Cellular Manuf
Content
Article 1
CHANGES IN
PERFORMANCE
MEASURES ON THE
FACTORY FLOOR
ROBERT F. MARSH
School of Business Administration, University of Wisconsin-Milwaukee, Milwaukee, WI 53201
JACK R. MEREDITH
Babcock Grad. School of Management, Wake Forest University, Winston-Salem, NC 27109
sures should be found in most cases of cell implementation. It
might also be expected that data on these measures may have
been kept prior to cells.
The objective of this study is to compare how management
measures performance both before and after the move to cells.
As the saying goes, “what gets measured gets done.” On the
other hand, in The Goal [1], we learn that activation and utilization are not synonymous. Therefore measuring something
doesn’t necessarily mean that it is important. This study also investigates what performance measures management is evaluated on both before and after cells. Finally, we examine the
relationships between measures and methods. For example,
does the use of JIT correlate with measures of lead time or WIP
levels?
As a management method, cellular manufacturing (CM) continues to gain acceptance. The reasons are obvious; CM reduces
work-in-process (WIP) inventory levels and correspondingly
reduces lead times. Not coincidentally, many companies have
recently shifted focus to compete on time-based parameters,
like lead time. Also, the Just-in-Time (JIT) management philosophy of reducing waste (and thus WIP) is naturally complemented by CM practices on the floor.
Essentially, CM involves finding repetitive procedures in an
otherwise random set and then performing that work in a more
efficient manner. In terms more congenial to manufacturing, it
is moving some production from a job shop to a line process design, or moving down the diagonal in Hayes and Wheelwright’s
product/process matrix [2] to gain efficiency. Customization is
sacrificed, but only for a portion of the work load. And for many
companies, the commitment to customization was never needed
and was just another form of waste. Thus, CM often leads to
lower costs and higher productivity than previously realized in
job shops.
METHODOLOGY
To answer these questions, a survey was designed and administered to managers from 42 companies, all but three from
the Midwest. All companies were involved in metal machining
and held Standard Industrial Classifications (SIC) beginning
with 34 or 35. All had operated cells for at least one year. Table
These two factors are the impetus for cells: lower costs and
shorter lead times. If a company’s management measures performance according to its objectives, cost and lead-time mea1
ANNUAL EDITIONS
1 shows the year CM started and Table 2 classifies the size of
the 42 companies. Most of these firms had assembly operations
in the same plant as fabrication, but most of the part fabrication
took place in cells (as indicated in Table 3).
PERFORMANCE MEASURES
VERSUS PLANT DEMOGRAPHICS
As companies grew in their experience with cells, performance measures generally became more refined. It often occurred that many measures were kept to validate the conversion
to cells but some fell out of date as time went on. Three of the
most experienced cell users only tracked one performance measure. The size of the plant (small, medium, large) didn’t correlate
with the number of performance measures kept, although it did
relate to the progress toward converting to cells. Production at all
five of the small plants in this study was between 91% to 100%
in cells, while almost half of the large plants had less than 50%
of production in cells. More than half of the plants in the study
indicated that conversion to cells was ongoing. There was no significant correlation between plant size, CM experience, or percent of production in cells and the type of performance measure.
TABLE 1: Beginning of
Implementation
Year Cells Started
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
Pre-1985
Percentage
5
5
7
5
10
21
17
5
17
7
2
TABLE 2: Plant Size
Size of Plant (sales)
Percentage
Small (less than $50 million in
annual sales)
Medium (between $50 and $250
million)
Large (greater than $250 million)
When possible, surveys were conducted in conjunction with
a plant trip. This improved the reliability of the responses and
even led to some data modifications. It was occasionally found
that rater bias factored into questions concerning the application
of current management techniques and collection of data. For
example, most managers considered their firm to be an advocate
of JIT, yet the preponderance of evidence (inventory) suggested
otherwise. Further questioning usually resolved such issues.
Elsewhere, managers would indicate quality was maintained
using control charts. If the manager could not show evidence of
charts, the measure was not counted.
Measures varied greatly from plant to plant and some of that
variation was attributable to the specific product or process.
