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1. Introduction With the growing advancement of Information Technology (IT), public organizations structural departments were compelled to deploy new systems to not only match their business needs but also execute operations through the best IT practices with the necessary efficiency and speed. Organizations may use changing processes in the current globalized market to implement these systems [1]. Despite previous studies, various project management approaches do not consider the uncertainties that exist in projects [4]. It is easy to believe that when risks are managed, uncertainties are also managed: these concepts are not identical, as risks are typically quantified regarding probability and the impact of their consequences, but uncertainties are not [5]. This researchs initial motivation involves the complexity in implementing an integrated business management system in the public sphere and adopting uncertainty management approach. This implementation revealed the opportunity to investigate adherence to this type of management in an ERP systems educational module within a federal teaching institution (the Unified Public Administration System - UPAS). This paper presents a case study from the public sector, using Marinhos [11] proposed approach for the management of uncertainties in software projects. This study primarily aimed to coordinate and control an ERP project. In addition to this introductory section, this paper is structured as follows: Section 2 presents the background; Section 3 presents the adopted research method; Section 4 presents the case study; Section 5 analyses and finally, Section 6 contains conclusions and some directions for future work. 2. Background 2.1 Uncertainty Management Uncertainty Management in Software Projects: A Case Study in a Public Company Karina Macedo, Marcelo Marinho, Simone Santos Journal of Convergence Information Technology (JCIT) Volume14, Number1, Jan. 2019 61 Johansen et al. [12] provide a process for uncertainty management but do not instruct the project manager and team regarding how to become aware of the early signs of uncertainty, or identifying their associated risks. Martinsuo, Korhonen, and Laine [13] noted how to address uncertainty in program management. Specifically, the authors aspire to understand how portfolio managers handle the threats and opportunities that generate uncertainty. Ramasesh and Browning [14] presented a theoretical framework in which factors and relationships are proposed that increase projects levels of uncertainty unknown unknowns. Further, Marinho et al. [3] presented a series of publications in the software management field, including an exploratory literature review aiming to identify the basic concepts and primary research sources in uncertainty management. Marinho et al. [6] offered a systematic literature review on managing uncertainty in projects, and action research was conducted using an innovative software project [8]. Semi-structured interviews demonstrated project managers practical perspectives as well as project management researchers perspectives [9]. Finally, Marinho et al. [11] denoted an approach to managing software uncertainties. 2.2 Uncertainty Management in Software Projects Marinho [11] presents a theoretical approach to managing uncertainties in software projects, (hereafter referred to as MUSP), that is based on six practice sets to guide the software team: Characterizing Projects: understanding the best management for the approach to be applied; Identification of Uncertainty Sources: identifies the most unfamiliar sources of uncertainty in the project; Early Signs Detection: observes the signs of uncertainties perceived in the project; Sensemaking: the sense is created to detect signs; Risk Management: the phase in which identified risks are managed; Unexpected Results: the preparation for, and reaction to, events not anticipated in the project. Additionally, MUSP presents general guidelines for managers to handle uncertainties and displays a set of proactive techniques, practices, and strategies to reduce or eliminate project uncertainties. 3. Research Method The research was divided into four stages: a research proposal, applied methodology, case study and results analysis. Therefore, the following topics will introduce and detail the steps in this research. 1) Research Proposal: This research study began with an ad hoc literature review [15], with the purpose of studying the elementary concepts that guide this study. In this phase, we also studied systems that could be adapted to conduct this research within the chosen organization. The research problems and their objectives were then defined. 2) Applied Methodology: We used this case study selection phase to study which systems in the chosen organization were in a phase to begin implementation. The case study was designed and planned in stages to provide a better perception of the problem [16]. 3) Case Study: MUSP [11] was applied to begin in January 2017, during the case study, with the following phases selected by the project manager: characterization, identification of uncertainty sources, early signs detection and sensemaking. The data collected from meetings and workshops, used to elaborate upon and apply the steps in the approach, were then transcribed and analyzed. These meetings consisted of conversations with project participants as well as on-site observations. After this analysis, the collected documentary evidence and the researchers observations were consolidated, and the results reported. 4) Results Analysis: Data analysis aims to derive conclusions from collected data both clearly and systematically by maintaining a consistent chain of evidence [16]. Further, Merriam [17] posited that data collection and analysis should be a simultaneous process in qualitative research, and not sequential. Specifically, while the collection and analysis of data is a resourceful and dynamic process, this does not mean that the analysis ends when all data is collected, but that this analysis becomes more intense as the study progresses. Thus, the data were collected, coded and analyzed throughout the research process. Uncertainty Management in Software Projects: A Case Study in a Public Company Karina Macedo, Marcelo Marinho, Simone Santos Journal of Convergence Information Technology (JCIT) Volume14, Number1, Jan. 2019 62 4. Case study findings and analysis We applied this study Department of Information Systems (DIS) at the federal institute. Intensity sampling, which targets a larger number of interview participants with different responsibilities within the same unit of analysis, was employed to obtain richness and depth in the study. Perspectives from participants with different responsibilities were obtained to triangulate the data. Responsibilities included: developers, testers, project management, and corporate-level executives. Further, we applied this study in UPAS Software Team, which was chosen to meet the institutions most urgent demands. The first module to be deployed was UPAS-EDU, the academic module, due to a substantial collective interest in the unified use of school records. 4.1. MUSP Application in UPAS-EDU We conducted some evaluations to assess how the organization perceives the uncertainties arising from a software project development. This led to a collection of the organizations perceptions and actions regarding the uncertainties in software projects. The study was supported with documentary sources, such as publicly available white papers, technical reports, case studies and web hosted marketing materials. On-site visits to secure work environments enabled first-hand observation of working practices and workplace environments. Teams coordination meetings were observed. However, the primary data collection technique employed in the study was face-to-face interviews conducted with practitioners performed in January and November 2017. The following subsections will present each phases results. 1) Project Characterisation: Table 1 illustrates a data set identified in project characterization phases, such as the chosen management methodology, a stakeholder analysis, and project definition criteria.

QUESTIONS

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