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Aligning the balanced scorecard and a firm's strategy using the Analytic Hierarchy Process: the Analytic Hierarchy Process (AHP) is used to provide insights to six companies to determine if each company's performance system is aligned with its strategic objectives of implementing lean enterprise policies. A step-by-step process for using excel in AHP applications is described.

In the Spring 2002 issue of Management Accounting Quarterly, B. Douglas Clinton, Sally A. Webber, and John M. Hassell illustrated the use of the Analytic Hierarchy Process (AHP) in implementing a balanced scorecard. Their article demonstrated the power of the AHP in resolving multicriteria decisions such as developing a balanced scorecard. They mentioned that the first level of a balanced scorecard hierarchy contains the four balanced scorecard (performance) categories, while the second level of the hierarchy contains the metrics used within each category. The authors demonstrated how the AHP can be used to help select the metrics of a balanced scorecard as well as to help understand the relative importance of each metric for a firm's management.

The purpose of this article is to investigate the use of the AHP at the first level of the balanced scorecard hierarchy with data from six firms. Using the AHP to determine the relative weight of the performance categories may give some insight into the alignment between the balanced scorecard and a company's strategic initiatives. In addition, the article will demonstrate the use of Excel in AHP applications.

THE COMPANIES

The six companies involved were part of an overall assessment project aimed at measuring the extent that lean enterprise tools are implemented successfully. (1) The common thread among all six companies was their involvement with implementing lean enterprise principles. In other words, lean was the strategic initiative undertaken across all six companies. Robert Kaplan and David Norton state, "The scorecard should be the translation of the business unit's strategy into a linked set of measures that define both the long-term strategic objectives and the mechanisms for achieving those objectives." (2) For the companies under study, any scorecard developed should align with the strategic initiative of implementing lean.

LEAN ENTERPRISES

In their book Lean Thinking, James P. Womack and Daniel T. Jones state that lean thinking can be summarized in five principles: "precisely specify value by specific product, identify the value stream for each product, make value flow without interruptions, let the customer pull value from the producer, and pursue perfection" (italics theirs). (3) They also state that by clearly understanding these five principles and integrating them together, managers can make full use of lean tools and techniques to compete in the marketplace. Lean can be defined as the effective utilization of various tools and techniques in a systematic, customer-focused manner that increases the flexibility of the manufacturing/ logistical processes with the goal of producing the highest-quality product and/or service within an environment of continuous improvement through the absolute elimination of all forms of waste. The overarching values of lean are customer prosperity and the view that the workforce is the most important resource of an organization.

As implied by the definition, lean is multi-dimensional. Two important characteristics of a lean enterprise are customer focus and employee empowerment. The customer is the driving force behind lean, which suggests that customer-focused measures should be an integral part of any performance system. In a similar vein, lean enterprises empower their employees to make operational decisions. The employees are trained to uncover and correct errors as well as find continuous improvement opportunities. (4) Measuring employee performance should also be a prominent aspect of any performance system in a lean enterprise.

Firms attempting to implement lean need a performance system that collects, stores, aggregates, and reports on the many dimensions of lean. The structure of a balanced scorecard, with its four performance categories, is a good start for developing a performance system that meets the requirements of a lean enterprise.

THE BALANCED SCORECARD

The four performance perspectives of Kaplan and Norton's balanced scorecard are well documented. (5) While those perspectives provide a comprehensive picture of most companies, two additional perspectives were added based on the lean initiatives under way at the companies being studied: quality and safety.

Interviews with managers at some of the participating companies indicated that quality and safety were critical aspects worthy of separate categorization. Increase in quality is a direct benefit of successfully implementing lean and, therefore, should be measured separately. In addition, safety was a major concern to the parties involved.

Figure 1 displays the scorecard categories used. Basically, the four perspectives of Kaplan and Norton's balanced scorecard are retained, but the internal business processes perspective is subdivided into three subcategories: operating performance, safety, and product quality. The financial performance, customer focus, and employee satisfaction perspectives align with Kaplan and Norton's financial, customer, and learning and growth perspectives.

