Machine learning in supply chain: 8 use cases to consider

Early implementations focus on optimizing logistics operations, improving decision-making processes, and enhancing customer support functions. AI also contributes to supply chain resilience by addressing disruptions and complex global operations. Additionally, the adoption of AI spans various industries, from food and beverage to pharmaceuticals and agriculture. Chicago-based Echo Global Logistics provides innovative solutions that help businesses manage transportation and logistics.

However, successful implementation requires investment in data infrastructure, analytics capabilities, and organizational change. The extent of the benefits depends on how well a business integrates these insights into its decision-making processes and overall supply chain strategy. Automated systems like robots, conveyors, and automated guided vehicles (AGVs) constantly collect data on inventory levels, location, movement, and performance metrics. This data is then analyzed using advanced analytics tools to identify trends, bottlenecks, and areas for improvement. For example, analyzing data on picking times and travel distances can optimize picking routes and improve efficiency.

However, it faces increasing complexities due to growing customer expectations, rapid market fluctuations, and a rising need for sustainable practices. But a company doesn’t need a pandemic-sized disruption to knock a normally operating supply chain off kilter if the company lacks access to vital information. This so-called “bullwhip effect” has been known for decades, but now the data and technology are available to finally do something about it. Underpinned by AI and the cloud, these digital doubles can help companies improve resilience by identifying potential vulnerabilities and optimizing key areas of their supply chain. The keys to simultaneously addressing relevance, resilience and responsibility are advanced analytics and AI. Our study shows that Leaders are adopting these powerful tools at scale and, in the process, getting a head start in capitalizing on the significant opportunities created by human and machine collaboration.

real-life blockchain in the supply chain use cases and examples

For instance, Walmart uses blockchain to track the movement of leafy greens which helps them ensure the safety of their products. Similarly, IBM Food Trust, Walmart Food Traceability, and Mediledger are other examples of blockchain used in the supply chain. To target reductions in carbon emissions, companies need primary sources of information from their suppliers, and are starting to use hybrid carbon accounting methodologies to produce a more accurate assessment of Scope 3 emissions.

The shift demonstrated how important it is for companies to anticipate changes in demand. As an example, in an oil and gas manufacturing plant, IoT-based predictive maintenance can identify corrosion and pipeline damage, Hung said. These IoT applications then use sensors installed across the pipeline to obtain data on potential for hydrogen and gaseous content. The applications retrieve sensor data in real time and transmit it to the cloud for evaluation, analysis and prediction. RPA can improve supply chain management and logistics by automating repetitive time-consuming tasks such as data entry, in turn improving workflows. RPA uses automated software robots — or bots — to extract data from one application and paste it into another.

Automated execution equips an organization with a powerful tool that allows demand planners to shift focus to more complex issues and improve organizational efficiency. There is an explicit link from forecast demand signals back to the production schedule and plan, ensuring that sufficient raw materials are in place. Overall operations become more cost- and resource-efficient, resulting in a reduction in supply chain costs of 5 to 10 percent, freeing resources of time and capital to support investment and fuel growth.

Top 10 Use Cases: Supply Chain Management

Traditional solutions constrain users in how to engage, utilize and investigate the large volumes of enterprise data. While chat bots have attempted to make it easier to get information, they rely on extensive model training and are frequently limited to “how to” questions. Generative AI brings the force of machine learning to everyday tasks by leveraging foundation models that allow users to interact with structured and unstructured data like never before. Our supply chain digital assistant allows users to interact with data conversationally to interrogate vast transaction data, such as hundreds of thousands of documents and visual images. Users can go from getting an overall picture of the health of the supply chain to understanding a specific transaction by just asking. They get contextual information as well as factual data (PDFs, visual images, RFID tags information).

