In this report we will be looking at the critical areas of weakness displayed by the Galactic Enterprises Group (GEG) in their methodology of project management which have led to miscalculations in the potential outcome of their product development project, we will also aim to propose solutions to rectify the issues which have risen due to a lack of a clear Project Management strategy. We will look at How KPIs needed to be set out at the start of the project, and how they could have helped indicate the health of the project at every stage Whether the use of spreadsheets for data analytics is an effective application The use of data analytics in measuring the effectiveness of the Project We will also dive deeper into the culture of the organization and some of the problems associated with it which have made effective management of the project almost impossible. The Role of KPIs Projects in every organization are undertaken to achieve specific goals by undergoing change and/or development in addition to the regular operations, in order to achieve those goals there need to be measuring tools to indicate how well the project is doing at achieving those goals and how far along they are in their targets these measuring tools are known as Key Performance Indicators (KPIs), As defined by Florida Tech. Another important question is about how the goals of the project are linked to the strategic objectives of the organization and how they can progress the overall organization strategic process Alsadeq et al. (2010). KPIs have a target and a range of performance that can be measured against its achievement, one of the most implemented ways of KPIs in organizations is to keep them SMART which is an acronym for Specific, Measurable, Attainable, Relevant and Time-Bound as explained by Ward (2021). As it relates to GEG and the Widget 2 project, clear KPIs needed to be set out at the initiation stage of the project management life cycle Singh (2016) by the project manager and an evaluation needed to be carried out at every subsequent stage of operation, the project plan and the work breakdown structure needed to be set according the to KPIs of each team, which would have made the project objectives clear to follow along the execution stage and easily measurable and reportable during the closing stage. The specific areas for which the KPIs needed to be displayed and communicated periodically to the project team and the upper management by the project manager are Delivery Timeline Budget Process improvements Relationships and communication Risk management Customer orientation These KPIs along with the performance measurement against each of them e.g. Exceeded expectations, Good, Acceptable, Poor etc. Harvey. would have kept everyone informed of the health of the project, however it seems that only delivery timelines was the only KPI being stressed upon and the rest were either ignored or merely skimmed through. All of this with little to no communication to the project team which is a deeper issue which will be addressed later in the report. The Role of Data Analytics and Suitable Tools Data has an important role in managing a project, the more data that is available on previous instances of similar situations in a current project, the easier it is going to be for the project team to make an informed and correct decision to resolve the situation Misra (2021). However, raw data if not organized and arranged in a readable sequence, will not be of any help to the project team, therefore it needs to be put through the processes of data mining, Chen et al (2000). Sharma (2015) condenses data mining techniques down into five major categories, which are Classification Analysis, Association Rule Learning, Anomaly or Outlier Detection, Clustering Analysis and Regression Analysis, these will condense the data into readable patterns which can be used to forecast the risk and reward of management decisions with probabilism. If there is a very large amount of data which needs to be put through the data mining processes, it cannot be done manually or by normal computing means, in this case, computing with high processing power and artificial intelligence need to be combined with data mining processes to aid in accurate decision-making, Witten et al. (2017). The three types of business analytics are called descriptive analytics (DA), predictive analytics (PDA), and prescriptive analytics (PSA), Frazzetto (2018). Prescriptive analysis is the most suitable type to be used in the widget 2 project because there is a lot of back data available to the team, with needs to be input into a data analytics tool, and using that data the tool can predict the best course of action for the project team to take based on previous similar instances. The subject of use of spreadsheets for data analytics is a very pertinent one here, as during the Widget 2 project at GEG the data mining, management and presentation was done via spreadsheets. Spreadsheets can be a great tool to present and process simple data and noncomplex models, they are easy and convenient to use, do not require much training and are virtually ubiquitous in a corporate environment, so data presentation for small projects and routine tasks is fast, convenient and easily understandable. However when it comes to complex models, large volumes of data, processing power and real-time processing, there are several drawbacks to spreadsheets as data mining tools. According to Tackels the five big disadvantages of using Miscrosoft Excel are: It is not good at analyzing unstructured data and has trouble in converting data from other formats especially those with complex structure It is non-interactive in its data presentation, you cannot raise further questions and answer them with dashboard, rather you can only present the final product It cannot connect to a live database with ever-changing data which it can be used to analyze on the go, and visualize quickly Several people cannot work on the same file, even in the newer versions, where it has been made possible, the file slows down considerably as users are added There are security vulnerabilities which makes