Supply chain trends 2024: The digital shake-up

This data is then embedded into AI systems to predict delays and ensure the cargo safety. Intellias has developed a fleet truck tracking system that captures data through IoT devices and can determine the location of a stolen vehicle. One of the other widespread use cases of Machine Learning in the supply chain is predictive equipment maintenance. ML ensures reactive and preventative equipment maintenance based on real-time asset data rather than a predefined calendar. By improving asset maintenance, supply chain professionals can significantly decrease maintenance costs. The choice of the best blockchain for supply chains depends on specific requirements and use cases.

• Antuit.ai offers a Demand Planning and Forecasting solution that uses advanced AI and machine learning algorithms to predict consumer demand across multiple time horizons.
• AI enabled sales and operational planning (S&OP) and integrated business planning (IBP) applications will help eliminate the gap between supply chain planning and execution.
• The revelation in Shein’s 2023 sustainability report comes as it is understood to be planning to sell shares on the stock market.
• Generative AI (GenAI) is a subset of AI that has the potential to revolutionize supply chain management, logistics and procurement.

Generative AI models can analyze transaction data, identify patterns and anomalies, and detect potential cases of fraud in the supply chain. This helps businesses minimize financial losses, protect their reputation, and ensure the integrity of their supply chain operations. Moreover, the use of generative AI in supply chain financial services and operations can significantly benefit supply chain management by improving efficiency, reducing risks, and enhancing decision-making processes. Generative AI can play a significant role in transportation and routing optimization within supply chain management. By analyzing vast amounts of data from various sources, AI can generate efficient transportation plans, save time, and improve the overall efficiency of supply chain logistics. Generative AI can process market data, customer feedback, and competitor information to generate insights about potential gaps or opportunities in the market.

The first transactions were recorded on Jan. 4, 2018 and the pilot wrapped up on Feb. 15, 2018. During the six-week test, the company did experience some technical glitches and noted challenges getting data from the physical world into the digital platform. Still, company leaders believe in blockchain’s traceability potential and may use the technology in the future. It’s taking a wait-and-watch approach as the technology matures and gains wider traction, which will make blockchain’s use in the supply chain more attractive.

Companies using AI in supply chain

You can foun additiona information about ai customer service and artificial intelligence and NLP. As the C-Suite now has a full understanding of the role supply chain plays, and more insight into the challenges it faces, resources are being freed up to create more efficient, resilient supply chains. Supply Chain Management Review spoke with Matt Stekier, principal at Plante Moran’s supply chain practice, on how creating value in the supply chain can help leaders drive the needed change for supply chain improvements. AI in supply chain management will help enterprises become more resilient and sustainable and will transform cost structures. While it does have limitations, generative AI presents a multiplier in what humans and technology can achieve together in building efficient and resilient supply chains — whether in planning, sourcing, making or moving.

This will represent a major change at many companies, a large number of which still set performance targets within individual functions or business units. Accordingly, companies may need to redesign their performance-management systems to be more integrated and cohesive. If you’re ready to harness the power of modern supply chain analytics and drive your organization towards greater success, we invite you to book a free data strategy session with our team of experts through this contact form. Together, we’ll explore the unique challenges and opportunities within your supply chain and develop a customized plan to leverage data and analytics for optimal performance. SPC works by collecting data on various quality parameters during production and analyzing it using statistical tools like control charts and process capability analysis. This helps determine if processes are stable and predictable or if they require intervention.

To address this issue, we have curated this article to highlight the top 12 AI applications in supply chain management and how supply chain leaders can implement them. If you are looking to tap into the transformative power of AI and digitize your supply chain for better visibility, resilience, and responsiveness, drop us a line. Our experts will answer your questions and help navigate the transformation process with little to no risks. At the end of 2023, the US government also announced a review of the semiconductor supply chain to reduce US corporate reliance on China with a specific focus on chip production. The US CHIPS Act opened up $280 billion in funding for semiconductor production and research over the next decade, while the EU Chips Act is similarly intended to drive funding that will diversify supply chains.

