AI in the Supply Chain: Use Cases & Implementation Roadmap
Let’s all agree: the pandemic has reshaped the global supply chain. Multiple lockdowns paired with temporary trade restrictions and workforce shortages unveiled vulnerabilities in supply chains that were previously unseen.
The drastic change of the landscape forced supply chain executives to up their strategic management game. To do so, some of them have turned to innovative technologies, with supply chain AI leading the race.
In fact, while it is a standard practice for enterprises to hold up their digital transformation projects in times of economic uncertainty, the COVID-19 crisis did not stop supply chain decision-makers from turning to artificial intelligence solutions providers. A study suggests that 92% of the surveyed senior-level executives carried on with their investments. The reasoning? A positive value of AI in the supply chain.
Below, we look at how artificial intelligence helps supply chains run effectively, investigate the outcomes AI in the supply chain can yield, and share tips on jumping on the digital supply chain bandwagon in a risk-free manner.
AI in the supply chain: how it helps hit strategic management targets
At the beginning of 2021, Ernst and Young surveyed 200 senior-level supply chain executives to reveal their top priorities for the next 12 to 36 months. Here is what they found:
- The number one strategic target for supply chain decision-makers is increasing supply chain efficiency
- The second objective they look to achieve is to gain better visibility into their supply chains
- Other targets that top off the global supply chain agendas are boosting resilience and optimizing supply chain management costs
Artificial intelligence is becoming essential to bringing these strategic transformations to life. As a matter of fact, 95% of the highest-performing organizations see AI as a cornerstone of their supply chain success.
Here’s a more detailed look at the use cases of AI in the supply chain that help achieve the targets mentioned above.
Use cases of AI in supply chain management that increase operational efficiency
Understanding the sources of demand has never been more challenging. With customer expectations changing quickly and getting more diverse, businesses now rely on AI-powered supply chain tools to glean more demand-related insights — and tune their production strategies accordingly.
Integrating available data about each process within the supply chain (even real-time information) and running this data through AI algorithms can help businesses establish a unified view of demand and make more thoughtful decisions. The data for analysis can be gathered from internal sources, such as orders and sales, or external sources, for example, macroeconomic factors, brand sentiment, and the number of COVID-19 cases.
As a result of such planning, AI is capable to return a detailed report of potential consumption volumes, broken down by customer and location levels. And with the detailed demand data on hand, businesses can optimize their production volumes, reduce inventory levels, store more goods closer to their customers, and cut down on unnecessary shipments.
Laying out best delivery routes
Leveraging AI in supply chain management can also help design better delivery routes and optimize fleet utilization. Considering such criteria as supplier and manufacturing sites, storage locations, potential wear and tear of machinery, and fuel usage, AI-based supply chain solutions come up with an optimal route and provide for a speedier flow of items along the supply chain.
Last-mile deliveries and driverless cars
Driverless cars and last-mile delivery robots (that rely on AI, too) have a chance to transform supply chains by decreasing dependence on human drivers and optimizing delivery routes.
Still, according to BCG, merely 10% of trucks will likely operate autonomously by 2030. Delivery drones and bots, in turn, have already won significant popularity. They are increasingly being used to deliver at shorter distances or to places where the ground transfer is not safe, reliable, or sustainable.
Hybrid solutions combining delivery vans and drones have recently emerged, too. Amazon, for example, works on a system whereby vans deliver items close to their destination and then send out an AI-controlled drone for the final drop-off.
Use cases of AI in supply chain management that provide for better visibility
Adopting AI in supply chain management can help uncover the performance of inventory across various channels and sellers and identify anomalies, like delays or low inventory levels. With detailed inventory data, enterprises can adjust their inventory strategies to operate more efficiently.
AI-powered supply chain platforms facilitate order management and help bring together multiple supply chain players involved in the process. For example, an AI-based cargo management platform ITRex helped develop can predict order shipment costs based on up to 60 parameters, process customer requests to eliminate duplicate orders and suggest better carrier matching options, and track shipments from dispatch to delivery.
Use cases of AI in supply chain management that increase resilience
Predictive analytics for risk management
The pandemic has pushed risk management to the top of every corporate agenda. McKinsey reports that 59% of businesses have adopted a new approach to supply chain risk management over the past year.
