Issue link: https://iconnect007.uberflip.com/i/1253723
58 SMT007 MAGAZINE I JUNE 2020 issues for which there is little or no visibility other than symptoms that appear as business limitations or even failure. Analysis of data needs to be more intelligent. Machine learning and line-based closed-loop systems are great at using raw machine data to automate process improvement. Beyond these narrow solutions, however, the analysis of machine data in isolation is relatively point- less. We would expect these days that auto- mated machines work well when able to do so, providing that positive spin opportunity. The real challenge is how to perform analyses of what is happening in between the machines, where there is no data being reported. At the simple level, no machine data means potential loss. Machines are stopped and per- haps blocked, starved, not needed, or broken down; there could be material issues, quality concerns, lack of operators, a scheduled vaca- tion, or even a pandemic. The machines don't know; they only say that they are stopped. Through the correlation of data from multiple disciplines—such as material logistics, plan- ning, and quality management—what needs to be discovered are the root causes and net effects of any key exception in the process pre- venting operational progress. Then, there is a more complex level. We should look at the progress of a product through manufacturing rather than just look- ing at the performance of the machines them- selves. Consider the typical international tour- ist experience at an airport. How much time is actually needed to check-in, drop a bag, enjoy the security check, walk to the gate, and get on the plane? Probably about 10 minutes door to door, but we are told to arrive at the airport at least two hours before the flight leaves. There- fore, added-value time at the airport is about 8%, and the other 92% is waste, but owners of the shops and cafés may tend to disagree. To report about 8% efficiency in manu- facturing would probably get you fired, but I could go into most factories working nor- mally today and get reports that show efficien- cies measured in such a way as being much worse than 8%. We are fixated by looking at machine data rather than using the data to truly analyze the effectiveness of the factory in doing its job, taking materials, and mak- ing end products. The stock of raw materials should be minimized, as should be the hold- ing of sub-assemblies, areas of semi-finished goods, and finished goods in the warehouse. We should not have so many products await- ing repair or retest, being repaired or tested, going through quality inspection, being in quarantine, or being piled up in front of pro- cesses that are not yet set up and ready to exe- cute. All of these aspects of manufacturing have a far more significant effect on the busi- ness than the simple operation of any particu- lar machine. Machine data acquisition has been revo- lutionized of late, with data gathering from machines being easier, more detailed, timely, and accurate without the need for middleware or customized machine interfaces. This is nota- bly true in the case of using the IPC Connected Factory Exchange (CFX) standard. There are no interfaces for the gaps in between the machines. These are the areas that have a major impact on the operation. Take the example of an individual product simply leav- ing one process and moving to another. The product gets to the end of the line and stops. It is stored—somewhere, somehow—waiting for the others in the batch, job, or work order to be completed. The next process has to be as efficient as possible, so planning delayed the start time until it was sure that all prod- ucts had been completed by the prior process, a vacancy had opened up, and it was optimal timing to do so. The real challenge is how to perform analyses of what is happening in between the machines, where there is no data being reported.