Accepted for Publication at the International Journal of Production Research, Forthcoming, 2015, DOI:10.1080/00207543.2015.1106020
Relationships between Lean Product Development enablers and problems
Guilherme Luz Tortorella (
[email protected])* Federal University of Santa Catarina, Florianopolis – Brazil Campus Trindade, Centro Tecnológico/DEPS – Florianópolis/SC - CEP 88040-900 Phone: +5548 37217012 Giuliano Almeida Marodin (
[email protected]) The Ohio State University, Department of Management Sciences 600 Fisher Hall, 2100 Neil Avenue, Columbus, Ohio, 43120, Phone: +1 614 6207201 Diego de Castro Fettermann (
[email protected]) Federal University of Santa Catarina, Florianopolis – Brazil Campus Trindade, Centro Tecnológico/DEPS – Florianópolis/SC - CEP 88040-900 Phone: +5548 37217012 Flavio Sanson Fogliatto Federal University of Rio Grande do Sul, Porto Alegre – Brazil Av. Osvaldo Aranha, 99, 5o andar – Porto Alegre/RS CEP 90.035-190 Phone: +5551 33083491
Abstract The Lean Product Development (LPD) approach uses lean principles and enablers (tools, techniques, and practices) to reduce waste and continuously improve the product development processes. Other than reducing product development lead-time, LPD also aims at improving quality by reducing problems that might occur during the process. Several LPD enablers are proposed in the existing literature; however, there is limited knowledge on how these enablers may effectively counteract the occurrence of problems in the product development process. We empirically tested the association between four groups of LPD enablers and eleven groups of LPD problems in a sample of 64 companies that are undergoing lean implementation in the shop floor and administrative areas. There are two major contributions here. First, we propose an empirically validated instrument for assessing the adoption of LPD enablers and the incidence of LPD problems in companies filling an existing gap in the literature. Second, we provide understanding on how LPD enablers can mitigate the incidence of LPD problems, allowing companies undergoing lean implementation to better manage their product development processes. Moreover, some results demonstrate that the association between enablers and problems may not be as suggested in the existing literature. Keywords: Lean product development, Product development problems, Lean product development enablers.
1. Introduction Product development capabilities are becoming critical for companies, as the increase in global competition and markets’ segmentation accelerate the pace in which changes take place in many industries (Dal Forno et al., 2013; Oliver et al., 2004). However, companies that structured their Product Development Process (PDP) based on traditional models may be in disadvantage regarding important dimensions such as agility, flexibility, and productivity (Panizzolo et al., 2012). Traditional PDP models usually lead to a number of problems commonly seen in companies; some of them are: (i) work overload on designers and engineers that frequently perform unnecessary tasks, (ii) a PDP model that is not clearly understood by designers, (iii) project cost overruns, (iv) difficulty in retrieving knowledge from previous projects, and (v) ambiguity regarding tasks’ responsibilities due to insufficient commitment of functional departments (Rossi et al., 2012; Oppenheim, 2011; Liker, 2005). Such problems may result from the fact that companies have traditionally focused their improvement efforts on shop floor (materials flow) related issues or on supporting areas with a closer relationship with that, such as procurement and logistics (Dekkers et al., 2013; Jayanth et al., 2010). To overcome such deficiencies the application of lean thinking principles and enablers to PDP has been proposed by academics and practitioners (Rossi et al., 2012). Lean Product Development (LPD) departs from the customer’s perception of value to create new and profitable value streams within the organization, exploring synergies between processes, people, tools, and technology (Kumar et al., 2015; Kennedy, 2003; Slack, 1999). Browning and Worth (2000) emphasize that the concept of LPD goes beyond the goal of waste elimination: it aims at maximizing the value added to customers, shareholders, employees, society, and suppliers. The adoption of LPD has yielded benefits to companies
such as Toyota; however, achieving effective LPD implementation has proven difficult for many other organizations (Letens et al., 2011). Increasing practical significance has been attributed to the concept of LPD by companies that have succeeded in improving their manufacturing processes, and find that product design is the new operations bottleneck (Reinertsen, 2009). The lean approach applied to product development leads to greater interaction between teams, flexibility, and dynamism, as well as shorter development lead times (Reis et al., 2013). However, since some of the enablers included in LPD have been known before the coining of the concept, some confusion concerning definitions may arise among practitioners who may be familiar with the enablers but not with the concept of LPD (Karlsson and Ahlstrom, 1996). Information gathered from studies at Toyota provides a clear view of LPD as a system; it becomes clear that implementing individual techniques from the LPD toolbox will not transform a company’s product development process. Unfortunately, reported cases of successful LPD adoption apart from Toyota are rather limited (Liker and Morgan, 2011; Kennedy et al., 2008; Lander and Liker, 2007). Although giving organizations the means to provide the market high quality products in a faster and more efficient way, LPD is not yet fully recognized as a source of competitive advantage by many companies (Reis et al., 2013; Kreafle, 2011). A reason for that is that organizations evolve differently according to their characteristics (Leon and Farris, 2011). To achieve improvements in LPD, organizations must improve value creation and work flow efficiency not just within, but across levels (Letens et al., 2011). Thus, each organization’s maturity level will determine the results (Reis et al., 2013). In this paper we propose an instrument for assessing and understanding the relationships between enablers that promote LPD, providing means to mitigate product development problems in companies undergoing lean implementation. The aforementioned relationships
were determined and validated through a survey carried out with 64 Brazilian companies. We thus provide an empirically validated instrument for assessing LPD enablers and problems with no parallel in the existing literature. In addition to the method proposed here, this paper carries another significant contribution. Identifying relevant relationships between product development problems and LPD enablers may contribute to specify the context in which problems are expected to occur. With that knowledge, companies will be able to emphasize the development of enablers that tend to improve their operational performance. The rest of this paper is organized as follows. A literature review on LPD is presented in section 2. Section 3 contains the research method. Results are given in section 4, followed by conclusions in section 5.
