An Empirical Analysis of the PLM Implementation Effects in the Aerospace Industry

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automatically lead to their effective deployment at the organiza-

tional level, because of the effects related to network externalities

[7,8] and learning-by-using among adopters [9].

At a strategic level, there can be a negative impact if

management misjudges the effort required. Moreover, the success

of any PLM implementation (and consequently its strategic

outcomes) depends on organizational characteristics. These are

markedly different across firms, and this can significantly alter the

implementation strategy and its outcomes.

The research question faced in the paper is to analyze PLM

implementation effects at different levels. In particular, the paper

theorizes and empirically validates the existence of three distinct

dimensions (operational, organizational, and strategic) in the

implementation and their interactions. Besides these elements

that are more deeply considered in the paper, the entire research

regarded, also other factors affecting PLM assimilation such as

learning-by-using effects, end users’ acceptance of the technology,

etc. The underlying idea is that, through this analysis, it is possible

to make the relations among the elements which can lead to

effective PLM implementations emerge.

The first part of the paper is based on literature review, with the

objective of identifying the diverse dimensions of the problem and

the focal points of the analysis. The second step is represented by

the construction of a theoretical model that summarizes thedifferent perspectives coming from literature. Starting from the

main PLM system functionalities declared in literature, the paper

proposes three incidence matrices for analyzing effects interac-

tions, and then investigates the impact of PLM on individual work,

on organizational processes and at a strategic level. Finally, the

three dimensions of PLM effects are investigated by an explorative

study in a large enterprise operating in the aerospace industry. The

last section deals with conclusions and managerial implications.

2. Literature review

Many contributions in academic literature and among practi-

tioners give different definitions of PLM and propose a number of

viewpoints, which can be summarized in Table 1. The table doesnot refer to specific areas of PLM implementation, but to diverse

investigation perspectives. The main aspects which are objects of

investigation in literature are the implementation industrial

scenario [1], approaches [2], operative modalities [3] and

implementation effects [4].

At the same time, two main perspectives are present, depending

on whether contributors focus on adopters (i.e., a ‘‘demand-side’’

perspective) or on systems and their vendors (i.e., a ‘‘supply-side’’

perspective). Considering the industrial scenario [1], the demand-

side perspective focuses on drivers that lead firms to adopt PLM

systems (e.g. [10]) or integrated managerial approaches to NPD

process (e.g. [11]). Conversely, a supply-side perspective, quite

often found in practitioner-oriented papers (e.g. [12]), focuses on

enabling technologies. In this context it is possible to recognize

two technological trajectories, one that views PLM as a progressive

broadening of the scope of CAD systems (e.g. the one proposed by

Dassault/IBM, Siemens/UGS, Enovia, think3, etc.), the other as a

specific module of enterprise systems (e.g. SAP PLM; Oracle PLM,

etc.).

Concerning implementation approaches [2], a first stream

concerns a top-down perspective on the NPD process and

considers PLM a holistic and strategic activity addressing many

components such as products, organizational structure, working

methods, processes, people and information systems [4]. The

second stream is related to a bottom-up perspective, leading from

tools to problems. It considers that knowledge of available tools

can allow finding appropriate solutions for company-specific

problems. This perspective identifies PLM as a set of tools for

gathering, managing, spreading and using information and

knowledge on products. It focuses on specific technological

solutions, and is stressed by consultants and business analysts

(for a survey, see [13]).

Some authors [14,15] claim that PLM should be approached

through these two perspectives together. The implementation of tools without the comprehension of strategy and business

processes cannot be useful, just like the application of business-

level PLM fundamentals without an adequate knowledge of the

supporting technologies.

