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

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TABLE OF CONTENTS

Executive Summary 3

Introduction 4

Approach 5

Methodology 8

Results 15

Recommendations 21

References 25

Appendices 26

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EXECUTIVE SUMMARY

This project demonstrates methods to better organize turn-aroundtime (TAT) data used to measure performance in the Materiel Service Center

(MSC). The data can be meaningfully analyzed using the ideas and methodsof Statistical Process Control (SPC), such as control charts and scatter plots. Byconstructing these charts and graphs, an improved methodology for TAT dataanalysis 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 incontrol for each of the four different delivery types (SUPP, STAT, REQ, PAR).This information can help management come to conclusions regarding thedelivery system.

By calculating appropriate control limits, unusual or unsatisfactorytimes can be easily seen on graphs. By following a progression of charts overtime, management will be better equipped to locate problem areas anddetermine 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 ofmeaning.

Seven major recommendations are being made:

1. The control charts introduced by this project should be made a part ofthe 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.

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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.

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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.

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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.

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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

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( 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 by many 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 (• -

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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 value, a value which an ideal process

would consistently achieve. It can be seen that total TAT for a material

delivery does not satisfy this criteria. One can not expect, or even desire, zero

variability in turn-around times, unless all deliveries consisted of the same

items, going to the same location. If variable charts are to be used, they must

track a more appropriate statistic. One such measure may be the “TAT per

line filled”, which displays much less variance than absolute TAT data.

During September 1991, TAT data for PAR orders exhibited a statistical

variance of over 1800. The same data, when transformed to TAT per line

filled, showed a variance of only 0.557, indicating it is much more suited for

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control chart analysis. More on the TAT per line filled statistic is discussed

later in this report.

Attribute charts that monitor fraction nonconforming imply that there

is a well-defined and acceptable method of classifying process output as

“good” versus “bad”. The MSC does this through the use of standards. For

example, it is expected that any PAR order should be filled within six hours.

Any order taking longer than six hours is classified as “nonconforming”.

Thus, it appears as though attribute charts could be utilized in this instance,

perhaps to track the percentage of orders that are delivered late. However, it

shoi.ild be kept in mind that the effectiveness of such a chart really depends of

the legitimacy of the process standards. If six hours is an unreasonable

expectation, then the resulting chart will have little meaning.

Regardless of what types of charts are developed, an important issue is

the organization of the TAT data. Most of the data is currently organized by

employee, or by the destination department. For SPC control charts to be

useful, data must be in chronological order, or else the meaning of

“improvement over time” is lost. Another issue is the particular time frame

of a proposed control chart, such as whether charts are created to examine

day-to-day, week-to-week, or month-to-month performance. This is closely

related to the issue of how much of the data will be considered in the analysis.

For example, utilizing each piece of data collected each day may result in a

more thorough study, but will most likely be outweighed by the additional

administrative burden it would cause.

The majority of charts developed for this analysis are variable control

charts. More specifically, they are (“x—bar”) control charts. Appendix A

provides the statistical equations used in creating this type of chart. Although

the formulas seem elaborate, they involve well-known measures such as

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mean, standard deviation, and sample size, which can be easily calculated

using many scientific calculators or computer programs. Although it may be

helpful to understand how these formulas are derived, it is much more

important to be able to make meaningful use of the charts that result from

them.

A number of SPC techniques have been used to analyze MSC data. The

resulting charts, graphs, tables, etc. have been collected in the appendices of

this report. In the “Results” section of this report are brief descriptions of

each chart, and explanations of their usefulness. The “Recommendations”

section discusses the implications of these results for the Materiel Services

Department, as well as listing specific recommendations that will help to

improve the overall effectiveness of the department. The statistics and

relationships that were examined in the TAT analysis are listed below:

PAR Orders:

1. TAT per each line filled (control chart): As me n t ion e d

above, absolute TAT is not an appropriate statistic for use

with variable control charts. The measure of “TAT per line

filled” eliminates the variations caused by the number of

lines delivered per order, and can thus be correctly applied to

SPC methods.

2. Percent of total TAT spent waiting for pick sheet (bar chart):

As the PAR employees wait for their pick sheet to be printed,

they are helping others in the MSC, or possibly are on break.

By looking at these percentages, unsatisfactory TAT data can

be better evaluated, and individual workers can be compared

on a more equal basis.

