AI in Supply Chain Management | 5 Application of AI in Supply that escalated the service experience and profitability.

Artificial Intelligence (AI) and Machine Learning (ML) are already changing the supply chain business, widening the split between winners and losers. They drive enterprise-wide insight into all elements of the supply chain by removing deep-rooted inefficiencies and uncertainties, with granularity and methodology that humans just cannot replicate at scale. AI in supply chains is assisting in the delivery of significant optimization skills necessary for more accurate capacity management, enhanced efficiency, high quality, reduced costs, and increased production while encouraging safer working conditions.

Adopting new technologies is one of the most effective methods to optimize your supply chain. Artificial intelligence is crucial to integrate into your logistics organization since it provides a competitive edge while also bringing in a useful and cost-effective shift. Artificial intelligence may increase not just the efficiency but also the accuracy of your company’s procedures, which promotes cost-effectiveness.

AI-enabled increment in supply chain member satisfaction creates a positive feedback loop, resulting in improved overall SCM performance. One of the key aims is to use AI to enhance supply chains from manufacturing to consumption in product-related businesses, as well as to generate chances for product-service innovation in a cloud-based service paradigm. AI will find chances for supply chain stakeholders to have more ownership of outcomes as a service and control over the entire product/service experience and profitability. According to research, the contribution of AI in SCM will reach $16.7B globally by 2027. Moreover, cloud-based AI-as-a-service for SCM will contribute $3B by 2027.

Top 5 applications of AI in Supply chain

Some of the benefits of artificial intelligence in supply networks are less obvious than others. Determining the impact of predictive analytics based on supply chain data, for example, can provide advantages in the long run, but some organizations are finding a clear relationship between revenue changes and the adoption of AI in supply chains. According to McKinsey & Company study, 61 percent of CEOs who have used AI in their supply chains claim lower costs and more than 50 percent report greater revenues. AI has the potential to significantly improve supply chain management internationally, as well as to alleviate the burdens associated with traditional methods of doing operations. For example, AI may assist in the generation of analytical reports for performance improvement criteria such as revenue optimization, supply chain satisfaction, and cost reduction. The top 5 applications of AI in SCM are described below:

Improved Warehouse supply and demand management

Modern supply chains are complex networks of companies, people, activities, information, and resources that are engaged in the movement of a product or service from supplier to customer. The supply of products is determined by market demand because if demand is high, the items required are high as well, and supply must be raised, while the opposite is true in the reverse case. The warehouse is the focal location of supply management where items and supplies are stored. To lessen the likelihood of a product scarcity in the market, it is critical to be prepared ahead of time with the necessary supply of items. This is characterized as warehouse supply and demand management.

Machine learning algorithms and constraint-based modeling, a mathematical technique in which the outcome of each action is bound by a minimum and maximum range of constraints, are being used to uncover patterns and significant aspects in supply chain data. These pattern evaluation algorithms are used to estimate future product demand and hence assist companies in increasing or decreasing supply accordingly. Improper demand forecasting has caused critical losses to major companies in the past for instance Nike had to incur a loss of $100 million in sales due to the failure of demand forecasting software thus effective demand forecasting is essential for proper warehouse supply and demand management.

Improved routing efficiency using AI

Since the covid-19 pandemic, online purchases have increased drastically all over the world. Whether it’s groceries or food items everyone is turning out to online platforms for placing their order and receiving the products at their homes. As per the survey by UNCTAD, more than half of the respondents have started to purchase products online more frequently. In this online technology world, it is vital to have a better logistics system and AI-enabled Supply chain management. Artificial intelligence helps delivery agencies by providing routing information using GPS and other technological aspects.

Google maps is the best example of AI providing routing services to the public for finding the correct directions to their preferred destinations.

AI models assist firms in analyzing existing routes and optimizing routes. Route optimization employs shortest path algorithms from the graph analytics field to determine the most effective route for logistics trucks. As a result, the company will be able to minimize transportation expenses while also speeding up the shipment process. Valerann’s Smart Road System, for example, is an AI-powered web-based traffic control platform that provides information about road conditions to autonomous cars and users. The route optimizers also reduce the impact of fuel consumption on the environment and help the courier boy to deliver more parcels at the same time.

ML improving the health of transportation vehicles

Data from IoT devices and other sources collected from in-transit supply chain vehicles can give crucial insights into the health and durability of the costly equipment necessary to keep commodities moving through supply networks. Based on historical and real-time data, machine learning produces maintenance suggestions and failure forecasts. This enables enterprises to remove vehicles from the supply chain before performance concerns cause a cascading backlog of delays. Predictive maintenance has the potential to save a significant amount of money. It is essentially the new tool for better asset management. While six sigma and agile management were earlier strategies for increasing efficiency, they have had limited returns for today’s businesses after being applied for more than a half-decade.

The PwC report suggests that predictive maintenance can improve the uptime of machines by more than 9%, reduce repair costs by 12%, reduce safety, health, environmental, and quality risks by 14%, and extend the lifetime of aging assets by more than 20%. Predictive equipment monitoring solutions assist businesses in lowering the costs associated with unplanned downtime by allowing them to schedule repairs ahead of time rather than dealing with unexpected equipment breakdowns that result in production delays or excessive product waste due to outdated vehicle parts.

AI helps in improving loading and storage processes

Supply chain management demands considerable attention to details, such as how commodities are loaded and unloaded from shipping containers. To identify the quickest and most effective ways to load and unload items from trucks, ships, and planes, both art and science are required. Moreover, AI also helps to decide the loading and unloading time of the products in the warehouse. It can help the company to decide on the storage positions of products.

For Example, Lineage logistics is a company that keeps cold storage for restaurants and grocery stores. They use AI to identify the path of their orders. The AI technology can predict when orders will arrive and depart a warehouse, allowing personnel to place boxes correctly. Those that will stay in the warehouse for a long time are placed further back, while items that will move rapidly and will not be in the warehouse for as long are placed closer to the front. Lineage’s efficiency has been boosted by 20% since incorporating smart placement through AI. Rather than shifting pallets about like a huge game of Domino to get them in the right sequence, AI helps the organization to be wiser about where products are placed from the start.

AI helps to provide transparency to customers

Machine learning approaches, such as a mix of deep analytics, IoT, and real-time monitoring, may be used to significantly increase supply chain visibility, allowing organizations to alter customer experiences and meet delivery obligations more quickly. Machine learning models and workflows do this by analyzing historical data from many sources and then identifying links between operations throughout the supplier value chain. The major online platforms like Amazon are offering real-time tracking facilities to provide transparency and a better user experience.

Correct estimation of delivery time is also a vital application of AI as the customer who requires a product urgently shall like to look at the delivery date before placing an order for their product. Infinera uses Machine learning to examine production timelines and logistics to better anticipate delivery dates. The AI program then communicates this information to sales colleagues and consumers, letting them know which goods are available and when they may be delivered. Instead of relying just on production and shipping schedules to estimate when goods will arrive, AI integrates previous delivery data with consumer input, weather reports, and logistics to provide an accurate prediction of when products will arrive at customers. As a consequence, the organization is more coherent, able to make choices more quickly, and consumers are happier since they know when their things will arrive.

Conclusion

Innovative technologies such as machine intelligence make it simpler to deal with the volatility and properly estimate demand in global supply chains. Improving the performance of the supply chain is critical in every business. Operating within tight company profits, every type of process optimization may have a significant influence on the profit.