Best Quality Improvement Tools

A Study on the Use and Effects of Quality Improvement Tools By Bjørn Andersen, Associate Professor, Department of Production and Quality Engineering, Norwegian University of Science and Technology, N-7034 Trondheim, Norway Henrik Sverre Løland, MSc.Eng. Student, Department of Production and Quality Engineering, Norwegian University of Science and Technology, N-7034 Trondheim, Norway ABSTRACT This paper describes a study with the objectives of understanding which improvement tools produce the best effects in given situations. Enterprises were asked to provide experiences with different improvement tools. In the analysis of the survey data, improvement was defined as a function of; (1) actions, (2) variable attributes of the improvement situation, and (3) non-variable attributes of the enterprise. A correlation analysis was undertaken that revealed many findings. Several very strong correlation factors were found linking some of these three factors to improvement results achieved. Six different mechanisms of interplay between factors were identified that seemed to govern the improvement outcome. Finally, a ranking of the tools was established based on the improvement effects. These findings were merged into one improvement toolbox presenting enterprises with guidelines to which tools to select and how to apply them to maximize the probability for success. KEYWORDS Quality improvement tools, improvement toolbox, improvement survey. Bjørn Andersen, Ph.D., is an associate professor at the Norwegian University of Science and Technology in Trondheim, Norway as well as scientific advisor to the research foundation SINTEF. During his doctorate studies, he spent eight months at Rochester Institute of Technology working on the subject of benchmarking and the results achievable through use of the tool. He has co-authored and authored several books and papers and has been involved in or managed several research and implementation projects on benchmarking, quality improvement, productivity, and material and production management during the last years. Henrik Sverre Løland…

1 Introduction – Improvement Tools and Areas of Competitive Advantage As everyone working with quality improvement and process innovations knows, there are a very large number of different improvement tools or philosophies available for use. In Norway, many, if not most, of these tools or philosophies have been applied in enterprises around the country. However, very little work has been undertaken to analyze the use and results achieved in actual implementations of them. Based on the resulting lack of understanding of which tools seem to work better and what factors seem to govern the success of an implementation of a tool or philosophy, a small study was undertaken during the first half of 1997 to capture such experiences. At the outset of the study, it was decided it was necessary to classify the different improvement tools and philosophies in some way to render the survey in companies more

December 22nd, 1997

-1-

Version 2.0

systematic. Such a classification could have been done in a number of ways, but possible areas generating competitive advantage were seen as a good starting point. Seven main areas were listed which, if a company were to excel within, was believed to lead to some form of competitive edge compared to competitors: Œ  Ž   ‘ ’

Time, especially delivery time and delivery precision. The customers’ perceptions of quality and service. The use and flow of information. Strategy and vision. Process orientation and process flow. Employees. Overview and control with operations.

To classify the improvement tools and philosophies, they were attempted grouped according to which of these seven areas they would mainly contribute to when being applied. This list is far from complete, but reflects the most widely used tools and philosophies in Norway currently. Furthermore, some tools obviously contribute to many of the seven areas for competitive advantage. The area each single tool has been assigned to merely reflects the most predominant feature. For the complete list of tools considered in this study, see the list below. 1. Time · Just-in-Time (JIT). · Time Based Management (TBM). · Single Minute Exchange of Die (SMED). 2. Quality and Service · Concurrent Engineering. · Design for Assembly (DFA). · Design for Manufacturability (DFM). · ISO 9000 to 9004. · Statistical Process Control (SPC). · Total Quality Management (TQM). · Internal Quality Management System. 3. Information · System/database for storage of experiences, knowledge, and information. · Organization-wide IT system. 4. Strategy and Vision · GAP-analysis. · Strategy for customer focusing of the enterprise (LOTS). · McKinsey’s 7S Model. · Profit Impact of Marketing Strategy (PIMS). 5. Process flow · Activity Based Costing (ABC). · Work unit analysis. · Benchmarking. · Business Process Reengineering (BPR). · Idealization. · Total Productive Maintenance (TPM). · Reliability Centered Maintenance (RCM). 6. Employees · Incentive programs. · Work environment programs. 7. Overview and Control

December 22nd, 1997

-2-

Version 2.0

·

Systems for Internal Control

When reviewing this list, it is obvious that it represents a rather unstructured blend of tools and philosophies that work at different levels in the organization. To clarify the differences further, they were also placed in appropriate cells in the pyramid shown in Figure 1.

System Tools: Quality Systems IT Systems Internal Control Strategy Tools: GAP-analysis, LOTS, PIMS, 7S Holistic Philosophies: JIT, TQM, TBM Complex Improvement Tools: Benchmarking, BPR Improvement Tools: SMED, Concurrent Engineering, DFA/DFM, TPM, SPC, ABC, Work unit analysis, Idealization, Incentive programs, Work environment programs

Figure 1 Improvement Tools and Philosophies As part of the study, a detailed literature search was conducted and in-depth descriptions of each tool and philosophy were generated. For those unfamiliar with some of these, please consult the literature list attached to this paper.