2. Lean Product Development (LPD) Researchers and practitioners took different approaches once they realized the potential benefits that the product development process could attain by becoming lean (Khan et al., 2011). Such approaches may be classified in five categories: (i) those that rebranded concurrent engineering as LPD (Karlsson and Ahlstrom, 1996); (ii) those that viewed lean as lean manufacturing, and tried to adapt its various components to product development (Cooper and Edgett, 2008; Reinertsen, 2009); (iii) those that considered the foundation of LPD to be the Toyota Product Development System, but combined with other ideas from lean manufacturing, applying it to product development (Oppenheim, 2004; Hines et al., 2006); (iv) those that identified the Toyota Company as the foundation of lean and went to great extents to study its product development system, identifying a more comprehensive set of principles and mechanisms directly related to product development that were argued to be
theoretically superior to conventional theory (Sobek et al., 1998; Oppenheim, 2011); and (v) those derived from group (iv) with a greater focus on applications in industrial companies (Panchak, 2009; Liker and Morgan, 2011). A comparison of traditional product development and LPD projects is presented in Table 1. Ward (2007) conceptualized LPD as a set of operational value streams that should be designed to consistently execute product development activities effectively and efficiently, creating usable knowledge through learning. The building blocks of such value streams and knowledge creation cycle were organized in five LPD principles: value focus, entrepreneurial system designer, teams of responsible experts, set-based concurrent engineering, and cadence (pull and flow). Flores et al. (2011) proposed an LPD model based on four supporting tools: designing a lean transformation tool kit, mapping of the LPD value stream, creation of a learning kit for LPD, and methods and techniques based on lean design. Pardal et al. (2011) synthesized LPD in seven key elements: (i) set-based concurrent engineering, (ii) chief engineering as connection between functional areas, (iii) strong integration with suppliers throughout the development process, (iv) superior technical competence, (v) simplified visual communication, (vi) search for excellence, and (vii) continuous improvement-driven culture. Traditional product development
Lean product development
Team structure
Teams were not used
Cross-functional teams
Development phases Integration vs. coordination Project management
Small overlap
Simultaneous
N/A
Meetings
Functional team structure
Heavyweight manager
Black box engineering
No
Yes
Supplier involvement Towards the end of the project From the beginning of the project Table 1: Traditional and lean product development projects Source: Karlsson and Ahlstrom, 1996
Machado and Toledo (2006) point to the difference in the application of five lean principles proposed by Womack and Jones (2003) in manufacturing and product development activities,
as shown in Table 2. In manufacturing, lean principles are methodically implemented, and the product is easily visualized (Karim and Zaman, 2013); in product development, the process is not as tangible, and targets emerge as the process takes place (Venkatamuni and Rao, 2010; Fiore, 2005). Lean principles 1 - Define value
Manufacturing Visual in each step, target prespecified
Product development Difficult to see, emerging targets
2 - Identify flow of value
Parts and material
Information and knowledge
3 - Make the value flow
Interactions are waste
Interactions are usually beneficial
4 - Pulled from customer demand
Driven by takt time
Driven by company's need
Repeatable and error-free Process allows innovation and processes reduces cycle time Table 2: Lean principles applied to manufacturing and product development
5 - Look for perfection
Source: Machado and Toledo, 2006
2.1 LPD Enablers Several methods have been proposed to improve the traditional product development process (e.g. Clark and Wheelwright, 2010; Eppinger, 2002). Such methods, although providing some benefits to companies, do not seem sufficient to achieve the breakthrough improvements that characterize LPD (Letens et al., 2011; Liker and Morgan, 2011; Morgan and Liker, 2008). Much of the LPD literature concentrates on suggesting solutions to a number of problems that it establishes to be commonly found in the LPD process (Hines et al., 2006; Kosonen and Buhanist, 1995). Such tools and techniques that focus on the integration and coordination of product development are essential to improve flow within the organization as a whole (Rauniar and Rawski, 2012; Letens et al., 2011). Wang et al. (2012) identify three families of enablers required for LPD according to their goals; they are: (i) those aiming at collecting information and providing feedback for the design process, (ii) those supporting the core activities of product design and development,
and (iii) those providing administrative support to chief engineers and staff. Womack et al. (1991) identify what they believe to be the set of core LPD enablers: heavyweight project managers, dedicated cross-functional teams, joint decision making involving all team members, and concurrent engineering. Karlsson and Ahlstrom (1996) argue that the key set of interrelated enablers comprised in LPD include supplier involvement, simultaneous or concurrent engineering, cross-functional teams, and strategy. Furthermore, Leon and Farris (2011) outline seven knowledge domains in the LPD literature: (i) performance based, (ii) strategy, (iii) knowledge-based networks, (iv) decision-based, (v) process modelling, (vi) supplier/partnership, and (vii) lean manufacturing principles. Definitions in the literature for different LPD enablers’ are provided in Table 3. Hoppmann et al. (2011) developed a framework for organizing LPD enablers. Their proposition listed the following 11 interdependent components: strong project manager, specialist career path, workload levelling, responsibility-based planning and control, crossproject knowledge transfer, simultaneous engineering, supplier integration, product variety management, rapid prototyping/simulation/testing, process standardization, and set-based engineering. Oppenheim (2011) also offers a comprehensive checklist named “lean enablers for systems engineering”. The checklist is an amalgamation of recommendations for systems engineering organized around 6 principles: (i) capture value defined by customers, (ii) map the value stream and eliminate waste, (iii) flow the work through the planned streamlined value-adding steps and processes, (iv) let customers pull value, (v) pursue perfection in all processes, and (vi) respect people. Despite the efforts to define LPD enablers, in Khan (2012)’s view the differentiation between critical enablers and those which may be substituted by equivalents is a topic still open in the literature. Field research may also be required to determine whether such enablers are actually adopted by industrial practitioners. Ballé and Ballé (2005) comment that any enabler taken
