a total quality management-based incentive system supporting total quality management implementation

PRODUCTION AND OPERATIONS MANAGEMENT Vol. 4. No. 3. Summer 1995

Primed in U S.A

A TOTAL QUALITY MANAGEMENT-BASED INCENTIVE SYSTEM SUPPORTING TOTAL QUALITY

MANAGEMENT IMPLEMENTATION *

RICHARD T. SYMONS AND RAYMOND A. JACOBS Department of Business Administration, Ashland University,

401 College Avenue, Ashland, Ohio 44805, USA

Although there have been many cases of total quality management (TQM) success, embracing TQM does not always lead to performance improvements. Many companies resist the changes in organizational processes such as compensation and performance appraisal systems that are required to link TQM efforts to bottom-line performance. We present the basic structure of a TQM-based compensation system that can provide incentives based on a variety of performance measures, including an explicit incentive for the reduction of variability in product variables. As a result, this approach encourages the continuous improvement central to the TQM philosophy, rather than serving as a disincentive for such improvement as do many traditional compensation systems. The set of performance measures can be adjusted periodically to focus on those measures deemed most likely to yield significant increases in customer satisfaction, further supporting the core elements of TQM. The approach is described using examples from the paper manufacturing operation where it has been successfully implemented. A longitudinal analysis of several performance measures is used to demonstrate the effectiveness of the new compensation system. (TOTAL QUALITY MANAGEMENT; INCENTIVE SYSTEMS)

1. Introduction

One would be hard pressed to find any progressive company that is not emphasizing quality as the key to its competitive edge and long-term growth. The approach subscribed to by most of these firms, most often known as total quality management ( TQM), has been defined in part as “a people-focused management system that aims at continual increase of customer satisfaction at continually lower real cost . . . (stressing) learning and adaptation to continual change as keys to organizational success” (Evans 1992). In recent years TQM has been heavily touted as the best approach for improving an organization’s productivity, profitability, and competitiveness.

Although there are numerous cases of TQM success, embracing TQM does not always lead to performance improvements. There are a variety of reasons why a TQM program may fail, but many problems encountered when implementing TQM are due to the dra- matic changes typically required in the way the organization does business. A Business Week Special Report on Quality (Port, Carey, Kelly, and Forest 1992) cited several recent studies that observed a large number of ineffective TQM initiatives and asserted

* Submitted July 1993; revised May 1994 and July 1995; accepted July 1995.

228 ~059-~4lg/95/0403/22g~l.25

Copyright 0 1995, Production and Operations Managemenr Society

A TOTAL QUALITY MANAGEMENT-BASED INCENTIVE SYSTEM 229

that “an all-out quality commitment requires companies to uproot entrenched habits and business methods and virtually start over” (p. 67).

To facilitate the changes required by TQM implementation, management must concentrate on revising and realigning organizational processes and systems to make them more supportive of people-focused continuous improvement. The Core Body of Knowl- edge Working Council of the Total Quality Forum concluded that “the difficult task of implementing TQ in practice, which is clearly the most urgent applied challenge . . . has little to do with improved techniques and much to do with management and organizational processes” (Evans 1992, p. 2-42). Scherkenbach ( 1986) concurs that management “must first take the lead in examining every management system and operating procedure to determine if it supports or inhibits continuous improvement” ( p. 47).

Among the management systems likely to require changes, the compensation system used to reward and motivate employees has perhaps the most direct and immediate impact on employee behavior. A poorly suited compensation system may send signals to employees that conflict with the TQM message delivered by other management systems. On the other hand, an incentive system that supports the core concepts of TQM can provide the link between quality efforts and the bottom line that American Quality Foundation president Joshua Hammond feels is missing in many TQM efforts (Port, Carey, Kelly, and Forest 1992). In spite of the fact that incentives play a key role in managing employees motivated by “what’s in it for me,” there have been few, if any, compensation systems that adequately support TQM and continuous improvement.

In this paper we present the basic structure of a TQM-based compensation system that can provide incentives based on traditional production measures as well as a variety of quality-related performance measures, including an explicit incentive for the reduction of variability in product variables. This allows for a direct link to process variability, a feature that sets this system apart from others that have been suggested. As a result, this approach explicitly encourages the continuous improvement central to the TQM philosophy, rather than serving as a disincentive for such improvement as do many traditional compensation systems. In addition to the initial establishment of performance measures, any measures can be adjusted periodically to focus on those most likely to yield significant increases in customer satisfaction, further supporting the core elements of TQM.

