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Cross industrial PLM benchmarking using maturity
models
Philipp Steck1 , Felix Nyffenegger2, Helen Vogt3, Roman Hänggi4
Hochschule für Technik (HSR), Rapperswil, Switzerland
1 philipp.steck@hsr.ch
2 felix.nyffenegger@hsr.ch
4 roman.haenggi@hsr.ch
Zürcher Hochschule für Angewandte Wissenschaften (ZHAW), Winterthur, Switzerland
3 vogh@zhaw.ch
Abstract. Maturity models are commonly used to assess a company’s position
on a roadmap towards a defined mature PLM environment. The models focus
mainly on an internal point of view, comparing a current state to a possible future
state. The high level of adjustments in these models to the individual companies
needed, make it difficult to compare companies across industries using existing
maturity models.
The aim of this paper is to introduce a generic, cross-industrial maturity model
suitable for the benchmarking of companies. The model uses an ability-based
approach, similar to a case form report.
Using the model, a first benchmarking study has been conducted among ten
Swiss companies. This initial study allowed to verify and discuss the suitability
of the developed model. Furthermore, the actual results of benchmarking lead to
interesting insights into potential success factors to achieve higher PLM maturity.
This paper discusses both the maturity model and the actual results.
Keywords: PLM, Maturity Model, Benchmarking.
1 Introduction
Product lifecycle management (PLM) is a widely accepted practice in today’s compa-
nies. In many places it has become a strategic initiative to support the overall goal of
an enterprise. However, due to its complexity, it is hard to clearly explain the impact
and value created by the implementation of PLM. Accordingly, arguing for investment
into new PLM initiatives on top management level can be hard.Maturity models help
to asses a company’s current state and are able to show the potential next steps towards
a higher maturity level. However, this is not enough to get a clear view on the added
value to the company by this next maturity level. To better qualify such an investment,
it would be interesting to do an industry wide comparison of the impact of maturity
levels on a company’s performance. Since companies in a particular economic system
differentiate in very dimension (product, organization, business model, tools, …) the
IFIP 17th International Conference on Product Lifecycle Management,
July 5th – 8th, 2020, Rapperswil, Switzerland
2
only way to achieve this is cross industrial benchmark. The initial question of the pre-
sented work was to evaluate weather maturity models can help to do a cross industrial
benchmark and extract common success factors of PLM. As a result of this analysis a
new assessment model was created and tested.
2 Related works
The main goal of a PLM maturity assessment tool is to improve the PLM implementa-
tion process. This poses a great challenge for many companies [1]. PLM maturity mod-
els have been developed and used to assess the PLM implementation situations and
determine the relative position of the enterprise by comparing PLM maturity levels with
other enterprises [2]. Various frameworks for PLM maturity models have been de-
scribed in literature [3] and been benchmarked [4]. As Vezzetti has shown, several im-
portant maturity models are worth analyzing: The Capability Maturity Model Integra-
tion (CMMI) is widely recognized within the PLM community [5] and has developed
into an established model in the field of information systems development [6]. It is
composed of five maturity levels: Initial, Repeatable, Defined, Managed and Opti-
mized. Batenburg’s model [7] focusses on the assessment of PLM implementations.
The model applies four maturity levels: ad-hoc, departmental, organizational and in-
terorganizational. Sääksvuori [8] determines the maturity of a large international cor-
poration for a corporate-wide PLM development program and describes business and
PLM related issues on the product lifecycle management. The origin of the model lies
in the idea of phases or stages, which a company usually goes through as it adapts to
new cultural issues, processes, management practices, business concepts and modes of
operation [4]. In contrast to the previous models, which mainly focus on internal com-
pany processes, Kärkkäinnen [9] proposes a model that focuses on the customer aspects
of PLM maturity. The authors distinguish between the following main levels, namely
Chaotic, Conscientious, Managed, Advanced and Integration stages, and use elements
such as level of proactivity, extent of coordination, extent of integration and quality and
type of customer knowledge to characterize and measure these levels.
