Artificial Intelligence in the Supply Chain
According to analysts consulted by Reuters, the market for new supply chain technologies could be worth more than $20 billion annually in the next five years. According to a Gartner study, by 2026, more than 80% of commercial supply chain management applications will offer AI and data science. These forecasts demonstrate the importance of Artificial Intelligence in supply chains to improve labor productivity or the services that companies offer to users.
For the consulting firm McKinsey, the successful implementation of AI in the supply chain has allowed ‘early adopters’ to “improve logistics costs by 15%, inventory levels by 35%, and service levels by 65% compared to slower competitors.” With these figures and the above, how to integrate the supply chain with Artificial Intelligence?
Why Introduce Artificial Intelligence in the Supply Chain?
Among the reasons for introducing AI in supply chains are:
- Improve performance
- Design possible work scenarios
- Organize demand
- Control suppliers
Supply Chain Management & AI
Artificial Intelligence is already a contemporary classic in decision-making: companies and public administrations are increasingly aware of the importance of analyzing the large amounts of data they generate. Thanks to this, insights are obtained with which to reorient the business.
With this in mind, how can we create an AI-driven supply chain?
Artificial Intelligence in Supply Chain: Examples
Digital twins reproduce a supply chain at a lower cost to check how all its parts fit together: warehouses, inventory, workflows…
At a glance and in a scenario that can be easily modified for new tests, the digital reproduction of an artificial intelligence-powered supply chain shows:
- Where there are failures or bottlenecks that make you lose money.
- Where to save costs (could the inventory in that warehouse be moved to another location to save on rent?).
- Where to gain productivity (e.g., by changing the layout of the space to make employees move faster or work more agile).
Also, just as the digital twin of a building under construction predicts the behavior of the real building in the face of natural or climatic catastrophes, the same philosophy can be applied to parts of the supply chain: How strong are those shelves? Where should hot or cold air machines be installed for the well-being of employees?
Digital modeling also serves as a technology to improve the sustainability of companies; for example, to optimize transportation routes, which means less fuel expenditure and thus less carbon emissions. We will come back to transportation optimization later.
These digital twins identify repetitive tasks to be robotized in warehouses or factories. But this is not the only example of supply chain automation.
Chatbots are also a sample of the integration of artificial intelligence in the supply chain to automate processes. They can be used in customer service functions such as resolving queries and making reservations or purchases.
Artificial intelligence can also be used to automate administrative tasks in the supply chain, such as the collection and processing of invoices, delivery notes, documentation…
Benefits of Artificial Vision
Work chain robots with artificial vision systems identify errors or needs, remedy them and alert the human team to do so. In this sense, Artificial Intelligence can also be used to program or execute maintenance tasks.
Knowing what customers need, the final link in the supply chain, also improves the organization of the supply chain. The best way to do this is to have data.
Data collected by the company itself, but also external data (statistical, macroeconomic, consumer trends…), help to make decisions that improve the supply chain.
AI models include predictive components with which planning can be adapted to the different situations that arise, both in the supply chain itself and in response to developments in world markets: changes in demand, fluctuations in prices, changes in suppliers, etc.
Therefore, the predictive nature of AI makes it possible to plan ahead:
- On-the-fly changes in the face of these different situations that arise.
- Greater system robustness.
The system learns from these changes and improves over time.
The introduction of AI allows models with a richer description to solve logistic problems. If experimentation and simulation phases are added, their validation is closer to reality.
Optimization of Transportation Routes
Another artificial intelligence in supply chain example is related to roads, volume of trucks, weight of packages… Again, analytics and Big Data, combined with Artificial Intelligence, help reduce costs in the transportation of materials and products. And, as we have seen before, they improve the environment and the company’s sustainability indicators.
Warehouse and Stock Management
The SKU or Stock-Keeping Unit is the code of a product for sale, which identifies it within the inventory. Managed with Big Data and machine learning, they can be used to:
- Locate products in real time
- Track sales
- Know the inventory status
- Predict needs
- Detect supply chain scams
What happens if a supplier is late in delivering a part or component? The supply chain grinds to a halt. Again, artificial intelligence, combined with analytics, responds quickly to these unforeseen events, anticipates them or predicts what would happen if they were to occur. For example, by building more finely tuned digital twins for these predictive and diagnostic tasks.
Do you want to learn more about how to implement AI in your supply chain?
At Plain Concepts we have extensive experience in the implementation of Artificial Intelligence projects, which we adapt to each situation thanks to our multidisciplinary team of AI, Machine Learning and Deep Learning. Contact our team; together we will find the product that best suits your company.