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STJPP Orders (STAT orders would follow similar analysis):

1. TAT per line filled (control chart): Again, variations in

times because of different numbers of lines per delivery are

eliminated, effectively standardizing the absolute TAT values

for each order.

2. Number of deliveries per run vs. TAT per line filled

(scatterplot): This analysis was an attempt to verify a

theory concerning total TAT versus the number of deliveries

per run. It was believed that, all else being equal, a larger

number of deliveries per run would lead to longer TAT

times for the affected deliveries. If true, this would give

management another way of standardizing the TAT data, for

easier comparison.

REQ Orders (deliveries and stagings):

1. TAT per line filled (control chart): This is the same statistic

as is used with the SUPP/STAT and PAR delivery types.

2. Number of deliveries per run vs. TAT per line filled

(scatterplot): The assumptions and analysis used for SUPP

orders holds true for REQ orders as well.

All Orders:

1. Frequency of Item Stockouts (bar graph): The most

frequently out of stock items were plotted to demonstrate that

several items were out of stock a disproportionate number of

times.

C: :

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2. Distance of Unit Areas from the MSC vs. TAT Performance (bar

graph): Unit Areas were plotted by the number of times they

appeared on the list of 20 worst TAT’s. It was expected that units

further away from the MSC would have longer TAT’s.

In the future, these and other measures of departmental performance

could be applied directly to new MSC data on a periodic basis, details of which

would depend on what type of chart is being used. In terms of control charts,

appropriate control limits for each chart would be calculated and plotted.

Unusual or unsatisfactory times can then be examined to determine their

root cause and whether or not such “outliers” call for action on the part of

management. If all points are within the control limits, then it is still

appropriate to ask questions about the process overall, and seek the root

causes of current levels of variation.

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RE SULTS

1. PAR control charts. Control charts were developed for the TAT

per line filled, as described earlier. Appendix B consists of a summary of the

data used to create the charts, as well as the charts themselves. These can help

management decide where to focus its attention when problems arise. If

there are a number of out-of-control points, employees may be at fault.

However, if all points are in-control, most improvement in performance will

come from a change in the system, not from increased efforts of employees.

In this case, it can be seen that the data points lie well within the control

limits. Therefore, if management is unsatisfied with the TAT values, it

should look for an opportunity to improve the system, rather than the

people.

2. Pick sheet waiting time (PAR orders). Appendix C shows the percent

age of total TAT time spent waiting for the pick sheet, as tracked over several

days. It is seen that this amount is relatively stable, fluctuating between 3%

and 10%. This type of graph would be useful when investigating unusual or

unsatisfactory TAT data. For çxample, if a PAR control chart indicated an

out-of-control point on a certain day, management could make note of the

percentage of time spent waiting on that particular day. An unusually high

percentage would inform management that the point is most likely an

outlier, and can be effectively ignored in interpreting departmental

performance.

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3. SUPP control charts. The development of control charts for SUPP

orders illustrates the importance of understanding the role of outliers in

control chart analysis. The first chart in Appendix D was created with the

presence of an outlier — one delivery was seen to have taken 726 minutes to

complete, when the average of the other points was 13.8 minutes. This was

assumed to be due to an out-of-the-ordinary event, and was declared an

outlier. Even though the resulting control limits do not indicate any out-of-

control points, the limits are too wide to be effective. Also included is the

same chart after the point has been removed (An assignable cause must be

present to justifiably remove any outlying data point). The new control

limits result in two out-of-control points. These points would then be

investigated to determine whether or not they have assignable causes, or if

they represent an actual shift in performance on the part of the employees.

4. Number of deliveries per run (SUPP orders). Appendix E repre

sents the results of the scatterplot previously described in the “Approach”

section. Initially, a plot was made which included data from several

employees. The resulting graph is not conclusive regarding any upward

trend in the data. Such a trend would be expected if the number of deliveries

per run had a significant effect on TAT per line filled. However, when

individual employee data was extracted and plotted on its own, clearer

patterns emerged. These patterns indicate that, for a given employee, the

theorized relationship may be true. However, there are sufficient differences

in performance between employees to mask out any such relationship on a

department-wide scale.

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5. Staging REQ control charts. The control chart developed for the

staging of REQ orders is unique, in that it illustrates an extreme shift in the

process mean. The chart in Appendix F clearly shows a process that is “in

control” for a period of over two months. However, at that point the average

TAT per line filled jumps dramatically. These extreme data points were

examined, and judged not to have readily apparent assignable causes.