Most of these models have a clear scope on manufacturing companies and mainly focus
on an as-is or a future state analysis. Hence, the models have primarily an intra-com-
pany view. They do not aim at comparing or benchmarking different companies. Fur-
thermore, most models empathize the organizational perspective rather than a technical
or functional point of view. The study carried out by Kärkkäinnen and Silventoinen
analysed a broad spectrum of different models. Base on their analysis, it can be said
that most maturity models do not have an indepth description of quantifiable factors
that differentiate the different levels [10]. Finally, the analyzed models evaluate ma-
turity using open-ended questions or ask the participants to rank their own maturity
based on a description of functionalities or of a use-case. The approach of using closed
yes or no questions to verify whether a PLM system can fulfill a functionality is a more
objective way of evaluating. It does not depend as much on the interviewee’s personal
opinion and in this way overcomes the typical limitations of a scale based questioning
survey as described by Franzen [11].
3
The proposed model differs from the commonly used ones in the following points:
• Maturity is defined in this study as the capability of a company’s PLM environment
to fulfill a certain task. This allows a cross-industrial comparison of capabilities.
Whether or not a certain capability is beneficial for a company depends on the mar-
ket context.
• Main target of this model is the comparison of different companies for benchmarking
purposes. Hereby the model helps companies to asses their level of maturity and
compare themselves across indsturies. The models described in Chapter 2, on the
other hand, have a mainly internal perspective and generally do not aim to compare
companies.
• Industrial focus: The proposed model is applicable across industries. The models
analysed all clearly focus on a limited number of industrial sectors.
• Survey format: The proposed closed yes or no questions focusing on PLM abilities
and enable the exact analysis of the available functionalities within a a PLM envi-
ronment. The analysed models use scale based ratings and ask the companies to rank
their abilities in different areas.
This makes the proposed model and its application new and unique. It could help to
quantify the influential factors for a successful PLM environment.
3 Method
To develop a cross-industry PLM maturity model, which is suitable for the benchmark-
ing of companies, the methodology as proposed in the SPICE model has been used.
SPICE is an international framework for assessment of software processes developed
jointly by the ISO and the International Electrotechnical Commission (IEC). According
to the literature search of Wangenheim et. al is it also one of the most commonly used
approaches to develop maturity models for software products [12]. It describes the pre-
conditions needed to conduct process analyses [13]. Additionally, it has been adapded
in other industries (construction) and for other applications (elarnings) [14][15].
The model was developed and validated along the process shown in figure Fig. 1.
Fig. 1. Chosen methodology for establishing the new PLM maturity model
Literature search and analysis of existing models
Defintion of the structure of the PLM model
Defintion of maturity levels and level descriptions
Qualitative and Quantitative analysis
Conduction of interviews with participating companies
Creation of assesment questions
Expert Review of setup
Expert Review of assesment questions
Review of findings with participating companies
Maturity model
development
Cross-industrial
benchmarking
study
• All interviewpartners have direct responsiblity for the PLM
environment.
• Minimum time of 2 hours per company
• Questionnaire as well as in depth discussion of the c urrent
set up.
• Based on the overall description of the PLM abilities
• Questions cover the full lifecycle
• The structure is based on the reviewed maturi ty models
• Key target of the model is the cross industrial comparison of
companies
• Approach is to evaluate the abilities of a company.
• Exact defintion of what functionalities are expected
within the maturity levels
4
3.1 Maturity model development
Maturity models can be characterized by the number of dimensions (such as the ‘pro-
cess areas’ in CMM), the number of levels , a descriptor for each level (such as the
CMM’s differentiation between initial, repeatable, defined, managed, and optimizing
processes), a generic description or summary of the characteristics of each level as a
whole, a number of elements or activities for each dimension, and a description of
each element or activity as it might be performed at each level of maturity [16]. The
introduced model distinguishes five levels of maturity (Initial, Low, Intermediate,
Mature and Best Practice) as shown in Table 1. The five-level approach has been suc-
cessfully used in similar application by Hchicha et al [17]. Further is it also a recom-
mended approach to asses processes on the base of ISO/ IEC15504 [13]. For each of
the five levels, Table 2 provides a brief description of the expected traits and abilities.
