Kaizen one approach to improving the organization’s performance
Learning From Less Successful Kaizen Events: A Case Study Jennifer A. Farris, Texas Tech Eileen M. Van Aken, Virginia Tech Toni L. Doolen, Oregon State University June Worley, Oregon State University Abstract: This paper describes results from an ongoing research program focused on identifying determinants of Kaizen event effectiveness, both in terms of initial event outcomes and the sustainability of outcomes. Although anecdotal published accounts suggest that increasing numbers of companies are using Kaizen events, and that these projects can result in substantial improvement in key usiness metrics, there is a lack of systematic research on Kaizen events. A particular weakness of the current published accounts is the lack of attention to less successful events – only strongly successful applications of Kaizen events receive much coverage in the accounts; however, the organizational learning literature suggests that understanding less successful cases is a key component of organizational learning. We present a case study from a less successful Kaizen event to demonstrate how the case study event contributed to organizational learning. We also present a set of methods and measures that can be sed by practicing engineering managers and engineering management researchers to evaluate and analyze Kaizen event performance. The implications of the case study event for the current body of knowledge on Kaizen events are also examined, and, finally, directions for future research are described. Keywords: Productivity, Teams, Lean Manufacturing, Quality Management EMJ Focus Areas: Strategic and Operations Management, Program and Project Management, Quality Management A “Kaizen event” is a focused and structured improvement project, using a dedicated cross-functional team to mprove a targeted work area, with specific goals, in an accelerated timeframe (Letens, Farris, and Van Aken, 2006). During the relatively short timeframe of the event (generally 3 to 5 days), Kaizen event team members apply low-cost problemsolving tools and techniques to rapidly plan and, often, implement improvements in a target work area. Evidence suggests that Kaizen events have become increasingly popular in recent years as a method of rapidly introducing improvements. In particular, Kaizen events have been associated with the implementation of lean production (Womack, Roos, and Jones, 1990).
In fact Kaizen events apparently originated with Toyota, which used this method to train its suppliers in lean production practices during the 1970s (Sheridan, 1997). Anecdotal published accounts suggest that some Kaizen events have resulted in substantial improvements in key business metrics, as well as in important human resource outcomes (Sheridan, 1997; Melnyk, Calantone, Montabon, and Smith, 1998; Laraia, Moody, and Hall, 1999); however, despite their popularity and apparent potential for creating improvement, there is a lack of systematic research on Kaizen events.
The majority of current Kaizen event publications are focused on anecdotal results from companies that have implemented Kaizen events (Sheridan, 1997; Cuscela, 1998) and untested design recommendations from individuals and organizations that facilitate Kaizen events (e. g. , Laraia et al. , 1999; Vitalo, Butz, and Vitalo, 2003). There is no systematic, empirical evidence on what sort of Kaizen event designs may be most effective for achieving and sustaining improvements in business performance or human resource outcomes. A particular weakness of the current published accounts s the lack of attention to less successful events – only strongly successful Kaizen events receive much coverage in the accounts. This is despite the fact that Laraia et al. (1999) suggest that most companies will have difficulty sustaining even half of the results from a given event. In addition, the organizational learning literature suggests that understanding less successful tool applications is a key component of organizational learning (Sitkin, 1992; Lounamaa and March, 1989; Cole, 1992). This paper describes results from an ongoing research program focused on identifying determinants of Kaizen event ffectiveness, both in terms of initial event outcomes and the sustainability of outcomes. In particular, the paper presents a case study of a less successful Kaizen event studied in the current research to demonstrate how the event contributed to organizational learning, and to highlight how the methods and measures used in the current research allowed triangulation of multiple data types and sources. The implications of the case study event for the current body of knowledge on Kaizen events are also examined, and, finally, directions for future research are described.
Current Research Program Based on the lack of previous research on Kaizen events, in 2002, Oregon State University and Virginia Tech began a joint research effort aimed at understanding Kaizen events. This effort is aimed at measuring the outcomes of Kaizen events (both technical performance and human resource outcomes), and identifying what design and context factors relate to the effectiveness of Kaizen events, both in terms of generating positive initial results and sustaining event outcomes over time.
The goal of the research is to sample multiple Kaizen events in multiple organizations, in order to better understand how these events (and associated outcomes) vary both within and across organizations and industry types. A total of six organizations participated in the first phase of this research program, which Refereed management tool manuscript. Accepted by Associate Editor Landaeta. A previous version of this work was presented at the ASEM 2006 Conference. 10 Engineering Management Journal Vol. 20 No. 3 September 2008 Exhibit 1. Research Model for Initial Outcomes ontained the case study event described here (a total of 51 events were studied), and nine organizations are currently participating in the second phase of the research program. Industry types range from heavily manufacturing-focused companies (e. g. , secondary wood products manufacturers) to heavily engineering-oriented companies (e. g. , electronic motor manufacturer). Within each organization, events within different organizational process areas (e. g. , manufacturing, engineering, sales, and product development) are being studied.
The first phase of this research focused on initial outcomes of events, while subsequent phases are focusing on results sustainability. This case study was completed before the first phase of the larger study was complete, and an earlier work focused on the analysis of the case study data alone (Farris, Van Aken, Doolen, and Worley, 20062). In this paper, we compare the results from the case study to findings from the now-complete first phase of the larger study, to further evaluate case study findings; however, it is important to stress that the contributions of the findings from the larger data set are entirely post hoc.
