10 Reasons Supply Chain Management Should Start Using Machine Learning
Businesses now and before have relied on intelligence gathering operations to assess the performance of the supply chain. The supply chain is a crucial element of business operations as it propels the factors that make a business stand out amongst its competitors. Therefore, the velocity and swiftness originate from how businesses adapt to the required changes in demand and supply.
However, despite the importance of logistics and procurement operations, few companies have been able to merge their supply chain in their processes. According to a survey, only 23% of procurement firms consider using supplier collaboration.
With inconsistencies in the procurement-supply chain dynamics, there are challenging outcomes like supply shortages or uncompetitive pricing, or even delivery delays. But machine learning is changing supply chain management and solving the issues that the logistics industry faces today.
What is Machine Learning?
Machine learning is a technology by which computer systems can get learning based on some given data. Companies can use machine learning to come up with a good algorithm relevant to the market. Machine learning is different because it produces special algorithms, which unlike traditional algorithms learn from the market factors.
Another special feature of machine learning is the least human intervention in computer systems. It is a progressive tool in which, with every addition of data in the data bank the system becomes more intelligent. This feature helps the data to be interpreted and makes it more manageable.
Machine learning can also be incorporated with big data sources like digital markets and social media. This enables companies to use data signals from other sites that consumers generate.
The Ways in Which Machine Learning is Changing the Logistics Industry
- Reducing the time that is wasted on repetitive functions
Those involved in the supply chain process are aware of the wasted time on repetitive tasks. Machine learning allows a business to carry out better quality control tasks with a fraction of the time spent via manual methods.
Thus, machine learning is effective at automated quality inspections. Machine learning algorithms can determine if a product is damaged. These algorithms even suggest the most appropriate corrective action to repair products and assets.
- Informed Forecasts based on Empirical Evidence
Machine learning enabled systems can interpret data from supply chain operations. This makes the system make an informed guess or forecast on the upcoming performances based on these past pieces of evidence. The algorithms created by machine learning learn from all the past data. This can be revolutionary in terms of predictive analytics for supply chain management strategies and help them shape their decision-making plans.
- Automated Alerts to Avoid or Mitigate Crisis
Any company that has to deal with supply chain management has to follow a set of protocols for its operations to be foolproof. But in case of any crisis in the functioning, companies depend on many experts of the field to review existing protocols and blueprints in order to fix the point of failure.
Once it is found what the point of failure is, companies have to make a range of decisions to resume production back to normal. Machine learning is useful in this situation because instead of asking people for help, turning to powerful algorithms that give insights, warnings and automated recommendations is a better deal.
- Monitoring Supplies through Natural Language Processing
Natural language processing enables ease in supply chain management. It monitors overseas suppliers by collecting news about them from all over the world and translating it into the preferred language.
This aspect of machine learning allows a system to understand and interpret human colloquies like social media engagements, publications or any other data. It empowers executives to monitor their vendors across borders in the realm of supply chain management.
- Judicially Improving Delivery Performance
Machine learning combines the positives of unsupervised learning, supervised learning and reinforcement learning to find all the catalysts that affect supply chain management. Not just that, it does all of this by reducing product costs and appropriating processes that mitigate any risks in operations.
10 Reasons Supply Chain Management Should Start Using Machine Learning
- Consumer Engagement
Through machine learning algorithm, businesses can personalize solutions by automating customer services. Chatbots are one such consequence. Through a tailored algorithm, one can create a bot that deals with supplier queries and concerns. These bots reduce the amount of required manpower.
An efficient interactive communication channel can help businesses in tracking worldwide suppliers, manufacturing unit staff, warehouse distribution, and transportation towards retailers. It is important to let go of the myth that chatbots are not intelligent; their smartness depends upon the people that create them.
- Predictive Analysis for New Products
Machine learning can be a boon if businesses want to drive up the sales of new products. Algorithms created by artificial intelligence can be so sincere that they can apply practical approaches of asking partners, coming in contact with direct or indirect sales teams. These manoeuvres can help companies to know how much of a new product or service they will be selling.
What machine learning does in this respect is that it considers all the causal factors that can influence demand (which have not been pointed out before). This can help in proper supply management of a new product.
- Supplier-Specific Data
One of the key functions machine learning based algorithms can perform is to improve the supplier quality management. It does so by tracking or tracing all data pertaining to the particular supplier and then finds patterns in the quality levels of the supplier.
It does all this in isolation and is unassisted. Machine learning systems can independently chalk out product hierarchies. Finally, the systems can also save many manual hours as it organizes track-and-trace reporting.
- Assessing Equipment Performance
Every business that uses machinery and equipment wants to reduce long-term costs by being able to reduce the depreciation rate of its assets. They try to extend the lifespan of their fundamental assets involved in supply chain management. These assets can be machinery, engines, or warehouse equipment.
Machine learning plays a role here as well. It analyses machine-driven data to find out what factors influence the efficiency (or a lack of) in machinery.
- Comprehensive Operations: End-to-End Visibility
Machine learning’s biggest advantage is that it functions from the beginning to the end. In logistics, it begins its work even before the data has to process. First, it can disaggregate all the data by removing unnecessary data. Next, it estimates the volume that may be in demand. Furthermore, it estimates dimensions as well by applying industry-specific standards and all business rules and protocols. Finally, it predicts any gaps in the operations based on historical data.
In the terminology of those involved in machine learning systems, this is a 3D loading visualisation.
The human touch in technology is revolutionizing everything around it. To understand, think of Leonardo da Vinci-he was an expert in mathematics, biological and evolutionary sciences, philosophy and physics, fine arts and critical thinking all at once. Machine learning is the Da Vinci of the logistics industry.
Supply chain management and the greater industry of logistics can benefit from machine learning. As per Harvard Business Review, as of the first quarter of 2018, only a mere 7% of all companies are using Artificial Intelligence to automate their operations.
But machine learning gives quantifiable results. It reduces the time wasted on manual labour functions, allows businesses to make better decisions based on empirical data, and even sends important precautions and alerts. But the beauty of machine learning is that it does not stop there. It recommends the solutions to avoid a crisis, helps monitor suppliers across the world and across various lines of industries.
Therefore, machine learning simplifies the intricate operations of supply chain management systems by optimizing the very operations and functions prone to faults. Machine learning is not the future, it is the present.