Quality, for example, was sometimes measured as amount of
scrap, percentage of good pieces, number of rework hours, customer satisfaction, etc. For simplicity, performance measures
were categorized into nine different types: productivity, quality,
inventory, lead time, preventive maintenance, schedule performance, utilization, cell completeness, and other costs. This classification sufficiently covered the data collected from the 42
companies without creating discrepancies in the assignment of
measures to categories. Managers from two firms mentioned a
safety-based measure but the authors did not consider this related to manufacturing performance. Although important, some
form of safety measure is required of all companies. An argument can be made that WIP levels (inventory) and lead time are
inversely related [4] and therefore one of these categories is redundant, but since the managerial objective differs, i.e., cost
versus time, the categories were not merged.
12
57
31
PERFORMANCE MEASURES
BEFORE AND AFTER CM
The first observation from the comparison of before and after
CM was that the number of performance measures increased
after the conversion to cells, from an average of 2.7 to 4.3. Table
4 summarizes how performance was measured in these 42
plants before and after the implementation of cells. The greatest
beneficiary of this increase was in the quality area, with inventory and lead time close behind. The tremendous improvement
in these latter two areas after the conversion to cells was definitely related to their measures. In many companies, the
tracking of lead time led to improvements in the marketing of
products. The shorter and more stable lead times meant increased sales and decreased delinquencies. A few companies
even dropped schedule performance measures in lieu of
tracking lead time.
The most common inventory measure was the number of
times inventory was turned each year. In many cases, JIT and
CM were undertaken simultaneously and the reduced WIP was
used to justify the conversion expense. Tracking WIP or turns
then became a gauge of how well the conversion went and a tool
for continuously improving the velocity of material throughout
the plant.
Similar to the schedule performance and lead-time relationship, a drop in the use of productivity-based measures coincided
2
Article 1. CHANGES IN PERFORMANCE MEASURES ON THE FACTORY FLOOR
TABLE 3: Production Completed in Cells
Portion of Production
in Cells
91%–100%
81%–90%
50%–80%
Under 50%
TABLE 4: Measuring Operating
Performance
Percentage of Plants
Type of Measure
50
14
14
21
Productivity measures
Quality measures
Inventory measures
Lead-time measures
Preventive maintenance
Schedule performance
Utilization
Cell completeness
Other costs
with the increase in measuring turns. The managers indicated a
preference for the latter because it more accurately assessed
costs. Most of the productivity measures centered on direct
labor, a much smaller portion of total costs than materials (5%
to 15% versus 40% to 60%). These same managers also felt
more in control of inventory levels than work-force levels.
The increase in quality-related performance measures is not
as easily explainable. Many companies did adopt quality improvement programs in conjunction with CM, but many others
already had the programs in place. Anecdotal evidence from
managers’ comments indicates that tools to collect data on
quality have become more affordable and understandable in recent years. The underlying issue here is the fundamental change
in how management treated performance data. Fully 79% of
these 42 companies posted results for all employees to see. No
definitive data was collected to determine how this has changed
over time but many managers implied that posting was relatively new and growing. The message from virtually every new
manufacturing improvement program included more worker involvement in the process. Along with that, support functions
like accounting and management information systems are using
tools like activity-based costing (ABC) and the personal computer to improve the reliability and accessibility of performance
data. Openness in the workplace and the ability to compress
volumes of data probably explain the increased emphasis on
tracking quality performance.
The other changes in measures are understandable. Total
preventive maintenance (TPM) programs are frequently
adopted by CM and JIT users. The reduction in WIP means less
buffer inventory so machines must keep running. Maintenance
measures like time spent on PM and downtime show an understanding of TPM significance and a commitment to practice it.
Utilization was only tracked by one company that extensively
used computer numerically controlled (CNC) equipment, and
they mentioned it might be abandoned in the future. Cell completeness was a meaningless measure before cells. Some of the
“other costs” measures concerned setup time, depreciation, and
budget performance.
Before
Cells (%)
After Cells
(%)
93
33
24
31
5
81
2
0
7
79
93
74
64
24
71
2
5
21
partially responsible for the increase in measures kept. But do
all of these measures reflect what is actually important to the
company? Are managers evaluated on the same performance
criteria they measure? Indirectly, the question being asked is:
Have company objectives changed since the adoption of CM?
