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PLM Maturity Model Development and Implementation in SME
Available online at www.sciencedirect.com
ScienceDirect Procedia CIRP 63 (2017) 651 – 657
The 50th CIRP Conference on Manufacturing Systems
PLM Maturity model development and implementation in SME Marko Paavela,*, Kristo Karjusta, Jüri Majaka a Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia * Corresponding author. Tel.: +372 58089134; fax:+0-000-000-0000.E-mail address:[email protected]
Abstract The product lifecycle management (PLM) is an important tool for managing products and its data throughout the life cycle. The advantages of the PLM systems are well-known in the case of large enterprises and groups. In the current study the objective is to simplify and optimize PLM implementation through PLM maturity model in small and medium size enterprises (SME). The introduced maturity model allows estimating the current situation in SME through use of questionnaires. The information gathered includes the expectations of the SME and will answer different questions like how are benefits connected with different business dimensions and their sub-categories described in maturity model. The business dimensions are evaluated by the expert group. The maturity model proposed in this paper is based on combining the know-how of the expert group and Fuzzy analytical hierarchy process (FAHP) techniques. The proposed PLM maturity model is implemented in Estonian SME bringing out current situation in: Strategy & Policy, Management & Control, Organization & Processes, Information technology, People & Culture. © Published by by Elsevier B.V.B.V. This is an open access article under the CC BY-NC-ND license 2017The TheAuthors. Authors. Published Elsevier © 2017 (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility ofthe scientific committee of The 50th CIRP Conference on Manufacturing Systems. Peer-review under responsibility of the scientific committee of The 50th CIRP Conference on Manufacturing Systems Keywords:PLM; Maturity model; company analyze model
1. Introduction 2. State of the art In changing business environment, companies are looking for different advantages on how to get better position in the market. One way to do that is to find help from Product Data Management (PDM) or PLM systems [1-2] for optimizing the production processes and methods, to give better and quicker overview of the actual situations in the shop floor and according to the real situation changing the processes to react quicker for changed situations [3-5]. If company has chosen to implement PDM or PLM system, then it is needed to know the current status of the enterprise. One way to get better understanding about current PLM or PDM situation is to determine the maturity level. For this reason, the PLM maturity model is utilized in this paper. In this maturity model is used decision-making methodology considered for PLM implementation. This is based on utilization of PLM maturity models [6-8] and Fuzzy analytical hierarchy process techniques [9-12]. In the current study the PLM maturity model is developed and implemented in SME in order to raise awareness of PLM and its functionalities, focusing for Estonian and EU market.
PLM-related research issues are rather young accordingly PLMs young age. In literature, there is brought out different approaches in maturity modelling which are of one or the other way important aspects in PLM implementation process. [13] There are three different opportunities how to execute current PLM maturity situation, which target group performs the actions concerned with the maturity model [6]: x Self-assessment; x Third party assisted assessment; x Certified practitioners. There are models which are based only academic, like; " PLM framework for the assessment and guidance of PLM implementations" by Batenburg, "Process oriented framework for support PLM implementation" by Schuh, "Product lifecycle management" by Saaksvuori and "Defining the customer dimension of PLM maturity" by Kärkkäinen. Starks model is based on consultant knowledge [1,2,13,14,15,16]. Usually model consist from 3 to 7 different business dimensions which are describing different valuated sectors.
2212-8271 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of The 50th CIRP Conference on Manufacturing Systems
doi:10.1016/j.procir.2017.03.144
652
Marko Paavel et al. / Procedia CIRP 63 (2017) 651 – 657
The number of maturity levels in different maturity models are varying most commonly between four and six. The advantages provided by maturity model can be outlined as follows [13,15]: Companies expected benefits; Current situation (As is Analyse); Expected situation (To Be); Weak spots description and overview.
Biggest problem with existing maturity models is that the number of questions included in models are most commonly limited or the questions are not pointed out. Thus, the decision criteria are not well covered, etc.
3. Maturity model description As a rule, the PLM implementation starts from over viewing business vision of the company. Based on that the PLM vision and its objectives are defined. After that is needed to fix the existing PLM and its implementation strategies. Based on these strategies the PLM and its implementation plans should be determined and optimized to lower the implementation cost and time [1]. For all of these activities and decisions trustable information is needed. Here is proposed a company analysis model for evaluating PLM maturity and to get extra needed information.
BENEFITS
Software Configuration Management
Visualization
System integration
Team Culture
People in PLM
Skilled, Competent People
Customer Service and Support
NPDI
Fig. 1. Principal scheme of maturity model.
Document/ File Management
Information technology
People & Culture
Lifecycle and Processes
workflow & process Management
Change Management
BUSINESS IMPROVEMENT
Data exchange
QUALITY IMPROVEMENT
Organization & Processes
Management of Product portfolio and structure
Requirements Management
Management of Product data and information
Quality Management
Reporting & Analytics
Configuration/BOM Management
Management & Control
Program and Project Management
(PLM) Plan
(PLM) Metrics
(PLM) Vision
(PLM) Strategy
Strategy & Policy
TIME REDUCTION
Maintenance, repair and operations (MRO)
FINANCIAL PERFORMANCE
Sourcing and Supply Chain Management
x x x x
Applying real time monitoring and digitalization can be considered as new trends in PLM system development [19,20].
