OPSMGT 357 Individual Assignment

Overview

The case seeks to assess the use of the essential tools that a project manager should handle and the ability of students to report the most relevant results and insights of the execution of a project. Students must take the role of consultants and deliver an analysis and recommendations about the revisions conducted on the project.

Requirements

1. Read the case on the following pages – read the Overview, Requirement and Deliverables carefully.
2. Complete this assessment individually. The University’s Academic Integrity policies regarding cheating, plagiarism and third-party assistance apply. Turnitin will be used.
3. For this assignment, you may use generative artificial intelligence (AI) software, such as ChatGPT, to assist you in an appropriate way. Things that would be considered appropriate include:
   • summarising information.
   • to help generate ideas.
   • refine your writing.

However, the work to submit must be substantially your own work, you must carry out your own analysis, write your own text and create your own figures and charts.

Deliverables

• The assessment must be submitted electronically to CANVAS | OPSMGT 357 | Assignments | Individual Assignment before 23:59, Friday, 03 Apr 2026. The report to be submitted must be named “IndAssignment_yourUPI.docx”.
• All late assignment will receive 5 marks penalty per late day if you have not been granted an extension of time. Assignment will not be accepted after Sunday, 05 Apr, 2026.
• Submit a report for the case with its own cover page, analysis, and issues addressed to the client

Course Learning Outcomes Addressed

This assessment is marked out of 100 marks and is worth 20% of the coursework grade.

This assessment addresses the following learning outcomes:

1. Analyse and assess the needs of organisations with respect to managing projects.
2. Apply and assess the inter-disciplinary nature of Project Management concepts, with respect to their wide application and the multiple stakeholders involved.
3. Effectively use analytical tools to develop competence in the management of projects.
4. Develop generic skills in time management, analytical reasoning and decision making, for problemsolving in the context of project management tasks.

Case. Predict Now: The Race to Launch an AI Forecasting Tool

Overview

Emma Torres sat staring at the message from PredictNow’s Chief Product Officer for the third time that morning. The email, concise yet pressing, confirmed that the company’s new AI-powered demand forecasting product—an initiative Emma had personally championed—had been approved for development. It was the opportunity she had hoped for ever since joining the Auckland-based analytics startup two years earlier. But it came with a challenge that made even her, a seasoned product analytics lead and former project manager, hesitate for a moment.

PredictNow wanted the product launched by November 2, 2026, in time for the pre–Black Friday marketing cycle. The company believed that a forecasting tool specifically tailored for small and

medium retailers could quickly gain traction if released before the holiday season, when retailers felt the pressure to stock the right products at the right time. If launched later, the tool would miss the biggest retail demand peak of the year, making the product far less compelling for early adopters. The deadline, therefore, was non-negotiable.

Emma understood the potential impact immediately. PredictNow had grown rapidly by offering customizable analytics dashboards to mid-size businesses, but competitors were also accelerating their development of AI-driven prediction tools. Timing was everything. A delay of weeks—even days—could be the difference between being seen as an innovative leader or as a late follower in a crowded market. The leadership team had made it clear: the schedule was not merely important, it was the constraint around which the entire project must revolve.

Yet the situation inside the company was more complicated. PredictNow was in the middle of several major internal initiatives—cloud migration, cybersecurity enhancements, and three enterprise client deployments—all demanding both human and financial resources. Budgets had been tightened across the organization; every team was instructed to justify even minor expenses. Emma had been warned that while some additional resources might be made available for the forecasting project, the company did not want the project to overshoot its allocated budget unless the expense was essential to meeting the strict timeline. The management team emphasised that completing the project earlier than November 2 was not a priority and should not justify additional spending. If an activity could be completed more cheaply by taking slightly longer, and if that extra time did not jeopardize the delivery deadline, then the cheaper option should be taken.

Scope, meanwhile, was a different story. PredictNow had always prided itself on building highly customizable and feature-rich analytical tools, but Emma was told explicitly that this project did not need to deliver every feature in the first release. The leadership team was comfortable with launching a smaller set of features—if necessary—with the possibility of releasing enhancements incrementally over the following months. As long as the core forecasting engine and the basic dashboard interface functioned reliably before the November deadline, some advanced visualisation components or integration capabilities could be deferred.

The product vision itself, however, was compelling. PredictNow’s idea was to create an accessible forecasting tool that could process sales data in near real-time and provide small business owners with demand predictions without requiring any background in analytics. The tool would use a combination of sales history, local events, seasonal patterns, and machine learning algorithms to help retailers avoid stockouts and overstocking. The interface needed to be intuitive—retailers had neither time nor the expertise to navigate complex dashboards. For Emma, the project represented everything she loved about data science: taking advanced analytics and translating them into tools that genuinely helped people make better decisions.

