ANALYSIS AND CONTROL OF ISSUES THAT DELAY PHARMACEUTICAL PROJECTS

. Every project will have certain objectives and service levels to be achieved. The success of a project depends on several dimensions like time, cost/budget, quality, etc. and managing a project involves completing the project within time, within budget and with quality to satisfy the users. Because of the significance of health, pharmaceutical companies realized the importance of project management methods and techniques to make available the life saving drugs in time to the needy patients and hospitals. In literature, there is meager information about pharmaceutical project management oriented towards analysis of issues and factors that contribute to the failure or success of projects. This study attempts to analyse different issues that contribute to time delays in pharmaceutical product­based projects, group them under a finite set of prominent factors and identify remedial measures to control those delays. The feedback of project people of some big pharmaceutical firms of Indian sub­continent was collected for this purpose. Exploratory factor analysis (EFA) has been used to reduce the reasons for time delays to a limited number of prominent factors and the EFA model has been further examined by confirmatory factor analysis (CFA) for its validation. Remedial measures under each factor of time delays have been gathered and a framework designed to mitigate the time delays in pharmaceutical projects. The derived factors that delay the pharmaceutical projects include resource, monitoring & control, scheduling and planning problems. Important remedial measures like blended resource approach, estimation and forecast of shortage of labour and skills, regular quality training, etc. have been recommended.


