Increasingly, supply chain managers are shifting from how to best respond to problems to how to prevent problems from arising. That’s where AI in the supply chain steps in to help.
Specifically, data in supply chains is increasing each day. And the need for more sophisticated solutions for planning and optimization is more urgent than ever. Thankfully, AI can transform supply chain management (SCM) and planning. For example, it can improve operations and logistics and maximize productivity by reducing uncertainties.
A supply chain links multiple functions, such as logistics, production, procurement, marketing, and sales. More specifically, it’s a network of individuals and companies representing the whole process of building a product and delivering it to consumers.
Increasingly, global supply chains are becoming more complex. And disruption in one part of the chain can negatively impact the others. Thus, it’s critical to have supply chain systems and processes in place to mitigate or avoid delays from occurring.
The good news is that AI in the supply chain enables managers to avoid disruptions and helps retailers go global to grow their reach. And this is made possible through the ongoing adoption of big data in any landscape.
Specifically, AI helps identify data patterns across complex financial networks worldwide. As a result, it helps build customized products, and personalized user experiences, and conduct deeper data analyses. Additionally, AI helps create more sophisticated chat interfaces to help retailers successfully reach customers around the globe.
For example, companies in the consumer-packaged goods (CPG) space rely on AI and machine learning (ML) to dramatically improve performance. This means improving production, demand forecasts, inventory allocation and planning, dynamic pricing, customer segmentation, and deliveries.
CPGs are products with a short lifespan and unique packaging that customers regularly purchase, such as food and beverages. Namely, automation helps them reduce human involvement in handling more than one task, such as predicting consumer demand for goods.
According to GlobeNewswire, the global AI in the supply chain market accounted for $5,610.8 million in 2021 and is forecast to make up $20,196.6 million by 2028.
Think of a supplier as a person, company, or organization selling or supplying goods or equipment to customers. Specifically, suppliers provide products or services, and buyers receive them. For example, a company selling printed circuit board assemblies (PCBAs) to a laptop manufacturer is a supplier.
In addition, the term “supplier” is also used for countries. For instance, Japan imports its oil and gas, and Saudi Arabia is its biggest oil supplier.
As the first link of a supply chain, the supplier plays a considerable role in providing goods and services to businesses as efficiently and economically as possible. And AI in the supply chain can help achieve this thanks to automation, faster data analysis, simplified inventory management, improved distribution, and faster tracking of production.
A supplier acts as the first link in the supply chain and is typically a business-to-business (B2B) relationship. Conversely, a vendor serves as the last link and can participate in B2B or business-to-consumer (B2C) relationships.
Specifically, a supplier is a business that sells raw materials to another company for manufacturing purposes. On the other hand, a vendor is in the business of providing items that can be inventoried. These are goods and products ready to sell.
For instance, restaurant owners serve several types of soda. By ordering soda from a vendor instead of a supplier, they avoid the hassle of mixing, bottling, and storing the product themselves.
Another example is when a health-focused restaurant owner specializing in organic, made-from-scratch meals partners with a local farm. The owner receives ingredients that can be used in the kitchen. In this case, these farms act as suppliers.
And AI in the supply chain helps better manage these processes. How? You’ll learn below.
Supply chain management (SCM) is the handling of product or service flow. More specifically, SCM is about the movement and storage of raw materials, work-in-process inventory, and finished products from their creation to delivery to consumers.
This type of management allows companies to reduce costs and deliver products to consumers faster and more efficiently. Specifically, SCM is based on five essential elements: strategy development, raw material sourcing, production, distribution, and returns.
According to the Association for Supply Chain Management (ASCM), AI and ML are among the 10 supply chain trends in 2022. And it’s no wonder since AI in the supply chain provides more efficient, visible, and optimized management.
Though the terms “logistics and SCM” are sometimes used interchangeably, they’re different concepts with different meanings.
Specifically, logistics is the movement, storage, and flow of goods or services in a supply chain. For instance, logistics in the natural gas industry includes managing pipelines, trucks, storage facilities, and distribution centers that manage oil transformation along the supply chain.
And AI in the supply chain can strengthen the execution of end-to-end logistics performance by connecting the main activities. These activities include planning, booking, settlement, and shipment tracking.
