Reducing Check-In Errors at Brigham Young University Through Statistical Process Control

Introduction of statistical process control in the setting of a small blood centre was tested, both on the regular red blood cell production and specifically to test if a difference was seen in the quality of the platelets produced, when a change was made from a relatively large inexperienced occasional component manufacturing staff to an experienced regular manufacturing staff. Production of blood products is a semi-automated process in which the manual steps may be difficult to control. This study was performed in an ongoing effort to improve the control and optimize the quality of the blood components produced and gives an example of how to meet EU legislative requirements in a small-scale production centre. Data included quality control measurements in 363 units of red blood cells, 79 units of platelets produced by an occasional staff with 11 technologists and 79 units of platelets produced by an experienced staff with four technologists. We applied statistical process control to examine if time series of quality control values were in statistical control. Leucocyte count in red blood cells was out of statistical control. Platelet concentration and volume of the platelets produced by the occasional staff were out of control, which was not the case with the experienced staff. Introduction of control charts to a small blood centre has elucidated the difficulties in controlling the blood production and shown the advantage of using experienced regular component manufacturing staff.

Questions about the validity of measurements made in an E/sup 3/ laboratory using software to control the instrumentation are a continuing concern for EMC engineers. In previous work, it has been shown that the use of statistical process control (SPC) methods to gage and track the variability of system measurements can provide confidence in the repeatability of the system hardware setup, and in the data it produces. Once the hardware system stability has been demonstrated, it is possible to also utilize SPC historical information to assist in confirming the validity of the data gathered using the system's control software.

Software process monitoring a complex activity. Recently many authors have been suggesting the use of Statistical Process Control (SPC) for software process monitoring while others have pointed out potential pitfalls in using this approach. SPC often used as is without the appropriate customizations or extensions to software, given the peculiarities and differences of software processes compared to manufacturing ones. As so the starting point to understand how and whether SPC can be used in software to understand its contribution in monitoring processes. This work puts together experience collected by the authors in using SPC in industrial contexts, points out the main issues concerning software process monitoring and highlights how the technique addresses them. The main contribution of the paper to formalize and put a set of guidelines together in a disciplined process for guiding practitioners in correctly using SPC during process monitoring.

The use of statistical process control (SPC) procedures such as Shewhart charts has been one of the pillars of the quality revolution. The Shewhart charts display process data under test and provide statistical decision making tools to assess the process quality (Shewhart 1931). These simple charting methods have proven to be powerful tools for monitoring the ‘health’ of a process, for detecting special causes and events, and showing if the process in under control. In quality monitoring (Box and Luceno 1997) applications of process industry we must often use uncertain measurements and SPC is a good tool for handling them.

This paper reports on the findings of a questionnaire survey into the use of statistical process control (SPC) in a sample of United Kingdom automotive suppliers. All the 76 respondents had attended the Ford‐approved three‐day course on SPC held at the University of Manchester Institute of Science and Technology (UMIST). The survey investigated the following broad issues of SPC: motivation, implementation, application, suppliers and customers, and benefits. A number of findings surprised the authors; these included the number of organizations who had not appointed a SPC facilitator, organizations experiencing difficulties in deciding what type of control charts to use, control charts being ‘filled‐in’ by people not directly responsible for control of a process, charts being displayed away from work areas and the frankness of some suppliers in declaring that the vast majority of their manufacturing processes are not in a state of statistical control. If there is an overall message arising from the findings it is that the introduction and application of SPC should not be overcomplicated.

To investigate and illustrate the use of statistical process control (SPC) for prospective monitoring and detection of regional changes in prescription opioid abuse rates. A demonstration project was conducted by applying several types of SPC charts to Louisiana TANF (Temporary Assistance for Needy Families) applicant self-reported opioid illicit use data collected during 21 months. These charts were constructed and examined for signals of abuse rate changes both at the aggregate statewide level and for each state region (parish) separately. SPC identified increases in opioid illicit use in 2 parishes and subsequent decreases in both regions. These fluctuations were not as apparent when the data were examined using traditional bar graphs and summary tables. The remaining parishes exhibited stable abuse rates with no statistically significant changes over time. These results illustrate proof of concept for monitoring prescription drug abuse regionally via statistical control charts and the potential of this approach for real-time prospective surveillance for abuse rate changes.

Use of statistical process control to detect important changes in failure rates is well-ingrained across industries, including manufacturing, services, and healthcare. Although significant research has focused on optimizing signal detection, less is known about the optimal manner by which to respond to signals once detected. To address this important practical question, we develop a response optimization framework assuming process changes of low through high severity can occur, each of which is probabilistically detected and classified correctly or incorrectly. Based on perceived severity, a practitioner responds in one of several manners varying in cost, effectiveness, and timeliness, which in turn produces different costs until the process is reset to statistical control. Experimental results from an application to hospital-acquired Clostridium difficile infections indicate the optimal response can differ significantly based on organization size, baseline rate, and infection cost but less so on classification accuracy. Hospitals with 80 to 600 beds can save between 30% and 60% of their expected total cost per out-of-control event by using optimized versus non-optimized response policies. Simulation analysis across a range of settings indicates that optimal solutions also result in significantly lower total cost variances. The average annual number of deaths prevented ranges between one and six per out-of-control event. This approach also can be used in other applications to identify context-specific optimal response policies generalizable to any type of surveillance or chart method.

