“Impacts Productivity on Profitability for Organizational Performance Analysis” By Chansiri Singhtaun Kongkiti Phusavat, Ph.D. International Graduate Program in Industrial Engineering Department of Industrial Engineering Kasetsart University Abstract: There are two primary purposes for this research. The first purpose is to examine the possible impacts from productivity on profitability. Sink and Tuttle (1989) propose the hypothetical interrelationships among the seven key performance criteria, i.e., profitability, productivity, innovation, QWL, quality, effectiveness, and efficiency. In this hypothetical framework, the level of profitability is to be directly impacted by changes in productivity. To test this premise, several quantitative measures in the ratio format reflecting both productivity and profitability were identified. Then, the data from each measure was collected from an organization, which can be categorized as labor- and material- intensive, operating in the made-to-order products’ environment. There are two methods used for this examination. The first one is the Multi-Criteria Performance/ Productivity Measurement Technique (MCP/PMT). This technique requires the conversion of information from all the identified measure into one dimensionless scale. The second method involves the use of the regression analysis to examine on their possible interrelationships. The second purpose is to apply these findings (the interrelationships) for the target setting activities. This second purpose is accomplished by the interview session with the company’s executives. The overall results demonstrate the significant influences from productivity on profitability without explicitly including the time and time-lag factors. At the same time, top management, specifically for the target setting task, supports the adaptability and usefulness of the findings. Finally, the limitations and recommendations for future research are stated. Key Words: Performance Measurement, Performance Criteria, Profitability, and Productivity Harper (1984), and Sink and Tuttle (1989) suggest the likely impacts from productivity on profitability of an organization. Commonly, the term productivity is defined as the relationship between outputs from and inputs into a system. Productivity can be measured from single-, multiple-, and total-factor perspectives. Single measures include the relationship between outputs and labor, materials, or facility space. The multiple measures are, for example, the relationship between outputs and labor plus materials. The total factor productivity measure includes all possible inputs into a system. On the other hand, the term profitability implies the ability to continuously generate profits over the sustained period of time. As a result, there are several ratios that reflect this implication, e.g., the ratio of outcomes over inputs (such as revenue ÷ cost or profit ÷

cost) or of outcomes over outcomes (such as profit ÷ revenue or revenue from rework ÷ revenue). Both productivity and profitability are part of the basic seven performance criteria in which an organization needs to measure, analyze, and evaluate (Sink and Tuttle, 1989). Others are quality of work life, innovation, quality, effectiveness, and efficiency. Simply put, the attribute to become more profitable is productivity while impacts from productivity stem from QWL and other remaining performance criteria. Figure 1 illustrates the interrelationships among the seven performance criteria. Figure 2 shows the research premise.

Quality of Work Life

Effectiveness

Productivity

Efficiency Quality

Profitability

Innovation (Product/ Processes)

Figure 1: Interrelationships among Performance Criteria (Adapted from Sink and Tuttle, 1989)

Productivity

+ (?)

Profitability

Figure 2: Research Premise Performance measurement has long been regarded as an important management tool for continuous improvement. The “useful” performance measurement system provides feedback to the following three questions. How well an organization is performing? Is the organization achieving its objectives? How much has the organization improved from a last period? In addition, performance measurement helps create feedback to managers with respect to the effectiveness of improvement interventions (implying corrective and preventive decisions). To simplify the scope and the roles of performance measurement, Sink (1990) provides the Kurstedt’s management system framework. This framework depicts the components and interfaces in the management system. The Kurstedt’s management system consists of three components. They are “who manages,” “what is managed,” and “what is used to manage.” The three interfaces are “decision/action,” “measurement/data,” and “information portrayal/information perception.” Sink further elaborates the “what is managed” component or the domain of responsibility for a manager should be divided into five elements. These elements are in the sequential order of “upstream system or providers,” “inputs,” “value-added

processes,” “outputs,” and “downstream system or outcomes.” Figure 3 demonstrates the Kurstedt’s management system. Other formal and Informal systems Perception

Who manages?

