Question

The account receivable department of R&W Company has received complaints from many customers about incorrect billing, i.e., the bills do not reflect the materials that arrived at their receiving docks. The department has decided to implement statistical process control in its billing process. Number of complaints over randomly selected 8 days of a month were recorded. The observation is given below:\

Day 1 2 3 4 5 6 7 8
No. of incorrect bills 10 3 5 4 7 6 8 2

Develop appropriate control chart(s).
Find the control limits

Answer 1

To develop control charts and find control limits for incorrect billing complaints at R&W Company, one would use a p-chart, which monitors the proportion of defective items. Control limits are calculated using the average proportion of defects, sample size, and Z-value from the standard normal distribution.

Step-by-step explanation:

The student has asked how to develop appropriate control charts and find the control limits for the number of incorrect bills recorded over 8 randomly selected days by the accounts receivable department at R&W Company. To address this question, we would typically use a type of control chart known as a p-chart, which is used to monitor the proportion of defective items in a process where we have count data (like the number of incorrect bills) and can calculate proportions.

The control limits for a p-chart are defined as:

• Upper Control Limit (UCL) = p-bar + Z×(√(p-bar(1 - p-bar)/n))
• Lower Control Limit (LCL) = p-bar - Z×(√(p-bar(1 - p-bar)/n))

where p-bar is the average proportion of defects, 'n' is the sample size, and 'Z' is the Z-value from the standard normal distribution for the desired confidence level. However, to fully compute the control limits, we need the exact number of items billed each day to calculate the proportions of incorrect bills. With that information, we could calculate the average proportion p-bar, and then determine the control limits applicable to their process.