Project Management and Resource Allocation, Essay Example

Abstract

Timely completion of projects before schedule deadlines is normally the most significant concern for project managers. Resource allocation policies in such projects establish the portion of resources to be allocated to component tasks. The selection of allocation policies in projects has a strong influence on project durations. Performance can be improved through the understanding of how resource allocation policies impacts on project schedules. Despite the potential of improved resource allocation policies to minimize project, relatively little research has explored allocation policy designs.

Introduction

Minimizing the duration taken in project implementation is significant in the success of development projects. Resource allocation policies in such projects establish the portion of resources to be allocated to component tasks. The selection of allocation policies in projects has a strong influence on project durations. However, policies for minimized project duration are complex to design and execute as a result of closed loop flow of work that produce dynamic demand patterns and interruption in the transference of resources between activities. The demand estimates of resources and resource modification times are two features of policy that project managers can readily modify to manipulate project durations. These features are utilized in the illustration of allocation policies in a rather uncomplicated project model. Foresighted and myopic policies are identified by their utilization or lack thereof of rework and manifold backlogs in allocation. Optimal policies in limited and perfect managerial control are illustrated by examination of foresighted and myopic policies across a variety of project complications and modification times under uncertain and deterministic conditions. Counter-intuitive outcomes may signify that minimum resource allocation delays do not create minimum durations, and increasing ambiguity decreases durations in specific conditions (Sterman, 2000).

The Project Duration Problem

Timely completion of projects before schedule deadlines is normally the most significant concern for project managers. There are two approaches that facilitate improved schedule performance. These approaches are resource improvements and management. A range of process improvement techniques for the improvement of schedule performance have been investigated. These include concurrent development and dynamic planning cross-functional development panels and the utilization of information technology instruments. However, the project management challenges of ambiguous project conditions and limitations imposed by product architecture, cost, and project stakeholders’ relationships usually hamper managers’ capability to successfully improve projects by means of process improvement (Joglekar et al., 2001). Consequently resource management is imperative to the timely conclusion of projects and minimizing project durations. Development projects generally as well as the resource management of the specific projects in can profit from a systems perspective. Development projects are systems of varied components connected by rich interactions. These interactions could also be more essential to controlling and understanding project behavior as well as performance as the comprehensive characteristics of specific components. One outcome is that project systems develop over time in ways that could be complex to elucidate and control successfully. Systems techniques have been employed to identify and model development projects and can offer insight into the manner in which project structures influence performance and behavior (Sterman, 2000).

Total quantities of resources as well as associated productivities are in most cases limited and complex or expensive to develop, leaving resource utilization as a principal management instrument to minimize project durations. Project managers can have a huge influence on resource utilization by means of the policies they employ to allocate resources between development activities, even in the case of fixed total quantity and productivity of the resources. Application of reasonably few resources to any particular activity impedes progress, while the application of too many resources result into crowding that minimizes productivity and squanders resources that could have been utilized more resourcefully in other activities. Therefore the efficient and effective allocation of limited resources in the development phases and between activities within phases is a pragmatic management prospect for developing project schedule performance. The figure 1 below is utilized in this paper to illustrate the various constituents in projects.

The project work flow formation illustrated in Figure 1 above, is a value chain through preliminary project conclusion, quality assurance, and work endorsement and release with a rework cycle by means of the invention of requisite rework, rework backlog and rework activity. The portion of work discovered to necessitate rework is utilized to model project intricacy. More intricate problems are assumed to entail more iteration for conclusion. Quality assurance initiatives are assumed to determine all rework requirements.

The focus on resource allocation policies as a vehicle employed to minimize project duration as well as the impacts of those policies on project durations via project systems modeling and analysis requires further research investigation. Sterman’s (2000) explanation of pertinent policies as decision-making regulations is adopted in this paper. In this context resource allocation policies become formal guidelines or heuristics which project managers employ to make individual assessments concerning where to apply particular resources. For instance, the critical path technique mantra ‘supply the critical path, with requisite resources,’ is an informal heuristic for resource allocation that can be formalized and applied as a policy of filling the entire resource requirements of critical path activities prior to allocating resources to any other activities. Performance can be improved through the understanding of how resource allocation policies impacts on project schedules. Despite the potential of improved resource allocation policies to minimize project, relatively little research has explored allocation policy designs. Resource allocation policies can embrace several types of data, such as including resource requirements across activities and timeframes, productivities of various types of resources, as well as resource availability (Sterman, 2000).