This assumes that objectives have been communicated
throughout the company and management understands it must
operationalize these objectives into performance measures for
feedback purposes.
Productivity and schedule performance were the most
common measures upon which managers said they were evaluated, with no other measure coming close (Table 5). Little has
changed here over time because these same two categories finished first and second before CM; in fact, 52% of managers indicated their evaluation criteria did not change. Interestingly,
many companies still consider the comparison of standard to actual labor times (usually referred to as “efficiency”) as the most
important indicator of improvement even though labor may
only be 5% of the cost of goods sold. As noted before when
tracking general manufacturing performance, there was a slight
drop-off in the significance of productivity and schedule performance with the conversion to cells, replaced by an increase in
quality, inventory, and lead-time measures. This increase can
logically be attributed to the 48% of companies that did change
their management evaluation measures and probably indicates
a strategic change in direction facilitated by shorter lead times
and better quality.
Comparing the “after implementation” columns of Tables 4
and 5 gives the impression that many of the performance measures being tracked are of little significance in either evaluating
the managers or steering the company toward its objectives. Although productivity and schedule performance measures held
steady, the other categories appear to be kept for show by many
companies. Quality and inventory measures drop significantly
when it comes to evaluating managers. So why are they even
tracked? A few managers said the measures started as part of the
conversion and justification of JIT or CM and were never abandoned. Also stated was, “The employees like to see how they
PERFORMANCE OF
MANAGEMENT MEASURES
As indicated, there was a new openness among managers to
share performance data with all employees, and this might be
3
ANNUAL EDITIONS
JIT success can be measured in a variety of ways including
WIP levels, inventory turns, lead times, and worker productivity. Of the 30 companies claiming to be JIT, 27 used an inventory measure, 23 used a lead-time measure, and 28 used a
productivity measure. TPM is claimed to be practiced by 27
firms, yet only ten of those 27 kept track of a measure like machine uptime or percentage of TPM completed. The success of
TPM could show up indirectly from increased productivity or
quality, but the implementation of TPM probably did not result
in adding a measure in one of these categories. Some managers
viewed TPM as a necessary burden for a JIT environment.
Therefore justification of this policy didn’t require additional
proof.
are doing in these areas so we keep it (data collection) up as a
motivational factor.”
Another possible explanation for the divergence on quality is
based upon Terry Hill’s [3] description of order qualifiers, a
competence that customers assume exists. In this case, all managers are assumed to be delivering quality. Managerial performance is thus differentiated on order winners like productivity
and schedule measures.
TABLE 5: Evaluation of
Operations Managers
Type of Measure
Productivity measures
Quality measures
Inventory measures
Lead-time measure
Preventive maintenance
Schedule performance
Utilization
Cell completeness
Other costs
Before
Cells (%)
After Cells
(%)
93
26
10
28
0
81
0
0
5
79
43
26
40
10
71
0
0
10
TABLE 6: Management Methods Currently
in Use
Table 6 summarizes what percentage of the 42 companies in
the sample are using some of the newer management methods.
Seven such methods gaining acceptance in industry were
studied: JIT, TPM, statistical quality control (SQC), setup reduction, concurrent engineering, work teams, and ABC. Selfdirected work teams, used by 62% of the surveyed companies,
were often responsible for the bulletin board’s content, including the display of performance measures. Some managers
gave the workers authority to collect data as long as they
seemed relevant and were obtained at a reasonable expense. In
two firms, work teams actually created a business plan containing a mission statement and planned objectives, so tracking
performance became a matter of pride rather than a management manifesto.
Management Method
Plants Using (%)
Just-in-Time
Total Preventive Maintenance
Statistical or Total Quality Control
Setup Reduction
Concurrent Engineering or
Design for Manufacture
Work Teams
Activity-Based Costing
71
64
93
43
48
62
29
Shorter setups could show up in productivity, inventory, or
lead-times measures. Of the 18 firms practicing single minute
exchange of die (SMED) or similar setup reduction methods, 17
also measured productivity, 15 measured inventory, and 17
measured lead times. Not all companies employed setup reduction to the same degree. Many would only look at setups to improve capacity at a bottleneck operation. Anecdotally, those
companies using setup reduction appeared to be shifting to
time-based objectives and it was very common for these same
firms to be using concurrent engineering (17 out of 18). As expected, all but one of the 20 users of concurrent engineering also
measured lead times.