Portrayal

Decisions

Actions

Data-toinformation conversion

Measurement

Data

Organization System Upstream Systems

Inputs

Value-added processes

Outputs

Downstream Systems

Figure3: Modified Management Systems Model (Adapted from Sink and Tuttle, 1989) The MCP/MT is one of the most widely used performance measurement techniques at the functional and organizational levels. This technique can assist during an attempt to identify the overall performance level or the overall level of each of the seven performance criteria (given that each criterion is measured by several ratios). This technique is based on the concept of the multi-attribute decisions. The primary mechanism for this technique involves the use of the performance scale and the preference curve. See Sink and Tuttle (1989) for more details. Given the limitation on data availability, not all measures representing productivity and profitability is utilized. Table 1 illustrates these measures in the ratio format. Table 1: Productivity and Profitability Measures Productivity Profitability 1.Revenue ÷Indirect Labor (denoted as ILAB) (Baht per Hour) 2.Revenue ÷Direct Labor (denoted as DLAB) (Baht per Hour) 3.Revenue ÷Material (denoted as MAT) (Baht per Piece) 4. Revenue ÷ Utility (denoted as UTI) (Baht per Baht)

1. Profit ÷ Revenue (denoted as PFR) (Baht per Baht) 2. Profit ÷ Cost (denoted as PFC) (Baht per Baht)

Then, the data for each measure was collected over the period during June 2001 to 2002. To test whether the impact from productivity on profitability exists, the framework for the experiments and the analyses is to be shown as follows.

Analysis

Test for Applicability By Executive Interview

Measures -> To Be Combined By MCPMT

Combined Measures -> Models By Regression Analysis

Suggested Models

Figure 4: Experimental and Analyses Framework For the application of the MCP/MT, the performance scale of 0 to 100 is to be used for all productivity and profitability measures. The best result over the June 2001 to 2002 duration from each measure is to receive the score of 100 while the worst number is to be assigned the score of 0. The score of 50 is for the average value. Then, other results in each month can be assigned the numerical scores between the score 0 to 100 (since the three points, namely the maximum, minimum, and average values, form the so-called preference curve) as demonstrated by Table 2. Table 2: Demonstration of the Values from the Scale of 0 to 100

Month

2001 June July 2001 August 2001 September 2001 October 2001 November 2001 December 2001 January 2002 February 2002 March 2002 April 2002 May 2002 June 2002 Average

Productivity Criterion Revenue / Indirect Labor Score from the Scale of 0- 100 (Baht / Hour) 15.66 72.00 14.83 62.48 18.10 100.00 14.91 63.40 16.22 78.43 14.80 62.14 14.85 62.71 15.34 68.33 15.63 71.66 10.27 31.40 4.41 0.00 12.97 45.86 10.66 33.49 13.742

50

Profitability Criterion Profit / Revenue Score from the (Baht / Baht) Scale of 0- 100 0.16 17.26 0.22 52.44 0.28 100.00 0.25 76.22 0.25 76.22 0.25 76.22 0.26 84.15 0.27 92.07 0.25 76.22 0.13 0.00 0.17 23.01 0.19 34.51 0.14 5.75 0.217

50

50

50

Scale

Scale

Figures 4 and 5 exhibit the preference curves for the revenue-to-indirect labor and the profit-to-revenue ratios respectively. By having developed the preference curves, the scores of 0-100 were assigned to all selected measures. 100 100

0

4.41

13.742

0

18.10

Figure 4: Preference Curve for ILAB

0.13

0.217 0.28

Figure 5: Preference Curve for PFR

Given the scores for all 6 measures (4 productivity measures and 2 profitability measures), the weighted score for each criterion can be identified. In this research, it is assumed that each measure in both performance criteria to be received an equal weight. Table 3 and 4 illustrate these weighted scores. Table 3: Sum Weighted Scores for Overall Productivity (PROD)

Month June July August September October November December January February March April May June

2001 2001 2001 2001 2001 2001 2001 2002 2002 2002 2002 2002 2002

ILAB 72 .00 62 .48 100 .00 63 .40 78 .43 62 .14 62 .71 68 .33 71 .66 31 .40 0.00 45 .86 33 .49

Index ( Common Scale ) DLAB MAT 35 .22 3.16 5.85 86 .18 66 .65 64 .72 100 .00 0.00 82 .90 9.04 66 .14 56 .21 83 .93 100 .00 85 .29 31 .24 64 .43 73 .48 30 .09 20 .71 34 .37 21 .94 28 .24 90 .60 0.00 74 .69

