hence why i insist on calculated averages and not exact takt times... with averages you can implement a more controlled flow speed that will feed the lines and not over supply the inventory.. while working on averages that coincides with all the operators, the operators can begin improving their own takt time as well as gain new habits to continually improve upon...i never said the technicians performing the takt time collecting were wrong.. i only said to set the takt times to the best performer is wrong.. that its stacking the decks.. or for want of a better word..cheating it... to have perfect jit.. you must set it on averages.. if done corectly then the high performance of the team will increase greatly each new day.. continuely.. kaison... the tchnitions must demonstrate how, to each operator, they got the definitive time.. to not show them is only inviting failure.. and in highly competitive world class operations this is more prevalent than one thinks.. as in dont move him because.. with newer reasons daily.. fact is he can do it.. its because of you hes not flagy Out-of-control Variables During the implementation of SPC, organizations may have problems managing the amount of items that are out of control in the early stages. The usual reasons for the large number of out-of-control factors include the following: Processes are, simply, out of control. In the early stages of an SPC implementation, control charts typically show a large percentage of subgroups are out of control. Examples of out-of-control variables are incorrect measurements and overadjustment of the process by operators. When SPC software is used in the SPC implementation, the number of control charts can quickly proliferate. Control limits are calculated automatically and, therefore, every disturbance in the process will lead to an out-of-control signal. Characteristics being monitored are often not classified, so all control charts get the same priority. But some charts are extremely important and other charts are used only as help for calculations and are of no real importance. Significance of factors, in order of priority, would come from a failure mode and effects analysis (FMEA). In many production environments, the process average is unstable. Reasons for this may include tool wear, setup differences, differences in raw material and machine warm-up times. All of these situations will show out-of-control points on the average (e.g., x-bar) control chart, but for some of these situations, it is not possible to bring the process average into control. For example, it may not be financially feasible. The instability of the process average will be visible with a large difference between Cp and Pp (e.g., Pp/Cp > 1.25). But what is more important in terms of this reason is that the out-of-control variables on the control chart monitoring the process average will place the emphasis on the wrong chart. The more important control chart is the one monitoring process dispersion (commonly the range or sigma chart) and, in general, this chart should be in control first before attention is given to the average chart.
Posted on: Mon, 08 Sep 2014 09:06:11 +0000
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