This dichotomy is present also when dealing with implemen-

tation operative modalities [3]. According to the first perspective,

the key focus should be on understanding and eventually re-

engineering the business processes in which products are

developed [14,15]. According to the other perspective, implemen-

tation decisions should be guided by the features and functionali-

ties of IT applications (e.g. product data management systems, data

exchange and collaboration technologies). In this sense, firms must

look for the specific applications they need, and not all possible

tools will be adopted.At last, a somewhat limited amount of research contributions

has been dedicated to investigate the effects, in term of benefits

and problems, of PLM implementation [4].

One way is to assess the impact of PLM in improving

effectiveness, efficiency and control of the NPD process. It regards

the reduction of design mistakes [16], the improved possibility of

design alternative comparisons [13,14,16], a better understanding

of product architecture and components features [13], the

achievement of design parameter optimization [17], the reuse of

past design information (e.g. [14,16,18]), the anomaly detection in

Table 1

Investigation perspectives on PLM in literature.

Implementation industrial scenario [1] Implementation approaches [2] Implementation

modalities [3]

Implementation effects [4]

Demand side Basic drivers

- Saaksvuori and Immonen [16];

- Dutta and Ameri [41];

- Sharma [21].

- Rangan et al. [10]

Integrated approaches to NPD

- Hage et al. [11];

- Nobelius and Sundgren [42];

- Von Corswant and Tunfilv [43].

Comprehensive approach to NPD

and product information management

- Stark [4]);

- Rangan et al. [10]

Focus on processes

- Grieves [15];

- Schuh et al. [14].

Impact given by the adoption

of a PLM strategy

- Schuh et al. [14];

- Stark [4]

Supply side Existent supporting technologies

- Garetti, et al. [12]

- Dassault/IBM UGS, Enovia,

Think3, SAP PLM; Oracle PLM, etc.

Tool- driven approach in order to

provide a specific support to specific

processes [13]

Focus on technological

choices

- Garetti, et al. [12];

- Sudarsan et al. [44]

Benefits given by specific

solutions

M. Cantamessa et al. / Computers in Industry 63 (2012) 243–251244



the first phases of NPD and the management of design changes

[16,19]. This impact can be considered as an enabler of the

development of more innovative products [14]. Moreover, data

integration, reduction of data redundancy, real-time updating

[16,20] and, more in general, integrated information and

knowledge management (e.g. [4,14]), as provided by PLM systems,enhances cross-functional collaboration among employees [21–

23]. This leads to a decrease of transaction costs in the NPD process

[38], more effective project coordination and control on product

engineering [13,14,24,25], besides a greater rigor in the manage-

ment of NPD, especially for product planning [4,14].

Finally, it is also possible to attempt an evaluation of the

revenue increase or the cost reduction that PLM can lead to. These

‘‘bottom line’’ results stem from an improved management of

design alternatives, a greater design diversification [14,16], a

deeper comprehension of product architecture and components

features [14], a lower impact of product changes on process

[16,26], the possibility of re-using past design information

[14,16,18], a higher quality of design and a lower number of

design mistakes [16], an improved management of complex tasks[27], the reduction of time needed for information research [14,16]

and for low added-value activities [16], the anomaly detection in

the first phases of NPD and the management of design changes

[16,19], an effective support for teamwork and cross-functional

collaboration [21–23].

3. Theoretical model developed and focal points of the analysis

None of the previously mentioned contributions examines the

elements of the firm on which PLM impacts by considering

different implementation levels.

Stark [4] considers three levels of PLM implementation in the

pursuit of opportunities and benefits, but does not consider the

associated

organizational

issues.

This

is

an

important

limitation,since PLM systems require the reengineering of relevant business

processes [28,29], with a dramatic impact on the firm’s organisa-

tion [30].

This paper attempts to take into account the organizational

effects of PLM implementation by considering workers in the NPD

process as the unit of analysis. In so doing, the paper distinguishes

between three different dimensions of PLM impact, individual

operational effects, organizational process effects, and strategic

effects. The first dimension analyzes the impact of PLM on users’

individual work and operational procedures. The second dimen-

sion reflects the benefits that users may have perceived in the

entire NPD process, in terms of impacts on coordination routines,

idea exchanges and workflows. The third dimension reflects how

employees

perceived

how

PLM

affected

firm

performance

from

a

strategic point of view. The basic idea is that, through the

identification of the effects of PLM at different levels, the dealings

among the elements which lead to effective PLM implementations

can result.

