The electronics industry is facing numerous challenges from the new technology landscape and disruptions in the supply chain caused or amplified by the pandemic. The industry has been dealing with shortages of essential parts and sub-assemblies, and the demand for smart products is reducing the commercial life of manufactured products. High pressure is being placed on faster product development and deployment but, the need to be sustainable and create eco-friendlier products is keeping their recyclability and component re-use also at an all-time high.

This means, manufacturing operations, whether EMS or CM need to figure out how to be more productive, innovative, and flexible. Forbes highlights the strategy of Just-in-case inventory management instead of Just-in-time, so manufacturers can better control their component supply, even if it means storing more than usual inventory at their warehouses. Automation, larger order sizes, better financial management, and supply chain partners are prescribed as ways of dealing with industry challenges.

Science Times highlights five major challenges faced by the electronics manufacturing industry as:

Increased Demand – Increased demand for ‘smart everything’ products applies direct pressure to maximize productivity. As process inputs get smaller, assembly operations need to evolve and adopt better technology, which requires higher precision in micro-dispensing and application of micro components.

Sustainability – For electronics manufacturers to work towards being carbon-neutral, they need to be more energy efficient and deliver ‘greener’ products. Providing sustainable products puts pressure on the sourcing of components that need perfect traceability throughout the process, down to individual component level.

Short Product Life Cycle – The appetite for newer and better products extends beyond the consumer electronics market and applies immense pressure on manufacturers to bring products to market faster. The market is punctuated with high volatility, and demand patterns are not as clear as they may have been in the past. Manufacturers need tools to better study their products’ performance and plan future versions accordingly.

Complex Global Supply-Chain – Manufacturers component suppliers are spread across the world, which creates challenges with uncertain markets and demand. Global markets demand compliance with multiple regulations to ensure components are sourced in accordance with set regulations and remain fully traceable.

Service and Warranty Management – With the increased complexity and intelligence of electronic products, the need to have better quality management becomes a requirement for EMS and CM companies.

McKinsey highlights how the application of advanced analytics in maintenance, asset management, and overall process management can help manufacturers gain between 4-10% in EBIDTA profitability. Advanced analytics only works when there is a reliable and continuous supply of process data that has been standardized and contextualized. McKinsey underscores the importance of three applications for advanced analytics that combined maximize factory and financial performance (see figure 1 below).

Predictive Maintenance – Advanced analytics helps manufacturers by predicting potential breakdowns, allowing process owners to be better equipped to deal with potential downtime and ensure maximum uptime. According to McKinsey, predictive maintenance improves machine life by 20-40% and reduces downtime by 30-50%.

Yield-Energy-Throughput Analysis – Yield and throughput analysis (YET) has the potential of improving asset performance. When data is analysed for multiple performance indicators and advanced algorithms are employed to reveal previously unknown patterns process engineers alter conditions, equipment settings, or material inputs to drive better asset results. What sets such analysis apart from traditional analytics is the amount of data employed, the higher number of parameters, and the complex algorithms which create new intelligence. All of this helps boost productivity of an asset.

PPH maximization Analysis- The profit per hour maximization (PPH) analysis deploys highly advanced process modelling algorithms and analysis to deliver optimal processes. It may suggest a different sales mix or procurement mix. PPH analysis is a complex assimilation of data from multiple sources and evaluates it against multiple performance metrics before delivering recommendations.

The real question now is, how does the electronics industry make the most of AI and analytics, and how does it help overcome industry-specific obstacles?

McKinsey clarifies that data is the core of future improvements that go beyond traditional CI efforts. Most electronics manufacturers face challenges gathering and contextualizing siloed data from multiple disparate applications, and they lack a single source of standardized data from automation, equipment sensors and the enterprise. Unless electronics manufacturers deploy the right MES and look at their current process management application suite, it will be virtually impossible to gain the benefits of advanced analytics.

The MES application goes beyond the collection of data; it also acts as the vehicle which deploys AI and advanced analytics within the process. The tool allows process owners to perform more than SPC and visualize data in ways they have not ever before. With MES predictive maintenance, YET and PPH analysis happens continually and organically.

Electronics manufacturers with multiple sites, can use the same MES to standardize their processes in terms of manufacturing and leverage the MES to create learnings that are applicable across their manufacturing footprint.

The ideal MES for electronics manufacturing acts a platform that establishes more than predictive maintenance and advanced analytics in the manufacturing process. It enables a high level of automation and process orchestration, through integration across shopfloor and enterprise applications. Eliminating data silos and standardizing data across the enterprise, forms the basis of effective AI and advanced analytics. High end component level traceability improves process control and compliance management and contributes to improved throughput, product quality and profitability.