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Introduction of Statistical Process Control to Turn-Around Time Analysis

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Introduction of Statistical Process Control to Turn-Around Time Analysis MATERIEL SERVICE DEPARTMENT UNIVERSITY OF MICHIGAN HOSPITALS Introduction of Statistical Process Control to Turn-Around Time Analysis DATE: December 12, 1991 TO: John Gialanella Director, Materiel Services University of Michigan Hospitals Richard J. Coffey, Ph.D. Director, Management Systems University of Michigan Hospitals FROM: George K. Chen Laurie D’Alleva Douglas M. Donaldson Management Systems SUBJECT: Final project report. C (2) TABLE OF CONTENTS Executive Summary 3 Introduction 4 Approach 5 Methodology 8 Results 15 Recommendations 21 References 25 Appendices 26 (3) EXECUTIVE SUMMARY This project demonstrates methods to better organize turn-around time (TAT) data used to measure performance in the Materiel Service Center (MSC). The data can be meaningfully analyzed using the ideas and methods of Statistical Process Control (SPC), such as control charts and scatter plots. By constructing these charts and graphs, an improved methodology for TAT data analysis became apparent. The control charts show whether or not the TAT’s, when standardized by the number of lines and deliveries for each run, are in control for each of the four different delivery types (SUPP, STAT, REQ, PAR). This information can help management come to conclusions regarding the delivery system. By calculating appropriate control limits, unusual or unsatisfactory times can be easily seen on graphs. By following a progression of charts over time, management will be better equipped to locate problem areas and determine possible courses of action to improve performance in the MSC. The charts are also excellent for monitoring improvements as changes in the delivery system are made, as the graphs are easy to interpret and full of meaning. Seven major recommendations are being made: 1. The control charts introduced by this project should be made a part of the standard routine in the MSC. 2. The MSC should increase inventory levels for the 13 items identified as major contributors to the number of stockouts. 3. It is recommended that the MSC keep better track of changes in stockout items. 4. A hospital wide, synchronized time system should be used when recording delivery times to increase the accuracy of the TAT’s. 5. The databases used by the department should be reconstructed for more efficient data storage. 6. Further study of the relationship between the distance of a unit from the MSC and the service provided to that unit is recommended. 7. The department should utilize SPC methods in order to effectively summarize and reduce the data collected to concise, meaningful charts and graphs. (4) INTRODUCTION The purpose of this project was to demonstrate methods to more effectively represent the overall efficiency and performance of the Materiel Service Center. The MSC delivers items from its inventory supply to various units throughout the University of Michigan Hospital. The elapsed subtimes for specific steps in this order-filling process are recorded and summed for a net TAT for each delivery. This project has implemented the techniques and ideas of statistical process control (SPC) to meaningfully organize this historical data, to represent the overall efficiency and performance of the department. Effective measures of organizational performance have been developed, summarized and applied toward producing graphical representations of the data. In the future, analyzing TAT data by these methods will help management reach better conclusions about departmental performance, and, in turn, better manage the Materiel Services Department. (5) APPROACH In order to determine the most effective and useful manner in which to organize the TAT data, the following approach was used: 1. Operational Definitions. Before any analysis can take place, it is necessary to have art understanding of departmental terminology. Through discussions management, and observation of the order-filling process, the “language” of the MSC became apparent. 2. Flowcharting. In order to effectively analyze the organization, a thorough understanding of departmental processes is necessary. This was achieved by mapping the actual flow of people, information, and materiel goods through the process of receiving, picking, and delivering a materiel order. 3. Client Input. After soliciting input on how data collected in the MSC will ultimately be used, current performance measures were evaluated for relevance. By determining precisely what uses the client envisions for the data (and, conversely, what uses are not anticipated), the portions of the data that are important were discriminated from those which are not. 4. Historical Data Analysis. The historical raw data collected over the past several months was examined, to develop effective measures of performance for the department. Relevant issues included speed, accuracy and reliability of the delivered orders. Appropriate statistics were found, which focus on patterns of performance rather than individual events. Unusual or unsatisfactory times were examined to determine their root cause and whether or not such “outliers” call for action on the part of management. (6) 5. Graphic Analysis. After effective measures of performance were determined, they were applied toward producing graphic representations of the data. Traditional SPC methods of graphical monitoring were utilized, including control charts, scatter plots, and Pareto diagrams. (7) METHODOLOGY One of the most common methodologies for quality improvement is statistical process control, or SPC. The goal of SPC is to achieve process stability and improve capability, through the reduction of variability (Montgomery, 1991, p. 101). It uses seven major tools in reaching this goal, as listed below: 1. Control Chart 5. Pareto diagram 2. Process flow diagram 6. Scatter plot 3. Cause-and-effect diagram 7. Histogram 4. Checksheet This project sought to find the most effective manner to graphically represent TAT data, using one or more of the above methods. It was found that control charts were most useful in interpreting MSC data. However, Pareto diagrams and scatter plots were also used, albeit to a lesser extent. The intent here is to demonstrate the use of these techniques in analyzing the effectiveness of the department. Admitedly, the following discussion is nothing more than an introduction to the SPC methodology. The greatest benefit to be gained from such a demonstration will be a new perspective on the Materiel Services Department. Simply adopting an “SPC way of thinking” will help promote the type of environment in which actual SPC techniques can be successfully implemented. Although traditionally applied to manufacturing processes, SPC can also be utilized in nonmanufacturing situations, such as the case in the MSC. This type of SPC application requires more flexibility and creativity than that (8) ( normally required for a typical manufacturing setting. In the literature concerning SPC, two main reasons have been observed to account for this difference: 1. Most nonmanufacturing operations do not have a natural measurement system that allows the analyst to easily define quality. 2. The system that is to be improved is usually fairly obvious in a manufacturing setting, while the observability of the process in a nonmanufacturing setting may • be fairly low. (Montgomery, 1991, p. 137) Although the Materiel Service Department may appear to have a “natural measurement system”, in the form of TAT data, there are numerous other measures that could be used to judge departmental performance. In addition, the process by which materiel orders are filled is complex, influenced many • by factors which may not be readily apparent. This is in contrast to a straightforward assembly line process, common in most manufacturing environments. The fact that the MSC is a nonmanufacturing organization has no special implications for the construction of charts other than control charts. The main area of concern when creating Pareto diagrams, scatter plots and the like, is the use of relevant statistics for the desired analysis. These types of graphical tools are generally much more intuitive than control charts, and a background of statistical training is not always a prerequisite for their use. Control charts, on the other hand, are grounded more in statistical theory, and those not familiar with such concepts may have difficulty interpreting - Er (• (9) them. As a result, these potential educational demands should be considered whenever an organization plans to implement SPC techniques. In applying SPC to develop control charts, a primary issue is deciding which types of charts to construct: (1) Variable charts, (2) Attribute charts, or (3) Charts for individuals. Variable charts track such measures as the mean, range, and variance of a process, well-suited to traditional manufacturing procedures. Attribute charts monitor fraction nonconforming, number nonconforming, and the like. This may be appropriate when quality is measured in terms of “good/bad”, satisfactory/unsatisfactory, and so on, rather than any hard numerical specifications. Control charts for individuals are sometimes used when it is difficult to obtain more than one process measurement at a time, or when the end-product does not follow from a constant, standardized procedure. Variable control charts have an underlying assumption that influence the type of data they can be used to track. The use of such charts implies that the ideal state of the system is a state of zero variability. It implies a quality characteristic with a desired target
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