out of the LPD system will not yield significant efficiency gains in the development process. Moreover, no integrated framework of the identified LPD enablers has been put forward in the surveyed literature, nor has a methodological guide been formulated to support the application of lean thinking in engineering projects (Dekkers et al., 2013; Reis et al., 2013; Leon and Farris, 2011; Jayanth et al., 2010). Constructs of enablers Set-based concurrent engineering
Value-focus (planning and development)
Knowledge-focus (knowledge-based environment) Continuous improvement (Kaizen) culture
Definitions References It is a unique product development process, and is considered the main enabler of LPD. Instead Forno et al. (2013); Oehmen and of only one concept, the product development Rebentich (2010); Ward (2007); Oliver et process is conducted based on various concepts, al. (2004); Kennedy (2003). in which the team develops solution sets in parallel and relatively independently. Largely mentioned by researchers, it differentiates between product/customer value Letens et al. (2011); Gautam and Singh and process/enterprise value. This enabler (2008); Cooper and Edgett (2008); Kato focuses on satisfying customers' needs, (2006); Sobek et al. (1999). emphasizing techniques such as value stream mapping. Learning more about design alternatives is the Khan (2012); Oehmen and Rebentich focus of product development activities, (2010); Kennedy et al. (2008); Hines et supported by mechanisms for capturing, al. (2006); Sobek et al. (1998). representing, and communicating knowledge. Comprises standardization of processes, skills, David and Goransson (2012); Oppenheim and design methods allowing continuous (2011); Morgan and Liker (2008); Matsui improvement to be regularly considered upon et al. (2007); Ward et al. (1995). review.
Table 3: LPD enablers and definitions
2.2 LPD problems In iterative processes such as product development, a key factor for value addition is getting the right information in the right place at the right time (Browning and Worth, 2000). LPD consists of many interrelated enablers, which to be successfully adopted demand changes in basic values and ideas. For the most part, LPD enablers are not conceptually complex; therefore, problems that may arise in their implementation are mostly management-related (Meybodi, 2013; Cusumano and Nobeoka, 1998; Karlsson and Ahlstrom, 1996). The use of techniques detached from the LPD mainframe is also not recommended. Browning and Worth (2000) emphasize that removing waste in a product development process context requires a system perspective, rather than focus on individual activities. Thus, a certain degree of
organizational unlearning must be pursued such that old beliefs regarding procedures and measurements are deconstructed to welcome change (Leon and Farris, 2011). Problems addressed in the LPD literature may be grouped in two classes. The first one congregates problems dealing with the effectiveness of the development process in terms of market success of newly developed products (Hines et al., 2006). Problems within this class include lack of alignment between product development strategy and the wider business strategic plan, unnecessary development activity, lack of understanding of customer requirements, and high new product failure rates (Graebsch, 2005; Haque and Moore, 2004; Bauch, 2004). The second class of problems is concerned with the efficiency of the development process itself. These include the lack of a formal or standardized process, ineffective control of high volume development environments, poor internal communications, lack of common focus, inability to improve or learn from mistakes, and ultimately poor project deadline achievement and fiscal control (Reinertsen, 2009; Oppenheim, 2004). To address problems listed above a major topic in the LPD literature is the identification of best practices that may lead to their mitigation (Hoppmann et al., 2011; Kato, 2005). Similar to waste classification in materials flow analysis, Oehmen and Rebentich (2010) propose eight categories of waste in LPD: (i) waiting of people, (ii) overproduction of information, (iii) overprocessing of information, (iv) miscommunication of information, (v) stockpiling of information, (vi) generating defective information, (vii) correcting information, and (viii) unnecessary movement of people. Two fundamental root causes for such wastes are information overproduction and miscommunication; waiting of people may be interpreted as the end point in the waste chain (Liker and Morgan, 2011; Oehmen and Rebentich, 2010; Morgan and Liker, 2008). Pessoa (2008) proposes two additional types of waste (external events, and wishful thinking), while Bauch (2004) adds lack of system discipline and limited IT resources to the list. Finally, re-invention and hand-offs are also cited as types of product
development wastes by Rauniar and Rawski (2012). Table 4 describes LPD problems mentioned in the literature and their corresponding citations. LPD Problems
Definition
References
Project leader without formal authority
Problems related to absence of formal management practices that support leadership duties.
Wang et al. (2012); Leon and Farris (2011); Liker and Morgan (2011); Oppenheim (2011).
Achieve true crossfunctional integration
Encompasses issues related to teamwork integration and vertical organizations.
Meybodi (2013); Letens et al. (2011); Oehmen and Rebentich (2010); Reinertsen (2009).
Lack of Problems in communication and information communication and sharing among areas and personnel. feedback
Meybodi (2013); Cooper and Edgett (2008); Schuh et al. (2008); Ward (2007).
No simultaneous engineering and partnership with suppliers
Suppliers are not seen as partners in the product development process; instead, they are only expected to deliver what is requested.