Weight UTI 19 .44 44 .22 0.00 47 .64 76 .89 49 .12 70 .17 100 .00 63 .24 40 .22 49 .57 35 .02 8.90

1/4 1/4 1/4 1/4 1/4 1/4 1/4 1/4 1/4 1/4 1/4 1/4 1/4

Sum Weighted Score (PROD ) 32 .46 49 .68 57 .84 52 .76 61 .82 58 .40 79 .20 71 .22 68 .20 30 .60 26 .47 49 .93 29 .27

Table 4: Weighted Scores for Overall Productivity (PROF) Month June July August September October November December January February March April May June

Index ( Common Scale ) PFR PFC 17 .26 17 .86 52 .44 46 .43 100 .00 100 .00 76 .22 77 .27 76 .22 72 .73 76 .22 72 .73 84 .15 86 .36 92 .07 90 .91 76 .22 68 .18 0 .00 0 .00 23 .01 25 .00 34 .51 32 .14 5 .75 7 .14

2001 2001 2001 2001 2001 2001 2001 2002 2002 2002 2002 2002 2002

Weight 1 /2 1 /2 1 /2 1 /2 1 /2 1 /2 1 /2 1 /2 1 /2 1 /2 1 /2 1 /2 1 /2

Sum Weighted Score (PROF ) 17 .56 49 .43 100 .00 76 .75 74 .47 74 .47 85 .25 91 .49 72 .20 0 .00 24 .00 33 .33 6 .45

For an attempt to establish the interrelationships, the following 16 patterns are examined (to be illustrated by Table 5). There are 3 types of the series to be studies. Each represents the overall profitability (denoted as PROF), the profit-to-revenue measure, and the profit-to-cost measure respectively. Table 5: Patterns (Models) to Be Examined Part 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

MODEL 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Independent Variable(s) PROD ILAB DLAB MAT UTI ILAB and DLAB ILAB and MAT ILAB and UTI DLAB and MAT DLAB and UTI MAT and UTI ILAB, DLAB, and MAT ILAB,DLAB, and UTI ILAB, MAT and UTI DLAB, MAT, and UTI ILAB,DLAB,MAT, and UTI

Dependent Variable Series 1 Series 2 PROF PFR PROF PFR PROF PFR PROF PFR PROF PFR PROF PFR PROF PFR PROF PFR PROF PFR PROF PFR PROF PFR PROF PFR PROF PFR PROF PFR PROF PFR PROF PFR

Series 3 PFC PFC PFC PFC PFC PFC PFC PFC PFC PFC PFC PFC PFC PFC PFC PFC

The proposed 16 patterns have been tested with the regression-analysis method. Given the comprehensive testing, without the consideration into the time or time- lag factor (see Appendix A), the following relationships are identified (Figure 6). Impacts from the Overall Productivity Impacts from Each Productivity Measure on the Overall Profitability

PROF = -36.065 + 1.758 (PROD) PFR = -37.093 + 1.791 (PROD) PFC = -35.04 +1.725(PROD)

PROF = -2.241 + 0.977(ILAB) PROF = 9.186 + 0.858(DLAB) PROF = -11.565 + 0.5631(ILAB) + 0.633(DLAB) PROF = -25.009 + 0.961(ILAB) + 0.51(UTI) PROF = -17.289 + 0.99(DLAB) + 0.402(MAT)

Impacts from Each Productivity Measure on the Profit-to-Revenue Measure

PFR = -2.847 + 0.999(ILAB) PFR = 9.96 + 0.856(DLAB) PFR = -11.955 + 0.594(ILAB) + 0.619(DLAB) PFR = -26.277 + 0.982(ILAB) + 0.524(UTI) PFR = -17.366 + 0.992(DLAB) + 0.415(MAT) PFR = -29.032 + 0.453(ILAB) + 0.786(DLAB) + 0.338(MAT)

