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52 SMT007 MAGAZINE I NOVEMBER 2020 ogy is very fortunate for vendors of test and inspection equipment. These processes actu- ally provide a large part of the required data for smart decision-making. Can we finally put aside the stigma that these machines and solu- tions are indirect costs, and position them as adding direct value to the production opera- tion? We need to bear in mind that the key people we need to convince are not technically minded. The engineering-orientated explana- tion of how the role of these machines has changed is not going to be easy. Let's first take a look at where we are in terms of maturity with the use of test and inspection data, as the technology is not yet at the point where it can be regarded as mature. Many years ago, the application of Six Sigma showed that defect occurrence is not required and is avoided by understanding the variations in processes and how to control them. Analy- sis of the data statistically as machine learning, closed-loop solutions, or as MES-level quality analysis exposes trends that risk defects, com- municating the issue—and even executing the appropriate corrective action—before any defect occurs. Whilst this is simple in theory, the prac- tice itself is multi-dimensional, as there are so many complex and interacting factors. Machine-learning and closed-loop algorithms are yet to be perfected to the point where the root cause of variation can be correctly iden- tified and appropriate action taken. A simple example of this is the analysis of the deviation of placement coordinates, comparing positions as specified in the machine program against those inspected after placement. Simply put, too much deviation raises the alarm before the risk of making a poor joint or an unwanted short. However, how much is too much depends on many factors, such as the size, shape, and orientation of the pads on the PCB. Consider the size, shape, pitch, and con- tact profile of component leads, as well as the location, size, shape, and thickness of the sol- der paste. Then, there is the need to understand the nature of the patterns of deviation, especially with respect to adjacent parts. Patterns in the activities—including repair and re-work—are seen as being indirect. Applying the same principle to test and inspection, imagine a set of production pro- cesses where there is one machine, such as a pick-and-place, and a reflow oven both involved in the making of the product, and then an SPI, AOI, and ICT machine. Applying the direct/ indirect rule, the direct ratio of this line is just 40%. In a perfect world, we would only need the two direct machines making the product. It means that considerable investment has been made, together with a high proportion of the operational cost, which is—in a sense—waste. However, few would be bold enough to do without the test and inspection machines, as quality performance would no doubt suf- fer, bringing potentially more substantial costs associated with poor quality later. The reluc- tance to invest in equipment and software solutions that are regarded as being indirect is understandable. But as this and other similar business-level metrics are adversely affected, this leads to concerns with business sponsors. How poor is the actual team at the site that they need so much more indirect support than others? Why should we invest in their inade- quacy? These are old-school questions that the smart factory still has to answer if investment in smart technology associated with test and inspection, as well as software solutions, are to be readily accepted, as opposed to being a con- tinuous battle for funding. The fact that the smart factory is run on the contextualization of data provided by the IIoT- driven MES solution's built-in defined ontol- Applying the direct/indirect rule, the direct ratio of this line is just 40%. In a perfect world, we would only need the two direct machines making the product.

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