Following a brief discussion of the use of incentive and compensation systems in companies implementing TQM, we describe the basic elements of our TQM-based incentive system and explain how each element is both grounded in and supportive of the TQM philosophy. The approach is described with the use of examples from the paper manufacturing operation where it has been successfully implemented. We then present a longitudinal analysis of several performance measures to demonstrate the effectiveness of the new compensation system at the firm that has implemented the approach. We continue by describing how the system can be adapted to react to process improvements as well as other environmental changes. We provide some concluding thoughts in the final section.

2. TQM and Employee Compensation Systems

Compensation systems have traditionally been built around three components: salary, benefits, and rewards (such as incentives). Salary and benefits, which are typically based on initial rates and annual appraisals, are hygiene factors and therefore are not an effective source of motivation. On the other hand, a properly structured incentive system that rewards initiative and teamwork as well as output can provide elements of achievement, recognition, and other motivator factors (Herzberg 1987 ). Furthermore, such an incentive system can reward performance as frequently as each payday. It is arguable that the reward system can have at least as much effect on quality as yearly evaluations while also encouraging desired actions in a more timely fashion, yet many companies have

230 RICHARD T.SYMONS AND RAYMOND A.JACOBS

been slow to link incentives to quality issues. As Lawler, Mohrman, and Ledford ( 1992 ) note, “practices that manage the performance of individuals have not been a central focus of implementation in total quality management” ( p. 102).

The use of incentive and compensation systems by companies implementing TQM varies widely. In their discussion of systems for compensating members of self-directed work teams, Wellins, Byham, and Wilson ( 199 1) single out skill-based pay as being used by many companies,because it supports multiskilling. They point out several drawbacks of pay-for-skill systems, however, including the need for extensive training, lost proficiency due to acquired but rarely used skills, and the difficulty in developing and implementing such systems. Furthermore, there is no direct linkage to output, quality, or system improvement with this approach.

Gain sharing or profit sharing is another compensation approach that has been used with empowered teams, with division or unit profit sharing being the most prevalent system of this form (Wellins, Byham, and Wilson 199 1). This type of system does have the advantage of a direct connection to bottom-line profits or other performance gain, but there often is no direct linkage to quality and safety incentives. In many firms, quality and safety awards, when present at all, “usually take the form of certificates, trophies, small cash bonuses, or some sort of motivational event” (Wellins, Byham, and Wilson 199 1, p. 59) rather than being an integral part of the compensation system.

The use of merit ratings, evaluation of performance, and annual review has been singled out by Deming as one of the “Seven Deadly Diseases” plaguing US management. Among the problems Deming attributes to individual performance appraisal is that “it nourishes short-term performance, annihilates long-term planning, builds fear, demolishes teamwork, nourishes rivalry and politics . . . (and) is unfair, as it ascribes to the people in a group differences that may be caused totally by the system that they work in” (Deming 1986, p. 102).

Despite the objections of Deming and others, many companies implementing TQM use merit ratings or performance ratings in some form (Evans 1992). Walton suggests that many companies that otherwise subscribe to Deming’s approach find it difficult to change their existing compensation and promotion systems, and she cites US Navy con- sultants who concluded from their study of eight quality-minded firms that “performance management tends to be the last component addressed in TQM transformation” (Walton 1991, p. 221).

The Core Body of Knowledge Working Council of the 1992 Quality Forum asserted that “traditional ideas about systems of merit ratings, incentives, and compensation of employees have come under critical scrutiny, and the case for them has become more and more uneasy” (Evans 1992, pp. 2-47). Cleary and Cleary ( 1993) point out “the need to design systems that reflect a commitment to statistical thinking and the organization’s long-term goals is nowhere more pressing than in compensation systems” (p. 69). The incompatability between many incentive systems and the idea of continuous improvement is pointed out by Hayes and Wheelwright, who observed that in many firms “wage incentives are designed or applied in a manner that inhibits performance improvements rather than encouraging them” (Hayes and Wheelwright 1984, p. 247). Obviously, new approaches are needed to motivate and reward employees working under the TQM philosophy-approaches that satisfy management’s desire to encourage good performance while remaining consistent with the basic tenants of TQM .

In spite of the lack of attention given to reward systems in the quality literature, Lawler, Mohrman, and Ledford ( 1992) found in their 1990 survey of Fortune 1000 companies that total quality practices often are utilized by “organizations with a rewards orientation to employee involvement” ( p. 103 ). They point out that many involvement- related reward systems are compatable with TQM, particularly when problem-solving and


process-improvement groups are used in conjunction with gainsharing and team incentive programs. It is exactly this sort of system that is described here.

3. Elements of a TQM-Based Incentive System

The incentive system described in this paper was developed to provide rewards and feedback in a timely fashion (every pay period) while adhering to the philosophies of TQM . The following objectives were established for the incentive system:

1. maintain focus on a measurable key output variable, 2. promote a teamwork approach to problem solving, and 3. include explicit incentives for quality and safety measures.