Marko Paavel et al. / Procedia CIRP 63 (2017) 651 – 657
The methodology employed for Maturity model development is based on combining questionnaire and expert group evaluations for input data acquisition and application of FAHP techniques for evaluation of decision criteria included in model. Current model is built up based on the company’s selfassessment and it works by answering questionnaire. Through answering the questions information is gathered for implementing the lowest level of the model i.e. subcategories. Model is built up so that in the first stage the expectations and hoped benefits of the company are mapped and described. The benefits are divided into four bigger categories: x x x x
Financial performance; Time reduction; Quality improvement; Business improvement.
There is brought out only upper level as final result, actually the lower level criteria are evaluated first and through them are evaluated higher level criteria. For example, financial performance is covering; greater productivity, decreased cost of new product introduction etc. Time reduction includes; improved product cycle times, insight into critical processes and better resource utilization etc. Quality improvement consists of fewer errors and better product quality. Also, less scrap and rework etc. Business improvement includes product faster time to market, greater design efficiency, standards and regulatory compliance, improved communication etc. In this model the activities of the company are divided into groups based on the Batenburg "PLM framework for the assessment and guidance of PLM implementation"[7]: x x x x x
Strategy & Policy; Management & Control; Organization & Processes; People & Culture; Information technology.
The groups have also subcategories for more detailed description of the current situation in the company. Principal scheme of the PLM maturity model is depicted on Fig. 1. In this model, expert group holds the central place. PLM Expert group consists of 11 persons from academic and industrial field. They are from universities, industrial companies, PLM vendors and development centres. In this model, expert group evaluates; questions used in questionnaire starting from job position and work experience in scale from 1-
10. Ending by evaluating all possible answers for each question. There has been used different outlier methods. Expert group has also evaluated benefit categories and business dimensions. Model is built up so that employees holding up different positions in company are answering to web based questionnaire. This information is gathered and most of the information is automatically analyzed using expert group knowledge. Based on gathered information there is also brought out the expectations what company has in mind. For output of the current as is situation is used Saaksvouri Maturity model. Description of this model is brought out in table 1. [2] Table 1. Maturity level descriptions. [2] Level Unstructured
Repeatable but intuitive
Defined
Managed and measured
Optimal
Description Topic has been recognized and importance agreed. PLM concept and standards are not defined. Not defined approach and all issues are solved individually case-bycase. Lifecycle and processes are developed so far that different process executors are acting the same. There are no formal development, definition, or communication of standard process. Processes and basic concepts are standardized, defined, documented and communicated through manuals and trainings. There is no end-to-end PLM process supporting IT systems. All work is manual from the process view. Compliance between processes is monitored and measured to take actions where process is not functioning. Concept and processes are under constant improvement. PLM processes are well supported by IT systems and developed through clear vision. Processes and concept have been refined to the level of best practices, based on continues improvement and benchmarking with other organizations. IT is used and integrated. Processes are automated to end.
3.1. Questionnaire description The developed and modified questions in the web based questionnaire will give information based on the different principles starting with respondent position, current and previous work experience, mission, vision and expectations. Also about activities, metrics and modules currently fallowed and what interest in future. In general, the questionnaire covers all subcategories in the lower level of the model (Fig.1). Answering possibilities are different and are dependent on particular question type/content. The respondent can express opinion by using scale from strongly agree to strongly disagree, give fact based answers by using dropdown box or multiple
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Marko Paavel et al. / Procedia CIRP 63 (2017) 651 – 657
choice answers. In some cases, the use of free text box is provided to give detailed description of certain problem/situation linked with PLM and connected with the certain company situations. The questionnaire is built up based on principle that everyone is answering general questions and questions concerning his/her area of expertise. There is extra questionnaire for management and department managers consisting from 123 questions. The total capacity of the
questionnaire exceeds 700 rows (multiple choice answers included). Table 2 gives overview of the current questionnaire. There is brought out questions what are giving information about business dimension Strategy & Policy sub-categories PLM Vision and PLM Strategy. There are several questions which are giving information for different categories.
Table 2. Business dimension Strategy & Policy subcategory PLM Vision questions Vision x x x x -
Strategy x x x x x x x
-
x
x x x x x x x x X
x x x x x x x X
Question Enterprise mission: Enterprise vision: Management has clear vision of future plan of company. Vision of future of the company is strongly formulated. Too difficult to explain goals to each level in organization. All employees are aware of company's goals, missions and visions. Investments on R&D projects is one of the important trends in company's politics. During last 5 years, the technology and methodology of production has been changed according last trends. Are you interested in PLM system or PLM functionalities in your company? Have you done PLM feasibility study? Have you done PLM ROI (return of Investment)? How big is/was the payback time for PLM? Do you have PLM vision? What it is it now and in 5-year perspective? The company's overall vision matches PLM vision? Company's PLM vision is in accordance with company's overall vision? Do you have PLM strategy? Do you have PLM implementation strategy? What kind of PLM related metrics are you following in company? What kind of PLM related metrics you wish to follow in company?
X
X
Company is more interested in?