Autonomous planning can help supply chains function more effectively in volatile environments, and with less direct human oversight and decision making required. Real-time tracking of assets is a technology-driven approach that provides continuous monitoring and reporting of the location, condition, and status of goods and equipment as they move through the supply chain. Warehouse layout is about leveraging technology to gather real-time data, analyze it to gain valuable insights, and optimize warehouse operations. When customers search for diamonds on the Brilliant Earth website, they can use a filter that lets them view and select diamonds that are blockchain-enabled.

Step 4: Ensure the solution’s smooth adoption and scale the implemented capabilities

Generally, while implementing an SCM solution, ABC analysis of SKUs (classifying products based on their importance i.e. on sales value or volume (quantity) or the margin, etc.) is done. Such classification is used for configuring, applying, and implementing a customized strategy for every class. Such analysis makes the implementation more effective because A-class products need completely different treatment as compared to the ‘C’ class.

By optimizing routes, businesses can ensure faster delivery times, which can lead to increased customer satisfaction and a competitive advantage in the marketplace. This can be achieved by minimizing travel time, reducing transit delays, and Chat GPT optimizing the use of transportation resources. Demand forecasting, when implemented with the power of supply chain analytics, transforms from a guesstimate into a strategic tool for optimizing operations and maximizing profitability.

This can help supply chain stakeholders better manage financial risks and maintain supply chain stability. Analytics, AI and the cloud play a powerful role here, enabling companies to continuously monitor and respond to disruptions within the multi-echelon supply chain. Just as we said about demand, having better information about what’s happening throughout the entirety of the supply chain leads to better, more informed decisions.

But you can enable easy data exchange by using the cloud to create a data fabric that instantiates a common data model across the enterprise. Cloud-based data fabrics enable the consumption and publishing of core data through services and APIs. In turn, these support downstream and collaboration tools to visualize data while applying intelligence to workflows and processing. In a 2024 survey conducted by SPS Commerce, suppliers identified significant difficulty in responding to market changes and emerging opportunities. In the same survey, 73% of the respondents noted integrating and adopting new technology as a challenge that was at least moderately impactful to their organizations’ resilience.

Autonomous delivery vehicles

Predictive analytics is critical for unlocking scenario planning and simulations, which are critical for optimizing the performance of the modern supply chain. McKinsey recently interviewed senior leaders from large CPG manufacturers in Asia about the state of their planning processes. In our sample, approximately 80 percent of companies still follow traditional or collaborative sales and operations planning (S&OP) processes, with limited real-time decision making or automation (Exhibit 1). Current processes often depend on unreliable sources of data and outdated IT systems, with coordination limited across functions. If you have to manage a vast network of suppliers, warehouses, and logistics service partners, supply chain management can become daunting.

While this is not a new use for AI, the generative component offers added dimensions of customization — say, optimizing based on less fuel, or prioritizing certain deliveries or considering many other factors in a user-friendly application. Chatting with its customized tool helped the company understand if its trade network was optimized, and it even offered suggestions for improvement. Corporations have been increasingly deploying artificial intelligence (AI) in supply chains for demand planning and procurement, while exploring its use in other areas, such as standardizing processes and optimizing last-mile delivery. Even in the relatively nascent area of sustainability tracking and measurement, AI adoption is as high as 62%, according to an EY study.

It works by establishing a baseline of emissions, using data on energy consumption, transportation, manufacturing, and waste management. Advanced analytics tools process this data to calculate the carbon footprint and identify areas for improvement. Customer segmentation divides a company’s customer base into distinct groups based on shared characteristics and behaviors. In the context of supply chain analytics examples, this method allows businesses to tailor their supply chain operations to meet the specific needs of each customer segment more effectively. Risk assessment modeling has become a strategic necessity for businesses seeking to thrive in an increasingly complex and volatile world. A proactive approach to identifying, assessing, and mitigating risks is necessary for companies to build supply chains that are resilient and adaptable – a critical advantage in the ever-evolving global marketplace.

