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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-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.
(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 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 (• -
(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 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
(10)
C
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
(11)
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.
C.
(2)
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: :
(13)
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.
(14)
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.
(15)
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.
(16)
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
(17)
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
(18)
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
(19)
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.
(20)
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
(21)
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
(22)
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
(23)
the Materiel Services Department will be effectively summarized and reduced
to meaningful charts and graphs that are easily understood.
(24)
REFERENCES
Montgomery, Douglas C., Introduction to Statistical Quality Control,
John Wiley & Sons, Inc., 1991 (second edition).
(25)
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
(26)
Ac?toi A.
C Control Charts forandS
= sample mean
S = sample standard deviation
S—
weighted grand standard deviation:
, (p,1 — r) sj
control limits:
upper control limit (UCL) = X +
center line = X
lower control limit (LCL) = — A3 S
definitions:—
=
weighted grand
5E
(Fr- Z/3z13c1)Ecr tF(NtnQtJ
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(27)
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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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Effect of Deliveries per Run on Avg.TAT/Routine Supp orders (combined)
80.0 - -
70.0 - -
60.0--I
50.0 - -
40.0 -
30.0 - -
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Weekly avg. number of deliveries per run
(35-)
Effect of Deliveries per Run onAvg. TAT/Routine Supp orders
• (employee A)
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C
(3E)
Effect of Deliveries per Run onAvg. TAT/Routine Supp orders
(employee B)
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Weekly avg. number of deliveries per run
:
(37)
Effect of Deliveries per Run onAvg. TAT/Routine Supp orders
(employee C)
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Weekly avg. number of deliveries per run
(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
10.812.3
4.89.76.77.58.39.09.59.07.88.3
9.78.9
13.38.2
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
8.67.7
23.411.1
7.213.0
7.01 .70.49.08.16.9
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 -
__
60.0 -
C
50.0 - -
40.0 - -
30.0 - -
20.0 - -
10.0 1
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— — — — 0) 0) 0) 0) 0. 0. 0.D D D
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100.0 -
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
TAT per line filled/Req delivery orders(outliers removed)
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Effect of Deliveries per Run on Avg.TAT/Req deliveries (combined)
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Weekly avg. number of deliveries per run
—‘
Effect of Deliveries per Run on Avg.TAT/Req deliveries (employee X)
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.
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Weekly avg. number of deliveries per run
Effect of Deliveries per Run on Avg.TAT/Req deliveries (employee Y)
20.00 - -
15.00 -
a)= U
a). 10.00
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a)a)
0.00— I I I I I I I
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Weekly avg. number of deliveries per run
q7
STOCK OUTS OCCURRING MORE THAN TWICE05/27 to 09/26
12
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