Efficient supply chain planning is usually synonymous with warehouse and inventory-based management. With the latest demand and supply information, machine learning can enable continuous improvement in the efforts of a company towards meeting the desired level of customer service level at the lowest cost. Cost inefficiencies, technical downtimes, labor shortages, and bad customer experience can be disastrous for any business. However, the complexity and volatility across global supply chains present new problems every day. Innovative AI in supply chain use cases tackles these issues at the micro and macro levels.
One of the main concerns about using AI in a supply chain is that the algorithms that run on computers can learn and make mistakes. If an algorithm got trained on bad data, it might not be able to recognize there’s a problem with its analysis. There are often discrepancies between ordered and received items when receiving shipments from suppliers or distributors. AI helps analyze the product code numbers on each package and ensure everything matches what’s listed on the invoice before it gets unloaded from the truck into storage or onto shelves for sale.
Benefits of Artificial Intelligence in supply chain management
This can be a challenge for retailers for not putting older products out of the warehouse. AI algorithms can predict the arrival and departure of the product in and out from the warehouse more easily. This is useful in assisting employees put the pallet in the correct order and release product as per their shelf life. Do you know where your product is, where it is going to be and in what quantities? This is the challenge that many manufacturers and logistics companies face in the management of their supply chains.
It is a prime example of an environment where artificial intelligence can help improve efficiency and reduce costs. Businesses can leverage AI to make better decisions about the purchase of materials, inventory storage capacities, production plans, and more. Luckily, demand prediction is one of the most popular uses of artificial intelligence in operations and supply chain planning. For this purpose, you can use ready-made platforms like Demand Guru, by LLamasoft. The platform is forecasting demand in the supply chain using machine learning algorithms and identifying demand patterns.
Use case 1: Inventory optimization
“Technological awe aside, autonomous delivery has proven incredibly useful during the pandemic,” she notes. Intelligent technologies and connected end-to-end data, when combined and scaled, can add immense value to any company’s supply chain. IoT device data is generated from in-transit supply chain vehicles to deliver real-time insights on the longevity of the transport vehicles. The machine learning systems integrated into the vehicles make maintenance recommendations and failure predictions based on past data. This will allow you to take fleeting vehicles out of the chain before the performance issue causes any kind of delay in the deliveries.
Join us at 11 AM EST for a hands-on data and decision-making in the supply chain grand rounds. We will be reviewing success stories in ML/AI and network optimization as applied to real world supply chain use cases in supply and demand management and inve…https://t.co/xj6P6lyznu
— Dr Bill Panak (@PanakBill) August 24, 2022
Businesses have better visibility across the supply chain, and better decision-making capabilities in real time – not just across their own organization, but across the entire business partner ecosystem. Includes the creation of a strategic supply chain deployment plan, inventory planning, and the coordination of assets to optimize delivery of goods, services and information from supplier to customers — balancing supply and demand. Artificial intelligence seeks to study the workings of the human mind and to replicate them in operations. When done successfully, AI can reduce costs, increase revenue, and improve overall productivity.
The parameters for maintaining inventory are well-curated and recorded using artificial intelligence. Hence, operations managers can make relevant decisions that support warehouse supervisors. The use of predictive models is particularly popular given the uncertainty that is prevalent in virtually all markets.
- The platform is forecasting demand in the supply chain using machine learning algorithms and identifying demand patterns.
- Within most organizations, there is usually an abundance of data being generated, stored and forgotten.
- According to experts, these two phenomena are expanding its boundary to offer more tangible uses cases in the coming years.
- This is where ML and AI can help by analyzing previous data to pinpoint future demand and fortify pricing decisions.
- CGs have been battling several challenges to meet the ever-increasing and shifting consumer demands.
- Overcoming these obstacles isn’t easy—but it must happen for AI to scale and deliver genuine business value.
Data also suggests that the last mile delivery in supply chain constitutes28% of all delivery costs. Normally supply & production planning processes are run as batch jobs on a weekly, fortnightly, and monthly basis as it is not feasible to run them daily and possibly impossible to run on a real-time basis. Rather it may not make sense to run them in real-time as it will create more confusion! So, if AI/ML algorithms can amend, adjust, AI Use Cases for Supply Chain Optimization and refine plans on a daily basis without running all logic embedded in the SCM systems, then it will be very useful to business users. At its core, SNP involves generating & solving a large mathematical optimization problem using Mixed Integer Linear Programming technique from the Operational Research tools repository. MILP is a very effective optimization technique, where variables defined can be either continuous or integer .
