When work days (and nights) are spent conforming to customer compliance standards, investigating customer complaints, or struggling with data collection software, then finding time for continuous process improvement seems unrealistic if not impossible. Tasks pile up, demands increase, and products become more diverse. How do you find ways to create real value?
The first step is to look closely at how you are collecting data. Typically, quality data are sampled during the process to determine if the product (or part) is in compliance. These data are often appended to traceability information such as a lot, purchase order number, or work order number. This is the bare minimum required for satisfying customer needs or being able to respond to a recall. However, adding machine, part, and shift data to this set can make it more valuable.
Ultimately, one or more machines are responsible for certain quality criteria that are measured on a given part. Capturing the machine as an identifier, such as asset number, rather than simply “Line 1” gives you increased resolution of the process. If machines are switched out, upgraded, or replaced, you can focus on the process performance of each one individually. This enables you to provide a more accurate benchmark of your process. Remember, the process can be defined how you see fit, and adding resolution to your data collection gives you more options.
You are probably collecting a “part” with your quality data, but you may want to consider adding more detail. For example, assign a work in progress part number at different stages of production. This will enable comparative analysis at multiple levels and give you greater resolution for recalls. It will also allow you to pinpoint with more certainty not only underperforming products but also specific problem areas within your process.
There is always a human component to your process performance. Appending shift information to quality data is not the same as simply noting the time that a check was performed. Adding a shift indicator will allow you to aggregate data by shift, regardless of the specific date or time the data were collected. Try looking at the Cpk (a value in process capability calculation) of a given process/part/test combination. Now look at the same data, but by individual shift. What if the third shift’s performance yields a Cpk of 0.87, while the first shift yields 1.34? This could help you quickly identify issues such as understaffing or inadequate training.
Process improvement is all about collecting the right data so you can see your process not only at a high level but also with high resolution. Comparative analysis of quality data by machine, part, and shift can quickly identify areas to improve on rather than tackling too many variables at once. These three additions will make your quality data truly valuable.