[FIGURE 1 OMITTED]

Kaplan and Norton comment that a properly constructed balanced scorecard should tell the story of a company's strategy. (6) In their article, Clinton, Webber, and Hassell state, "Different methods of employing a balanced scorecard are used to clarify and update strategy, to communicate intentions, to align organization and individual goals, and to learn about and improve strategy." To learn about and improve strategy is the focus of this article. The real question to ask is: Does the company's performance system support or hinder its strategic objectives? For example, if a company's strategic objective is to be the highest-rated customer service company in the industry, then it would be expected that customer-focused metrics would rank very high in the performance system for that company relative to other performance categories. If, however, customer-focused metrics are rated last or near the bottom in importance, a misalignment has occurred between the stated strategic objective and the performance system. Not only can the AHP assist companies with developing a balanced scorecard, but the relative weighting of the performance categories can be used to provide insights into a company's strategy.

THE ANALYTIC HIERARCHY PROCESS (AHP)

The AHP uses paired comparisons of objects with respect to a common goal or criteria. The end result of the AHP is a set of weights derived from the pair-wise comparisons. (7) See the Appendix for the AHP instrument completed by the management-level employees of the participating companies. Each manager made two decisions when completing the AHP instrument:

1. Selecting the more important performance category for measuring and monitoring the company's performance.

2. Recording the magnitude of importance the category selected has over the category not selected.

Thomas Saaty, the developer of the AHP, recommends a one-to-nine ratio scale when deciding between the two alternatives. A one-to-five ratio scale (Table 1) was used in this study. Using a different scale does not violate the theoretical foundation of the AHP as long as the scale used is a bounded ratio scale and the alternatives are homogeneous with respect to the scale. (8) Both requirements are met in this study.

USING EXCEL IN AN AHP APPLICATION (9)

Once the managers have completed the AHP instrument, five steps are necessary to develop the first hierarchical level of a balanced scorecard for each company:

1. Enter pair-wise responses in Excel.

2. Calculate a company-level matrix.

3. Normalize the comparisons.

4. Calculate the score.

5. Determine consistency.

Step 1: Enter pair-wise responses in Excel

Entering the pair-wise responses of each manager in Excel in a matrix format is the first step. The Performance Pair-Wise Comparisons section of Table 2 displays the matrix format for the performance AHP. The key to remember when entering the pair-wise comparisons is the assumption of reciprocity. For example, if the quality performance category is evaluated as three times more important than the operating performance category, the reciprocity axiom states that the operating performance category is 1/3 times more important than the quality performance category. The AHP matrix shown in Table 2 is designed to account for reciprocity by entering the reciprocal formula in the lower half of the matrix.

When entering data in the upper half of the matrix, one must enter the preference denoted by the respondent (i.e., [P.sub.ij], where i = column and j = row) or its reciprocal (i.e., [P.sub.ji] = 1/[P.sub.ij]). For example, a respondent indicates that safety is two times more important than quality. Notice that cell D11 represents the magnitude of quality over safety. Because the respondent indicates safety is more important than quality, the reciprocal (i.e., 1/2 = 0.50) is entered in cell D11, and Excel automatically places a "2" in cell C12, indicating safety is two times more important than quality. Likewise, cell E11 indicates the respondent thinks quality is two times more important than financial measures in measuring and monitoring the respondent's company performance. The reciprocal is calculated automatically and entered in cell C13. Once an AHP matrix is completed for each respondent, the company-level matrix is created.

Step 2: Calculate a company-level matrix

The objective of step two is to develop a single matrix for each company from the individual respondents. This step is accomplished by taking the geometric mean across the respondents for each company. (10) The geometric mean is calculated for each cell across all of the individual matrices. The result is a single, composite matrix. Table 3 displays the composite matrix for Company A.

Step 3: Normalize the comparisons

Normalizing the pair-wise comparisons is accomplished with two procedures. Calculating the sum of each column is the first procedure (i.e., the formula for cell C17 in Table 2: = SUM(C11:C16)). The formula for cell C17 is copied to cells D17 through H17. The next procedure involves dividing each entry in the matrix by its column sum (i.e., the formula for cell C21: = C11/C$17). The formula for cell C21 is copied to cells C21 through H26. Completing those two procedures creates the performance normalized comparison matrix found in Table 2.

Step 4: Calculate the score

The average of each row in the normalized matrix is used as the score for each alternative (i.e., the formula for cell C30 in Table 2: = AVERAGE(C21:H21)). That formula is copied to cells C31 through C35. The result is the score (relative importance weight) for each performance category for measuring and monitoring the company's performance. The performance scores in Table 2 indicate that the respondent ranks safety (0.28) as the most important performance category, followed by quality (0.252), financial (0.163), customer (0.138), operating (0.129), and employee (0.038).

