artificial Intelligence and Machine Learning (ML) are already beginning to change the face of the supply chain industry, which will further exacerbate the divide between the winners and the losers.

AI can help mitigate human error to reduce millions of tons of wasted food. When it comes to food packaging, it is common practice for workers to perform checks at the end of the line, but by the time a product issue is identified at the end of a run, it is too late.

By culling out deep-rooted inefficiencies and uncertainties, Artificial Intelligence and Machine Learning drive enterprise-wide visibility into all aspects of the supply chain and with granularity and methodologies that humans simply can’t mimic at scale.

Moving from production to goods delivery, AI can be used in logistics to automatically locate expiration dates for packages, read them and output standard format dates that can be logged in the customer's control system, a process often subject to failures that result in incorrect data, extra manual labour and delays in shipping.

Supply chain issues can be resolved with flexible AI and Robotics, deployed alongside people, but it doesn’t come without some challenges.

Benefits of AI in Supply Chain:

  • Accurate Inventory Management
  • Warehouse Efficiency
  • Enhanced Safety
  • Reduced Operations Costs
  • On-time Delivery

Challenges of AI in Supply Chain:

  • System Complexities:

AI systems are usually cloud-based and require expansive bandwidth which is needed for powering the system. Sometimes, operators also need a specialised hardware to access these AI capabilities and the cost of this AI-specific hardware can involve a huge initial investment for many supply chain partners.

  • The Scalability Factor:

Since Most AI and cloud-based systems are quite scalable, the challenge faced here is the level of initial start-up users/systems needed to be more impactful and effective. Since all AI systems are unique and different, this is something that supply chain partners will have to discuss in depth with their AI service providers.

  • Cost of Training:

Like any other new technology solution, training is another aspect which needs significant investment in terms of time and money. This can impact business efficiency as supply chain partners will need to work with the AI providers to create a training solution that is impactful yet affordable during the integration phase.

  • Operational Costs Involved:

An AI-operated machine has an exceptional network of individual processors and each of these parts need maintenance and replacement from time-to-time. The challenge here is that due to the possible cost and energy involved, the operational investment could be quite high. Manufacturers would also need to replace these which can shoot up the cost of utility bills and could directly impact the overhead expenses of keeping them running.

Supply chain strains continue to be an ongoing concern for everyone. So, it’s never been more important to create resilient solutions. Digital solutions and AI will enable end-to-end transparency and faster decision making.