Employee work teams of various levels of control were
found in 26 of the companies including all but two of the firms
with more than 90% of production in cells. Again, no direct
measure of success can be attributed to teams, but productivity
is the likely beneficiary with many secondary scenarios likely.
Of these 26 companies, 24 measured productivity and all included at least one measure of performance. ABC was another
method without logical correlation to one of the measure categories. This was also the method most difficult to verify. Nine
of the 12 ABC users were classified as large companies. Based
on the small number of firms using ABC, the significance of
any relationships is difficult to determine.
MANAGEMENT METHODS AND
PERFORMANCE MEASURES
There should be some correlation between management
methods and performance measurement. To gauge the impact
of these improvement programs on the manufacturing setting, a
performance measure may be operationalized. For example,
tracking rework or warranty costs would be a natural measure
of success in implementing SQC. And the correlation was perfect for the case of SQC and quality-related measures; all companies using SQC measured quality.
4
Article 1. CHANGES IN PERFORMANCE MEASURES ON THE FACTORY FLOOR
About the Authors—
CONCLUSIONS
Despite the moderate sample size of this research, enough
evidence has been gathered at this time to suggest that performance measures in metal machining firms are increasing in
number and variety. More emphasis has been placed on improving quality and lead-time performance since the adoption
of cells. And posting performance results on the factory floor is
now the rule rather than the exception, although this may be
done more for worker motivation reasons than for measuring
success toward corporate objectives. Managerial performance
is still predominantly evaluated in terms of how well the plant
achieves cost objectives and on-time deliveries. In other words,
many items are measured for “show,” but cost and schedule are
tracked for “dough.” This could, however, be changing in the
future as a few managers indicated more significance is now
being placed on quality and lead-time performance by their
managers.
ROBERT F. MARSH is an assistant professor of business at the University of Wisconsin—Milwaukee. He received his PhD in operations
management from the University of Cincinnati. Prior to that he held
positions at General Electric Aircraft Engines and Diebold. His recent
articles have been published in Journal of Operations Management,
International Journal of Technology Management, Omega, and this
journal. His research interests include cellular manufacturing, enterprise resource planning, and lead-time compression.
JACK R. MEREDITH is professor of management and Broyhill Distinguished Scholar and Chair in Operations at the Babcock Graduate
School of Management at Wake Forest University. He received his undergraduate degrees in engineering and mathematics from Oregon
State University and his PhD and MBA from University of California,
Berkeley. His current research interests are in the areas of research
methodology and the strategic planning, justification, and implementation of advanced manufacturing technologies. His recent articles in
these areas have been published in Decision Sciences, Management
Science, Journal of Operations Management, Sloan Management Review, and Strategic Management Journal. He has three textbooks that
are currently popular for college classes: The Management of Operations (John Wiley & Sons), Fundamentals of Management Science (R.
D. Irwin), and Project Management (John Wiley & Sons). He is the Editor-in-Chief of the Journal of Operations Management, an area editor
for Production and Operations Management, was the founding editor
of Operations Management Review, and was the production/operations management series editor for John Wiley & Sons, Inc.
REFERENCES
1. Goldratt, E. M., and J. Cox. The Goal. 2nd rev. ed. New York:
North River Press, 1992.
2. Hayes, R. H., and S. C. Wheelwright. “Link Manufacturing Process and Product Life Cycles.” Harvard Business Review 57, no. 1
(1979): 133–144.
3. Hill, T. Manufacturing Strategy: Text and Cases. 2nd ed. Burr
Ridge, IL: Richard D. Irwin, 1994.
4. Kekre, S. “Performance of a Manufacturing Cell with Increased
Product Mix.” IIE Transactions 19, no. 3 (1987): 320–339.
From Production and Inventory Management Journal, First Quarter 1998, pp. 36-40. © 1998 by APICS, the American Production and Inventory
Control Society. Reprinted by permission.
