Issue link: https://iconnect007.uberflip.com/i/1078362
64 SMT007 MAGAZINE I FEBRUARY 2019 of all types and from all vendors to talk freely with each other through a commonly defined language. As human intelligence came out of data from the five senses, digital intelligence starts primarily with the evolution of the sixth. The Evolution of Digital Intelligence Continuing with the smart warehouse solu- tion, once the Industry 3.0 hardware was in place, software AI stepped in with Industry 4.0. The idea of the word random immediately puts a question in many people's minds as to how efficient a system can be based on anything that is random. Having operators searching around for an available empty location to put materials is quite a major waste. The intelligence of digi - tal warehouse systems comes from the need to address that. The warehouse solution software knows the content of each location and can di- rect putting away materials, either by human or machine, to specifically chosen locations. The cleverness comes with the selection of the location. If the software developer knows the travelling distance between bins and the material checkout point is an issue, the soft- ware can be developed to look at which mate- rials are more commonly used and locate them closer to the checkout, for example; it can even start to optimize the path of travel to collect sets of materials as needed. This speeds up the access of materials, decreasing the material re- plenishment cycle times. The size, type, and form of materials can be considered as well as whether the materials have specific storage constraints, such as electrostatic or moisture sensitivity. Material storage location decisions may also be affected by the ownership of the material, cost, whether new or used, whether on a carrier such as a feeder, tax exemption, or critical for specific industry sectors, etc. Ma- terials are being stored optimally rather than randomly—an automated process performed by an AI—but is just a software algorithm where decisions are based on the knowledge of the programmer to make certain things hap- pen in certain ways. Discrete software algorithms for the majority optimization application were originally devel- oped to mimic the ways that humans would approach a problem, and then do it faster and more accurately. Over time, as humans no lon- ger wanted to approach increasingly complex optimization problems in this discrete fashion, digital modelling of operations took over. Rath- er than create a specific logical procedure in software to follow, starting with so-called "ge- netic" algorithms, the software was developed to define a set of rules that would measure the effectiveness of a solution through a process of scoring what worked and what didn't against the targets. Random combinations of solutions criteria were set up, each of which was measured by the software. This was then repeated with dif- ferent combinations, often millions of times, until the best result was found, which was very much a trial-and-error process. This method was found to be less suitable than expected for more complex problems because with each additional element to the problem, the num- ber of iterations required to reach the best re- sult increased exponentially. However, on the measurement side, software became easier to develop an application of these kinds of algo- rithms to different problems simply meant the alteration of the measurement system. Tech- niques were created to reduce the randomness of the criteria arrangement in attempts to speed up the process. Even so, the best optimization of an SMT machine program would take hours. Compromises to finish early were introduced to allow the customer to accept a fairly good optimization in a reasonable time. The warehouse solution software knows the content of each location and can direct putting away materials, either by human or machine, to specifically chosen locations.