Therefore, the resulting control limits show a process for which every point is

technically out-of-control. In this case, it is likely that some kind of system

change occurred during the first week of September 1991, to account for the

shift in performance. We suggest that this process shift, which created the

poorer performance, be investigated. In actual application, a given set of

control limits is appropriate only so long as the process mean remains stable.

When a major system change causes a shift in this mean, the control limits

should be recomputed, and a “new” control chart begins.

6. Delivering REQ control charts. The effect of outliers and their

implications for control chart analysis are seen again in the charts developed

for REQ deliveries. The first chart in Appendix C shows a very erratic

pattern, and would result in several out-of-control points if the control limits

- were computed at this stage. Upon closer inspection, it was found that 5

extreme points (from the 15 weeks under study) were causing this instability

in the process mean. It was assumed that these data points would be found to

have assignable causes, and as a result, they would be excluded from the

initial chart. After these outliers have been removed, the chart pattern is

much more stable. Although the tail end of the chart suggests that the

process mean may be drifting slightly upward, the control limits computed

here indicate a process that is currently in-control. The proper use of control

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charts, then, requires two steps. One, creating an initial chart with an

appropriate subset of data points (i.e., excluding outliers), in order to create

meaningful control limits. These control limits are then used to track future

data points, and out-of-control points are examined as they arise.

7. Number of deliveries per run (REQ deliveries). The results of this

analysis are similar to those found for SUPP orders. When several

employees’ data are combined and plotted, a slight upward trend can be

observed [See Appendix H]. However, when employees are plotted

individually, the trend becomes much more noticeable. This indicates what

may be a larger problem for the Materiel Services Department. It suggests that

TAT times are dependent on the person delivering the items. Ideally, this

should have no influence. The idea behind the assembly line in

manufacturing is worker interchangability. The productivity of the line is

unaffected by the particular people working on it. Although the method may

not be applicable to the MSC, the concept is. This is an area worthy of future

study. The variability in TAT that comes as a result of worker variation may

be reduced through stricter operating procedures, additional training, and so

on.

8. Inventory stockouts. The stockouts that occurred in the MSC over

the four months between 5/27/91 to 9/26/91 were analyzed through the use

of Pareto diagrams. It was found that 745 stock outs occurred during those

four months, comprised of 341 different items. The significant discovery in

this analysis was found when the most frequently out-of-stock items were

plotted. The 13 items that were out of stock 4 or more times during the four

month period accounted for 78 stockouts, or 11% of the total (745). Put

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another way, among those items that are experiencing stockouts, 3% are

causing 11% of the stockouts. If a larger inventory is kept for these 13 items,

many stockouts could be avoided. The 13 parts and their contribution to the

total number of stockouts are shown in Table 1, below [See Appendix I for

more detailed diagrams].

Table 1. Stock items most frequently out-of-stock.

stock # item description # of stockouts

1624 11Leadwire, ECG. Cr. Blk. Wht. Set

1684 11Tray, Minor Dressing Prep Custom

4432 8Cath, Thermo. w/Hep. RA/RA 8FR

1807 6Crutches, Aluminum Adult Tall

2722 6Tubes, Centrifuge (15 ml)

I443 5Leadwire, ECG 40” (Cr. Bk. Wh. Rd.)

1647 5Bandage, Elastic Rubber 6” Sterile

1729 5Tray, Towel & Gauze Sterile

2814 5Tray, CVC Double Lumen 5FR 8CM

1625 4Leadwire, ECG Red w/Pinch Connect

1726 4Gauze, Fine Mesh 6”X9” FMG Sterile

1924 4Tube, RAE Oral Cuff 7.0MM Sterile

2685 4Tray,_Epidural

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9. Delivery distances. Analysis was performed in an attempt to find

a relationship between the distance from the MSC of the destination units,

and the turn-around times for deliveries to these units. Although no direct

relation was found, it is seen that the units which are located far from the

MSC are frequently included in the list of 20 worst TAT’s [See Appendix JI.

These graphs provide a quick and simple presentation of the unit areas which

are receiving the worst service from the MSC. Knowing which units have

longer TAT’s may point out problems that had not previously been seen.

Even though these problems may turn out to have little relation with the

distance from the MSC, they may. expose some other common factor between

units that is influencing the turn-around times.