Impacts from Each Productivity Measure on the Profit-to-Cost Measure

PFC = -1.634 +0.955(ILAB) PFC = 8.412 + 0.86(DLAB) PFC = -11.174 + 0.531(ILAB) + 0.648(DLAB) PFC = -17.213 + 0.988(DLAB) + 0.389(MAT)

Figure 6: Results (with the Significant Relationships) of the Testing The findings demonstrate the impacts from productivity (both the overall level and the levels from an individual measure) on the term profitability (both the overall level and the levels from the Profit-to-Revenue and Profit-to-Cost Measures). The next step is to discuss and verify the results with the company’s executives. In addition, this discussion focuses on the potential use of these findings for planning and target-setting purposes. The results from this discussion can be described as follows. • It is difficult to measure the productivity level when the company is operating in the made-to-order business environment. This is because the term outputs are not driven by the production process and capability alone. Therefore, the productivity measures that are used in this research are acceptable. • The relationships confirm the contribution to profitability from key input attributes, especially in the area of indirect labor. In other words, the coefficient values are extremely important and should be monitored on the continuous basis. • This target-setting approach is robust and dynamic. However, it considers only the internal influences on the target level. Further integrations of external influences and factors still have to be made. • The database has to be ready and timely.

In conclusion, the research has demonstrated that, without explicitly considering the time and time-lag factors, the profitability level is impacted by changes in productivity. Additional research needs to focus on the integration of these two factors to confirm or validate this initial conclusion. Furthermore, the analysis needs to be made on the possible interrelationships among each measure in both performance criteria before testing on the impacts. Finally, the future testing should be performed on the possible two-way relationships between profitability and productivity.

References: 1. Blanchard, B. S. 1998 “System Engineering and Management” Wiley-Interscience: Singapore 2. Blanchard, B. S. and Fabrycky, W. 1990 “System Engineering and Analysis” Prentice-Hall: Englewood Cliffs, NJ 3. Deming, W. E. 1986 “Out of Crisis” MIT Center for Advanced Engineering Study: MA. 4. Dixon, J. R.; Nanni, A. J.; and Vollmann, T. E. 1990 “The New Performance Challenge: Measuring Operations for World-Class Competition” Dow Jones- Irwin: New York 5. Hodgetts, R. M. 1998 “Measures of Quality and High Performance” AMACOM: New York 6. Kurstedt, H. A. 1992 “Management System Theory and Practices” Course Lectures for ISE 4015, 4016, and 5004, Department of Industrial and Systems Engineering at Virginia Tech 7. Kurstedt, H. A. 1985 “Management System Model Helps Your Tool Work for You” Responsive Article Published by the Management Systems Laboratory, Department of Industrial and Systems Engineering at Virginia Tech. 8. Montgomery, D. C. 1982 “Introduction to Linear Regression Analysis” John Wiley & Sons: New York. 9. Sink, D. S. and Rossler, P. E. 1990 “Roadmap for Quality and Productivity Management and Improvement” Engineering Management Journal: September 10. Sink, D. S. 1990 “Theory and Practice of Measurement for Improvement in the Organization of the Future” Productivity Management Frontier II 11. Sink, D. S. and Tuttle, T. C. 1989 “Planning and Measurement in Your Organization of the Future” IE Press: Norcross, GA.

Appendix A: Future Research Framework for Interrelationship or Impact Analysis The future research, to examine the impact from productivity and profitability, may have to incorporate both the time and time-lag factors into the analysis. This incorporation is necessary to ensure the validity of the possible conclusion of the impact or interrelationship. Verification of Interdependency among Measures within Each Criterion

Testing the Interrelationships between the Two Performance Criteria (Productivity as in “X” and Profitability as in “Y”) • Set of Individual Measures with Set of Individual Measures • Set of Individual Measures with Overall Level • Overall Level with Set of Individual Measures • Overall Level with Overall Level

Excluding the Time Consideration (the focus on this research) Y = a + b(X) X= d + e(Y)

Time Factor Y = a + b(X) + c(t) X = d + e(Y) + f(t)

Incorporating the Time Consideration

Time-lag Factor (for t-1, t-2. t-3, etc.)

Y = a + bXt-1 X = d + e(Yt-1)