Supply chain operations, with varying degrees of global interruption, have recently come into public focus due to huge transport congestion, labour strikes, and general added complexity and interconnectivity, which have aggravated age-old inefficiencies.
AI is being used to study these networks and give them better health as the supply chain planners begin to disentangle their knots. In an increasingly globalised future, AI will help them in making supply chains more efficient and also more resilient.
What is Artificial Intelligence in Supply Chains?
AI is an application that differs from traditional software applications in its efficiency when it comes to supply chain activities such as monitoring product quality, balancing inventory levels, and mapping delivery routes for ideal fuel efficiency.
These systems are trained using huge amounts of data on certain classifications instead of being programmed step-by-step. Some other practical fields in AI include machine learning, which is more of a subfield of AI, where systems learn from the data they've been fed without explicitly programming it with every single step of the task.
AI systems can surpass conventional software in the ability to acquire information from video streams, process spoken or written text, anticipate future market trends, make decisions in intricate scenarios, and draw insights from an ocean of data.
These kinds of capacities are proving tremendously beneficial to the management of workflows across almost all aspects of the supply chain. For instance, supply chain systems powered by ML algorithms can detect patterns and relationships in data that humans or non-AI systems can rarely see, thereby predicting customer demand fairly accurately, resulting in economically efficient inventory control. AI can also analyse factors, such as traffic or weather, that may allow it to recommend alternate shipping routes to minimise the risk of unexpected delays and create better delivery schedules. It can oversee production environments to identify poor quality control practices and health and safety violations. And new use cases continue to unfold as supply chain professionals test the technology.
Benefits of AI in Supply Chain
An AI-based supply chain has many potential benefits:
1. Cut down on errors and waste.
AI technologies can track behaviours and patterns; thus, manufacturers and warehouse operators are able to instruct the algorithms to find weaknesses, whether they be human error or product defects, well in advance of situations that would cause even greater headaches. This knowledge could, of course, also assist in the streamlining of an ERP framework via direct embedding.
2. Improved warehouse efficiency
More efficient warehousing with AI will help organise the racking and design layouts. ML models may analyse how many materials are moving through warehouse aisles and suggest floor layouts that accelerate inventory access and travel time from receiving racks to packing and shipping stations. In addition, they can plan the quickest paths for workers and robots searching for inventory, which helps greatly in increasing fulfilment rates. From the demand signals of marketing, production line, and point of sale systems, forecasting systems using AI can help manufacturers balance inventory against carrying costs and optimise warehouse capacity.
3. Reduced operating costs
As AI has gained the ability to encapsulate complex behaviour and act in unpredictable environments, basic repetitive activities, such as counting, tracking, and documenting inventory, can now be done more accurately, with less labour, and without bottlenecks being detected and circumvented. AI can also substantially bring costs down by minimising inefficiencies in the operations of a complex supply chain through repetitive learning.
AI can also help save manufacturers and distribution managers money by reducing downtime of critical equipment. Smart systems, particularly those that process data from IoT devices in smart factories, can identify malfunctions and breakdowns in real time or predict them before they occur, thereby limiting disruptions and any associated financial losses.
4. Improved accuracy in inventory management
Taking full advantage of the capabilities of AI, manufacturers are able to manage their inventory levels more precisely and efficiently. For example, AI-enabled forecasting systems can utilise inventory information shared from a downstream customer with a view to gauging that customer's demand. If it was determined that the customer's demand was decreasing, the system would accordingly lower the manufacturer's demand forecasts. The British Academy for Training and Development offers the Modern Trends in Warehouse & Inventory Management course, where professionals gain practical insight into AI-powered inventory strategies.
Workers are increasingly deploying computer vision systems, wherein cameras are attached to supply chain infrastructure, racks, vehicles, and drones, to count goods in real time while monitoring space in warehouses. AI is also preserving such workflows in inventory ledgers while simultaneously automating the process for creation, updating, and extraction of information from inventory documentation.
5. Enhanced worker and material safety
AI systems can monitor work environments during the supply chain, including assembly floors, warehouses, and freight vehicles, and also report on conditions endangering the safety of workers and the public. It means that they are using computer vision to ensure PPE or company measures for safe operation and OSHA standards. It could also go as far as managing data onboard the vehicles, such as trucks and forklifts, to ensure that the drivers are safe and sober in their operation. When it comes to factory equipment, AI can help detect potential malfunctions or dangers. AI wearable safety devices can also increase protection; for example, sensor-equipped vests interface with AI systems monitoring the workers' movements in the warehouse and alerting them of injury risk based on posture, movements, and location in the warehouse.
This AI system fed by sensors all over the distribution facilities and vehicles will ensure proper handling and disposal of hazardous materials, protecting those who live and work nearby. By taking the automation approach for such hazardous work, the intelligence will save the exposure for workers from hazards. Smart robots could leverage AI algorithms in tandem with cameras and sensors to process a route to the warehouse with minimum congestion, transporting hazardous materials while avoiding obstacles of all types in their pathways, and reporting back to the warehouse management system. In case of accidents and failures, AI can help do root cause analysis to discern the exact cause and help eliminate it from happening again.
Uses for AI in supply chains
Here are the following uses for AI in the supply chain:
1. Manufacturing
AI can be used to process data on matters related to production or optimisation of throughput, product quality assurance, predictive or preventive maintenance, energy conservation, and other manufacturing processes. Other manufacturing possibilities for AI include collaborative robots, AI-assisted design and prototyping, autonomous handling of materials and even enhancing human-machine collaboration.
AI, machine learning, and cloud computing have not made their way in a massive way to the manufacturing industry, where data and intellectual property protection have been chief concerns for adoption. Nevertheless, certain aspects of food and beverage products and processes include enhancement of production workflows and food safety through machine learning technology, as well as precision of aerospace manufacture has seen an entry for the technology due to these causes.
2. Farming
AI helps farmers watch over soil, weather forecasts, market potential, and other factors to judge when and how much of a certain crop to plant. An IoT system has currently been put in place to monitor and diagnose sick cattle. AI and machine learning are helping this system to monitor animal behaviour and identify early signs of sickness. Farmers get real-time data, allowing them to make quick decisions and catch illnesses in cattle before they really become severe.
Retailers, too, are now using conversational assistants to provide 24/7 service to shoppers.
3. Retail
Like other businesses are bringing in chatbots to serve more customers even when they are not present in the physical stores. In addition, with AI modelling, it is also possible to optimise inventory through demand prediction and stock balance, thus improving operational efficiency. For example, some of the warehouses in Amazon use AI to screen items for any damage before their shipment to customers to shorten logistics time and reduce costs.
Challenges of AI in the supply chain
There can be complexities in the implementation of AI, and organisations need to understand the challenges and risks of bringing in any new technologies.
1. Downtime for training
Every time an organisation introduces a new technology, it needs a period for training the people who will use that technology at any level. This will result in disruption downtime, so it is better to plan for it. All professionals in the supply chain should know that downtime may happen and be open with the partners about the event.
2. Startup costs
There are lots of expenses involved in the installation of AI. In addition to buying software to run the system, the organisation must have machine learning models. Some of these come out of the box, while others are developed from scratch. However, they all must be trained using clean historical data before putting them into the AI algorithm.