Table 2 shows the effects as described in literature and classifies

them in the defined three categories.Really despite these three dimensions are likely to go together

within a firm, it is plausible to expect that when PLM effects are

investigated at the individual level, some users may have perceived

benefits in their individual job without experiencing concrete

results at the NPD process level. This is more likely where the

technology is scarcely used as a coordination tool, but simple as a

knowledge repository for his/her own use. Vice versa, some other

users may have not perceived any particular impact in their

individual work, despite at the process level PLM resulted into

improved coordination and knowledge management. This is more

likely in individuals with a low acceptance of the new technology.

In addition, some individuals may do not have a greater vision or

sensitiveness on the strategic implications of PLM implementation

despite their use of PLM is regular and the advantages on theircoordination routines is evident. This is the reason why the study

of the perception of the users about these diverse effects is the first

focal point of the analysis.

After having considered the three levels of PLM implementation

effects, the mutual interactions among them remain to be

explored. Operational effects can influence the development

process and – in turn – this can have an influence at strategic

level. This knowledge on the PLM implementation effects and

relations at multiple levels (by referring to Table 1, [4]) may allow

to understand its antecedents concerning modalities [3],

approaches [2] and industrial scenarios [1].

In order to assess the mutual relations at different levels,

incidence matrices can be used to explore potential causal links

among

effects

[31], systematically

reporting

in

the

matrices

thefindings deriving from literature.

Specifically, three matrices have been considered. The first

matrix connects PLM functionalities (i, on the rows) to individual-

level operational effects (j, on the columns). The second matrix

connects functionalities i or the individual-level operational effects

j on the rows to process-level effects (r, on the columns). The third

matrix connects the operational-level j or process-level effects r

(on the rows) to strategic effects (s, on the columns). These

matrices can be translated in a causal map that shows the different

interactions, as in Fig. 1. Obviously, it is important to underline that

the possibility of a direct link between two distant groups is

allowed by the model. For instance, the direct link between a PLM

individual operative effect and a strategic effect can be allowed

even

without

intermediate

individual

and

operative

effects.

Table 2

PLM effects classification.

Individual operative effects Organizational process effects Strategic effects

Improved possibility of design alternative

comparisons and management [14,16]

Reduction of data redundancy and real-time

data integration [16,20]

Higher product innovation level [14]

Better comprehension of product architecture

and components features [14]

Information and knowledge management [4,14] Product cost reduction [14,16]

Past design information reuse [14,16,18] Transaction cost reduction in the NPD process [38] Time to market reduction [14]

Reduction of design mistakes [16] More effective project coordination and control [14,24,25] Process cost reduction [14]

Support

for

complex

task

execution

(e.g.

[27])

More

rigorous

NPD

Management

(especially

forproduct planning) [4,14] Customer

satisfaction

[14]

Design optimization [17] Product diversification [14]

Time reduction for research and

information gathering [14,16]

Higher design quality [16]

Reduction of low value added activities [16] Anomaly detection in the first phases of

NPD and design changes [16,19]

Support for design in team and

cross-functional collaboration [21–23]

Lower impact of product changes on process [16,26]

M. Cantamessa et al. / Computers in Industry 63 (2012) 243–251 245



Matrix X, as shown in Fig. 2 and whose description below

explains the logic used for its construction, considers the relations

between PLM functionalities (as listed in the introduction and

described in [4]) on the rows and the main individual operative

effects, which are claimed by literature on PLM systems (as shown

in Table 2),on the column. Matrix Y and Z are similarly constructed

and shown in Figs. 3 and 4.This theoretical model has then been validated by a panel of

industry experts, however must also be validated on a broader

empirical base. Moreover, especially in the evaluation of user

perception, moderator effects due to individual traits of PLM users

(e.g. age, gender, type of tasks performed) and to the organizational

context (e.g. the functional department and its involvement in the

NPD process, the facilitating conditions set the and middle

managers for encouraging and supporting the PLM use) may be

relevant and may affect the interdependencies among the PLM

effects described by the matrices.

