A recent study (Pre-COVID19) found that companies lose between 9% and 20% of their value, in just 6 months due to problems with their supply chain. The problems, accentuated by the current Pandemic, range from shortages of parts, excess finished product inventory, underutilized installed capacity, unnecessary storage costs, to unnecessary transportation of supplies and merchandise.
Due to the obligation of suppliers to keep their information systems and operating advantages confidential, problems reappear more frequently, without the possibility of breaking the inertia.
Artificial Intelligence (AI)
Crises demonstrate that weighing risks and preparing contingency plans in advance of a drastic change in the Supply Chain can prevent significant losses. To achieve this, proactive strategies and the ability to accurately predict the probability of occurrence, as well as the impact of risks, are required .
This ability can be achieved using any of the various techniques provided by Artificial Intelligence, until now little used in local industries:
-Mathematical Optimization Techniques.
-Neural Network Approaches, which represent a set of possible states and transitions. Artificial Neural Networks (ANN).
-Methodologies that adopt multi-agent interaction modeling.
-Approaches that use automated reasoning based on knowledge.
-Machine Learning and Big Data techniques.
-Classification Machine Algorithms, within which SVM (Support Vector Machine) is one of the most popular.
How does it work?
Artificial Intelligence must be enabled to autonomously decide on the course of action that leads to the successful achievement of objectives of the Supply areas and do so under a partially unknown environment of the Supply Chain. AI takes foundations from mathematical representations, evolutionary computing and statistics.
Data Analytics
The standardization of processes is the baseline to mitigate the risks inherent in the supply of goods and services. With it we achieve “expected” behaviors of the agents and with data analytics we can measure and understand with broad certainty the impact that each process has and finally, establish if adjustments are required.
With its implementation we can also ensure the accuracy of the transactions, that there are no misunderstandings with suppliers, it suggests through interpretation how to handle exceptions and, above all, it allows us to make strategic decisions that guarantee the continuous improvement of objectives.
Use Cases
1. Automated selection of Suppliers based on risk identification.
2. Automated Bid Selection using parameters defined in Sorting Machine algorithms.
3. Modeled risk propagation in the supply network.
4. Identification and Prediction of deceptive practices in the supply chain.
5. Data-oriented disaster explanation .
6. End-to-end support to facilitate collaborative disruption in Management.
Advanced Applied Examples
1. Identification of critical variables ( “Rapid trust”, and “Transparent information sharing” ) during a disaster event (such as the 2015 Nepal earthquake) to achieve a reliable supply chain.
2. The Chinese government analyzes its supply data to train sorting machines. This data is used to determine if a company has the capacity to produce, demand, supply through external disruptive events.
3. Bayesian predictions are used to determine the risks of delays in air supply chains .
4. Food sensors in supermarkets that dynamically adjust prices depending on their expiration profile and temperature.
Take the first step towards using data
Although Latin America has so far ventured to take the first initiatives, it requires a brave management team that believes in the adoption of new technologies to unleash the power of the data that its companies produce daily.
This does not mean that Artificial Intelligence and Data Analytics are the only ones that should be taken into consideration when making strategic decisions, but they should be taken as crucial tools to discover new knowledge. In this way, true decision makers can combine them with their own experience and reach optimal resolutions.
Sources:
–Supply chain risk management and artificial intelligence: state of the art and future research directions.