Pessoa (2008); Oliver et al. (2004); Karlsson and Ahlstrom (1996).
Lack of product portfolio strategy
Related with absence of market orientation and link with business long term strategy.
Haque and Moore (2004); MIT (2001); Cusumano and Nobeoka (1998).
LPD performance measurement system
Deficient methods to quantify and follow up effectiveness of product development processes.
Letens et al. (2011); Pessoa (2008); Womack et al. (1991).
No IT integration
IT tools not included or neglected in the product development process.
Liker and Morgan (2011); Baines et al. (2006); Bauch (2004).
Poor operational decision making process
Decisions are superficially analyzed by few people, raising both long and short term problems in product development.
Leon and Farris (2011); Schuh et al. (2008); Browning (2000).
Lack of discipline
No adherence to standards and definitions is observed along the product development process.
Forno et al. (2013); Tsinopoulos and McCarthy (2002); MIT (2001).
Lack of knowledge reutilization
Previous knowledge is ignored, and current knowledge is not captured nor registered.
Forno et al. (2013); Reis et al. (2013); Womack et al. (1991).
Coordination and time-consuming activities
Activities are usually redundant or not performed due to poor individual scope definition.
Oppenheim (2011); Oehmen and Rebentich (2010); Reinertsen (2009).
Lack of project vision sharing
Employees perform their tasks without any sense of common target or engagement with the business’ mission.
Hines et al. (2006); Bauch (2004); Karlsson and Ahlstrom (1996).
Inexistence of leveled workload
Overload and idleness are commonly observed in process activities
Liker and Morgan (2011); Haque and Moore (2004).
Table 4: LPD problems and references
3. Method
There are three stages to the research method proposed here: (i) questionnaire development and data collection, (ii) construct validity and reliability assessment, and (iii) regression models. These stages are detailed in the sections to follow. 3.1 Questionnaire development and data collection 3.1.1 Sample We used the following criteria to select companies and respondents. First, we targeted at companies that were (i) implementing lean both at the shop floor and office levels, and (ii) geographically located in the south of Brazil, in order to control the effect of environmental factors, such as availability of skilled labour. Non-random selection of companies in surveys on lean is a common approach; examples may be found in Saurin et al. (2010), Boyle et al. (2011), Eroglu and Hofer (2011), and Taj and Morosan (2011). Second, respondents should have experience in lean and product or process development. Questionnaires were sent by e-mail to former students of executive education courses on lean offered by a large Brazilian University since 2008. The institution is the only one in its region offering short courses on lean. Courses are open to the general public. The same database of respondents was used in previous studies (e.g. Marodin and Saurin, 2015; Tortorella et al., 2015). A first e-mail message containing the questionnaires was sent in March 2014, and two follow-ups were sent in the following weeks. The final sample was comprised of 64 valid responses; respondents’ demographics are presented in Appendix A. Most respondents were from large companies (76.2%); the majority of companies belonged to the automotive supply chain (69.8%). Most respondents (87.4%) had 6 or more years of experience with LPD. Regarding the job title, there were a predominance of manufacturing or continuous improvement engineers (30%), product
development managers (23.3%), supply chain analysts (23.3%), and product development supervisors (13.3%). 3.1.2 Questionnaire The questionnaire had three parts. The first part was comprised of 30 questions based on Khan et al. (2011), and aimed at measuring the degree of adoption of the four constructs of LPD enablers described in the literature (Table 3). These questions are presented in Appendix B. Each question was answered on a scale ranging from 0 (not used) to 9 (fully adopted). The second part of the questionnaire intended to assess the frequency of occurrence of LPD problems. The forty-four questions from Paula et al. (2012) used to measure 13 LPD problems (Table 4) are presented in Appendix C. A 6-point scale ranging from 1 (vary rare) to 6 (very frequent) was used in the questionnaire. Demographic information of respondents and their companies was asked in the final part of the questionnaire. 3.1.3 Sample and method bias We tested for non-response bias as proposed by Armstrong and Overton’s (1977) using Levene's test for equality of variances and a t test for the equality of means between early (respondents of the first e-mail sent) and late (respondents of the two follow-ups) respondents. Results indicated no differences in means and variation in the two groups, with 95% significance. Thus, there is no statistical evidence that our sample is significantly different from the rest of the population. Surveys that use data from single respondents may be affected by respondent’s bias; to minimize that we followed some directions given by Podsakoff et al. (2003). First, dependent variables (LPD problems, in this case) were placed first and physically far from independent variables (LPD enablers) in the questionnaire, and using different scales (1 to 9 for LPD enablers, and 1 to 6 for LPD problems). Second, we verified that respondents had the proper
knowledge to answer the questionnaire, in terms of job title and work experience (Appendix A). Third, we used Harman’s single-factor test following a Factor Analysis with all measures to test for the existence of a single factor that could account for the majority of the variance among variables. As the first factor accounted for 44% of the overall variance in the data we concluded for the nonexistence of any common method bias. 3.2 Construct validity and reliability assessment We adopted a comprehensive multi-step approach for scale development and validation based on Shah and Ward (2007). The first three steps aimed at assuring the perspectives of content validity, discriminant validity and convergent validity, as presented in Figure 1. Discriminant validity was not tested for LPD problems, as we used only one construct for each problem as independent variable in the regression models of section 3.3. We performed a discriminant validity analysis of LPD enablers, which are dependent variables in those regression models, to reduce problems associated with multicollinearity.
