Kaizen Event Follow-up Mechanisms and Goal ... - Semantic Scholar

Proceedings of the 2008 Industrial Engineering Research Conference J. Fowler and S. Mason, eds.

Kaizen Event Follow-up Mechanisms and Goal Sustainability: Preliminary Results Wiljeana J. Glover and Eileen M. Van Aken Industrial and Systems Engineering Virginia Tech, Blacksburg, VA 24061, USA Jennifer A. Farris Industrial Engineering Texas Tech University, Lubbock, TX 79409, USA Toni L. Doolen and June M. Worley Mechanical, Industrial and Manufacturing Engineering Oregon State University, Corvallis, OR 97330, USA

Abstract Despite the anecdotal recognition of factors that may influence Kaizen outcome sustainability, there has been little empirical investigation of sustainability determinants. This research reports on the preliminary sustainability results from one manufacturing organization. The organization is participating in an ongoing field study of Kaizen events containing multiple manufacturing organizations. Researchers collected data on event characteristics, follow-up mechanisms, goal sustainability, and work area context for 14 events from the case study organization. A preliminary set of perceptual follow-up mechanism measures is identified and summarized. The study results also provide preliminary evidence of how the follow-up mechanisms may influence goal sustainability.

Keywords Lean manufacturing, continuous improvement, kaizen blitz, rapid change, improvement sustainability

1. Introduction and Background A Kaizen event is a focused and structured improvement project, using a dedicated cross-functional team to improve a targeted work area, with specific goals, in an accelerated timeframe [1]. Recent evidence suggests that Kaizen events are becoming increasingly popular as a method for targeting initial improvement both in business performance and social system outcomes [1, 10-12]. However, the initial success of a Kaizen event does not guarantee the sustainability of event outcomes [2]. In fact, some practitioners note that Kaizen events should not be performed unless they are done with the intent and activities necessary to sustain results [3]. While the process and continuous improvement sustainability literature reaches some conclusions about long-term sustainability of improvement initiatives, most do not address Kaizen events specifically [4-8]. The work of Bateman [9] does specifically address Kaizen event sustainability but does not differentiate between the types of results outcomes. In addition, only a limited set of context factors and follow-up mechanisms were investigated and the relationship between event characteristics and goal sustainability were not investigated. This papers presents results from a subset of data from a multi-site, long-term study of Kaizen event outcomes that investigates the relationship between technical and social system outcomes, work area factors and follow-up mechanisms [1, 10-12]. Thus, this paper presents preliminary results on the follow-up mechanisms that may influence the Kaizen event technical system outcome of goal sustainability. Researchers analyzed data captured from a field study of 14 Kaizen events in a single organization. The case study organization is a manufacturer of secondary wood products, which has consistently conducted Kaizen events since 1998. Overall, the Kaizen event program is recognized as a success within the company, and the company has received national recognition for their lean manufacturing program and results.

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2. Measures of Follow-up Mechanisms and Goal Sustainability As described in [1, 10-12], a Kaizen event is a complex organizational phenomenon that requires a varied set of measures in order to be explained and studied. Researchers collected measures of initial event characteristics and outcomes at the time of the event [1, 11, 12]. For the study of Kaizen event outcome sustainability, additional factors related to the target work area, follow-up mechanisms, and sustainability of results initially achieved must also be included. Through a review of process and continuous improvement sustainability literature [4-9], the authors identified several factors that may relate to and influence Kaizen event outcome sustainability, including the 21 follow-up mechanisms that are a focus of this paper. Data on the characteristics, content, follow-up mechanisms and results sustainability of each event studied were captured through a questionnaire (Post-Event Information Sheet) which was administered approximately one year after the end of the event. The case study organization event facilitator completed the Post-Event Information Sheet, which contained a mixture of closed-ended (rated) and open-ended questions. All follow-up mechanism items were worded to capture the perceived extent to which the mechanism was used, and were rated on a six-point Likert-type scale, with 1 = “not at all,” 2= “to a small extent,” 3= “to some extent,” 4= “to a moderate extent,” 5= “to a large extent,” and 6 = “to a great extent.” The Post-Event Information Sheet also measured the current performance level on each of the Kaizen event‟s primary technical goals. These data were then compared to initial post-event performance levels (fully realized or estimated) to compute Goal Sustainability % (that is, the average level of sustainability across the event‟s primary goals one year after the event). The results, presented in Figure 1, suggest that the case study organization is relatively successful in sustaining its initially achieved improvements in results. Specifically, the case study organization‟s average goal sustainability was 84.9%. In the sole case where 0% of the goal was sustained, the Kaizen event team did not implement any changes during the event itself but made several projections for improvements related to the primary goals that depended upon the implementation of post-event action items. The set of action items was never implemented after the event.