One typical example of how applying AI in the supply chain advances risk management is optimizing supplier evaluation, flagging suppliers as low-, medium-, or high-risk. For that, an AI solution could weigh out such metrics as the impact on revenues a business is likely to face if a particular source is lost, the time it would take a specific supplier to recover from a disruption, the availability of alternative sources, and other data.
Another way of leveraging AI for the supply chain is predicting supply chain disruptions. Feeding off historical operational data, AI could help identify and correct operational inefficiencies in real time, providing an in-depth look into the supply chain performance, opportunities, and risks. Doing so proactively allows supply chain executives to operate at lower costs without sacrificing efficiency. In fact, according to McKinsey, organizations that have adopted AI report a 44% reduction in operating expenses.
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 in the supply chain: five real-world examples
Let’s look at the examples of companies that have already adopted AI for supply chain management.
The ecommerce giant uses AI-based predictive analytics to power its supply chain and predict demand for products before purchasing and stocking its warehouses. The company says predictive analytics has become a backbone for its supply chain strategy. AI-powered demand forecasting “kicks off the supply chain” and helps the company determine which products, and how much of each, to buy.
The company uses AI to manage the flow of packages. The company’s staff gets a bird’s eye view of the number of packages in the delivery network, the expected peaks in the volume of goods en route, as well as potential disruptions. The AI-based supply chain solution relies on historical and real-time information, including weather and traffic data, to devise the fastest and safest ways to deliver packages.
The company employs an AI-based tool for predicting air freight delays. The solution analyzes 58 data points and predicts delays or speed-ups a week in advance. The tool also indicates the reasons why the changes to the itinerary might occur.
The delivery service uses Roxo — a robot that relies on AI to automate last-mile deliveries. The robot is designed to be used in a three-to-five-mile radius of storage facilities. It has helped the company satisfy the needs of its customers better and improve its performance benchmarks.
Echo Global Logistics
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.
Embarking on an AI-driven supply chain transformation journey
About 60% of projects dealing with AI in supply chain management are either delivered late or over budget. We have laid out an AI adoption roadmap to help you overcome AI implementation challenges and ease your supply chain transformation journeys.
Step 1. Formulate a business case and think over the strategic aspects of AI adoption
Only a third of companies ushering in AI-driven transformation performs a diagnostic audit before rolling out the technology. To make sure you are not missing out on the opportunities of AI, we recommend kicking off your digitization project by identifying and prioritizing the possibilities for value creation across all supply chain segments — from procurement to manufacturing to shipping. With an all-rounded assessment carried out, define the supply chain digitization strategy, and make sure it reflects the findings. It makes sense to start with digitizing one segment of the supply chain that shows the highest value-creation potential to drive ROI faster. And once the base solution is rolled out, you could evolve further, both vertically, expanding the list of available features, and horizontally, extending the capabilities of AI to other supply chain segments.
Step 2. Look for an optimal vendor to bring your solution to life
Due to the complexity and the multifaceted nature of the supply chains, all of your expectations could hardly be met by a single vendor. So, don’t be afraid to examine what the supply chain technology market has to offer and integrate the optimum offerings into a solution that addresses your specific needs. Another piece of advice is going for a vendor-agnostic integrator, so you prevent technology and solution lock-in.
Step 3. Oversee the development and integration of the solution
According to McKinsey, only 15% of businesses involved in supply chain management report feeling like their objectives are in line with those of their vendors. To prevent that and ensure a smooth roll-out, map the development process to the initial supply chain digitization strategy and keep in mind the key value you intend to tap into. Prioritizing the value-creation opportunities and dividing the development process into increments according to the set priorities might help navigate end-to-end AI implementation.
Step 4. Ensure the solution’s smooth adoption and scale the implemented capabilities
Powering a supply chain with AI is a complex endeavor that is more than just rolling out the technology. 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.
On a final note
Over the past years, artificial intelligence has become a vital element of a resilient supply chain. AI-based supply chain management tools are helping organizations speed up the flow of materials and finished products, cut down operational costs, and effectively navigate through changes.
If you are looking to tap into the transformative power of AI and digitize your supply chain for better visibility, resilience, and responsiveness, drop ITRex a line. Their experts will answer your questions and help navigate the transformation process with little to no risks.
Originally published at https://itrexgroup.com on February 21, 2022.