5
CHANGES IN
PERFORMANCE
MEASURES ON THE
FACTORY FLOOR
ROBERT F. MARSH
School of Business Administration, University of Wisconsin-Milwaukee, Milwaukee, WI 53201
JACK R. MEREDITH
Babcock Grad. School of Management, Wake Forest University, Winston-Salem, NC 27109
sures should be found in most cases of cell implementation. It
might also be expected that data on these measures may have
been kept prior to cells.
The objective of this study is to compare how management
measures performance both before and after the move to cells.
As the saying goes, “what gets measured gets done.” On the
other hand, in The Goal [1], we learn that activation and utilization are not synonymous. Therefore measuring something
doesn’t necessarily mean that it is important. This study also investigates what performance measures management is evaluated on both before and after cells. Finally, we examine the
relationships between measures and methods. For example,
does the use of JIT correlate with measures of lead time or WIP
levels?
As a management method, cellular manufacturing (CM) continues to gain acceptance. The reasons are obvious; CM reduces
work-in-process (WIP) inventory levels and correspondingly
reduces lead times. Not coincidentally, many companies have
recently shifted focus to compete on time-based parameters,
like lead time. Also, the Just-in-Time (JIT) management philosophy of reducing waste (and thus WIP) is naturally complemented by CM practices on the floor.
Essentially, CM involves finding repetitive procedures in an
otherwise random set and then performing that work in a more
efficient manner. In terms more congenial to manufacturing, it
is moving some production from a job shop to a line process design, or moving down the diagonal in Hayes and Wheelwright’s
product/process matrix [2] to gain efficiency. Customization is
sacrificed, but only for a portion of the work load. And for many
companies, the commitment to customization was never needed
and was just another form of waste. Thus, CM often leads to
lower costs and higher productivity than previously realized in
job shops.
METHODOLOGY
To answer these questions, a survey was designed and administered to managers from 42 companies, all but three from
the Midwest. All companies were involved in metal machining
and held Standard Industrial Classifications (SIC) beginning
with 34 or 35. All had operated cells for at least one year. Table
These two factors are the impetus for cells: lower costs and
shorter lead times. If a company’s management measures performance according to its objectives, cost and lead-time mea1
ANNUAL EDITIONS
1 shows the year CM started and Table 2 classifies the size of
the 42 companies. Most of these firms had assembly operations
in the same plant as fabrication, but most of the part fabrication
took place in cells (as indicated in Table 3).
PERFORMANCE MEASURES
VERSUS PLANT DEMOGRAPHICS
As companies grew in their experience with cells, performance measures generally became more refined. It often occurred that many measures were kept to validate the conversion
to cells but some fell out of date as time went on. Three of the
most experienced cell users only tracked one performance measure. The size of the plant (small, medium, large) didn’t correlate
with the number of performance measures kept, although it did
relate to the progress toward converting to cells. Production at all
five of the small plants in this study was between 91% to 100%
in cells, while almost half of the large plants had less than 50%
of production in cells. More than half of the plants in the study
indicated that conversion to cells was ongoing. There was no significant correlation between plant size, CM experience, or percent of production in cells and the type of performance measure.
TABLE 1: Beginning of
Implementation
Year Cells Started
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
Pre-1985
Percentage
5
5
7
5
10
21
17
5
17
7
2
TABLE 2: Plant Size
Size of Plant (sales)
Percentage
Small (less than $50 million in
annual sales)
Medium (between $50 and $250
million)
Large (greater than $250 million)
When possible, surveys were conducted in conjunction with
a plant trip. This improved the reliability of the responses and
even led to some data modifications. It was occasionally found
that rater bias factored into questions concerning the application
of current management techniques and collection of data. For
example, most managers considered their firm to be an advocate
of JIT, yet the preponderance of evidence (inventory) suggested
otherwise. Further questioning usually resolved such issues.
Elsewhere, managers would indicate quality was maintained
using control charts. If the manager could not show evidence of
charts, the measure was not counted.
Measures varied greatly from plant to plant and some of that
variation was attributable to the specific product or process.