Figure 1: Goal Sustainability % Values for the 14 Kaizen Events in the Case Study Organization.

3. Follow-up Mechanism Constructs and Reliability Because the Post-Event Information Sheet measures the extent to which the Kaizen event facilitator and targeted work area used each of the 21 identified follow-up mechanisms, being able to reduce the data to a smaller number of variables is a desired analysis step. Previous researchers did not provide any a priori groupings for these follow-up mechanisms. Thus, the authors proposed four initial constructs for the follow-up mechanism based on face validity (i.e., apparent conceptual similarity between the items identified). The four constructs and their corresponding items are provided in Table 1. Table 1: Initial Follow-up Mechanism Constructs Construct Employee Empowerment

Item

Description

EE1

Involving work area employees (not on the Kaizen event team) in follow-up and completion of action items from the event.

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EE2

IChange3

Providing work area employees with freedom to make changes to work area. Work area management allowing work area employees time to work on continuous improvement activities. Formal documentation of follow-up action items (e.g., through a Kaizen newspaper) from the Kaizen event. Individual team members working on follow-up action items from the Kaizen event. Training work area employees in new work methods and processes from the Kaizen event.

IChange4

Updating work method and process documentation (e.g., standard work charts, formal job descriptions, etc.) for changes made due to the Kaizen event.

EE3 IChange1 IChange2

Institutionalizing Change

ICulture1

ICulture2 ICulture3 ICulture4

Improvement Culture

ICulture5

Avoiding blame or negativity when team goals are not achieved. Work area management encouraging work area employees to apply continuous improvement knowledge and skills.

ICulture6

Work area management supporting the use of Kaizen events in the organization.

ICulture7

Work area management championing the value of continuous improvement.

PR1

Regularly reviewing performance data related to Kaizen event goals.

PR2

Conducting regular audits on changes made due to the Kaizen event. The Kaizen event team meeting as a whole to review progress and/or develop follow-up strategies for the Kaizen event.

PR3 PR4 PR5 PR6 Performance Review

Rewarding or recognizing Kaizen event team members for their contributions. Rewarding or recognizing work area employees (not only those on the Kaizen event team) for progress on sustaining changes or completing action items from Kaizen event. Avoiding blame or negativity when changes are made, but results are different than expected.

PR7

Meetings with higher-level management about Kaizen event progress or follow-up. Meetings with Kaizen coordinator or facilitator about Kaizen event progress or follow-up. Meetings with work area management about Kaizen event progress or follow-up. Informing higher-level management of issues with follow-up and sustaining results from the Kaizen event.

Next, the authors performed a separate exploratory factor analyses on each construct as described in [13] to analyze the initial item groupings. Due to the very small initial sample size (i.e., 14 events), the factor analysis was used as a preliminary analysis to investigate the validity of the initial groupings and must be revisited as additional data become available from more events in order to confirm results. Although the suggested sample size for stable factor analysis results is roughly 5-10 observations per item, smaller sample sizes can be used with caution for exploratory purposes [16]. For the four factor analyses presented here, the ratio of observations to items ranged from 2:1 to 4.7:1. It is also noted here that the use of data from a single organization means that the constructs defined from this factor analysis may also be specific to the case study organization, further supporting the need for additional factor analysis to verify these initial results. Principal components extraction with an oblique rotation method was used, because theory suggested that measures (i.e., survey constructs) may be correlated [14]. Following convention, in all four of the factor analyses, items were considered to have loaded onto a given factor when the primary loading was 0.500 or greater and all cross-loadings were less than 0.300. The factor analyses partitioned the original four constructs into the nine constructs included in Table 2. An examination of the item descriptions and the data values supported the creation of the new constructs. For example, ICulture3 and ICulture4 are the only two items in the Improvement Culture construct that both address the extent to which blame and negativity are avoided and the two loaded together as a separate factor. As another illustration, the Performance Review item PR2 refers to the extent to which regular standard audits are conducted