Ͳ
ݔ൏ ͳ ݔݎ ݑǡ ݈ ݔ ݉ǡ
ି௫
݉ ൏ ݔ ݑǤ
௨ି
For evaluating business dimensions’ expert group has used Analytic Hierarchy Process (AHP), proposed by Saaty [9]. The AHP is widely used method in multiple-attribute decision making [9,10]. It structures the alternatives into hierarchical framework. Fuzzy Analytic Hierarchy Process (FAHP) is an extension of AHP method that uses fuzzy logic. Also, fuzzy sets and fuzzy numbers for determining the ranking of certain criteria's [11]. Fuzzy Set Theory (FST) was introduced by Zadeh in 1965 to deal with uncertainty and vagueness [12]. A tilde "~" will be placed above a symbol is the symbol shows FST. The membership functionߤ ሺݔሻof a triangular fuzzy number can be introduced as [19]
consent Yes/No Yes/No Yes/No Years from 1 to 5 Yes/No text consent consent Yes/No Yes/No List of Metrics List of Metrics company`s expansion/ modification of current systems
௫ି
ߤ ሺݔሻ ൌ ൞ ି
3.2. Application of FAHP
Answer text text consent consent consent consent consent
(1)
In table 3 the Fuzzy logic was utilized for evaluation Saaty's 9-point scale [20]. Table 3. Linguistic terms and the corresponding triangular fuzzy numbers Saaty scale
Definition
Fuzzy Triangular Scale
1 3 5 7 9
Equally importance Weakly important Fairly important Strongly important Absolutely important
(1,1,1) (2,3,4) (4,5,6) (6,7,8) (9,9,9)
2 4 6 8
The intermittent values between two adjacent scales
(1,2,3) (3,4,5) (5,6,7) (7,8,9)
Table 4. The scores of experts in terms of triangular fuzzy numbers Time Reduction
Strategy & Policy
Management & Control
Strategy & Policy Management & Control
(1,1,1) (1,2,3)
(1/3,1/2,1) (1,1,1)
Organization & Processes (1/5,1/4,1/3) (1/4,1/3,1/2)
People& Culture
Information technology
(1/5,1/4,1/3) (1/4,1/3,1/2)
(1/4,1/3,1/2) (1,1,1)
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Marko Paavel et al. / Procedia CIRP 63 (2017) 651 – 657
Organization & Processes People & Culture Information technology
(3,4,5) (3,4,5) (2,3,4)
(2,3,4) (2,3,4) (1,1,1)
The decision criteria are evaluated by experts by performing pair-wise analysis. The obtained scores are converted to triangular fuzzy numbers and presented in Table 4. The results given in Table 4 cover five decision criteria. Let us introduce the-th triplet corresponding to geometric mean values as ෩୧ ൌ ሺ୧ ǡ ୧ ǡ ୧ ሻ
Strategy & Policy Management& Control Organization &Processes People& Culture Information technology
where భ
(3) భ
݉ ൌ ൫ςୀଵ ݉ ൯ ǡ
(4)
భ
ݑ ൌ ൫ςୀଵ ݑ ൯ ǡ
(5)
The number of decision criteria used in (3) -(5) is denoted by n. In Table 5 are given the values of the i-th triplet ሺ୧ ǡ ୧ ǡ ୧ ሻ for five criteria considered and corresponding sums. Table 5. The geometric means of fuzzy comparison values ୧
୧
୧
Strategy & Policy Management& Control Organization & Processes People& Culture Information technology
0.320 0.574 1.084 1.644 0.740
0.401 0.740 1.516 2.352 0.944
0.561 0.944 1.974 2.993 1.320
Sum
4.362
5.954
7.791
Criteria
The normalized values of the ݈ ǡ ݉ and ݑ can be computed as ሾ݈ҧ ǡ
௨ ೕసభ σೕసభ σೕసభ ௨
݉ ഥ ǡ ݑത ሿ ൌ σ
ǡ
ǡ
൨
(6)
(1/4,1/3,1/2) (1,1,1) (1/3,1/2,1)
(1,2,3) (1,2,3) (1,1,1)
Table 6. Eigenvectors of fuzzy comparison values Criteria ୧ҧ
(2)
݈ ൌ ൫ςୀଵ ݈ ൯ ǡ
(1,1,1) (2,3,4) (1/3,1/2,1)
0.07 0.13 0.25 0.38 0.17
ഥ୧
ത୧
0.07 0.12 0.25 0.40 0.16
0.07 0.12 0.25 0.38 0.17
The eigenvectors of fuzzy comparison values for Time reduction are presented in Table 6. In the following the consistency index CI and consistency ratio CR are calculated for ୧ in order to convince that the judgments remain at the limit of consistency. Let us start with computing vector ɉ୫ୟ୶ ɘ as product of the judgments ୧୨ and normalized eigenvector ഥ ୨ [18] ͲǤ͵Ͷ ͲۍǤ͵ې ێ ۑ ߣ௫ ߱ ൌ σୀଵ ݉ ݉ ഥ ൌ ͳێǤ͵ͷۑ ʹێǤͳʹۑ ͲۏǤͺͳے
(7)
The estimate value of the ɉ୫ୟ୶ is obtainedby dividing the componentsof the vector given in (7) by ୧ and computing the average value of resulting vector ( ɉ୫ୟ୶ ൌ ͷǤͳͻ ). The consistency index CI and Consistency Ratio CR are computed as ൌ
ౣ౮ ି୬ ୬ିଵ
ൌ ͲǤͲͷ
ൌ
େ୍ ଵǤଵଶ
ൌ ͲǤͲͶǤ
(8)
In generally the CR values less than 0.1 indicates that the judgments are at the limit of consistency. Thus, the obtained values of CR=0.04 means that the results are trustworthy. Table 7 shows how expert group has evaluated different business dimensions in view of different benefit groups.
