MG308 2024/25 Summative Assessment

A European healthcare technology company specialises in the design, manufacture, and distribution of diagnostic solutions for chronic conditions including cardiovascular disease, type 2 diabetes, and other metabolic disorders. The company recently appointed a new CEO. The CEO previously worked for a company that extensively used Monte Carlo simulations for many of its internal projects. Having seen the benefits of these simulations for that business, she decided to implement them for the healthcare technology company as soon as possible. As part of this, the CEO asked different departments within the company to put forward proposals for projects that would benefit from simulation modelling and analysis.  These proposals were considered by the board of directors and two initial projects were chosen to be prioritised. The first project was put forward by the commercial strategy team in relation to the launch of a new point-of-care diagnostic device. The second project, put forward by the supply chain planning team, focuses on analysing the allocation of injectable treatments for the company's recently established distribution capability.

The CEO has already convinced the board to hire a Monte Carlo simulation expert to join the healthcare technology company permanently. However, this expert cannot join the company before completing the three-month notice period of his current role. As the CEO is keen to start implementing these simulations as soon as possible, she approached your consultancy to work on these priority projects. The company will review your analysis and recommendations, and use them to inform their decision-making process. Afterwards, the  CEO is planning on passing your models to the newly hired simulation expert to review and expand on for future projects.

Data and Assumptions

Where required for financial analysis for any of the projects below, you have been asked to use a discount rate of 10%.

1)  New Point-of-Care Diagnostic Device

The company is preparing to launch a next-generation point-of-care diagnostic device designed for use in community-based healthcare settings such as general practices, local health centres, and diabetes prevention programmes. The device performs rapid HbA1c screening along with additional metabolic markers to support early identification and proactive management of type 2 diabetes.

While many portable HbA1c testing kits currently exist, most operate as standalone units with basic measurement outputs. This new device goes further by integrating directly with existing electronic health records (EHRs), automatically updating patient profiles, and providing real-time clinical prompts to guide treatment escalation, referrals, or lifestyle interventions. This functionality is particularly valuable in high-volume settings where clinicians have limited time but require actionable data at the point of care.

The commercial strategy team would like you to model and analyse the financial viability of this launch in an initial target region. This focused regional launch strategy allows the company to test the market with controlled investment, refine the product offering based on early feedback, and establish a strong presence before expanding through phased rollouts to additional European markets. Therefore, your analysis should focus solely on the financial viability of the initial regional launch. They are seeking to understand the risks associated with this launch and the range of potential outcomes. To this end, they asked you to share any relevant insights from your analysis to aid in their decision-making process.

Furthermore, they have asked you to put forward one recommendation for enhancing the financial success of the launch based on your analysis, and demonstrate how exactly this recommendation will help them.

The commercial strategy team consulted various internal stakeholders and shared the following information with you regarding the launch of the new diagnostic device. All data provided to you has been checked and reviewed, and the team confirmed it does not contain any errors.

Analysis Period

In line with the company’s standard financial modelling guidelines, a 10-year analysis period has been established, reflecting the expected market lifespan of the device. No significant hardware replacements are anticipated within the analysis period, and routine maintenance and software support obligations over the product's lifetime have been pre- estimated and are included in the per-unit variable cost for the purpose of this analysis.

Product Economics

The company plans to sell the device directly to clinics. The planned sale price of each unit in the first year of the launch is €2,400, benchmarked against comparable advanced point-of-care devices with EHR integration. The estimated variable cost per unit in the first year of the launch is €900, reflecting the direct costs of producing each device, including sensors and connectivity components, and the per-unit cost of ongoing maintenance and software support. The company assumes the prices and variable costs increase by 3% annually to account for inflation, reflecting both general price trends and  the specific impact of global supply chains and imported components in the medical device industry. The product will leverage the company’s existing manufacturing infrastructure and distribution channels, so fixed costs are already covered by other product lines and do not need to be included in your analysis.

While diagnostic devices typically generate ongoing consumable sales, the company has an established reagent supply chain with stable margins. To maintain focus on the device's core value proposition, this analysis considers only hardware sales, as clinical sites already purchase compatible reagents through existing contracts.

Initial Investment

To bring the product to market, the company must complete the final stages of Conformité Européenne (CE) marking, obtain other regulatory approvals, and fund targeted marketing campaigns to clinicians and practice managers. The initial investment is expected to fall between €7 million and €10 million, with all values within  this range considered equally likely. This reflects historical launch costs for comparable devices, as well as variations in regulatory timelines and regional marketing spend.