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due to a Kaizen event, while the other Performance Review items refer to the use of meetings to review performance data. Cronbach‟s alpha was used to assess the reliability of the constructs (see Table 2). All Cronbach‟s alpha values were greater than 0.70, which is commonly taken to indicate acceptable scale reliability [17]. Examining the means of each variable shows that the case study organization uses the follow-up mechanisms Avoiding Blame and Work Area Mgt Support to a great extent and with little deviation, while other constructs had slightly lower means and higher variability.

Factor 1 2 3 4 5 6 7 8 9

Table 2: Revised Follow-up Mechanism Constructs and Reliability New Variable Name Items Mean Standard Deviation Employee Follow-up EE1 2.714 1.437 Employee Involvement EE2, EE3 4.071 0.852 Follow-up IChange1, IChange2 3.786 0.955 Infrastructure Updating Work IChange3, IChange4 4.321 1.683 Methods Employee ICulture1, ICulture2, 3.571 0.744 Encouragement ICulture5 Avoiding Blame ICulture3, ICulture4 5.071 0.616 Work Area Mgt ICulture6, ICulture7 5.321 0.541 Support PR1, PR3, PR4, PR5, 3.690 1.090 Performance Review PR6, PR7 Audits PR2 4.571 1.555

Cronbach’s Alpha n/a .8712 .8133 .9161 .8822 1 .7136 .9325 n/a

4. Correlation of Follow-up Mechanisms with Goal Sustainability To gain a preliminary understanding of the possible correlation between follow-up mechanism constructs and Goal Sustainability %, the researchers used a pairwise correlation. Because of the small sample size, the statistical significance threshold was adjusted from the traditional .01 to .01/9 or .00111 to account for the nine constructs. The results of the pairwise correlation analysis are provided in Table 3. None of the correlations were statistically significant at the adjusted α. However, the Audits construct was nearly significant with a p-value only slightly greater than 0.0011 (p = 0.0015) and noticeably large for field data (0.76). For the case study organization, the audit is a frequently used follow-up mechanism and outliers were noted on the item. In particular, in 2 of the 14 cases, audits were not used at all, while in the remaining 12 cases, audits were either used to a large extent (5) or to a great extent (6). Upon examining the data, it does appear that the 2 cases that did not utilize regular audits had the lowest Goal Sustainability % , at 0% goal sustained and 67% goal sustained. Thus, it seems reasonable to posit that there is a relationship between the use of audits and goal sustainability, even based on this small sample size. This apparent relationship should be further explored in future research involving additional organizations. Table 3: Pairwise Correlations of Goal Sustainability % by each Follow-up Mechanism Construct Variable by Variable Correlation Signif Prob Goal Sustainability % Employee Follow-up 0.0650 0.8253 Goal Sustainability % Employee Involvement -0.2241 0.4412 Goal Sustainability % Follow-up Infrastructure -0.1070 0.7158 Goal Sustainability % Updating Work Methods -0.2820 0.3287 Goal Sustainability % Goal Sustainability % Goal Sustainability % Goal Sustainability %

Employee Encouragement Avoiding Blame Work Area Mgt Support Performance Review

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-0.1304 -0.0048 0.1832 -0.2297

0.6569 0.9870 0.5308 0.4296

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Variable Goal Sustainability %

by Variable Audits

Correlation 0.7644

Signif Prob 0.0015

5. Insight from Qualitative Data The authors also included open-ended questions in the Post-Event Information Sheet to capture additional data that may support the quantitative findings. In particular, the Kaizen event facilitators of the case study organization answered the questions, “In your opinion, what have been the biggest obstacles to date in sustaining the results from the Kaizen event?” and “In your opinion, what have been the biggest contributors to date in sustaining the results from the Kaizen event?” The authors grouped these comments from the facilitators at the case study organization according to their common themes [15]. Figure 2 presents facilitator perceptions of the biggest sustainability obstacles. It is noted that personnel turnover is the most frequently cited (cited in five out of 14 events). Also, most of these obstacles are not directly controllable by the facilitator but can be at least partially influenced by senior management in the organization – for instance the time and capital available for follow-up, as well as efforts to control personnel turnover.