We now describe how each of these objectives is met. A flow chart summarizing the process used for calculating each worker’s bonus is presented in Figure 1. As shown in

Select key output variable (da//y oa&wf h tans)

Calculate mea” (ADO) and standard deviation (a) of key output vorlable durtng bosellne period (3 months)

Calculate mean (ad@) of key output vartable during most recent pay period (2 weeks)

Determine level of bonus @ercenfage of each employee’s base pay) related to key output variable:

, I

IF odoP<ADO-2a REWARDfADO)= 0 IF AW2odo2<ADOu REWARDfADO)= 2% IF AWcSodo2<ADC IF ADCSodoP<ADO+o

REWARD(ACC)= 4% REWARD(ADO)= 6%

IF ADO+oSodoP<ADO+Po REWARDfADO)= 8% IF ,ADO+loSodo2 REWARDfADO)=lO%

Determine level of bonus related to teamwork problem- soMng lncentlve 1 Teanu will either: (1) try to solve a oblem related to an

out-of-contra t” point. or (2) work on vorla”ce reduction for a

product vartoble (If no out-of- control points exkt)

IF team formed and fishbone diagram completed REWARD(Tl)=l% IF plan developed for Implementing change REWARDfTI)=3% IF plan Is Implemented and change Is successful REWARDgl)=5%

+ Use Pareto Analysts to ldenttfy product vartable(s) to be used as quality ‘gates’

I I

Calculate mecm (MEANI) and standard devtatlon (cl) of the devlatlon from target for product variable 1 Cpaperthlckness) during bosellne period (3 morrfhs)

I Calculate mean (dev/afbn I) of the devtatlon from target for product variable 1 durtng most recent pay period (2 wee&s)

IF deviationl>MEANl+2(ol) REWARD(Ql)= 0 Determlne level of bonus related to quality gate 1 (paper thickness) IF MEANlcdeviotlon~EANl+2bl) REWARD&X)= 2%

IF M5ANl-2(ol)Sdeviotionl9MEANl REWARD(Ql)= 4% IF devtotlonl<MEANl-2fol) REWARD(Ql)= 5%

Calcukrte mean (MEANZ) and standard deviation (62) of the devlatlon from target for product variable 2 (mofsfure conten dudng baselIne period (3 monfhs)

Calculate mean (dev/at/onZJ of the deviation from target for product vortable 2 during most recent pay period (2 weeks)

IF deviotlon2>MEAN2+2(o2) REWARDG2)=0 Determine level of bonus related to quality gate 2 (moisture confertO: IF MEAN2<detiatlon2SMEAN2+2@2) REWARD(Q2)=2%

IF MEAN2-2(c2)sdevlotlon~MEAN2 REWARDG2)=4% IF devlotton2cMEAN2-2(cr2) REWARD(Q2)=5%

I Determlne total bonus prior to flnol safety gate

TOTAL=(Employee base pay)x(REWARD(ADO)+REWARDfTl)+REWARD(QI)+R2WARD~Q2)) I

Determine level of bonus related to safety gate

IF no In urles from Job-related accidents during most recent TOTAL=TOTAL IF one n]ury from a Job-related accident during most recen 1 Fy:$:d TOTAL=TOTALx50% IF two or more Injuries from job-related accidents during mo recent pay period TOTAL.0

FIGURE 1. Flow Chart for Bonus Calculation in TQM Incentive Plan.


the flow chart, the various incentive components total to a maximum bonus of 25% of the worker’s base pay.

Focus on A Measurable Production Variable

The primary focus of the incentive system is on a measurable key output variable, production. This output variable was selected for several reasons. First, it provides for a direct link between bottom-line performance and the incentive plan. This link is essential for top management support and for the overall effectiveness of the system. Second, the variable must be easily measurable to facilitate system accounting as well as to gain employee support. In general, if the system is too complicated, employees may become confused and, consequently, may not respond to the incentive. Third, the focus should be on a variable that is affected by the entire organizational unit rather than just one individual or team, so that the incentive promotes a sense of unity throughout the workforce. In many organizations (such as the one in which this system has been implemented), this last consideration may involve eliminating an existing incentive based on individual or team performance.

To this point, the incentive system has much in common with traditional production- based incentive packages. The remaining elements of the system, however, are quite different. Rather than establishing a single performance baseline to be targeted, this incentive plan is based on the statistical deviation from a periodically updated moving average. (Clearly, employees working under this system must have received prior training in Statistical Process Control ( SPC), or this aspect of the incentive plan will make no sense to them.) Specifically, the level of the incentive reward earned is determined by the position of the average daily output during the most recent pay period in terms of ‘how many standard deviations it is above or below the average daily output during a span of several past pay periods.