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RECOMMENDATIONS

After examining the above results, seven major recommendations can

be made to the Materiel Services Department. These suggestions will

improve the way in which turn-around time data is gathered, reported, and

interpreted. By following these recommendations, the overall effectiveness

of the department can be improved.

1. The control charts that are presented in the Appendices should be

made a part of the standard routine in the Materiel Services Department. SPC

software packages are available for many types of computer systems, some of

which may be found within the University Hospitals environment. Such

programs are designed to produce SPC charts with little effort, although those

employees using the charts should still have a good understanding of

statistical concepts, to aid in interpretation. In the end, SPC-inspired charts

and graphs will be much more suitable and meaningful than most of the

charts currently used in the department. By following the progression of

these charts over time, management will be better equipped to locate

problem-areas within the department, and determine possible courses of

action which would improve departmental performance.

2. It is recommended that the MSC keep a larger inventory for the parts

listed in Table 1, in the “Results” section. A larger inventory of these 13 parts

should prevent a large amount of stockouts that are currently occurring. It is

assumed that the incremental cost of this extra inventory space will be less

than the “cost” associated with having an item be out of stock. Whether or

not this recommendation is implemented could depend on the size and cost

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of individual items on the list, and management should consider each of the

13 items separately. The time to recover different items that are out of stock

must also be included when making the decision of whether or not toincrease

inventory for an item.

3. It is also recommended that the department begin to produce bar charts

like those in Appendix I, to better track improvements and changes in

stockout items. If inventory is increased for the items listed above, then these

items should not be frequently out of stock afterwards. By producing such

charts every few months, the MSC can alter its inventory according to

changing demand and significantly reduce the total number of stockouts in

the future.

4. One recommendation that should be relatively simple to implement is

a hospital-wide, synchronized time system for use in recording acceptance

times from the MSC. When a delivery has been completed, the recipient at

V the unit area writes the time of delivery on a time sheet. If each recipient uses

a different watch or clock, the reliability of these times could fall off

significantly. If everyone were to use the time kept by the hospital computer

system, for example, this time variance can be avoided. This standardization

will result in more accurate TAT data, and thus, a more accurate analysis of

departmental effectiveness.

5. A more extensive recommendation is that the databases used by the

department be reconstructed for more efficient data storage. The quantity and

appropriateness of the data currently used to evaluate the MSC’s performance

V presents a large area for potential improvement. The data sheets appear to

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include far too many inconsistencies and contradictions, and the format of

the output tends to be confusing and, in some cases, misleading. It is strongly

recommended that the data collection and data presentation routines

currently used in the MSC be changed, possibly introducing some of the

statistics mentioned earlier, as well as the sample data outputs shown in the

appendices.

6. Further study into the relationship between the distance of a unit from

the MSC and the service provided to that unit is recommended. One possible

approach would be to standardize the TAT by accounting for the number of

lines and deliveries, as discussed in the “Approach” section, then plot the

worst 20 standardized TAT’s with their respective unit area. If a relationship

is then seen, a multiplication factor could be found that would further

standardize the TAT data, to allow for direct comparison of measures between

units. Implementation of such a factor is recommended only if a strong

relationship is found, i.e. if units twice as far from the MSC have TAT’s that

are twice as long, etc. Such a relationship is not expected for PAR or REQ

data, but could very well exist for STAT and SUPP deliveries, as a larger

percent of these TAT’s is actual travel time through the hospital system.

7. The SPC charts used in this analysis could be enlarged and posted on

walls throughout the department, so trends in performance are apparent to

workers as well as management. Everyone would be alerted to a decline in

performance, either on a departmental or individual basis, and be able to alter

their work accordingly. Everyone would also be aware when performance is

high, and would know what behaviors to continue in order to remain

productive. By utilizing SPC methods, the vast amount of data collected by

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the Materiel Services Department will be effectively summarized and reduced

to meaningful charts and graphs that are easily understood.

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REFERENCES

Montgomery, Douglas C., Introduction to Statistical Quality Control,

John Wiley & Sons, Inc., 1991 (second edition).