The evaluation of overall event performance and identification of likely contributing factors were completed based on the case study data alone. Findings from the larger data set, as included here, are used only as a further test of the robustness of case study conclusions. Measures A Kaizen event is a complex organizational phenomenon, with the potential of impacting both a technical system (i. e. , work area performance) and a social system (i. e. , participating employees and work area employees). In addition, Kaizen events typically use a semi-autonomous team (a social system) to apply a specific set of technical problem-solving tools.
Thus, a complex and varied Engineering Management Journal Vol. 20 No. 3 set of measures – including both technical system measures and social system measures – is necessary in studying Kaizen events. Exhibit 1 presents the research model developed to study initial Kaizen event outcomes. (See Farris, Van Aken, Doolen, and Worley, 20061, for a description of how the factors in the model were identified. ) For the study of longitudinal outcomes, additional factors related to the target work area and sustainability mechanisms will be included.
The variables studied in the research on initial outcomes are summarized in Exhibit 2. The longitudinal study of Kaizen event outcomes investigates the relationship between technical system outcomes, work area factors, and follow-up mechanisms. Technical system outcomes to be studied include perceived success (overall success), impact on the area, and the percentage of improvement sustained. Work area factors to be investigated include work area employee learning behaviors, work area employee stewardship, work area employee Kaizen capabilities (i. e. task- and problem-solving knowledge, skills, and abilities gained from Kaizen event participation), work area employee commitment to Kaizen events, and the history of Kaizen events in the target work area (both before and after the target Kaizen event). Follow-up mechanisms to be investigated include the amount of resources invested on follow-up (hours), as well as the specific mechanism used (e. g. , management review meetings, audits, training on new standard work procedures, etc. ) Methods Each participating organization first completes a semi-structured interview on its Kaizen event program as a whole (i. . , the way it plans, implements, sustains, and supports Kaizen events). This interview guide contains 35 open-ended questions related to the Kaizen event program. Then for each Kaizen event studied September 2008 11 Exhibit 2. Variables Studied in Research on Initial Outcomes Goal Clarity Team perceptions of the clarity of their improvement goals Goal Difficulty Team perceptions of the difficulty of their improvement goals Team Kaizen Experience Average number of previous Kaizen events completed by team members Team Functional Heterogeneity
Diversity of functional expertise within the Kaizen event team Team Autonomy Amount of control over event activities given to the Kaizen event team Team Leader Experience Number of previous Kaizen events the team leader has led or co-led Management Support Team perceptions of the adequacy of resources dedicated to the event Event Planning Process Total person-hours invested in planning Work Area Routineness General complexity of the target system, based on the stability of the product mix and degree of routineness of product flow Action Orientation
Team perceptions of the extent to which their team focused on implementation versus analysis Affective Commitment to Change Team member perceptions of the need for the Kaizen event Tool Appropriateness Facilitator rating of the appropriateness of the problem-solving tools used by the team during the event Tool Quality Facilitator rating of the quality of the team’s use of these tools Internal Processes Team member ratings of the internal harmony and coordination of their team. Goal Achievement Aggregate percentage of major improvement goals met Perceived Success (Overall Success)
Stakeholder perceptions of the overall success of the Kaizen event. Impact on Area Team member perceptions of the impact of the event on the target area Kaizen Capabilities Perceived incremental gains in employee task- and problem-solving knowledge, skills, and abilities resulting from a specific Kaizen event Attitude Team perceptions of the degree to which members gained affect for events within participating organizations, four research instruments are used to collect data for the study of initial event outcomes – two questionnaires completed by team members (a Kickoff
Survey and a Report Out Survey), one log of team activities, and one questionnaire completed by the event facilitator (an Event Information Sheet). These instruments, their contents, and administration procedures are summarized in Exhibit 3. In addition to these instruments, organizational documents (e. g. , the report out presentation file, team charter if used, etc. ) are also collected. Besides the quantitative measures described in Exhibit 3, the research instruments are designed to collect additional, qualitative data on the event design, context, process, and outcomes. For instance, the Report Out Survey contains two open-ended uestions that ask team members to describe the biggest obstacles to the success of their team and the biggest contributors to the success of their team, respectively. The Event Information Sheet contains additional open-ended questions asking the facilitator to describe his/her perceptions of the biggest obstacles and biggest contributors, as well as the event planning process, the kickoff meeting process, team training, and management interaction with the team during the event. In addition to the data collected on initial outcomes, a PostEvent Information Sheet is used to collect longitudinal data on 12 he sustainability of event results approximately 12 months after event completion. Longitudinal data is being collected for the 51 events studied during the first phase of this research, as well as for subsequent events studied during additional phases of the research. Some non-response is expected for a variety of reasons (e. g. , work area no longer exists, etc. ). Exhibit 4 describes the types of data collected in the Post-Event Information Sheet. It is noted that pilot efforts in studying the sustainability of event outcomes led to a change in the methodology from the version presented in previous work (Farris et al. 20062), specifically the combination of two instruments and the reduction of the number of repeated observations. Both changes were designed to reduce the measurement burden on participating companies and to increase the likelihood of response, as pilot efforts suggested that the initial methodology was likely to result in high non-response. As can be seen, the current research methodology uses a mixture of measures, methods, and data sources to provide a holistic assessment of the event design, context, process, and outcomes. For instance, technical success is measured through multiple measures (e. . , goal achievement, overall perceived success, perceived impact on area) of multiple types (i. e. , survey scales and objective measures) and multiple data sources (i. e. , data from team members and the event facilitator – as well as Engineering Management Journal Vol. 20 No. 3 September 2008 Exhibit 3. Study Instruments for Initial Outcomes Instrument Kickoff Survey • • • Team Activities Log Person(s) Completing Measures Timing Method Team members First day of event — end of the “kickoff meeting,” which introduces event goals Group administration None directly – provides an understanding of vent context and can be compared to Action Orientation scale results One team member During event – end of each day Self-administered Report Out Survey • • • • • • • • Attitude ImpactonArea KaizenCapabilities TeamAutonomy ManagementSupport ActionOrientation InternalProcesses OverallSuccess(PerceivedSuccess) Team members Last day of event — end of the “report out meeting,” where the team presents results to management Group administration Event Information Sheet • • • • • • • • TeamFunctionalHeterogeneity TeamLeaderExperience EventPlanningProcess WorkAreaRoutineness ToolAppropriateness
ToolQuality OverallSuccess(PerceivedSuccess) GoalAchievement Facilitator One to two weeks after event Self-administered or administered via phone interview GoalClarity GoalDifficulty AffectiveCommitmenttoChange Exhibit 4. Study Instruments for Sustainability Study Instrument Measures Person(s) Completing Timing Method Post-Event Information Sheet • • • • • • • • • • Work Area Manager T2 = 12 months after event Self-administered or administered via phone interview Follow-UpInvestment KaizenEventHistory OverallSuccess(PerceivedSuccess) PercentageofImprovementSustained ImpactonArea WorkAreaLearning
WorkAreaStewardship WorkAreaCommitmenttoKaizenEvents WorkAreaAttitude WorkAreaKaizenCapabilities secondary, supporting data from organizational documents). In addition, the collection of qualitative contextual data can be used to further explain conclusions drawn from the quantitative data. For instance, team member and facilitator descriptions of the biggest contributors and biggest obstacles to team success can provide rich data useful for understanding the event context. Case Study The case study organization is a manufacturer of large equipment which is participating in the current research.
The case study organization has been conducting Kaizen events since 1998 and conducts events in both manufacturing and non-manufacturing work areas, with a roughly 70/30% ratio in favor of nonmanufacturing areas. On the whole, the case study organization Engineering Management Journal Vol. 20 No. 3 management believes that the Kaizen event program is recognized as a success within the company. The results for the specific event described in this case study demonstrate how even organizations with substantial maturity and experience in conducting Kaizen events experience variation in outcomes across events.
The study results also demonstrate how the measurement tools and methods used in this research provide valuable information that the organization, as well as the researchers, can use to learn from all of its events (both more and less successful events). Finally, this case study demonstrates how a less successful case can be useful for deriving testable propositions regarding the contributing factors to event success. As part of the current research, the case study organization collected data from a manufacturing-focused event, which
September 2008 13 occurred in February 2006. The event focused on improving the quality of the raw material delivered to production, and will be referred to here as the Raw Material Quality (RMQ) event. The event was five days long and the primary intended outcome was an action plan for improving raw material quality. None of the changes suggested during the event would be implemented until after the conclusion of the event. In the current research, events of this type are referred to as “non-implementation” events. While the Kaizen vent literature and the authors’ experience both suggest that many Kaizen events include full or partial implementation of the team’s solution as part of the formal event activities (i. e. “implementation” events), the current data set and some published accounts reveal that some organizations hold events where implementation of changes is not a part of event design. In these events, the intended output is a plan for change, which could be a detailed action plan for implementing changes to a work area or product or a more general list of action items (e. g. , from a value stream mapping activity).
In the 51 events studied in the first phase of this research, 75% (38) were implementation and 25% (13) were non-implementation. It can also be observed from this that the averages for individual variables in our research are likely more representative of implementation events, rather than non-implementation events. Because of this, we note in this paper how the event compared to the other 12 non-implementation events in the sample, as well as to the larger data set as a whole. The detailed objectives for the RMQ event are summarized in Exhibit 5. There were 11 members on the RMQ event team.
All 11 team members completed the Kickoff Survey (100% response rate) and 10 team members completed the Report Out Survey (91% response rate). The objective here is not to present all the data collected from the event, but rather to highlight how the current research contributed to a holistic understanding of the event and how the event process could be improved. This is considered both in terms of the relative levels of the different variables measured in this case study event and the comparison of this event to the other events studied in the first phase of the larger research program. Exhibit 5.
RMQ Event Goals Goal 1. Result Achieved Standardize the raw material inspection process throughout supply chain 1. 2. Communicate incoming quality requirements to vendors 2. Action plan developed to communicate standard criteria to vendors 3. Reduce raw material inventory levels 3. Action plan developed to reduce inventory by implementing just-intime delivery of raw material and a kanban system for safety stock 4. Decrease product throughput time 4. Estimated 59% decrease in throughput time if all changes implemented 14 Action plan developed – includes plans to designate a single inspection process owner (work roup), create standard inspection criteria, and develop standard training. Exhibits 6 and 7 display the boxplots of team member ratings for the Kickoff Survey variables and Report Out Survey variables, respectively, while Exhibit 8 lists the summary statistics for the Kickoff Survey and Report Out Survey. On the boxplots, the y-axes denote the response scales for the survey questions. All survey questions used the same 6-point Likert-type (Likert, 1932) response scale (1 = “strongly disagree”, 2 = “disagree”, 3 = “tend to disagree”, 4 = “tend to agree”, 5 = “agree”, and 6 = “strongly agree”).
