Artificial intelligence in supply chain management

When supply chain disruptions predominate and there is a growing need to strengthen them, emerging technologies such as Artificial intelligence services have been of paramount importance. From guesswork to shipping and delivery management, AI is powering supply chain operations, improving their efficiency.
According to a Mckinsey report, the value of goods traded globally has tripled to more than $10 trillion annually since 2000. Businesses around the world aspire to a well-designed and efficient AI model to manage the inventory levels, reduce lead times and ensure all-time on-complete deliveries.
Today, we’ll look at eight such examples where artificial intelligence is helping modern organizations transform their supply chains at lightning speed.
Artificial intelligence in the supply chain

AI in Logistics and Transportation
Fleet management and optimization are the most underrated applications of AI in the supply chain. Fleet managers amplify the crucial connection between the consumer and the provider. Therefore, they are responsible for the unimpeded flow of trade.

Coupled with rising fuel costs and resource scarcity, fleet managers face data overload issues. If companies do not collect data and process it, quickly or properly analyze the collected data and they will soon become an unproductive swamp.

AI intervenes in such a scenario to ensure efficiency in all activities. With the help of predictive analytics, it assesses truck response time and ad-hoc vehicle demands. It studies historical demand patterns and, with the help of statistical techniques, predicts the demand for trucks by shipping route. It uses powerful multidimensional analytics to reduce unplanned fleet downtime, increase fuel efficiency, and detect and eliminate bottlenecks.
Supplier risk assessments
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Read: Cost to develop travel App
Businesses and enterprises can build AI and ML-based models based on their risk assessment infrastructure. The model can extract deeper insights on real-time data from various sources (such as social networks, news, media, etc.) 24 hours a day in as many categories as you want.

Using data science-based techniques, AI can perform noise reduction and relevance-based normalization to provide actionable insights. Taking into account the most relevant and significant data from the large amount of big data, it calculates a risk score/index for the providers. These risk scores alert the organization to any potential vendor failures.
AI in demand forecasting and inventory management

Also Check: AI use cases in manufacturing

According to a survey, 90% of respondents believe that AI will transform the supply chain for the better by 2025. When applied to demand forecasting, the AI ​​and ML framework generates accurate predictions about future demands.
For example, it is easy to predict the decline and end of life of a product accurately in the sales channel along with market growth when introducing a new product. Deep learning deciphers both linear and non-linear dependencies to make demand forecasting more scientific and accurate.

Similarly, in supply chain forecasting, AI and ML ensure that bills of materials and PO data are structured and accurate deductions are made on time. Field operators leverage this data to drive operations and maintain threshold levels needed to meet current demand.
Maintaining optimal stock levels is one of the biggest challenges facing supply chain organizations — the AI ​​and ML framework works to maintain stock while creating a revenue generation path for businesses.

Artificial Intelligence In Supply Chains
1. Demand Forecasting Is Improving Warehouse Supply And Demand Management
Machine learning is being used to identify patterns and influential factors in supply chain data with algorithms and “constraint-based modeling,” a mathematical approach where the outcome of each decision is constrained by a minimum and maximum range of limits. This data-rich modeling empowers warehouse managers to make much more educated decisions about inventory stocking.

This type of big data predictive analysis is transforming the way warehouse managers handle inventory by providing deep levels of insight impossible to unravel with manual, human-driven processes and endless, self-improving forecasting loops.

C3 AI uses AI to power its Inventory Optimization platform, which gives warehouse managers data on inventory levels in real-time, including information about parts, components, and finished goods. As the machine learning ages, the platform produces stocking recommendations based on data from production orders, purchase orders, and supplier deliveries.

2. AI Is Optimizing Routing Efficiency And Delivery Logistics
In a world where just about anything can be ordered online and delivered within data, companies that don’t have a firm handle on delivery logistics are at risk of falling behind. Customers today expect quick, accurate shipping, and they’re all too happy to turn somewhere else when a company is unable to deliver on that expectation. McKinsey & Company reports that around 40% of customers who tried grocery delivery for the first time during the COVID-19 pandemic intend to keep using these services indefinitely. Customers in major markets like New York and Chicago have dozens of choices.

Delivery logistics is a detail-oriented, challenging field. This Economist article unpacks some of this complexity, pointing to the “devilishly complex” task of delivering 25 packages by van — the number of possible routes for a single van adds up to around 15 septillion.

AI-driven route optimization platforms and GPS tools powered by AI like ORION, a company used by logistics leader UPS, create the most efficient routes from all the possibilities, a task untenable with conventional approaches, which have been inadequate for fully analyzing the myriad route possibilities.

3. Machine Learning AI Is Improving The Health And Longevity Of Transportation Vehicles
IoT device data and other information taken from in-transit supply chain vehicles can provide invaluable insights about the health and longevity of the expensive equipment required to keep goods moving through supply chains. Machine learning makes maintenance recommendations and failure predictions based on past and real-time data. This allows companies to take vehicles out of the chain before performance issues create a cascading backlog of delays.

Chicago-based Uptake uses AI and machine learning to analyze data to predict mechanical failures for a wide range of transportation vehicles and cargo containers, including trucks, cars, railcars, combines, and plans. The company uses data from IoT devices, GPS information, and data pulled directly from vehicle performance records to arrive at its predictions, which can greatly reduce downtime.

4. AI Insights Are Adding Efficiency And Profitability To Loading Processes
Supply chain management includes a great deal of detail-oriented analysis, including how goods are loaded and unloaded from shipping containers. Both art and science are needed to determine the fastest, most efficient ways to get goods on and off trucks, ships, and planes.

Companies like Zebra Technologies use a combination of hardware, software, and data analytics to deliver real-time visibility into loading processes. These insights can be used to optimize space inside trailers, reducing the amount of “air” being shipped. Zebra can also help companies design quicker, less risky, more efficient processing protocols to manage parcels.

5. Supply Chain Managers Are Uncovering Cost-Saving And Revenue-Increasing Methods With AI
Moving goods around the world is expensive, and only becoming more expensive. Bloomberg reports that the cost of moving goods by ship, for example, increased by 12% in 2020, the highest level in five years, according to the Drewry World Container Index.

Companies like Echo Global Logistics use AI to negotiate better shipping and procurement rates, manage carrier contracts, and pinpoint where changes in supply chains could deliver better profits. Users access a centralized database that takes virtually every aspect of supply chains into account to deliver financial decision-making advice.

AI in supply chain innovations are paving the way for a future where we can eventually expect to see AI-powered, autonomous vehicles used throughout supply chains.