2 Material and Methods To conduct the study, a questionnaire was designed to identify any connections between how the companies utilized the improvement tools and the results achieved in organizations. The questionnaire contained questions related to the following issues: · · · · ·

Characteristics of the company, e.g., size, sales, type of industry, etc. The improvement process, i.e., which tools were used, how, and in what situations. To make sure that the enterprises fully understood what they were responding to, a four-page appendix was attached to the questionnaire explaining the different tools. Internal resistance toward change. Achieved improvement results. Open-ended questions concerning general experiences and advice.

To the extent possible, the questions were designed to produce numerical answers in order to enable comparison and statistical analysis of the data. Typically, a scale from 1 to 5 or 9,

December 22nd, 1997

-3-

Version 2.0

depending on the nature of the question, was used to rate different questions. Some examples of questions are: · · ·

To what extent was top management directly involved in the improvement project? To be answered on a scale from 1 to 9, 9 indicating the highest extent of involvement. How many employees spent most of their time working in the improvement project? To be answered by the actual number. How strong was the internal resistance to change before the improvement project was started? To be answered on a scale from 1 to 9, 9 indicating the highest extent of resistance.

The questionnaire along with an accompanying letter was sent to 17 enterprises, resulting in 13 returned completed questionnaires. All questionnaires had been answered extensively including many and lengthy comments to the open-ended questions, and were mainly answered by the quality manager or someone in a similar position in the enterprises. The distribution of the respondent enterprises among industrial sectors was as follows: · · ·

Manufacturing – 10 enterprises. Within these, there was a wide distribution among subsectors. Service – 1 enterprise. Finance and administration – 2 enterprises.

With regard to size, the distribution among the companies of annual sales figures was as follows: · · · ·

10 to 30 million £ - 7 enterprises. 30 to 50 million £ - 3 enterprises. 50 to 100 million £ - 2 enterprises. More than 100 million £ - 1 enterprise.

Depending on which classification one uses, this meant that all of the respondent companies in the study had to be considered relatively large and not SMEs.

3 Data Analysis The entire survey was based on the following general framework for improvement: Improvement = f (Actions + Variable attributes + Non-variable attributes) In words, improvement was believed to be a function of one or more of the three entities: Œ Actions, the conscious activities the company performs before and during the improvement process, e.g., improvement planning, top management support, etc.  Variable attributes, attributes of the organization that the enterprise has the power to change, e.g., resistance to change, improvement understanding, employee motivation, etc. Ž Non-variable attributes, attributes of the organization not possible to impact by the enterprise in the short term during the improvement process, e.g., sales, number of employees, competition, etc.

December 22nd, 1997

-4-

Version 2.0

The ultimate objective of the analysis of how these factors impacted the improvement function was to arrive at conclusions pertaining to the connections between achieved improvement results and: · · · ·

The improvement tool or philosophy used. The situation in which the improvement tool was used. Characteristics of the organization that used the tool. The improvement process and how the tool was used.

3.1 Numerical Response Variables To determine any such connections, correlation factors (based on the covariance between the two variables divided by the product of the standard deviation) were calculated for all pairs of variables. The variables yielding numerical responses and thus encompassed by the correlation analysis are presented in Table 1. In the table, the variables have been sorted according to which variable type they are. The numbers correspond to the numbering of the questions in the questionnaire and references to them in the remainder of the paper. Variable attributes 6. Employees motivated for improvement

Non-variable attributes 1. Last year’s sales

15. Communication of the improvement objectives in advance of the process

2. Last year’s financial result

16. Internal resistance to the improvement process in advance of start-up 17. Internal resistance to the improvement process during the process 18. Average assessment of the difficulties in using a tool

3. Number of employees in total

19. Satisfaction with the achieved results compared to expectations

4. Number of employees in the part of the organization affected by the improvement 5. The competition in the most important market segments 7. Experience in improvement processes before the new process was started

Actions 8. The number of employees that spent most of their time on planning the improvement project 9. The number of employees that spent most of their time on performing the improvement project 10. The duration of the planning 11. The duration of the execution 12. Degree of top management involvement in the improvement process 13. Did the company have clear plans for the future and the activities of the improvement process 14. To what extent were results compared with objectives of the improvement process

Table 1 Variables included in the correlation analysis

December 22nd, 1997

-5-

Version 2.0

3.2 Correlation Factors The generated correlation factors are portrayed in Table 2. # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

