Six Sigma and Warranty Reduction at Harley-Davidson

Six Sigma and Warranty Reduction at Harley-Davidson

            Bill Smith developed the six-sigma method of eliminating defects in manufacturing processes, products, and services. The sigma, which represents a population standard deviation, measures the deviation in sets of data collected on the process. Six-sigma usually has a mean in a process where specification limits define defects. The six-sigma or six standard deviations is the mean from the nearest specification limit. Working with six sigma, therefore, provides an allowance between the normal variation of a process and limits of specification (Stamatis, 2019). For instance, if Harley-Davidson’s piston rings must have a diameter between 10.32 and 10.38 inches to optimum efficiency of the engine, then the manufacturing procedure means should be about 10.35 inches with a 0.005 standard deviation. That is, a piston with a diameter of 10.38 inches would be six standard deviations from the mean.

Three factors cause warranty claims for companies. A poor product design that compromises reliability, a poor manufacturing design that compromises quality and inappropriate handling of the product once it is sold. The most common causes are the first two – produce and manufacturing designs. To reduce warranty costs, Harley-Davidson must focus on these two factors, and this is where the six-sigma approach is useful.

Reduction of warranty costs requires the use of the six sigma four-phased methodology, which can be used in the improvement of the quality of the motorcycle manufacturing process and enhance product design for reliability. The four phases include measuring, analyzing, improving, and controlling (Stamatis, 2019).

Measuring – in this phase, the company should measure its existing systems and determine reliable and valid metrics that will be used in monitoring the manufacturing process. The process must be geared towards providing the best design based on customers’ expectations. This phase involves the identification and description of the potential critical processes in manufacturing. The critical aspects of the process are identified from historical data of the company, failure analysis report, and yield reports. Once identified, the company should determine the accuracy precision, reproducibility, and repeatability of each instrument used in the process to ensure that they are suitable (Jin, Janamanchi & Feng, 2011).

Analyze – In this phase, the system will be analyzed to identify the existing gaps between the company’s product performance currently and the target performance. Here the company should identify the reasons for the defects in the product and manufacturing process that causes warranty claims. Once the potential variables are identified and isolated, then a list of priority factors necessary for achieving the desired goal is developed.

Improvement – in this phase, the company will create an alternative optimal solution and test it to confirm is it will lead to improvements in the manufacturing process and in the design of the motorcycles. The current model used in the manufacturing should be modified, and the improvements confirmed through an experiment. The phase involves conducting design experiments to determine the most important factors in the process (Jin, Janamanchi & Feng, 2011). The new process model must reduce variability on both the product and the process to ensure that they are stable and predictable.

Control phase – this is the last stage in the six-sigma methodology of warranty reduction. Here, the company needs to control the new, improved system to ensure that the new metrics are followed for the desired outcomes (Jin, Janamanchi & Feng, 2011). There must be continuous monitoring to avoid the recurrence of previous problems that caused warranty claims. Finally, the new system should be institutionalized through policy, procedure, and operating instruction modifications. Documentation of the improvement processes is key. This can be accomplished using reaction plans and a decision tree.

References

Jin, T., Janamanchi, B., & Feng, Q. (2011). Reliability deployment in distributed manufacturing chains via closed-loop Six Sigma methodology. International Journal of Production Economics, 130(1), 96-103.

Stamatis, D. H. (2019). Six Sigma Fundamentals: A complete introduction to the system, methods, and tools. CRC Press.