The x-axes denote the different survey variables. The middle line of each boxplot represents the within-team median score for that variable, while the box captures 50% of the data, and the whiskers capture the upper and lower quartiles. Outliers and extreme outliers are indicated by an open circle and asterisk, respectively. As shown in Exhibit 8, we calculated parametric statistics (mean, standard deviation), as well as nonparametric statistics (median, range), for the Kickoff Survey and Report Out Survey scales.
Along with the majority of researchers utilizing psychometric data, we subscribe to the measurement paradigm that considers parametric analysis methods appropriate as long as the distributional properties of the data support them and the latent variables being measured can be assumed to be continuous. An analysis of the distributional properties of the scale-level data for the larger sample of 51 events studied in this research suggested that most rated variables were approximately normally distributed (Farris, 2006), thus supporting the use of parametric methods.
In addition, an examination of the boxplots (Exhibits 6 and Exhibits 7) suggest that the data for the RMQ event were also approximately normal for many variables (e. g. , Affective Commitment to Change, Kaizen Capabilities), although for other variables, a skew is noted (e. g. , Team Autonomy). Thus, given our philosophy of measurement and the actual characteristics of the data, we consider parametric, as well as non-parametric, statistics to be meaningful for these analyses.
However, we note that the use of parametric statistics with psychometric data is not completely without controversy, with a minority of social science researchers, primarily those subscribing to representational theory (Stevens, 1946, 1951), opposing the use of parametric statistics with psychometric data under all or most conditions (for a more detailed review of the topic see Borgatta and Bohrnstedt, 1980; Gaito, 1980; Goldstein and Hersen, 1984; Hand, 1996; Lord, 1953; Michell, 1986; Townsend and Asbhy, 1984; Velleman and Wilkinson, 1993; Zumbo and Zimmerman, 1993).
Interpreting Team Outcomes Analysis of the raw technical system outcomes—that is, team performance versus goals—would suggest that the team was successful in meeting its objectives (see Exhibit 5); however, the team’s goals were not measurable per se—especially since exact targets (e. g. , “decrease product throughput time by 30%”) had not been specified. The conclusion at the time of the initial case study analysis was that this event was highly successful in terms of “objective” results. This finding is supported by the data set of 51 events studied in the first phase of the arger research program, where these results place this event in the top two-thirds of the events studied in terms of goal achievement. In addition, this event was similar to the majority of the other 12 non-implementation events, where the mean goal achievement was 83%, with a mode of 100% (9 out of 13 observations) and a minimum of zero. Survey results would more tentatively support the successfulness of the event—most of the team members tentatively agreed that the event was a technical success. For the Overall Success variable Engineering Management Journal
Vol. 20 No. 3 September 2008 Exhibit 6. Kickoff Survey Results Exhibit 7. Report Out Survey Results (see Exhibits 2 and 7), both the within-team median rating and the within-team mean rating were between 4 (“tend to agree”) and 5 (“agree”) on the 6-point survey response scale (4. 20 and 4. 50, respectively). Meanwhile, team member perceptions of the impact of the RMQ event on the target system (Impact on Area) were also tentatively positive. The within-team median for Impact on Area was 4. 00 and the within-team mean (representing the eam-level variable score) was 4. 04; however, compared to the larger data set of 51 events, the RMQ event had the fourth lowest Engineering Management Journal Vol. 20 No. 3 score for Impact on Area (bottom 8%), while the average Impact on Area score was 4. 94. Even among the 13 non-implementation events only, the RMQ event had the third lowest score for Impact on Area, while the average score for non-implementation events was 4. 69. Finally, team member responses to the Attitude and Kaizen Capabilities scales suggest that the event had omewhat positively impacted employee affect toward Kaizen events and employee continuous improvement capabilities (see Exhibit 7). Both the within-team median and mean scores for September 2008 15 Exhibit 8. Summary Statistics for Kickoff Survey and Report Out Survey Variables Variable Median Mean Standard Deviation Range Kickoff Goal Clarity 4. 00 3. 98 0. 69 2. 25 – 5. 00 Survey Goal Difficulty 3. 88 3. 98 0. 75 3. 00 – 5. 50 Commitment to Goals 4. 50 4. 45 0. 59 3. 33 – 5. 50 Report Attitude 4. 25 4. 23 0. 63 3. 00 – 5. 00 Out Impact on Area 4. 00 3. 97 1. 6 1. 75 – 5. 00 Survey Skills 4. 25 4. 20 0. 48 3. 00 – 4. 75 UnderstandingofCI 4. 63 4. 45 0. 81 2. 50 – 5. 25 Team Autonomy 4. 00 3. 88 1. 04 1. 50 – 4. 75 Management Support 4. 40 4. 22 0. 82 2. 60 – 5. 20 Action Orientation 2. 75 2. 43 0. 85 1. 25 – 3. 50 Internal Processes 4. 80 4. 64 0. 72 3. 00 – 5. 60 Overall Success 4. 50 4. 20 1. 32 2. 00 – 6. 00 these variables were between 4 (“tend to agree”) and 5 (“agree”). The within-team median was 4. 00 for Attitude and 4. 56 for Kaizen Capabilities, while the within-team mean was 4. 20 for Attitude and 4. 9 for Kaizen Capabilities; however, even though team member responses were on the positive (“agree”) side of the survey scale, out of the 51 events studied in the first phase of the larger research program, the RMQ event had the second lowest score (based on within-team means) for Attitude (bottom 4%) and the seventh lowest score for Kaizen Capabilities (bottom 14%). In addition, for the 13 non-implementation events only, the RMQ event had the lowest score for Attitude and the third lowest score for Kaizen Capabilities. The averages across all 51 events in the data set were 5. 0 for Attitude and 4. 87 for Kaizen Capabilities; for non-implementation events, the averages were 4. 98 and 4. 76, respectively. The error structure of the data set (i. e. , the 51 teams nested within six organizations) makes the results of ordinary least squares-based tests for differences across events suspect due to violation of independence assumptions; however, the relative position of the RMQ event in the data set suggests that this was one of the least successful events in phase one of the research in terms of human resource impact and perceived impact on area.
