How to Apply AI in Supply Chain to Drive Better Results

“It was the best of times, it was the worst of times” for supply chain companies. On the one hand, they have technological advancements and globalization. But on the other hand… The zero-Covid policy in China, the Russo-Ukrainian War, sanctions on Russia, and the China-U.S. trade war. These are the major factors causing rerouting, delays, high transportation costs, labor and material shortages, and a long trade flow.

Is there a way to mitigate the uncertainties and risks, increasing profits and customer brand loyalty at the same time? Artificial intelligence (AI) in the supply just may do the trick.

AI can provide advanced analytics that will help companies with real-time data visibility, proactive alerts, prescriptive insights, and even autonomous driving. Keep reading to discover how businesses apply AI in the supply chain. 

Supply chain market state through the prism of AI

Decision-makers in the supply chain industry need better ways to handle forecasting, supplier relations management, quality assurance, automation, and sustainability. In the food and consumer goods industries, 100% of respondents had experienced production and distribution problems, and 91% had problems with suppliers. At the same time, as much as 85% of respondents struggled with inefficient digital technologies in their supply chains.

Fortunately, artificial intelligence can help with many supply chain challenges, and that’s why companies invest tremendous budgets in AI in logistics, trying to get the most out of its implementation. In fact, the global market of artificial intelligence in the supply chain is expected to reach $21.8 billion by 2027.

In the 2021 MHI Annual Industry Report, 17% of respondents said they use AI already, and another 45% said they plan to use it in the next five years. The survey of more than 1,000 supply chain professionals worldwide also found that 25% plan to invest in AI products in the next three years.

And since companies need to sustain the competition and bring value to their customers in this volatile market, they should really consider using AI tools. Let us show you some successful examples.

AI for supply chain resilience: Use cases that work

Accenture discovered that 3/4 of chief supply chain officers want to rework their supply chains to make them more resilient. And their best bet would be to implement AI and software solutions instead of resorting to traditional approaches. Let’s look at the cases when AI investments appear to be paying dividends to supply chain businesses. 

Demand forecasting

In the growing recession, accurate demand forecasting is essential for cutting supply chain costs, improving financial planning, capacity planning, and risk management. There are many ways in which AI-driven operations allow managers to make accurate supply chain forecasting and facilitate warehouse and shop floor management. Some of them include:

• Analyzing and interpreting massive datasets. AI can interpret datasets quickly to discover trends and pinpoint changes. 
• Making forecasts based on extensive criteria. AI can make advanced forecasts based on internal and external data sources like demographics, social media information, online reviews, weather, etc. When used correctly, AI-powered supply chain networks can outperform manually controlled networks managed by data analysts. AI allows supply chains to adapt to changes quickly yet smoothly.
• Making accurate estimates on demand against the stock. AI can help make accurate estimates regarding future demand against the current stock. For instance, it can anticipate a decline of a product by the end of its life cycle on a sales channel. AI can even create models for new expected products that might make a breakthrough on the market.

Business application

IKEA is using an advanced AI-powered tool called Demand Sensing developed for demand forecasting. The tool uses the existing and new data to make highly accurate forecasting insights. Though it’s currently only used in Norway, the results are still impressive: the tool increased the accuracy of IKEA supply chain forecasting from 92% to 98%. That’s four times fewer incorrect predictions.

Before the Demand Sensing tool rolled out, IKEA was using statistical sales, meaning the predictions were based on the sales and demand patterns from the previous year. Demand Sensing, in its turn, can use up to 200 data sources for its forecasts regarding every product. It builds up knowledge from a local perspective, making local customers in local stores the center of its forecast. The tool also considers specific influencing factors like festival shopping patterns, weather, season, and so on.

IKEA says this solution helps improve accuracy and minimize the inventory they don’t need. All in all, the company sees much potential in applying AI in different areas of supply chain management beyond forecasting. They expect AI to enhance planning and problem solving on top of automation.

Supplier risk management

Supply chain risk management (SRM) includes different strategies to identify and mitigate events and conditions that can negatively impact any aspect of the supply chain. For example, the global pandemic outbreak caused severe disruptions to supply chains across different industries, especially food and automotive.

The infographic below shows a breakdown of the risks every supply chain has to deal with right now.

Although the risks supply chain companies face today vary greatly, AI-based SRM software can be applied to almost any of them. All because trained machine learning models can identify the source of risk and even predict it thanks to big data analytics, assess the risk based on past impact data, and suggest the most effective mitigation strategies for each scenario. 