For example, in the implementation of this system in a paper manufacturer, the key production variable is average daily tonnage. A 3-month history is used to calculate the performance baseline average daily output (ADO) and the standard deviation ( a) of daily output. A total of 10% of each employee’s base pay is available from this portion of the incentive, with the following threshholds. (Note: as described below, percentage points earned from one part of the bonus ultimately may be reduced or eliminated if other parts of the incentive plan are not satisfied.) If average daily output during the most recent 2-week pay period is less than 2a below the baseline ADO, no reward is earned. If the output is between 2a below the baseline ADO and the baseline ADO, a 4% reward is earned. Output between the baseline ADO and la above the baseline ADO earns an additional 2% (for a total of 6%). Output between 1 and 2a above the baseline ADO earns an additional 2% (for a total of 8%). Output greater than 2a above the baseline ADO, likely indicating an improvement to the production system, earns the final 2% (for a total of 10% ) .

If the full 10% bonus is earned, the work crews are asked to document what was done differently during the 2-week recording period. Assuming that the changes to the production system were logically linked to the increased performance and did not have a negative impact on either safety or quality, the changes are incorporated into the standard operating procedures.

The exact percentages and the number and location of the performance threshholds may, of course, be adjusted as desired, but several main points should be adhered to. First, there should be some reward earned as long as the system maintains itself in statistical control, even if below average output is experienced (in this case, the first 4% of the incentive). Second, the reward should be increased for above average performance.

Perhaps the most interesting feature of this part of the incentive system is the way in which a reduction in output variability is rewarded. As variability is reduced and (T


becomes smaller, the performance threshholds move closer to the mean (baseline) ADO

and therefore become more attainable (smaller improvements in the process are required to increase output enough to earn the top reward). This aspect of the system was almost immediately noted by the workers themselves once the system was implemented. (Iron- ically, management had been trying for 4 years prior to the implementation of this incentive system to get workers to pay attention to variation, with little if any success.) To our knowledge, a.reward system containing such an explicit incentive for workers to reduce process variability has not previously been described in the quality management literature.

Promote Teamwork Approach to Problem Solving

The second objective of the incentive plan is to promote a teamwork-oriented approach to solving problems as they arise, for example, when a machine is discovered to be out of control according to its control chart. In many firms, workers assigned to teams by management may lack self-motivation for working with the other team members to solve a problem. Under this system, any individual employee can request (by asking management or a steering committee of other employees formed for this purpose) that a team be formed to address a particular problem. Workers thus are given the responsibility for creating their own quality action teams and analyzing the problem using the appropriate statistical process control ( SPC) tools. The reward allocated to this section of the incentive plan is earned depending on the success that the team has in dealing with the problem, as described below.

In the paper manufacturing unit, a maximum of 5% of the base pay is available in each pay period for the teamwork incentive. If the work crew forms a team to work on a problem and creates a fishbone diagram to analyze the problem, they earn 1%. If the fishbone analysis results in a prescribed fix for the problem and the team develops an operating plan for implementing the change, they earn an additional 2% (a total of 3%). If the plan is actually implemented and the change is successful, they earn the final 2% of the incentive (for a total of 5%). It is important to note that the workers are being rewarded here for the process by which they are addressing problems rather than the level of their success in making changes. Thus, a solution yielding a $50 cost saving will result in the same reward as an improvement resulting in a $5000 cost saving. The intent of this part of the incentive is to reward team-based problem solving initiatives to encourage future team-based efforts.

If during a pay period there are no “out-of-control” situations to be dealt with (or if all out-of-control situations are readily explainable), the workers are encouraged to develop a standing team to work on variation reduction for product variables. The basic structure for the reward in this case is the same as for a problem-solving team. Workers are en- thusiastic about working on variation reduction because they recognize how reduced variation benefits them in the other parts of the incentive plan. In practice, this portion of the incentive meant that the workers are always working on either solving a particular problem or else working to reduce variation of some product variables, resulting in the continuous improvement Central to TQM .

Incentives for Quality and Safety

In addition to the primary focus on a key production variable and the support for team problem solving, the incentive system contains explicit incentives for key quality and safety measures. These measures are referred to as gates, a term that reflects their position as figurative portals that must be passed to reach the incentive reward. Any number of quality and safety gates can be included in the system, although it is recom- mended that only a few be included at a time to concentrate efforts on what currently are perceived to be the most important areas. Pareto analysis is helpful here since em-

234 RICHARD T. SYMONS AND RAYMOND A. JACOBS

ployees are familiar with the technique and it gives management an opportunity to share information with employees. As described in the next section of the paper, the specific gates can be changed over time as the environment changes. In its initial implementation, two quality gates and one final safety gate are employed.