Step 5: Determine consistency

The final step in applying the AHP is determining the consistency of the results. The respondents should be consistent in their pair-wise comparisons. In other words, if the respondent considers quality two times more important than safety, and customer two times more important than quality, then the respondent should consider customer four times more important than safety. If the results are not consistent, the scores should not be used.

Multiplying each alternative in the original matrix by the normalized scores and dividing the result by the respective normalized score calculates the consistency measure found in Table 2. In other words, you multiply each row in the performance pair-wise comparison matrix by the performance scores, then divide the result by the respective performance score (i.e., the formula for cell D30 in Table 2: = MMULT(C11:H11,$C$30: $C$35)/C30). The formula is copied to cells C31 through C35. If the respondent is perfectly consistent, the consistency measure will equal the number of alternatives (i.e., six).

Table 2 indicates the respondent was not perfectly consistent. As long as the inconsistency is not excessive, however, the performance scores can be treated as reasonably accurate. A consistency ratio is calculated to determine reasonable consistency. The consistency ratio is calculated as follows: (11)

Consistency ratio = CI / RI, where

CI = Consistency Index = ([lambda] - n) / (n - 1)

[lambda] = the average consistency measure for all alternatives

n = the number of alternatives

RI = the appropriate random index (1.24, when n = 6)

The formula for cell F35 is: = (AVERAGE(D30:D35) -6)/(5 * 1.24). The consistency ratio calculated in Table 2 is 0.091. A consistency ratio of 0.10 or less is considered acceptable, so the performance scores calculated in Table 2 appear reasonably consistent.

BALANCED SCORECARD/STRATEGY ALIGNMENT

Table 4 displays the performance scores for each company participating in the study. Are the scorecards balanced? With six performance categories, a perfectly balanced scorecard would weight each category equally at approximately 16.6%. None of the company's scorecards is perfectly balanced; however, Company E is the most balanced, with scores ranging from 0.192 to 0.132. Company C has the largest dispersion between the top-ranked performance category and the lowest-ranked performance category (0.197). While we would not expect any scorecard to be perfectly balanced, the analysis does give insight into the emphasis placed by management on particular performance categories.

An examination of the ranking of the performance categories reveals a couple of interesting points. First, the employee performance category is ranked last across all companies. When compared to the other performance categories, employee performance is just not as important. In particular, Company C's scorecard ranks safety three and a half times more important than employee performance in measuring and monitoring the company's performance. The result is somewhat surprising because employee empowerment is a key principle in implementing lean. A higher focus on employees is expected as companies become more mature in implementing lean throughout their facilities.

The second interesting point is the relative ranking of the customer performance category. A strong customer focus is at the heart of lean enterprises. Companies successful in implementing lean principles are "in-tune" with their customers. Two companies, however, have the customer performance category ranked next to last, while three companies ranked the customer performance category as second-to-last. For firms implementing lean, it would be expected that customer performance would be the highest-ranked performance category.

Another point worth mentioning is the relative ranking of the financial performance category. None of the companies ranked financial performance as the top performance category, a refreshing development. With the dominance of financial measures in most companies, it was surprising to find the managers of the participating companies did not view financial performance as the most important performance category. While Kaplan and Norton do not rank financial performance as the most important performance category, they believe that the metrics used in the other performance categories should be linked to financial performance. It is important that improvements in nonfinancial performance eventually translate into financial performance.

Does the balanced scorecard perceived by the companies' management support or hinder the strategic objective of implementing lean? Table 4 appears to suggest some misalignment between the performance system and strategy. The relative weighting of the performance categories does not reflect the importance of customers and employees to lean strategies. The operational performance categories dominate the higher-ranked categories. Presenting the results of the AHP to the management team to discuss the relative weights placed on the different performance categories may be useful. An open dialogue discussing the lean initiative and how the balanced scorecard affects the initiative also would be beneficial. Developing an understanding with a company's management on the importance of a properly structured balanced scorecard and its link with a company's strategic objectives is key.

COMPLETING THE BALANCED SCORECARD

The analysis conducted has only examined the performance category level of a balanced scorecard. The next step involves determining the specific metrics in each performance category to collect and measure. It is likely that the number of metrics within each performance category will vary. The steps outlined above can be used at the metric level. Once the metrics are defined and scored, a scoring system is developed. From that scoring system, an overall performance score can be calculated. (12) A single composite score allows for comparison across companies as well as comparisons across facilities within companies.

