Artificial Intelligence (AI) in Supply Chain and Logistics - Top 8 Requirements

Artificial Intelligence (AI) in Supply Chain and Logistics - Top 8 Requirements

Written By : Bakkah

15 Jan 2024

Table of Content

This article discusses the basics that supply chains need to get actual results from AI applications.

What is Artificial Intelligence?

AI is the intelligence that machines display, especially in the context of thinking, making decisions, and acting. It is when machines make smart decisions, whether deciding which raw material to move where, or how to forecast based on changing demand.

Artificial Intelligence (AI) can offer a great benefit to your supply chain when based on foundations that consider the nature of today's modern supply chains. The effectiveness of artificial intelligence depends on the availability of accurate data needed for decision-making.

Key Requirements for AI in Supply Chain Management

There are eight criteria that are required for a successful AI implementation for an AI solution to deliver optimal value in the supply chain It is important to: 

1. Access to Real-Time Data

To improve the traditional systems with older planning systems, new AI systems should eliminate the old data problems. Most supply chains today attempt to implement plans using days-old data, that improves the supply chain or requires manual user intervention to process it.

2. Access to Community (Multi-Party) Data

The ability to access data outside of the organization to see the data that is relevant to your trading process must be made available to any type of artificial intelligence algorithm, deep learning, or machine learning.

Unless the AI tool can see the most advanced supply and final demand, and all related constraints and capabilities in the supply chain, the results will not be better than those of the traditional planning system.

3. Support Objective Functions

The objective function of an AI engine should be to level consumer service at the lowest possible cost. This is because the final consumer is the sole consumer of the true final products. If we ignore this fact, the partners will not get the full value that comes from improving service levels and service cost, which is important as increased sales through drives value for everyone. The further enrichment of the decision algorithm should support cross-client customization at the organization level to address product scarcity issues and the business policies of the individual enterprise. 

4. Decision-Making Process should be Incremental

Re-planning and changing execution plans can create stress in the organization. Continuous change without weighing the budget of the change creates more cost and reduces the capacity for effective implementation The AI tool must consider the trade-offs in terms of cost of change versus incremental benefits when making decisions.

5. The decision process should be Continuous

Variability is a recurring problem, and implementation efficiencies vary continuously. The AI system must look at the problem constantly, not just periodically, and it must learn as it continues to develop its own policies to best adjust its capabilities. Part of the learning process is measuring the effectiveness of the analyzes, and then applying what you have learned.

6. AI Engines should be Autonomous

Value can only be realized if the algorithm can not only make smart decisions but can also implement them. Moreover, they need to be implemented not only within the enterprise but, where appropriate, across business partners. This requires an AI system and core execution system to support multi-party execution workflows.

7. AI Engines should be Highly Scalable

For the supply chain to be optimized from consumers to suppliers, the system must be able to process massive amounts of data very quickly. Large supply chains can contain millions of storage sites. AI solutions need to be able to make smart, fast, and large-scale decisions.

8. Finding a Way for Users to Interact with the System

You should give users insight into decision criteria, and the impact of diffusion, and enable them to understand the problems that the AI system cannot solve. The AI system must lead the system itself and only engage the user on an exception basis or allow the user to add the latest information the AI may not know at the request of the user. There is a great opportunity here. However, a different approach is needed to meet all these requirements.

How to Realize the Benefits of AI Today?

Executives should focus on ensuring that the basics are laid to maximize the return on their AI investment now and in the future. An AI system receives data and adds significant value by providing insights, making decisions, conveying, and implementing them using machine learning to monitor results and adapt its algorithms, as necessary.

Businesses can prepare for artificial intelligence, digitize their supply chains, and deliver significant benefits, but by tapping all levels of engagement and intelligence to perception, they can achieve truly transformative results.

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Best Regards,

Bakkah Team!