Accurate predictive analytics/forecasting
Once you have an idea of the expected ROI of AI, the potential impacts of digital transformation and an estimate of costs, start thinking about your project timeline. Here, your focus should be on long-term efficiency gains, rather than immediate fixes. The benefits of AI supply chain management are cumulative in nature, and you’ll likely have to make near-term sacrifices to achieve significant future advantages. AI-lead supply chain optimization software amplifies important decisions by using cognitive predictions and recommendations on optimal actions. It also helps manufacturers with possible implications across various scenarios in terms of time, cost, and revenue.
For example, let’s say you are running an online business where you sell furniture. You could use AI to track customer orders and predict what items they will order next based on previous purchases. Cost inefficiencies are usually the result of inaccurate planning and inflexibility, which causes sluggish operations. Eventually, this leads to a drop in on-time in-full delivery performance, which spawns unhappy customers and falling revenues. Companies falter at this stage and adopt piecemeal approaches to solve specific problems like excess inventory, unavailability of raw materials, employee overtime, inferior quality, and so on. Whereas, saving both cost and time are a few benefits of using AI in the supply chain.
Supply Chain Trends to Watch for in 2022
Digitizing a supply chain also requires comprehensive change management and reskilling. So, before you jump on the AI bandwagon, we recommend laying out a change management plan to help you handle the skills gap and the cultural shift. Start with explaining the value of AI to the employees and educate them on how to embrace the new ways of working. The transportation management company builds on AI to ship goods quickly, securely, and cost-effectively. The areas where the company employs AI are manifold — from optimizing the procurement of transportation to carrier management to intelligent shipment tracking. AI in the supply chain can recognize relationships between different datasets and identify fluctuations in demand.
- Having equipment reliably up and running is key to ensuring a smooth end-to-end workflow.
- As a result, you will receive the right type of AI that drives meaningful outcomes and uncover a clear path for further improvements.
- Now imagine a piece of machinery unpredictably breaking down, and others following suit over the next couple of months.
- A well-organized warehouse space streamlines the job of employees, like product pickers, enabling them to be more productive when it comes to order fulfillment.
- With detailed inventory data, enterprises can adjust their inventory strategies to operate more efficiently.
- Powering a supply chain with AI is a complex endeavor that is more than just rolling out the technology.
Germany’s leading railway operator has launched many AI and ML projects to transform into a Digital Rail. Some of them are digital signaling, predictive maintenance of switches, and integrated command and control. However, It has achieved world-class procurement status and invests in digital start-ups globally.
Therefore, deploying the model in a stable, reliable and secure environment is extremely important. Overcoming these obstacles isn’t easy—but it must happen for AI to scale and deliver genuine business value. In our experience, three things can help minimize the roadblocks and allow AI to flourish across the enterprise.
Expanding the reliance on artificial intelligence in the supply chain even further,, businesses can create so-called digital twins — virtual simulations of all corporate assets, warehouses, routes, and materials and product flows. Digital twins help design more resilient and effective supply chains and allow testing out the supply chain performance and foreseeing risks. AI-powered supply chain platforms facilitate order management and help bring together multiple supply chain players involved in the process. But if you over-order at point A, some of it will go unsold, costing you money. AI in the supply chain helps with these challenges by providing real-time data analysis, which allows managers to make quick decisions. AI and machine learning can also increase efficiency and speed up the supply chain processes.
How is artificial intelligence changing how supply chains are being managed and optimized?
AI will be able to provide supply chain management with a better understanding of the business's needs and, therefore, be able to make more accurate predictions. AI will also allow for more accurate projections of demand and inventory levels.
Here’s a more detailed look at the use cases of AI in the supply chain that help achieve the targets mentioned above. This means that the AI can forecast whether there will be enough stock left over when it gets delivered! And because it knows exactly how much should be ordered at each interval, you also save yourself from over-ordering items.
What are the benefits of using AI in logistics?
The benefits of using AI in logistics include improved efficiency, reduced costs, and enhanced customer experience and satisfaction.