The disruptions of global supply chain wreaked havoc throughout the electronics and semiconductor industries in 2022, making it difficult to source materials and operate efficient manufacturing and logistics ecosystems, shortage was followed. The shortage impacted numerous sectors and dented the availability of products such as mobility, smart devices, and computers.

Looking at Asia areas, 76% of the market’s growth will originate from APAC during 2020-2024, according to Technavio. The strong growth is due to increasing demand as well as continued high prices in the memory segment, while the design complexity of semiconductor components will be a major challenge, in the meantime, the pandemic, the ongoing ramifications of war in Europe, the decoupling of major world economies, as well as shifting geopolitical dynamics calling for the reshoring of manufacturing for key technologies – all of these challenges and more like them still to come, will play major roles in reimaging a new generation of global supply chains for electronics and semiconductor markets.

However, turmoil often presents opportunities for innovation and improvement. And today, some companies are leveraging recent advances in digital technology to tackle tough challenges like the ongoing supply chain crisis. For the electronics and semiconductor industries, artificial intelligence (AI), cloud-based ecosystem collaboration, as well as model-based systems engineering (MBSE) are poised to play a major role in helping these companies overcome challenges associated with ongoing supply chain problems.

One byproduct of the rapid digitalization underway across virtually all global industries is data. And since data is the lifeblood of AI, digitalization paves the way for unique, AI-based techniques for capturing expert knowledge and leveraging past design data to develop surrogate models and AI-driven simulations that can help to speed up product development. But these techniques also hold great promise for tackling current supply chain challenges.

As AI technology continues to evolve and permeate both mature and new industries alike, its role in addressing supply chain challenges in the year ahead has become clearer. For example, AI can discern recurring or deviating patterns in data. It can use algorithms to calculate the ideal line for a supply chain, making it possible to determine more precise delivery dates. Further, industrial enterprises can integrate these analytical components in various ways to make their processes more intelligent and efficient. This kind of intelligent fusion of production and logistics presents a new degree of plannability and transparency that touches every link in the process chain.

Meanwhile, recent advances in and adoption of smart cloud enterprises provide electronics and semiconductor firms with more valuable tools needed to heal their supply chains. Today’s secure cloud merges industry and logistics through a cloud-based IT platform and provides cross-location, cross-enterprise integration of the supply chain partners.

In the cloud, everyone who needs to be there is there. Today’s intelligent supply chains merge industry and logistics through a cloud-based IT platform, providing cross-location, cross-enterprise integration of all supply chain partners. It is about live collaboration: everyone who needs to be there is there. Suppliers, OEMs, manufacturers, logistics experts, carriers, customs authorities, service partners, customers: they all have the same view of the same event at the same time. Cloud-enabled supply chains are quickly turning estimated time of arrival (ETA) into yesterday’s metric. The new standard in industry and logistics will be the precise time of arrival (PTA). Algorithms capable of analyzing data streams from participating partners in mere fractions of a second and continuously syncing data with the current event status are able to nail down exact arrival times, and all of this is made possible via cloud collaboration.

Model-based systems engineering also has a key role to play in helping electronics and semiconductor firms tackle supply chain challenges. Rather than constructing system models in PowerPoint or other applications. In an MBSE methodology, data is stored centrally with secure connections to other relevant information to constitute the system architecture – the roadmap for development processes from concept to manufacturing. MBSE also plays a key role in developing strategies for dealing with the growing complexity of systems engineering, specifically in the areas of production and supply chain. With the growing complexity of today’s electronics and semiconductor products, modeling the product, talent and processes involved in developing and shipping these products has become paramount.

MBSE can address the complete spectrum of model-based dataflows for systems engineering, helping companies orchestrate their engineering program to manage scope and minimize risk. In fact, MBSE is a prerequisite for the development of digital twins, which help ensure the seamless and secure provenance and sharing of critical data, while ensuring trusted traceability. MBSE practices can help companies model global supply chains themselves, leading to new insights that can help avoid future supply chain disruptions.

Finally, MBSE enables continuous virtual verification and validation of the intended product, combining functional and physical behavior. This allows companies to effectively deliver consistent execution with customers and suppliers — from planning and scheduling verification activities, performing analysis, configuration and management of test resources and equipment through to confirmation of regulatory conformance and product compliance.

Looking to 2023 and beyond, it’s clear that these advanced digital technologies will play key roles in helping electronics and semiconductor companies mitigate the risks and vulnerabilities associated with their supply chains by fostering collaboration, gathering, leveraging, and securing key data, and exploring innovative solutions while saving time and money.

By: DocMemory
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