How Machine Learning Impacts Supply Chain?

Machine learning is one such technology which has the potential to become one of the most disruptive technologies of the 21st century. Machine learning refers to the ability of computers to learn on their own without being explicitly programmed. It is driving innovation across sectors such as healthcare, agriculture, education, supply chain, manufacturing, etc. The supply chain is one such sector which has also joined the bandwagon as it now leverages machine learning algorithms to find out new patterns in supply chain data to revolutionize every element of the supply chain for a business.


Managing a supply chain is a crucial process as an optimized supply chain helps reduce costs and increase the speed of the production cycle. A supply chain is a crucial aspect of a business process. A supply chain helps boost the profitability of a business. Even if one link of a supply chain goes down or does not function properly, it will affect the rest of the chain and cost the company dearly. Machine learning is a perfect tool for the supply chain process as it provides a detailed analysis of factors based on demand and helps companies make the right business decisions.


What Is The Supply Chain?


A supply chain refers to a network between a company and its suppliers, and this network includes activities, entities, people, information, and resources. This network is set up to produce and distribute specific products to final buyers. A supply chain denotes the steps involved in getting a product or service from its original state to the consumer. So, what is supply chain management? Supply chain management refers to managing the flow of goods and services and covers all the activities needed to plan, control, and execute the flow of a product. Supply chain management includes the active streamlining of an enterprise’s supply-side activities to gain a competitive advantage and enhance customer value.


Ways in which machine learning impacts supply chain


1.Predictive Analysis


Demand forecasting refers to analyzing customer demand for optimizing supply chain processes. Accurate demand forecasting offers advantages such as reduced holding costs and optimal inventory levels. Machine learning models are proficient in conducting predictive analysis for demand forecasting. These models help identify patterns of in-demand data. For example, there are models which can help correlate a customer’s purchasing behavior with a change in weather patterns.

2. Inventory Management


Nowadays, machine learning and deep learning make image classification more feasible. This means that computer systems will be able to recognize and classify objects in images. They perform with a high degree of accuracy and sometimes even outperform humans. Computer vision is an important aspect of AI, which is now also being used to enhance the capabilities of Enterprise Resource Planning (ERP) systems and machines. Computer vision is a field of computer science which lets computers see, identify, and process images. Computer vision will help a supply chain management system to perform accurate inventory management. A perfect of computer vision is the testing robots introduced by Target, the retail giant, which will help track inventory on store shelves.


3. Improved Compliance


In certain industries, it is necessary for manufacturers to comply with an array of industry-specific regulations which govern product quality. In verticals such as healthcare and aerospace, supplier quality is of paramount importance. Supply quality management is time-consuming and is a costly affair. This is because manufacturers in heavily-regulated industries need to track and monitor thousands or sometimes even millions, of component parts from numerous suppliers, to ensure that they meet the compliance standards. Machine learning models can be used to help streamline auditing and compliance monitoring of component parts.

4. Production Planning And Factory Scheduling


Machine learning improves production planning and factory scheduling by taking multiple constraints into account. For manufacturers relying on build-to-order and make-to-stock production workflows, machine learning helps balance each constraint more effectively than the way in which it was done manually in the past. Machine learning is similar to continuous improvement. For production and demand planning, the machine learning engine improves forecasts in an iterative, ongoing manner. The machine learning engine looks at the forecast accuracy from the model and questions itself as to whether the forecast can be improved if the model was modified in some manner.

5. Extending The Life Of Supply Chain Assets


The manufacturing industry leads others mainly based on the volume of data it produces per year. The key supply chain assets include engines, machinery, and transportation and warehouse equipment by finding new usage patterns through data which is collected via IoT sensors. Machine learning invaluable support in analyzing Machine-derived data for determining the causal factors which influence machinery performance to the maximum. Machine learning also leads to more accurate measures of Overall Equipment Effectiveness (OEE). OEE is a key metric relied upon by many supply chain operators and manufacturers. It helps identify the percentage of manufacturing time which is truly productive.




Technologies such as artificial intelligence (AI) and machine learning augment the roles of skilled workers, thereby allowing them to render more value to their organizations. The new knowledge and insights gained from machine learning are thus revolutionizing supply chain management. Attaining the entire benefits of machine learning is an evolutionary process. Though the implementation of machine learning in a majority of the supply chain organizations may take years, supply chain professionals must take the wise decision of planning for the future by making use of the machine learning solutions available today.