Introduction
Usually any project ends up with either success or failure in achieving all the objectives in a satisfactory way. According to PapkeShields et al. (2010), project management evolved over the past two decades as both researchers and practitio ners have attempted to identify the causes of project failure and the various factors that lead to success. Meredith and Mantel (2012) reported that there are three major forces involved for the development of new methods in project management -(i) the exponential expansion of human knowledge; (ii) the growing demand for a broad range of complex, sophisticated customized goods and services; and Hammer, Champy 2003;Zhang et al. 2003;Evans 2005;Ward 2014;Mejillano et al. 2007) demonstrated that most of the projects fail either in meeting time and budget goals or in satisfying customer and/or company expectations. According to Tukel and Rom (1998), completion of a pro ject by scheduled due date was treated as one of the most frequently used measurements of project success. Time delays will be so severe that they can reap up in any task/ activity/phase of a project and reinforce in the connected stages. Hence considerable attention is needed for their control and this is becoming one of the big challenges for project people. This study focuses on time delays in pro jects.
Since health needs have top most importance in human society, both public and private pharmaceutical companies have taken up the challenge of providing affordable medici nes to more people around the world at lower costs. Despite aggressive application of good tools, methods and techniqu es, many pharmaceutical companies have been struggling to make their projects more economical and schedule oriented to achieve maximum service levels. Most of the major and big pharmaceutical companies are committed to provide affordable and innovative medicines by focusing on customer requirements and delivering the products at right time. Currently, these companies have been aggres sively using project management techniques to complete the projects in time and within budget and maintain their competitive advantage by meeting the market demands. The very nature of drug development cycle or product de velopment through its different stages and the competition prevailing amongst the pharmaceutical companies by en suring an early product launch to capture the market are just a few reasons for the growing importance of project management in pharmaceutical industry.
The pharmaceutical industry is unique in its procedures and methods of manufacture since the integrity of its pro ducts must be ensured by three main functions -current good manufacturing practices, quality assurance, and qua lity control (Cole 1998). According to Hwang et al. (2008), pharmaceutical projects often demand a tailored benchmar king approach because of their intensive qualification and validation procedures. They developed and validated a ben chmarking framework for pharmaceutical capital projects by taking into consideration three major drivers -schedule, cost and dimensional performance. In pharmaceutical in dustry, competitive advantage and increased sales revenue would be achieved by reduced timetomarket (Nalewaik 2005). In the management of pharmaceutical projects, the re has been a growing interest in finding ways and means to control delays in making drugs and taking to market in time. For example, Yang and Yau (2013) developed a computerbased method that integrates two processba sed schedule delay analysis methods simultaneously based on information flow analysis. The present study took the support of statistical factor analysis and applied both explo ratoratory and confirmatory factor analyses on the collected data to analyse time delays in pharmaceutical projects.
Statistical factor analysis is a multivariate statistical met hod used to identify common underlying variables called factors within a larger set of measures. There are two statis tical approaches, namely, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) that are used to examine the internal reliability of a measure. EFA is helpful in the initial stages of analysis to explore interrelationships among sets of variables and reduce them to a few factors so as to arrive to a measurement model. CFA is used in the later part of analysis to test the model derived from EFA, by testing its "goodness of fit" and conformity of the fac tors. Several researchers applied statistical factor analysis to several fields including project management for analysis of data collected on different issues. Bryson and Bromiley (1993) used factor analysis to identify major factors from various variables describing the context of the projects, their planning and implementation processes and project outco mes. They reported that a number of contextual variables strongly influence project planning and implementation process, and indirectly influence the outcomes through planning and implementation process. They added that both process and contextual variables affect project outco mes directly. Shi and Wright (2001) used both exploratory and confirmatory factor analyses to examine and valida te factor structures of international business negotiator's profile. They used the commonly accepted goodnessoffit indices as reported by Jöreskog and Sörbom (1993) to assess the overall fit of the measurement model. Sureshchandar et al. (2002) identified the critical factors of service quality from the customer perspective, developed an instrument to measure customerperceived service quality based on those factors and with the help of CFA, they empirically tested and validated the instrument. Wang and Ahmed (2004) developed an organizational innovativeness construct and assessed its validity and reliability using confirmatory factor analysis. This study, after identifying the prominent factors that delay the pharmaceutical projects from EFA, attemp ted to test the "goodness of fit" of the model and validate it further using CFA. Chan and Tam (2000) examined the underlying factors affecting the quality of building project and found that pro ject management action by the project team was the most powerful predictor of client's satisfaction with quality. Li et al. (2005) investigated into the relative importance of various potential critical success factors for construction projects in UK with publicprivate partnerships (PPPs) and private finance initiative (PFI) and identified the three most important factors out of them. Doloi (2009) applied factor analysis to identify the prominent factors that influence contractors' performance in construction projects. Aubry et al. (2010) made a survey on transitions of project mana gement offices (PMOs), which are dynamic organizational entities and found that the transition of a PMO from one configuration to the next is not a question of being right or wrong. Nwachukwu and Emoh (2010) analysed materials as an integral part of direct and indirect factors that hinder project management success of public and private sector construction in Nigeria. They used factor analysis to derive potential factors. Jou et al. (2010) used factor analysis to identify the key elements that affect new product develo pment (NPD) in a semiconductor equipment manufactu ring firm, whereas Thomas and Vilakshan (2011) used for software project risk management. Much of the literature concerned with projects in various fields except pharma ceutical industry. Hence this study attempted to examine and validate important timedelay factors in pharmaceuti cal projects and as a first step the study focused on product based projects only.
The objective of this work is to come out with a fra mework to control time delays in pharmaceutical product projects in order to help the project people to meet the plan ned service levels. It has been attempted to analyse in depth the various issues that contribute to time delays. By perfor ming surveys in pharmaceutical companies and interacting with experienced project people, the study explored several issues that hamper achievement of service levels in terms of time in pharmaceutical projects. To derive the major factors from the feedback data, exploratory factor analysis was used and to validate the constructs thus derived, confirmatory factor analysis was used. Taking the results to the notice of experienced pharmaceutical project managers, valuable information on different remedies to control time delays was collected. Finally, a framework was developed to give an overall picture of time delays and remedial measures to control them in pharmaceutical projects.

