Nowadays, logistics companies strive to lower costs, provide the best customer service, and increase profit. Supply chain management is not one of the lightest tasks in the current market situation. AI and ML prove helpful in solving the above problems. In today’s article, we will focus on how machine learning affects SCM.
- 1 What is supply chain management?
- 2 Machine Learning in supply chain management
- 3 What are the limitations of traditional SCM methods?
- 4 6 ways that ML is transforming SCM
- 5 Conclusion
What is supply chain management?
Supply Chain Management (SCM) is the management of data, processes, and people related to a product or service. It includes activities related to purchasing, sourcing, production, and logistics.
SCM enables companies to reduce their logistics costs and improve the efficiency of their entire business.
Machine Learning in supply chain management
Both artificial intelligence and machine learning have a huge impact on the functioning of many industries. As Gartner predicts, more than 75% of commercial SCM applications will rely on some form of AI by 2026. Machine learning in supply chain management allows you to:
- Automate tasks
- Optimize inventory
- Maintain efficient business operations
Moreover, ML uses complex algorithms to transform supply chain data into useful predictions. As a result, you save time as well as money.
What are the limitations of traditional SCM methods?
- No quick response to changes in market conditions.
- Traditional SCM methods use outdated software that slows down all operations.
- Delays due to the need to approve each decision.
- By utilizing traditional SCM methods, companies cannot respond as quickly to sudden disruptions.
- No comprehensive data analysis.
Machine learning in supply chain management allows companies, first of all, to gain a competitive advantage, save resources and increase efficiency. Today, when global supply chains are so complex, the use of outdated SCM solutions is not beneficial. Here are some examples of how machine learning is turning traditional SCMs into modern ones.
6 ways that ML is transforming SCM
By leveraging ML tools, companies can take advantage of predictive analytics to forecast demand. And admittedly, these solutions are able to do it with high accuracy. Besides, with a robust supply chain forecasting system, companies can easily detect problems and threats.
Here are a few of the benefits companies get from predictive analytics:
- Reducing the cost
- Optimal stock level
- Quick reaction to threats
- Greater company efficiency
- Accurate planning of marketing and financial activities
Fraud prevention is another application of Machine Learning in Supply Chain Management. ML algorithms analyze information in real-time to detect anomalies and deviations. Supply chain managers will be notified if the algorithm finds anything wrong.
Thus, the use of ML reduces the risk of fraud. But apart from that, it:
- Improves product quality
- Allows identifying defective goods
- Prevents equipment failure
AUTOMATED QUALITY INSPECTIONS
Automation is one of the main benefits of using ML in supply chain management. This way, employees don’t have to perform repetitive, mundane tasks.
Logistics companies carry out manual inspections quite frequently. Especially when it comes to checking packages for damage during transport. Fortunately, with the implementation of ML, logistic centers can automate these processes. What’s more, ML uses techniques that allow for automatic defect analysis in industrial equipment and products. It is possible to use the image recognition method.
Automation of quality inspection reduces the risk of delivering defective products to consumers.
Effective supply chain planning often involves warehouse and inventory-based management. Thanks to ML, accurate forecasts of supply and demand are possible. It enables logistics companies to achieve a high level of customer service at a low cost.
Machine learning in the supply chain:
- Solves the problem of shortage or overloading the warehouse
- Affects better warehouse management
- Tracks the goods throughout his journey
- Optimizes the location of goods in the warehouse
PLANNING PRODUCTION MORE EFFICIENTLY
Planning the production process is based on the fact that organizations have to deliver the materials as quickly as possible. With the aid of ML, they can optimize production plans. Machine Learning allows them to optimize factory schedules and reduce delays in deliveries. By using ML models and techniques, it is also possible to identify possible inefficiencies and waste in production.
IMPROVED CX WITH REAL-TIME VISIBILITY
The survey published by Statista shows that the biggest challenge companies face in the supply chain is visibility. The success of a supply chain relies heavily on visibility and tracking. That’s why businesses are constantly seeking technology that can enhance them. ML techniques to help improve supply chain visibility. Thus, it allows to change the customer experience and to deliver commitments more quickly.
REDUCE DELIVERY TIME
Last but not least is the use of machine learning to reduce delivery time and costs. Machine learning algorithms enable supply chain operators to change delivery routes. As a result, it suggests the most time-saving and cost-effective delivery route.
Many components go into the supply chain. These are raw materials, materials supply, manufacturing, and delivery. Traditional SCM methods have many shortcomings. As we showed in the article, machine learning is very helpful in the proper management of the supply chain. Find out more about MLOps consulting.