Eliminate variation that causes inefficiency or defects, while maintaining flexibility to scale.
A configure-to-order assembly process helps reduce finished goods inventory and enhance scheduling flexibility. However, it also introduces variation in the production process. Use of Lean manufacturing principles in designing production flow can ensure efficiency and minimize the defect opportunities this level of variation could otherwise create.
SigmaTron International’s facility in Acuna, Mexico, has a dedicated assembly and test “focused factory” area to provide configure-to-order (CTO) services for a manufacturer of industrial products. The customer has outsourced over 50 different product types that are a mix of legacy and current product.
Design for manufacturability (DfM) analysis is performed during the new product introduction (NPI) phase to identify potential issues prior to the product entering production. Test strategy and programming development is conducted for new products, and test programming is optimized for legacy products, where needed.
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.
There are times increasing inventories and AVLs makes sense.
A constrained supply chain represents a challenge to Lean manufacturing processes, but in the electronics manufacturing services (EMS) market, the bigger challenge is often OEM misperceptions about strategies to address this. From a Lean perspective, navigating a constrained supply chain often requires taking one step back to move two steps forward.
Our November 2017 column discussed several areas where the best strategy was “at odds” with Lean manufacturing principles, including:
One measures the variability of process outputs. The other assesses the tests themselves.
People often confuse measurement system analyses and capability studies. Far too often, I hear, “When will we run the capability study on the tester?” And while I’m sure those few brave souls who read my column do not fall into this trap, you might know of people who do. Maybe this column will help.
MSAs are for tests and gages. Capability studies are for the processes being measured. Or, to state it another way, MSAs give us confidence we can measure the capability of our process to produce parts to our customer’s specification.
One can talk about the “capability” of a tester, but only when the word is being used in its classic sense, for instance, “the extent of someone’s or something’s ability.” Let’s review.
A PCB carrier can reduce variations on solder joint geometries.
Eliminating defect opportunities by minimizing process variation is a key concept of Lean manufacturing. Fixturing is often a key ingredient in that. However, many contract manufacturing customers see fixturing as an unnecessary non-recurring engineering (NRE) expense. The reality is fixturing does add cost, but it can also save money in production. More important, printed circuit board (PCB) technology is driving the need for greater use of fixturing. Consequently, the decision on whether to fixture or not is being made more frequently.
Use of a printed circuit board assembly (PCBA) carrier for fixturing has several benefits, including:
As most readers know, statistical tests calculate a mean and confidence intervals on the mean. We are all familiar with the fact that as our sample size decreases, our knowledge of the “true” mean becomes less and less certain. This is important for tests that use the mean, such as the t-test and ANOVA.
FIGURE 1 is an example of two data sets: “apples” and “oranges.” In the first experiment we had 15 samples of apples and 15 samples of oranges. Plotting the means with their calculated confidence intervals shows we cannot differentiate between apples and oranges. (Since the confidence intervals overlap, we cannot be certain both means are not equal.)