Market Dynamics

The strategic marketing team has identified several factors that will influence the commercial success of the new device.

o Initial Market Size

The initial addressable market for this diagnostic device is estimated at 5,000 clinical sites, each expected to require a single device, across the company's target launch region in Central Europe. This represents community-based healthcare settings including general practices, local health centres, and diabetes prevention programmes in mid-sized markets including Austria, Switzerland, and parts of Germany, where patient volume and clinical focus make them well suited to benefit from advanced HbA1c testing capabilities with EHR integration.

o Market Growth Assumptions

Demand for community-based diagnostics in the target region is expected to grow, driven by increased awareness of chronic conditions and a broader shift toward decentralised models of care. Based on a review of external market forecasts and  internal planning benchmarks, the strategic marketing team estimates that future  growth for each year over the analysis period is expected to average 5%. The team believes annual growth rates are symmetrical around this average figure. Furthermore, they are approximately 95% sure that annual growth rates will be between 3% and 7%.

o Initial Market Share

While basic point-of-care HbA1c testing devices are already present in the market, this next-generation device with EHR integration represents a significant advancement. Initial adoption of this new device is expected to vary by region and clinic type. Clinics already investing in preventive care are more likely to adopt early, while others may wait until the device's clinical and financial value is demonstrated.

The team consulted clinical affairs specialists, regional sales managers, and external primary care advisors to inform their assumptions. They believe the most likely market share in the target region in the first year of the launch is 30%, based on results from a recent pilot. It could reach as high as 50% in the best-case scenario, if key opinion leaders endorse the device and funding support is available. However, there is also a downside risk, with initial market share potentially as low as 20% if uptake is limited to early adopters and wider traction is delayed.

After the first year, market share is expected to hold steady unless altered by competitive entries.

o Competitive Landscape

The team has identified three potential competitors with the technological capabilities, regulatory readiness, and commercial focus to enter this segment of the diagnostic market in the target region in the near term. These estimates are based on historical patterns of competitor response in the point-of-care sector, as well as recent patent activity and acquisitions. At the start of each year, each competitor that has not yet entered has an independent 40% probability of launching a rival device.

Once a competitor enters, it is reasonable to assume it remains in the market, as exit barriers in regulated medical devices are substantial due to high initial investments. Based on retrospective analysis of market share erosion following competitive entry  in similar product categories, each new entrant reduces the company’s market share by 25% of its current level.

2)  New Injectable Treatment Distribution Model

As part of its strategic expansion into integrated healthcare delivery, the company recently acquired a specialised pharmaceutical distribution firm with expertise in regulated product handling and supply chain operations. This acquisition has enabled the company to connect its diagnostic offerings with downstream treatment distribution and support the broader aim of delivering more cohesive chronic care solutions.

To test the feasibility of this new distribution model, the company is piloting the sale of two formulations of a perishable injectable treatment across a selected region. This pilot is designed to validate internal forecasting methods, assess inventory management capabilities under uncertainty, and evaluate the financial implications of operating under tight shelf-life constraints.

The two products in scope are a premium formulation, which offers an extended-release profile and is typically reserved for complex or specialist care settings, and a standard formulation, which follows a conventional dosing schedule and is used more broadly across general clinics.

The supply chain planning team will use your analysis to inform both commercial decision-making and potential adjustments to the distribution agreement with the manufacturer, as part of broader future planning. The primary objective of the project is to determine the optimal allocation between premium and standard formulations that maximises total expected revenue from both product types within each 28-day distribution cycle. For this initial pilot phase, the team is focusing specifically on revenue optimisation to establish baseline performance metrics for the distribution model and asked you to ignore the costs in your analysis. This approach allows them to validate their demand forecasting accuracy and  inventory management capabilities before introducing more complex financial planning including detailed cost analysis in subsequent phases. While expected revenue serves as the primary optimisation criterion, the company is also interested in the potential variability of revenue outcomes under your proposed allocation strategy, which should be covered in your analysis.

In addition, the supply chain planning team needs to understand the implications for unsold vials that must be discarded at the end of each 28-day distribution cycle under your recommended approach. This insight is important both from an environmental sustainability perspective and for operational planning, as the disposal of biological products requires specialised handling and documentation. The analysis should determine both the probability that there will be any unsold vials under your proposed allocation strategy and, separately, examine the range and variability of unsold vials. These complementary analyses will support planning for the company's waste management protocols.

The supply chain planning team shared the following data with you to support your analysis. The dataset has been reviewed internally, confirmed to be error-free, and deemed suitable for use in this project.

Product Economics and Shelf Life

The company will generate revenue of €190 for each vial of the premium formulation and €85 for each vial of the standard formulation. These rates reflect the relative clinical value and manufacturing complexity of each formulation and have been validated against comparable products in the market.

Both formulations are biologically sensitive and arrive with 56 days of shelf life remaining when the company receives them. However, pharmaceutical regulations stipulate that any vial sold to a clinic must have at least 28 days of shelf life remaining at the time of sale. As a result, the company has a 28-day window in which to sell the vials after receiving them at the beginning of each distribution cycle. The company’s 28-day distribution cycle is aligned with this sales window to ensure compliance with minimum shelf-life requirements. Any unsold vials after this point must be discarded, in accordance with safety regulations, and will not generate any revenue.