The use of the Analytic Hierarchy Process in investigating the degree of alignment between management's ranking of balanced scorecard performance categories and the company's strategic initiatives was illustrated here using six lean enterprise companies. The use of Excel to perform AHP calculations also was demonstrated. Results of the process indicate two performance categories (customer and employee) were not highly ranked when compared to the other categories. Those two categories are the foundations of lean programs and were expected to be highly ranked. Armed with the results of the AHP, management can begin to realign the performance system to measure lean better.
Appendix--Performance Categories AHP Instrument

For the 15 pairs of evaluation criteria to be compared, first
indicate which category in the pair is more important for
measuring and monitoring the performance of your site, then
record your judgment as to the magnitude of its importance
over the other item in the pair. There are no right or wrong
answers. The response scale for magnitude of importance
is as follows:

INTENSITY OF
 IMPORTANCE DEFINITION EXPLANATION

 1 Equal importance Two criteria contribute
 equally to the evaluation of
 performance

 2 Weak importance of one Experience and judgment
 item over another slightly favor one criterion
 over another

 3 Strong importance Experience and judgment
 strongly favor one criterion
 over another

 4 Very strong importance A criterion is strongly
 favored, and its dominance is
 demonstrated in practice

 5 Absolute importance The evidence favoring one
 criterion over another is of
 the highest possible order of
 affirmation

Example: Quality of a leader

 COMPARISON PAIR EVALUATION CRITERIA

 More
 A VS B Important Magnitude

Communication Technical
 Skills Skills A B 1 2 3 4 5

Communication skills are 4 times more important than
technical skills of a leader.

 COMPARISON PAIR EVALUATION CRITERIA

 More
 A VS B Important Magnitude

Product/Service
 Quality Safety A B 1 2 3 4 5

 COMPARISON PAIR EVALUATION CRITERIA

 More
 A VS B Important Magnitude

 Financial Customer
 Performance Focus A B 1 2 3 4 5

 COMPARISON PAIR EVALUATION CRITERIA

 More
 A VS B Important Magnitude

 Operating Employee
 Performance Satisfaction A B 1 2 3 4 5

 COMPARISON PAIR EVALUATION CRITERIA

 More
 A VS B Important Magnitude

 Financial
 Safety Performance A B 1 2 3 4 5

 COMPARISON PAIR EVALUATION CRITERIA

 More
 A VS B Important Magnitude

Product/Service Financial
 Quality Performance A B 1 2 3 4 5

 COMPARISON PAIR EVALUATION CRITERIA

 More
 A VS B Important Magnitude

 Employee
 Safety Satisfaction A B 1 2 3 4 5

 COMPARISON PAIR EVALUATION CRITERIA

 More
 A VS B Important Magnitude

 Operating
Custumer Focus Performance A B 1 2 3 4 5

 COMPARISON PAIR EVALUATION CRITERIA

 More
 A VS B Important Magnitude

 Employee Product/Service
 Satisfaction Quality A B 1 2 3 4 5

 COMPARISON PAIR EVALUATION CRITERIA

 More
 A VS B Important Magnitude

 Operating Financial
 Performance Performance A B 1 2 3 4 5

 COMPARISON PAIR EVALUATION CRITERIA

 More
 A VS B Important Magnitude

Customer Focus Employee
 Satisfaction A B 1 2 3 4 5

 COMPARISON PAIR EVALUATION CRITERIA

 More
 A VS B Important Magnitude

Product/Service Operating
 Quality Performance A B 1 2 3 4 5

 COMPARISON PAIR EVALUATION CRITERIA

 More
 A VS B Important Magnitude

Customer Focus Safety A B 1 2 3 4 5

 COMPARISON PAIR EVALUATION CRITERIA

 More
 A VS B Important Magnitude

 Employee Financial
 Satisfaction Performance A B 1 2 3 4 5

 COMPARISON PAIR EVALUATION CRITERIA

 More
 A VS B Important Magnitude

 Product/Service
Customer Focus Quality A B 1 2 3 4 5

 COMPARISON PAIR EVALUATION CRITERIA

 More
 A VS B Important Magnitude

 Operating
 Performance Safety A B 1 2 3 4 5

Table 1: The Ratio Scale

For the 15 pairs of evaluation criteria to be compared,
first indicate which category in the pair is more important
for measuring and monitoring the performance of your site,
then record your judgment as to the magnitude of its
importance over the other item in the pair. There are no
right or wrong answers. The response scale for magnitude
of importance is as follows:

