Critical Thinking Assignment 100 Points Important Read First

Critical Thinking Assignment100 Pointsimportant Read Firstchoose On

Choose one of the following two assignments to complete this week. Do not do both assignments. Identify your assignment choice in the title of your submission. When you are ready to submit, click the Module 4 Critical Thinking header on the Assignments page to upload the document.

Assignment Choice #1 : Decision Support Systems

The owner of a hardware store wants to design a decision support system to predict how many and which type of nails she should sell and what information she needs to do so. The scenario is described below: Consider that you offer six types of nails and can make as many as you need of each. These are: 4-inch nails, 3.5-inch nails, 3-inch nails, 2.5-inch nails, 2-inch nails, and 1.5-inch nails. The cost of making each type of nail depends on the size of the nail. The costs and selling prices are listed in the table below, along with the weights. The nails will be sold in boxes of up to 30.

There must be no more than 10, but no less than five, of each of three types of nails in each box. The nails in each box should weigh no more than 20 ounces. You’re looking for the combination with the highest profit using a trial-and-error method. A spreadsheet would be helpful for completing this project. You’ll most likely find that you identify some promising paths to follow right away and will concentrate on those to reach the best one.

Justify at least three different considerations you looked at while attempting to come up with the most profitable solution. What would go into such a system if you were the owner of the store and your business profits were at stake? Your submission must be a minimum of two pages, double-spaced and comply with CSU-Global Guide to Writing and APA Requirements.

Assignment Choice #2 : How Would You Classify People?

Some have suggested that neural networks could be applied to people to indicate how likely they are to develop a disease or even become criminals. The idea is to add a child’s personal characteristics, demographics, and genealogy into a neural network, and the neural network will classify if that youngster is at risk for a disease or for aberrant behavior. For this assignment, choose either susceptibility to disease or to criminal behavior. Make the following lists, explaining why you chose each one. Justify at least two different considerations: What personal characteristics would be useful? What demographic factors would strongly influence a person’s future? What, if any, inherited characteristics can predict a child’s future? Your submission must be a minimum of two pages, double-spaced and comply with CSU-Global Guide to Writing and APA Requirements.

Paper For Above instruction

Critical thinking is an essential discipline in decision-making and predictive modeling, especially in fields such as retail management and social sciences. When approaching complex problems—whether designing a decision support system for a hardware store or predicting future health risks—critical analysis involves evaluating relevant factors, formulating strategic considerations, and applying technical knowledge. This essay explores two distinct but interconnected scenarios: developing an optimal nail packaging strategy to maximize profits and identifying key characteristics for classifying individuals at risk of developing diseases or engaging in criminal behavior.

Decision Support System for Nail Packaging and Sales

The first scenario entails creating a decision support system (DSS) that predicts the most profitable mix of nails for a hardware store. The primary considerations include optimizing profit margins, adhering to packaging constraints, and ensuring product variety meets customer demand. From a critical perspective, three major considerations influence the development of an effective solution:

1. Cost-Benefit Analysis of Nail Sizes: Each nail size has distinct production costs and selling prices, directly impacting profitability. For example, 4-inch nails cost 4 cents and sell for 8 cents, resulting in a gross profit of 4 cents per unit. Conversely, 1.5-inch nails cost 1.5 cents and sell for 2 cents, yielding a profit of 0.5 cents. Prioritizing sizes with higher profit margins ensures maximum revenue, but also requires balancing inventory diversity to meet customer preferences and market demand.
2. Weight Constraints and Packaging Regulations: The total weight per box must not exceed 20 ounces, and each box should contain between five and ten nails of three types. These constraints necessitate calculating combinations that maximize profit while satisfying these bounds. For example, larger nails weigh more but may have higher profit margins, whereas smaller nails are lighter and allow a larger quantity per box. The critical consideration involves selecting compositions that optimize the sum of profits without violating weight or quantity limitations.
3. Order Size and Quantity Limits: Each box can contain up to 30 nails, with restrictions on minimum and maximum counts for specific types. These limits influence the search space for the optimal combination. A potential approach involves employing trial-and-error methods supported by spreadsheet modeling to iteratively evaluate configurations and identify the highest-profit assembly of nails within the criteria. Critical analysis here involves simulating different options and analyzing their profitability impacts.

If I, as the store owner, were building this system, integration of real-time sales data, inventory levels, and customer preferences would be essential. The system would need a robust database that captures historical sales trends, seasonal fluctuations, and customer feedback to refine predictions over time. Predictive analytics could aid in adjusting the nail mix dynamically, responding to evolving market needs and maximizing profitability.

Moreover, a decision support system in this context would benefit from features such as automated scenario analysis, graphical visualizations, and user-friendly interfaces for trial-and-error testing. Incorporating machine learning algorithms could further enhance decision accuracy by learning which combinations historically generate the highest margins. Overall, critical considerations include balancing economic factors with operational constraints, leveraging data analytics, and ensuring flexibility to adapt to market shifts.

Classifying Individuals at Risk Using Neural Networks

The second scenario involves applying neural networks to classify children based on their likelihood of developing diseases or criminal behaviors. Critical analysis here involves selecting relevant personal and demographic characteristics and understanding inherited traits that influence future risks. Two primary considerations include:

1. Relevant Personal Characteristics and Demographics: Personal attributes such as genetic predispositions, health history, behavioral tendencies, and psychological profiles significantly influence risk assessment. Demographic factors like socioeconomic status, family background, education level, and community environment also bear strong predictive power. For instance, children from lower socioeconomic backgrounds with limited access to healthcare may face higher disease susceptibility, while behavioral tendencies might be influenced by early childhood experiences and familial instability.
2. Inherited and Genetic Factors: Genetic inheritance plays a crucial role in predicting future health and behavioral outcomes. Specific inherited traits, such as genetic mutations associated with diseases or predispositions to antisocial behaviors, can act as indicators when incorporated into neural network models. For example, research links certain gene variants to increased risks of conditions like schizophrenia or cerebral palsy, which could inform predictive classifications.

In choosing these considerations, the goal is to develop a multidimensional profile that accurately reflects an individual’s future risk. Integrating personal, demographic, and genetic data enables a neural network to identify patterns that might not be obvious through traditional assessments. Ethical concerns, data privacy, and the potential for bias must also be critically examined when deploying such models.

Overall, the classification of individuals at risk through neural networks hinges on selecting relevant, measurable features that have proven associations with outcomes of interest. Combining informed insights from genetic research, epidemiology, and social sciences enhances the model’s predictive capabilities, ultimately contributing to targeted interventions and preventive strategies in healthcare and criminal justice contexts.

Conclusion

Both scenarios demonstrate the importance of critical thinking in decision-making processes involving complex data analysis, optimization, and ethical considerations. Developing an effective decision support system for nail sales requires balancing profitability constraints with operational limitations, while classifying individuals at risk demands a nuanced understanding of multidimensional factors influencing future outcomes. Critical evaluation and strategic considerations underpin successful implementation in both contexts, emphasizing the indispensable role of analytical reasoning in practical applications.

References

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