Achieving Optimization in Supply chains using ML
How machine learning can be used in real life to enhance supply chains.
Today, the field of Machine Learning which is a subset of Artificial Intelligence is moving at a staggering pace. It has found many applications in various different fields from manufacturing, psychology, automation, etc. and many more. Machine learning is successfully being used in so many fields such that, “Machine Learning” has become the most famous technical buzz word these days. On the other hand, in the business world, Supply chains have become a very important part of businesses. So, there comes a question, can ML help in any way, to solve problems faced in Supply chains?
The answer is yes! In fact, Supply chains can, in the true sense, achieve optimization by using ML and many legendary SCs like Amazon, Flipkart are using ML algorithms to solve their problems!
How can we use ML in Supply chains?
First of all, what is a supply chain?
A supply chain is a network that comprises of every supplier and company, which is used to produce and distribute a specific product to the final buyer. A supply chain deals primarily with three things, that is, Money, Matter, Information. The very nature of a Supply Chain is very complex as many people and many activities are involved. Through an optimized model, supply chains try to achieve low input cost, higher profit, less delivery time, overall efficiency. Achieving such an optimized model is possible with ML. ML can provide many insights and efficient methods of doing things within supply chains. Some of the ways ML can help Supply chains to achieve optimization are explained in the following sections:
1) Demand Forecasting:
It is used to predict future demands for a product. Many e-commerce supply chains buy goods from sellers and then sell them to the customer upon placing the order by the customer. For this purpose, these supply chains store the goods bought from sellers in their Distribution centres. The amount of goods bought is dependent on the current demand. But, if there is any sudden increase in the demand for that product in the near future, which was not thought of, then the company will have to buy that good again from the seller. The biggest issue with this approach is the late delivery time. If the seller fails to provide goods on time in the high demand, then further the company won’t be able to deliver that product to the customer on time. Thus, leading to bad feedback from customers.
To solve this problem, companies like Amazon, make a demand forecast of all the products registered on their website. A database is made which contains each product’s name and its no. of orders on the website. Next, this database is given as an input to ML classification algorithms. These algorithms predict the demand for a product in the near future. If a high demand is detected then that product will be ordered in a high amount more than that of the orders. Thus, companies won’t have to wait for the supplier to provide the product and thus the company can deliver that product faster.
2) ML in Cross-docking:
Many supply chains need big inventories or warehouses to store products. These warehouses cost very high and managing these big warehouses is a complex task. Considering these issues, Cross-docking is an efficient way of handling products. Essentially, cross-docking removes the “storage” link of the supply chain. Products are unloaded from incoming trucks, then sorted, and directly reloaded onto outbound trucks to continue their journey. The sorting mechanism is based on the destination of the product or the type of product. This sorting mechanism is completely based on ML classification algorithms. The products from the incoming trucks are mounted on conveyor belts. These belt assemblies have cameras that are used for computer vision. These computer vision techniques use ML algorithms to classify these products. These classified products are sent to the corresponding stations for loading in trucks.
Consider the example of a company. It has to deliver products in locations A, B, C and D. The distribution centre of the company will have one station for unloading all the products from all the products. These trucks have many different types of products thus they are initially unsorted. On the other end of the DC, there will be Four stations corresponding to four different destination locations namely, A, B, C, D. In cross-docking, the incoming trucks will unload the products at one end of the Distribution centre. These products will be sorted and then loaded in the trucks located in the four end stations which will further deliver the product to the customer.
Sorting of these many products by humans is practically very difficult and takes so many hours to complete. But ML algorithms can easily sort many products in some minutes. This is the power of ML.
The most frequent use of cross-docking is when the demand for any given inventory item is high and shows strong consistency and when handling time-sensitive and perishable inventory. In these cases, Cross-docking reduces the amount of delivery time and eliminates the cost of having an inventory. This effective model of Cross-docking is only possible because of ML.
3) Vehicle Routing Problem:
In the Vehicle Routing Problem (VRP), the goal is to find optimal routes for multiple vehicles visiting a set of locations. In logistics, many types of problems occur in the transportation of goods. While transporting goods from distribution centres to customers, no. of vehicles are limited and the number of products is very high. So if those limited vehicles have to cover all the customers in the least amount of transportation cost in the least amount of time. Other than this, sometimes bad weather conditions, high traffic, road problems are also responsible for delaying the product delivery and increasing transportation cost. Thus, an optimal route has to be determined which can assure that the delivery time will be as least as possible and the route will have the shortest distance travelled thus low transportation cost. Finding such an optimal route is the solution to the Vehicle Routing Problem. Finding such an optimal route becomes a very complex task for a human. This can be easily solved by ML.
The large scale VRP problem is first broken down into smaller distinct problems and then solutions are merged to give a final solution. First, the total number of vehicles serving each customer is found to analyze which vehicles should be used to deliver products to each customer. Then the result is optimized by giving the processed data to a classification algorithm that calculates the minimum number of vehicles that will deliver products to each customer. Then clustering techniques are used to form clusters of customers given the maximum distance of a cluster. So, each cluster represents a stop. By using this technique, we can easily find the optimal route. This complex task is achieved only by using ML.
In this way, ML can help supply chains to operate efficiently. Here only 3 ways are listed in which ML can help supply chains, but there are many more ways by which ML can help supply chains. Thus, supply chains are able to achieve optimization through ML in the true sense!