Table 7. Eigenvectors according to business dimensions Strategy & Policy Financial Performance Time Reduction Quality Improvement Business Improvement
0.38-0.41 0.07 0.10-0.11 0.38-0.40
Management & Control 0.27-0.29 0.12-0.13 O.14-0.18 0.14-0.15
4. Results and discussion The PLM systems are commonly not implemented in SMEs in Estonia. In the process of the current study 51 employees from Estonian manufacturing company answered to
Organization & Processes 0.14-0.17 0.25 0.31-0.35 0.11
People & Culture 0.09-0.10 0.38-0.40 0.30-0.31 0.06-0.07
Information technology 0.07-0.09 0.16-0.17 0.10-0.11 0.29
questionnaire in summer of 2015. Based on the data obtained, the current As Is situation by business dimensions, are given in the Figure 2. The results presented in Figure 2are showing company situations from PLM implementation point of view. The results can be affected by employee’s lack of experience and ignorance on the current field.
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From the results given in the Figure 2 and Table 7it can be seen that for the financial performance the key impact factors, with which company has to work with are the strategy and management issues. For time reduction point of view the biggest effect is concerned with people, organization and processes activities. For quality improvement work with the organization and processes needs to be done in the near future.
Key impact factor for business improvement is the strategies and policy activity. Even if the graphic is showing small numbers the situation is not so dramatic because the starting point or lower limit is set quite high compared with other Estonian SMEs.
Fig. 2. Principal scheme of maturity model.
References 5. Conclusion and future study The results show weak preparation of the evaluated company from the aspect of PLM and its maturity. Such a conclusion has been made by utilizing PLM maturity model for particular SME. The PLM maturity model proposed include thoroughgoing questionnaire composed and evaluated by expert group and covering 26 subcategories in lower level of model (over 120 questions, featured by position of the employees) and FAHP method employed for evaluation of model criteria, decision making. Application of Fuzzy AHP technique allows to obtain more detailed information in comparison with standard AHP method including lower and upper limits for evaluated criteria. Future study is focused on improving current model by testing and adaption for wider range of companies. Currently main focus has been paid for companies working in area of machinery. Another not less important trend planned to follow is improving the level of digitalization by increasing data gathered from information system of the company (from general economic indicators up to product quality parameters measured by sensor systems) and modifying the automatic data analysing process. Acknowledgements
This research was supported by Innovative Manufacturing Engineering Systems Competence Centre IMECC (supported by Enterprise Estonia and co-financed by the European Union Regional Development Fund, project EU48685) and by the Estonian Centre of Excellence in Zero Energy and Resource Efficient Smart Buildings and Districts, ZEBE, grant TK146 funded by the European Regional Development Fund.
[1] Stark, J., 2011, Product Lifecycle Management 21st Century Paradigm for Product Realisation. Springer-Verlag, London. [2] Saaksvuori, A., and Immonen, A., 2008. Product Lifecycle Management.3rd Edition. Springer-Verlag, Berlin, Heidelberg. [3] Ferreira, W. P., Silva, A. M., Zampini, E. F., Pires, C. O., 2016, Applicability of Lean Thinking in Bakeries. Espacios (Caracas). [4] Karaulova, T., Bashkite, V., 2016, Decision-making framework for used industrial equipment. InzinerineEkonomika-Engineering Economics, 27(1), 23-31. [5] Sahno, J., Shevtshenko, E., Karaulova, T., 2015, Framework for Continuous Improvement of Production Processes. Inzinerineekonomika – engineering economics, 26(2),169-180. ISSN: 1392-2785. [6] Mettles, T., Rohner, P., Winter, R., 2010, Towards a Classification of Maturity Models in Information Systems. Management of the Interconnected World. The Italian Association for In formationSystems.,D'Atri. PhysicaVerlag HD, 333-340. ISBN 978-3-7908-2403-2. [7] Batenburg, R., Helms, R., Versendaal, J., 2005, The maturity of Product Lifecycle Management in Dutch organizations A strategic alignment perspective.institute of information and computing sciences, utrechtuniversitytechnical report UU-CS-2005-009 [8] H. Zhang, Y. Ouzrout, A. Bouras, A. Mazza, M. Savino., 2013, “PLM Components Selection Based on a Maturity Assessment and AHP Methodology”.International Conference on Product Lifecycle Management Conference (PLM’13), Nantes, France, 6-10July. [9] Saaty, T. L., 1980, The Analytic Hierarchy Process. McGraw-Hill, New York. [10] Franek, J., Kresta, A., 2014, Judgment scales and consistency measure in AHP, Procedia Economics and Finance, 12, 164-173 [11] Chang, D.-Y., 1996, Applications of the extent analysis method on fuzzy AHP, European Journal of Operational Research,95(3). 649–655. [12] Shu, M. S.; Cheng, C. H.; Chang, J. R., 2006, Using intuitionistic fuzzy set for fault-tree analysis on printed circuit board assembly. Microelectronics Reliability, 46(12), 2139-2148. [13] Vezzetti, E., Violante, M. G., Marcolin, F., 2014, A benchmarking framework for product lifecycle management (PLM) maturity models. Int J AdvManufTechnol 71:899–918. Springer-Verlag London [14] Batenburg, R., Helms, R.W., Versendaal, J., 2016, PLM roadmap: stepwise PLM implementation based on the concepts of maturity and alignment. Int. J. Product Lifecycle Management,1(4), 333-351. [15] Schuh, G., Rozenfeld, H., Assmus, D., Zancul, E., 2008, Process oriented framework to support PLM implementation. Computers in Industry, 59, 210-218. [16] Kärkkäinen,H., Pels, H., Silventoinen, A., 2012, Defining the customer dimension of PLM maturity. In: Rivest L, Bouras A, Louhichi B (eds)