1 X 0,22 0,92 0,94 0,06 -0,01 -0,13 0,79 0,88 0,13 -0,41 -0,14 -0,03 0,00 0,13 -0,39 -0,16 0,34 0,40

2 0,22 X 0,77 0,91 0,76 -0,31 0,71 0,85 0,97 0,18 -0,77 -0,48 -0,25 0,55 -0,32 -0,07 0,53 -0,34 -0,10

3 0,92 0,77 X 0,93 0,26 -0,20 0,37 0,69 0,76 0,24 0,54 -0,11 -0,21 0,15 -0,39 0,29 0,30 -0,04 -0,11

4 0,94 0,91 0,93 X 0,12 -0,19 0,35 0,74 0,82 0,36 0,21 -0,13 -0,38 0,16 -0,33 0,24 0,25 -0,14 -0,06

5 0,06 0,76 0,26 0,12 X -0,15 0,56 0,32 0,35 -0,17 0,45 -0,14 0,54 0,57 0,52 -0,30 0,08 -0,23 0,37

6 -0,01 -0,31 -0,20 -0,19 -0,15 X -0,48 -0,44 -0,36 0,38 -0,49 0,68 0,15 -0,35 -0,09 -0,54 0,02 0,43 -0,20

7 -0,13 0,71 0,37 0,35 0,56 -0,48 X 0,79 0,69 -0,40 0,03 -0,48 0,09 0,38 0,19 -0,03 -0,10 -0,48 0,04

8 0,79 0,85 0,69 0,74 0,32 -0,44 0,79 X 0,95 0,24 0,20 -0,53 -0,23 0,22 -0,18 0,10 0,18 -0,14 -0,08

9 0,88 0,97 0,76 0,82 0,35 -0,36 0,69 0,95 X 0,40 -0,36 -0,46 -0,25 0,21 -0,26 0,06 0,27 -0,24 -0,07

10 0,13 0,18 0,24 0,36 -0,17 0,38 -0,40 0,24 0,40 X 0,01 -0,02 -0,15 -0,02 0,11 -0,35 -0,03 0,08 0,04

11 -0,41 -0,77 0,54 0,21 0,45 -0,49 0,03 0,20 -0,36 0,01 X 0,06 0,34 0,19 -0,33 0,76 -0,09 0,00 0,08

12 -0,14 -0,48 -0,11 -0,13 -0,14 0,68 -0,48 -0,53 -0,46 -0,02 0,06 X 0,42 0,10 -0,04 0,05 -0,15 0,38 0,23

13 -0,03 -0,25 -0,21 -0,38 0,54 0,15 0,09 -0,23 -0,25 -0,15 0,34 0,42 X 0,60 0,51 -0,22 -0,27 0,08 0,63

14 0,00 0,55 0,15 0,16 0,57 -0,35 0,38 0,22 0,21 -0,02 0,19 0,10 0,60 X 0,71 0,06 -0,18 -0,35 0,85

15 0,13 -0,32 -0,39 -0,33 0,52 -0,09 0,19 -0,18 -0,26 0,11 -0,33 -0,04 0,51 0,71 X -0,45 -0,21 -0,20 0,71

16 -0,39 -0,07 0,29 0,24 -0,30 -0,54 -0,03 0,10 0,06 -0,35 0,76 0,05 -0,22 0,06 -0,45 X 0,06 -0,02 -0,04

17 -0,16 0,53 0,30 0,25 0,08 0,02 -0,10 0,18 0,27 -0,03 -0,09 -0,15 -0,27 -0,18 -0,21 0,06 X 0,24 -0,44

18 0,34 -0,34 -0,04 -0,14 -0,23 0,43 -0,48 -0,14 -0,24 0,08 0,00 0,38 0,08 -0,35 -0,20 -0,02 0,24 X -0,14

19 0,40 -0,10 -0,11 -0,06 0,37 -0,20 0,04 -0,08 -0,07 0,04 0,08 0,23 0,63 0,85 0,71 -0,04 -0,44 -0,14 x

Table 2 Correlation factors Based on general interpretation norms for correlation factors, the following meaning was attached to different intervals, irrespective of the sign of the factor: · · ·

0,00 to 0,30 – negligible. 0,30 to 0,50 – medium strength correlation. 0,50 to 1,00 – high strength correlation.

When removing the correlation factors that represented negligible connections, the simplified table looked as in Table 3. # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

1 X 0,92 0,94

0,79 0,88 -0,41

2 X 0,77 0,91 0,76 -0,31 0,71 0,85 0,97 -0,77 -0,48

3 0,92 0,77 X 0,93

4 0,94 0,91 0,93 X

0,37 0,69 0,76

0,35 0,74 0,82 0,36

0,54

0,53 -0,34

7

0,76

-0,31

0,71 0,37 0,35 0,56 -0,48 X 0,79 0,69 -0,40

-0,39

0,56 0,32 0,35 0,45

-0,33

-0,39 0,34 0,40

6

X

-0,38 0,55 -0,32

5

0,54 0,57 0,52 -0,30

X -0,48 -0,44 -0,36 0,38 -0,49 0,68

-0,48

8 0,79 0,85 0,69 0,74 0,32 -0,44 0,79 X 0,95

-0,53

9 0,88 0,97 0,76 0,82 0,35 -0,36 0,69 0,95 X 0,40 -0,36 -0,46

10

11 -0,41 -0,77 0,54

0,38 -0,40 0,40 X

0,45 -0,49

-0,36

-0,38 0,54 0,68 -0,48 -0,53 -0,46

X X 0,42

0,38

-0,54

-0,35

13

-0,48

0,36

0,34 -0,35

12

-0,33 0,76

14

15

0,55

-0,32 -0,39 -0,33 0,52

0,57 -0,35 0,38

0,34 0,42 X 0,60 0,51

-0,33 0,60 X 0,71

0,63

-0,35 0,85

16 -0,39

17 0,53 0,30

-0,30 -0,54

18 0,34 -0,34

0,37 0,43 -0,48

-0,35 0,76 0,38

0,51 0,71 X -0,45

0,30

-0,35 -0,45 X X

0,43

-0,48

0,38

0,37

19 0,40

0,63 0,85 0,71 -0,44

X 0,71

-0,44

x

Table 3 Simplified correlation overview In Figure 2, a graphic representation of Table 3 is given. Please notice that in order to enhance the readability of this figure, the correlation factors between 4 and 7, 8, and 9 have been omitted since they are almost identical to those between 3 and 7, 8, and 9. In this figure, circles are used to indicate variable attributes of the company, rhombs for non-variable