Quality, for example, was sometimes measured as amount of
scrap, percentage of good pieces, number of rework hours, customer satisfaction, etc. For simplicity, performance measures
were categorized into nine different types: productivity, quality,
inventory, lead time, preventive maintenance, schedule performance, utilization, cell completeness, and other costs. This classification sufficiently covered the data collected from the 42
companies without creating discrepancies in the assignment of
measures to categories. Managers from two firms mentioned a
safety-based measure but the authors did not consider this related to manufacturing performance. Although important, some
form of safety measure is required of all companies. An argument can be made that WIP levels (inventory) and lead time are
inversely related [4] and therefore one of these categories is redundant, but since the managerial objective differs, i.e., cost
versus time, the categories were not merged.
12
57
31
PERFORMANCE MEASURES
BEFORE AND AFTER CM
The first observation from the comparison of before and after
CM was that the number of performance measures increased
after the conversion to cells, from an average of 2.7 to 4.3. Table
4 summarizes how performance was measured in these 42
plants before and after the implementation of cells. The greatest
beneficiary of this increase was in the quality area, with inventory and lead time close behind. The tremendous improvement
in these latter two areas after the conversion to cells was definitely related to their measures. In many companies, the
tracking of lead time led to improvements in the marketing of
products. The shorter and more stable lead times meant increased sales and decreased delinquencies. A few companies
even dropped schedule performance measures in lieu of
tracking lead time.
The most common inventory measure was the number of
times inventory was turned each year. In many cases, JIT and
CM were undertaken simultaneously and the reduced WIP was
used to justify the conversion expense. Tracking WIP or turns
then became a gauge of how well the conversion went and a tool
for continuously improving the velocity of material throughout
the plant.
Similar to the schedule performance and lead-time relationship, a drop in the use of productivity-based measures coincided
2
Article 1. CHANGES IN PERFORMANCE MEASURES ON THE FACTORY FLOOR
TABLE 3: Production Completed in Cells
Portion of Production
in Cells
91%–100%
81%–90%
50%–80%
Under 50%
TABLE 4: Measuring Operating
Performance
Percentage of Plants
Type of Measure
50
14
14
21
Productivity measures
Quality measures
Inventory measures
Lead-time measures
Preventive maintenance
Schedule performance
Utilization
Cell completeness
Other costs
with the increase in measuring turns. The managers indicated a
preference for the latter because it more accurately assessed
costs. Most of the productivity measures centered on direct
labor, a much smaller portion of total costs than materials (5%
to 15% versus 40% to 60%). These same managers also felt
more in control of inventory levels than work-force levels.
The increase in quality-related performance measures is not
as easily explainable. Many companies did adopt quality improvement programs in conjunction with CM, but many others
already had the programs in place. Anecdotal evidence from
managers’ comments indicates that tools to collect data on
quality have become more affordable and understandable in recent years. The underlying issue here is the fundamental change
in how management treated performance data. Fully 79% of
these 42 companies posted results for all employees to see. No
definitive data was collected to determine how this has changed
over time but many managers implied that posting was relatively new and growing. The message from virtually every new
manufacturing improvement program included more worker involvement in the process. Along with that, support functions
like accounting and management information systems are using
tools like activity-based costing (ABC) and the personal computer to improve the reliability and accessibility of performance
data. Openness in the workplace and the ability to compress
volumes of data probably explain the increased emphasis on
tracking quality performance.
The other changes in measures are understandable. Total
preventive maintenance (TPM) programs are frequently
adopted by CM and JIT users. The reduction in WIP means less
buffer inventory so machines must keep running. Maintenance
measures like time spent on PM and downtime show an understanding of TPM significance and a commitment to practice it.
Utilization was only tracked by one company that extensively
used computer numerically controlled (CNC) equipment, and
they mentioned it might be abandoned in the future. Cell completeness was a meaningless measure before cells. Some of the
“other costs” measures concerned setup time, depreciation, and
budget performance.
Before
Cells (%)
After Cells
(%)
93
33
24
31
5
81
2
0
7
79
93
74
64
24
71
2
5
21
partially responsible for the increase in measures kept. But do
all of these measures reflect what is actually important to the
company? Are managers evaluated on the same performance
criteria they measure? Indirectly, the question being asked is:
Have company objectives changed since the adoption of CM?
