Bus 606 Fall 2021 Homework Module 5 Assigned November 2
Bus 606 Fall 2021homework Module 5assigned Monday November 22 202
Analyze decision problems involving launching timing, advertising strategies, and production scales, including sensitivity analysis and influence diagrams to maximize expected profit under uncertain conditions.
Paper For Above instruction
In this paper, we explore two complex decision-making scenarios involving business strategies, uncertainty, and probabilistic analysis: the launch timing of a new magazine by Westward Magazine Publishers and the production and competitive strategies of ABC Chemicals. Each scenario involves assessing options to optimize expected outcomes amidst uncertain events, with the application of influence diagrams, decision trees, and sensitivity analysis to identify optimal policies and understand the robustness of those policies under varying assumptions.
Introduction
Decisions in the business environment often involve uncertainty and multiple interconnected factors. Companies need tools that can model these complexities and support strategic choices that maximize expected benefits or minimize costs. Influence diagrams, decision trees, and sensitivity analysis are core methodologies in this context, allowing firms to visualize relationships, evaluate different scenarios, and test the robustness of their decisions under varying probability estimates.
Case 1: Westward Magazine Publishers’ Launch Timing and Advertising Strategy
The first case involves Westward Magazine’s decision about whether to bring forward the launch of their new fashion magazine from April to January. The decision hinges on multiple uncertain elements: whether they can beat a rival publisher’s launch, the probable circulation outcome, and the impact of advertising in boosting circulation if the rival launches first.
Key variables include the decision to bring the launch forward or not, the probability of launching before the rival under each choice, the probability of high circulation conditioned on launch timing and advertising, and associated profits. The basic structure involves evaluating the expected profit based on different policies, considering the additional costs and benefits of each option.
Using probabilistic calculations, the optimal policy involves comparing the expected profits of different strategies: launching early without extra advertising, launching early with advertising, or waiting until April with or without advertising. For example, if the probability of launching before the rival when launching early is 70%, and the probability of high circulation in that scenario is 65%, then expected profits are calculated considering these probabilities and profit margins.
The analysis suggests that bringing forward the launch could be beneficial if the expected profit exceeds that of delaying, factoring in the extra costs and probabilistic outcomes. Sensitivity analysis indicates how the optimal decision might change if probabilities such as the chance of beating the rival or the effect of advertising vary, highlighting the importance of accurate estimates and robustness of strategy under uncertainty.
Case 2: ABC Chemicals’ Production Scale, Competition, and Market Conditions
The second case centers on ABC Chemicals’ strategic decision to choose between large-scale or small-scale manufacturing of a new pharmaceutical product. This decision depends on external factors: the economic climate, whether a competitor launches a similar product, and their marketing efforts. Each combination influences the profitability of their choices, requiring a comprehensive influence diagram and decision tree approach to capture dependencies and outcomes.
Constructing an influence diagram involves nodes representing the initial decision of production scale, followed by chance nodes for the economy state, rival activity, and marketing expenditure. The diagram helps visualize the causal and informational relationships, enabling the derivation of a decision tree outlining all possible sequences and outcomes.
Expected monetary values (EMV) are calculated for each pathway. For example, high economic growth combined with no competitor’s launch and high advertising expenditure might yield higher profits under large-scale production, but at increased costs. Conversely, small-scale might be more suitable under less favorable conditions. Sensitivity analysis evaluates how changes in the probability estimates of key uncertainties influence decision outcomes, allowing the company to determine the robustness of their choices.
Through these analyses, ABC Chemicals can select a strategy that maximizes expected profit while understanding the risk sensitivities involved. The importance of flexible decision frameworks is underscored, as assumptions regarding market conditions and competitor actions significantly impact optimal policy selection.
Conclusion
Both case studies demonstrate the utility of influence diagrams, decision trees, and sensitivity analysis in strategic business decision-making under uncertainty. These tools enable firms to quantify risks, evaluate different strategies, and assess the stability of their decisions against varying assumptions. Accurate probabilistic estimates and understanding the impact of uncertainty are essential for making informed decisions that align with corporate objectives and market realities.
References
- Proceedings of the Ninth International Conference on Uncertainty in Artificial Intelligence (UAI), 77–84.