Research methodology
Industries like pharmaceutical, biotech, life sciences and R&D are quality and schedule driven sectors marked by strong competition to launch their product first in the mar ket and capture high marginal profits for their survival and future growth. In the light of treating reduced timetomar ket as one of the important factors to achieve competitive advantage and increased sales revenue in pharmaceutical projects (Nalewaik 2005), this study was initiated to exa mine the reasons behind not achieving service levels in pharmaceutical projects in terms of time dimension.
To study the importance of time dimension in achieving the service levels of pharmaceutical projects, a simple ques tionnaire was distributed to managers at different levels dea ling with projects in four big pharmaceutical companies -A, B, C and D (to maintain confidentiality, the names of the companies are suppressed) in India. They were requested to express their perception on importance of time dimension in achieving the service levels of projects on a scale of 0-5, '0' representing 'not important' and the remaining scores representing importance of time proportionately with '5' the highest importance. A total of 137 managers responded and the weighted average was found to be around 3.5, with no one opting for zero importance. This result provided good support for the fact that time is an important dimension in the success of pharmaceutical projects. In continuation of this, another survey was taken up simultaneously in the companies to check the status of different projects during the financial year 2006-07 in terms of time. Four categories of status of 91 projects were considered -completed in time, completed with delays, workinprogress projects as per schedule, and workinprogress projects with delays. It was observed that about 70% of the projects were completed in time and remaining ones completed with delays in all the four quarters. Most of the workinprogress projects were moving as per schedule in the first two quarters, and in the remaining two quarters, the percentage was coming down with more delays.
The above two preliminary studies motivated the pre sent research work by establishing a strong base to proceed further for a detailed analysis of the reasons behind time delays and thereby finding remedies to mitigate them in improving the service levels of pharmaceutical projects. For this, a detailed survey was conducted in the four phar maceutical companies to collect useful feedback about the reasons behind time delays in the projects.

Survey instrument
As a first step, facetoface interviews were conducted with several pharmaceutical project managers, who directly involved in managing the projects. This attempt led to preparation of a draft list of reasons behind success and failure of projects in terms of time. The list is further refi ned by interactions with a group of selected senior project managers having long experience with pharmaceutical projects. Based on these interactions, a final list consisting of a total of 13 reasons behind time delays in pharmaceu tical projects was prepared. To get the feedback of people on the significance of each of these 13 reasons for time delays, a questionnaire was developed and circulated to different people who are working in those four companies and are all internal project stakeholders only. They were requested to fill the questionnaire by assigning a signifi cance level to each reason on a Likert scale of 1-5, which ranges from 'not at all significant' (assigning a score of 1) to 'most significant' (score of 5) and the remaining ones representing the relative significance. All the reasons are listed in Table 1 and the questionnaire is given in Table  AI of Appendix.
To analyse the feedback data, statistical factor analysis, both exploratory and confirmatory, has been used. For exploratory factor analysis of survey data, the method of principal component analysis is used along with Varimax rotation method to reduce the variables to a minimum number of factors. To check the adequacy of the sample, KMO and Bartlett tests are conducted. Based on eigenvalues and proper loadings of the variables, a finite set of factors has been selected. Using the feasible value of Cronbach's α (alpha), the reliability of each factor is checked. The con formity of the factors thus extracted from factor analysis is further examined with the help of confirmatory factor analysis. Thus, both EFA and CFA are used to extract reliable and well validated factors from the feedback data collected on various reasons related to time delays in pharmaceutical projects.

Data collection
Out of a total of 170 questionnaires distributed, 150 emplo yees from the four big pharmaceutical companies respon ded with proper and clear feedback. Hence the response rate was above 88%, which is reasonably very good. 17% of respondents were senior project managers, 11% project managers, 11% senior managers, 18% managers, 22% depu ty managers and remaining 21% were assistant managers. Regarding length of experience, 21% of the respondents had above 10 years, 26% had 6-10 years, 30% had 2-5 years of experience, and remaining 23% had about 2 years of experience. Regarding companywise responses, 24% contribution was from company A, 29% from B, 28 % from C and the remaining 19% of respondents from company D. All these demographic details are given in Table 2.

Factor extraction
KeiserMeyerOlkin (KMO) measure of sampling adequacy was found to be 0.593, which is very close to the required merging of 0.6 (Alhaji et al. 2011) and also mat ching the requirements reported by Hair et al. (1995) and Tabachnick and Fidell (2007). In addition, the Bartlett's Test of Sphericity produced a χ 2 (Chisquare) of 1937.934, degre es of freedom of 78 and a significance level of 0.000, which is less than 0.05. These results indicate the significance of the sample. Using the latent root (eigenvalue) criterion, five factors were identified with eigenvalues greater than 1.0. It is found that all the five factors altogether account for about 83.8% of the total variance, with first factor (F1) of 22.6%, second factor (F2) 21.8%, third factor (F3) 15.8%, fourth factor (F4) 13.8 % and fifth factor (F5) of 9.8% of variance. After rotating of the factors by Varimax method, the degree of association (correlation) of each variable with each factor was identified. A cutoff for all loadings followed here was 19 above 0.40 (Conway, Huffcutt 2003;Mathur et al. 2007). Each of Factor 1 and Factor 2 has three variables, whereas Factor 3 and Factor 4 have two variables with significant loadings greater than 0.9. Factor 5 has 3 variables with loa dings of 0.646, 0.411 and 0.800 respectively.

