A review of analytical models, approaches and decision support tools in project monitoring and control

JPMA-01691; No of Pages 8

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ScienceDirect International Journal of Project Management xx (2014) xxx – xxx www.elsevier.com/locate/ijproman

A review of analytical models, approaches and decision support tools in project monitoring and control Öncü Hazır TED University, Faculty of Economics and Administrative Sciences, Ziya Gökalp Caddesi No. 48, 06420, Kolej, Çankaya, Ankara, Turkey Received 30 January 2014; received in revised form 29 August 2014; accepted 9 September 2014

Abstract This paper reviews the problems, approaches and analytical models on project control systems and discusses the possible research extensions. We focused on literature in Earned Value Analysis (EVA), optimization tools, and the design of decision support systems (DSS) that will contribute to helping project managers in planning and controlling under uncertain project environments. The review reveals that further research is essential to develop analytical models using EVA metrics to forecast project performance. It also suggests that DSS should be model driven, function as early warning systems and should be integrated to commercial project management software. © 2014 Elsevier Ltd. APM and IPMA. All rights reserved. Keywords: Project management; Project monitoring and control; Earned Value Analysis; Decision support systems; Project management software

1. Introduction Projects are one of the most important components of today's organizations. In almost any firm and sector, organizations are becoming more and more project based. This may be perceived as a consequence of the contemporary management practices that have transformed organizations from hierarchical to more flat ones. As they have receded from a hierarchical and isolated nature, projects have become the medium for interdepartmental or even inter-organizational activities. Another factor that reinforces the rise of projects is the increasing competitive pressure. Competition, becoming fierce day by day, leads the firms to seek excellence in accomplishing the tasks. This pursuit of excellence in management has increased the importance of coordination, monitoring and control functions. From this perspective, project based organizational structures support accomplishing specific purposes/outcomes, focusing on responsibility and authority, ensuring better control and coordination, and facilitating better communication and customer relationships (Meredith and Mantel, 2011). E-mail address: [email protected].

In order to ensure these gains and the accomplishment of goals even under the threat of various uncertainties (Aytug et al., 2005; Herroelen and Leus, 2005), employing effective project monitoring and controlling systems has become essential in project based organizations (Shtub et al., 2005). Considering this need and importance, in this paper we focus on development of these systems, their content and scope. Specifically, we investigate models and algorithms that will support managerial decision making and constitute the foundations of these systems. To put formally, a project monitoring and control system works to minimize the deviations from the project plans and consists of identifying and reporting the status of the project, comparing it with the plan, analyzing the deviations, and implementing the appropriate corrective actions. Hence it includes the set of policies, methods and tools that would ensure the achievement of the project targets. An effective system should clearly define the following policies: (a) monitoring policy: what, how, where, when and by whom to monitor, (b) intervention and control policy: what, how, where, when and by whom to prevent, intervene and correct.

http://dx.doi.org/10.1016/j.ijproman.2014.09.005 0263-7863/00 /© 2014 Elsevier Ltd. APM and IPMA. All rights reserved. Please cite this article as: Ö. Hazır, 2014. A review of analytical models, approaches and decision support tools in project monitoring and control, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2014.09.005