it unsuitable for sensitive confidential data to be stored on it Other than the above, there are other issues with data accuracy and a glaring lack of selflearning capability with AI to enable spreadsheets to forecast probabilities and aid in decisionmaking. As per Alexander (2021) Ideally a data analytics tool for project management needs to have several functions to be of use to project managers: Importing data easily Simple analyses for quick decisions Charting and graph templates for visualization Modeling and forecasting capabilities Customizability Easy user interface Seamless integration with other applications Easily accessible through mobile devices Able to easily communicate information to project team (integration with email, easy sharing) Report templates easily extractable An example of an application of a project management tool is Monte Carlo, which was used in the Quantitative risk management for an Oil and Gas project in Oman, Aggarwal (2007), it combined several inputs to probabilistically assess the risk of project failures, including budget, safety, delivery delays and contractor non-compliance and treated them as interlinked aspects of the project rather than each one in isolation. It gave a weighted probability for each failure, including criticality and consequence for each outcome, which helped the project team determine on the priorities and resources to be allocated to each aspect of the project during the execution phase for smooth closure and achievement of goals. Recommendations It is recommended that a project management office be set up at GEG as soon as possible, the current ad-hoc and haphazard methodology of project management is leading to project failure. A consistent philosophy and methodology to project management needs to be set up to ensure project success for example PRINCE2 methodology, which as Chapman (2019) explains allows project teams to break the project down into phases and so they can designate duties timelines and resources accordingly. The data collection and input have been inconsistent and experienced change in personnel midway through the data collection process, it is unclear whether the data quality was checked and verified. According to Snee (2015) the data pedigree, understanding for data sources, collection and measurement methods are all fundamental to good analytics results. Since the input data was erroneous in the Widget 2 project, it is recommended that the data pedigree be checked and verified in order to find the true potential results of the project. It is recommended that the expectations of project goals be revised, as the goals set by the project manager were challenged as unrealistic by a project team member, also because of the erroneous input data Resource Management needs to be revised since there are some incongruencies between the roles handed to employees and their functional expertise, the financial control is being handled by engineering rather than the team member from Finance. During any product development a Total Quality Management (TQM) philosophy needs to be applied, however it seems that both RnD and QA have been given backseat advisory roles, rather than being the leading functions like they should be. Procurement needs to be consulted and involved in budgeting meetings to keep a handle on the costs, which has not been the case during the Widget 2 project. Communication between the project manager and project team needs to be completely revamped, the progress on budget, timelines and other KPIs have not been properly communicated to the team, the limited presentations have been unclear to most of the team members, in addition to that there was a lack of comprehensive reporting of meeting minutes which can lead to a confusion in roles and responsibilities. There need to be clear risk management KPIs which need to be discussed and presented in every phase of the project which would keep the project team aware of criticalities and priorities to avoid failure. The deepest issue within the organization is that of culture and lack of trust amongst organization members, which goes on all levels of management. Below are some examples The top management used favoritism and bias to select projects leader The head of marketing engaged in blame game as soon as problems with the project were provided to him and looked to scapegoat the data scientist The person who was passed over for the project lead position was given the task of investigating the project, which could lead to bias and potential misreporting The project was run on the decision making of one or two people rather than inputs from the whole project team The project leader picked and chose on whose input he was going to value rather than going by the expertise of the personnel There is deep mistrust amongst team members In the current environment people are unwilling to admit to and report mistakes, as is the case with the bonding issue, instead tend to cover up mistakes, which could lead to bigger failure down the These issues with the organization culture can only be resolves through vigorous retraining of employees, an ownership mindset from the leadership team, changes to the structure of the organization and an emphasis on meritocracy in assigning roles and positions rather than playing favorites. Ownership can only come in the employees if they feel that they are valued and that their input makes a difference to the organization, an attitude change from the leadership to a solution based approach rather than assigning blame would put employees at ease to report problems when they arise rather than trying to hide them which is very detrimental in the long run. Annexure A Interview Questions for Project Manager What methodology of project management do to ascribe to? How much experience do you have with prescriptive data analytics in PMO? How do you plan on communicating with the project team and the leadership team? Do you have experience in conflict resolution? What should be the fundamental KPIs for project management? How do you plan to maximize the potential of your team? 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