Generative AI creates new content, such as images, text, audio or video, based on data it has been trained on. While the technology isn’t new, recent advances make it simpler to use and realize value from. As investors pour cash into the technology, executives are racing to determine the implications on operations, business models and to exploit the upside. Because it is so comprehensive, autonomous supply chain planning leads can improve performance in a range of processes across the supply chain (see sidebar, “A CPG company’s initial success with autonomous planning”). The key benefits of S&OP include improved demand forecasting, better inventory management, enhanced resource utilization, increased responsiveness to market changes, and improved financial performance. However, the extent of these benefits depends on the comprehensiveness of implementation and the company’s commitment to acting on the insights gained.

“Digitally enabled processes reduce employee effort and time spent in back-end processes, freeing up time to engage with dissatisfied customers,” Hung said. For example, when a customer places a service request using a mobile app, an intelligent virtual assistant or chatbot can interact with customers and then place requests in the system, supply chain use cases Hung said. Intelligent document processing applications can read data from various service request document formats and then coordinate with RPA bots to capture and maintain service ticket data. The utilization of generative AI for financial operations of the supply chain can help supply chain leaders to solve many problems.

The test uses digital twin scenario modeling to assess potential operational and financial risks and impacts created by major market disruptions, disasters or other catastrophic events. The test can enable companies to not only understand how resilient their supply chain and operations are, but also to identify the weakest links and quantify the impact of those links’ failures on fulfilling their role. This analysis, in turn, can help companies develop mitigating actions to improve resilience, and can also be used to reallocate resources away from areas that are deemed to be low risk to conserve cash during difficult times.

MediLedger is an initiative of Chronicled, a San Francisco-based technology company that builds blockchain-powered ecosystems and supply chain products. As for Merck, Walmart, IBM and KPMG, each company has a special interest in improving the efficiency of the pharmaceutical supply chain. Merck is a drug manufacturer, Walmart is a leading U.S. pharmacy, IBM sells blockchain technology and KPMG provides consulting services to pharmaceutical companies. For instance, Tetra Pack uses AI to develop Tetra Rex packages that have a lower environmental impact than equivalent glass and plastic packaging and are recyclable.

AI will be able to analyze data at scale, identify anomalies, search for patterns that lead to unexpected disruptions, and make suggestions on how to solve them—almost instantaneously. Despite the tremendous investment our supply chains have received over the last several decades, today they still demonstrate the need for significant reinvention. Many supply chain structures remain functionally siloed and struggle to execute predictably end-to-end. Multiple ERP instances create challenges with fragmentation of orders and commitments across disparate systems. These combine to create unnecessary costs, increased execution latency and a suboptimal customer experience.

By analyzing data across various aspects of the supply chain, generative AI models can identify unusual patterns or deviations from the norm. This can help businesses quickly detect potential issues, such as bottlenecks, quality problems, or unexpected changes in demand, and address them before they escalate. As a result, companies are better positioned to meet demand, avoid being surprised by disruptions or changes in conditions, and even eliminate unnecessary shipments and, thus, fuel use and emissions. With this new approach, organizations ultimately establish a unified view of demand and a repeatable planning process that enhances accuracy and yields new insights to drive more meaningful decisions across the business. Scenario modeling also can help companies optimize their network, processes and inventory—which not only improves overall operating and business performance, but also helps enable companies to achieve ever-higher responsibility goals.

So they don’t consider factors like changing consumer preferences, market disruptions, and the impact of variables like weather events or economic shifts, leading to less accurate forecasts. This phenomenon occurs when small fluctuations at one end of the supply chain are amplified as they move upstream/downstream. AI-powered forecasting tools can help reduce demand and supply fluctuations to control bullwhip by leveraging data collected from customers, suppliers, manufacturers, and distributors. Supply chain AI can also provide detailed region-specific demand to help business leaders make better decisions.

