Introduction
Agile Project Management comprises several PMLC models. They provide guidelines on how to achieve project goals by prescribing specific solutions that are geared towards the success of those goals. Different PMLC models are applied in different projects to suit the specific characteristics of the project or project sub-system and the goals of the subsystem (Sawant). This paper analyzes the Pizza Delivered Quickly case study and proposes different PMLC models that should be applied to the various the subsystems of the project based on the unique characteristics of the sub-systems
Pizza Factory Locator Subsystem
The Adaptive PMLC model best fits the subsystem. The Adaptive PMLC model is suitable for projects with high uncertainty (Wysocki, Introduction to the Adaptive Project Framework). According to the case, there is a lot of information that is still unknown. The number of factories and their exact location is unknown. Each phase of the model is based on feedback loops which allow for modeling the entire system with limited and incomplete knowledge of all facts of the scenario. In this case, we do not know the number of factories and their location. Therefore, the system has to be built without the information. Consequently, the system will be used to make a decision on the two variables.
Order Entry system
A traditional PMLC model would be sufficient for the Order Entry System. The Order Entry System will be used for the ordinary business functions. Therefore, most aspects are well known in advance. Besides, the off-the-shelf commercial software will play a fundamental role in the sub-system. Therefore, a traditional PMLC model can adequately meet the intended purposes.
Order Submit Subsystem
The Adaptive PMLC model will be appropriate for the subsystem. The details of the solution are unknown, and they acknowledge that it is complex. Very sketchy information is provided. Even the decision variables are unknown. An Adaptive PMLC is suitable for complex systems with little information.
Logistics Subsystem
The Adaptive PMLC model will be appropriate for the subsystem. The subsystem is complex because it has six subsystems and the use of vans provides a mobile production facility. An adaptive model will allow the six subsystems to adjust when additional information is available since it has feedback loops.
Routing Subsystem
A linear TPM model would be sufficient for the system. All the details are known in advance, and it is very straightforward. They are aware of the solution, which is installing a GPS. No feedback loops are necessary since the details of each stage are already known during the planning phase.
Inventory Management Subsystem
This paper proposes an iterative PMLC. Similar to the Adaptive PMLC, the iterative PMLC is used in situations when there is uncertainty. However, an iterative PMLC is applied when the project manager has an idea of the final solution but needs the finer details of the solution (Wysocki, Effective Project Management: Traditional, Agile, Extreme). In this case, some of the solutions of the Inventory Management Subsystem has already been discussed. They just need for a detailed description of the solution. Therefore, the iterative PMLC perfectly fits the situation.
Conclusion
This paper proposes the different PMLC models that should be applied to the various Pizza Delivered Quickly subsystems based on the unique characteristics of the sub-systems. An Adaptive PMLC is applied for most sub-system since they are complex, and there is a lot of uncertainty. However, there are some straightforward subsystems which allow for the application of simple PMLC models.
Works Cited
Sawant, Vijaya . "http://oratechsolve.com." 3 March 2013. Project Management Life Cycle Models. Web. 02 July 2016. <http://oratechsolve.com/project-management-life-cycle-models/>.
Wysocki, Robert . Effective Project Management: Traditional, Agile, Extreme. New York: John Wiley & Sons, 2014. Web.
Wysocki, Robert . "Introduction to the Adaptive Project Framework." 17 February 2010. http://www.informit.com. Web. 02 July 2016. <http://www.informit.com/articles/article.aspx?p=1554968>.