December 22nd, 1997

-6-

Version 2.0

-0,33

December 22nd, 1997

Parameter

-70,54

0,52

-0,45

0,57 -0,33

-0,35

0,30

-0,39

0,54

0,71

0,51

14. Comparison of results with goals

0,40

0,38

11. Duration of the execution

-0,54

0,34

0,42

0,36 0,32

-0,49

-0,48

0,79

-0,48

0,79

12. Degree of involved top management

0,68

6. Degree of motivated employees before the imp. process

0,43

0,69

0,95

18. Average perceived difficulty in using the tool

7. Experience from previous imp. proc.

0,38

-0,36

-0,36 0,69

-0,40

9. Number of employees 100% involved in the execution

0,56

13. Clear plans for progress and activities

-0,33

0,60

-0,35

0,37

0,34

10. Duration of the planning

0,45

0,35

0,88

-0,38

4. Number of employees in unit to be imp.

-0,35

0,76

-0,30

5. Degree of competition

1. Sales

0,93 0,94 0,92

16. Internal resistance to the imp. process before start

15. Understanding of the imp. process among the employees before execution

17. Internal resustance to change during the imp. process

3. Total number of employees

0,76

-0,53

-0,44

-0,48

-0,46

8. Number of employees involved in the planning

0,38

attributes of the enterprise, and squares for actions undertaken during the improvement process.

Figure 2 Graphical representation of the correlations between the numerical survey responses

3.3 Analysis of Parameters in Direct Correlation to Achieved Results From the massive correlation table presented above, the factors that indicated a direct relationship between variables and the result variable, i.e., the degree to which the improvement results were satisfactory, were extracted. Sorted according to descending strength of the correlation factors, they are portrayed in Table 4:

Correlation with improvement results

Version 2.0

14. Comparison of results with goals 15. Understanding of the improvement process among the employees in advance of execution 13. Clear plans for progress and activities in the improvement process 17. Internal resistance toward change during the improvement process 1. Company sales 5. Degree of competition

0,85 0,71 0,63 -0,44 0,40 0,37

Table 4 Significant correlations between dimensions of improvement and results of improvement The correlation analysis showed that: ·

·

· ·

Enterprises that planned the improvement process thoroughly and also monitored progress compared to objectives, were most satisfied with the improvement results. The explanation probably lies in a better-structured improvement process resulting from a clear vision of what the objectives and ways to achieve them are. The better the understanding of the improvement process among the employees, the better the results. Clearly, this shows that even if an awareness process costs and takes time, it pays off. On the other hand, the larger the internal resistance toward the project, the poorer the results. Higher sales generally means that the enterprise controls more resources, both in terms of money and people, that can be exploited in such an improvement process. In markets of more fierce competition, the needs for improvement are perhaps more easily visible. Thus, the improvement projects might be better aligned with true improvement needs and yield more rewarding results.

In addition, most of these variables also seem to be correlated to each other, thus resulting in synergy effects and reinforcing the effects of others. 3.4 Analysis of Variable Parameters of the Enterprises To analyze which of the variable parameters, i.e., actions and variable attributes, available to an enterprise for impacting that seemed to influence the achieved results, these issues were studied in some more detail. To give an overview of the connections involved, these parameters were isolated in Figure 3, where the shaded boxes represent factors that correlated directly with achieved results.

December 22nd, 1997

-8-

Version 2.0

Figure 3 Correlations between action, variable attributes, and achieved results Based on these correlation factors, the contours of some mechanisms of impact could be seen in the data material. Further studies of these possible mechanisms were undertaken by the authors as a follow-up to the correlation analysis. What seemed to be relevant connections are listed below:

December 22nd, 1997

-9-

Version 2.0

ΠIntensity, connections between the intensity both before and during the improvement process and the variables number 12 (top management involvement), 6 (employee motivation), 16 (internal resistance before start), 7 (experience with improvement processes), and 18 (difficulties in using the tools). It seems as if an increased feeling of intensity surrounding the improvement situation works favorably pertaining to all of these five variables. The mechanism is depicted in Figure 4.