Content validity
4 LPD enablers (30 variables) Literature review
13 LPD problems (43 variables) Literature review
Discriminant validity
Factor analysis with all 30 LPD enabler variables
Convergent validity
Factor analysis with each LPD enabler (construct) individually
Factor analysis with each LPD problem (construct) individually
Reliability
Crombach alpha for each LPD enabler
Crombach alpha for each LPD problems
4 LPD enablers (constructs) validated (23 variables)
12 LPD problems (constructs) validated (40 variables)
Figure 1. Schematic representation of the steps followed for scale development and validation
The first step proposed by Shah and Ward (2007) is an extensive literature review for content validity purposes. Tentative 4 LPD enablers (Table 3) and 13 LPD problems (Table 4) are elicited and discussed in section 2. Measurable variables associated with LPD enablers and problems also emerged from the literature (Appendix B and C). As questionnaires were not validated before, further analysis was performed to validate constructs. Regarding the LPD enablers, we conducted a Factor Analysis with Principal Component extraction and Varimax orthogonal rotation for discriminant validity. Factor Analysis reduces dimensionality and eliminate multicollinearity by developing orthogonal constructs that are linear combinations of the original set of variables (Hair et al., 2006). Table 5 presents the Pearson correlation values between LPD enablers; of the 465 correlations analysed 427 were statistically significant. Bivariate correlations assess the degree to which variables are
associated; a high number of significant correlations, as occurred in this study, is essential to create meaningful constructs (Meyers et al., 2006).
CE1 CE2 CE3 CE4 CE5 CE6 CE1 CE2 .757** CE3 .753** .800** CE4 .640** .534** .516** CE5 .562** .431** .555** .565** CE6 .628** .525** .595** .559** .696** CE7 .546** .459** .635** .426** .587** .648** * * ** ** CE8 .331 .332 .581 .392 .445** .469** ** * ** CE9 .295 .265 .483 .370 .444 .432** CE10 .259 .337* .353* .265 .598** .372* * ** ** ** ** CE11 .357 .420 .580 .438 .608 .496** CE12 .302* .208 .473** .365* .727** .472** CE13 .359* .211 .476** .455** .667** .553** VF1 .239 .184 .416** .269 .694** .401** VF2 .178 .143 .370* .384* .491** .425** VF3 .342* .364* .560** .267 .389** .399** KF1 .164 .158 .367* .323* .681** .435** KF2 .150 .155 .325* .249 .700** .437** KF3 .206 .182 .398* .374* .591** .461** ** KF4 .416 .239 .392* .433** .676** .509** KF5 .325* .326* .536** .397* .628** .605** KF6 .307 .359* .454** .423** .786** .550** KF7 .198 .226 .414** .203 .627** .572** KF8 .216 .188 .403** .233 .680** .290 KF9 .361* .307 .448** .206 .699** .410** KF10 .304 .309 .323* .320* .667** .416** CI1 .316* .253 .360* .359* .693** .397** CI2 .275 .332* .491** .356* .681** .501** CI3 .218 .318* .382* .286 .682** .408** CI4 .223 .230 .392* .155 .579** .445** *Significant at 0.05 level/ ** Significant at 0.01 level
CE7
CE8
CE9
CE10
CE11
CE12
CE13
VF1
VF2
VF3
KF1
KF2
KF3
KF4
KF5
KF6
KF7
KF8
KF9
KF10
CI1
CI2
CI3
.685** .639** .396** .534** .601** .660** .624** .414** .785** .651** .564** .500** .512** .572** .620** .648** .508** .591** .528** .588** .635** .553** .662**
.919** .490** .582** .638** .652** .581** .626** .690** .630** .636** .573** .570** .759** .520** .728** .546** .583** .460** .580** .729** .525** .702**
.502** .529** .638** .672** .511** .588** .630** .623** .644** .547** .565** .686** .527** .732** .517** .515** .410** .528** .679** .444** .694**
.709** .744** .601** .556** .549** .399** .657** .670** .607** .591** .527** .635** .689** .615** .650** .570** .714** .705** .743** .699**
.780** .718** .692** .660** .634** .784** .686** .706** .704** .555** .731** .709** .707** .637** .591** .693** .764** .725** .699**
.891** .706** .748** .641** .885** .888** .884** .781** .753** .846** .841** .833** .749** .637** .800** .828** .753** .812**
.694** .796** .698** .852** .768** .869** .740** .796** .790** .784** .786** .650** .605** .712** .831** .659** .730**
.506** .612** .748** .718** .676** .554** .577** .679** .675** .821** .782** .681** .718** .741** .720** .695**
.591** .739** .735** .793** .673** .762** .616** .695** .645** .484** .437** .609** .757** .631** .582**
.745** .625** .598** .444** .558** .557** .620** .701** .639** .578** .590** .663** .593** .642**
.870** .829** .716** .701** .826** .827** .850** .785** .711** .856** .860** .832** .835**
.843** .781** .756** .813** .905** .828** .755** .714** .823** .910** .792** .857**
.756** .791** .769** .779** .782** .634** .595** .757** .823** .697** .751**
.722** .737** .740** .615** .572** .497** .735** .807** .607** .733**
.729** .801** .617** .595** .535** .581** .847** .604** .644**
.779** .802** .720** .756** .695** .822** .737** .683**
.704** .719** .636** .731** .900** .733** .892**
.827** .721** .731** .782** .750** .690**
.854** .808** .760** .834** .783**
.762** .705** .809** .681**
.833** .884** .869**
.853** .862**
.811**
Table 5: Bivariate correlations between variables measuring LPD enablers
Our sample was larger than 50, which is the minimum sample size recommended by Hair et al. (2006) to carry out a Factor Analysis. We used the following criteria thresholds in our analyses: (i) Kaiser-Meyer-Olkin (KMO) measure (KMO) higher than 0.6, and Bartlett’s sphericity test with significance of 0.001 or less (Meyers et al., 2006); (ii) eigenvalues not smaller than 1.0 in all retained constructs (Field, 2005); (iii) constructs variable loadings of at least 0.4 to be retained in the PC (Tabachnick; Fidell, 2001); and (iv) explained variance of 50% or higher (Tabachnick; Fidell, 2001). Table 6 presents the results of the second step. Factor Analysis results suggests four enablers with eigenvalues larger than 1.0; they explain 82.4% of the total variance. Enablers were: Continuous Improvement, Knowledge Focus, Concurrent Engineering, and Supplier Involvement. Seven variables were excluded from constructs for not meeting the criteria previously described.