The description is based on an interview with a PLM expert and on a literature review
[18].
Table 1. Description of traits for the different levels in the maturity model
Level 1
initial
• No data standards
• Reactive approach
• No master data plan
• No clear strategy
• Processes are not clearly defined
• Processes are not transparent
• Processes are not supervised
• Process automation is not existing
• All interfaces are handled manually
Level 2
low
• Some processes are clearly defined
• Processes are not transparent to all stakeholders
• There is no systematic KPI tracking for key processes in place
• Process automation is only implemented for a few processes/task
• Processes are partially supervised
• Some automated interfaces
• No cross-supply-chain integration
Level 3
intermediate
• Nominal data governance implemented
• Some sort of MDM in place
• Some KPI tracking regarding data quality in place
• Most processes are clearly defined
• Processes are not transparent to all stakeholders
• There is no systematic KPI tracking for key processes in place
• Processes are only partly supervised by tools
• Multitool processes are not implemented
• Automated interfaces widely used
• Some cross-supply-chain integration
Level 4
Mature
• MDM managing all companies meta data
• data layers implemented
• clearly defined data governance
• Data models enable a smooth and quick exchange
• All processes are clearly defined
• Processes are transparent to operators and managers
• There is systematic KPI tracking for key processes in place
• Processes are supervised by tools
• Multitool processes are implemented
• Process engines are implemented for key processes
• Automated interfaces widely used
• Cross-supply-chain integration is standards
5
Level 5
Best-practice
• Scalable and easy to adjust data models
• Data simplifies processes and is highly adjustable to different
tools
• Data is stored in a central database
• Data objects are minimized
• All processes are clearly defined
• Processes are transparent to all stakeholders
• There is systematic KPI tracking for all processes in place
• Processes are supervised by tools
• Processes enable a seamless workflow among the enitre supply
chain
• Multitool processes are implemented
• Process engines are implemented for all processes
• Processes interact directly with customers and suppliers
3.2 Development of the survey
For the creation of the survey, the decision was taken to create closed questions in
binary form (yes/no). This approach is commonly used in medical examinations where
a patient’s symptoms are evaluated using case report forms. These forms mostly use
closed yes or no questions. This approach helps to objectively evaluate the pattern of a
possible illness based on the patient’s description [19]. Transferring this to the field of
PLM maturity, a questionnaire with a total of 64 closed binary questions was created.
The questionionair was created using a five step approach.
1. Creation of a description of tasks for every phase of the lifecycle. Hereby the phases
of beginning, mid and end of life defined by S. Terzi et al were utilized [20].
2. For every task in each phase a description of functionalities that support or enable
the task was created.
3. The functionalities were transformed into closed yes or no questions.
4. For validation of the questions, the model was pretested with a selected partner com-
pany.
5. The questionnaire was reviewed by PLM experts (more than 15 years of experience).
The goal of this step was to ensure that all questions are unique and do not correlate
with each other.
Table 2 shows the complete questionnaire. On the left hand side the current life cycle
phase of the product is shown.
6
Table 2. Questionaire and categoristion
ID Question Lifecycle phase I D Question Lifecycle phase
1 Requirements for new products ar e collected syste matically and transfe rred to R&D via a c learly define d process. 33 Bills of materia l can be gene rated automatic ally using configurators.
2 Requirements ar e recor ded directly in a pr ocess engine. 34 On the base of bills of materia l the system can automatica lly generate pa rameter ized CAD models.
3 Information ab out the current portf olio is provided systematically and ca n be sorted by prope rties. 35 The re system enable s the management of adjusted bills of materials for e ach end use r accor ding to the respective r equirements.
4 Within the proce ss, resources a re alloca ted and the planning is supported by a n automated tool 36 The bill of material for se rvice purposes is ge nerated a utomatically.