Increased intensity (e.g., competition)

12. More involved top management

6. More motivated employees in advance of the improvement process

16. Lower internal resistance to the improvement process before start

7. Quicker accumulation of experience from improvement processes

18. The improvement tools are perceived as easy to use

Figure 4 Mechanism 1 – Intensity in the improvement situation  Motivation, resistance, and understanding, connections between the variables 6 (employee motivation), 16 (internal resistance before start), and 15 (pre-process employee understanding). An established high degree of motivation among the employees correlates strongly negatively with internal resistance before the improvement process is started. The internal resistance also shows negative correlation with the employees’ understanding of the improvement process. Thus, by maximizing 6, minimizing 16, or maximizing 15, the company can increase the chances of a successful improvement process. The mechanism is illustrated in Figure 5. 6. Increase the employee motivation

16. Pre-process resistance is reduced

15. The pre-process understanding is improved

Increased improvement

16. Reduce the pre-process resistance

Figure 5 Mechanism 2 - Motivation, resistance, and understanding toward the improvement project Ž Top management involvement, connections between the variables 12 (top management involvement), 18 (perceived tool difficulty), 13 (clear plans), and 14 (comparison of results with objectives). Generally, there is a correlation between tools that are seen as December 22nd, 1997

- 10 -

Version 2.0

more difficult to apply and the level of top management involvement. This is probably due to a higher propensity to get involved when tools that the enterprise views as difficult to use are being applied. Furthermore, there is a strong chain of correlation links between top management involvement, clear plans, monitoring of the progress with these plans and resulting achievements, and increased improvement. This chain of connections is shown in Figure 6. 18. More difficult tool

12. Higher top management involvement

14. More comparison of results with objectives

13. Clearer plans for progress and activities

Increased improvement

Figure 6 Mechanism 3 - Top level involvement in the improvement project  Complexity, connections between the complexity of the improvement process and the variables 10 (duration of the planning), 16 (pre-process internal resistance), 11 (duration of the execution), and 15 (pre-process employee understanding). The internal resistance is negatively correlated to the duration of the planning. This can be interpreted to mean that allowing the pre-process planning to take its time, the internal resistance will decrease. However, the planning duration does not correlate with neither increased employee understanding nor clear plans. The duration of the execution correlates strongly with the internal resistance, i.e., the higher the resistance, the more likely that the process will take time to accomplish or vice versa. As opposed to this, there is negative correlation between the pre-process understanding among the employees and the execution duration. Finally, there is strong correlation between the pre-process employee understanding of the improvement process and the results achieved in that process. This led to the following mechanism, see Figure 7. Increased complexity

15. Reduced pre-process employee understanding

11. More extensive process, i.e., longer duration

16. Increased pre-process internal resistance

Lower achieved improvement

Figure 7 Mechanism 4 – Complexity of the improvement project

December 22nd, 1997

- 11 -

Version 2.0

 Top level versus employee involvement, connections between the variables 12 (top management involvement), 8 (employees involved in the planning), and 9 (employees involved in the execution). The correlation analysis showed that the more involved the top management was in the improvement process, the less involved the employees were in both planning and execution. This might reflect that when the top management does get involved in an improvement project, they also do much of the work and delegate less of the tasks to lower level employees. There is, however, no direct correlation between any of the latter two variables and achieved results. ‘ Internal resistance, connections between the variables 16 (internal resistance in advance of the improvement process), 17 (internal resistance during the improvement process), and 19 (achieved results). A rather surprising finding was that the internal resistance during the process did not correlate with any other variables than the achieved improvement results. Furthermore, the resistance before the process did not correlate with the achieved results (-0,04). On the other hand, this variable was, as we have seen, important in many of the other improvement-related mechanisms. There was also reason to believe that there might exist a function between the two resistance variables: Resistance during the process = Resistance in advance x Resistance-reducing efforts In fact, there was a correlation of 0,33 between the change in resistance (resistance before – resistance during) and achieved results. In other words, the larger the change in resistance, the better results and vice versa. The lack of correlation between the two resistance variables can then be caused by resistance-reducing efforts. Such efforts were also investigated in the survey and some dominating factors were identified. These, along with the general topography of this mechanism, are illustrated in Figure 8. The numbers, e.g., 0,23 attached to additional information, indicate that 23% of the organizations had experienced this phenomenon. Inadequately coordinated top management

Reference to earlystage results Promises of more interesting work Additional information

