January 11, 2024

Artificial Intelligence is Growing in Supply Chain Management

Datalynq Team
An image of neon lines criss-crossing in a digitalized space

Artificial intelligence is quickly becoming necessary in both consumer and corporate technologies. Throughout 2023, tech leaders capitalized on the new capabilities offered by popular large language models (LLMs) and generative artificial intelligence (AI), implementing new programs based on technologies such as ChatGPT.  

LLMs, generative AI, and upcoming developments in semiconductor manufacturing, like 2-nanometer and 1-nanometer, will give industries new opportunities and solutions to adopt new technologies at a larger scale. One of these industry applications should be the electronic component supply chain, as AI can revolutionize manual processes, optimize strategic decision-making, and increase efficiency.  

Research by Statista reveals that artificial intelligence is one of the advanced technologies businesses utilize to stay competitive. Once implemented, AI can provide organizations with better inventory management, smarter manufacturing, and more dynamic logistic systems. Over the last few years, AI has aided companies in their goals to increase workflow efficiency and overall productivity. AI has helped push sustainability through improved process performance in the greater supply chain.

Similarly, a study by McKinsey found that AI implementation, specifically generative AI based on training neural networks, could quickly turn around and offer insights that revolutionized supply chain logistics by fast digesting massive volumes of data. Furthermore, generative AI technology supported six top-level categories: computer hardware, cloud platforms, foundation models, model hubs and machine learning operations (MLOps), applications, and services. These are part of the new generative AI supply chain, improving the previous, traditional AI supply chain management.  

Data-driven analytics has proven invaluable to modern supply chain management, now generative AI, and the evolution of it and other LLMs over the last year are building upon these critical benefits.  

Using Artificial Intelligence to Optimize Data Analytics

Organizations that utilize AI tools are more connected and, of course, intelligent. Connectivity between systems can produce more accurate data and analytics, which helps organizations make better decisions. AI uses the vast data collected by advanced technology to optimize workflows by providing insights into weak points in processes that organizations later improve, thereby driving innovation.  

Different use cases of AI combined with data analytics provide additional benefits based on their specialties. One such tool is predictive analytics.

Using statistical modeling and regression analysis, predictive analytics can identify and understand trends based on historical data. This information can then help determine predictions on future market trends or device maintenance. For semiconductor industries, predictive analytics can help forecast future disruptions and the likelihood of significant impact on specific electronic components.

Predictive analytics is growing in use in corporate artificial intelligence applications. At the core of enterprise AI tools, predictive analytics is used to help establish predictive maintenance for manufacturing production lines and risk modeling.  

Combined with artificial intelligence, “the insights gleaned from these advanced systems are the key to more accurate and timely forecasting going forward.” Predictive analytics has improved machine learning by leveraging historical data such as weather patterns, consumer behavior, and energy fluctuations. AI systems can use predictive analytics insights to proactively flag future events and provide vital forewarning for informed decision-making.

These can be used in inventory management and delivery optimization within an organization. Scheduled delivery and inventory management is especially vital for the electronic component supply chain, considering how vulnerable its operations are to the slightest disruptions.  

Key Steps to Optimize AI and Data Analytics in the Supply Chain

Implementing AI and utilizing data analytics in workflows isn’t a one-size-fits-all solution for most organizations. Available resources, environment, and specific company needs mean the requirements from an AI tool vary between organizations.  

However, to begin adopting AI in any company, most organizations should follow the same three essential steps when implementing advanced technology.  

Establishing a Proper Data Strategy

With the overwhelming number of new AI tools, many companies fall victim to one of the easiest missteps in AI adoption. In the race to stay competitive, implementing one too many programs without clearly defined rules can leave many organizations adrift in seas of insignificant data. AI can collect vast amounts of information, but things get dicey without a concrete and focused data strategy or the “long-term plan that defines the technology, processes, people, and rules required to manage an organization's information assets.”  

To utilize essential data-driven insights that can help revolutionize supply chain operations and management, an organization must define how the collected data should align with business goals. It should be laser-focused, making it easier for companies to concentrate on which artificial intelligence technology will support these objectives.  

Improving Existing Systems Through Integration

The supply chain is no stranger to disruptions. One of the challenges organizations encounter when implementing an AI strategy is the confusion and possible pushback from staff learning a completely new system and workflow. In an industry as fast-paced as electronics, transitioning to an AI system should reduce time-waste not contributed to it.  