Furthermore, additional sources of data examined in this research support a much less positive picture of the overall success of the event than the team’s objective performance versus its goals does. The facilitator rating for the Overall Success variable (obtained via the Event Information Sheet) was 1. 0 (“strongly disagree”). The RMQ event was one out of only two events in the phase one data set of 51 events to receive an Overall Success rating of 1. 0 from the facilitator (the other event was an implementation event). In an open-ended response describing management nteraction with the Kaizen event team, the facilitator said he felt that the event was a failure, even though he believed the team’s solution was technically sound. Furthermore, the member of management who was present at the team report out to review the team’s solution rejected the majority of the team’s suggestions outright. Other surrounding factors—especially lack of buy-in from a key function that was not represented in the event team 16 —make it almost certain that the improvements identified in the event will never be implemented.
On the balance, therefore, it is clear that, except for the potential for sustaining the gains in employee Attitude and Kaizen Capabilities from participation, the RMQ event was not ultimately a strong success (i. e. , it did not directly result in changes that were then implemented to improve organizational performance). Thus, it is clear that the multiple data sources and measures used in this research provided a more comprehensive picture of overall event impact and success than any of the measures would have alone – especially if only the raw technical outcomes were examined, as is common practice in ublished Kaizen event accounts. This case example highlights the need for multiple measures of event outcomes (e. g. , goal achievement, human resource outcomes, perceived success) from multiple sources (e. g. , team members, facilitator) to provide a holistic picture of the event and both its immediate and shortterm results. After analyzing the overall impact of the event, and determining that it was a less successful application, the next step was to investigate what factors may have contributed to these results. Identifying Obstacles to Team Success
In case study research, triangulation is one method commonly used to draw overall conclusions from case study data. In a case study context, triangulation describes the situation where data from multiple sources or multiple data collection methods are examined to determine to what extent these data lead to the same conclusions (Yin, 2003). Often, one or more data sources being examined are qualitative in nature and the cross-datum comparison is also typically made using qualitative analysis methods (looking for common themes, etc. ).
This form of triangulation is conceptually similar to, but methodologically distinct from, the mathematical triangulation used to determine a location of a given point in land surveying, etc. In the current research, triangulation was used to evaluate the overall effectiveness of the event. In addition, triangulation of the data collected on event input and process factors led to the identification of three major factors that likely contributed to the limited success of the RMQ event, Engineering Management Journal Vol. 20 No. 3 September 2008 hus suggesting ways the organization can improve its Kaizen event process and expanding the current body of knowledge on Kaizen event effectiveness. Although the identification of these factors is not based on experimentation, the triangulation of data strengthens the conclusion that these factors likely contributed to the overall event results. In addition, the factors identified in this case study are compared to results from analysis of the larger phase one data set (i. e. , 51 events from six organizations). First, the method used to communicate the event goals to he team appears likely to have resulted in lower than optimal goal clarity. Data provided by the event facilitator (via the Event Information Sheet) on the kickoff meeting process indicate that the event sponsor completed the goals presentation portion of the kickoff meeting and that he used a one-way, top-down delivery format. The sponsor’s presentation of the goals did not include any description of the motivation behind the goals (i. e. , how the event was identified or what the motivating business issues were) or any interactive discussion of the goals with the team.
That is, the team was not invited to discuss the goals with the sponsor or to ask questions to clarify the sponsor’s expectations. This method of communicating the goals to the team likely made it more difficult for the team to achieve improvements (due to confusion about what improvements were needed), and, more importantly, put the team at risk for failing to meet sponsor expectations, since those expectations appear not to have been clear to the team and the team had no opportunity of asking questions to clarify or deepen their understanding.
This apparent lack of understanding of sponsor expectations at least partly explains why management ultimately found the team’s solutions unacceptable despite positive “objective” results (see the previous section). Data collected using other study measures and sources are consistent with this interpretation. For instance, the within-team median for the Kickoff Survey variable Goal Clarity was 4. 00 (“tend to agree”) and the within-team mean (the team-level variable value) was 3. 98, indicating a somewhat positive orientation but an overall lack of confidence by team members about the clarity of the goals.
Furthermore, the majority of RMQ team members (six out of 10 responding team members) reported a Goal Clarity score of 4. 00 or lower. Only one team member reported a Goal Clarity score greater than 4. 50. In addition, compared to the larger data set of 51 events, the RMQ event had the sixth lowest score for Goal Clarity (bottom 12%), while the average Goal Clarity score was 4. 59. For the 13 non-implementation events only, the RMQ event had the second lowest score for Goal Clarity, while the average score for non-implementation events was 4. 61.
