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SMT007-Mar2020

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42 SMT007 MAGAZINE I MARCH 2020 than focusing solely on the return on invest- ment (ROI). Instead, focus on implementing IoT solutions that make the manufacturing process more streamlined for your employees, even if it returns a minimal ROI or an initial increase in the cost of goods produced. Third, be prepared to work with multiple IoT system providers. Few companies offer full- scale IoT solutions for manufacturers. How- ever, divisions within major IoT providers can help each manufacturer integrate their IoT solutions with solutions from other vendors. Building on the Data Flow Layers: From Manual to Digital When considering a data-management strat- egy, you can examine your data flow as mov- ing in four levels. Industry 4.0 means going from level one and building to level four. The first level is the data that is available, which is the status quo for many companies today. Data is available but difficult to use to make decisions or implement improve- ments. The data is in siloed systems and often requires manual work to integrate and trans- late into useful information. Problem-solving at this level is possible but extremely time-con- suming. When a product quality or machin- ery issue arises, operators and engineers must scramble to gather data from various sys- tems before they can decide what happened and how to fix it. This approach drains time, resources, and money from the factory. Man- ufacturers at level one should move to level two as soon as possible or risk wasting mil- lions of dollars in lost production output from unplanned downtime each day. Level two makes the data accessible. A level-two data-management system integrates all of the disparate information sources into one single source of truth and continuously gathers and tracks production data. With the data in one location and always available, problem-solving becomes almost frictionless. When an issue occurs, operators and engi- neers can access the data in the system using data visualizations and dashboards, essen- tially leveraging the system as a query engine. With easy access to all the data, they are able to answer questions quickly, increasing plant productivity. A level-two data flow allows engineers to address high-value issues that improve the product, conduct more efficient materials changeovers, or adopt a mass customization strategy. However, this reactive—and some- what proactive—analysis still requires time, effort, and engagement from engineers. To move from level one to level two, manu- facturers must implement a new data architec- ture, which usually takes less than a year. To do this, you need to evaluate whether to build Figure 1: Big data flow.

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