In the year 2016, Otto, Uber’s autonomous trucking brand, made a shipment of 50,000 beer cans for the first time in a self-driving vehicle that traveled from Fort Collins to Colorado Springs along existing Colorado highways. This is one of the many examples that show the scope of Artificial Intelligence in various aspects of Supply Chain Management. Machine Learning uses existing data to discover patterns in an industry and pinpoints its most significant factors that can then be used to improve outputs in that industry, and so is the case with Supply Chain Management.

Here Are 5 Ways How Machine Learning Applications Help Supply Chain Management:

1. Managing  Inventory-

In order to run a business successfully, it is crucial to maintain a well informed inventory of your goods. Studies show that on an average, retail inventory is accurate only 63% of the time. When dealing with huge numbers in inventory, it can be difficult to maintain inventory due to ill-maintained data, bad goods going unrecognized and not being able to predict out stock duration’s. All these issues can be handled with the help of machine learning in supply chain. It reduces admin and processing costs in inventory management by integrating all different data and processing them accurately.

2. Managing  Workflow-

Maintaining a smooth workflow is crucial to running a supply chain. If any one element of the chain does not function properly, all the following elements will also be affected resulting in the chain breaking down. Machine learning provides for easy maintenance of the workflow by aligning resources, labor equipment and timing. It also optimizes operations by running planned and unplanned maintenance operations that improve safety and productivity of the supply chain.

3. Production  Planning-

Companies are keen on using machine learning not only in supply chain demand forecasting but also in supply chain forecasting to carry out production planning. Production planning phase holds 25% of the overall market. Machine learning software is capable of providing daily, weekly and monthly production plans to improve production throughout. Artificial Intelligence is used to study consumer behavior, requirements and also purchasing power in order to produce goods highly optimized to sell well in the market and bring maximum profit.

4. Extending  Lifetime  of  Supply  Chain  Assets–

Overall Equipment Effectiveness (OEE) is an important metric that supply operations rely on for good manufacturing outputs. AI uses intelligent sensors in order to find and collect new patterns with regard to usage data of machinery, engines, warehouse equipment and transportation. Manufacturing is a leading industry when it comes to the volume of data it annually produces. This large amount of data is processed and used by machine learning to give better OEE and optimize manufacturing costs.

5. Improving  Product  Quality-

Maintaining product quality is a huge pain point in supply chain management. This includes not just the goods produced, but also the externally supplied spares and parts used in manufacturing the final product. According to research, on an average, over 80% of the elements assembled into the final product manufactured by any a company comes from external suppliers. This necessitates supplier quality checks, compliance and track and trace hierarchies in industries. Machine learning applications can now independently define product hierarchies, streamline track-and-trace reporting, and take care of quality checks to save thousands of  manual hours that are typically invested by manufacturers in these areas, thereby boosting cost-effectiveness and productivity.

One of the main reasons that reduce the effectiveness of a Supply Chain is that they still follow historic models in order to maintain inventory, predict demands and carry out their other tasks. These models are becoming more and more redundant as they can no longer keep up with the pace of demand changes in the market that fluctuate on a daily basis. Fast paced software’s are the absolute need of the hour and it is now inevitable for companies to use Machine Learning in order to keep up with the competition in the Supply Chain Management sector.