5 Findings from previous development pr ojects are systema tically incorporated into the definition of new deve lopment orders. 37 CAD mode ls of assemblies are automatically chec ked for er rors (e.g. c ollision check).
6 Currently running deve lopment projects are tracked w ithin a tool that enables sorting acc ording to defined re quirements. 38 The system checks the store d data for duplica tes and completene ss.
7 Initial concepts to fulf il the requirements ca n be clear ly assigned to require ments within the system. 39 The design review is ca rried out in a system-le d process.
8 The results of the product conce ption are systematically re corded a nd always ac cording to the same proc edure. 40
The system supports diff erent roles a nd thus enables the systematic control of engineer ing data (example: develope r and
reviewe r).
9 Concepts and intial draf ts are hande d over to the deve lopment teams via a clea rly defined r elease pr ocess. 41
If er rors are de tected during the r eview, any comme ntsc an be enter ed directly into the system and can be clear lya ssigned to a
responsible.
10 Documentation on first drafts ca n be clear ly assigned to the final ar ticle in the system. 42 CAD da ta can be extracted dir ectly from the system in a ne utral CAD format.
11 The system allows the search for modules that fulfill similar functions and re quirements. 43
If CAD da ta is share d with a stakeholder , this happens in a c learly structured form ( in ter ms of content) a nd following a
processe s based on clea r guidelines.
12 Articlenumbers are assigned automatically by the system. 44 I n the event of a c hange, it is clear to the executing engine er whethe r the data has be en shared e xternally or not.
13 Numbering structures are clearly de fined for standa rd and purch ased parts. 45 Da ta is always exc hanged via sec ure databa ses in which acc ess can be monitored.
14 The same article numbe r is always used for all processes throughout the whole life cycle . 46 All produc tion relevant data for individual parts can be found in the CAD model.
15
While working in the CAD the enginee r has acce ss to information on tools / machines tha t are use d in manufactur ing. The
engineer is using this information to adjust new pr oduct accor dingly.
47 Man ufacturing informa tion is transferred to the production sites in a defined f orm and acc ording to a clear ly defined proc ess
16 Models are enriche d with a defined se t of metadata. 48
Manufac turing information is release d fully automatically for the de fined manufac turing locations and is a utomatically available
after r elease.
17 CAD models of individual parts a re automatica lly checked for errors ( e.g. incomplete def initions) 49
Production resourc es such as tools and machines ar e stored in the system and are a ccessible to the engineer dur ing all
development stage s.
18 Faulty CAD models cannot be transferr ed to the next proce ss instance. 50 CAM-da ta is clearly to the a ctual CAD model and is consider ed a par t of the change process.
19
CAD models are the single source of truth and c ontain all relevant information for the whole life cylce (example: surface
condition, material data , tolerances, sur face tr eatment, raw ma terial)
51 All manuf acturing information re garding a produc t is stored in one consolidated data base.
20 The system enables the sea rch for pa rts using technical traits of c omopnonents. 52 As built bills of materials a re clea rly assigned to the order s and can be called up at any time.
21 Within the system one ca n systematically sear ch for possible, suitable standa rd parts. 53 Visua lizations can be gen erated a utomatically using the existing product para meters
22 While selecting suitable standardpa rts the engineer gets imediate information about c ost and availability. 54 Change requests ar e systematically rec orded and c learly assigned to the a rticles.
23 If a new standar d part would have to be introduced, the system allows a systematic check f or possible alternative s. 55 Change requests get systematica lly reviewed a nd categoriz ed with system support.
24 The system enables the systematic sea rch for suitable purchase d parts on the basis of tec hnical parame ters. 56 As pa rt of a cha nge order the required c hanges ca n highlighted directly in the CAD model.
25 On the base of selec ted technical a ttributes and description the system is able to pr opose standard suppliers f or certa in parts. 57 Change orders ar e clear ly assigned to a responsible.
26 The creation of f unctional descriptions for tende rs is supported by the system 58 I n case of a n index change, an overview of all affec ted articles is automatica lly generate d.
