Building better supply chains using AI
Our industry is built on disruptive developments. The latest example, artificial intelligence (AI), is driving unprecedented change. Both incumbents and startups are bringing new AI-based solutions to market with many collaborations, acquisitions and mergers. As the number of solutions and suppliers increases, so does innovation.
This innovation will lead to new products coming to market, enabled by systems that combine the old with the new. Every component will have its own supplier dependencies, which need to be managed holistically. Consequently, a sustainable and resilient supply chain is becoming even more important.
Tracking assets using connected Internet of Things (IoT) technologies provides raw data for trained AI models that produce refined predictions. This gives logistics companies an advantage by streamlining their operations, and using AI in this way helps businesses respond to sudden changes in demand.
It is harder to predict how an end market will react to external influences. These reactions have an impact that is felt further up the supply chain. Remember how the recent pandemic impacted global supply chains. AI, fueled by good data, makes predicting supply chain disruptions simpler and more reliable. Using AI in supply chains is growing at a compound annual growth rate of over 28%.
Real-world data builds better AI models
With over 100 years of experience, Avnet has extensive data sets it uses to build advanced AI models. Being at the center of the supply chain gives distributors access to upstream and downstream data from customers and suppliers.
When coupled with market analysis and trend data, AI can make confident predictions about future demand. Analyzing design trends also means Avnet can optimize inventory, which is the keystone of a dependable supply chain.
Having a large customer base means design trends can predict demand at a local level. AI models that scale provide the deep analysis required to deploy these insights on a global level. Sharing this level of market intelligence informs supplier partners, too, which also promotes a smooth supply chain.
The same intelligence can support the sales process. Account managers can identify equivalent or alternative solutions when concerns arise over supply. They can also suggest complementary parts based on AI insights into application trends.
When armed with these wider observations, account teams and subject matter experts are better equipped to help customers select the best solution. In this case, “best” may not depend on the part's technical credentials but also include the estimated demand, supply, future stability and potential design change notes from suppliers.
AI is directly impacting supply
It takes several months to process a semiconductor wafer and produce tested, packaged integrated circuits. Building the fabrication plant to process the wafers can take years. In terms of cost, building a sub-nm wafer fab runs to tens of billions in U.S. dollars.
The huge investment and long-term commitment required for IC manufacturing are justified by the future and ongoing demand for the products it produces. That demand is subject to many factors, from the global economic climate to the latest trend on Instagram.
Understanding the relevance and potential impact of all these disparate factors is challenging, but AI is uniquely placed to tackle that challenge. Access to this intelligence can inform fab schedules and product design cycles.
IC fabrication relies on a global supply chain, with many stages provided by specialists in their field. Manufacturing integrated solutions can involve shipping parts in progress across continents. More dependable, real-time information about each stage can feed models that can infer the consequences of the many factors affecting supply.
Using AI to increase supply chain resilience will lower overall risk and improve contingency planning. Predictive analytics, combining statistical analysis with machine learning, is becoming the standard for forecasting. As market conditions change, OEMs will be better prepared.
Supply chain partners will be assessed and selected based on their adoption of AI enablement and integration. AI will support many aspects of the relationship, including contract management and performance measurement. Ultimately, this will lead to stronger relationships built on real-time insights.
What does the future hold for AI in supply chains?
We can no longer look at instances of AI in isolation from larger systems. Deeply integrated AI solutions will define a new normal. What’s more, multiple instances of disparate AI solutions, or agents, will work together to provide greater context and value. Intelligence will then move to animate objects, such as autonomous mobile robots (AMRs). These mobile systems will be capable of carrying out more tasks, assisting human co-workers.
As confidence in AI and our reliance on the technology grow, more tasks will be assigned to AI agents. Within a decade, agents will operate on behalf of customers and suppliers to communicate directly. Autonomy, in all its forms, will support higher efficiency throughout the supply chain.
AI has passed an important milestone. The competitive advantage no longer resides with those using AI; rather, those not using AI are now disadvantaged. Avnet is using AI and ML to transform component distribution and procurement. The supply chain is changing forever through investments being made now that will benefit all customers in the future.