The approach used to select the quality gates is based on the TQM objective of increasing customer satisfaction while simultaneously reducing costs. It is critical to the effectiveness of this system that a clear justification exists for each of the specific product variables selected as gates. In the case of the paper manufacturer, a combination of customer surveys and customer complaint records were used to identify a list of product variables that impacted customer satisfaction. By applying the SPC tool of Pareto analysis to the list, the product variables for which improved control is most important to customer satisfaction are identified. The paper manufacturer identified moisture content and caliper (paper thickness) as the two initial quality gates.

For each quality gate, a target level based on customer requirements is identified. For example, a specific paper thickness is specified as the target for the caliper gate. Historical records are maintained to determine the average deviation from the target and the standard deviation of that average. In each period (assuming that the process is capable), the average deviation from the target is calculated and compared against the historical limits, with the use of the same basic approach employed with the key performance measure. If the deviation is greater than two standard deviations above the historical mean level, no reward is earned. If the deviation is between the mean and two standard deviations above the mean, a 2% bonus is earned. An additional 2% bonus is earned (for a total of 4%) if the deviation is between the mean and two standard deviations below the mean. If the deviation from the target is reduced by a statistically significant amount (likely due to an improvement in the process) so that it is less than two standard deviations below the historical mean, an additional 1% reward is earned (the maximum of 5%). The same basic approach is used for the other quality gate (in this case, moisture content). In this fashion, an explicit incentive exists for the reduction of variation in the key product variables. Note that a key advantage of measuring the average deviation from the target rather than the variable (for example, thickness) itself is that the control chart will then measure process capability and not just the ability to reach a specific target.

A final component to this incentive plan is a gate for safety. As in many firms, there has long been a strong emphasis on safety at the paper manufacturer, and they wanted the compensation system to retain this emphasis so that both workers and managers understood that safety considerations were still key. This final gate is implemened in such a way that if safety performance is good, the full percentage reward earned from the other components is actually paid. If safety problems exist, part or all of the entire reward could be eliminated for the current pay period, as described below.

One extreme would be to treat all injuries as preventable and decide that if any injuries occur, the entire incentive would be eliminated for that pay period. Another approach might be to establish the relationship between injuries and “near misses” and/or safety violations and then use an approach similar to the one used for the other gates as described above. In the specific implementation described here, the following approach is used. If during the 2-week period just ended there were no injuries resulting from job-related accidents, then the full percentage reward is paid. If there was one injury of any sort, then only half of the percentage reward earned from the other components in this period is paid. If there are two or more injuries, no incentive pay is received at all for the current period. The advantage of this approach is that it emphasizes to the workers that safety is everyone’s responsibility and cannot be treated individually, which in turn helps the entire work force pull together on this issue. (It should be noted that if safety-related incentives exist for management, they must be similar to and not in conflict with the incentives applied to the hourly workforce.)


4. Performance Improvements with the New System

The incentive system was successfully implemented at a paper manufacturing operation, and it has performed very well. In this section, we present the results of a longitudinal analysis of selected performance measures during a 26-month period. This time span includes a 12-month period (June 1992-May 1993) prior to full implementation and a 1Cmonth period (June 1993-July 1994) during which the new incentive system was fully implemented and used to determine worker bonuses. The data to be analyzed are given in Table 1. As described below, the analysis confirms the effectiveness of the new system in helping the firm increase output, reduce scrap, reduce the variability of the two critical product variables (caliper and moisture), increase safety awareness, and decrease the number of customer complaints. Because no major process changes occurred during this period, a large portion of the improved performance is attributed, by the plant’s top management, to be the direct result of the new incentive plan.

The primary focus of the incentive system is output; therefore, management’s primary measure for assessing the effectiveness of the new incentive system was the key output variable, average daily tonnage produced during each month. As shown in Table 1, tonnage increased from an average of 74.5 tons/d (u = 0.98) during preimplementation (months 1-12) to an average of 86.7 tons/d ( c = 4.10) during postimplementation (months 13-26). A t-test (assuming unequal variation) testing the alternative hypothesis of Ha: preimplementation average tonnage < postimplementation average tonnage is significant at the CY = 0.01 level, verifying the observation of an increase in output after implementation of the incentive plan.

TABLE I

Monthly Performance Measures

Period (TIME) Month

output Scrap (TONS) (in tons)

Caliper std. dev.

Moisture std. dev.