Marko Paavel et al. / Procedia CIRP 63 (2017) 651 – 657 Product lifecycle management. Towards knowledge-rich enterprises, Springer, Heidelberg,388, 623–634. [17] Snatkin, A.; Karjust, K.; Majak, J.; Aruväli, T.; and Eiskop, T., 2013, Real time production monitoring system in SME. Estonian Journal of Engineering, 19, 62–75. [18] Herterich, M.M., Uebernickel, F., Brenner W., 2015, The Impact of Cyber-physical Systems on Industrial Services in Manufacturing, Procedia
CIRP, 30, 323-328. [19] Kaufmann, A., Gupta, M.M., 1988, Fuzzy Mathematical Models in Engineering and Management Science, Elsevier, Amsterdam, 1988. [20] Ayhan, M., B., 2013, A Fuzzy AHP approach for supplier selection problem: a case study in a gearmotor company, International Journal of Managing Value and Supply Chains, 4, 3.
657
ScienceDirect Procedia CIRP 63 (2017) 651 – 657
The 50th CIRP Conference on Manufacturing Systems
PLM Maturity model development and implementation in SME Marko Paavela,*, Kristo Karjusta, Jüri Majaka a Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia * Corresponding author. Tel.: +372 58089134; fax:+0-000-000-0000.E-mail address:[email protected]
Abstract The product lifecycle management (PLM) is an important tool for managing products and its data throughout the life cycle. The advantages of the PLM systems are well-known in the case of large enterprises and groups. In the current study the objective is to simplify and optimize PLM implementation through PLM maturity model in small and medium size enterprises (SME). The introduced maturity model allows estimating the current situation in SME through use of questionnaires. The information gathered includes the expectations of the SME and will answer different questions like how are benefits connected with different business dimensions and their sub-categories described in maturity model. The business dimensions are evaluated by the expert group. The maturity model proposed in this paper is based on combining the know-how of the expert group and Fuzzy analytical hierarchy process (FAHP) techniques. The proposed PLM maturity model is implemented in Estonian SME bringing out current situation in: Strategy & Policy, Management & Control, Organization & Processes, Information technology, People & Culture. © Published by by Elsevier B.V.B.V. This is an open access article under the CC BY-NC-ND license 2017The TheAuthors. Authors. Published Elsevier © 2017 (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility ofthe scientific committee of The 50th CIRP Conference on Manufacturing Systems. Peer-review under responsibility of the scientific committee of The 50th CIRP Conference on Manufacturing Systems Keywords:PLM; Maturity model; company analyze model
1. Introduction 2. State of the art In changing business environment, companies are looking for different advantages on how to get better position in the market. One way to do that is to find help from Product Data Management (PDM) or PLM systems [1-2] for optimizing the production processes and methods, to give better and quicker overview of the actual situations in the shop floor and according to the real situation changing the processes to react quicker for changed situations [3-5]. If company has chosen to implement PDM or PLM system, then it is needed to know the current status of the enterprise. One way to get better understanding about current PLM or PDM situation is to determine the maturity level. For this reason, the PLM maturity model is utilized in this paper. In this maturity model is used decision-making methodology considered for PLM implementation. This is based on utilization of PLM maturity models [6-8] and Fuzzy analytical hierarchy process techniques [9-12]. In the current study the PLM maturity model is developed and implemented in SME in order to raise awareness of PLM and its functionalities, focusing for Estonian and EU market.
PLM-related research issues are rather young accordingly PLMs young age. In literature, there is brought out different approaches in maturity modelling which are of one or the other way important aspects in PLM implementation process. [13] There are three different opportunities how to execute current PLM maturity situation, which target group performs the actions concerned with the maturity model [6]: x Self-assessment; x Third party assisted assessment; x Certified practitioners. There are models which are based only academic, like; " PLM framework for the assessment and guidance of PLM implementations" by Batenburg, "Process oriented framework for support PLM implementation" by Schuh, "Product lifecycle management" by Saaksvuori and "Defining the customer dimension of PLM maturity" by Kärkkäinen. Starks model is based on consultant knowledge [1,2,13,14,15,16]. Usually model consist from 3 to 7 different business dimensions which are describing different valuated sectors.