The first point is completely treated in the paper. Results

described here are obtained by using a scale that has been

validated statistically through a Principal Components Analysis, of

the three identified dimensions and their relations.

The second point is mainly related to the nature of PLM as a

complex technology that can be accepted to a different extent by

individuals depending on the individual traits and their organiza-

tional context. The Technology Acceptance issue does not only affect

individuals but, due to network externalities in the organizationalprocesses, can also affect the benefits of the technology at

organizational and business process level. This issue was part of

the study and guided the construction of questionnaires but it is only

briefly mentioned in the paper. This because as Fig. 5 shows, the

focus of the analysis in this paper is on the operational,

organizational and strategic effects and their linkages of PLM

implementation; readers are referred to [34] for further details.

4. The survey

Two business units of an Italian industrial group, which

operates in the aerospace industry, were the setting for the

Fig. 1. Causal relation matrices.

Fig. 2. Matrix X-PLM functionalities vs individual operative effects.




empirical analysis. In both cases, PLM implementations were

driven by the willingness to enforce stronger relationships in the

firm’s network of customers, suppliers, and partners, together with

the need for a better management of concurrent engineering

activities. The business units introduced different PLM functional-

ities, with product data and configuration management, process

planning and resources management being the most important.

The study was based on a quantitative analysis conducted

through a structured questionnaire addressed to all adopters of

PLM systems in the two units. A representative sample made by

300 respondents in the two units was involved into the study: this

sample reflected the distribution of the population of PLM end-

users regarding age, educational level and type of organizational

function.

133

usable

responses

were

obtained

(63

from

firstBusiness Unit and 74 from the second one), which correspond to

about a 20% response rate in each of the two organizations. To

check for non-response bias, the respondents were compared with

the non-respondents through contingency tables, Kruskall–Wallis

non-parametric tests and a logit analysis.

In the surveyed sample 110 respondents were part of a cross-

functional product development team and 72 were affiliated with

the product design department. On average, 61% of PLM users’

working time was spent performing tasks associated with product

development projects. With respect to age, the sample was well

balanced (49.6% of the surveyed workers were under the age of 40).

Age was found to highly correlate with seniority (Pearson

correlation was 0.788 with a 0.1% p-value), thereby highlighting

the

low

turnover

rates

of

the

surveyed

users.

Data were collected following a three stage process. A

preliminary round of interviews was made with senior managers

accountable for the PLM implementation project and with some

program managers, in order to analyze the organizational impact

of PLM and the type of implementation. A structured questionnaire

was then sent to all the end users of PLM system. Findings obtained

from the survey were at last discussed and validated with the top

management.

Outcomes emerging from this survey can be generalized at the

industry level, as firms in the aerospace industry exhibit similar

business processes, with a high degree of standardization and

limited room for idiosyncratic operations, routines and human

capital. Standardization occurs because players in this industry

often

work

together

in

R&D

alliances

and

tend

to

follow

similarpractices both to ensure operating compatibility with partners and

to imitate partners’ good practices. Thus, when generalized, results

reflect how PLM is accepted and assimilated in the operational

routines and processes of large aerospace firms one year after its

implementation.

4.1. The questionnaires and measures

Based on previous PLM-specific literature, the questionnaire

included the measures presented in Table 3. Each item was

expressed on a 5-level Likert scale, wherein the value ‘00

corresponds to a neutral position between a strong disagreement

(‘20) and a strong agreement (‘+20). In order to identify higher-

level

factors,

item

responses

were

analyzed

using

factor

analysis.