INTENSITY OF
IMPORTANCE DEFINITION EXPLANATION

 1 Equal importance Two criteria contribute equally to
 the evaluation of performance

 2 Weak importance Experience and judgment slightly
 of one item over favor one criterion over another
 the other item

 3 Strong importance Experience and judgment strongly
 favor one criterion over another

 4 Very strong A criterion is strongly favored, and
 importance its dominance is demonstrated
 in practice

 5 Absolute The evidence favoring one criterion
 importance over another is of the highest
 possible order of affirmation

Table 2: AHP Calculations

A B C D E F
1 Performance Pair-Wise Comparisons
2 Quality Safety Financial Customer
3 Quality 1.00
4 Safety =1/D4 1.00
5 Financial =1/E4 =1/E5 1.00
6 Customer =1/F4 =1/F5 =1/F6 1.00
7 Operating =1/G4 =1/G5 =1/G6 =1/G7
8 Employee =1/H4 =1/H5 =1/H6 =1/H7
9
10 Quality Safety Financial Customer
11 Quality 1.00 0.50 2.00 3.00
12 Safety 2.00 1.00 2.00 2.00
13 Financial 0.50 0.50 1.00 0.50
14 Customer 0.33 0.50 2.00 1.00
15 Operating 0.33 0.50 0.33 2.00
16 Employee 0.20 0.20 0.20 0.25
17 Sum 4.37 3.20 7.53 8.75
18
19 Performance Normalized Comparisons
20 Quality Safety Financial Customer
21 Quality 0.229 0.156 0.265 0.343
22 Safety 0.458 0.313 0.265 0.229
23 Financial 0.115 0.156 0.133 0.057
24 Customer 0.076 0.156 0.265 0.114
25 Operating 0.076 0.156 0.044 0.229
26 Employee 0.046 0.063 0.027 0.029
27
28 Performance Consistency
29 Scores Measure
30 Quality 0.252 6.80
31 Safety 0.280 6.56
32 Financial 0.163 6.62
33 Customer 0.138 6.55
34 Operating 0.129 6.49
35 Employee 0.038 6.35 Consistency Ratio 0.091

A B G H
1
2 Operating Employee
3 Quality
4 Safety
5 Financial
6 Customer
7 Operating 1.00
8 Employee =1/H8 1.00
9
10 Operating Employee
11 Quality 3.00 5.00
12 Safety 2.00 5.00
13 Financial 3.00 5.00
14 Customer 0.50 4.00
15 Operating 1.00 4.00
16 Employee 0.25 1.00
17 Sum 9.75 24.00
18
19
20 Operating Employee
21 Quality 0.308 0.208
22 Safety 0.205 0.208
23 Financial 0.308 0.208
24 Customer 0.051 0.167
25 Operating 0.103 0.167
26 Employee 0.026 0.042
27
28
29
30 Quality
31 Safety
32 Financial
33 Customer
34 Operating
35 Employee

Table 3: Composite AHP Matrix for Company A

 Quality Safety Financial

Quality 1.000 1.048 1.188
Safety 0.954 1.000 1.130
Financial 0.841 0.885 1.000
Customer 0.729 0.698 0.664
Operating 0.773 0.899 1.220
Employee 0.411 0.402 0.635
Sum 4.708 4.932 5.838

 Customer Operating Employee

Quality 1.372 1.294 2.434
Safety 1.434 1.112 2.488
Financial 1.505 0.820 1.574
Customer 1.000 0.738 1.372
Operating 1.356 1.000 1.441
Employee 0.729 0.694 1.000
Sum 7.395 5.658 10.308

Table 4: Performance Scores

Company A

 PERFORMANCE PERFORMANCE CONSISTENCY
RANK CATEGORY SCORE MEASURE

 1 Quality 0.213 6.036
 2 Safety 0.205 6.038
 3 Operating 0.176 6.029
 4 Financial 0.172 6.032
 5 Customer 0.135 6.034
 6 Employee 0.099 6.029
 1.000 0.005 CR