Customer Complaints In.iuries

8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Jun 92 74.1 3.0 1.15 1.60 Jul92 74.7 4.0 1.16 1.70 Aug92 75.4 4.3 1.10 1.20 Sep 92 74.9 4.1 1 .oo 1.40 Ott 92 75.2 4.0 1.10 1.60 Nov 92 74.9 4.3 0.96 1.50 Dee 92 73.0 3.9 0.86 1.40 Jan 93 72.4 3.2 0.82 1.20 Feb 93 74.1 4.6 0.84 1.40 Mar 93 75.4 5.3 0.86 1.30 Apr 93 74.9 4.6 0.78 1.30 May 93 75.5 5.0 0.75 1.20 Jun 93 77.5 4.2 0.75 1.20 Jul93 80.6 4.2 0.72 1.10 Aug 93 81.9 4.9 0.71 1 .oo Sep 93 83.1 5.0 0.71 1.00 oct93 85.4 4.8 0.66 1.10 Nov93 88.1 3.6 0.64 1 .oo Dee 93 89.2 2.9 0.62 1.00 Jan 94 89.2 3.1 0.57 1 .oo Feb 94 89.5 3.3 0.61 0.99 Mar 94 90.2 2.8 0.56 0.96 Apr 94 88.9 3.1 0.58 0.92 May 94 89.9 1.8 0.55 0.89 Jun 94 90.1 1.8 0.55 0.88 Jul94 90.2 1.8 0.50 0.80

1 2 4 0 3 1 3 1 3 0 5 1 5 1 3 1 2 1 1 1 2 1 3 2 2 1 2 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 2 1 0 0 0 1 0


This simple mean separation test does not consider many of the more subtle questions that can be raised concerning the stability of the process, the impact of the “learning curve” effect, or the predictability of the output (tonnage) after implementation of the incentive plan. To answer these questions, we begin by observing a graph of tonnage over the time of the study (Figure 2). A simple regression model of the form Y = a + blXl , where Xi = time and Y = tons, yields the following:

TONS = 69.75 + 0.84X1,

where the b, coefficient is significant at the cy = 0.05 level and R-SQUARE = 0.86. This, in itself, is a forceful linear model. But, as is readily apparent from the graph shown in Figure 2, the data can be grouped into three distinct learning curve stages. The first stage, characterized by relatively stable output (u = 0.98), is the preimplementation period (months l- 12 ). The postimplementation period exhibits two distinct stages: a transition or learning stage (months 13- 18 ) characterized by increasing output and a posttransition stage ( months 19-26 ) characterized by relatively stable output at the significantly higher level. This is consistent with our expectations that the plant, which has had an active TQM program for some time, would exhibit relatively stable (“in control”) output prior to the change effected by the new incentive system. We would hypothesize that the new system would temporarily disrupt that equilibrium as improvements were made (which it did, as indicated by the increase in IS to 3.7), with an eventual return to stable output at a higher level (assuming that the new system was successful) once the transition or learning stage had transpired (which it did, as indicated by the reduction in CT to 0.5 1).

Given the emphasis on reduction in variability central to the new incentive system, we would hypothesize that the variability in output levels would be significantly reduced from preimplementation (stage I) to post-implementation (stage III). The variance of the output levels from periods 1-12 (stage I) is 0.962, and from periods 19-26 (stage III) is 0.265. An F-test confirms that this is a significant reduction in variability: the F- statistic is 0.962/0.265 or 3.63, which is statistically significant at the 0.05 level.

100

95

FIGURE 2. Average Daily Output (TONS).


The impact of the incentive plan on output first was examined with a linear regression model of the form TONS = a + b,TIME + &PLAN. The independent variables are: TIME, representing the time period (months l-26); and PLAN, a dummy variable differentiating preimplementation (PLAN = 1 for periods 1 - 12) and postimplementation (PLAN = 0 for periods 13-26). The dependent variable, TONS, represents the average daily output in tons for each of the 26 months. The fitted model is:

TONS = 75.8 + 0.56(TIME) - 4.9(PLAN).

The model fits the data well, and the R-SQUARE value is increased to 0.89. The coefficient associated with the variable PLAN was statistically significant at the 0.02 level, indicating a significant increase in output averaging 4.9 tons/d after the plan was fully implemented. Although this analysis does support the hypothesis that the incentive plan did result in significantly increased output, an analysis of all three stages of data provides some additional information.