This assumes that objectives have been communicated
throughout the company and management understands it must
operationalize these objectives into performance measures for
feedback purposes.
Productivity and schedule performance were the most
common measures upon which managers said they were evaluated, with no other measure coming close (Table 5). Little has
changed here over time because these same two categories finished first and second before CM; in fact, 52% of managers indicated their evaluation criteria did not change. Interestingly,
many companies still consider the comparison of standard to actual labor times (usually referred to as “efficiency”) as the most
important indicator of improvement even though labor may
only be 5% of the cost of goods sold. As noted before when
tracking general manufacturing performance, there was a slight
drop-off in the significance of productivity and schedule performance with the conversion to cells, replaced by an increase in
quality, inventory, and lead-time measures. This increase can
logically be attributed to the 48% of companies that did change
their management evaluation measures and probably indicates
a strategic change in direction facilitated by shorter lead times
and better quality.
Comparing the “after implementation” columns of Tables 4
and 5 gives the impression that many of the performance measures being tracked are of little significance in either evaluating
the managers or steering the company toward its objectives. Although productivity and schedule performance measures held
steady, the other categories appear to be kept for show by many
companies. Quality and inventory measures drop significantly
when it comes to evaluating managers. So why are they even
tracked? A few managers said the measures started as part of the
conversion and justification of JIT or CM and were never abandoned. Also stated was, “The employees like to see how they
PERFORMANCE OF
MANAGEMENT MEASURES
As indicated, there was a new openness among managers to
share performance data with all employees, and this might be
3
ANNUAL EDITIONS
JIT success can be measured in a variety of ways including
WIP levels, inventory turns, lead times, and worker productivity. Of the 30 companies claiming to be JIT, 27 used an inventory measure, 23 used a lead-time measure, and 28 used a
productivity measure. TPM is claimed to be practiced by 27
firms, yet only ten of those 27 kept track of a measure like machine uptime or percentage of TPM completed. The success of
TPM could show up indirectly from increased productivity or
quality, but the implementation of TPM probably did not result
in adding a measure in one of these categories. Some managers
viewed TPM as a necessary burden for a JIT environment.
Therefore justification of this policy didn’t require additional
proof.
are doing in these areas so we keep it (data collection) up as a
motivational factor.”
Another possible explanation for the divergence on quality is
based upon Terry Hill’s [3] description of order qualifiers, a
competence that customers assume exists. In this case, all managers are assumed to be delivering quality. Managerial performance is thus differentiated on order winners like productivity
and schedule measures.
TABLE 5: Evaluation of
Operations Managers
Type of Measure
Productivity measures
Quality measures
Inventory measures
Lead-time measure
Preventive maintenance
Schedule performance
Utilization
Cell completeness
Other costs
Before
Cells (%)
After Cells
(%)
93
26
10
28
0
81
0
0
5
79
43
26
40
10
71
0
0
10
TABLE 6: Management Methods Currently
in Use
Table 6 summarizes what percentage of the 42 companies in
the sample are using some of the newer management methods.
Seven such methods gaining acceptance in industry were
studied: JIT, TPM, statistical quality control (SQC), setup reduction, concurrent engineering, work teams, and ABC. Selfdirected work teams, used by 62% of the surveyed companies,
were often responsible for the bulletin board’s content, including the display of performance measures. Some managers
gave the workers authority to collect data as long as they
seemed relevant and were obtained at a reasonable expense. In
two firms, work teams actually created a business plan containing a mission statement and planned objectives, so tracking
performance became a matter of pride rather than a management manifesto.
Management Method
Plants Using (%)
Just-in-Time
Total Preventive Maintenance
Statistical or Total Quality Control
Setup Reduction
Concurrent Engineering or
Design for Manufacture
Work Teams
Activity-Based Costing
71
64
93
43
48
62
29
Shorter setups could show up in productivity, inventory, or
lead-times measures. Of the 18 firms practicing single minute
exchange of die (SMED) or similar setup reduction methods, 17
also measured productivity, 15 measured inventory, and 17
measured lead times. Not all companies employed setup reduction to the same degree. Many would only look at setups to improve capacity at a bottleneck operation. Anecdotally, those
companies using setup reduction appeared to be shifting to
time-based objectives and it was very common for these same
firms to be using concurrent engineering (17 out of 18). As expected, all but one of the 20 users of concurrent engineering also
measured lead times.