LPD Enablers
Continuous improvement (CI)
Knowledge Focus (KF)
Concurrent Engineering (CE)
Variables CE10 CE11 VF1 KF1 KF8 KF9 KF10 CI1 CI2 CI3 CI4 CE13 VF2 KF4 KF5 CE1 CE2 CE3 CE4 CE6 CE8 CE9 VF3
Average
CI
KF
CE
SI
5.28 5.51 5.63 5.89 5.23 6.11 6.30 5.78 5.33 6.11 5.12 5.33 6.26 5.02 4.88 3.86 3.12 3.49 5.43 4.78 5.11 4.77 5.14
.669 .609 .736 .722 .794 .867 .841 .811 .676 .880 .711 .499 .363 .442 .333 .100 .166 .219 .024 .152 .258 .230 .460 18.792 33.61%
.383 .415 .228 .509 .340 .125 .114 .349 .558 .253 .395 .663 .748 .709 .694 .025 -.103 .104 .390 .402 .438 .541 .153 3.080 54.43%
.146 .304 .171 .083 .064 .192 .208 .145 .170 .150 .059 .229 .076 .295 .264 .910 .850 .781 .751 .708 .220 .183 .215 1.551 69.88%
.073 .228 .312 .328 .256 .289 .135 .186 .351 .154 .429 .325 .262 .103 .380 .120 .188 .442 -.060 .150 .760 .710 .733 1.299 82.40%
Supplier Involvement (SI) Eigenvalue Proportion of variance explained (%) Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 8 iterations.
Table 6: Factor Analysis results for LPD enablers (second step)
The third step was intended to assure that variables assigned to each construct indeed represented a unidimensional construct of LPD enablers or LPD problems. Factor Analysis is often used to identify constructs of questions, as we did in the second step, but it may also be used to check the validity of pre-defined constructs (e.g. Dwivedi et al., 2006). Thus, we followed the procedure in Gopal et al. (2003) by conducting Factor Analyses on each LPD enabler and LPD problem individually, for discriminant validity. We validated constructs in which all variables loaded in one single component, as suggested by Dunn et al. (1994). Criteria thresholds in our analyses were the same described in the second step. Applying the criteria led to verifying that all LPD enablers and problems successfully achieved validity. LPD enablers explained a portion of the total variance that varied from 70.68% to 83.31%, while LPD problems displayed values ranging from 55.24% to 77.51%
Finally, we tested all constructs (LPD enablers and problems) for reliability determining their Cronbach’s alpha values. An alpha threshold of 0.6 or higher was used, since it is commonly adopted in the case of newly developed constructs (Meyers et al., 2006). LPD enablers displayed high reliability, with alpha values ranging from 0.894 to 0.970. Table 7 presents score averages and standard deviations for all LPD enablers, along with their alpha values. LPD problems displayed an average alpha value of 0.748, with one construct (“8-Poor operational decision making process”) not satisfying the reliability threshold, and being disregarded in the analyses to follow. Table 8 presents score averages and standard deviations for all LPD problems, along with their alpha values. Standard Cronbach’s Deviation Alpha Continuous improvement 5.68 2.82 0.970 Knowledge Focus 5.44 2.88 0.924 Concurrent Engineering 4.15 3.07 0.894 Supplier Involvement 4.95 2.97 0.897 Table 7: Score averages and standard deviation of LPD enablers, and their corresponding alpha values LPD enablers
LPD problems
Average
Average
Standard Deviation 3.07 3.03 2.93
Cronbach’s Alpha 0.797 0.782 0.744
1-Project leader without formal authority 7.34 2-Achieve true cross-functional integration 6.58 3-Lack of communication and feedback 6.61 4-No simultaneous engineering and 7.90 3.05 0.728 partnership with suppliers 5-Lack of product portfolio strategy 7.79 3.25 0.833 6-LPD performance measurement system 9.55 3.76 0.822 7-No IT integration 9.73 3.38 0.820 8-Poor operational decision making process 5.69 2.33 0.343 (excluded) 9-Lack of discipline 7.58 3.55 0.846 10-Lack of knowledge reutilization 9.47 3.97 0.856 11-Coordination and time-consuming 6.83 2.64 0.684 activities 12-Lack of project vision sharing 6.59 3.08 0.818 13-Inexistence of levelled workload 6.24 2.24 0.650 Table 8: Score averages and standard deviation of LPD problems, and their corresponding alpha values
3.3 Regression models Ordinary Least Squares (OLS) regression analysis with stepwise variable selection was used to describe how LPD enablers are associated with LPD problems. We build individual models
using each LPD problem as dependent variable and LPD enablers as independent variables. In our models, each construct was treated as a variable with outcomes given by their scores which were obtained considering answers provided by respondents on (i) questions related to enabler adoption, or (ii) questions related to problem incidence, depending on the construct under consideration. A total of 64 score outcomes were obtained for each construct; scores provided by each respondent on constructs were aligned. OLS models were tested for normality, linearity, and homoscedasticity (Hair et al., 2006). We examined the models’ residuals to check for normality; linearity was tested with partial regression plots for each LPD enabler; homoscedasticity was visually evaluated plotting standardized residuals against predicted values. Models satisfied all assumptions.