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Mathematical modeling is one of the means to formulate and analyze these policies. It has been used to investigate various project management problems and literature review papers were published (some of them are by Herroelen (2005), Herroelen and Leus (2005) and Kolisch and Padman, 2001). These reviews cover studies that address a wide range of managerial problems (time scheduling, resource allocation, quality assurance). Project monitoring and control from mathematical modeling perspective, conversely, have not received sufficient scholarly attention. Accordingly, we aim to address this lacuna in the literature. Different from its predecessors, this research presents a review with a narrower scope. A more in-depth analysis of approaches and models on monitoring and control systems is performed. Furthermore, an initiative approach for designing model-driven DSS for effective project monitoring and control is developed, with an emphasis on recent developments and studies. Current studies on project monitoring and control mainly examine financial control tools and various accounting techniques that managers use to monitor the project outcomes (Rozenes et al., 2006). We will not cover these accounting tools in this review. Instead, we will pay special attention to Earned Value Analysis (EVA), since it is the most widely used managerial control tool in the industry. We will elaborate on analytical aspects of EVA and relevant optimization models. We will give emphasis to the integration of these models into decision support systems (DSS) and project management software. Regarding this specific application area, research areas demanding further effort and promising extensions are explicitly listed. We organize the review and discussions as follows. First, in Section 2.1, we present existing studies on EVA, as it is widely used in practice. Then, in Section 2.2, we examine the optimization models to set project control decision variables, since these models serve as a basis for DSS design. Afterwards, decision support tools and relevant project management software are discussed in Sections 3 and 4. Finally, in Section 5, we present conclusions and make a summary of future research areas. 2. Literature review 2.1. A widely used managerial control tool: earned value analysis EVA is a managerial methodology to monitor and control projects and it uses monetary units as a common basis to measure and communicate the progress of a project. It is based on comparing the actual and the budgeted values of the work performed, the time taken and the costs incurred. Hence, time and cost perspectives of a project control system are integrated. Cost and schedule variance are calculated to evaluate the current project progress and also predict the total project cost and duration. We refer the readers to the books (Fleming and Koppelman, 2005; Shtub et al., 2005; Vanhoucke, 2009) for more detailed explanations on the basic principles and metrics. In practice, EVA has been generally used to measure project performance throughout the life of a project. However, it could also be used in forecasting the resulting project outcomes; specifically to estimate the expected project time and cost using

the current status of the project. In this aspect, Vandevoorde and Vanhoucke (2006), and Vanhoucke and Vandevoorde (2007) developed three forecasting methods that are based on EVA metrics and compared them in terms of prediction accuracy. For that purpose, nine scenarios and possible outcomes were considered and Monte-Carlo simulation was employed. In addition, activity sensitivity measures and their relationships with forecasting and use in deciding on project control strategy were investigated (Elshaer, 2013; Vanhoucke, 2010). In order to improve the prediction performance of EVA, statistical methods could be integrated to the analysis (Lipke et al., 2009; Narbaev and Marco, 2014; Tseng, 2011). In this regard, Caron et al. (2013) followed Bayesian approach and integrated experts' opinions in describing the probability of events. In addition to statistical analysis, learning curves and risk management tools were also combined with EVA. Plaza and Turetken (2009) investigated the effects of learning and developed a spreadsheet based DSS. Concerning risk management, Pajares and Lopez-Paredes (2011) developed two metrics that support managers in differentiating whether project over-runs are within the expected variability or due to structural deviations. In the case of deviations, decisions on corrective actions become critical. For supporting decision making, Aliverdi et al. (2013) and Acebes et al. (2014) used simulation and statistical control charts. In addition to analysis of risks, hedging against uncertainty is important to achieve project targets. For this purpose, Naeni and Salehipour (2011) modeled percent completions as fuzzy numbers and used fuzzy set theory for estimating project performance. All the abovementioned studies addressed single project organizations. However, firms invest in many projects and these projects have resource dependencies within the firms. Portfolio management, which aims to choose and manage multiple projects in a way that enhances business strategy and contributes to achieving organizational goals, has become more and more critical in organizations. To assess the performance of the projects in the portfolio, Vitner et al. (2006) combined EVA with a multidimensional control system and used Data Envelopment Analysis (DEA), which is a mathematical approach to evaluate the efficiency of decision making units (DMUs). In the project management context, every project was modeled as a DMU and its efficiency was measured as a weighted sum of its outputs divided by a weighted sum of its inputs (see Farris et al. (2006) to evaluate the performance of engineering design projects using DEA). Other than examining the use of EVA in forecasting and performance assessments, graphical illustrations of EVA parameters have been widely utilized by project managers before taking control decisions. To illustrate the deviations from plans and emphasize the need of corrective actions, graphical tools could be very helpful (Anbari, 2003; Cioffi, 2005). For this reason, Hazir and Shtub (2011) focused on graphical and tabular presentations of EVA. They questioned the relationship between information presentation format and project control. Monte-Carlo simulation was used to replicate and model the uncertain project environments. This simulation technique was used by other researchers to compare two project tracking methods: top-down or bottom-up