27 Simulation results and models are dir ectly linked to all other produc t data. 59 Communication with stakeholde rs regar ding changes is conducte d through a clea rly structured proc ess.
28
Transfe r of the data to any spec ial depar tments (example: simulation) is carried out in a c learly structured p rocess, follwing
clear guidelines.
60 The system supports the developer in deciding whether a new part numbe r is necessar y or not.
29 Simulation results are sent ba ck to the responsible deve lopment department via a clearly struc tured proce ss. 61 I f an article is discontinued, a ll data is automatically set to invalid..
30 Bills of material are crea ted in a clear ly defined proc ess, , follwing clear gu idelines. 62 I f a product is discontuined the system is able to deliver an ove rview of all interde pendencies.
31 Bills of material are derived dire ctly from the CAD-da ta. 63 The system supports communication if a product is discontinued to e xternal stakeholder s
32 Bills of material are transfer red to ERP without manual intervention. 64 Any suc cessor produc ts can be re corded within the system.
Beginning of life
Middle of life
End of life
Middle of life
7
The following data can be extracted from the assessment questions:
• Sum of questions answered with yes
• Overall maturity level reflecting on the number of yes-answers
• Number of times a specific question got answered with yes
• Additional descriptive data about the participants and additional commentary and
qualitative information
Using the sum of yes-answers the data can be used to create a benchmark of the com-
panies and rank them. On this base the different PLM environments of the companies
can be compared. On the base of the impact different factors can be estimated. Based
on these factors, possible success factors for a mature PLM environment can be as-
sessed using the qualitative data.
3.3 Crossindustrial benchmarking study
Using the questionnaire, a benchmarking study was conducted among potential best
practice companies. The companies have been selected based on their potentially ma-
ture PLM-environment rather than industrial criteria. The following selection criteria
were used:
i. Swiss headquarter
ii. Comparable product strategy
iii. Possible mature PLM-environment
iv. Similar manufacturing depth
v. Available during the required time period
Thus, the study does not represent an industrial benchmark. An overview of the differ-
ent traits of all the participating companies can be found in the Table 3. Due to agree-
ments with the participants, the data is displayed without any explicit description of the
companies. The given categorical data will be used for further statistical analysis.
All interviews were conducted with company employees. The interviewees hold posi-
tions with direct responsibilities for the improvement of the IT-infrastructure and/or the
PLM-environment in general. The interviews took place in the diffrenent locations. The
minimum timeframe per interview was two hours. The interview followed hereby the
following agenda:
1. Brief introduction of the research project (personal interview)
2. General questions about the PLM strategy and personal estimations (personal inter-
view)
3. Interview using the assement questions as shown in chapter 3.3 (personal interview)
4. Immediate discussion of the results to gain a deeper understandign of the case at
hand (personal interview)
5. Extended data analysis
6. Review of the findings by the participants
7. Creation of the final report
8
Table 3. Participating companies of the benchmarking study
ID
Industries:
Size
Last
PLM
change
Prod-
uct
strat-
egy
Changes
currently
planned:
Manu-
factur-
ing
loca-
tions
Engineer-
ing
locations
Company
age
A
Energy
me-
dium
2004
ATO
yes
multi-
ple
single
25 to 50
years
B
Energy
me-
dium
2019
ATO,
ETO,
MTS
yes
multi-
ple
multiple
50 to 100
years
C
Mechanical
Engineer-
ing
small
2019
ETO
yes
single
single
> 100
years
D
Building
industries
large
2017
ATO,
ETO
yes
multi-
ple
multiple
> 100
years
E
Building
industries
large
2014
MTS,
ATO
yes
multi-
ple
single
50 to 100
years
F
Mechanical
Engineer-
ing
small
2019
ATO
yes
single
single
< 25
years
G
Mechanical
Engineer-
ing
large
2018
MTS,
ATO
yes
multi-
ple
multiple
25 to 50
years
H
Mechanical
Engineer-
ing
small
2019
ETO
yes
multi-
ple
single
< 25
years
I
Mechanical
Engineer-
ing
small
2010
ATO
yes
single
single
25 to 50
years
J
Building
industries
large
2019
MTS,
ATO
no
multi-
ple
multiple
< 25
years
4 Results
4.1 Overall benchmarking results
Using the developed questionnaire, the participating companies have been interviewed.