Fig. 3. Functionalities and Individual operative effects vs. organizational process effects.




We employed a loading threshold of 0.5 for component identifica-

tion and a level of 0.6 for the Kaiser–Meyer–Olkin measure of

sampling adequacy. Items were aggregated into factors after

controlling for the internal consistency of the measures using

Cronbach’s alpha. A reliability threshold of 0.7 was used for this

purpose. Given the lack of previous theoretical studies that clearly

separated the individual from process-level dimensions of PLM

impact, exploratory factor analysis (EFA) to identify the dimen-

sions of PLM’s organizational impact was used.

5. Analysis of the results

5.1. PLM impact

Table 4 reports descriptive statistics and EFA results for factors

related

to

PLM

impact.

This

analysis

separated

three

dimensions

of benefits experienced by users. Each item loaded higher on only one

factor, thereby supporting the discriminant validity of the

measures.

The first factor identified by the analysis refers to the perception

that PLM produces strategic benefits: that it reduces the firm’s

product costs and enhances the development of new products

starting from platforms. It was labelled ‘Perceived Strategic

Benefits’ and confirms the first typology of effects considered by

the theoretical model.

Also the second factor EFA revealed confirms the theoretical

model and refers to ‘‘process effects’’. It explains the benefits users

perceived in the organization of NPD activities thanks to PLM use

and was labelled ‘Perceived Impact on the NPD process. This factor

considers:

(1)

more

effective

collaboration

with

colleagues,

(2)

Fig. 4. Individual operative effects and organizational process effects vs. strategic effects.

Fig. 5. Factors that describe PLM impact.




better cross-functional coordination and idea exchange, (3) more

structured ways to manage workflows, and (4) more frequent use

of

component

carryover.

Among

these

items,

there

was

diffusedagreement among users that PLM facilitated a more structured

way of managing workflows (item 2.3). Conversely, the perception

that PLM supports the coordination and exchange of ideas (item

2.4) exhibited a limited diffusion among the surveyed users.

The third factor too confirms the developed theoretical model

and refers to the impact of PLM on individual work and operational

procedures. This was labelled ‘Perceived Impact on Individual

Work’ and encompasses: (1) easier product data retrieval, (2) a

reduction in time wasted due to either ‘re-inventing the wheel’ or

to doing useless work because data is inaccurate or not updated;

(3) an increase in time devoted to individual technical work and (4)

individual productivity increase (i.e., less time needed to perform a

job). Among these factors, the items that describe the impact of

PLM

on

overall

individual

productivity

(item

3.1)

and

the

reduced

waste of time (item 3.2) show the lowest means and the highest

standard deviations. Conversely, diffused agreement was on the

greater

ease

of

retrieving

product

data.

These

results

are

consistentwith descriptive statistics of how users view the impact of PLM on

the allocation of their working time (see Table 5). On average, PLM

Table 3

Measures in the questionnaire.

PLM perceived benefits Item Main references

Impact on individual work 1. Increased ease of retrieving product data See Table 2 column 1

2. Increased individual productivity

3. Increased time available for individual technical work

Reduction in the time spent ‘re-inventing the wheel’ or re-doing

the same activity due to a prior use of wrong/non-updated product data

Impact on the NPD process 1. Enhanced cross-functional coordination See Table 2 column 2

2. Enhanced data integration and improved collaboration tools

3. Enhanced product knowledge re-use

More effective process management tools in NPD (i.e., workflow management)

Strategic effects 1. Product and process cost reductions See Table 2 column 3

2. Time-to-market reductions and more innovative product platforms

Table 4

Factors that describe PLM impact.