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(project or activity based) approaches (Vanhoucke, 2011). A project based method relies on EVA data that can be used as early warning signals, whereas an activity based one is founded on risk analysis and uses the activity sensitivity information to decide on the critical activities to focus on. The project based approach was found to be more efficient for networks with a serial activity structure, whereas the activity based one performs better in parallel structured networks (Vanhoucke, 2011). Even though EVA has been increasingly getting attention from project managers, there are some limitations in implementing EVA in practice. Hall (2012) listed them as: 1. 2. 3. 4. 5.

Critical and noncritical activities are not differentiated. Activities are assumed to be independent. Behavioral aspects of management are not taken into account. Quality of processes and output are not assessed. Information requirement is high.

In addition to these points, we note that EVA only considers two dimensions of project planning and control: time and cost. However, other performance measures such as technical, operational and quality specifications could be also critical. To broaden that limited focus of EVA, Rozenes et al. (2004) proposed a multi-dimensional control system. Their system emphasizes monitoring work breakdown structure (WBS) at work package level (El-Mashaleh and Chasey, 1999). Another recent critical point is on assessing schedule performance using monetary values. To eliminate this, Khamooshi and Golafshani (2014) focused on durations and measuring earned duration instead of earned value. All these limitations and concerns should be carefully analyzed and taken into account in the applications. 2.2. Optimization tools: how to set control variables In designing an effective project control system, it is critical to find out the optimal timing and magnitude of project control activities. For this purpose, operations research (OR) methods such as simulation, dynamic programming and stochastic optimization have been commonly applied. We will pay special attention to these optimization based studies and discuss how these OR tools are utilized. Among those who employ simulation, Partovi and Burton (1993) evaluated control-timing policies: equal intervals, front loading, end loading, random and no control. Their study revealed that the end loaded policy performs best in preventing time overruns; however, there were no significant differences among the policies for the cost required to recover from deviations. Falco and Macchiaroli (1998) focused on effort concentration and formulated a function that is linearly dependent on the total number of active operations and is inversely related to total slack. They increased the frequency of checkpoints at the critical time periods, for instance, when resource needs are maximal. On the other hand, Bowman (2006) examined activity duration specification limits. He assumed that durations that exceed the limits could be moved back to the limits with additional costs.

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To decide on timing of the control activities, Raz and Erel (2000) used dynamic programming. They maximized the amount of information that is produced by control operations. This amount depends on the intensity of the activities that have been performed after the last control operation and on the amount of time that passed since activity completion. This dynamic approach is based on the assumption that some of the information generated by control operations will be lost over time. To determine the inspection points, Golenko-Ginzburg and Gonik (1997) formulated a stochastic optimization model and developed a heuristic on-line control tool and set the proper project speed. In the model, the progress of a project is assessed only via inspection at control points and the project speed can be altered at these points. Note that in modeling stochastic project networks, usually a common PERT (Program Evaluation and Review Technique) assumption is made meaning that activity durations are independent. However, this assumption might not be valid for many real life cases. Regarding this, Markov chains have been used; this approach allows correction or repetition of earlier activities (Hardie, 2001). Regarding the content and magnitude of control activities, simulation and optimal control theory have been applied to model intervening policies and their impacts on project outcomes. By means of simulation, Hazir and Shtub (2011) modeled the impacts of two types of corrective actions: minor and major interventions. Minors refer to short-term, operational actions such as workers working overtime in a road construction project. However, majors such as new technology investments create structural changes on cost figures and have long-term effects. In their simulation application, watching the snapshot views in a given display format, simulation users decided on taking a corrective action, and which corrective actions should be taken for a given project in a decision period. In addition to simulation, another appropriate technique to model the effects of intervening activities is optimal control theory. It is a field of mathematics that aims to determine effective control policies for dynamic systems. Using this theory, a control problem is defined as a function of state and control variables and modeled using differential equations that express the impact of the control variables to minimize the given cost function. Various time-dependent control problems encountered in management science and economics have been modeled using this theory. In this regard, we refer to the book of Sethi and Thompson (2002) for various applications. Even though this literature offers a strong theoretical background to model dynamic, time dependent behavior of projects, optimal control studies on project management problems are scarce. In scheduling projects, optimal control theory has been used to allocate continuously divisible resources in a least costly way (Nowicki and Zdrzalka, 1984; Weglarz, 1981). Moreover, the relationship between control efforts and project cost deviations has been investigated: Kogan et al. (2002) developed a continuous model to determine the optimal control effort with the assumption that control activities have a negative effect on project performance deviations. Concerning real life implementations of the theory, a control model was developed to allocate testing resources in a software development project efficiently (Kapur et al., 2013).