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C - ( APPENDICES

Appendix A. Equations for x and S control charts 27

Appendix B. PAR control charts and data summary 28

Appendix C. Pick sheet waiting time (PAR) 30

Appendix D. SUPP control charts and data summary 32

Appendix E. Number of deliveries per run (SUPP) 35

Appendix F. Staging REQ control charts and data summary 39

Appendix G. Delivering REQ control charts and data summary 41

Appendix H. Number of deliveries per run (REQ deliveries) 45

Appendix I. Inventory stockouts 48

Appendix J. Delivery distances 49

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Rctine Supp TAT/Summary reportveKs beginning July - September 1991

Siweek of number a avg. #deliv. avg. dept. TAT avg. # lines avg. TAT standard

— delivery deliverie aer run per delivery per delivery per line filled deviatior range

1 5-Jul-91 32 0.89 39.72 1.72 23.11 126.98 726.00

2 12-Jul-91 23 0.00 8.22 1.70 4.85 4.48 19.80

3 19-Jul-91 21 0.00 11.29 1.71 6.58 4.43 15.17

4 26-Jul-91 20 0.00 11.55 1.95 5.92 5.26 20.00

5 2-Aug-91 19 1.49 26.42 1.89 13.94 11.93 52.50

6 9-Aug-91 19 1.48 22.68 1.79 12.68 9.31 38.50

7 16-Aug-91 16 1.54 26.69 1.69 15.81 10.18 43.50

8 23-Aug-91 25 1.44 23.48 1.56 15.05 11.90 48.00

9 30-Aug-91 23 1.57 35.78 1.61 22.24 17.85 69.50

10 6-Sep-91 27 1.45 30.96 2.33 13.27 14.35 52.50

11 13-Sep-91 27 1.62 29.89 1.74 17.17 11.65 43.50

12 20-Sep-91 23 1.56 32.09 1.57 20.50 19.56 48.00

2averages 22.9 1.09 24.90 1.77 14.05 20.66 98.08

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(35-)

Effect of Deliveries per Run onAvg. TAT/Routine Supp orders

• (employee A)

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C

(3E)

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(employee B)

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:

(37)

Effect of Deliveries per Run onAvg. TAT/Routine Supp orders

(employee C)

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(33)

St-’ing Req TAT/Summary reportbeginning July - September 1991 —

vu x4.

week of total # regs avg. # reqs avg. TAT avg. # lines avg. TAT per standard— delivery staged in dept per employeE per req stage per req stage line staged deviatior range

1 5-Jul-91 118 9.08 86.15 88.46 0.97 3.18 10.352 12-Jul-91 185 23.13 163.50 189.00 0.87 8.55 24.543 19-Jul-91 63 7.88 75.25 57.63 1.31 5.83 17.504 26-Jul-91 151 15.10 121.70 125.50 0.97 2.34 6.945 2-Aug-91 165 15.00 145.64 124.36 1.17 2.19 6.746 9-Aug-91 164 16.40 115.20 128.10 0.90 9.82 25.557 16-Aug-91 159 9.94 133.25 88.50 1.51 3.69 13.678 23-Aug-91 159 12.23 178.69 104.08 1.72 19.68 63.119 30-Aug-91 158 13.17 120.75 117.33 1.03 1.94 5.61

10 6-Sep-91 215 19.55 135.36 11.72 11.55 6.05 18.1311 13-Sep-91 195 17.73 82.91 8.34 9.94 5.29 17.6712 20-Sep-91 185 20.56 115.56 8.37 13.81 6.05 21.44

:f2averages 159.8 14.98 122.83 87.62 3.81 6.22 19.27

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Dcivering Req TAT/Summary reportwks beginning July - September 1991

week ofdelivery

total # reqdelivered

avg. # reqs avg. TAT per avg. # lines avg.per employee req delivered per req deliv. line

TAT perdeliv.

standarddeviation

123456789

101112

5-Jul-9112-Jul-911 9-Jul-9126-Jul-912-Aug-919-Aug-91

1 6-Aug-9123-Aug-9130-Aug-91

6-Sep-9113-Sep-9120-Sep-91

range545334

322453

18.640.3

8.836.344.732.343.765.069.542.030.648.0

105.2108.863.879.784.076.0

124.0152.5176.0134.898.0

116.0

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4.89.76.77.58.39.09.59.07.88.3

9.78.9

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12.610.114.916.918.515.012.613.9

averages

3.53.48.66.14.15.43.61 .20.34.33.13.6

3.9

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8.73.6 40.0 109.9 8.6 12.7

TAT per line filled/Req delivery orders

100.0

90.0 -

80.0 -

70.0 -

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60.0 -

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—‘

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q7

STOCK OUTS OCCURRING MORE THAN TWICE05/27 to 09/26

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