On top of that, AI provides a quick analysis, rapid assessment process, and rational mitigation strategies. For example, imagine a supplier location is affected by floods. To mitigate the flood-related risks, an AI-based system might analyze weather patterns, the data collected by a supplier, and mass media data to estimate the severity of the impact. Then, the system will decide and provide a corresponding output explaining whether supply chain redesign is required for business continuity or not.

Business application

We can find a nice example of an AI-powered supplier risk management tool at PwC. This supplier risk assessment framework allows accessing the complete network of a supply chain.

Another example is Versed AI, an NLP-based (Natural Language Processing) spinout of the University of Cambridge. It helps companies understand and manage potential risks in their multi-tier supply chains better. This tool can analyze millions of documents and reveal relationships among organizations, companies, products, and people. The connections that would have been hard to notice for humans expose potential sources of supply chain disruption, highlight risk concentrations, and allow companies to manage risks better.

Quality assurance

AI-powered computer vision systems can be applied for inventorying or during shipping. Just like with other applicable areas, AI and ML can analyze massive assets of data and spot trends here. For instance, they can discover the most frequent quality issues that arise during particular steps of the supply chain or unveil issues that can cause trouble.

Case in point: computer vision can improve manufacturing defect detection rates by up to 90%. Thanks to that, the number of defective products that get shipped and returned by the client decreases, and customer satisfaction grows.

Business application

We can see a good real-life example of AI-powered quality assurance in the supply chain at BMW. Here’s a video of how BMW uses computer vision to scan car models as they move on the assembly line. This approach to implementing AI into quality assurance brings:

• Transparency growth
• Product quality improvement
• Customer satisfaction growth

Generally speaking, AI in the form of computer vision, image analysis and recognition helps to ensure quality at every step of the supply chain, from warehouses and roadways.

Supply chain automation

The need for agility and adaptability brings about the importance of automation within supply chains. For instance, automation can help with document processing, transportation, and warehouse management.

With proper automation, you can optimize the planning stage as well. AI software can generate cognitive predictions and recommendations to facilitate the planning process. Automated planning can also help reduce the number of mistakes along the process. Other benefits include:

• Data-driven decisions
• Effective labor management
• Efficient inventory management
• Real-time visibility (storage and transportation transparency)
• Predictive control

There’s no doubt that AI-based supply chain automation solutions considerably improve resiliency, increase agility, and optimize operations.

Business application

Meet C3’s AI solution for healthcare. While the company was growing, it accumulated supply chain data stored across disparate ERP systems. C3 AI targeted the visibility challenges of the company and unified the data on the C3 AI Supply Chain Digital Twin. Then, the company configured machine learning algorithms and the application logic to predict order lead times and delivery risks. As a result, they achieved a 35% reduction in delayed sales orders.

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Sustainability

Modern customers tend to get more environment-conscious and choose businesses that share the same eco-friendly values. Sadly enough, the greenhouse gas emissions from companies’ supply chains are five times greater than those from direct operators. Can AI help them mitigate this issue?

Absolutely. Artificial intelligence can be used for precise demand forecasting. This, in turn, brings reduced inventory levels, reduced waste, and a cut in carbon emissions across the supply chain. Modern AI solutions bring data-driven insights that can help reroute supply lines, set environmental policies, and more. 

Business application 

AI allows optimizing logistics routes to reduce gas consumption, and UPS is an excellent example. Using the power of AI, the company has managed to boost its sustainability. The ORION (On-road Integrated Optimization and Navigation) system used by UPS allows the company to create optimal routes for delivery drivers with the help of advanced algorithms. The data is collected from the customers, drivers, other vehicles, and weather conditions.

As part of its commitment to be carbon negative by 2030, Microsoft is integrating emission goals into procurement processes. The company is launching a technology initiative to improve supplier practices. How? Using the AI for Earth program that monitors carbon emissions and enhances supplier practices regarding the environment.

Conclusion

When implemented correctly, AI can do wonderful things for supply chains. It can help optimize logistics, facilitate quality control, and boost the sustainability of the supply chain. Artificial intelligence is essential for quality prediction, planning, risk evaluation and management, as well as supply chain automation.

Numerous examples from leading businesses like IKEA and UPS show that efficient modern supply chains and AI have a bright future together. And if you too want to take advantage of technologies that help you balance between customer service and budget, avoid downtime and penalties, contact Unicsoft. We know how to make your supply chain predictable and profitable. Let’s talk today!