This paper has a focus on how three related policy features adapted by project managers in addressing resource allocation issues that influence project durations. The three related policy features are: (a) whether to prioritize project resource allocations on present or future circumstances, (2) how rapidly to regulate resources and (3) how much control to apply in resource adjustment rate. Basing the argument on model analysis this paper would propose and explore fine-tuning managerial delays as a probable innovation in project management. It is imperative that this paper highlights the application of fine-tuning these interruptions to resource allocation policy design.

Designing of resource allocation policies is complex because of two intrinsic features of development: iteration and delays in execution of decisions relating to resource allocation. Development processes are usually iterative by nature. Iteration generates closed loop flow of work in which deficits or discretionary modifications for development are discovered, modifications are done, and the work is verified or re-tested for supplementary modification requirements. Iteration may greatly amplify the total work effort required for completion since rework can expose or generate supplementary modification requirements, which generates extra rework. Emergent body of product innovation works employs the design structure matrix (DSM) process to investigate the iteration and allied interdependence issues (Sosa et al., 2004).

This process accounts for iterations by mapping the dependence between a value chain of innovation responsibilities in terms of precedence, data exchange needs and prospects of rework. Other literary works by Helo et al. (2004) have employed DSM with systems thinking process to evaluate economic effects of ambiguity within feedback loops and investigated several options in decision-making to probably circumvent iterative situations. Efficient and effective allocation of resources for iterative projects or project phases is complex as a result of challenges in precisely forecasting the volume of work backlogs, specially the quantity of work that must be completed initially, work to be tested or inspected to determine modification requirements, and reworks. These work backlogs develop during projects. For instance, a design phase devoid of the advantages or burden of beginning with earlier developed work. At the commencement of the phase the entire work packages ought to be initially concluded and not any are nevertheless on hand for rework or quality assurance. The design backlog as well as the necessity for designers, reduces as work is designed while the rework and quality assurance backlogs together with their resource requirements increase, except at dissimilar rates. The rework and quality assurance backlogs afterward reduce as work is endorsed and designs completed. The dynamics of rework cycles causes work backlogs to be complex to forecast.

Considering the limits of human cognition especially in the management of dynamic system, project managers are usually unable to accurately forecast resource requirements for successful resource allocation. Delays in making decisions regarding resource allocation, implementation of re-allocations, and productivity ramp-up of the re-allocated resources as well make the policy design for resource allocation complicated. Resource adjustment interruptions, or delays, may be huge as a result of the quantity of data and physical activities that ought to take place for a comprehensive change in allocation, the timeframe requirements for those activities, and the prerequisite data requirements in those processes. Much of the analytical studies on innovation project management presuppose that resource allocation delays are non-existent, or that these exchanges are absolutely synchronized, or both. Several research done on the systems dynamics tradition plainly model delays whilst allocating resources in a multiplicity of problem settings (Sterman, 2000).

The Time-Cost Tradeoff Problem

There are projects that are set on a short-term schedule only to learn later on that the time allocated for the program is not adequate for the project considering the dynamic nature of the projects. Project managers in such cases have to reschedule the whole program to fit within the time frame that was stipulated from the onset of the projects. However, the duration of projects can be shortened by crashing the projects Network. While crashing the network duration, it is recommended that the project manager focuses on the activities that lie on the critical path. The heuristic for choosing the best activity to crash may be varied but the lowest acceleration cost heuristic is the most preferred amongst project managers. The factors to consider when determining the  best activity to crash depends on factors like the cash equivalent gain, the bottleneck activities, the labor requirements and the amount of time the activity to be crashed takes compared to other activities (Hong et al., 2001).

Time/Cost Trade-Off Analysis an Introduction

This is the compression of the overall projects schedules so as to achieve a positive outcome in terns of revenue, project duration and cost. Using the Lowest Acceleration Cost Heuristic to determine which activity to crash, while determining the best activity to crash under the Lowest Acceleration Cost Heuristic, it is advisable to consider cost the normal cost and the crash cost together (Hong et al., 2001). The normal cost can be defined ad the cost of normal activity duration, the crash cost is the cost that can be associated with speed in carrying out an activity.

In the example below

Costs that can be accelerated includes: Activity A, £6,000/day, Activity B, £10,000/day, Activity C, £5,000/day, Activity D, £6,000/day. From the above examples it only makes sense to crash the activities since they are the only activities that lie on the critical path and have the lowest acceleration cost/time. Makes sense to crash only those activities on the critical path(s) with the lowest acceleration cost per time.