Distribution Agreement Parameters

Vials can only be purchased from the manufacturer in 100-unit batches for both formulations. Under the commercial terms of the agreement, at the beginning of each 28-day distribution cycle, the company receives a total of 20 batches (equivalent to 2,000 vials) across both formulations for distribution. The agreement further stipulates that the premium formulation must represent no less than 20% and no more than 50% of the total allocation for each cycle.

Demand Data

Following the acquisition of the pharmaceutical distribution firm, the company gained access to historical demand data for a comparable product that was previously distributed in the selected region, which also had two distinct formulations. The supply chain planning team, in collaboration with internal analysts, reviewed this dataset and   made adjustments to account for seasonal fluctuations in treatment demand, regional  differences in prescribing practices, evolving clinical guidelines, and recent policy changes. The supply chain planning team has confirmed that this adjusted dataset is a reasonable basis for use in your analysis to estimate demand for the premium and standard formulations of the new treatment. The dataset represents historical demand patterns over 28-day distribution cycles.

Discrete event simulations

The CEO recently met a high school friend who is now working for a Japanese car manufacturer. During their conversations, her friend mentioned that the manufacturer uses discrete event simulations to analyse and improve their production lines. Whilst the CEO is very familiar with the Monte Carlo simulation methods and the benefits they can bring to the healthcare technology company, she is not familiar with discrete event simulations, and she  is curious to find out more. When she heard that you have experience in this area, she asked you to include some information on discrete event simulations as part of your report for the  company.

In particular, she wants to understand the benefits this approach can bring in the context of  the operations of the healthcare technology company (over and above the benefits of Monte Carlo simulations). She does not require you to build or run a discrete event simulation model for this purpose. She is keen for you to exemplify and explain one possible discrete event simulation model for the company at a conceptual level. She would also like you to set out the potential insights this model can bring to the business if it were built and run. Furthermore, she requested that you explain how these insights could lead to specific operational improvements or strategic decisions for the company, and address any considerations and limitations that should be taken into account if such a model were to be developed.

Technical appendices

As the CEO intends to pass on your models to their in-house expert, she asked you to include the technical details of your Monte Carlo simulation models as appendices to the report. These appendices will allow the in-house expert to understand your models, and should include:

Inputs used in your model (where relevant, together with an explanation of any distributions you used and your rationale);

A sufficiently detailed account of your calculation approach and modelling logic – i.e., a clear description of the setup of your model including an explanation of the formulae and relationships between your inputs and how outputs of interest are calculated; and

Settings for the number of iterations and simulations you used to obtain the results.

The CEO said that all such information must be included in these appendices and not the spreadsheet itself.

Requirements

Report

In addition to a suitable introduction and conclusion, the main body of your report should include the following:

For each Monte Carlo simulation project:

-     Your assumptions

-     An explanation of your results addressing the company’s questions and requirements and the implications of your analysis

- For the new point-of-care diagnostic device project only: One well-justified

recommendation, which is supported by evidence from your analysis to convince the client

-     Any limitations of your analysis and possible next steps

A section addressing the request regarding discrete event simulations

Any graphs, outputs, results, assumptions, commentary, and analysis that you would like the client to consider should be included in the main body of the report (i.e., not in the appendices or the spreadsheet). In other words, the main body of the report should be a self-contained document that will inform the client in a satisfactory manner about your analysis and results.

Your report should have exactly two appendices (one for each one of the Monte Carlo simulation models you are asked to build). Any additional calculations and analysis performed for each project must be included in the corresponding appendix. No other appendices are allowed.

Word and page-limits

The main body of your report is subject to a limit of 3,000 words. Each appendix is subject to a limit of two pages. These appendices must have minimum single-line spacing and a font size of 11 or greater.

Spreadsheet

All your calculations must be submitted in one spreadsheet. You can use different tabs for different models, but your tabs must be appropriately titled for ease of reference.

As the company will be receiving your spreadsheet via the cloud, the CEO asked you to do the following to keep the file size small:

Once all your analysis is completed and your report is ready, save your spreadsheet after rerunning it one last time with 100 iterations and one simulation and upload this version*.

Mark Allocation

The total 100 marks for this assessment are allocated as follows:

Project 1: 35 marks (including 10 marks for Technical Appendix 1)

Project 2: 35 marks (including 10 marks for Technical Appendix 2)

Discrete event simulation section: 20 marks

Quality of presentation: 10 marks

Please refer to the DoM Undergraduate assessment criteria and the assessment-specific rubric on Moodle for further details on how these marks will be awarded.

Deadline

Both the report and spreadsheet should be submitted no later than midday (12pm UK time) on 8 May 2025.

* This does not mean you should be running 100 iterations and one simulation for all your models. It simply means the version of the spreadsheet you submit must be run one last time using these settings and saved prior to uploading. Otherwise, the file will be too large.