Finally, open-ended responses to perceptions of the biggest obstacles to team success (an open-ended question in the Report Out Survey and the Event Information Sheet) provide further support for the idea that team goal clarity could have been improved. One team member stated that the main obstacle to the success of his/her team was “[difficulty in] focusing on our main objective and who would do it. ” Thus, these results suggest that low levels of goal clarity may contribute to failure to meet sponsor expectations, even if the “objective” results achieved appear positive.
Meanwhile, results from statistical analysis of the larger data set indicate that low goal clarity also reduces the effectiveness of internal team dynamics (i. e. , Internal Processes), which, in turn, leads to lower levels of Attitude and Kaizen Capabilities (Farris, Van Aken, Doolen, and Worley, 2007). This is consistent with findings from this case study. Although Internal Processes was the highest-rated Report Out Survey variable for the RMQ event (the within-team median was 4. 80 and the within-team mean was 4. 64), the RMQ Engineering Management Journal Vol. 20 No. 3 vent had the fifth lowest score for Internal Processes in the larger data set (bottom 10%), while the mean for Internal Processes across all 51 events in the phase one data set was 5. 17. For the 13 non-implementation events only, the RMQ event had the lowest score for Internal Processes, while the mean score for nonimplementation events was 5. 25. Thus, case study results suggest that goal clarity is a key variable in predicting and managing event success, both in terms of technical system outcomes and human resource outcomes. Second, the team was lacking in representation from a key unction, thus likely limiting the appropriateness of the team’s solution and reducing the likelihood of post-event implementation, due to a lack of input and buy-in from the function. Data from the Event Information Sheet indicate that a key contributor to raw material quality was not represented on the team: the group responsible for actually performing the inspections of the material, and thus, ultimately responsible for implementing and sustaining changes. It appears that this may have been due to problems with resource availability from this group during the scheduled time for the event.
This lack of representation ultimately led to doubts both by members of the team and members of management over whether the event results were realistic. In describing management interaction with the team during the event (in the Event Information Sheet), the facilitator indicated that one of the criticisms that the member of management reviewing the solution had was that it did not take into account the “realities of the workplace. ” In addition, in the descriptions of the biggest obstacles to team success, one team member listed “not being allowed to be realistic” as the biggest obstacle.
A second respondent listed “lack of support from the process contributors to the team” as one of the biggest obstacles. Finally, one of the individual items related to the Management Support variable in the Report Out Survey is, “Our Kaizen event team had enough help from others in our organization to get our work done. ” The within-team median score for this item was 4. 00 and the within-team mean score was 3. 60 (between “tend to disagree” and “tend to agree”), indicating that the team thought that support from others in the organization could have been improved.
Finally, team decision-making authority (team autonomy) was ultimately limited in this event. Although apparently accorded the authority to make decisions to improve the work area in the stated goals of the event, in practice, the team’s decision-making authority was entirely negated by management at the report out meeting. As described earlier, data provided by the event facilitator indicate that management ultimately rejected many of the team’s solutions during the report out meeting. Additional data from the Event Information Sheet indicate that, during this meeting, the member of management reviewing the solution irectly criticized the team’s solution. In addition, the report out meeting was “informal” with only a management representative, rather than a broad spectrum of managers, which would have more strongly demonstrated support for the event and the team’s decision-making. Furthermore, in the descriptions of the biggest obstacles to team success, one respondent listed “management’s [lack of] willingness to change,” as one of the biggest obstacles, further indicating the fact that management support for the ecisions made during the event (and thereby its willingness to empower the team to make changes) was not evident. Most Kaizen event resources suggest setting certain boundary conditions (e. g. , cost, etc. ), and then giving the team a high degree of autonomy in deciding what solutions to implement, as long as boundary conditions are met (Oakeson, 1997; Sheridan, 1997; Minton, 1998; September 2008 17 Laraia et al. , 1999). Finally, although the team median and team mean score for the Report Out Survey variable Team Autonomy were tentatively positive (4. 33 and 4. 2, respectively), the RMQ event had the fourth lowest Team Autonomy score in the data set (bottom 8%) and the mean score for team autonomy across all 51 events in the data set was 4. 82. For the 13 non-implementation events only, the RMQ event had the second lowest score for Team Autonomy, while the mean score for non-implementation events was 4. 81. It has been suggested that the high degree of autonomy generally accorded to the team may be a key factor in why Kaizen event programs appear (at least based on anecdotal evidence) to be more accepted by employees than many previous improvement programs, such as quality circles.
Kaizen teams are often allowed to actually implement changes during the event, so participating employees can directly see that their effort makes a difference and that management trusts them to make decisions to improve the organization. In contrast, in traditional improvement approaches, such as quality circles, employees are generally only given the power to suggest changes, which must then be approved by management prior to implementation (Mohr and Mohr, 1983; Cohen and Bailey, 1997; Laraia et al. , 1999).
