Maintaining the supply chain performance at a high level might be a challenging task, but 79% of companies who do this observe revenue growth higher than average. At the same time, according to a survey by BCI, 63% of organizations don’t use any technology to monitor their supply chains, losing the opportunity to improve their productivity and achieve better customer satisfaction.
Machine learning is one of the most promising technologies that can make accurate predictions, enhance logistics, improve inventory management, and much more. We will tell you more about machine learning in logistics and supply chain in our article.
The Need for Change in Supply Management
To get from manufacturer to store, supplies take a long journey through multiple loading and unloading points, varied quality checks, traffic congestions, and so on. A delay at any stage can lead to potential revenue losses and affect customer service, diminishing your profits.
Supply chain management can face the following challenges:
- Demand fluctuation;
- Inadequate inventory planning;
- A huge backlog of orders;
- Logistical uncertainties;
- Communication gaps within the supply chain;
- Supply shortages, etc.
These variables can differ daily, making it difficult to manage the supply chain using predefined rules. Luckily, there are some powerful technologies to fight the operational inefficiencies at all involved stages.
What Should You Know about Machine Learning?
A branch of artificial intelligence, machine learning enables training a computing model on observations or data so it can adjust to conditions without being programmed to do so. This way, the machine can teach itself over time, improving the accuracy of its own algorithms and optimizing operations.
To predict demand, plan supply, and make other forecasts, people usually use old-school statistical methods that are hard to adapt to changing conditions. Machine learning algorithms work differently: they analyze the historical demand or any other dataset to derive relationship and then apply them to current data to make much more accurate predictions.
Machine learning has a variety of applications in the supply chain, so you can benefit from:
- Better inventory management;
- Supply quality improvement;
- Waste reduction;
- Reduced costs due to optimized operations;
- Real-time insights and inventory visibility.
10 Ways to Enhance Supply Chain with Machine Learning
Currently, 41% of retail businesses use machine learning for supply chain management and logistics, but that’s not the only application of this technology. So, how can you combine machine learning and supply chain in practice? Here are 10 proven methods to apply this technology and optimize logistics, manufacturing, and inventory management.
1. Improving Demand Forecasting
More accurate forecasting of demand is one of the most promising supply chain use cases of machine learning algorithms. In contrast to predefined algorithms, machine learning can take into account large amounts of real-time data, which will result in more efficient forecast generation. What is more, it can analyze customer behavior to spot tendencies and predict their future buying habits. Walmart has already found some unexpected connections between weather and customer habits.
Businesses will benefit from better forecast accuracy because they will be able to tailor manufacturing and logistics to actual demands and prevent both shortage and excess of supplies. What is more, understanding of customer behavior will help to improve the relationship with each particular customer by fulfilling their particular needs.
2. Inventory Management
Among all other applications, machine learning enables more accurate inventory management. It can predict demand growth so you can fill your stores in advance and prevent excesses or shortages. It has already been proven that better forecasting can reduce sales losses caused by production being unavailable up to 65%.
Based on computer vision, the ability to analyze images and recognize familiar patterns, the software will be able to classify objects it “sees”. You can imagine robots equipped with cameras, like those used by Amazon, that inspect the storages and automatically analyze products to build a real-time picture of your inventory.
3. Optimizing Logistics
There are numerous variables that can affect logistics, from past order volumes to current weather conditions. Machine learning algorithms can analyze these large datasets and make accurate predictions improving efficiency and profitability.
Real-time data combined with historical information can be used for proactive management decisions, which is one of the most promising machine learning use cases in logistics. Technologies can predict optimal shipment solutions and potential disruptions, choose the most effective route, etc.
4. Predicting Preventative Maintenance
Based on the previous breakdowns and equipment details, algorithms can learn tendencies connected to machine failures and predict them in the future. Machine learning software can analyze the data from sensors and detect the first signs of failure, so you can proactively schedule maintenance and prevent downtimes in manufacturing and logistics.
What is more, if you use IoT sensors, machine learning algorithms can help to derive insights about equipment quality and effectiveness so you can optimize its usage and extend the life cycle.
5. Reducing Fraud Risks
Machine learning can also grant you insights on possible sources of fraud, so you can quickly act and prevent it. Automated inspections will generate real-time reports stored in a secure environment of cloud-based platforms. Algorithms can analyze this data to track the items and determine the stages where fraud may occur.
6. Automating Quality Inspections
At certain stages of the supply chain, products are usually inspected by people who are responsible for detecting possible defects. Such manual inspection is time-consuming and bears risks of human error.
Supply chain intelligence software can automatically analyze packages for signs of damage or wear and accurately evaluate their quality. It can analyze visual data, storage conditions, and system-based information to automatically categorize faults. If an algorithm detects some defects, the maintenance teams will identify it in real time and will be able to fix them before the items get to end customers.
7. Supplier Quality Management
Most companies rely on external suppliers who are responsible for the final quality of their products. However, it can be quite challenging to track and trace every item or components used to make the final product. Luckily, you can save time and money by involving machine learning in this process, as it can help you to receive real-time reports on suppliers’ quality.
8. Production Planning
Machine learning use cases in manufacturing include better scheduling and production planning. You can train algorithms on your existing production data to identify the areas vulnerable to waste and inefficiency. Machine learning can also create a more adaptable environment that can be automatically adjusted to any changing conditions.
You can use this strategy for any production plant, and it can be particularly useful in job shop production environments where it eliminates the need to manually determine the optimal job shop schedule.
9. Security of Supply Chain
The smarter and more complex network you have, the more secure it should be. One source of risks is third party providers who might try to infiltrate your company the way it has already happened to Amazon and Apple. Therefore, you should pay attention to the security of your systems and verify anyone who attempts to access your data within the supply chain.
You can teach machine learning algorithms to evaluate the risks based on who asks for access, the content they request, and the current state of the environment. Try to adopt the strategy of granting only least-privilege access to secure your precious data.
10. End-to-End Visibility
69% of businesses don’t have full visibility on all stages of their supply chains. Artificial intelligence software combined with sensors and machine learning algorithms is giving an opportunity to receive end-to-end visibility from suppliers and manufacturers to stores and customers.
A wide network of sensors and devices generate huge amounts of data, and by using machine learning to analyze it you will be able to discover hidden interconnections between various processes. This way, you will identify and resolve inefficiencies faster and will have more understanding of which stages can be optimized.
If you have to operate a wide network of suppliers, storage points, transport and stores, supply chain management can become a challenging task. Luckily, technologies such as machine learning can help you at all stages. The advanced algorithms can accurately predict demand, optimize logistics, help you efficiently manage inventory, and automate inspections. All these factors will contribute to an end-to-end visible network that will be less vulnerable to fraud, work with more efficiency, and require fewer operational costs.