How Supply Chain Networks Benefit from Machine Learning?
An overview of the impact of machine learning in a supply chain network.
Supply chain management is one of the most complex business processes. It requires a considerable amount of proper and careful planning. Else a single loss of shipment can cause severe damage to the industry while disrupting the entire chain. Also, given the enormity of the data handled by this industry, manual processing might not be the best option.
Enter Machine Learning. It is a promising application of artificial intelligence that enables a system to learn from data recorded from actions and experiences to bring automation into the process and improve decision making. This allows technology to train itself over time so that it can improve operations. Using intelligent machine learning software, supply chain managers can augment inventory and find the most appropriate suppliers to keep their business running smoothly. It has numerous applications in supply chain networks and logistics.
According to analyst firm Gartner, by 2020, 95 percent of Supply Chain Planning (SCP) vendors will be relying on supervised and unsupervised machine learning in their solutions. Further, it also predicts that by 2023, intelligent algorithms and AI techniques will be embedded across 25 percent of all supply chain technology solutions.
The supply chain industry is already stressed due to an imbalance between supply and demand, changing weather conditions, scheduling maintenance, product security, order backlogs, inadequate area mapping, and more. By discovering insightful patterns in supply chain data and leveraging cloud power, machine learning can enable excellent customer experiences that shall transform the business prospects of most logistics hubs. This is helpful in a competitive market where each participant is striving to cut costs, increase profits, and enhance the customer experience.
Using machine learning, one can improve the quality of advanced analytics processes like demand and supply forecasting. This enables field staff to quickly evaluate the best and worst possible scenarios and suggest optimal solutions to make well-informed decisions. It also allows key stakeholders to gain higher contextual intelligence across supply chain operations. This translates into lower inventory and operations costs and quicker response times to customers. Machine learning eliminates credential abuses and security breaches by verifying the identity of anyone requesting access, as well as the context of the request and, most importantly, the risk associated with the access environment. Therefore, it reduces the possibilities of potential fraud in the supply chain.
Moreover, machine learning curtails the dependence on human factors, resulting in faster project completion with minimal reduces cost. Plus by assessing customer requirements it shall optimize the upstream supply chain to ensure timely supply of goods with marketplace demands. It also helps in monitoring how the quality of a shipping network varies over time and suggest improvements if required. All these enhance supplier relationship management due to more straightforward, proven administrative practices. This is because reducing freight costs, improving supplier delivery performance, and minimizing supplier risk are import attributes of collaborative supply chain networks.
Investing in machine learning and related technologies imply increased profitability and end-to-end visibility. It has also reinvented the last-mile-delivery experiences since it identifies the type of delivery address (whether it is an office or home) and then alerts the system to figure out the best time to make the delivery attempt. The process involves using algorithms, patterns, and predictive insights from data sets to differentiate categories. Additionally, it can analyze updates about the weather forecast, traffic situations, and other factors that can directly or indirectly impact delivery schedules. These measures help in increasing the probability of the receiver’s presence at the delivery address, ensuring successful delivery, and improving the customer experience. Machine learning also removed the ambiguity in interpretations of pieces of records and orders. Other than that, machine learning eases the maintenance of physical assets. E.g. IBM Watson’s Visual Recognition helps to spot defects on-site without manual intervention, revolutionizing this part of the industry. Even retailers can do a stock analysis using machine learning and link sales and promotional activities to demand and supply planning so that stores do not run out of stock.
To surmise, machine learning has become an essential asset in supply chain management and logistics. It solves complex constraints, cost, and delivery problems that corporations face today. Due to its virtually endless possibilities, one can expect the creation of new trading models in the future. Plus with new challenges, machine learning too shall evolve with time to respond to those threats.