0,23 0,38

16. Internal resistance before the process

0,15 0,31

0,15

0,23

17. Less resistance during the process

17. Increased resistance during the process

Higher degree of improvement

Lower degree of improvement

Lack of resources Disagreement between management and employees

Figure 8 Mechanism 6 - Internal resistance December 22nd, 1997

- 12 -

Version 2.0

3.5 Analysis of Verbal Responses Many of the respondents provided input of a verbal nature to the survey’s open-ended questions. These were difficult to analyze with the same degree of precision as the numerical data. However, some interesting hypotheses emerged from these answers as well: A. Educational level in the organization; the higher percentage of skilled operators, the more willing to change the organization seemed to be. B. Organizational type; process-oriented organizations were more willing to change than functionally oriented companies. C. Nicknames; companies that named their improvement processes with nicknames that become familiar and generally accepted throughout the organization seemed to create more enthusiasm for these processes. D. Triggering impulse; companies initiating change processes out of a general desire to improve more often experienced more indifference among the employees than when there was a need to improve in order to survive. E. Type of plans; the more thorough the activity and resource plans for the improvement project were, the more seriously the project was perceived by the employees. 3.6 Indicators of the Improvement Effects of the Tools To undertake an analysis of the effects of the individual tools and philosophies, two indicators of a tool’s improvement effect were defined. First, the survey respondent enterprises indicated which process or area of the organization they wanted to improve by using a specific tool or philosophy, for instance delivery precision. At the end of the questionnaire, the companies were also asked to explain which processes or areas had been improved as a result of the improvement project. Depending on whether the area desired to improve in fact was improved, it was possible to assess whether the tool worked as intended. Thus, the first indicator was: Improvement effect of the tool, indicator 1 = Degree of achieved objectives Secondly, the degree to which the enterprises that had applied the tools were satisfied with the achieved results said something about their effects. The second indicator was therefore defined as: Improvement effect of the tool, indicator 2 = Assessment of achieved results In sum, these two indicators were combined into the following expression: Improvement effect of the tool = Indicator 1 x Indicator 2 » “Area Precision” x “Improvement Contribution” Table 5 contains the data for the two indicators and the total improvement effect for the tools that had been applied by one or more of the respondent companies. In addition, the table shows how many of the respondents that had applied each tool and how difficult they were perceived to be in use.

December 22nd, 1997

- 13 -

Version 2.0

Improvement process or area

Delivery time and precision Product development Quality

Business processes

Maintenance Re-use of information Strategy/vision

Employee motivation

Overview and control

JIT

62

100

Average achieved improvement results (Indicator 2) (1-9) 5,8

SMED DFM ISO 9001-4

23 15 38

67 100 80

SPC

23

TQM Own quality system

Improvement tool or philosophy

Extent of use among the respondents (%)

Degree of achieved objectives (Indicator 1) (%)

Improvement effect of the tools (I1 x I2) (0-900)

Average perceived difficulty in use (1-5)

575,0

3,8

4,7 5,0 5,6

311,1 500,0 448,0

4,0 3,5 2,2

100

5,7

566,7

3,7

46 8

67 100

4,8 7,0

322,2 700,0

4,0 2,0

ABC Work unit analysis BM BPR

8 8 54 54

0 100 86 86

5,0 7,0 4,7 5,1

0,0 700,0 404,1 440,8

3,0 3,0 2,6 4,3

Idealizing TPM Knowledge database New IT system GAP Customer adaptation PIMS

15 8 8 31 15 15 15

100 100 100 75 100 100 50

6,0 5,0 7,0 5,8 6,0 6,0 6,0

600,0 500,0 700,0 431,3 600,0 600,0 300,0

3,5 4,0 4,0 4,5 3,0 4,5 2,0

Incentive program Work environment program Internal control

31 31

25 25

6,0 4,5

150,0 112,5

2,8 3,8

15

0

4,0

0,0

3,5

Table 5 The improvement effects of the tools Based on Table 5, it was now possible to generate a concluding ranking of the improvement effects of the different tools and philosophies. This ranking is shown in Table 6, where tools used by more than 20% of the respondent enterprises are in bold. Ranking 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

December 22nd, 1997

Improvement tools or philosophy Improvement effect (0-900) Work unit analysis 700,0 Knowledge database 700,0 Idealizing 600,0 GAP 600,0 Customer adaptation 600,0 JIT 575,0 SPC 566,7 DFM 500,0 TPM 500,0 ISO 9001-4 448,0 BPR 440,8 New IT system 431,3 BM 404,1 TQM 322,2 SMED 311,1 PIMS 300,0 Incentive program 150,0 Work environment program 112,5 ABC 0,0 Internal control 0,0

- 14 -

Version 2.0

Table 6 Ranking of the tools according to improvement effects 3.7 Verbal Responses Collected in the Survey In addition to the numerical answers, the respondent enterprises were also asked to input any explanations or useful hints in a verbal manner. For most of the questions, these responses were not very useful. However, when presented with the questions “What is the most valuable knowledge you can give to someone starting an improvement project for the first time?” gave some hints that could be useful for beginners in the area of business process improvement: · · · · · · · · ·

Things take time, results do not come easy, patience, a long-term view and endurance are crucial in improvement projects. If possible, plan the project so that only one issue is dealt with at the time. Initiate improvement projects out of strength when the company is doing well, do not consider improvements a fix to poor results. Create top management support and decide and define what the primary objectives of the project are. You can never be well enough prepared. Create teams with separate areas of responsibility during the execution of the project, preferable led by highly motivated sponsors. Allow the necessary resources to be available for the project. Make sure there is a steady flow of information about the progress and results of the project to the employees. Be realistic when it comes to expectations of progress.

3.8 Sources of Error The study resulted in a small data set. In fact, it contained only 13 completed questionnaires, a rather limited data set. This of course introduced some possible error sources in the study: ·

·

Did the respondent group represent a cross section of companies in Norway that had used one or more improvement tools? Perhaps not, but the selected enterprises were targeted to represent different geographical areas, industries, and sizes. Still, the results could certainly have changed if a larger sample had been collected and the results must be viewed in this light. The sample size of 13 data sets rendered it possible to undertake the performed correlations analysis. However, a larger sample size would have made the analysis more solid and eliminated some possible statistical inconsistencies.

