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32 SMT007 MAGAZINE I JUNE 2018 3. Digital Planning Process a. The decision about which choice of line configuration to use is made depending on the immediate customer demand requirement. The decision-making algorithm considers the capabilities and availability of machine processes, effects of the commonality of product mix and availability of resources. 4. Digital final process optimization and execution: a. Information is transmitted from the digital manufacturing engineering system to the identified machines. This is best done using the digital IPC Connected Factory Exchange (CFX) IoT protocol. b. The machine vendor receives an assured dataset, which can be used reliably to create the optimized machine operation. In high-mix or Industry 4.0 scenarios, the machine vendor can include optimization of the machine setup together with execution optimization based on the sequence of products that will be executed. Following this digital best practice, the manu- facturer receives their expected benefit, with a significant reduction in new product intro- duction lead-times, a step change in flexibility, the elimination of engineering mistakes, and the ability to support an Industry 4.0 opera- tion from an engineering perspective. For the machine vendor, there is a huge reduction in the work needed by the software to resolve exter- nal data issues. This enables them to focus on optimization, that includes grouping of prod- ucts planned in a flexible way, to be executed in a known sequence, such that changeover times can be reduced. Though the time needed to prepare and setup the machine has greatly reduced, the effectiveness and performance of the machine overall will increase, thus increas- ing the value proposition of the machine espe- cially in Industry 4.0 scenarios. This is the first part in a series in which we look at new digital best practices. The series includes looking at manual processes, the supply-chain, quality management as well as planning, and how digital data formats and communication standards combine to create the new paradigm of digital manufacturing and Industry 4.0. SMT007 Michael Ford is the European marketing director for Aegis Software. Scientists at the University of Campinas's Device Research Laboratory (LPD-UNICAMP) in Brazil, collab- orating with colleagues at the University of California San Diego in the United States, have developed a Fourier- transform infrared (FTIR) spectrometer based on silicon photonics. Resulting from Mário César Mendes Machado de Souza's PhD research and a research internship abroad, supported by scholarships from FAPESP and supervised by Professor Newton Frateschi, the new spec- trometer is described in an article published in Nature Communications. Various projects have appeared in recent years to develop an FTIR spectrometer based on integrated photon- ics, but progress had so far been scant owing to several technical challenges, such as the highly dispersive profile of silicon waveguides. The researchers succeeded in overcoming these chal- lenges by creating a laser calibration method to quantify and correct the distortions caused by silicon waveguide dispersion and non-linearity. As a proof of concept, they developed a 1 mm 2 FTIR spectrometer chip based on stan- dard silicon photonics fabrication procedures. The researchers now plan to engineer a device that is totally functional and integrated with photodetectors, light sources and optical fibers. Infrared Spectrometer on a Chip