To prevent large-scale disruption during AI implementation, companies should prioritize integration. Integrating AI capabilities into existing infrastructure and technologies, such as application programmable interface (APIs) that connect a global e-commerce site with an enterprise resource planning (ERP) system, can provide users with more accurate data without having to learn an entirely new system. These seamless integrations allow quick data transfers between the two programs. For example, organizations can optimize inventory management, easily schedule deliveries when stock becomes low, and remain aware of a component’s lifecycle status.  

Always Be Ready to Improve

Over the last year, AI technology moved at a breakneck pace thanks to the introduction of OpenAI’s ChatGPT, supported by Nvidia’s AI-capable line-up of graphic processing units (GPUs). During 2023, companies, big and small, added generative AI applications to their corporate technology suites. The overwhelming number of use cases contributed to bottlenecks of coveted AI-capable components and raised prices.

Generative AI is an example of modern technology's rapid development and adaptation. It is not the first and won’t be the last. A quintessential step in creating an applicable AI strategy for any business is to ensure it has the flexibility to adapt and change over time as new advanced technology tools become available. Being aware of new supply chain innovations and areas for optimization ensures fast and successful future development and retaining a competitive edge.  

3 Benefits of Artificial Intelligence in Supply Chain Management

Artificial intelligence has been shown to increase productivity, boost operational efficiency, reduce human error, and predict customer preferences for a more personalized experience. Organizations that embrace advanced technology and digital tools not only maintain a competitive edge, but often excel.  

The electronic component supply chain, as far-reaching and intertwined with hundreds of manufacturers, suppliers, and distributors, stands to benefit immensely from the incorporation of AI.  

1. Predictive Forecasting to Improve Inventory Management

When combined with predictive analytics, artificial intelligence can help companies make informed decisions regarding orders and scheduling future deliveries. As market demands change, employees can be alerted to optimal purchase windows through historical data and integrated systems.  

Similarly, many components are more at risk for supply chain disruptions, such as sole source parts, or if they are entering end-of-life (EOL). Intelligent tools can recommend alternatives or the opportune time to make a last time buy (LTB).  

2. Supply Chain Cost Optimization

Artificial intelligence has long been cited in helping organizations reduce costs and increase overall revenue. AI analytics remains one of the most effective options for pinpointing critical data insights and illuminating avenues for increasing process optimization, such as procurement. AI can deliver vital financial observations based on information from every global supply chain sector.  

According to Bloomberg, within the past two years, the overall cost in the supply chain has reduced to 12%, leading to a growth in profits. Advanced analytics contributes to new cost-effective revenue-building standards in organizations and leads the way for further innovation.  

3. Strategic Sourcing and Reduced Risk Through Data Analytics

Advanced analytics and artificial intelligence can identify critical suppliers and strategic partners in a company’s supply chain. Highlighting possible electronic component risks, such as obsolescence, NRND notices, and sole sources, organizations can locate different suppliers and part alternates and manage cases in advance, navigating future challenges ahead of time.

With this information, organizations can prioritize proactive strategies for future market demand shifts or supply chain disruptions without massively impacting operations and revenue.  

Fortify Your Supply Chain with Datalynq’s Advanced Analytics and Intelligence

Artificial intelligence, data analytics, predictive forecasting, and other advanced technology applications are fundamental to modern supply chain management. The global electronic component supply chain stretches beyond imagination and is expected to grow in the coming years as the semiconductor market grows. With the integration of supply chain AI solutions, it will be possible for any organization to remain competitive and handle the rising demands of the supply chain.  

Original equipment manufacturers (OEMs), contract manufacturers (CMs), and electronic manufacturing service (EMS) providers need a digital tool that can provide valuable insights for the electronic component supply chain to help optimize procurement, streamline risk management, and empower decision-making. Datalynq, the premier market intelligence tool for the electronic component supply chain, is what any OEM, CM, or EMS provider needs.  

Datalynq leverages data from the leading global e-commerce site for electronic components, Sourcengine–its partner under Sourceability–to deliver accurate insights on electronic component design risk, multi-source availability, pricing trends, and more. Now partnering with Cadence, Datalynq’s intelligent analytics can be accessed in Cadence’s OrCAD X platform to help design engineers make more informed decisions during product development.  

Through Datalynq, users can optimize their supply chain risk management, gain real-time supply chain visibility, utilize demand forecasting in proactive strategies, and avoid disruption with predictive analytics.  

Ready to future-proof your supply chain with Datalynq today? Get started here.  

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