Supply chain intelligence
The supply chain will be one of the areas affected by the fourth industrial revolution and digital technologies such as the Internet of Things, advanced robotics, and big data analytics in the near future. The concept of the smart factory can be found in the smartness of the supply chain. In some leading countries, the operational aspects of this topic have been well implemented to a very significant extent. Placing sensors in everything, creating networks wherever necessary, automating everything that is needed, and analyzing everything that leads to improved performance and customer satisfaction are among the achievements of this revolution in the supply chain.
Integrating machine learning into supply chain management can help automate a large number of everyday processes. This feature allows companies and organizations to focus more on their strategic and more effective business activities and not get involved in the implementation of repetitive activities. Smartening the supply chain and using intelligent software based on machine learning in the supply chain can improve the availability of goods and find the most suitable suppliers. In this way, it will directly affect the efficiency of an organization or business. By using a smart supply chain you can.
Take full advantage of the large volume of data collected by your warehousing, transportation and industrial logistics systems. Other important benefits of using such a supply chain include reducing business risks and risks, improving a deeper insight into the organization and its customers, improving the organization’s performance, and creating a fully functional model in this sector (Supply Chain).
Challenges in the supply chain
Regarding the supply chain and in general in the field of logistics management, there are various challenges, among which the following can be mentioned:
Inventory Management: Inventory management is critical to the supply chain and allows companies to deal with unexpected shortages. Machine learning can be very effective in managing the inventory of an organization.
Quality and safety: With increasing pressures to deliver products on time to keep the assembly line moving in the supply chain, maintaining quality may become a very serious challenge. Acceptance of substandard parts that do not meet the quality standards can bring great risks to the organization
Problems due to lack of resources: Issues related to lack of resources in logistics and supply chain management are very common for organizations. By using machine learning models, organizations can take advantage of this style of analysis and identify hidden patterns in historical supply and demand data.
Automated quality checks: Logistics centers used to perform quality checks manually. However, today, the growth of artificial intelligence and machine learning has increased the scope of automating quality inspection processes in the supply chain life cycle. These techniques have made it possible to analyze and check the defectiveness of industrial equipment in the digital supply chain
Improving customer experience: Today, having a deep insight in the field of supply chain is of great importance and has become a challenge in this field. Machine learning-based techniques, including a combination of deep analytics, Internet of Things, and real-time monitoring, can help dramatically improve supply chain visibility and help businesses improve customer experience. Improve yourself.
Warehouse management: By intelligentizing the supply chain, you can have an efficient planning for your warehouse. In this way, the latest supply and demand information is provided to a machine learning based system and this system tries to improve the level of service quality at the lowest possible cost. Machine learning with predictive models and techniques can solve the problem of shortage or excess inventory in the warehouse.
