Question

Tombro Industries is automating one of its plants and developing a flexible manufacturing system. In an effort to revise its performance measurement system, the company gathered the following data for the last four months: Month 1 2 3 4 Quality control measures: Number of defects 191 169 130 91 Number of warranty claims 52 45 36 33 Number of customer complaints 108 102 85 64 Material control measures: Purchase order lead time 10 days 9 days 7 days 5 days Scrap as a percent of total cost 2% 2% 3% 6% Machine performance measures: Machine downtime as a percentage of availability 5% 6% 6% 10% Use as a percentage of availability 95% 92% 89% 85% Setup time (hours) 10 12 13 14 Delivery performance measures: Throughput time ?question mark ?question mark ?question mark ?question mark Manufacturing cycle efficiency (MCE) ?question mark ?question mark ?question mark ?question mark Delivery cycle time ?question mark ?question mark ?question mark ?question mark Percentage of on-time deliveries 96% 95% 92% 89% The president read in industry journals that throughput time, MCE, and delivery cycle time are important measures of performance, but no one is sure how to compute them. You have been asked to assist the company and gathered the following data: Average per Month (in days) 1 2 3 4 Wait time per order before start of production 10.0 12.9 13.0 15.0 Inspection time per unit 0.8 0.7 0.7 0.7 Process time per unit 2.8 2.1 2.0 1.0 Queue time per unit 3.1 4.9 5.9 7.6 Move time per unit 0.3 0.4 0.4 0.7 Required: 1-a. Compute the throughput time for each month. 1-b. Compute the manufacturing cycle efficiency (MCE) for each month. 1-c. Compute the delivery cycle time for each month. 3-a. Refer to the inspection time, process time, and so forth, given for month 4. Assume in month 5 the inspection time, process time, and so forth are the same as for month 4, except the company

Answer 1

Productivity can be measured by quality, efficiency ratios like MCE, customer satisfaction, and resource utilization. Average wait times for deliveries can be calculated if the delivery distribution is known. For tasks with known defect rates or service times, statistical rules can predict the range or adequacy of budgeted times.

Step-by-step explanation:

Productivity can be measured in ways other than the amount produced per hour of work. Other metrics include quality of goods produced, efficiency ratios, customer satisfaction, and the use of resources. For instance, measuring the quality involves looking at the number of defects and warranty claims, which indicate the precision of production. Efficiency ratios like the manufacturing cycle efficiency (MCE) reflect the proportion of value-added time within the production process. Customer satisfaction can be assessed through surveys and the number of customer complaints. Lastly, resource utilization measures like scrap as a percentage of total cost or machine downtime indicate how well materials and equipment are being used.

For Richard's Furniture Company, we can use the continuous and uniform distribution of delivery times to calculate the average wait time. If deliveries occur between 10 a.m. and 2 p.m., and wait times are uniform, an individual's wait time can be evenly distributed across this four-hour period.

Looking at a production line with a known defect rate, such as the NUMMI assembly line with a 10% defect rate, the 68-95-99.7 empirical rule suggests that in a sample of 100 cars, the number of defective cars will generally be within a certain range around the mean, with a specific probability of one, two, or three standard deviations from the mean.

For a company planning maintenance on air conditioners with an average service time and a known standard deviation, using a simple random sample allows prediction of the required average time per technician. If this time is 1.1 hours, which is slightly above the average, it may or may not be enough depending on the variance in service times.