Company B

 PERFORMANCE PERFORMANCE CONSISTENCY
RANK CATEGORY SCORE MEASURE

 1 Safety 0.281 6.167
 2 Financial 0.173 6.161
 3 Quality 0.164 6.162
 4 Customer 0.156 6.150
 5 Operating 0.127 6.144
 6 Employee 0.098 6.126
 1.000 0.024 CR

Company C

 PERFORMANCE PERFORMANCE CONSISTENCY
RANK CATEGORY SCORE MEASURE

 1 Safety 0.275 6.101
 2 Quality 0.186 6.107
 3 Customer 0.175 6.091
 4 Financial 0.159 6.097
 5 Operating 0.127 6.079
 6 Employee 0.077 6.085
 1.000 0.015 CR

Company D

 PERFORMANCE PERFORMANCE CONSISTENCY
RANK CATEGORY SCORE MEASURE

 1 Quality 0.213 6.036
 2 Safety 0.205 6.038
 3 Operating 0.176 6.029
 4 Financial 0.172 6.032
 5 Customer 0.135 6.034
 6 Employee 0.099 6.029
 1.000 0.005 CR

Company E

 PERFORMANCE PERFORMANCE CONSISTENCY
RANK CATEGORY SCORE MEASURE

 1 Quality 0.192 6.178
 2 Operating 0.185 6.167
 3 Financial 0.174 6.165
 4 Customer 0.173 6.219
 5 Safety 0.144 6.190
 6 Employee 0.131 6.154
 1.000 0.029 CR

Company F

 PERFORMANCE PERFORMANCE CONSISTENCY
RANK CATEGORY SCORE MEASURE

 1 Operating 0.223 6.187
 2 Financial 0.190 6.179
 3 Quality 0.181 6.168
 4 Customer 0.159 6.168
 5 Safety 0.127 6.173
 6 Employee 0.120 6.173
 1.000 0.028 CR


(1) The project refers to the companies' participation in a Lean Enterprise Site Assessment conducted by Bradley Greene and DeWayne L. Searcy. The specifics detailed in this article are a small component of the overall assessment conducted with each company.

(2) Robert S. Kaplan and David P. Norton, "Why Does Business Need a Balanced Scorecard?" Journal of Cost Management, May/June 1997, pp. 5-10.

(3) James P. Womack and Daniel T. Jones, Lean Thinking: Banish Waste and Create Wealth in Your Corporation, Simon & Schuster, New York, N.Y., 1996.

(4) Steven Spear and H. Kent Bowen, "Decoding the DNA of the Toyota Production System," Harvard Business Review, September/October 1999.

(5) Robert S. Kaplan and David P. Norton, The Balanced Scorecard: Translating Strategy into Action, Harvard Business School Press, Boston, Mass., 1996.

(6) Kaplan and Norton, 1997, pp. 5-10.

(7) Thomas L. Saaty, The Analytic Hierarchy Process, McGraw-Hill, New York, N.Y., 1980.

(8) Patrick T. Harker and Luis G. Vargas, "The Theory of Ratio Scale Estimation: Saaty's Analytic Hierarchy Process," Management Science, November 1987 (vol. 33, no. 11), pp. 1383-1403. See also, "Reply to 'Remarks on the Analytic Hierarchy Process' by J. S. Dyer," Management Science, March 1990 (vol. 36, no. 3), pp. 269-271, by the same authors.

(9) See Cliff T. Ragsdale, Spreadsheet Modeling and Decision Analysis, 3rd ed., South-Western, Cincinnati, 2001, pp. 766-773.

(10) J. Aczel and Thomas L. Saaty, "Procedures for Synthesizing Ratio Judgments," Journal of Mathematical Psychology, 1983, vol. 27, pp. 93-102.

(11) The random index was generated through simulations conducted by Thomas L. Saaty.

(12) See B. Douglas Clinton, Sally A. Webber, and John M. Hassell, "Implementing the Balanced Scorecard Using the Analytic Hierarchy Process," Management Accounting Quarterly, Spring 2002.

DeWayne L. Searcy, PhD., CMA, CPA, CIA, is an assistant professor in the department of accounting at the University of Miami in Coral Gables, Fla. His research interests are supply chain management, lean enterprises, and continuous auditing. He can be reached at (305) 284-4821 or dsearcy@miami.edu.
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Date:Jun 22, 2004
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