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48 SMT Magazine • September 2016 ideal IT solution. However, we are talking about a lot of data. At each process, for each PCB or assembly, a range of data is collected, most of which is only available in real-time, such as: • Arrival of the unique product at the process • Start of the production cycle • Completion of the production cycle • Product leaves the process • Operational warnings such as material pick-up errors • Messages that describe various reasons that the machine might stop, such as material exhaust • Other process exceptions • Verification of each material • Unique feeder information • Traceability information, which can be a list of all reference designators and the code of the exact material ID that was used • Image information including material pickup and placement, leads, etc. • Machine usage statistics Beyond SMT, the messages can get more complex, for example: • Operational result information (pass or fail) • Electronics repair ticket • Detailed test results or operational measurements • Operation guidance step increments and confirmations • Diagnosis and repair information • Routing confirmation For a single operational production flow, end-to-end, many messages are generated each second. Some of the messages contain many ki- lobytes of data. Multiply that by the total num- ber of production flows in the factory and, now we are wanting to store more and more data in the cloud, second by second, month after month. What makes electronics yet more of a challenge compared to other industries is the sheer size and complexity of the bill of materi- als and the number and diversity of the various production processes. The danger of taking all of the data from all of the processes and simply stuffing it into a cloud is that it will make that cloud "heavy." Suddenly, impressions of the big fluffy white masses in the sky come a lot closer to the ground, and they look menacingly dark. Stan- dard data analytical tools make heavy work of looking through complex data to generate re- ports, based on time, processes, materials, or any of the dozens of key metrics. Generating near-real-time graphs, charts, or dashboards of live production information is a serious chal- lenge. The good news is that the latest generation of business intelligence or data analytics soft- ware is able to cope with immense volumes of information. However, the issue is that we are putting raw data into the cloud. Even where this data is fully normalized into a single lan- guage like OML, the process of reporting is an order of magnitude more complex than simply going through the data and adding up the num- bers in different ways. For example, consider a fairly standard SMT machine, labeled "Z." After working for some time, Z completes the placement process, and the current PCB leaves the machine. It then looks to start the next, but no PCB has arrived. An event or status message is sent into the ma- chine log and out to external systems, such as "Stopped. Waiting for PCB." Z has limited vis- ibility outside of itself. What happens inside the machine can be reported, but any external causes of issues can only be represented by the symptoms. For machine-based reports, around 80% of the information is just symptom, without a known reason or cause. Smart computeriza- tion, on the other hand, can take the "Waiting for PCB" message from machine "Z" and start the process to discover the reason behind the event. The Smart computerization knows the flow of the current production work-order or job, so the process immediately before "Z" can be identified, which may simply be a connect- ed machine upstream in the line, or it could be a completely different machine process or logis- tics operation. Using a common platform for the informa- tion such as OML, what is going is much easi- SMART FOR SMART'S SAKE, PART 1

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