4 Conclusions The study did clearly illustrate that the success achievable when applying a certain tool is strongly dependent on a number of factors that not only directly impact the outcome, but which are also linked in a complex network. Actions seen as favorable for improvement might negatively impact other factors reducing the effects of them. When posing the question which tool to use, the study showed that the enterprise’s ability to change is of pivotal importance to the decision. The fact that some companies succeed in applying a specific tool while others do not, seem to rely on how the ability to change is

December 22nd, 1997

- 15 -

Version 2.0

developed before and during the improvement project. Four of the six parameters that directly correlated with the achieved results, can be influenced during the improvement process. The key challenge for management during such processes is thus to maximize the organization’s ability to change. Improvement projects where highly complex or combinations of improvement tools have been applied, were often seen to have failed or produced results poorer than expected. According to the study, this did not only rely on available resources in the organization, but also on the fact that the organizations were unable to handle the extent of change advocated by the tools. Therefore, the boundaries for the improvement ambitions should be set so as to balance the deployable change ability of the organization. The tool box designed based on the study is supposed to aid the achievement of this balance by giving guidelines for the selection of: Œ The right tool compared to what the organization wants to improve.  The right tool related to the organization’s initial ability to change. Ž The right overall strategy for the improvement process based on the situation facing the company.  Activities to improve the ability to change before and during the improvement process. Figure 9 presents the overall improvement toolbox that can be used to design an improvement project to maximize the likelihood of success.

December 22nd, 1997

- 16 -

Version 2.0

Figure 9 The improvement toolbox

6 Literature

Since the paper itself more or less only describes the study that was undertaken, there are no references to literature in the paper. However, during the study, much literature was studied. The following list summarizes some of the most important literature scanned during this work.

· Andersen, Bjørn, Haavardtun, Lars Johan, and Strandhagen, Jan Ola: Kompendium i MPS-systemer (Translated from Norwegian: Textbook in Material and Production

December 22nd, 1997

- 17 -

Version 2.0 Overview and control

Employee motivation

Strategy/vision

Reuse of information

Maintenance

Business processes

Quality

Product development and design

Delivery time and precision

Desired improvement area

Companies with low willingness to change should follow:

Companies with high willingness to change can follow:

Fierce competition

Attributes that lead to higher willingness to change in the company High sales

1

Part of mechanism number:

Hard-to-use tools New IT system Strategy for customer adaptation Business Process Reengineering (BPR) Reduction of set-up times (SMED) Total Quality Management (TQM) Total Productive Maintenance (TPM) Knowledge systems Just-In-Time (JIT) Work environment programs Statistical Process Control (SPC) Design for Manufacturability (DFM) Idealizing Internal Control system Time Based Manufacturing (TBM)

I

300,0 448,0 404,1 150,0 0,0 700,0 600,0

I

431,3 600,0 440,8 311,1 322,2 500,0 700,0 575,0 112,5 566,7 500,0 600,0 0,0

II

2,0 2,2 2,6 2,8 3,0 3,0 3,0

II

4,5 4,5 4,3 4,0 4,0 4,0 4,0 3,8 3,8 3,7 3,5 3,5 3,5

I = Improvement effect II = Approximate difficulty in use

Profit Imp. of Mark. Strategy (PIMS) ISO 9001-4 Benchmarking (BM) Incentive Programs Activity Based Costing (ABC) Work unit analysis GAP-analysis Concurrent Engineering Design for Assembly (DFA) Rel. Centered Maintenance (RCM) McKinsey’s 7S model

Easier-to-use tools

Strategy B: Create an image that improvements are absolutely necessary

Strategy related to company situation

Strategy A: Blue Cheese Approach, careful progress

6

4

1

E

D

C

3

3

A B

3

Part of hypothesis:

2

Measures with a favorable effect on the execution of the improvement process Actions correlated with Part of mechanism number: results Consistently compare results with stated objectives Make clear plans for progress and activities Ensure top management and direct involvement Part of Consider: hypothesis: Aggressively nicknaming the improvement process Make clear the seriousness involved in the reasons for initiating the improvements Increase the level of detail and extent of the activity and resource plans

Process-orienting the company

Consider: Increasing the percentage of skilled operators

Counteract high internal resistance to change during the process

Measures to achieve a favorable application situation Factors correlated with Part of mechanism number: results Create a high understanding of the improvement process and in advance of it