Type of effects Meana S.D. Median Factor loadings

F1 F2 F3

F1. Perceived strategic effects 1.1 – PLM generated a reduction in product

development costs***0.23 0.79 0 0.76 0.39 0.25

1. 2 – PLM favoured the development of

product platforms***0.28 0.68 0 0.60 0.26 0.29

F2. perceived impact on

the NPD process

2.1 – PLM has favoured a more effective

collaboration among employees***0.27 0.75 0 0.21 0.59 0.34

2.2 – PLM supported more idea exchange

and more cross-functional coordination

0.02 0.77 0 0.12 0.64 0.49

2.3 – PLM encouraged employees to work in a

more structured way, following

workflows rules for validating/authorising

changes to product data***

0.64 0.80 1 0.23 0.67 0.34

2.4 – PLM favoured product carry-over*** 0.31 0.75 0 0.37 0.64 0.32

F3. Perceived impact on

individual work

3.1 – PLM contributed to reducing the time

required to do many tasks of my job

0.04 1.07 0 0.34 0.32 0.79

3.2 – Thanks to PLM, I do not have to spend

time ‘re-inventing the wheel’ or re-doing the

same task due to product data inconsistencies

0.12 0.92 0 0.08 0.39 0.80

3.3 – Thanks to PLM, I can more easily develop

new and effective solutions since I can spend

more time on technical aspects

0.05 0.76 0 0.45 0.39 0.76

3.4 – Once PLM has been implemented,

product data are more easily available

and more rapidly retrievable***

0.69 0.89 1 0.47 0.44 0.87

Initial eigenvalue 3.646 1.266 1.016

Proportion of variance explained [%] 36.4 12.6 10.2

Cumulative variance explained [%] 36.4 49.1 59.2

Cronbach’s Alpha 0.60 0.70 0.73

Kaiser–Meyer–Olkin measure of sampling 0.80

a +2 = strongly agree; 2 = strongly disagree.*** p-Value < 0.1% (Student’s t -test for assessing whether a variable’s mean significantly differs from 0).

Table 5

Estimated changes in the allocation of working time for PLM users after PLM

implementation.

Increased estimated percentage

of time spent doing

No. obs Mean Standard deviation

Individual technical work*** 97 5.14% 12.84%

Internal meeting 97 0.67% 4.87%

Reporting 96 0.52% 6.80%

Data retrieval*** 98 5.55% 8.00%

*** p-Value < 0.1% (Student’s t -test for assessing whether a variable’s mean

significantly

differs

from

0).




favoured a reduction in time spent retrieving information, as well

as an increase in time spent on individual technical work. The

average magnitude of this substitution effect was approximately

5% of users’ working time (a t -test underscored that these changes

were significantly different from zero at a 0.1% p-value).

5.2. PLM use as a coordination tool

The item 2.4 of Table 4 shows a limited agreement among the

surveyed users about the perception of PLM as a support to thecoordination. This result induced the idea of analyzing the role of

PLM in the NPD specifically in term of coordination. Obviously,

because the data collected, this has been made only considering

the collaborative technologies that were present in the company,

even is authors know that more collaboration oriented tools exist.

Two dimensions were considered (Table 6): the frequency of use

(F ) and its significance (I ) to employees in allowing them to get

information they need. Mann–Whitney non-parametric tests

highlighted different coordination patterns associated to users’

functional departments, but not to age. Users estimated that and

formal meetings are the most important coordination modes for

getting information relevant to their own tasks. In the product

design department – although PLM is more frequently used for

coordination than in the other departments, it is both less used andless important than direct verbal communication or exchanges

(paired samples t -test revealed significant differences at a 1% p-

value). Thus, the role of PLM in supporting knowledge exchange

does not go through explicit communication but, rather, in

allowing easier access to product data and embedded tacit

knowledge.

5.3. Implication of outcomes

Some considerations can bemade on theseresults. Thefirstone

is related to the PLM role in individual activities. The most

important benefit perceived from an individual point of view is

thatPLMhelpsa reduction in time spent retrieving information, as

well

as

an

increasein

time

spent on

individual technicalwork.