2212-8271 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of The 50th CIRP Conference on Manufacturing Systems
doi:10.1016/j.procir.2017.03.144
652
Marko Paavel et al. / Procedia CIRP 63 (2017) 651 – 657
The number of maturity levels in different maturity models are varying most commonly between four and six. The advantages provided by maturity model can be outlined as follows [13,15]: Companies expected benefits; Current situation (As is Analyse); Expected situation (To Be); Weak spots description and overview.
Biggest problem with existing maturity models is that the number of questions included in models are most commonly limited or the questions are not pointed out. Thus, the decision criteria are not well covered, etc.
3. Maturity model description As a rule, the PLM implementation starts from over viewing business vision of the company. Based on that the PLM vision and its objectives are defined. After that is needed to fix the existing PLM and its implementation strategies. Based on these strategies the PLM and its implementation plans should be determined and optimized to lower the implementation cost and time [1]. For all of these activities and decisions trustable information is needed. Here is proposed a company analysis model for evaluating PLM maturity and to get extra needed information.
BENEFITS
Software Configuration Management
Visualization
System integration
Team Culture
People in PLM
Skilled, Competent People
Customer Service and Support
NPDI
Fig. 1. Principal scheme of maturity model.
Document/ File Management
Information technology
People & Culture
Lifecycle and Processes
workflow & process Management
Change Management
BUSINESS IMPROVEMENT
Data exchange
QUALITY IMPROVEMENT
Organization & Processes
Management of Product portfolio and structure
Requirements Management
Management of Product data and information
Quality Management
Reporting & Analytics
Configuration/BOM Management
Management & Control
Program and Project Management
(PLM) Plan
(PLM) Metrics
(PLM) Vision
(PLM) Strategy
Strategy & Policy
TIME REDUCTION
Maintenance, repair and operations (MRO)
FINANCIAL PERFORMANCE
Sourcing and Supply Chain Management
x x x x
Applying real time monitoring and digitalization can be considered as new trends in PLM system development [19,20].
Marko Paavel et al. / Procedia CIRP 63 (2017) 651 – 657
The methodology employed for Maturity model development is based on combining questionnaire and expert group evaluations for input data acquisition and application of FAHP techniques for evaluation of decision criteria included in model. Current model is built up based on the company’s selfassessment and it works by answering questionnaire. Through answering the questions information is gathered for implementing the lowest level of the model i.e. subcategories. Model is built up so that in the first stage the expectations and hoped benefits of the company are mapped and described. The benefits are divided into four bigger categories: x x x x
Financial performance; Time reduction; Quality improvement; Business improvement.
There is brought out only upper level as final result, actually the lower level criteria are evaluated first and through them are evaluated higher level criteria. For example, financial performance is covering; greater productivity, decreased cost of new product introduction etc. Time reduction includes; improved product cycle times, insight into critical processes and better resource utilization etc. Quality improvement consists of fewer errors and better product quality. Also, less scrap and rework etc. Business improvement includes product faster time to market, greater design efficiency, standards and regulatory compliance, improved communication etc. In this model the activities of the company are divided into groups based on the Batenburg "PLM framework for the assessment and guidance of PLM implementation"[7]: x x x x x
Strategy & Policy; Management & Control; Organization & Processes; People & Culture; Information technology.
The groups have also subcategories for more detailed description of the current situation in the company. Principal scheme of the PLM maturity model is depicted on Fig. 1. In this model, expert group holds the central place. PLM Expert group consists of 11 persons from academic and industrial field. They are from universities, industrial companies, PLM vendors and development centres. In this model, expert group evaluates; questions used in questionnaire starting from job position and work experience in scale from 1-
10. Ending by evaluating all possible answers for each question. There has been used different outlier methods. Expert group has also evaluated benefit categories and business dimensions. Model is built up so that employees holding up different positions in company are answering to web based questionnaire. This information is gathered and most of the information is automatically analyzed using expert group knowledge. Based on gathered information there is also brought out the expectations what company has in mind. For output of the current as is situation is used Saaksvouri Maturity model. Description of this model is brought out in table 1. [2] Table 1. Maturity level descriptions. [2] Level Unstructured
Repeatable but intuitive
Defined
Managed and measured
Optimal
Description Topic has been recognized and importance agreed. PLM concept and standards are not defined. Not defined approach and all issues are solved individually case-bycase. Lifecycle and processes are developed so far that different process executors are acting the same. There are no formal development, definition, or communication of standard process. Processes and basic concepts are standardized, defined, documented and communicated through manuals and trainings. There is no end-to-end PLM process supporting IT systems. All work is manual from the process view. Compliance between processes is monitored and measured to take actions where process is not functioning. Concept and processes are under constant improvement. PLM processes are well supported by IT systems and developed through clear vision. Processes and concept have been refined to the level of best practices, based on continues improvement and benchmarking with other organizations. IT is used and integrated. Processes are automated to end.
