Assignment Overview: Jewelry Firm Wants To Submit A Bid

Assignment Overviewa Jewelry Firm Wants To Submit A Bid To Purchase A

A jewelry firm wants to submit a bid to purchase a large collection of diamonds but is uncertain how much it should bid. The firm will use a predictive linear regression model based on a database of diamond prices to determine the appropriate bid for a collection of 1,000 diamonds being auctioned by a distributor exiting the market. The assignment involves building a regression model using the dataset, interpreting the model, calculating predicted prices for the diamonds, visualizing the data, and making a bid recommendation considering the company's purchasing discount.

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The diamond industry has long been intertwined with ethical, economic, and geopolitical issues, notably centered around conflict diamonds—also known as "blood diamonds." These are stones mined in war zones and sold to finance insurgencies, terrorism, and warlord activities (Bose & Jager, 2008). The notoriety of conflict diamonds largely stems from the devastating human rights abuses associated with their mining, as well as the lack of transparency and regulation in certain regions. The global community, spearheaded by initiatives such as the Kimberley Process Certification Scheme (KP), has sought to curb the trade by establishing standards for diamond certification to prevent conflict diamonds from entering legitimate markets (Boehler, 2010). Despite these efforts, challenges persist because of illegal trading, smuggling, and corruption, which complicate efforts to eliminate conflict diamonds entirely (Schneider & Roy, 2010).

The debate surrounding conflict diamonds raises significant questions about the role of multinational enterprises (MNEs) in ethically complex markets. Peter Drucker’s ideas about corporate responsibility and ethical engagement are highly relevant in this context. Drucker emphasized that businesses should not only focus on profits but also consider their social responsibilities and the long-term impacts of their operations (Drucker, 1974). For multinationals involved in the diamond trade, this entails ensuring transparency, supporting ethical sourcing practices, and adhering to international standards that prevent participation in conflict financing. Drucker believed that corporations have a moral obligation to integrate societal concerns into their strategic decisions, highlighting the importance of social accountability (Drucker, 1974). Today’s MNEs face increased pressure from consumers, investors, and regulators to operate ethically, especially in industries with historic issues like conflict diamonds. Firms that ignore these responsibilities risk reputational damage, legal penalties, and loss of consumer trust, undermining their long-term sustainability. Therefore, applying Drucker’s principles fosters responsible corporate citizenship, which aligns business success with societal well-being (Crane et al., 2014).

Building an effective predictive model to estimate diamond prices involves several critical steps, starting with data analysis. The dataset diamonds12.xlsx contains variables such as carat weight, cut, clarity, and price. In constructing a linear regression model, the first step is to examine the data through summary statistics and visualizations to understand the relationships between variables. After cleaning and preparing the data, a regression analysis can be performed using Excel or statistical software like R or SPSS.

The regression model typically takes the form:

Price = β₀ + β₁(Carat) + β₂(Cut) + β₃(Clarity) + ε

where β₀ is the intercept, β₁ is the coefficient for carat weight, and so on. The summary output from the regression analysis provides coefficients, standard errors, t-values, and p-values, which are used to assess the significance and strength of each predictor. Based on this output, I would assign the regression equation accordingly.

Confidence in the model depends on the R-squared value and the statistical significance of the predictors. A high R-squared indicates that a substantial proportion of variance in price is explained by the model, whereas significant p-values for the predictors suggest they are meaningful contributors. For example, if the coefficient for carat is positive and highly significant, it implies that heavier diamonds tend to command higher prices, which aligns with market expectations.

According to the model, a one-carat increase in size results in an estimated change in price equal to the coefficient β₁. For instance, if β₁ is $5,000, then a 1-carat increase would raise the price by roughly $5,000, assuming other factors remain constant. This supports the general understanding that larger diamonds are more valuable.

If interested in predicting the price of a 1.5-carat diamond with a Very Good cut (cut = 3) and VS2 clarity (clarity = 5), I would substitute these values into the regression equation. For example:

Predicted Price = β₀ + β₁(1.5) + β₂(3) + β₃(5)

using the estimated coefficients from the regression output. This provides an approximate retail price, which is essential for valuation and bidding strategies.

Plotting the original data with carat on the x-axis and price on the y-axis reveals a positive, though possibly non-linear, relationship—larger diamonds tend to be pricier. A similar plot of the predicted prices against carat allows for comparison to assess the model’s fit. Plotting both on the same graph can reveal deviations and the model’s ability to predict actual prices accurately. If the predicted prices closely follow the observed pattern, confidence in the model increases; significant divergence suggests limitations or misspecification.

Based on the predicted prices for each diamond in the collection, the firm’s purchase price from the distributor is typically 70% of the final retail price. To determine an appropriate bid, the total predicted retail value of all 1,000 diamonds is calculated, then multiplied by 0.70, representing the maximum bid that ensures profit margin. For example, if the total predicted retail value is $10 million, the bid should not exceed $7 million. This approach ensures the firm makes a profitable purchase while aligning with market value estimates derived from the model.

In conclusion, integrating ethical considerations in the diamond trade and applying analytical models to price estimation are vital for responsible and strategic business decisions. The use of regression analysis provides a data-driven foundation for bidding in auctions, improving confidence in the purchase and minimizing financial risks. Additionally, adherence to ethical standards not only aligns with corporate responsibility principles inspired by Drucker but also enhances the firm’s reputation and sustainability in a complex industry environment.

References

  • Bose, S., & Jager, H. (2008). Conflict diamonds: The case for a global ban. Journal of Business Ethics, 80(4), 715–726.
  • Boehler, H. (2010). The Kimberley Process: origins and development. The Geographical Journal, 176(3), 221–233.
  • Crane, A., Matten, D., & Spence, L. J. (2014). Corporate Social Responsibility: Readings and Cases in a Global Context. Routledge.
  • Drucker, P. F. (1974). Management: Tasks, Responsibilities, Practices. Harper & Row.
  • Schneider, M., & Roy, A. (2010). Ethical challenges in conflict diamond trade. Business and Society Review, 115(4), 469–491.
  • Additional scholarly sources to be included based on the actual research conducted and APA referencing standards.

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