The current case example was designed to be a non-implementation event (i. e. , planning only), but even in this case, management could have accorded the team more authority. Failure to allow the team’s solutions to be implemented after the event demonstrates a lack of confidence in the team’s decisions, and also changes the Kaizen event model to be more similar in practice to the quality circle model in both process and results. In fact, Lawler and Morhman (1985, 1987) argue that this type of management response is a key factor leading to the demise of U. S. uality circles in the 1980s – employees were initially enthusiastic about participating in quality circles, but soon saw that they had no actual power to achieve change, as management often rejected their suggestions and decisions. Similarly, work motivation theory suggests that inequity between perceived inputs (e. g. , employee time and effort) and perceived outcomes (e. g. , management acceptance of a team’s solution) could lead to decreased motivation to perform similar tasks in the future, employee withdrawal and, ultimately, lower outcomes for future events (Adams, 1965; Aldefer, 1969).
Thus, it remains to be investigated how this change to the “prescribed” Kaizen event process, if it is replicated across additional Kaizen events, will ultimately impact the sustainability of individual event results and the Kaizen event program as a whole. Discussion and Future Research This research demonstrates how the detailed examination of a less successful case provided a valuable opportunity for organizational learning. The identification of the three factors presented above, which apparently limited the success of the specific RMQ event, provided valuable information for learning for the case study organization, and may ultimately ontribute to improved effectiveness of the organization’s Kaizen event program as a whole. By identifying three key variables that appear to impact event success, the case study organization can now seek to develop mechanisms that can be used to influence or control these variables. A few of the mechanisms recommended were: communicating upfront with sponsors to insure an interactive presentation of team goals, including the opportunity for the team to ask questions to clarify sponsor expectations; getting management agreement to institute a policy of not moving forward with an event without representation on the team from all key 18 unctions; negotiating with management to more clearly define the boundaries of team authority and to develop mechanisms for more clearly and strongly communicating management support for the team’s decision-making. The organization has already implemented some of these corrective mechanisms, with apparent success. On the next event studied within the organization, the facilitator of the event indicated that, based on the findings from the RMQ event, he ensured that the event sponsor presented the goals to the team in an interactive format, where team members were encouraged to ask questions to clarify the goals.
The Goal Clarity score was noticeably higher for this event, although it is, of course, impossible to prove that this was solely the result of his intervention. In general, the case study organization has already demonstrated that it is proactive in learning from its events. The Kaizen event facilitators routinely hold informal sessions where they share more and less successful elements from their events with one another. It is likely the organization will continue to seek to improve Kaizen processes based on examination of both more and less successful events. From a research perspective, the case illustrates how he methods and measures used in the current study allow triangulation of multiple data types and sources to draw more robust conclusions. As has been demonstrated, the importance of all three factors to the event outcomes is supported by two sources of data (the facilitator and the team) and by both qualitative and quantitative measures, as well as by results from the larger research program. In addition, the case study results highlight the need for additional research on Kaizen event effectiveness by illustrating how the design and results of Kaizen events can vary, even within organizations that are experienced in using Kaizen events.
The results also indicate how even organizations with significant maturity in their Kaizen event programs still sometimes experience difficulty in achieving optimal event designs. Finally, the case illustrates the importance of research on the sustainability of event outcomes, and event programs as a whole, by illustrating a factor (management support for team decision-making authority), which may ultimately impact the sustainability of event outcomes and the Kaizen event program as a whole.
Future research should focus on identifying factors related to the sustainability of outcomes, as well as further testing of factors associated with initial outcomes. For instance, future research should continue to investigate the impact of the three factors identified in this study to confirm their importance to Kaizen event effectiveness. This research should include additional case studies of more and less successful events and analysis of larger, multi-event data sets. As discussed, the larger research study of 51 events, which was completed after an earlier version of this paper was published (Farris et al. 20062), has already confirmed the importance of goal clarity and team autonomy to initial outcomes. The importance of adequacy of functional representation (i. e. , whether or not all key functions were represented on the team) has not been specifically investigated in the larger study, although cross-functional heterogeneity is a variable in the larger research. Perhaps additional case studies of less successful events could be used to determine whether or not this factor is a common theme in less successful Kaizen events and whether or not its impact should be investigated statistically in the future.
Finally, for the practicing engineering manager – who may be responsible for sponsoring Kaizen events, conducting Kaizen events, participating in Kaizen events, or overseeing employees who do—this research highlights three variables that should be Engineering Management Journal Vol. 20 No. 3 September 2008 considered in event design, as well as the consequences of not carefully managing these variables. For instance, engineering managers sponsoring Kaizen events might be tempted to solely reserve decision-making authority while relegating the team to an advisory role.
This research, and related literature, suggests that the manager would be better off chartering the creation of a competent team with full functional representation, giving them clear goals (allowing them to clarify these goals, if necessary), specifying important boundaries, and then abiding by whatever solution the team develops during the solution process, at least until this solution is proven inadequate. In addition, this research provides a set of measurement instruments and methods that can be used to assess Kaizen event design and initial outcomes.
