Introduction to OEMs
The increasing complexity of smart products, more demanding users, and stiff competition are driving the focus of electronics OEMs (original equipment manufacturers) on product quality and productivity. These companies are struggling to improve their operations to increase their production yield, operational efficiency, and product quality. This has been driving demand for Data Visualization & Analytics solutions over the past few years and as part of their evolution, electronic OEMs, particularly those highly quality-sensitive such as automotive, networking, and smart electronics, are moving in the direction of Data Visualization & Analytics product analytics and predictive analytics.
Challenges
However, the enormous amount and variety of data electronics OEMs deal with and lack of control over manufacturing data and standardization make it a challenging task for these organizations to extract value from the data they collect. At times, engineers end up wasting their precious time trying to locate the data they need for analysis and contextualizing it to perform analytics and finally act on it.
In addition to these challenges that are common across industry verticals, most electronics OEMs have adopted a highly complex supply chain typically consisting of multiple contract manufacturers and semiconductor suppliers. To realize the benefits of Data Visualization & Analytics, these companies thus require insights throughout their distributed operations, including into facilities they might not own.
Furthermore, the separate supply chains of electronics manufacturers and semiconductor companies prevent overall supply chain management and there are silos within each supply chain. Data are not shared between the different phases of the manufacturing process, even those taking place at the same location.
Benefits of visualizing data
Despite these challenges, the benefits from Data Visualization & Analytics are simply too good to pass on. The savings generated by an electronics giant for its customers, for example, are a testament to this. Analyzing customer data from over 50 billion devices, the company estimated to have saved its semiconductor customers over $250 million in only 12 months by reducing test time through eliminating redundant and unnecessary tests, reducing test costs by using advanced test methodologies, detecting product drifts that would have affected defective parts per million (DPPM) performance, increasing productivity by using automated rules that optimized manufacturing throughput and eliminated supply chain inefficiencies, and increasing visibility into their entire supply chain.
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