By crashing C by one day, the critical path changes to from C-D= 9 + 8 = 17, then we also then crash B by 1 day: n=7, £50,000, C=5, £62,000 and Finish

D: N=8, £30,000

C=6, £42,000

C: N=10, £40,000

C=9, £45,000

Step 1: Crash C by 1 day

Step 2: Crash D by 1 day

Step 3: Crash D by 1 day and crash A by 1 day.

Potential Problems Associated With Crashing

There is usually rigidity little margin for error as well as potential to poor quality as employees fear failing to complete the project. The time cost trade of system is a sure start of staff burnout and raises the activity based costs (Chan & David, 1996).

Resource leveling problem

Suppose a ship is preparing for a voyage and the Shipboard operations dwell in a complex system that forces us to determine the types of operations that must be considered to accomplish voyage. The voyage is inhibited by sequential relations that must hold among some of them or by the requirement of resources (Hong et al., 2001).

Taking A? as any feasible plan to obtain a very feasible solution,  We solve a resource leveling that minimizes the number of  possible overlapping task in V. assigning A?(t) the set of activities that are in progress at the time  t, (t is the active time set), i.e.,

x ij is defined as a binary variable which will be equal 1 if both activities i and j overlap and 0 otherwise. We also define end ij as the maximum end time of i and j, start ij as the minimum start time of i and j. The problem of resource leveling can then be described as:

Subject to:

Temporal constraints

Start ij ?ti   ? (i, j)   € V * V, j>i…………………….. (2)

Start ij ?tj   ? (i, j) €V * V, j>i …………………… (3)

End ij?ti 1wi    (?i, j) € V* V, j>i…………… (4)

End ij?tj 1wj   (?i, j) € V* V, j>i………………… (5)

M * ij?wi + wj – (end I, j –start ij)……………….(6)

? (i,j)  €V * V, j>i

rV?t€ ?Niter “t 2 f0,., _Tg (7)

ti?0, Xij € {0, 1}

When the figures on the right-hand side of (6) is positive, then *ij will be forced to be 1. Constraints (7) describe the resource constraints, i.e., the entire resource utilization r? (t) at time; t, of the isolated resource sailors can’t exceed its capacity (Niter) at the iterat

Conclusion

Research on resource allocation usually focuses on a solitary resource type such as equipment, finances, labor, or managerial effort because of the distinctive impacts of diverse resource types on performance. For instance, Shohet and Perelstein (2004) recommend a technique for the prioritization of rehabilitation projects in regard to allocation of funding. In particular construction projects allocation of resources is usually investigated as a unique case of scheduling problems such as the resource constrained scheduling problem, in which the resource is equipment or labor. These studies have employed heuristics (Hong et al., 2001) and genetic algorithms (Chan & David, 1996) in association with activity modeling for resource allocation.

In many cases activity priorities are employed to fully meet the demands of a single activity prior to allocating outstanding resources to lesser priority activities. This method is highly implausible to be utilized extensively in practice since project managers would be compelled to absolutely neglect a few activities that require resources whilst completely fulfilling others. This would lead to an increase in discontinuity of allocation, thus creating more problems in the project (Joglekar & Ford 2005).

References

Chan W-T & David KHC. (1996). Construction Resource Scheduling with Genetic Algorithms.  ASCE Journal of Construction Engineering and Management.

Helo P, Hilmola, O & Maunuksela A. (2004). The Economic Nature of Feedback Loops–Some Experiments with Ashby’s ‘System Thinking’ and DSM. International Journal of Innovation and Learning.

Hong Z, Tam CM, Shi J. 2001. Resource Allocation Heuristic in Construction Simulation. Construction Management and Economics 19: 643–651

Joglekar N & Ford D. (2005). ‘Product Development Resource Allocation with Foresight’. European Journal of Operational Research

Shohet IM & Perelstein E. (2004). Decision Support Model for the Allocation of Resources in Rehabilitation Projects. ASCE Journal of Construction Engineering and Management

Sosa ME, Eppinger SD & Rowles CM. (2004). The Misalignment of Product Architecture and Organizational Structure in Complex Product Development. Management Science.

Sterman JD. 2000. Business Dynamics, System Thinking and Modeling for a Complex World. Irwin McGraw-Hill: New York.

Sterman JD. (2000). Business Dynamics, System Thinking and Modeling for a Complex World. Irwin McGraw-Hill: New York.