To test the hypothesis that there are three distinct stages in the data, we expand the regression model to represent all three stages with dummy variables and to test whether there is a significant increasing trend during the transition in stage II. The new model is of the form TONS = a + b,PLAN + bzSl + b3S2, where the variables TONS and PLAN are defined as above and the following variables are used to represent the stages: a dummy variable, Sl , indicating stage I, the preimplementation stage (Sl = 1 for periods 1- 12, Sl = 0 for periods 13-26); and a dummy variable, S2, indicating stage II, the transition stage (S2 = 1 for periods 13-l 8 and S2 = 0 for periods l- 12 and 19-26). This model yielded the following results:

TONS = 85.0 + 0.20(PLAN) - 11.8(Sl) - 5.5(S2),

with an R-SQUARE of 0.94 and a standard error equal to 1.8. We also note that with the inclusion of stage variability, the beta value of PLAN becomes insignificant and will be dropped as an independent variable. However, it will be analyzed in terms of the interaction with the three stages of the implementation model. To analyze the significant variables plus the interaction, we use the model: TONS = a + b, Sl + bzS2 + b3T1 + b4T2 + b5T3, where the following variables are used to test for interaction between the three stages and the time period (TIME) : Tl , representing the potential interaction in stage I ( Tl = TIME X Sl ); T2, representing the interaction in stage II ( T2 = TIME X S2); and T3, representing the interaction in stage III (T3 = TIME X S3). The dependent variable, TONS, represents the average daily output in tons for each of the 26 months. The initial model fit is as follows:

TONS = 86.60 - 12.12(Sl) - 34.21(S2) + O.Ol(Tl) + 1.96(T2) + O.l4(T3).

The model fits the data very well; in fact, the R-SQUARE value equals 0.989. One might ask if the improvement in R-SQUARE from 0.93 to 0.989 is significant. To test this, we used an F-test with Ha: R-SQUARE( full model) > R-SQUARE( restricted model). This model gives

Ii = (R-SQUAREfull - R-SQUAREmictedI 1 = to.99 - 0.93)/ 1 ( l-R-SQUARE&/( 12 - k) (1 - 0.99)/21 Z 15”

which is significant; thus the full model is an improvement. Furthermore, all coefficients are statistically significant at the 0.001 level, except for the coefficients associated with Tl and T3. We can infer from this that output is relatively stable in stage I and stage III and thus does not contribute to an explanation of variability, but that there is a statistically significant increase in average daily output of 1.96 tons during the postimplementation


transition months in stage II. .This is confirmed by refitting the model after dropping the variables Tl and T3, yielding the following model:

TONS = 89.65 - 15.11(Sl) - 37.26(S2) + 1.96(T2).

This model has an R-SQUARE value of 0.988, and all coefficients are statistically significant.

In summary, the firm has experienced a significant increase in output following the transition from the old incentive plan to the new incentive plan, accompanied by a significant reduction in the variability of output. We now turn our attention to an analysis of the other performance measures affected by the new incentive plan.

An examination of the scrap levels during the 26-month period (see Table 1) shows a temporary increase in scrap during the initial 6 months of postimplementation (stage II) from a preimplementation (stage I) average of 4.2 tons of scrap per day to an average of 4.5 tons. This can be attributed to the increased level of experimentation with the process that accompanied the emphasis on variance reduction under the new incentive plan. This was followed by a decline in scrap during the final &month period (stage III) to an average of only 2.6 tons of scrap per day. Considering the last 3 months of data, where scrap consistently averaged only 1.8 tons/d, there was a reduction in scrap of well over 50% from preimplementation to postimplementation. This is shown graphically in Figure 3.

Also targeted for improvement under the new incentive plan were the variability of two process variables, caliper (paper thickness) and moisture content. The ongoing TQM program had had success in reducing the variability of both, but management felt that the new incentive system would enable them to continue to reduce variability beyond their current levels. As seen from the data in Table 1, also shown graphically in Figure 4, the variability of both caliper and moisture continued to decrease after the new plan was implemented. Although a regression analysis with dummy variables does not indicate a significant change in the rate of reduction in variability from preimplementation to

FIGURE 3. Average Daily Scrap.


FIGURE 4. Caliper and Moisture Standard Deviation.

postimplementation, management has attributed to the new incentive plan much of the continuing success in reducing variability to levels better than the industry standard. F- tests confirm that for both caliper and moisture content, the variability is significantly reduced after implementation of the new incentive plan.

Another important performance measure relates to customer satisfaction. After nearly 1 year under the new system, the plant’s top management feels that the workers have become more conscious of product quality and will no longer ship poor quality products, as had been the case in the past. According to the plant manager, the workforce now feels that the company is “backing up their talk concerning TQM” by empowering all employees to play a major role in managing the company. As a result, the firm has experienced significant improvements in customer satisfaction because of improved product quality, including a more than 70% reduction in returned sales. An additional indication of improved customer satisfaction can be measured by the number of customer complaints. As shown in Table 1, the number of customer complaints has declined from an average of 2.92 complaints per month during the 12 months prior to implementation of the new incentive plan, to an average of only 0.64 complaints per month during postimplementation periods 13-26.

In addition, there has been an increase in safety awareness, since everyone is now involved in and directly affected by the plant’s overall safety record. As shown in Table 1, at least one injury was recorded in 10 of the 12 periods (83%) prior to the new system’s implementation, while injuries were recorded in only 4 of the 14 periods (29%) after the new incentive system was in place.