The overall scores (amount of yes-answers) have been used to rank them. Figure 2
shows the results. The barplot on top shows the total sum of yes answers and the cor-
responding maturity levels. The matrix shows the individual answers of the participants
of every question. The histogram on the right shows the amount of times a question has
been answered with yes. The data is sorted in a ascending order using the sum of yes-
answers from the lowest scoring to the highest one. For the further analysis, the overall
PLM capability of a company is being considered. This means that only the sum of yes-
answers is considered relevant, thus the answer to a single questions does not reflect
the maturity of a company.
9
Fig. 2. Results Benchmarking overall
4.2 Additional insights from top performing companies
Based on the results, the three top-scoring companies have been asked to describe or-
ganizational aspects that they felt were most critical in their opinion to the success of
their PLM strategy and implementation. The following success factors were identified:
• Established a long-term road map regarding the system-landscape and the goals
• Running smaller, but more frequent process improvement projects.
• Having designated teams working on the improvement of the PLM-environment.
10
• Frequent exchange with users on problems, possible ideas and upcoming projects
• Frequent exchange with other companies and field experts
• Implementing small changes on their own – this includes programming
• PLM trainings are coordinated by designated specialists
• Special tools implemented for PLM-users to propose changes
4.3 Analysis of influencing factors
The further analysis focuses on the total number of yes-answers (scores) achieved by
the companies. Table 4 shows the number of yes-answers given by the different com-
panies and a summary of the basic statistical values.
Table 4. Number of yes-answers per company
ID
A
B
C
D
E
F
G
H
I
J
Score
24
24
33
42
36
38
23
29
19
34
Mean
Median
Variance
Standard deviation
30.2
31
6.84
7.54
A factor comparison of the mean values has been conducted. The analysed factors are
shown in Table 5.
Table 5. Analysed factors
ID
Factor
Factor levels
1
Company Size
Small, Medium, Large
2
Main Product strategy
ATO, ETO, Mixed
3
Industries
Building, Energy, Machine
4
Manufacturing locations
Single, Multiple
5
Engineering locations
Single, Multiple
6
Company age
<25 years, 25 to 50 years,
50 to 100 years, > 100 years
7
Termination of the last PLM Project
<1 year, >1 year
Fig. shows the results. The mean values are visualized as horizontal lines within the
diagrams. All differences in the mean values that are greater than the standard deviation
of the overall data set (6.84) were considered relevant.
This analysis shows that the following factors could be relevant for a high level of ma-
turity:
• Company age
• Company size
• Industries
• Termination of the last PLM Project
Further analysis using a regression model would require a lager sample size.
11
Fig. 4. Factorwise analysis
4.4 Sample size calculation
To gain representative results a larger sample group would be required. The minum
required sample size has been calculated on the base of the formula defined by Cochran
[21]. The required number of participants is 247. For the calculation the following val-
ues have been used:
• Relevant population = 1956. The relevant population size has been defined as all
European companies with at least 250 employees working in the sector of machine
engineering [22, 23].
• Standard deviaton relative to median = 0.24. This value has been transferred from
the current sample
• Margin of error of 5% and a z-value of 1.96 in accordance of a expected confidence
interval of 95%.
12
5 Discussion
The overall results show a broad spectrum, ranging from the lowest score of 19 yes-
anwsers to the highest value of 42. The maturity of five companies has been classified
as intermediate (level 3). Four have been classified as low (level 2) and only one has
achieved the level 4, which is described as mature. First, this distribution can be intrep-
reted as a validation of the assement questions. This is due to the fact that the broad
spectrum can only be achiveved with a set of questions that is able to differentiate the
companies on the base of their abilities. If the questions would be less generalistic, the
expected distribution of the results would be either polarized or very strongly concen-
trated on a single score. Second, the distribution can be interpreted as a sign for possibly
huge differences among the supported PLM functionalities within the companies. Some
potential influential factors have been detected. These will be discussed in chapter 5.1
and 5.2. Some of the questions never got answered with yes (19, 26 and 61). This may
have two possible reasons. First, the questions simply may not reflect a relevant func-
tionality of a PLM environment. Second, the participants may not be mature enough to
achieve the required functionality. To ensure that the questions are relevant and realis-
tic, a second round of review of the current questions may be needed.
