Quality bottleneck transitions in flexible manufacturing systems with batch productions

Reducing Bottlenecks to Improve the Efficiency of the Lung Cancer Care Delivery Process: A Process Engineering Modeling Approach to Patient-Centered Care

The process of lung cancer care from initial lesion detection to treatment is complex, involving multiple steps, each introducing the potential for substantial delays. Identifying the steps with the greatest delays enables a focused effort to improve the timeliness of care-delivery, without sacrificing quality. We retrospectively reviewed clinical events from initial detection, through histologic diagnosis, radiologic and invasive staging, and medical clearance, to surgery for all patients who had an attempted resection of a suspected lung cancer in a community healthcare system. We used a computer process modeling approach to evaluate delays in care delivery, in order to identify potential 'bottlenecks' in waiting time, the reduction of which could produce greater care efficiency. We also conducted 'what-if' analyses to predict the relative impact of simulated changes in the care delivery process to determine the most efficient pathways to surgery. The waiting time between radiologic lesion detection and diagnostic biopsy, and the waiting time from radiologic staging to surgery were the two most critical bottlenecks impeding efficient care delivery (more than 3 times larger compared to reducing other waiting times). Additionally, instituting surgical consultation prior to cardiac consultation for medical clearance and decreasing the waiting time between CT scans and diagnostic biopsies, were potentially the most impactful measures to reduce care delays before surgery. Rigorous computer simulation modeling, using clinical data, can provide useful information to identify areas for improving the efficiency of care delivery by process engineering, for patients who receive surgery for lung cancer.

A Hierarchical structure of key performance indicators for operation management and continuous improvement in production systems

Key performance indicators (KPIs) are critical for manufacturing operation management and continuous improvement (CI). In modern manufacturing systems, KPIs are defined as a set of metrics to reflect operation performance, such as efficiency, throughput, availability, from productivity, quality and maintenance perspectives. Through continuous monitoring and measurement of KPIs, meaningful quantification and identification of different aspects of operation activities can be obtained, which enable and direct CI efforts. A set of 34 KPIs has been introduced in ISO 22400. However, the KPIs in a manufacturing system are not independent, and they may have intrinsic mutual relationships. The goal of this paper is to introduce a multi-level structure for identification and analysis of KPIs and their intrinsic relationships in production systems. Specifically, through such a hierarchical structure, we define and layer KPIs into levels of basic KPIs, comprehensive KPIs and their supporting metrics, and use it to investigate the relationships and dependencies between KPIs. Such a study can provide a useful tool for manufacturing engineers and managers to measure and utilize KPIs for CI.