This post illustrates the Statistical Thinking tool, the flowchart or process map, using an example taken from the author’s consulting experience. A flowchart of a process is sometimes referred to as a process map. A manufacturer produced automotive door frames, depicted in the following figure. The door frame consists of four parts which were joined by a welding operation. The shape and finished product dimensions were important quality characteristics of the finished product. However, they had a problem meeting dimensional specifications on the assembled final product. As a result they did considerable rework to insure satisfactory quality for the finished product.
The manufacturer formed a team to recommend corrective action to reduce rework costs and the time to meet shipment schedules. Shop floor personnel thought that variations in incoming raw material caused the quality problems An analysis showed that the header was the primary quality problem.
The following figure gives the flowchart or process map for producing a header. The roll mill takes sheet metal, cuts the input material to the proper length, forms the two parts for a header, and spot welds them together. The bender bends the header to the proper shape punches two holes which will be used to position the part in subsequent operations. The saw forms the proper angles at the two ends of the header. The data on the flow chart below each operation specify important quality characteristics. The symbols h1, h2, g, D1, D2, D3 and SC 4 through SC20 specify dimensions.
The manufacturer collected data for the team for relating the quality characteristics on the flowchart to finished part dimensions. Collecting and analyzing data for individual steps in the flowchart is an example of disaggregation. A regression analysis resulted in the following conclusions:
- Variation in material characteristics has little effect on quality characteristics.
- D1, D2 and D3 have considerable variation and affect finished product quality
- The left and right headers have significantly different variation for D2 and D3.
The above conclusions motivated corrective action, and the manufacturer eliminated the need for rework. This example reinforces the conclusion that data-driven decision making gives Statistical Thinking a significant advantage over expert opinion.