Analyzing the economic value of IBM storage FlashSystem built-in resilience

This segmentation approach can be applied to various aspects of the supply chain, including inventory management, distribution network design, transportation planning, and production scheduling. Companies often see improved customer satisfaction due to more tailored service, cost optimization through efficient resource allocation, and enhanced demand forecasting accuracy. It also allows businesses supply chain use cases to focus on their most valuable customer segments, potentially increasing profitability and market share. Organizations increasingly need to pull data across the value chain from intelligent sensors, programmed to identify critical events, assess their impact, and adjust planning and control variables. Similarly, software capable of modeling the implications of various disruptions is also vital.

“Meanwhile, the trucking company says when it delivered the beef patties everything was fine and puts the blame on the restaurant.” For example, the region encompassing Ghana and Ivory Coast produces 60% of the 3.5 million tons of cocoa beans produced annually in Africa — in part by using child labor and slave labor. Naturipe Farms, LLC uses the SAP Cloud Platform Blockchain service to track blueberries from the point of harvesting to the dinner table. Naturipe, based in Salinas, Calif., is a partnership between four fair trade-certified berry growers across the globe. The blockchain application helps to fuel Bumble Bee’s sustainability and traceability efforts. If the information FFF Enterprises receives confirms the product it inquired about is legitimate, it can go back into inventory to be resold.

Companies leverage generative AI across marketing, sales, product development, and IT functions to streamline workflows, enhance production efficiency, and enable virtual logistics management. As someone working in the supply chain space, you must have, at some point, wondered, “Is the hype around AI in the supply chain real? According to Statista, AI adoption has spiked, with 68% of companies adopting AI in their supply chain operations by 2025. The benefits are tangible, with respondents reporting cost decreases and revenue jumps in the business units deploying generative AI. With customer expectations changing quickly and becoming more diverse, businesses now rely on AI-powered supply chain tools to glean more demand-related insights, tune their production strategies, and restock accordingly. Another example of strategic supply chain investment through this holistic lens is nearshoring.

While initial costs can be high, many businesses find long-term value in this investment for efficiency, risk management, and reputation. Businesses can utilize real-time supply chain visibility to gain unprecedented visibility into their supply chain operations. It allows them to monitor shipments and assets throughout their journey, enabling proactive problem-solving and more efficient inventory management. This approach allows companies to identify vulnerable areas, develop contingency plans, and optimize resource allocation. It informs decisions about supplier diversification, network redesign, and technology investments to improve resilience.

The process involves analyzing customer data such as purchasing patterns, order frequency, volume, and service level requirements. Using advanced analytics techniques, businesses identify meaningful segments and develop targeted strategies for each group. For instance, high-volume customers might receive priority in inventory allocation, while those with unpredictable demand patterns could be managed with more flexible supply chain arrangements. Through advanced predictive modeling, companies can quantify the likelihood and impact of these risks, enabling them to make informed decisions and develop targeted mitigation strategies.

Artificial intelligence technology speeds up the digitization of warehouses, automating picking and packing of goods, inventory, order fulfillment, and product transportation. It also equips business leaders with deep insights into their warehouses, which leads to smart and informed decisions such as where to place goods, how to route orders, and which staff to hire. According to a McKinsey report AI-driven systems aid in cutting warehousing expenditures by up to 15%. Artificial intelligence (AI) is a game-changer for supply chains, becoming a need rather than a luxury. A 2023 Meticulous Research study reports the market for AI in supply chain is expected to reach $41 billion by 2030, growing 39% yearly from 2023. Envision a world where supply chains are self-aware, can forecast tomorrow’s customer demand, and can analyze their own inefficiencies and re-route shipments in real time based on rapid weather changes.