Factor reliability
The internal consistency of a measuring instrument is es tablished by using a reliability coefficient, Cronbach's α (Cronbach 1951). Nunnally (1988) considered Cronbach's α value of 0.6 and 0.7 or above as the criteria to demonstrate internal consistency of new scales and established scales respectively. In the most reliable form, the coefficients should be as close to 1.00 as possible (Reinard 2006). In the present work, the Cronbach's α was derived for each of the five factors as 0.977, 0.966, 0.974, 0.718 and 0.273. Table 3 lists the communalities and factor loadings of all the 13 reasons, whereas Table 4 gives the eigen values, per centage of variance explained and reliability in terms of Cronbach's α of all the five factors extracted. It is found that the fifth factor (F5) attributed very low value for both percentage of variance explained (9.8%) and Cronbach's α (0.273) and hence it cannot be treated as a pro minent and reliable factor. Therefore, the first four factors have been treated as significant factors, based on their rea sonably and relatively high values of percentage of variance explained and reliability. In addition, all these four factors attained a cumulative percentage of total variance explained as 75.76, implying a satisfactory degree of construct validity. In addition, these factors have variables loaded with higher values, greater than 0.9.
Depending on the type of variables grouped under each of the four factors, proper naming has been done for them. Factor 1 is named as 'Resource problems' , Factor 2 as 'Monitoring & Control problems' , Factor 3 as 'Scheduling problems' and finally Factor 4 is named as 'Planning pro blems' . The factor of resource problems has contribution from the issues of improper resource mapping, nonavai lability of skilled labour and delays in drawing approvals. Similarly, 'Monitoring & Control problems' has contribu tion from wrong selection of consultants, improper designs and improper followups. Improper schedules and delays in order processing contribute to the factor of scheduling problems. Planning problems is loaded with the issues of improper planning and project scope creep. Table 5 lists all the four named factors along with the associated time delay issues (variables).