By analyzing an organization’s data, GenAI can potentially improve operational efficiency and supply chain resiliency. Many company leaders are looking to integrate AI with their business processes to gain a competitive advantage in their industry, and generative AI can potentially help optimize key supply chain processes. In the past five years, analytics and AI have become increasingly important to many companies’ business. These powerful tools are enabling companies to automate tasks they never could before while providing much deeper insights companies can use to make faster, better decisions to improve business performance. And “business performance” today requires delivering simultaneously against traditionally competing KPIs like customer satisfaction, revenue, efficiency, cost control and carbon emissions. Digital twin-driven modeling allows companies to design a network that optimizes cost and customer service levels, while simultaneously analyzing its carbon footprint.

However, while implementing such solutions, you need to ensure their feasibility and calculate their long-term benefits; otherwise, such initiatives can lead to failure. Artificial intelligence (AI) is one of those solutions that is bringing advancements to almost every industry and department, including the supply chain. However, according to a survey by BCG, despite the efforts, supply chain leaders have not been able to truly harness the power of AI in the sector. They found that the fault does not lie in the technology but in where and how it is applied. Also, consider finding a reliable tech partner who will consult you on AI and help you build and customize AI-driven solutions. We will not only support you with building and integrating AI tools but also with aggregating and preparing the data that AI models need to function.

Our artificial intelligence development company will also share four steps that will help you succeed in AI implementation. The modern supply chain faces numerous challenges, primarily driven by its increasing complexity and globalization. The potential for disruption grows as supply chains extend across multiple countries and continents.

Risk management may be the most promising area, particularly in preparing for risks that supply chain planners haven’t considered. The “chat” function of one of these GenAI tools is helping a biotech company ask questions that inform its demand forecasting. For example, the company can run what-if scenarios on getting specific chemicals for its products and what might happen if certain global shocks occur that disrupt daily operations. Risk management may be the most promising area for GenAI’s input, particularly in preparing for risks that supply chain planners haven’t considered. 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. So, if AI/ML algorithms can amend, adjust, 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.

AI algorithms also aid with precisely forecasting customer demand, thereby decreasing overstock inventory. For example, Nestlé leverages AI to predict demand for their goods in various countries and decrease the number of overstocked products by 10%. AI systems, along with NLP (natural language processing) and OCR (optical character recognition), can promptly review contracts and categorize their content. This speeds up the supplier contract vetting process and guarantees that all necessary details are included. In particular, AI algorithms can verify whether a contract adheres to the company’s policies and external requirements.

The lack of visibility across the layered tiers of a supply chain has major implications for organizations across industries, particularly for meeting regulatory requirements, and for the identification and mitigation of supply chain risks. Consequently, data availability, quality, cadence, and consistency – are now critical considerations. Supply chain professionals must manage the complexities within their data landscape efficiently; to be able to make informed decisions and enhance their operations. However, users should be aware of concerns surrounding what’s known as AI hallucinations, which could hamper GenAI’s ability to improve supply chain operations. What happens after a sale is becoming increasingly important, and RPA can work with several other technologies to improve that aspect of supply chain management.

Sales and operations planning

He notes that this also allows for better data to be kept on supply, giving customers and the wider industry greater visibility in the face of shortage risks, Fairbairn says. Supply chain resilience is the ability of a supply chain to prepare for, respond to, and recover from unexpected disruptions. A resilient supply chain can maintain its core functions and continue to deliver products to customers despite adverse conditions. This resilience is crucial for mitigating risks, ensuring continuous operations, and maintaining customer trust and satisfaction. If you’re not ready to adopt a totally new solution, your business can make small but meaningful enhancements to your existing processes to boost efficiency and accuracy. The first changes don’t have to be huge, but you can remain on the lookout for innovations in your process and stay up to date on the benefits that automation technology can bring to your company.

• An example is the new Taiwan Semiconductor Manufacturing Corporation (TSMC) construction in Dresden, Germany.
• Finding new ways to boost supply chain management efficiency is more critical than it’s ever been.
• The lack of visibility across the layered tiers of a supply chain has major implications for organizations across industries, particularly for meeting regulatory requirements, and for the identification and mitigation of supply chain risks.
• Digitized supply chains enable more insight and visibility for both you and your customers, who will be happy to see that you’re up to date on the latest technology.