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We note that the abovementioned optimal control applications assume continuously available resources or model continuous time systems. However, in real life, resources are usually available in discrete quantities, such as the workers or the machines. In addition, modeling the discrete time characteristics of projects such as the timing of the beginning and end of activities and the corresponding time–resource profile is important. Considering these characteristics, Azaron et al. (2007) presented a multi-objective model to optimally control the resource allocation. They addressed the time–cost trade-off problem where the cost of each activity is a non-decreasing function of the amount of resource allocated to it. They optimized the project direct cost, the mean of the project completion time and its variance. Regarding these criteria, non-dominated solutions were determined by using discrete-time approximation. More recently, Hazir and Schmidt (2013) analyzed multi-mode project networks assuming a discrete time/cost function. They described an optimal control problem that reflects the dynamic evolution of the cost savings that depend on the level of control and the processing technology (modes). Having summarized the limited number of analytical models on project control systems in the literature, we notice that further studies, especially on optimization models and tools are in need. Moreover, in order to make the results of all these summarized studies concrete and applicable to the problems of the industry, it is crucial to embed them in support tools that guide managers in deciding on corrective actions. 3. Decision support systems DSS are computer-based systems that support decision making by combining and analyzing data and providing analytical models and tools that contribute to the selection of alternatives (see Shim et al., 2002) for a discussion on the definitions, components and evolution of DSS technologies). They have been widely used in planning, organizing and managing manufacturing or service operations (Eom and Kim, 1997). From the project management perspective, project planning and control are suitable application areas for DSS, since unstructured or semi-structured decision-making problems are confronted; and many alternative solutions should be considered. Moreover, in today's rapidly changing, competitive environment, organizations faces to manage portfolios of projects. In this regard, we will discuss which OR methods could be used to facilitate planning and control under uncertain, multi-project environments and how these methods could be integrated to DSS. OR literature has contributed to solving operational and level problems in project management, such as various scheduling and resource allocation problems (Tavares, 2002; Williams, 2003), but recently practitioners have also been working on developing support tools for strategic-level needs. Although DSS tools have been increasingly used in production and operations management (Eom and Kim, 1997), applications in project management are scarce. We start by reviewing this limited number of applications. DSS applications in the literature mostly concentrate on scheduling and risk analysis. Kolisch and Padman (2001)