Employee work teams of various levels of control were
found in 26 of the companies including all but two of the firms
with more than 90% of production in cells. Again, no direct
measure of success can be attributed to teams, but productivity
is the likely beneficiary with many secondary scenarios likely.
Of these 26 companies, 24 measured productivity and all included at least one measure of performance. ABC was another
method without logical correlation to one of the measure categories. This was also the method most difficult to verify. Nine
of the 12 ABC users were classified as large companies. Based
on the small number of firms using ABC, the significance of
any relationships is difficult to determine.
MANAGEMENT METHODS AND
PERFORMANCE MEASURES
There should be some correlation between management
methods and performance measurement. To gauge the impact
of these improvement programs on the manufacturing setting, a
performance measure may be operationalized. For example,
tracking rework or warranty costs would be a natural measure
of success in implementing SQC. And the correlation was perfect for the case of SQC and quality-related measures; all companies using SQC measured quality.
4
Article 1. CHANGES IN PERFORMANCE MEASURES ON THE FACTORY FLOOR
About the Authors—
CONCLUSIONS
Despite the moderate sample size of this research, enough
evidence has been gathered at this time to suggest that performance measures in metal machining firms are increasing in
number and variety. More emphasis has been placed on improving quality and lead-time performance since the adoption
of cells. And posting performance results on the factory floor is
now the rule rather than the exception, although this may be
done more for worker motivation reasons than for measuring
success toward corporate objectives. Managerial performance
is still predominantly evaluated in terms of how well the plant
achieves cost objectives and on-time deliveries. In other words,
many items are measured for “show,” but cost and schedule are
tracked for “dough.” This could, however, be changing in the
future as a few managers indicated more significance is now
being placed on quality and lead-time performance by their
managers.
ROBERT F. MARSH is an assistant professor of business at the University of Wisconsin—Milwaukee. He received his PhD in operations
management from the University of Cincinnati. Prior to that he held
positions at General Electric Aircraft Engines and Diebold. His recent
articles have been published in Journal of Operations Management,
International Journal of Technology Management, Omega, and this
journal. His research interests include cellular manufacturing, enterprise resource planning, and lead-time compression.
JACK R. MEREDITH is professor of management and Broyhill Distinguished Scholar and Chair in Operations at the Babcock Graduate
School of Management at Wake Forest University. He received his undergraduate degrees in engineering and mathematics from Oregon
State University and his PhD and MBA from University of California,
Berkeley. His current research interests are in the areas of research
methodology and the strategic planning, justification, and implementation of advanced manufacturing technologies. His recent articles in
these areas have been published in Decision Sciences, Management
Science, Journal of Operations Management, Sloan Management Review, and Strategic Management Journal. He has three textbooks that
are currently popular for college classes: The Management of Operations (John Wiley & Sons), Fundamentals of Management Science (R.
D. Irwin), and Project Management (John Wiley & Sons). He is the Editor-in-Chief of the Journal of Operations Management, an area editor
for Production and Operations Management, was the founding editor
of Operations Management Review, and was the production/operations management series editor for John Wiley & Sons, Inc.
REFERENCES
1. Goldratt, E. M., and J. Cox. The Goal. 2nd rev. ed. New York:
North River Press, 1992.
2. Hayes, R. H., and S. C. Wheelwright. “Link Manufacturing Process and Product Life Cycles.” Harvard Business Review 57, no. 1
(1979): 133–144.
3. Hill, T. Manufacturing Strategy: Text and Cases. 2nd ed. Burr
Ridge, IL: Richard D. Irwin, 1994.
4. Kekre, S. “Performance of a Manufacturing Cell with Increased
Product Mix.” IIE Transactions 19, no. 3 (1987): 320–339.
From Production and Inventory Management Journal, First Quarter 1998, pp. 36-40. © 1998 by APICS, the American Production and Inventory
Control Society. Reprinted by permission.
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