With the increasing adoption of AI in the supply chain, companies are also exploring its potential to optimize logistic jobs, such as route planning, shipment tracking, and inventory management.
Specifically, AI in the supply chain enables adopters to quickly and efficiently analyze vast volumes of data to make a sophisticated analysis and predict future events based on the analysis results. For example, in partnership with Google, Rolls Royce is creating autonomous ships.
Specifically, AI algorithms enable existing ships to sense what surrounds them in the water. In addition, they allow ships to classify items based on the level of danger. As a result, shipments become safer, and goods can be delivered faster and more efficiently.
Based on a McKinsey study, AI-enabled supply-chain management allows companies to improve logistics costs by 15%, inventory levels by 35%, and service levels by 65%.
Supply chain planning (SCP) refers to all stages, from manufacturing and distribution to procurement and resource management. Specifically, SCP and supply chain execution (SCE) are the two key categories of supply chain management (SCM).
SCE is about execution-oriented applications, such as:
In addition, SCE is focused on execution applications, including real-time decision (RTD) support systems such as GPS route planning. The latter determines the fastest and most-suitable route between two points by analyzing and comparing several possible options.
What about planning? AI in the supply chain helps with better planning. Notably, companies often use explainable AI tools (XAI) to improve their operations and planning.
XAI includes computers and programs that think and make decisions like humans. Namely, precise and real-time data improves decision-making and facilitates time-sensitive processes, including manufacturing and inventory management.
For example, XAI assesses all transportation options to help logistics managers make real-time shipping decisions during poor weather conditions that may cause delays.
Supply chain optimization (SCO) is about the tools and processes used to improve supply chain performance and efficiency. Why is supply chain optimization important? It enables businesses to maximize profits, reduce operating expenses, and build a successful customer experience. As a result, consumers get what they wish, when, and where they wish.
According to a recent IBM study, 47% of surveyed chief supply chain officers (CSCOs) have relied on new automation technologies in the last two years. The purpose is to increase predictability, flexibility, and operational intelligence. These technologies refer to AI in the supply chain.
Specifically, an AI-powered platform learns from past decisions. Then, using historical data, it recommends actions that managers can use to respond to new, similar situations. Moreover, the platform continuously analyzes real-time data so managers can make better decisions and optimize performance more quickly.
Supply chain optimization should be framed around solutions that can help eliminate business-related challenges. That’s where AI and ML come into play. Such challenges can include production planning, demand forecasting, inventory management, routing, dynamic pricing, fraud detection, and quality control on the production line.
Here are the top application trends of AI in supply chain optimization:
A study shows 61% of executives using AI in their supply chains reduced costs. In addition, over 50% increase in revenue.
The benefits of AI in the supply chain become more impactful when combined with ML. For instance, many companies now rely on robotics to automate repetitive tasks. As a result, they reduce staffing costs and boost efficiency.
Moreover, cognitive automation creates new opportunities like robotic process automation (RPA). The latter technology is used to automate digital tasks such as demand forecasting. Specifically, it leverages AI technologies such as optical character recognition (OCR) or text recognition, text analytics, and ML to improve customer and workforce experience.
Specifically, the use of chatbot technology to respond to basic customer inquiries is an example of this. And chatbot use has reduced call center costs and increased customer response times.
Let’s look at the key benefits of AI in supply chain planning and optimization:
By providing higher-quality data and analysis, AI makes items flow properly in and out of a warehouse. This results in more accurate inventory management, which helps prevent overstocking, inadequate stock, and unexpected stock shortages.
By powering smarter, data-driven decision-making, AI in the supply chain creates agility. Specifically, companies start quickly adjusting their procurement, inventory management, and delivery strategies to fast-changing supply chain requirements. And this is extremely helpful for end-to-end projects as they can’t take more than 30 days.
Intelligent algorithm-based AI systems predict and unveil new consumer habits and forecast seasonal demand. Specifically, such analytical insights enhance decision-making and help managers predict possible problems, unforeseen abnormalities, and solutions to streamline production scheduling.
