Issue link: https://iconnect007.uberflip.com/i/1058015
DECEMBER 2018 I SMT007 MAGAZINE 79 als, then why not keep the same materials in place on the machine and eliminate the time to change them? Unfortunately, this created a new restriction for machine program opti- mization. The path travelled by the machine head to pick the most commonly used materi- als to the points of placements could no longer be optimized through the choice of where the materials should be set up on the machine. As a result, the program execution time was lon- ger and less efficient. Many overlooked this as the focus on machine program optimization had changed once the effects of higher mix came in; it became all about the changeover time. How- ever, in many scenarios, as time went on, the losses in the programs exceeded those avoid- ed in the changeover process. This lost pro- ductivity due to reduced program efficiency was almost never included in the productivity reports. Throughput performance was simply measured against the machine program time. Invisible losses like these started to become an increasingly common part of the regular high- mix production paradigm. Production planning is another black hole example of lost productivity where fixed pro- duction configuration assignment rules for product allocation to line configurations by engineers have to be followed due to the sheer time and effort needed to prepare product data, thereby significantly restricting the optimiza- tion process. Generic legacy scheduling tools are useless in this environment. In reality, pro- duction is mainly scheduled utilizing Excel as a just-in-time (JIT) planning tool; no one has time to think about whether more optimized production allocation plans could be made. The associated losses of opportunity contin- ue to increase due to these kinds of problems, many of which are hidden and not reported due to the narrow focus of individual opera- tions and lack of overall visibility. Productivity reports made from each perspective look good on the shop floor and everyone is making an excellent effort, but if you step back and look at the bigger picture, the overall result is going in the wrong direction, which is exactly what the German reports are highlighting. The real- ization of this is a good thing because even if the numbers themselves are very poor, with- out this information, there is no opportunity to investigate and improve. In the meantime, it is fine to continue to measure the internal perfor- mance of each element within manufacturing with the current metrics and key performance indicators (KPIs); one should not replace the other. However, what the German reports don't offer is a solution to this issue. From a high- level perspective, the situation is complex and bewildering with numerous variables and bar- riers, many of which are buried in the techni- cal detail of operations. Discovery of hidden issues—as well as the consequences of actions taken—are very difficult to understand, never mind quantify. To start to gain a sense of it all, there are two main things to address. The first is to understand the real need of the business—both current and future. For exam- ple, in Germany, the most active sector of the industry is automotive. The pride of German automotive assembly lines has a final produc- tion assembly line running at a fixed takt rate like a heartbeat delivering cars reliably and on time every time. The line is also fully flexible and capable of making any of the millions of combinations of options and features that any customer could require. It all sounds good and appears to be a supreme achievement of auto- mation, which includes human activity. However, if you focus away from the final line operation, it is possible to see the damage that the assembly-line operation is causing. Looking downstream, we see that the factory Discovery of hidden issues— as well as the consequences of actions taken—are very difficult to understand, never mind quantify.