Artificial Intelligence and its implementation in supply chains

Artificial Intelligence has been in development for several years, its implementation in the day-to-day operations of companies is one of the most revolutionary changes that organizations have experienced in decades. This is mainly due to three factors. One is the enormous storage capacity of our systems; on the other hand, a large amount of data available; and, finally, the availability of great processing capacity for this data. These three elements allow the application of algorithms in a very powerful and efficient way. These data are processed by the algorithms and produce a result that is evaluated, and the system learns from that result, improving future outputs according to established parameters.

The implementation of Artificial Intelligence impacts the entire value chain of companies, and especially in the management of supply chains, from end to end, helping to substantially improve the decision-making process. This allows improving not only the physical flow of materials and products but also the flow of financial information, producing a substantial improvement in productivity in all elements of the chain through greater visibility among all agents, improving processes and product quality, and achieving an increase in customer service, reducing errors and defects produced.

Supply chains generate data every time activity or process is carried out, or a product or service is obtained. This data can be stored and used to feedback into AI systems that will lead to better results in the future.

AI also allows us to simulate scenarios to compare costs, times, productivity, and evaluate risks, customer service, benefits, and also CO2e emissions. The generation of these scenarios will allow AI systems to make recommendations so that supply chain managers can make decisions based on accurate and reliable information.

Artificial Intelligence has to be an agent that allows the development of supply chains that work in a transparent way, without interventions. Although the idea of ​​a fully autonomous supply chain is still a long way off, AI can allow us to augment, complement, assist, predict, improve and measure the performance of current workers and teams in each of the functions performed in the chain, optimizing time and resources to obtain greater productivity. In addition, AI can help with other functionalities for better decision-making in the daily operation of the supply chain, identifying patterns and preferences that allow companies to deliver products that are better suited to the needs of customers in less time, adding value, and improving customer service.

Therefore, organizations have to prepare to adopt this change and lead it within themselves, collaborating with their suppliers and customers. Supply chain leaders have to take advantage of all available data, information, and technologies to increase productivity and create value for all stakeholders.

How to implement AI in supply chains?

To implement AI in our supply chain, it is necessary to do an end-to-end analysis of the state of the chain, considering all the agents involved. In this way, we can define the needs and opportunities that can be created in the implementation process. This analysis process should use a gradual approach, which gradually improves current capabilities rather than replaces them, and incorporates the following pillars:

  1. Establish an AI strategy. This strategy has to be aligned with the overall strategy of the company. AI is a very powerful set of tools. It may be the most important that a company has had in its entire history, but it is still that, a set of tools. A first step should be to understand these technologies well. Supply chain managers have to think about what they want AI for. How does AI adapt to current processes? Is it necessary to redesign the processes? As at the time of installation of an ERP, it is first necessary to ask ourselves what we do in the supply chain. End-to-end providers and customers have to participate in this reflection and try to identify the alignment between all agents on a common AI strategy.
  2. Decide what type of AI the company needs to satisfy its strategy. Any technology that we apply has to be oriented to the contribution of value, which can come from an improvement in the customer experience or from the increase in productivity and the reduction of waste in the processes. It is important to establish what the needs are and define which are the areas or activities in the chain that are potentially “ quick wins ”, that is, which are the technologies within AI that allow me to obtain the maximum benefit with the least investment in costs, time, capabilities, and resources.
  3. Study how to integrate technology in the current company environment. Once the technology we want to work with has been chosen, it is convenient to design a pilot implementation project. It is important to understand the level of maturity and technological integration between all the agents in the supply chain and also their level of automation. In many cases, there may be different levels of automation that do not allow the flow of information and materials in a homogeneous way.
  4. Measure improvements. The implementation of any system must have metrics that allow evaluating performance, and AI is no exception. It is important to establish robust indicators for a correct evaluation of the performance of the tools.
  5. Fit and scale. Continuous improvement is a key aspect of Artificial Intelligence systems: being able to learn from the environment and prepare a new solution to the same problem is one of the advantages that this technology allows us. This will make the components of the supply chain using AI smarter. In the learning process, it is important to identify biases that cause the learning of the system to be inappropriate.

We can clearly see that the application of Artificial Intelligence in supply chain management is a generator of value creation for companies, through improved productivity, better customer service, better working conditions for employees at all levels of the chain, and the reduction of emissions. However, implementing AI in supply chains has to take into account the security of the people and systems with which it operates end-to-end. This requires computer security systems that ensure the integrity and maintenance of the data generated and stored.

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