·

· · · · · · · · · · · · · · · · · · ·

Management), Department of Production and Quality Engineering, NTNU, Trondheim, Norway, 1997. Andersen, Bjørn, Moe, Einar Printz, Moseng, Bjørn, and Rolstadås, Asbjørn: Produktivitet og konkurranseevne i norske bedrifter - på vei mot TOPPen (Translated from Norwegian: Productivity and Competitiveness in Norwegian Enterprises – On the Way to the TOP), Ad Notam, Oslo, Norway, 1996. Andersen, Bjørn: The Results of Benchmarking and a Benchmarking Process Model, Ph.D. Dissertation published at the Department of Production and Quality Engineering, NTH, Trondheim, Norway, 1995. Aune, Asbjørn: Kvalitetsstyrte bedrifter (Translated from Norwegian: QualityManaged Enterprises), Ad Notam, Oslo, Norway, 1996. Bockerstette, Joseph A. and Shell, Richard L.: Time Based Manufacturing, McGrawHill, 1993. Born, Gary: Process Management to Quality Improvement: The Way to Design, Document, and Re-engineer Business Systems, John Wiley, 1994. Committee of Sponsoring organizations of the Treadway Commission: Internal control integrated framework, 1994. Cristopher, Martin: Logistics: The Strategic Issues, Chapman & Hall, 1992. Durö, Robert: Konkurrensöverlägsenhet: I tio konkreta steg (Translated from Swedish: Competitiveness: In Ten Specific Steps), Liber, Stockholm, Sweden, 1988. Eccles, Tony: Succeeding with Change: Implementing Action-Driven Strategies, McGraw-Hill, 1994. Federal Aviation Administration: Business Process Improvement (Reengineering), Handbook of Standards and Guidelines, 1995. Gellerman, Saul W.: Motivation and Productivity, American Management Association, 1963. Grøtte, Hauknes, Qvistgaard, Engh, and Persson: Håndbok for flytorientert MPS (Translated from Norwegian: Handbook for Flow-Oriented Production Planning and Control), Tapir, Trondheim, Norway, 1989. Hotvedt, Einar and Westhagen, Harald: Samarbeid om bedriftsutvikling (Translated from Norwegian: Cooperation on Enterprise Development), Tanum-Norli, Oslo, Norway, 1984. http://www.asedb.no. http://www.capgemini.no. http://www.sap.com. Institute of Management Accountants: Practices and Techniques: Implementing Activity-Based Costing, Statement on Management Accounting, Statement no. 4T, 1993. Ishiwata, Junichi: Productivity through Process Analysis, 1991. ISI: Markedsdrevet IT-logistikk, Et viktig middel for internasjonal konkurranseevne (Translated from Norwegian: Market-Driven IT: An Important Source for International Competitiveness), Theme Book no. 25, Oslo, Norway, 1993. Johannessen, Jon-Arild and Olaisen, Johan: Endringsledelse: Mål og resultatstyring i privat og offentlig virksomhet (Translated from Norwegian: Change Management: Objectives and Result Management in Private and Public Sector), Fagbokforlaget, Oslo, Norway, 1994.

December 22nd, 1997

- 18 -

Version 2.0

· Karlöf, Bengt: Håndbok i strategi for bedrifter og organisasjoner (Translated from Norwegian: Handbook in Strategy for Companies and Organizations), Damm, Oslo, Norway, 1989. · Killing, J. Peter: Perspectives for Managers, Managing Change: The Urgency Factor, IMD, vol. 29, Issue no. 1, February, 1997. · LaMarsh, Jeanenne: Changing the Way We Change: Gaining Control of Major Operational Change, Addison-Wesley, 1995. · Persson, Göran and Virum, Helge (editors): Logistikk for konkurransekraft (Translated from Norwegian: Logistics for Competitiveness), Ad Notam, Oslo, Norway, 1995. · Peters, Glen and Price Waterhouse: Riding the Wave: Imagining Your Future, CDROM/book, 1996. · Porter, Michael E.: Competitive Advantage: Creating and Sustaining Superior Performance, Free Press, 1985. · Price Waterhouse: Paradox Principles: How High-Performance Companies Manage Chaos, Complexity, and Contradiction to Achieve Superior Results, 1996. · SAP AG: Efficient R/3 Implementation, Business Engineering Workbench, CD-ROM, 1996. · Skorstad, Egil: Lean Production: Conditions of Work and Worker Commitment, Economic and Industrial Democracy, vol. 15, no. 3, 1994. · Solberg, Svein Linge and Andersen, Åge Borg: I forkant: Konkurransefordeler ved strategisk bruk av informasjonsteknologi (Translated from Norwegian: Ahead: Competitiveness Advantage through Strategic Use of UT), Tano, Oslo, Norway, 1990. · Spencer, Lyle M.: Reengineering Human Resources: Achieving Radical Increases in Service Quality - With 50% To 90% Cost And Head Count Reductions, Wiley, 1995. · Statoil: Benchmarking in Statoil, 1994. · von Krogh, Georg and Roos, Johan: Fra kunnskap til konkurransefortrinn (Translated from Norwegian: From Knowledge to Competitiveness), Norwegian School of Management, Oslo, Norway, 1992. · Wheelen, Thomas L. and Hunger, J. David: Strategic Management and Business Policy, Addison-Wesley, 1986. · Willoch, Bjørn-Erik: Business Process Reengineering - en praktisk innføring og veiledning (Translated from Norwegian: Business Process Reengineering – Practical Introduction and Guidelines), Fagbokforlaget, Oslo, Norway, 1994.

December 22nd, 1997

- 19 -

Version 2.0