BUSINESS ANALYTICS June 2026 Solved Assignment

Description

NMIMS

BUSINESS ANALYTICS

APPLICABLE FOR SEM 1 JUNE 2026 EXAMS

Q.1 A national retail chain, FreshStyles, is facing declining sales and customer complaints about product availability. The management suspects that the underlying issue stems from inconsistencies in their sales and inventory data collected from multiple branches. Their current datasets contain missing values, duplicates, and inconsistent formatting in date and product codes. Despite using Excel for analysis, the results remain inconclusive and are met with skepticism by stakeholders. The company’s analytics team has been tasked with resolving these data issues to enable trustworthy business insights and inform better inventory and sales strategies.As the lead data analyst for FreshStyles, apply appropriate data cleansing techniques (including missing value treatment, duplicate removal, and format standardization) to this real- world dataset. Describe the sequential steps you would take and explain how your approach ensures data reliability and supports more effective business decision- making? (10 Marks)

Ans.1

Introduction

Data serves as the compass that directs inventory management, marketing, and sales strategies in the fiercely competitive retail environment. The current actuality of declining sales and customer complaints regarding stockouts is a classic symptom of a disrupted data pipeline for FreshStyles. Any analysis that is derived from sales and inventory data from multiple branches that are afflicted with absent values, duplicate entries, and inconsistent formatting is rendered 

Q2(A). A manufacturing business has recently implemented a probability distribution analysis to better understand and reduce process defects. The operation team is considering whether to fit the data to a Poisson (discrete, PMF-based) or an Exponential (continuous, PDF-based) distribution. Corporate leadership is concerned about the accuracy and effectiveness of using each approach to drive quality improvement initiatives and continuous adaptation. Critically evaluate the merits and drawbacks of modeling defect data using Poisson versus Exponential distributions. Assess how the choice between the two would impact quality assurance, predictive accuracy, and the company’s adaptability to dynamic production environments, justifying your position. (5 Marks)

Answer 2a 

Introduction

Statistical modeling is the foundation of continuous quality enhancement in contemporary manufacturing. The fundamental question of how an operation defines “failure” is whether to use a Poisson distribution or an exponential distribution to represent process defects. Although they are mathematically closely related, they approach defects from entirely distinct perspectives: the Poisson distribution is discrete and counts events per interval, whereas the Exponential distribution is continuous and measures the time or distance between those events.

Strategic Evaluation: Poisson vs. Exponential:

Corporate leadership’s approach to operational adaptability, predictive accuracy, and quality assurance is significantly influenced by their selection of one of these two distributions.

Q2(B). A consumer goods company deploys a simple linear regression model to predict monthly sales from advertising spend, yielding an R-squared value of 0.82. However, regional marketing managers note that in some months, major events (such as festivals and supply chain disruptions) may cause large, unpredictable deviations in sales that the regression model does not explain. The executive team must decide how much to trust the model outputs for future campaign planning, and whether to introduce more explanatory variables or develop alternative analytics approaches.Critique the company’s reliance on the current regression model for campaign planning in light of the marketing managers’ observations. How should the executive team weigh the strong R-squared value against external factors, and what improvements or complementary analyses would you recommend to enhance decision-making robustness? (5 Marks)

Answer 2B 

Introduction

A classic, high-value starting point in retail analytics is FreshStyles’ utilization of a simple linear regression model to forecast sales based on advertising expenditures. Advertising accounts for the variance in monthly sales, as evidenced by a  value of 0.82, which is statistically significant. However, the regional managers’ observations regarding festivals and supply chain disruptions underscore a critical limitation: the model is predicated on a stable, isolated environment.