On the morning of April 13^th, 2026, Emma began to sketch the first version of the project plan. She listed out the essential activities required to bring the forecasting tool from concept to release. Her preliminary table, drafted before any consultation with specialised teams, included tasks such as market research, model architecture work, data infrastructure assessment, model selection, marketing strategy development, pipeline building, dashboard development, beta testing, final integration, procurement tasks, and provisioning of technical resources. Her early estimates reflected her experience but had not yet been vetted by the data science, engineering, or procurement teams. The initial version of Emma’s drafted activities looked as follows:

Table 1. Emma’s Initial Draft of Activities for the PredictNow Forecasting Tool

With this draft in hand, Emma organised a cross-functional meeting to validate her sequence of activities and confirm she had captured the technical requirements accurately. The discussion revealed several points that would eventually require adjustments. The data engineering and machine learning teams indicated that coordinating the completion of the Model Architecture Design and Data Infrastructure Assessment tasks at the same time would make the subsequent model selection review more efficient. Ideally, both outputs would be available together so the teams could conduct a joint evaluation without delays or repeated meetings.

Procurement provided a different type of insight. While placing the orders for cloud compute credits or GPU compute packages reflected the estimated durations shown in Emma’s initial table, the actual provisioning of these resources required additional time beyond the ordering period. Cloud compute credits typically took an extra ten days to become accessible to engineering teams, and GPU compute packages often took up to fifteen additional days before they could be used for model training or deployment. These delays were not optional and would need to be integrated into any realistic scheduling plan.

Procurement also explained that these additional delays involve no internal work and therefore do not consume PredictNow personnel time. The company simply waits for the cloud providers to complete their setup processes. Those ten or fifteen days represent vendor-side configuration periods during which no PredictNow employee is assigned, no labour hours are incurred, and no internal resources are occupied. Only once the cloud credits or GPU packages become available does PredictNow’s technical team begin the actual provisioning work.

Resource availability further complicated matters. PredictNow employed a small team of data scientists, machine learning engineers, data engineers, marketing analysts, and one very overloaded procurement officer (see Table 2). Many of these individuals were already assigned to other projects and could only commit limited hours to the forecasting tool. Emma knew that resource over-allocation could easily derail the schedule if not detected early. The constraints of an eight-hour workday, the upcoming public holidays, and the tight pool of available personnel meant the schedule had to be carefully analysed and possibly restructured.

A few days after the kickoff, Emma received confirmation from the leadership team that additional temporary resources could be hired, but again, with conditions. These resources could be allocated only to specific activities where they clearly shortened the schedule and helped meet the November deadline (see Table 3). They were not to be used to accelerate work that was already on track or to add functionality that went beyond what was necessary for launch. PredictNow simply did not have the financial flexibility for that.

As she refined the project plan, it became increasingly clear to Emma that she needed a rigorous analysis of the project timeline, resource usage, potential bottlenecks, and cost implications. She needed to know whether the project could realistically be completed by November 2 under the current conditions. She needed to understand where delays would be most dangerous and which activities were critical. She needed to identify whether resource over-allocation was present and whether a revised plan would have to be created. And, importantly, she had to decide judiciously whether the additional resources available should be used or avoided to preserve the budget.

Recognising the complexity of the problem, Emma approached the students of the BUSINFO 710 Project Management course for assistance. Their task would be to assess the feasibility of the project, construct a refined and realistic activity schedule, identify critical paths, evaluate resource conflicts, determine whether additional resources should be assigned, estimate the total cost, and propose a plan that would allow PredictNow to meet its strict November 2 deadline without exceeding necessary financial constraints.

For Emma, the stakes could not have been higher. Delivering the forecasting tool in time for the holiday season could significantly strengthen PredictNow’s position in the competitive analytics market. Failing to do so could mean missing the most lucrative window of the year and losing valuable ground to competitors.

Note: The project team works eight-hour days, Monday through Friday, but not on public holidays. April 13^th, 2026, is the start date.

The following public holidays are observed in New Zealand in 2026: New Year’s Day (Jan 1st ), Day after New Year’s Day (Jan 2nd), Waitangi Day (Feb 6th ), Good Friday (Apr 3rd), Easter Monday (Apr 6th), Anzac Day (Apr 25^th – Observed Monday 27 April), King’s Birthday (Jun 1st), Matariki (Jul 10th), Labour Day (Oct 26th), Christmas Day (Dec 25th), Boxing Day (Dec 26th).

Table 2. PredictNow Project Resources

 Table 3. Additional Resources (Only if needed for timeline)

 Deliverables Format

• Prepare a business report with insight and recommendations to Emma. Show simple tables or graphs summarising relevant information, and include Gantt charts, Network Diagrams, Dashboards, etc, that can support your analysis and recommendations. Max 2,500 words.
• Use the recommended report structure (adapt the names for headings and subheadings to communicate the contents of each section accordingly):
• Cover page.
• Executive Summary
• Introduction (include a priority matrix)
• Problem and Objectives of the project
• Analysis of the initial plan
• Proposed plan
• Final Recommendations to the client
• Label any images and tables clearly and consistently in your report. Referencing and citations are not required.