Contextual intelligence is the insights gained through analyzing content engagement online. And AI helps supply chain companies gather large amounts of data from the content published online and build knowledge. As a result, they can cut operational costs and inventory and respond to customers or partners more quickly through personalized messages.
For instance, supply-chain-related content starts trending at the beginning of November and steadily grows in popularity through December, when it peaks. Then, it drops off as the holiday season ends.
Automation saves time and ensures a smooth journey of products to customers. In addition, AI in the supply chain helps solve warehouse issues more quickly and accurately than humans. Moreover, it simplifies complex procedures and speeds up work.
For example, autonomous mobile robots (AMR) used for intelligent robotic sorting provide more effective and speedier sorting of letters, parcels, and palletized shipments.
AI-powered visual inspection uses computer vision to analyze machinery, production processes, inventory levels, and workplaces. Specifically, this is about taking photos of cargo using special cameras.
As a result, managers can identify damage and take appropriate corrective action. And this leads to safer, more efficient, and more effective business processes.
AI in the supply chain analyzes workplace safety data and informs managers about possible risks. Specifically, AI in a warehouse constantly monitors login activity on its servers. And when there is a suspicious login attempt, AI can alert the security department and even block the suspicious account.
This is particularly important in 2022 as Ransomware as a Service (RaaS) has appeared as a new underground market. Here, fraudsters purchase ransomware to conduct rapid attacks on businesses.
Since automated intelligent operations mostly work error-free, they reduce the number of errors and workplace incidents. In addition, warehouse robots work faster and more accurately, thus increasing productivity. All these results in money saved.
Automated systems make traditional warehouse procedures faster and eliminate operational problems along the supply chain. As a result, goods are delivered on time. According to Statista, 41% of surveyed shoppers hoped to receive goods within 24 hours, and 24% in less than two hours.
When managers lack visibility and react by focusing only on past events, they end up with the bullwhip effect, also called the “whiplash” or “whipsaw” effect.
The latter is a supply chain phenomenon when small changes in demand generate large fluctuations while moving along the chain. This can cause excess inventory, lost revenue, and overinvestment in production.
For example, when each party in the chain gradually escalates a spike in demand, each member of the chain overcompensates for it with excess product. This results in increased production, poor demand forecasting, and inconsistent inventories.
Thankfully, AI in the supply chain helps companies accurately estimate demand before a shortage happens. In addition, it helps companies reduce inventory before demand falls.
Insufficient staff is a growing supply chain issue. For example, 58% of companies surveyed by PwC see above-average employee turnover. In addition, only 23% think they have the necessary digital skills to meet future goals. That’s why more companies now rely on AI to understand who might quit and why.
Specifically, AI helps provide companies with responsive and real-time feedback to better understand employees’ thoughts, feelings, and expectations in the workplace. For instance, human resources specialists rely on data analysis to identify which employees are unhappy and might quit. As a result, they can retain employees with success.
Supplier-related risks, such as delays and shortages, are another primary concern for logistics professionals. The good news is that AI in the supply chain helps select suppliers more wisely and manage supplier relationships more effectively.
Specifically, AI analyzes supplier-related data, including delivery performance, audits, evaluations, and credit scoring. Then, it provides supplier-related predictions. As a result, managers make better supplier decisions.
AI-based analyses help personalize the relationship between logistics providers and customers. For instance, DHL Parcel’s cooperation with Amazon is a vivid example. Specifically, DHL offers a voice-based service to track parcels and receive shipment information via Amazon’s Alexa-powered Echo.
So, customers can query Alexa about the shipment by asking, “Alexa, where is my parcel?” or “Ask DHL where my parcel is.” If there is a shipment issue, Echo users can ask DHL for help and contact DHL’s customer support department.
Usually, supply chain processes are associated with multiple documents such as contracts, invoices, bills, and packing slips. AI automation helps automate and organize the flow of goods, finances, and information regarding supply chain transactions. As a result, human errors decrease.
AI technology gives birth to more sophisticated solutions that speed up and improve product creation and delivery. Thus, AI in the supply chain improves operational efficiency and customer service and reduces the impact of delays or shortages. Also, it unveils better and safer ways to move goods from one point to another. So, it’s no surprise that AI is becoming a deciding factor in many industries.
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