Thisoutcome is coherent with literature [14,16] and emerges very

clearly by thesurvey. An explanation could be that these benefits

are short term perceivable in an explicit way. On the contrary,

understand that the own competence in developing design

solutions is improved surely requires more experiential time

besides a bigger effort of internalization.

Another important result is related to the PLM role in

coordination. In literature, PLM is described as a facilitating

system for coordination [13,14,21,24,25] but users do not perceive

it in the same way. Table 4 shows that PLM is not commonly

perceived as a support to the coordination and Table 6 shows that

and formal meetings are considered more important than PLM for

coordination. This should be due to the fact that and formal

meetings

are

proved

procedures.

The importance attributed to PLM does not fit to frequency in

use and in fact what is really interesting is analyzing the

frequencies. The dichotomy between and informal communica-

tions on one side, in respect to PLM and formal meetings on the

other side is evident. It seems that PLM, obliging somehow to

formalize procedures, becomes de facto assimilated in its use to

formal meetings. This induced formalism in the procedures

actually persuades users to prefer for coordination informal

mechanisms, such as emails and direct contacts.

This is significant, more than even before, if one considers theproduct design department. Although here PLM is more frequently

used for coordination than in the other departments, it is both less

used and less important than direct verbal communication or

exchanges. The reason therefore may be, on the one hand, that PLM

would not provide the right technological solutions for coordina-

tion and hence users prefer other tools, on the other hand, the

studied implementations may be immature so that users have not

completely assimilated PLM potentialities.

These results anyway stimulate specific managerial implica-

tions. They make evident that in order to take advantage of all the

possible benefits that PLM can provide, technology, changes in

business processes and organization must be combined consis-

tently. It is important take into account cultural issues that easily

recognize in the PLM systems the data gathering facilities, butimpose resistance for an effective use of knowledge sharing tools.

Rather than, it is important keep in mind the user inertia in the

process reshaping and standardization; considering that users

easily agree to a better workflow management but slowly,

familiarize with new coordination procedures.

6. Conclusive remarks

At the moment, there is debate in literature on how obtain

successful PLM implementations. The paper presents an investi-

gation on PLM implementation effects at different levels. In

particular, the paper has theorized and empirically validated the

existence of three distinct dimensions (operational, organizational,

and

strategic).The outcomes generally confirm what is stated by literature.

The real contribution of the paper consists in the way they are

obtained. In order to evaluate benefits and effects of a PLM

implementation, besides anecdotal evidence on the impact of IT on

NPD processes at the macro-level, as usually made in literature, the

paper proposes a micro-level analysis on the impact of the

technology on individuals’ job that allows to analyze the linkages

between the individual use, the impact on the NPD process and the

strategic implications. This analysis, by using statistical methods,

allowed a deeper knowledge of the individual and organizational

phenomena linked to PLM implementations.

Two types of contribution for managers arise from these results.

First, the paper provides managers with a framework to measure

the

benefits

produced

by

PLM

technologies.

Second,

the

results

Table 6

Frequency and importance of different coordination modes.

Exchanges Formal meetings Informal direct verbal

communication

PLM

F I F I F I F I

Product design department 1.77 1.67 0.97 1.53 1.39 1.32 0.91 1.22

Other departments 1.57 1.48 0.98 1.31 1.36 1.26 0.67 1.39

Entire sample 1.68 1.59 0.98 1.43 1.38 1.29 0.81 1.29

p-Value (ANOVA) 5.7% 5.5% 92.5% 5.0% 75.2% 66.6% 9.1% 25.9%

p-Value (Mann–Whitney test) 9.4% 6.6% 92.7% 3.8% 96.2% 79.6% 9.2% 23.4%

F = frequency of use; I = importance for getting key information to accomplish own job (1 = low; 3 = high).




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Documents

An Empirical Analysis of the PLM Implementation Effects in the Aerospace Industry