reviewed some of the scheduling studies that make use of exact and approximate optimization tools. More recently, Trietsch and Baker (2012) proposed a new stochastic scheduling framework to enhance DSS so that reliable solutions are produced. Also in risk management, planning activities are shown to be effective in reducing the negative effects of uncertainty on project targets (Zwikael and Ahn, 2011). Integrating scheduling and risk analysis, Megow et al. (2011) developed a DSS for planning large scale maintenance operations in chemical manufacturing. To model and estimate the risks and quantify the consequences on project targets, Monte Carlo simulation has been widely used in the literature. The reviews of Kwaka and Ingall (2007) and Vanhoucke (2013) summarized the advantages and disadvantages of using Monte Carlo simulation to model projects, especially for risk analysis and control. In addition, we cite four nice risk analysis applications. In the first one, a DSS to predict project risk and assess impact on the project cost was developed (Nguyen et al., 2013). The decision making tool followed a scenario based probabilistic approach. In three others, Analytical Hierarchy Process (AHP), simulation and fuzzy logic were used (Dey, 2001; Fang and Marle, 2012; Liu et al., 2006). As a result of easiness and simplicity in modeling and solving, existing academic studies have usually modeled projects individually. However, in practice, organizations engage in managing project portfolios and work to share and distribute resources and capabilities over many projects effectively. Following the existing approaches and treating the multi-project planning problem as a set of independent single-project problems result in local optimum solutions for the organizations but a global analysis is required to facilitate effective Project Portfolio Management and achieve better results regarding strategic objectives of organizations. Also in program management, there exist a limited number of studies on using DSS. Slowinski et al. (1994) developed DSS that address non-preemptive scheduling problems that contain multiple activity modes. To solve these problems, they employed three different heuristics: priority rules, simulated annealing and branch-and-bound based approximation algorithms. Differently, Arauzo et al. (2010) used simulation to evaluate various scheduling policies for multiple projects. Considering the scarcity in theoretical studies, we emphasize that multi-project organizations require novel program management techniques to cope with the inherent complexity in distributing resources among various projects. We note that application areas and DSS needs are not limited to scheduling and risk analysis. Next, we summarize these limited numbers of applications. Cohen et al. (2005) focused on how projects are accepted and added to the system. Thereby, they found out the optimal loading of projects. Considering the dynamic and stochastic behavior of the problem, they used cross entropy, which is a technique to model and solve rare event simulation and stochastic combinatorial optimization problems. On the other hand, Hans et al. (2007) examined multi-project planning under uncertainty. They reviewed the studies in hierarchical planning and introduced a generic hierarchical project planning-and-control framework. Multi-criteria decision approaches and methods could be effectively used in DSS (Olson, 2008). A recent interesting

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project management DSS application of Lauras et al. (2010) addressed many performance criteria (cost, time, quality, risk, etc.). Their performance assessment system contained a method of defining the weights for criteria and allows making a global analysis of project performance. To assess the performance, Wi and Jung (2010) considered a project-oriented virtual organization and developed an index that contains quality, time and budget components. For this purpose, fuzzy approach to decision making was also employed (Dweiri and Kablan, 2006). We also note that in project management DSS, scheduling/ rescheduling and control functions have been usually examined separately. However, integration of these directly linked functions is important for effective management. Control policies should be determined in coordination with scheduling objectives and data on the outcomes of control activities should be used in determining the re-scheduling needs. Despite this importance, this relationship has not been studied sufficiently. The relationship between scheduling and control functions, characteristics of data sharing among them and possible integration strategies should be theoretically investigated. Moreover, from the application perspective, DSS are required in preparing schedules that can tolerate uncertainties, and for determining when control is necessary and when and how corrective actions should be taken. As a final point, considering the assumptions and properties of all these academic studies and projecting on the requirements of the practice, we realize that there exists a theory–practice gap. Academic studies mainly investigate closed systems, many times assume that information is known in advance, and work on less complex problems. However, managers face multidimensional, dynamic and open systems and require solutions/ predictions to more complex and non-deterministic problems. To fill this supply and demand gap between academia and industry, Herroelen (2005) emphasized the integration of scheduling theory and risk analysis tools into the current project management practice, particularly the use of commercial project management software. We believe that further research on DSS, and their practical use in program management will help to close the abovementioned theory–practice gap. The compliance of DSS with program management applications and project management software will facilitate these approaching efforts. 4. Project management software and DSS integration Software support is indispensable in performing various functions of project management (Shtub et al., 2005). Professionals widely use project management software packages such as Microsoft Project, Primavera, etc. Some surveys (by Liberatore and Pollack-Johnson, 2003; Liberatore et al., 2001) revealed that these packages have been mainly used for critical path planning (87% of 200 responses), whereas there is much less use of more complicated methods like time–cost trade-off analysis and probabilistic analysis or simulation (19%, 21% respectively). On the other hand, for controlling, earned value is employed by 53% of the managers. Interestingly, critical path analysis is also used for control purposes (66%).