Once customers click on the descriptions of individual diamonds, they can see more detailed information about the chain of custody, as well as additional insights and assurances of the supply chain, Gerstein said. The blockchain technology facilitates diamond tracking along with all the supporting documentation, including invoices and certification, as it moves through the supply chain, securely storing this chain of custody information, she said. Everledger’s technology provides a blockchain-enabled database that supports the independent tracking of every step in the supply chain from mine operator through each manufacturing step, she said. AI can process external factors such as social media posts to increase the accuracy of shopper demand predictions. Big firms like PepsiCo have leveraged AI to analyze what people are discussing and searching for.

Today’s algorithms can analyze a company’s network of suppliers and determine the total impact if a specific supplier goes down. During the COVID-19 pandemic, one of the largest branded consumer food and beverage product companies in Asia sought to improve its supply chain performance through autonomous planning. The company had historically used traditional processes, including an annual budget plan for forecasting, and it made highly manual, rule-of-thumb decisions in areas such as inventory levels and dispatch planning. Response times were slow—the company typically required more than five days to create a demand plan, and more than two days to create a dispatch plan. The company wanted to use analytics more effectively so that it could react faster to changes in supply or demand and do so in the most profitable way.

With the help of predictive analytics and demand forecasting, you’ll see valuable returns in no time. Processes that previously took days to complete can now be checked off much faster, leaving plenty of time for tasks that really matter. COOs are redesigning operating models and supply chains to navigate today’s complexities and evolving business models.

Based on the report info, the RPA bot can communicate with the appropriate supplier via email or ordering portal to place an order. The purchasing manager would only need to monitor the notifications and approve the request. In organizations with multiple suppliers and high-volume production, this process can occur hundreds of times per day.

As supply chains gained favor with the C-Suite, added scrutiny was, in some cases, an unwelcome consequence. Not only was more attention being paid to the operation, but so too was focus shifted onto cost, sustainability, https://chat.openai.com/ and increasingly the journey to an outcome. Whereas supply chains were previously tasked with getting products to the end customer, now, management wants to know how it is getting there as well.

• The keys to simultaneously addressing relevance, resilience and responsibility are advanced analytics and AI.
• This method uses advanced analytics to model and evaluate various future scenarios that could impact a company’s supply chain.
• AI and its subtypes can help you transform your supply chain management tactics and minimize dependence on a single supplier.
• As supply chain leaders, we’ve seen unprecedented levels of disruption, challenges with our ecosystem trading partner networks, tariff wars, real wars and tremendous turnover in human capital.
• The potential benefits include improved forecast accuracy, reduced inventory levels, fewer stockouts, increased agility in responding to market changes, significant cost savings, and potential revenue growth.

AI-powered with big data can help the supply chain become not only sustainable but resilient at the same time. To learn more about how to improve supplier relationship management, check out this quick read. With an all-rounded assessment carried out, define the supply chain digitalization strategy and make sure it reflects the findings. It makes sense to start with digitalizing one segment of the supply chain that shows the highest value-creation potential to drive ROI faster. However, a whopping 79% reported struggling to scale the technology to cover broader initiatives. The multinational transportation and delivery giant, FedEx, uses several robots in its operations.

The use cases presented in the article are at a conceptual level and need further analysis and detailing to implement them. Many of the berries are produced in Central and South America, so being able to onboard the data captured by the growers is important, said Sathya Narasimhan, senior director of blockchain business development at SAP. Blockchain has the potential to provide insight into a company’s sustainability practices.

These tools are useful to quickly extract information from large contracts and help you better prepare for renewal discussions, for example. Beyond negotiations, GenAI presents an opportunity to improve supplier relationships and management, with recommendations on what to do next. In many scenarios, KPIs are reported at the month-end or quarter-end, and sometimes, it becomes a ritual because, by that time, SCM teams would have already initiated actions towards the next period.