Confirmatory factor analysis
Exploratory factor analysis (EFA) is concerned with the question of how many factors are necessary to explain the relations among a set of indicators and with the estimation of the factor loadings, whereas confirmatory factor ana lysis (CFA) is concerned with parameter estimation and tests of hypotheses regarding, for example, the number of factors underlying the relations among a set of indicators (Pedhazur, Schmelkin 1991). CFA is a type of factor analysis conducted to test hypotheses or confirm theories about the factors one expects to find and it is a subtype of structural equation modeling (Vogt et al. 2008) and is the initial step of a complete test of a structural model (Hair et al. 2006). According to Schumacker and Lomax (2004), CFA is com monly used to confirm that the indicators sort themselves  into factors corresponding to how the research has linked the indicators to the latent variables. While analyzing the measurement for selfdirected learning, Harvey et al. (2006) used CFA to check the reliability of the results of explo ratory factor analysis (EFA) and the responses in the case of students' selfdirected learning. Since EFA resulted in a finite number of significant factors that are required to explain the intercorrelations among the measured varia bles, and CFA is more appropriate than EFA (Bentler 1995), the present study applied CFA to confirm the results of EFA.
In CFA, a model is built based on a priori information about the data structure in the form of knowledge derived from previous studies with extensive data. The confirmatory factor models will be displayed as path diagrams, where squares represent the observed variables, ellipses represent latent concepts (constructs or factors) and circles represent any errors in correlating variables to the respective cons tructs. Singleheaded arrows show the direction of assumed causal influence and the curved doubleheaded (bidirectio nal) arrows represent covariance between two latent varia bles, that is, correlation among the paired dimensions. Each indicator reflects (has a loading on) one factor only and the errors are said to be not correlated (Pedhazur, Schmelkin 1991). Taking into account the four factors derived from EFA, the path diagram of time delays in pharmaceutical projects is developed as shown in the Figure 1. In the path diagram, e1 to e10 represent the errors in correlating varia bles to the respective factors.
To support the results of EFA, there are several clas ses of model fit indexes in CFA and Marsh et al. (1996) recommended that individuals utilize a range of fit indices. These classes of fit indices include discrepancy functions (chisquare test, relative chisquare, and RMS); comparing the target model with the null model (CFI, NFI, TFI, and IFI); information theory goodness of fit measures (AIC, BCC, BIC and CAIC); and noncentrality fit measures (NCP). According to Jaccard and Wan (1996), usage of indices from different classes would overcome the limita tions of each index. Various authors (Bentler, Bonett 1980;Hoelter 1983;Jöreskog, Sörbom 1993;Bollen 1989;Steiger 1990;Browne, Cudeck 1993;Byrne 1994;Hu, Bentler 1999;Schumacker, Lomax 2004;etc.) proposed different fit indi ces and recommended the cutoff values to them to assess the acceptance of model.
According to Child (2006), three common measures of overall goodness of fit are a chisquare (χ 2 ) measure, go odness of fit index (GFI) and root mean square residual (RMR). Carmines and Zeller (1990) suggested the ratio of χ 2 to df (degrees of freedom) of 2 or 3 as criterion of fit. This relative χ 2 should be less than 2 or 3 (Kline 1998;Ullman 2001). According to Hair et al. (2006), the recommended values for relative χ 2 is 3.0 or below. In this study, the χ 2 value is derived as 82.271 and df as 29 and hence the relative χ 2 is found as 2.837, which is within the acceptable range as spe cified in the literature reports. RMR and GFI fall between 0 and 1 with GFI to be as near to one as possible, whereas RMR as near to zero as possible (Child 2006). According to Byrne (1994) and Hair et al. (2006), GFI value should exceed 0.90, the adjusted goodness of fit index (AGFI) should be 0.8 or above (Hair et al. 2006) and Normed fit index (NFI) should be greater than 0.90 (Byrne 1994) or 0.95 (Hu, Bentler 1999;Schumacker, Lomax 2004) and both GFI and AGFI may range from 0 to 1 (Pedhazur, Schmelkin 1991). Cole (1987) stated that values greater than 0.9 and 0.8 for GFI and AGFI Fig. 1. Path diagram for confirmatory factor analysis of time delays respectively, usually indicate good fit. In the present study, the derived values for RMR, GFI and AGFI are 0.047, 0.911 and 0.832 respectively, which are all within the acceptable ranges. Hence overall goodness of fit has been established for the model.
The unidimensionality of the measure represents the existence of a single construct underlying a set of measures. According to Anderson and Gerbing (1991), the unidimen sionality of the measure is a highly mandatory condition for checking construct validity and reliability. According to Bollen (1989), a comparative fit index (CFI) value of 0.85 represents progress and should be acceptable. CFI of 0.90 or above represents strong evidence of unidimensionality for a model (Byrne 1994;Hair et al. 2006). In this study, the derived value for CFI is 0.972, which strongly supports the unidimensionality of the measurement model. Table 6 lists all the derived values of measures of goo dness of fit and unidimensionality and these results well confirm the goodness of model fit and validate the unidi mensionality of the model. Hence CFA provided significant support for the grouping of reasons behind time delays in pharmaceutical projects under the said four major factors.

Interpreting results
All the extracted and validated factors are described below in the light of grouped reasons: 1. Resource Problems: In pharmaceutical projects, pe ople work for multiple projects at a time, and this situation leads to keeping same people on more than one project. Such multitasking by single resource keeps lot of pressure on that resource. When suffici ent labour with skill set matching the requirements of projects is not available, many tasks will be kept pending and those completed tasks would have poor quality. In such case, rework by other skilled labour will be awaited leading to time delays. While doing detailed engineering in projects, external consult ants would send execution drawings for approvals of project team members who spread across many functional departments or divisions. The issues of multitasking, integration failure among the project team members, etc. lead to delays in approving the drawings.

Monitoring & Control problems: Selection of low
profile consultants due to cost cutting procedures and improper negotiations by project team needs lot of followups to get the drawings, detailed Bill of Quantities (BOQ), etc. in time from the consul tants. When such followups are absent, lot of time delays happen. And at the same time, improper de signs of equipment, for example selection of MOC (Material of construction), design parameters, etc. lead to time delays in projects. This is because of reordering with proper designs. Similarly, when important activities like delivery of long lead equi pment by the vendors, services from external agen cies, etc. are not properly tracked and controlled, the project or the concerned project tasks may be delayed.

