Striking the right balance between costs and cycle time.
Decisions made in product design can impact assembly cost, defect opportunities and inventory cost. While design for manufacturability (DfM) analysis can eliminate many issues, less commonly analyzed decisions related to cost targets, scheduling and work team assignments can have unintended consequences that generate unacceptable levels of waste.
Lean manufacturing practitioners are aware of Taiichi Ohno’s concept of the seven wastes (muda) in manufacturing as part of the Toyota Production System (TPS). To recap, those seven wastes are:
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Leveraging the IT department to reduce operation-caused variation.
One continuing trend in electronics manufacturing services is the increasing role IT-related solutions have in supporting a Lean manufacturing-driven organizational culture. This is particularly true of proprietary solutions that automate processes in ways that minimize normally occurring variation or help eliminate non-value-added activity.
One example of this is SigmaTron International’s proprietary Manufacturing Execution System (MES) system known as Tango, whose Phase III system went live at the EMS company’s Elk Grove Village (IL) facility in June. The overarching goal of Tango is to centralize tools used throughout the company for production management, while adding enough flexibility via customization to address facility-specific or customer-specific situations.
Ten steps for achieving good design for excellence.
But don’t obsess over the distribution.
Yes, I said it. Normal data are nearly never normal.
In Six Sigma classes we study outliers, shift, drift and special cause events. But what we don’t always consider is that these “unexpected” data points may be part of the process and not as rare as we think.
First, let’s look at a set of screw torque data. The chart in FIGURE 1 is for a set of screw torques taken sequentially from a “smart” driver. We can see the data are normal (p=0.895), and the histogram and time series plot back that up.