5.1 Additional insights from top performers
The measurements and approaches presented are mainly based on the discussion with
the interviewees. Their influence on the actual PLM maturity has not been investigated
as part of this study. At this point, the mentioned measurements can interpreted as fac-
tors that may contribute to a higher level of maturity.
However, the data has only been collected for the top three and not for all participants.
For further research, a number of structured additional questions, targeting the chosen
strategy and PLM tools utilized by these companies, would help to gain further insights.
This data should definitely be collected for all participants, since the analysis of the
chosen approaches of the low scoring companies could be just as insightfull as shown
by Cooper [24].
5.2 Influecing factors
The factor by factor by factor comparison of means clearly shows that certain factors
lead to a larger difference among the means than others. Using the 6.84 threshold, the
following relevant factors have been selected:
Company size
Large and small companies scored higher than medium sized companies. The higher
scores of the smaller companies can be explained by the expected lower level of organ-
izational complexity. In contrast, the higher scores of the large companies would be due
to the larger amount of ressources. The middle sized companies low scores could be
caused by the increasingly complex set up without the right means to overcome this
complexity.
13
• Industries: Companies from the building industry achieved significantly better re-
sults than companies from the machine engineering and energy industry. All three
companies active in the building industry have a broad product portfolio with mixed
product strategies. This is a clear sign of the comparatively high complexity of the
overall PLM environment. This increased complexity may have led to a selection
process within the industries, leaving only the mature companies.
• Change:The highest score was achived by the companies that recently changed their
environment. This is a clear sign, that PLM projects overall lead to an improvement
in PLM maturity. Companies are capable to conduct such projects and gain benefits
from them even if the change was only recently.
• Companies age:The highest mean was achieved by the oldest companies. The sec-
ond highest by the youngest. These values are comparable close to one another and
the difference to the companies older than 50 years is rather small. The companies
older than 25 but younger than 50 years lag considerably behind. The root causes of
this cannot be evaluated at this point. However, all companies scoring low in this
category have the commonality that their last change in the PLM environment took
place more than one year ago.
All the discussed results are based on a small sample group, the results are not sta-
tistically singifcant and the selected group is not representative for their corresponding
industries. Additionally, the set of factors currently used is not comprehensive. As part
of further research additional evaluation of factors would be required.
5.3 Sample size calculation
The calculated value of 247 European companies is based on the assumption, that
the standard deviation of the current sample is transferable to a larger case group. Not
all of the current participants would meet the criteria for the proposed study. This leads
to the conclusion that the calculation has to be regarded as an estimation and a first
indicator. Furthermore, any change in the assessment questions or the overall model
would require a recalculation of the standard deviation on the base of a new study.
6 Conclusion
The chosen methodology can deliver meaningful results. Hereby, the frequent valida-
tion by experts and the review of the companies of the results have been proven to be
crucial elements. The developed model is able to differentiate the companies and de-
livers a broad spectrum of results. Within these results certain patterns can be detected.
These patterns lead to factors that may influence the maturity of a PLM environment
of a company. The additional discussion of the results with the high scorring companies
has been a useful approach to learn about measurements and approaches which may be
beneficial for the maturity of the PLM environment. On the base of Cooper additional
analysis of the low scorring companies would be beneficial. Conducting an additional
study with a larger sample group would allow the data to be ananlyzed using statistical
means and to quantify the effects of different factors. As part of this expanded study an
14
expansion of the current set of questions could be beneficial to cover additional aspects
of the PLM environment and get an even more holistic point of view.
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