OMIS 3750 - CASE 2
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Feb 20, 2024
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Case 2 Analysis: The New York City Department of Parks and Recreation
OMIS 3750 A
The New York City Department of Parks and Recreation’s (DPR) poor oversight has
led to a history of over-budget capital expenditures and delayed deadlines. The following
analysis showcases the existence of the planning fallacy at DPR.
Budget Analysis
The compiled data was first used to calculate the dollar variance of each project using:
$ Actual - $ Budgeted = $ Variance. This calculation highlighted the amount each project
went over or under budget in dollar amount. Similarly, the percentage variance was calculated
using: Actual/Budgeted - 1 = % Variance. These two values were then averaged to understand
the variance in dollars and percentages (Schmidt, 2022). Furthermore, an understanding of the
dispersion of the data was required through the standard deviation, which was calculated
using excel (=STDEV). The findings presented in Appendix 1 reveal an average dollar
variance of $259,436.72, corresponding to a percentage variance of 43.52%. Further analysis
of the data brings attention to a notable standard deviation of $702,014.89. This indicates a
considerable dispersion in the variance of projects highlighted a negative variance between
registered project budgets and actual expenditure.
Deadline Analysis
From the analysis of deadlines conducted, it was found that NYC DPR has an average
delay of 78.2 days in completing their projects. This was calculated by subtracting the
scheduled completion date from the final inspection date. From there, the average in days
projects from the NYC DPR were late were found, along with standard deviation, using excel
(=STDEV). A standard deviation of 190.6 days signals a significant amount of variability in
the project completion times (see Appendix 3). Then able the normal distribution for the
difference in days late was calculated and graphed along a normal curve (see Appendix 3) to
show the variance in project completion times. Appendix 3 also highlights the inconsistency
of project outcome times.
Next, using an analysis of the count of project designs delayed, early, and exactly on
time (Appendix 5) showed 70% of the time designs were late, 20% were early, and 11% of the
time they were completed exactly as projected (Appendix 6). This showed that only about 1 in
10 project designs were completed within the time frame suggested regarding the design
phase. Other data analyzed was scheduled completion compared to actual completion of the
full project. According to the results, 61.5% of the time projects were late, 32% were early
and about 6% exactly on time (Appendix 6).
Recommendations
Enhance Budget Estimation Accuracy
The variance between the departments budget projections and actual expenditures
highlights errors in their current budget estimation process. A thorough review involving
project managers, financial experts and relevant stakeholders should be prioritised to
understand whether a top-down estimate or bottom-up estimate method should be used.
Top-down estimation can provide a quick initial estimate based on past projects, allowing for
rapid decision making, making it fundamental for initial project planning (Dobre, 2023). As
the project progresses, a more detailed account of budgetary requirements can be estimated
for tasks and project phases using the bottom-up approach. The department must focus on
regularly updating estimates based on actual performance. This allows the project to be
budgeted on the bases of expertise, provided by professionals with past experience in the
project task (Dobre, 2023).
Scheduling
With an average of 78.2 days over schedule, a more rigorous scheduling plan could
help the NYC DPR improve its cost and deadline accuracy since they have over 1700 city
parks and facilities to maintain. To address scheduling problems, the DPR needs to identify
whether a project is time-constrained or resource-constrained. If time-constrained, they can
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use resource smoothing to reduce peak demand for resources and minimize fluctuations in
resource demand (Dobre, 2023). If resource-constrained, the DPR can use the Parallel Method
to apply heuristics which prioritize which activities are allocated resources and which are
delayed (Dobre, 2023). The heuristics priority is; 1. minimum slack, 2. least duration, 3.
lowest activity identification number (Dobre, 2023). In any period when two or more
activities require the same resource, the priority rules are applied. From there, the DPR can
create a proper resource schedule, assign work appropriately and use the WBS to create a
time-phased budget baseline.
Risk management
Risk management is defined as recognizing and managing potential and unexpected
circumstances that may occur once the project begins (Dobre, 2023). In order to improve
project costs and meet deadlines, risk should be identified and assessed. A risk response needs
to be developed to decide if risk needs to be mitigated, avoided, transferred, escalated, or
retained (Dobre, 2023). An essential aspect is contingency planning. Without a contingency
plan, a manager may delay decisions or accept the first proposed solution due to panic and
being forced to make a decision under pressure (Dobre, 2023).
Communication
Although not much is mentioned on communication in the case, the author of the case
explains on a podcast the importance of efficient communication procedures (Dubner 2018).
For better project control, communication channels need to be streamlined. Some suggestions
to implement this include implementing software that allows for easy regularly scheduled
communication, both internally and externally (Forbes 2021). Additionally, the DPR can
schedule regular meetings to discuss project progress and to realign deadlines if need be.
Communication can look different based on each channel, but it is important to make it as
efficient as possible to ensure success at the NYC DPR.
References
:
Dobre, M. (2023).
Lecture 7: Scheduling Resources and Costs
[PowerPoint slides]
Dobre, M. (2023).
Lecture 5: Managing Risk and PERT
[PowerPoint slides]
Dobre, M. (2023).
Lecture 3: Estimating Time and Costs
[PowerPoint slides]
Dubner, Stephen J. (Host). (2018, March 7). Here's Why All Your Projects Are
Always Late - and What to Do About It [Audio podcast]. Retrieved from
https://freakonomics.com/podcast/heres-why-all-your-projects-are-always-late-
and-what-to-do-about-it/
Forbes Magazine. (2021, August 2). 11 Ways Communications teams can prepare for
long-lead deadlines. Forbes.
https://www.forbes.com/sites/forbesagencycouncil/2021/08/02/11-ways-comm
unications-teams-can-prepare-for-long-lead-deadlines/
Khan Academy. (n.d.).
Standard deviation: Calculating step by step (article)
. Khan
Academy.
https://www.khanacademy.org/math/statistics-probability/summarizi
ng-quantitative-data/variance-standard-deviation-population/a/calculating-stan
dard-deviation-step-by-step
Schmidt, J. (Ed.). (2022, October 31).
Budget to actual variance analysis
. Wall Street
Prep.
https://www.wallstreetprep.com/knowledge/budget-actual-variance-analy
sis-fpa/#comments
Appendices:
Appendix 1: Results Table - Budget Estimations
Average Variance ($)
Average Variance (%)
Standard Deviation
$
259,436.72
43.52%
$
702,014.89
Appendix 2: Data Calculation - Budget Estimation
Appendix 3: Results Table - Deadline Estimations
Average Days Late
Variance
Standard Deviation
78.2
36,324.3
190.6
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Appendix 4: Data Analysis of Deadline Estimation
Appendix 5: Count of late, early and on time projects
Design
Final Project
Projects Late
1256
1101
Projects Early
343
576
Project Exactly on Time
191
113
Appendix 6: Percentage of late, early and on time projects
Percentage
Design
Final Project
Projects Late
70.16%
61.51%
Projects Early
19.16%
32.18%
Project Exactly on Time
10.67%
6.31%