These instruments consist of two short survey questionnaires completed by Kaizen event team members and a questionnaire completed by the Kaizen event facilitator, as well as a team activities log. Due to scope and space constraints, these instruments are not presented here, but are available in other publications (Farris, 2006). We invite interested individuals to contact the authors for copies of these instruments. Acknowledgments This research was supported by the National Science Foundation under grant No. DMI-0451512. We also gratefully acknowledge he Kaizen event team members, facilitators, and coordinators for their participation in data collection and validation. Finally, we thank the four anonymous reviewers for their constructive and insightful feedback. An earlier version of this paper appeared in the 2006 American Society for Engineering Management Conference Proceedings. References Adams, John S. “Inequity in Social Exchange,” in Advances in Experimental Social Psychology Leonard Berkowitz (ed. ) Vol. 62 (1965), pp. 335-343, Academic Press. Aldefer, Clayton S. “An Empirical Test of a New Theory of Human Needs,” Organizational Behavior and Human Performance, (1969), pp. 142-175. Borgatta, Edward F. , and George W. Bohrnstedt, “Level of Measurement: Once and Over Again,” Sociological Methods and Research, 9:2 (1980), pp. 147-160. Cohen, Susan G. , and Diane E. Bailey, “What Makes Teams Work: Group Effectiveness Research from the Shop Floor to the Executive Suite,” Journal of Management, 23:3 (1997), pp. 239-290. Cole, Robert E. , “The Quality Revolution,” Production and Operations Management, 1:1 (Winter 1992), pp. 118-120. Cuscela, Kristin N. , “Kaizen Blitz Attacks Work Processes at Dana Corp. ,” IIE Solutions, 30:4 (April 1998), pp. 9-31. Farris, Jennifer, “An Empirical Investigation of Kaizen Event Effectiveness: Outcomes and Critical Success Factors,” Ph. D. Dissertation (2006), Virginia Tech. Farris, Jennifer, Eileen M. Van Aken, and Toni L. Doolen, “Studying Kaizen Event Outcomes and Critical Success Factors: A Model-Based Approach. ” Proceedings of the Industrial Engineering and Research Conference (May 20061), CD-ROM. Farris, Jennifer, Eileen M. Van Aken, Toni L. Doolen, and June Worley, “Learning from Kaizen Events: A Research Methodology for Determining the Characteristics of More and Less – Successful Events,” Proceedings of the American Society for Engineering Management Conference (October Engineering Management Journal Vol. 20 No. 3 20062), CD-ROM. Farris, Jennifer, Eileen M. Van Aken, Toni L. Doolen, and June Worley, “A Study of Mediating Relationships on Kaizen Event Teams,” Proceedings of the Industrial Engineering and Research Conference (May 2007), CD-ROM. Gaito, John, “Measurement Scales and Statistics: Resurgence of an Old Misconception,” Psychological Bulletin, 87:3 (1980), pp. 564-567. Goldstein, Gerald, and Michel Hersen, Handbook of Psychological Assessment, Pergamon Press (1984).
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Jones, and Daniel Roos, The Machine that Changed the World, Rawson Associates (1990). Yin, Robert K. , Case Study Research Design and Methods, 3rd edition, Sage Publications, Inc. (2003). Zumbo, Bruno D. , and Donald W. Zimmerman, “Is the Selection of Statistical Methods Governed by Level of Measurement? ” Canadian Psychologist, 34 (1993), pp. 390-400. About the Authors Jennifer A. Farris is an assistant professor in the Department of Industrial Engineering at Texas Tech University. She received her BS in industrial engineering from the University of Arkansas and her MS and PhD in industrial and systems ngineering from the Grado Department of Industrial and Systems Engineering at Virginia Tech, where she also served as a Graduate Research Assistant and a Postdoctoral Associate in the Enterprise Engineering Research Lab. Her research interests are in performance measurement, product development, kaizen event processes, and healthcare performance improvement. She is a member of IIE and Alpha Pi Mu. Eileen M. Van Aken is an associate professor and associate department head in the Grado Department of Industrial and Systems Engineering at Virginia Tech. She is the founder nd director of the Enterprise Engineering Research Lab, conducting research with organizations on performance measurement, organizational improvement methods, lean work systems, and team-based work systems. Prior to joining the ISE faculty at Virginia Tech in 1996, she was employed 20 for seven years at the Center for Organizational Performance Improvement at Virginia Tech, and was also employed by AT&T Microelectronics in Virginia where she worked in both process and product engineering. She received her BS, MS, and PhD degrees in industrial engineering from Virginia Tech.
She is a senior member of IIE and ASQ, and is a member of ASEM and ASEE. She is a Fellow of the World Academy of Productivity Science. Toni L. Doolen is an associate professor in the School of Mechanical, Industrial & Manufacturing Engineering at Oregon State University. Prior to joining the faculty at OSU, Toni gained 11 years of manufacturing experience at Hewlett-Packard Company as an engineer, senior member of technical staff, and engineering manager. Toni received a BS in electrical engineering and a BS in mterials science and engineering from Cornell University, an MS in manufacturing ystems engineering from Stanford University, and a PhD in industrial engineering at Oregon State University. She is a senior member of SWE, IIE, and SME. She is also a member of ASEE. June M. Worley is a PhD candidate in the Mechanical, Industrial, and Manufacturing Engineering Department at Oregon State University. She received a BS in computer science and a BS in mathematical sciences from Oregon State University. She received an MS in industrial engineering from Oregon State University, completing a thesis on the sociocultural factors of a lean manufacturing implementation.
Her research interests include systems analysis, information systems engineering, lean manufacturing, performance metrics, and human systems engineering. Prior industry experience includes five years in operations management at a regional bank and over three years of technical experience in project analysis and implementation. Contact: Jennifer A. Farris, PhD, Texas Tech University, Industrial Engineering Building Room 201 (3061) Lubbock, TX 79409-3061; phone: 806-742-3543; jennifer. [email protected] edu Engineering Management Journal Vol. 20 No. 3 September 2008