4. Results and discussion Table 9 presents the regression models relating LPD problems and enablers. Dependent variables (LPD problems) are shown in the columns, and independent variables (LPD enablers) in the rows. Only beta coefficients significant at 0.05 and 0.01 levels are shown. Negative beta values indicate that adopting a given LPD enabler mitigates the occurrence of a given LPD problem, while positive beta values indicate the opposite. All regression models were significant (p-values < 0.05) with at least one independent variable acting as a significant predictor of the dependent variable. The lowest adjusted R-squared value was 0.129 for response “5-Lack of product portfolio strategy”, while the highest was 0.401 for response “9-Lack of discipline”.
0.386*
-0.400
-0.570
13-Inexistence of leveled workload
-0.556
12-Lack of project vision sharing
-0.796**
11-Coordination and timeconsuming activities
**
10-Lack of knowledge reutilization
10.890**
*
10.496**
14.495**
12.807**
12.842**
14.535**
10.070**
11.014**
8.715**
**
**
**
**
**
**
-0.451**
*
-0.520
-0.866
0.585**
9-Lack of discipline
4-No simultaneous engineering and partnership with suppliers
7.951**
**
7-No IT integration
3-Lack of communication and feedback
9.894**
6-LPD performance measurement system
2-Achieve true cross-functional integration
9.947**
5-Lack of product portfolio strategy
1-Project leader without formal authority Constant Continuous improvement Knowledge Focus Concurrent Engineering Supplier Involvement F-value p-value Adjusted R2
-0.907
-0.944
-0.928
-0.556
-0.763
0.384*
-0.368* 10.756
0.004
9.844
11.109
7.214
21.398
0.000
28.500
16.117
12.858
19.870
12.376
0.000
0.004
0.000
0.002
0.010
0.000
0.000
0.000
0.000
0.001
0.000
0.001
0.194
0.129
0.327
0.327
0.401
0.274
0.229
0.321
0.221
0.317 0.163 0.377 *Significant at 0.05 level/ ** Significant at 0.01 level
Table 9- OLS regression models relating LPD enablers and problems
Continuous Improvement displayed negative beta values in all regression models; thus, empirical evidence gathered in this research suggests that this enabler is the most important if the objective is to reduce problems associated with LPD. Such result is consistent with findings in previous studies (e.g. Karim and Zaman, 2013; Liker and Morgan, 2011; Flores et al., 2011; Kreafle, 2011; Ward, 2007). Continuous Improvement presented a particularly large negative association with LPD problems “9-Lack of discipline” (beta = -0.944) and “10-Lack of knowledge reutilization” (beta = -0.928). One of the key elements for reaching continuous improvement is associated with the reflection activity inherent to learning processes, denoted by the Japanese word Hansei (Liker, 2005). Such element demands high discipline both to avoid missing learning opportunities and to deeply understand the achievements that derive from improvement initiatives (Kumar and Wellbrok, 2009). Moreover, the result is consistent with findings in David and Goransson (2012), who mention the importance of a holistic approach which allows the creation of a more dynamic and integrated process, with overlapping stages.
Another fact that might explain the strong impact of enabler Continuous Improvement on LPD problems is the paradox between efficiency and creativity, as described by Sehested and Sonnenberg (2011), Fowler (2009), and Hoque et al. (2007). According to those authors, there are different needs of creative freedom at different stages of the development process. In the early stages, such as during concept generation, the rate of innovation is high, whereas too much innovativeness in later stages only causes disruption and delays. Therefore, execution discipline should be increasingly dominant as the development process progresses. Contrary to popular belief, results show that LPD enabler Knowledge Focus does not have a significant impact on any LPD problem. Such result is somewhat surprising in light of the conventional wisdom on the difficulty of improving any development process without a minimum adoption of mechanisms for capturing, representing and communicating knowledge within the organization. From a modelling perspective, the primary output of most product development processes is information (Reinertsen, 2009). Knowledge focus in product development processes is founded on information, transferred in the form of interim deliverables. According to Yang et al. (2011), Ward (2007), and Womack and Jones (2003), information is only valuable if useful; that is, valuable information reduces the risk of producing an unsatisfactory product or performing a superfluous development activity. The result above is also consistent with the findings in Qahtani and Ghoneim (2013), who describe the case of a Saudi Islamic University. Despite the high level of knowledge inherent to the scenario the authors mention that, unless a collaborative and encouraging environment is established, knowledge will not improve product development capability. Nonaka and Takeuchi (1995) argue that organizational knowledge is created when tacit knowledge is communicated and shared within the organization. For that purpose, the conversion of tacit knowledge into explicit knowledge is required so everyone can understand and learn, minimizing, thus, communication and feedback problems. Nevertheless, there are other
factors that contribute to building knowledge – e.g. strategic employee rotation between functions or projects, and free access to company information – that need further investigation in order to better explain such correlation (Shankar et al., 2013; Leon and Farris, 2011; Zhen et al., 2011). LPD enabler Supplier Involvement was negatively associated with problem “3-Lack of communication and feedback”. This result emphasizes the importance of companies to enhance communication and cooperation across functions, stressing the need to develop a network of lateral linkages, involving both suppliers and customers early on in the development process, as noted in studies by Trott (2012), and Cooper and Edgett (2008). On the other hand, Supplier Involvement did not present significant association with problem “4No simultaneous engineering and partnership with suppliers”, which is contrary to the considerations in Nepal et al. (2011). This result indicates that although such enabler has some impact on the capability of improving partnership with suppliers, the direction of the effect is not always as predicted. However, the findings are coherent with studies by Leon and Farris (2011), and Karlsson and Ahlstrom (1996), who suggested that involving suppliers without a clear understanding of exactly which practices are more likely to be effective for the organization may lead to wrong investments and waste of resources; as a consequence supplier involvement may become a hindering factor in the implementation of LPD. Finally, regarding the enabler Concurrent Engineering results present an opposite relationship with LPD problems than the conventional wisdom would suggest. Our findings demonstrate that the higher the enabler’s adoption more likely the company is to present LPD problems “1-Project leader without formal authority”, “3-Lack of communication and feedback”, and “7-No IT integration”. Further, the influence of that enabler on the frequency of LPD problems seems to be less pervasive than the literature indicates (Wu et al., 2014). Such finding may be indicative either of the limited applicability of the construct in product
