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ACCA · Question 32 · Decision-making techniques

Section C

CyberShield is a SaaS company developing a new AI threat detection module.

Part A (12 marks)
The launch of the new module faces uncertainty regarding market demand. The management accountant has prepared a payoff table showing the Net Present Value (NPV) of the project under three pricing strategies and three market demand scenarios.

Pricing StrategyLow Demand (Prob: 0.3)Medium Demand (Prob: 0.5)High Demand (Prob: 0.2)
High Price-$2m$4m$10m
Medium Price$1m$5m$7m
Low Price$3m$4m$5m
  1. Calculate the Expected Value (EV) for each pricing strategy and recommend the best strategy based on EV.
  2. Determine the optimal strategy if the management team adopts a 'Maximin' approach.
  3. Determine the optimal strategy if the management team adopts a 'Minimax Regret' approach (show your regret table).
  4. Briefly discuss the limitations of using Expected Values for this decision.

Part B (8 marks)
CyberShield currently uses incremental budgeting for its R&D department. The CFO is proposing a switch to Zero-Based Budgeting (ZBB) to better control costs and allocate resources to the most promising AI projects.

Evaluate the suitability of Zero-Based Budgeting for CyberShield's R&D department. Include both advantages and disadvantages in your evaluation.

How to approach this question

For Part A, systematically apply the mathematical rules for EV, Maximin, and Minimax Regret. Ensure the regret table is constructed correctly by finding the best outcome in each *column* (state of nature). For Part B, define ZBB and apply its pros and cons specifically to an R&D environment (e.g., the risk of cutting long-term research).

Full Answer

This question combines quantitative risk analysis with qualitative budgeting evaluation. Expected values are useful for risk-neutral decision-making but fail for one-off decisions. Maximin suits risk-averse managers, while Minimax Regret suits those fearing competitive disadvantage. ZBB is theoretically excellent for controlling discretionary costs like R&D, as it prevents budget slack, but its practical implementation is often hindered by the sheer administrative burden and the risk of stifling long-term innovation.

Common mistakes

Part A: Constructing the regret table by looking at rows instead of columns. Part B: Giving generic ZBB pros/cons without applying them to the specific context of an R&D department in a tech company.
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