Figure 2: Frequency of Outcome Sustainability Obstacles Identified by the Case Study Organization With respect to the biggest sustainability contributors (see Figure 3), the case study organization most often cited the willingness, dedication, and drive of work area employees and management to sustain the outcomes. Aligned with the quantitative findings, the case study organization also cited the completion of audits as a contributor and the delay of audit completion as an obstacle. Overall, the comments from the case study organization provided additional insights into the quantitative results and supported the conclusion that these factors should be considered in the overall study of Kaizen event outcome sustainability.

Figure 3: Frequency of Outcome Sustainability Contributions Identified by the Case Study Organization

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6. Conclusions and Future Research Preliminary findings from a subset of Kaizen events from a larger Kaizen event field study suggest that the 21 identified follow-up mechanisms may be reduced to form nine constructs, with those constructs having multiple items demonstrating acceptable levels of reliability. The study also suggests a relationship between the use of standard performance audits and results sustainability (supported by both quantitative and qualitative analysis), as well as a relationship between organizational and work area culture and results sustainability (supported by qualitative analysis only). Researchers should use these preliminary results to inform the ongoing study of Kaizen event (or other improvement project structure) outcome sustainability to identify the full set of factors that are critical for organizations to sustain technical system outcomes.

Acknowledgements This research was supported by the National Science Foundation under grant No. DMI 0451512. The authors gratefully acknowledge the support of all facilitators and team members who participated in this research.

References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

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13. 14. 15. 16. 17.

Farris, J.A., 2006, “An empirical investigation of Kaizen event effectiveness outcomes and critical success factors,” Ph.D. dissertation, Virginia Polytechnic Institute and State University. Friedli, D., 1999, “UK firms may suffer from „kamikaze‟ kaizen strategy,” The Engineer. Mackle, K., 2000, “There's no quick fix,” The Engineer. Dale, B.G., et al., 1997, “Total quality management sustaining audit tool: Description and use,” Total Quality Management and Business Excellence, 8(6), 395 - 408. Kaye, M. and R. Anderson, 1999, “Continuous improvement: the ten essential criteria,” International Journal of Quality & Reliability Management, 16(5), 22. Keating, E., et al., 1999, “Overcoming the improvement paradox,” European Management Journal, 17(2), p. 120-134. Oxtoby, B., T. Mguiness, and R. Morgan, 2002, “Developing Organisational Change Capability,” European Management Journal, 20(3), 310-320. Upton, D.M., 1996, “Mechanisms for Building and sustaining operations improvement,” European Management Journal, 1(3), 29. Bateman, N., 2005, “Sustainability: the elusive element of process improvement,” International Journal of Operations & Production Management, 25(3), 261-76. Farris, J., et al., 2004, “Longitudinal analysis of kaizen event effectiveness,” Proc. of the Industrial Engineering and Research Conference, May 15-19, Houston, TX, CD-ROM. Farris, J., Van Aken, E. M., and Doolen, T. L., 2006, “Studying kaizen event outcomes and critical success factors: a model-based approach,” Proc. of the Industrial Engineering and Research Conference, May 2024Orlando, FL, CD-ROM. Farris, J.A., et al., 2006, “Learning from Kaizen Events: A Research Methodology for Determining the Characteristics of More- and Less-Successful Events,” Proc. of the American Society for Engineering Management Conference, October 25-28, Huntsville, AL, CD-ROM. Tamimi, N., Gershon, M., & Currall, S., 1995, “Assessing the psychometric properties of Deming‟s 14 principles,” Qualtiy Management Journal, 2(3), 38-52. Jennrich, R., 2002, “A simple general method for oblique rotation,” Psychometrika, 67(1), 7-19. Yin, R.K., 1994, “Case study research: design and methods,” 2nd edition, Applied social research methods series. Sage, Thousand Oaks, CA, xvii-171. Johnson, D.E., 1998, “Applied multivariate methods for data analysts, Duxbury Press, Pacific Grove, Calif., xiv-567 Nunnally, J.D., 1978, “Psychometric Theory”, 2nd edition, New York: McGraw-Hill.

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