Process capability index Cpk for monitoring the thermal performance in the distribution of refrigerated products

The temperature of refrigerated products along the cold chain must be kept within pre-defined limits to ensure adequate safety levels and high product quality. Because temperature largely influences microbial activities, the continuous monitoring of the time-temperature history over the distribution process usually allows for the adequate control of the product quality along both short- and medium-distance distribution routes. Time-Temperature Indicators (TTI) are composed of temperature measurements taken at various time intervals and are used to feed analytic models that monitor the impacts of temperature on product quality. Process Capability Indices (PCI), however, are calculated using TTI series to evaluate whether the thermal characteristics of the process are within the specified range. In this application, a refrigerated food delivery route is investigated using a simulated annealing algorithm that considers alternative delivery schemes. The objective of this investigation is to minimize the distance traveled while maintaining the vehicle temperature within the prescribed capability level261546

A Process Capability Analysis Method Using Adjusted Modified Sample Entropy

Citation: Koppel, S., & Chang, S. I. (2016). A Process Capability Analysis Method Using Adjusted Modified Sample Entropy. Procedia Manufacturing, 5, 122-131. doi:10.1016/j.promfg.2016.08.012The evolution of sensors and data storage possibilities has created possibilities for more precise data collection in processes. However, process capability analysis has become more difficult. Traditional methods, such as process capability ratios, cannot handle large volumes of process data over time because these methods assume normal process distribution that is not changing. Entropy methods have been proposed for process capability studies because entropy is not dependent on distribution and can therefore provide accurate readings in changing distribution environments. The goal of this paper is to explore the use of entropy-based methods, specifically modified Sample Entropy to identify process variations over time. A study based on simulated data sets showed that the proposed method provides process capability information. © 2016 The Author