Micro, Small, and Medium Enterprises (MSMEs) play a strategic role in Indonesia’s economy, including in the garment industry. The subject of this study is MSME X, located in Cimahi City, which specializes in the production of knitted clothing. This research aims to identify production problems, analyze the causes of defects, and design improvement strategies using the Six Sigma method with the DMAIC (Define, Measure, Analyze, Improve, Control) approach. In the Define stage, the main problems are identified through the Critical to Quality (CTQ) approach, which reveals three major types of defects: button defects (39%), stitching defects (32%), and fabric or pattern defects (29%). Next, the Measure stage is conducted to assess process performance using quality indicators. The calculation results show a Defects per Million Opportunities (DPMO) value of 10,968.95 and a Sigma Level of 3.79, indicating room for improvement. The Analyze stage utilizes Pareto and Fishbone diagrams to identify root causes. The Improve stage applies Failure Mode and Effect Analysis (FMEA) to determine improvement priorities. The Control stage involves the use of Statistical Process Control (SPC), regular inspections, and an incentive system. This approach is expected to reduce defect rates and sustainably improve production efficiency.

In this paper, we propose the novel use of Statistical Process Control (SPC) as a tool for identifying anomalies in the image acquisition process of a digital camera, for the purpose of camera identification. Control charts are used to illustrate the overall level of control associated with several devices (models include Apple iPhone 3G and 3GS, Nokia N97, and Leica D-Lux4), which are in turn reviewed in accordance with the Western Electric Rules for identifying assignable causes for the observed variation. X-Moving Range and Exponentially Weighted Moving Average (EWMA) control charts are used to highlight the variation for a subset of the devices. By implementing such a statistical model, the forensic investigator is much better positioned to understand the behaviour of a particular device, and is ultimately able to identify the most unstable feature of the cameras image acquisition process, thereby establishing a suitable fingerprint for matching images to their source.

<p>TQM implementation and impact research</p>

A Systematic Approach for Evaluating and Upgrading Academic Science Journal Collections

This paper reports on the use of undergraduate students enrolled in an Applied Anthropology course as researchers for a library use study at Brigham Young University's (BYU) Harold B. Lee Library (HBLL). This is a common practice at BYU, but has not been reported extensively in the literature. The study was carried out by the authors with the assistance of undergraduate students, the students being the researchers and was conducted in order that the HBLL could determine student ideas for reconfiguring some newly opened space in the Periodicals room. Using students assisted the library as well as met a curricular need in the Anthropology course. Ethnographic methods were the primary means used to gather the data. The paper asserts that the peer interaction during the data collection significantly enriched the quality of the data collected.

Business librarians at Brigham Young University's Harold B. Lee Library are confronted with considerable and consistent declines in usage of print books in business and economic disciplines. Inspired by commercial businesses, where in-store displays are commonly used to increase sales, business librarians decided to highlight the library's business and economics collections by creating book displays. The study revealed that displays produced a substantial increase in circulation. Prior to the displays, featured books had an average usage of less than four times per year; after these books were featured in the displays, their usage per year increased by 58%.

Introduction Evolution is a progressive change from something inadequate in a given circumstance or environment to a new or modified state with the ability to perform better in that new environment or situation. Evolution occurs in organizations, organisms and processes. One aspect of evolution is the ‘survival of the fittest’, which is that the most adept at evolving will continue and propagate. In technology it is much the same: as one technology becomes obsolete (dies), it gives way to the next better able to serve. For examples consider the cassette tape or the LP record and their demise. Often technology changes occur as a result of what the user is using at the time, or in other words from external forces. It is also the case that changes occur because of internal forces or pressures from within the organization. Academic libraries have moved to mobile technologies for both these reasons: handheld devices are the main means of access to information for university students, faculty and parents (external); and mobile technologies are also useful for librarians in their day-to-day management of the library and services (internal). Libraries, like other organizations, often make changes to procedures, processes or services in an evolutionary manner. These changes or adaptations are made to organizational schemes, technology and other services. Libraries have changed the tours of the library building, its organization, services and resources in a similar manner. Being the main consumers of academic library information, students have the ability to influence the direction of library technology and services. These consumers determine how they want to utilize the information and it is up to librarians to provide the information in a way that it will be best appreciated. This paper will describe changes that occurred throughout the history of the library tour in the Harold B. Lee Library (HBLL) at Brigham Young University (BYU): specifically, how the tour evolved from in-person to analogue to digital to mobile. The benefits and drawbacks of each mode will be mentioned as well as the compelling reasons for following the path to the current implementation of the tour.

Dr Timothy Davis, Asian Studies Librarian, Harold B. Lee Library, Brigham Young University