3.1. Questionnaire description The developed and modified questions in the web based questionnaire will give information based on the different principles starting with respondent position, current and previous work experience, mission, vision and expectations. Also about activities, metrics and modules currently fallowed and what interest in future. In general, the questionnaire covers all subcategories in the lower level of the model (Fig.1). Answering possibilities are different and are dependent on particular question type/content. The respondent can express opinion by using scale from strongly agree to strongly disagree, give fact based answers by using dropdown box or multiple
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choice answers. In some cases, the use of free text box is provided to give detailed description of certain problem/situation linked with PLM and connected with the certain company situations. The questionnaire is built up based on principle that everyone is answering general questions and questions concerning his/her area of expertise. There is extra questionnaire for management and department managers consisting from 123 questions. The total capacity of the
questionnaire exceeds 700 rows (multiple choice answers included). Table 2 gives overview of the current questionnaire. There is brought out questions what are giving information about business dimension Strategy & Policy sub-categories PLM Vision and PLM Strategy. There are several questions which are giving information for different categories.
Table 2. Business dimension Strategy & Policy subcategory PLM Vision questions Vision x x x x -
Strategy x x x x x x x
-
x
x x x x x x x x X
x x x x x x x X
Question Enterprise mission: Enterprise vision: Management has clear vision of future plan of company. Vision of future of the company is strongly formulated. Too difficult to explain goals to each level in organization. All employees are aware of company's goals, missions and visions. Investments on R&D projects is one of the important trends in company's politics. During last 5 years, the technology and methodology of production has been changed according last trends. Are you interested in PLM system or PLM functionalities in your company? Have you done PLM feasibility study? Have you done PLM ROI (return of Investment)? How big is/was the payback time for PLM? Do you have PLM vision? What it is it now and in 5-year perspective? The company's overall vision matches PLM vision? Company's PLM vision is in accordance with company's overall vision? Do you have PLM strategy? Do you have PLM implementation strategy? What kind of PLM related metrics are you following in company? What kind of PLM related metrics you wish to follow in company?
X
X
Company is more interested in?
Ͳ
ݔ൏ ͳ ݔݎ ݑǡ ݈ ݔ ݉ǡ
ି௫
݉ ൏ ݔ ݑǤ
௨ି
For evaluating business dimensions’ expert group has used Analytic Hierarchy Process (AHP), proposed by Saaty [9]. The AHP is widely used method in multiple-attribute decision making [9,10]. It structures the alternatives into hierarchical framework. Fuzzy Analytic Hierarchy Process (FAHP) is an extension of AHP method that uses fuzzy logic. Also, fuzzy sets and fuzzy numbers for determining the ranking of certain criteria's [11]. Fuzzy Set Theory (FST) was introduced by Zadeh in 1965 to deal with uncertainty and vagueness [12]. A tilde "~" will be placed above a symbol is the symbol shows FST. The membership functionߤ ሺݔሻof a triangular fuzzy number can be introduced as [19]
consent Yes/No Yes/No Yes/No Years from 1 to 5 Yes/No text consent consent Yes/No Yes/No List of Metrics List of Metrics company`s expansion/ modification of current systems
௫ି
ߤ ሺݔሻ ൌ ൞ ି
3.2. Application of FAHP
Answer text text consent consent consent consent consent
(1)
In table 3 the Fuzzy logic was utilized for evaluation Saaty's 9-point scale [20]. Table 3. Linguistic terms and the corresponding triangular fuzzy numbers Saaty scale
Definition
Fuzzy Triangular Scale
1 3 5 7 9
Equally importance Weakly important Fairly important Strongly important Absolutely important
(1,1,1) (2,3,4) (4,5,6) (6,7,8) (9,9,9)
2 4 6 8
The intermittent values between two adjacent scales
(1,2,3) (3,4,5) (5,6,7) (7,8,9)
Table 4. The scores of experts in terms of triangular fuzzy numbers Time Reduction
Strategy & Policy
Management & Control
Strategy & Policy Management & Control
(1,1,1) (1,2,3)
(1/3,1/2,1) (1,1,1)
Organization & Processes (1/5,1/4,1/3) (1/4,1/3,1/2)
People& Culture
Information technology
(1/5,1/4,1/3) (1/4,1/3,1/2)
(1/4,1/3,1/2) (1,1,1)
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Organization & Processes People & Culture Information technology
(3,4,5) (3,4,5) (2,3,4)
(2,3,4) (2,3,4) (1,1,1)
The decision criteria are evaluated by experts by performing pair-wise analysis. The obtained scores are converted to triangular fuzzy numbers and presented in Table 4. The results given in Table 4 cover five decision criteria. Let us introduce the-th triplet corresponding to geometric mean values as ෩୧ ൌ ሺ୧ ǡ ୧ ǡ ୧ ሻ
Strategy & Policy Management& Control Organization &Processes People& Culture Information technology
where భ
(3) భ
݉ ൌ ൫ςୀଵ ݉ ൯ ǡ
(4)
భ
