Issue link: https://iconnect007.uberflip.com/i/1433652
36 SMT007 MAGAZINE I DECEMBER 2021 way, but has already started to make those de- cisions for us. We accept results, for example, from automated inspection and test machines that alert us to potential quality issues, instruc- tion from Lean supply-chain tools that orches- trate material logistics based on production progress and planned future activities, and ac- tionable information from dashboards that alerts us to trends. Hang on a minute. Do we trust all these deci- sions? Smart soware technology is still evolv- ing, with many challenges yet to fully over- come. It starts with the problem of data col- lection. Other than IPC-CFX, for which not every machine vendor has yet received IPC qualification, other data exchange technolo- gies do not guarantee the integrity, content, or meaning of information, leaving gaps in the data that oen go unobserved. Decisions are being made by soware automation that are based on access to only a certain part of the true holistic picture. Proof of this is apparent as we see examples that include: • Instances of work-orders being created and scheduled by ERP without the actual physical materials being available • e failure of an in-circuit test operation resulting in a "no fault found" • A dashboard indicating a ridiculous number for OEE at the start of a production run ese are all examples of where data and soware are incomplete, that contextualiza- tion is being assumed or even guessed. How many times do we turn off and on again our computers, phones, tablets, printers, televi- sions, etc.? Something went wrong, some- thing quite unknown to us, but we know enough about what to do to fix it. is does not inspire confidence. Today's reality is that we will not transcend into the full digital world of Industry 4.0 un- til these kinds of problems have been solved and trust created. is is not an unexpected issue per se. We continue to use human op- erators for assembly tasks that automation, even aer many years of evolution, has not yet found a way to competently and cost effective- ly replace. e same is, of course, happening in soware, with "AI" algorithms evolving over time, with improved visibility and therefore decision-making, though we are much closer to the start of the journey with soware than we are with hardware automation, a whole +1.0. We find ourselves therefore on a progres- sive journey. e trust element is vital for ongoing adop- tion. Almost all of us are still driving our cars manually, even though more automated fea- tures are being added, such as LIDAR-driv- en emergency braking and automated steer- ing following the lines on the road, are suc- cessfully being introduced. However, we don't yet totally trust that the soware can provide full autonomous driving in all conditions. Is it the soware itself that we don't trust or some- thing else? Hardware failures are also possible, which are addressed, we expect, by a degree of redundancy and fail-safe design. Far more like- ly would be the problems with the vast variety of road conditions due to unexpected or un- recognized fixed and mobile hazards, weath- er events, and human nature. Some very clever people work on the development of automo- tive soware, yet the most important people in the mix are those who implement and expe- rience it, then provide feedback as it works in the real world. e same is true in the evolution of artifi- cial intelligence (AI) manufacturing soware. Smart software technology is still evolving, with many challenges yet to fully overcome.