Helping clients meet their business challenges begins with an in-depth understanding of the industries in which they work. GenAI can run simulations and potential what-if scenarios, assess risk https://chat.openai.com/ and compile findings into a report. Traffic accidents can delay a shipment or extreme weather events can cause unexpected shortages, making it difficult to maintain on-time shipping schedules.

An example is the new Taiwan Semiconductor Manufacturing Corporation (TSMC) construction in Dresden, Germany. First, the people who are often most familiar with the internal processes may not be familiar with new, innovative alternatives. In much the same way, it’s often also difficult to objectively review existing processes and determine if a viable, more effective approach exists. Because of this, inventory management should be an initial focal point of any efforts to increase supply chain value.

Digital platforms are providing a centralized system for suppliers to input their emissions data, which can then be easily integrated into a company’s sustainability reporting. The use of AI is an enterprise-wide consideration, organizations must avoid dissipating effort across several single point disconnected AI implementations. Core business processes should be strategically rethought and redesigned to effectively leverage GenAI. Training GenAI models on a company’s current material use as well as market projections for renewable materials can give insight into how to make processes more sustainable while also considering cost-effectiveness and long-term scalability.

The 2020 pandemic and other geopolitical disruptions have demonstrated how weak supply chains can bring down entire organizations. Many companies are, therefore, investing in digital solutions to optimize their supply chain operations to get ahead of the curve. UPS’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. You can foun additiona information about ai customer service and artificial intelligence and NLP. 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. In today’s data-rich world, data inherently lives in silos and is not harmonized to easily drive insights and actions. For example, much of the data that supply chain analysts use lives outside of ERP systems in quality systems, manufacturing execution systems (MES) and warehouse management systems (WMS).

Certainly, the criticality of supply chains has become more recognized from a general economic point of view. Enterprise planning must be broader, cross-functional, active, and ongoing, if the sector is to avoid future supply chain shocks. Across all aspects of their supply chain, leaders must examine diverse options and consider alternatives – no matter how much they trust their current options. It’s all about ensuring enough margin of error is built in to cover the unexpected, Mohamed emphasizes. This means businesses must do more than just build out the warehouses near them, focusing instead on shoring up a resilient web of suppliers and partners.

Another piece of advice is to go for a vendor-agnostic integrator so you can prevent technology and solution lock-in. You can use AI in supply chain to analyze historical and real-time data on your suppliers to anticipate any performance issues and spot unsustainable practices and deviations from the agreed upon schedule. Taking a pragmatic approach to solving supply chain disruptions and infusing innovation into the process can drive significant business outcomes.

Coordination of cross-functional planning is a critical, strategic initiative that can affect future top and bottom lines more than most companies realize. However, it often doesn’t have an immediate impact, and therefore is unfortunately harder to drive. An EDI solution, like SPS Commerce Fulfillment, will enable you to send and receive all documents and data fully electronically, eliminating many manual errors and saving you time. You may not be ready for an entirely new system yet, but that doesn’t mean you can’t optimize your existing ERP, CRM or OMS. Newer, updated versions of your existing systems likely include more recent technology like AI or automation, easily alleviating your employees’ heavy lift of daily manual tasks. Beyond negotiations, generative AI presents an opportunity to improve supplier relationships and management, with recommendations on what to do next.

The ability to make informed decisions is necessary to not stay behind your competitors. AI systems consider sales data, expiration dates, inventory levels, market trends, and even customer feedback to understand what goods are no longer in demand. Employing Chat GPT AI for supply chain optimization helps companies reduce waste, free up warehouse space, and decrease the costs of storing unneeded goods. For example, IKEA launched a buyback and resell initiative that allows shoppers to sell back their used furniture.