ݑ ൌ ൫ςୀଵ ݑ ൯ ǡ
(5)
The number of decision criteria used in (3) -(5) is denoted by n. In Table 5 are given the values of the i-th triplet ሺ୧ ǡ ୧ ǡ ୧ ሻ for five criteria considered and corresponding sums. Table 5. The geometric means of fuzzy comparison values ୧
୧
୧
Strategy & Policy Management& Control Organization & Processes People& Culture Information technology
0.320 0.574 1.084 1.644 0.740
0.401 0.740 1.516 2.352 0.944
0.561 0.944 1.974 2.993 1.320
Sum
4.362
5.954
7.791
Criteria
The normalized values of the ݈ ǡ ݉ and ݑ can be computed as ሾ݈ҧ ǡ
௨ ೕసభ σೕసభ σೕసభ ௨
݉ ഥ ǡ ݑത ሿ ൌ σ
ǡ
ǡ
൨
(6)
(1/4,1/3,1/2) (1,1,1) (1/3,1/2,1)
(1,2,3) (1,2,3) (1,1,1)
Table 6. Eigenvectors of fuzzy comparison values Criteria ୧ҧ
(2)
݈ ൌ ൫ςୀଵ ݈ ൯ ǡ
(1,1,1) (2,3,4) (1/3,1/2,1)
0.07 0.13 0.25 0.38 0.17
ഥ୧
ത୧
0.07 0.12 0.25 0.40 0.16
0.07 0.12 0.25 0.38 0.17
The eigenvectors of fuzzy comparison values for Time reduction are presented in Table 6. In the following the consistency index CI and consistency ratio CR are calculated for ୧ in order to convince that the judgments remain at the limit of consistency. Let us start with computing vector ɉ୫ୟ୶ ɘ as product of the judgments ୧୨ and normalized eigenvector ഥ ୨ [18] ͲǤ͵Ͷ ͲۍǤ͵ې ێ ۑ ߣ௫ ߱ ൌ σୀଵ ݉ ݉ ഥ ൌ ͳێǤ͵ͷۑ ʹێǤͳʹۑ ͲۏǤͺͳے
(7)
The estimate value of the ɉ୫ୟ୶ is obtainedby dividing the componentsof the vector given in (7) by ୧ and computing the average value of resulting vector ( ɉ୫ୟ୶ ൌ ͷǤͳͻ ). The consistency index CI and Consistency Ratio CR are computed as ൌ
ౣ౮ ି୬ ୬ିଵ
ൌ ͲǤͲͷ
ൌ
େ୍ ଵǤଵଶ
ൌ ͲǤͲͶǤ
(8)
In generally the CR values less than 0.1 indicates that the judgments are at the limit of consistency. Thus, the obtained values of CR=0.04 means that the results are trustworthy. Table 7 shows how expert group has evaluated different business dimensions in view of different benefit groups.
Table 7. Eigenvectors according to business dimensions Strategy & Policy Financial Performance Time Reduction Quality Improvement Business Improvement
0.38-0.41 0.07 0.10-0.11 0.38-0.40
Management & Control 0.27-0.29 0.12-0.13 O.14-0.18 0.14-0.15
4. Results and discussion The PLM systems are commonly not implemented in SMEs in Estonia. In the process of the current study 51 employees from Estonian manufacturing company answered to
Organization & Processes 0.14-0.17 0.25 0.31-0.35 0.11
People & Culture 0.09-0.10 0.38-0.40 0.30-0.31 0.06-0.07
Information technology 0.07-0.09 0.16-0.17 0.10-0.11 0.29
questionnaire in summer of 2015. Based on the data obtained, the current As Is situation by business dimensions, are given in the Figure 2. The results presented in Figure 2are showing company situations from PLM implementation point of view. The results can be affected by employee’s lack of experience and ignorance on the current field.
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From the results given in the Figure 2 and Table 7it can be seen that for the financial performance the key impact factors, with which company has to work with are the strategy and management issues. For time reduction point of view the biggest effect is concerned with people, organization and processes activities. For quality improvement work with the organization and processes needs to be done in the near future.
Key impact factor for business improvement is the strategies and policy activity. Even if the graphic is showing small numbers the situation is not so dramatic because the starting point or lower limit is set quite high compared with other Estonian SMEs.
Fig. 2. Principal scheme of maturity model.
References 5. Conclusion and future study The results show weak preparation of the evaluated company from the aspect of PLM and its maturity. Such a conclusion has been made by utilizing PLM maturity model for particular SME. The PLM maturity model proposed include thoroughgoing questionnaire composed and evaluated by expert group and covering 26 subcategories in lower level of model (over 120 questions, featured by position of the employees) and FAHP method employed for evaluation of model criteria, decision making. Application of Fuzzy AHP technique allows to obtain more detailed information in comparison with standard AHP method including lower and upper limits for evaluated criteria. Future study is focused on improving current model by testing and adaption for wider range of companies. Currently main focus has been paid for companies working in area of machinery. Another not less important trend planned to follow is improving the level of digitalization by increasing data gathered from information system of the company (from general economic indicators up to product quality parameters measured by sensor systems) and modifying the automatic data analysing process. Acknowledgements
This research was supported by Innovative Manufacturing Engineering Systems Competence Centre IMECC (supported by Enterprise Estonia and co-financed by the European Union Regional Development Fund, project EU48685) and by the Estonian Centre of Excellence in Zero Energy and Resource Efficient Smart Buildings and Districts, ZEBE, grant TK146 funded by the European Regional Development Fund.
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