Client Name

ITT Foods Pvt Ltd

Faculty Advisor

Dr. Fawad Ahmad

SBS Thought Leadership Areas

Investment Decision Making

SBS Thought Leadership Area Justification

Our project fits well with Investment Decision Making, which is a key focus of Thought Leadership area at IBA. The project provided useful insights to Dipitt’s management by merging financial data analysis with strategic business tools.

One key example is the use of ABC analysis to identify the most cost-efficient and profitable SKUs. This enabled the company to prioritize high-performing pack sizes and allocate marketing resources more effectively across different regions and distribution channels. Rather than relying on gut instinct, Dipitt could make informed decisions backed by real financial patterns.

Beyond current operations, we used predictive analytics to forecast future sales and logistics costs. This forward-looking approach will help the company create a data-driven budget that anticipates market dynamics instead of simply reacting to them. More than just an academic exercise, this demonstrates how advanced analytical models can directly shape executive decision-making- helping Dipitt optimize resources, streamline operations, and confidently explore growth opportunities in both domestic and international markets.

As the company expands, the tools and insights from our project will serve as a scalable decision-making framework, ensuring that investment strategies remain data-driven, efficient, and strategically sound. This reflects the essence of Investment Decision Making, transforming raw data into tangible business actions that strengthen financial outcomes and long-term stability.

Aligned SDGs

GOAL 12: Responsible Consumption and Production

Aligned SDGs Justification

The project also promotes resource efficiency and reduces waste. By examining the root causes of rejected deliveries and adjustment losses, we suggested changes that improved inventory planning and cut avoidable waste. This not only lessens environmental impact but also encourages the adoption of circular economy practices within Dipitt’s supply chain, directly contributing to more responsible production and consumption patterns.

NDA

Yes

Abstract

The following is an integrated review of Dipitt's sales performance, distribution efficiency, and business strategy under IBA’s ELP program. Using 2023 data and the first half-year of 2024 data, the project made use of diagnostic analysis and forecasting for evidence-based decision support. The most important deliverables were an interactive sales dashboard, a cost and return diagnostic model, and a time series forecasting model that predicts monthly revenues through 2027. Python was used for data cleaning, transformation, and adjustment entry classification, while Power BI was employed to create dynamic visual dashboards. Meta’s Prophet model was also implemented for forecasting sales, enabling us to account for seasonality and growth trends. Logistics costs, which lacked product-level granularity, were mapped to SKUs via a quantity-weighted allocation using DC numbers.

The analysis revealed several critical insights. Adjustment entries, which were initially dominated by vague “Miscellaneous” categories, were successfully reclassified, showing a 44% drop in 2024, indicating improved internal controls. High return rates and reverse logistics costs were concentrated in low-volume Local Trade regions, while channels like E-Commerce and Food Solutions emerged as more cost-efficient. Seasonality also significantly affected sales, with noticeable dips post-Ramadan in April. The Prophet model projected that, if current trends continue, monthly revenues could exceed PKR 2.7 billion by 2027.

The study demonstrates how data-driven insights can guide investment decisions and improve resource allocation in Pakistan’s evolving FMCG landscape. By embedding analytics into Dipitt’s core operations, the project supports scalable, sustainable growth both domestically and in export markets.

Document Type

Restricted Access

Document Name for Citation

Experiential Learning Project

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Available for download on Saturday, June 15, 2030

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