As with any new incentive plan, an important consideration is whether the plan is providing benefits in excess of its costs. Under the old incentive plan (based solely on reaching an output target), employee bonuses were consistently at 12%, the maximum under that plan. Under the new plan, which included an adjustment to the base pay scale, there no longer was a cap at 12%. To date, there has been approximately a 2% increase in overall worker compensation under the new incentive system compared to


the old system. Since labor cost makes up less than 20% of the company’s total production cost, they have achieved significant performance improvements from less than a 0.5% increase in overall production cost. Given the success of the new incentive system in improving performance, as described above, the new incentive plan is considered by management to be very cost-effective.

5. Adapting the System as the Environment Changes

An important aspect of this incentive system is the way in which it can be adapted as the environment changes, providing encouragement and rewards for continuous improvement. One way in which the incentive plan rewards improvements to the process is by explicitly rewarding efforts to reduce variability as well as implicitly rewarding performance along the measures in which variability has been reduced.

A second area in which the incentive system adapts to change is in the area of the quality gates. As described above, Pareto analysis is applied to prioritize the product variables most important to customer satisfaction, as determined by a direct analysis of customer needs. The variables deemed most important to customer satisfaction are selected as the initial quality gates. However, if improvements made to the process reduce variability in a specific target variable so that any problems related to that variable have been eliminated, then that product variable can be removed as a quality gate and the company can go “down the list” to select the next variable needing attention. Of course, the product variable dropped from the incentive plan as a quality gate remains important to customer satisfaction and continues to be monitored to make sure that it does not go out of control in the future. In general, the plan is flexible enough to allow for any number of quality gates as required by the situation.

6. Conclusions

In summary, the incentive system described in this paper provides an effective way for a company to reward its employees for behavior directly supporting TQM implementation. It is based on TQM concepts of continuous improvement, teamwork, adaptation to change, and a focus on customer satisfaction, and it incorporates the methodologies of SPC familiar to TQM devotees. It is designed to focus on a key output-related variable while allowing for any number of secondary incentives in areas such as teamwork, quality, and safety.

The focus on an output-related variable may seem inconsistent with the SPC adage to “observe the system and production will take care of itself.” In actuality, the output- based portion of the incentive merely provides a tangible reward to all workers when the efforts directed at system variables (such as teamwork, safety, and process variability) pay off in terms of output. This clearly and directly demonstrates to employees how an emphasis on continuous improvement of processes will indeed drive production. In the implementation described in this paper, the new incentive system has provided a clear incentive for continuous improvement that has facilitated the implementation of TQM, yielding measurable improvements in performance.

References

CLEARY, TIMOTHY J. AND MICHAEL J. CLEARY ( 1993), “Designing an Effective Compensation System,” Quality Progress, 26, No. 4, 69-72.

DEMING, W. EDWARDS (1986), Out of the Crisis, MIT Center For Advanced Engineering Study, Cambridge, MA.

EVANS, JOHN P. ( 1992), A Report of the Total Quality Leadership Steering Committee and Working Councils, The Procter & Gamble Company, Cincinnati, Ohio.

HAYES, ROBERT H. AND STEVEN C. WHEELWRIGHT ( 1984), Restoring Our Competitive Edge: Competing Through Manufacturing, John Wiley & Sons, New York.


HERZBERG, FREDERICK ( 1987), “One more time: How do you motivate employees?,” Harvard Business Review, September-October, 65, No. 5, 109-120. (A reprint of an article originally published in the January- February 1968 issue, with a “Retrospective commentary”.)

LAWLER, EDWARD E. III, SUSAN ALBERS MOHRMAN, AND GERALD E. LEDFORD, JR. ( 1992), Employee In- volvement and Total Quality Management: Practices and Results in Fortune 1000 Companies, Jossey- Bass, San Franscisco, CA.

PORT, OTIS, JOHN CAREY, KEVIN KELLY, AND STEPHANIE ANDERSON FOREST ( 1992), “Quality,” Business Week, November 30, 3295,66-72.

SCHERKENBACH, WILLIAM W. ( 1986), The Deming Route to Quality and Productivity: Road Map and Road- blocks. CEE Press Books, Washington, DC.

WALTON, MARY ( 199 1 ), Deming Management at Work. Perigee Books, New York. WELLINS, RICHARD S., WILLIAM C. BYHAM, AND JEANNE M. WILSON ( 1991), Empowered Teams: Creating

Self-Directed Work Groups That Improve Quality, Productivity, and Participation. Jossey-Bass, San Francisco, CA.

a total quality management-based incentive system supporting total quality management implementation

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