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Microsoft Project is the most commonly used software and is mainly used for planning. Even if EVA tools are integrated to this software, in practice, these tools have been mostly used only to collect and present the data in various formats. This limitation might stem from the following: • Managers might not be aware of the relevant DSS or they might find them too sophisticated to use. • Acquiring these tools could be found infeasible regarding the project budget, or expensive regarding the benefits expected. To compare the well known software packages, Kastor and Sirakoulis (2009) and Trautmann and Baumann (2009) concentrated on scheduling and resource allocation capabilities. These packages contain scheduling and resource leveling heuristics. They can display the schedules using Gantt-charts and resource usages and capacities with bar charts. However, regarding scheduling outputs, there exists a great variance among these packages and also when compared to the existing theoretical results. Using the same data, the packages might output schedules with considerably different project completion times. Moreover, they perform evidently worse compared to optimal solutions generated by well-known scheduling algorithms in the academic literature. Therefore, better performing schedule-generation methods should be integrated to commercial software (Kastor and Sirakoulis, 2009; Trautmann and Baumann, 2009). Other than scheduling and planning, the second wide application area of project management software is risk analysis. Plug-in software such as @ Risk and Crystal Ball are well known. The basic strength of these simulation based tools is integrability to Microsoft Excel. Today, spreadsheets are inevitably used in every industry. Many decision support functions can be performed, even some optimization models could be implemented using them. For this reason, spreadsheet based DSS offer a significant research and application potential (see Buehlmann et al., 2000 as a good example in manufacturing). In addition to these tools that support scheduling and risk analysis, project managers require reliable early warning systems (Vanhoucke, 2012), and the majority of existing software does not include these systems (Trietsch and Baker, 2012). Analytical project control tools that support the managers to intervene effectively and stimulate them to undertake corrective measures at the right time are needed. In this regard, statistical process control techniques could be utilized (Bausch and Chung, 2001; Chang and Tong, 2013). Simulation software could integrate the effects of such managerial intervening actions (Williams, 1999). Integrating DSS that counsel decision makers in determining the timing and magnitude of corrective actions effectively will contribute to fulfilling the needs of project managers. For instance, this DSS could inform managers about the optimal frequency of rescheduling during project application. Moreover, the industry has a considerable need for the model driven DSS in which quantitative models are the basic components that allow decision makers to manipulate model parameters, and perform a sensitivity or what/if analysis (see the survey of Hahn and Kuhn (2012) on model-driven

Please cite this article as: Ö. Hazır, 2014. A review of analytical models, approaches and decision support tools in project monitoring and control, Int. J. Proj. Manag. http://dx.doi.org/10.1016/j.ijproman.2014.09.005