development or the lack of knowledge as to how it should be applied, which corroborates statements by Khan (2012), and Khan et al. (2011). A strategic approach to product development is employed by Toyota, which allows projects to be used to enhance the development of solution sets in parallel (Kennedy et al., 2008). Moreover, the management of interdependent activities is not a trivial task. One complicating factor is that the structure of the task network, such as scope and time, can itself be difficult to define (Kennedy, 2013). Thereby, the differentiation between set-based concurrent engineering and conventional development process is a potentially misunderstanding issue among companies, as argued by Browning (2003), who claims that the structure of the activity network determines the value trajectory of the LPD process, and, thus, its efficiency and effectiveness.
5. Conclusion 5.1 Contribution to theory This research presents some important theoretical contributions to the state-of-the-art on LPD. We propose a new approach to identify enablers that may contribute to the improvement of the product development process. The specialized literature on product development frequently identifies enablers through the analysis of organizations that successfully manage their product development projects; e.g. Garver (2003), Song and Noh (2006), Barczak et al. (2009), Barczak and Kahn (2012), Kahn et al. (2012), and Markham and Lee (2013). Being organization specific, such analyses may lack reproducibility; i.e. practitioners may not always verify the positive effects reported in the literature in their companies since they do not share the same characteristics of the organizations in which the benefits of adopting the enablers were originally observed (Bessant and Francis, 1997; Markham and Griffin, 1998; Ozer and Chen, 2006).
Our approach identifies lean enablers that may improve the product development process focusing on specific characteristics of the organization in which they will be implemented. That is accomplished through the identification of LPD problems that may be tackled using a particular set of enablers. Using our proposition, researchers may choose the construct of enablers with the highest likelihood of mitigating problems that appear frequently in the product development process of the company under analysis. A set of 23 different enablers that represent the operational space surrounding lean product development were identified and grouped into 4 main constructs, contributing to establish an operational complement to the conceptual definition of LPD. We also provide a deeper understanding on how LPD enablers can mitigate the presence of LPD problems, allowing companies undergoing lean implementation to better manage their product development processes. We argue that, viewed individually, enablers may not benefit operations involved in an LPD process, but together, grouped as constructs, they are likely to mitigating certain problems. That is the case of constructs Continuous Improvement and Supplier Involvement, which jointly enable companies to address product development problems. Moreover, some results demonstrate that the association between enablers and problems may not be as suggested in the existing literature. For instance, in opposition to evidences available in the literature there is a positive relationship between construct Concurrent Engineering and the occurrence of LPD problems. Such finding may suggest that Southern Brazilian companies may not be properly implementing the construct, thus facing more problems in communication, IT integration, and project leadership along the product development process. 5.2 Contribution to practice We presented empirical evidences on how LPD enablers and problems are associated. For instance, enabler construct Continuous Improvement was negatively associated with the
occurrence of all LPD problems in companies undergoing lean implementation. Supplier Involvement was negatively associated with only one LPD problem. Enabler construct Knowledge Focus does not seem to affect LPD problems in those companies, and the efforts to implement Concurrent Engineering are in fact increasing three LPD problems (“1-Project leader without formal authority”, “3-Lack of communication and feedback”, and “7-No IT integration”). Overall, evidences presented here suggest that the studied LPD enablers, viewed in the literature as fundamental for successful lean implementation, significantly affect the likelihood of LPD problems’ occurrence. For example, if a company is facing “3Lack of communication and feedback”, it is recommended the adoption of enablers Continuous Improvement and Supplier Involvement to mitigate that problem. 5.3 Limitations and future research There are some limitations due to the nature of the sample used in the survey that must be highlighted. First, respondents were mostly from companies located in the South of Brazil; their answers might thus be linked to regional issues. That may be relevant since recent data from 24 countries suggested that lean implementation is highly dependent on cultural aspects (Kull et al., 2014). Thus, as this limitation restricts the results to this geographic condition it also increases the certainty that they apply to those companies, and to others in regions with similar characteristics. Regarding the proposed objective, this investigation empirically validated the association between LPD enablers and problems. Due to poor evidence in literature on the likelihood of any interdependent influence, further investigation would add more information and help to establish a holistic perspective about the problem, identifying interactions between LPD enablers and their influence on LPD problems. Such extension would require a more elaborate data collection and analysis.
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