Scheduling problems: When microlevel (individual)
and macrolevel (combined) activities are not pro perly scheduled according to the interconnections and concurrence among different functional de partments, projects would face delays. Due to lot of procedural requirements in ordering process, like preparation of user requirement specifications (URS), collection of quotations from multiple ven dors, technical bid analysis (TBA) and final nego tiations, etc., the procurement cycle period would be enhanced and becomes a source of time delays. 4. Planning problems: Improper planning of manpo wer, equipment, resources, etc. in projects contri butes to time delays. In most of the pharmaceutical projects, continuous change requests from users would be common. They enhance the scope of the project further and further. Such scope creep would require additional time to accomplish all the added requirements. Another round of personal interactions and brainstor ming exercise with experienced project managers and study of various projects in the four big Indian pharmaceutical companies helped to prepare a list of remedies to control time delays. These remedies stood as great support to design the framework to improve service levels. The remedies thus collected are discussed in the following sections.

Remedies to control time delays
Based on the derived factors and their loaded reasons that contribute to time delays, a list of remedies to control time delays was prepared with the help of personal interactions established with the experienced and senior project managers working in the five big pharmaceutical compa nies. Figure 2 provides a framework that shows the contri butions and remedies to time delays in pharmaceutical projects and the following paras describe those remedies.
-Blended resource approach: It is a pool of talented pe ople from different disciplines of projects working for multiple projects as per the individual project requi rement which will be useful for optimum utilization of resources. -Estimation and forecast of shortage: Estimation and forecast of skilled labour at regular intervals will be used to maintain required strength at any point of time and thereby avoiding any shortages of required skills and skilled labour.
-Regular quality training: Regular quality training pro grammes in special areas improve the skill set of the people, who can show better performance in complex project activities. -Common talent pool: Maintaining a pool of talented people across the organization, can be useful for de veloping the required resources on multiple projects wherever necessary. -Coordination among departments: Consultants submit the project related drawings for approval of multiple departments, which have stake in the con cerned projects. In such cases, instead of individual study of the drawings by each department, it would be better to form a team among departments to study change management plan with a positive approach could be adopted by involving all the project stake holders and incorporating their needs thorough out the project life cycle. To avoid any project disputes, it is important to always seek approval of changes from users and communicate them to concerned team members in a timely manner.

Conclusions
There are several factors that contribute to the success or failure of a project and every project will be evaluated on the basis of some important dimensions, including time, cost, and quality. All these issues should be properly analysed and handled while managing the projects. Like other in dustries, pharmaceutical industry realized the importance of project management to meet the agreed service levels and many big pharmaceutical companies have been aggressively adopting various project management methods, tools and techniques. Meager research has been done in the direction of project management in pharmaceutical industry. In order to fill the research gap in literature on pharmaceuti cal project management and to have an indepth analysis of the reasons that delay the projects, this study took the help of statistical factor analysis including both exploratory and confirmatory factor analyses. Four big pharmaceutical companies in the Indian subcontinent were selected for survey and feedback data of the internal project people was collected. The measurement model based on the extraction of four reliable factors from the exploratory factor analy sis has been examined for its goodness of fit and further validation by confirmatory factor analysis. The results are quite satisfactory. The timedelay factors include resource, monitoring & control, scheduling and planning problems and each factor groups certain reasons. Interactions with the senior project managers of pharmaceutical projects helped to collect useful information on various possible remedial measures to mitigate the time delays in projects. Based on the results of factor analysis and interaction with senior project people, a framework has been designed to control time delays in pharmaceutical projects. These fin dings provide valuable support to the pharmaceutical in dustry to control the time delays. This study focussed on time dimension in meeting the service levels of productbased pharmaceutical projects. In addition to time dimension, there will be many other dimensions that can further improve the service levels of pharmaceutical projects. The data and information requi red for the present study was collected from four big phar maceutical companies in Indian subcontinent only. Future research would cover more number of big companies to improve the sample size and scope of analysis. In addition to productbased projects, other types of projects like capital projects in pharmaceutical industry would also be survey ed. Next, other dimensions like cost and client satisfaction would also be considered so as to enhance the analysis useful to improve the service levels of pharmaceutical projects.