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approach). DSS should provide a simplified representation of the problem in a simple, understandable way. In that sense, project control problems are a promising research area for model-driven DSS that have been utilized in various fields such as production planning and supply chain management (Power and Sharda, 2007). These DSS should contain these two important components: 1. Analytical models and solution algorithms Model base which encompasses simulation and optimization models: The simulation module uses random distribution functions to sample activity costs and durations and facilitates an understanding of the complex, stochastic and dynamic behaviors of real project systems. The distribution of the total project cost or completion time could be estimated. The optimization module addresses when and how the intervention should be undertaken. Linear and integer programming, and network analysis are the OR tools that have been increasingly utilized in model bases (see Eom and Kim (1997) for the tools embedded in DSS). In addition to these techniques, dynamic programming and optimal control could be used because of the dynamic and timedependent behaviors. From the multi-criteria decision making perspective, AHP has been increasingly applied to resource allocation and conflict resolution problems and project control is claimed to be a prominent area (Subramanian and Ramanathan, 2012). Finally, simulation and optimization modules should interact. For instance, the optimization module could invoke the simulation module to generate random data. Using this data, optimization models could be solved. 2. Data presentation and graphical interface Managers are usually required to consider many alternatives before deciding. They prefer to generate these alternatives, modify them and make sensitivity analysis using various visual representations. These visual interactive capabilities are increasingly embedded in commercial software packages. Information presentation format was shown to be influential on the quality of managers' decisions (Hazir and Shtub, 2011). Particularly, variance graphs and numerical tables are found to be effective. In this aspect, there is a managerial demand for visual interactive systems that use appropriate presentation formats and facilitate monitoring and controlling projects. Simulations supported with effective visual tools might also aid learning, and they might be used as an efficient and effective way of teaching and learning complex and dynamic systems (Davidovitch et al., 2010; Shtub et al., 2006). 5. Conclusions The aim of the article is to review the current studies on project control systems. The focus is on analytical models, algorithms, and DSS applications. We comprehensively discussed the managerial tools (basically EVA) and examined both the progress in academic knowledge and the current needs of the practitioners. In these regards, we emphasized the importance of embedding these

optimization methods in DSS and its integration to commonly used project management software. We underlined that DSS should be model driven and serve as an early warning system to trigger effective corrective actions. We also noted the components that it should involve. The software integration is crucial for reducing the current gap between project management theory and practice. To conclude, we highlight the research areas that are worth further investigation and summarize some possible research directions. There is a need to develop analytical models to forecast project performance more accurately especially studies on predicting performance based on new comprehensive EVA metrics and integration of statistical control tools. Regarding EVA, there are some limitations (Section 2.1) and new approaches that are more flexible and suitable for more general cases are required to better explain the practice. Optimal control theory could be further studied in control systems design. Accordingly, integrated models could be developed. How to combine scheduling/rescheduling and how to control functions to facilitate information sharing, coordination and effective resource allocation are open questions. The models and algorithms developed could constitute the foundations of DSS to determine the possible needs for corrective actions. These systems should contain early warning mechanisms, and user friendly interfaces and be integrated to commercial software packages. Both interface design and integration to software systems are effectively challenging but important issues. Finally further studies on modeling and solving complex real life instances are needed. Efficient approximate algorithms to solve portfolio management and, multi-objective program management problems shall be developed. Conflict of interest There is no conflict of interest. Acknowledgments This study was supported by The Scientific and Technological Research Council of Turkey under grant SOBAG 113K245. References Acebes, F., Pajares, J., Galan, J.M., Lopez-Paredes, A., 2014. A new approach for project control under uncertainty. going back to the basics. Int. J. Proj. Manag. 32, 423–434. Aliverdi, R., Naeni, L.M., Salehipour, A., 2013. Monitoring project duration and cost in a construction project by applying statistical quality control charts. Int. J. Proj. Manag. 31, 411–423. Anbari, F.T., 2003. Earned value project management method and extensions. Proj. Manag. J. 34, 12–23. Arauzo, J.A., Pajares, J., Lopez-Paredes, A., 2010. Simulating the dynamic scheduling of project portfolios. Simul. Model. Pract. Theory 18, 1428–1441. Aytug, H., Lawley, M.A., McKay, K., Mohan, S., Uzsoy, R., 2005. Executing production schedules in the face of uncertainties: a review and some future directions. Eur. J. Oper. Res. 161, 86–110. Azaron, A., Katagiri, H., Sakawa, M., 2007. Time–cost trade-off via optimal control theory in Markov PERT networks. Ann. Oper. Res. 150, 47–64.

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