Client Name

Lipton Pakistan Limited

Faculty Advisor

Dr. Mohsin Zahid Khawaja

SBS Thought Leadership Areas

Investment Decision Making

SBS Thought Leadership Area Justification

This project is best classified under the Investment Decision-Making thought leadership area.

Investment decision-making involves allocating limited resources to generate the greatest return. This includes decisions about which products to invest in and where to spend trade and marketing budgets. The SKU-wise P&L framework transforms financial data into structured insights that drive resource allocation in terms of products and budgets. The framework allows management to make informed investment decisions at the product level.

The outcomes of this project have several implications for Investment Decision Making as a field.

1. Portfolio Optimization: Optimizing or discontinuing loss-making SKUs is fundamentally an investment decision as it frees up resources that can be used towards higher profit- making products.

2. Data-Driven Decision Making: As with most corporate institutions, Lipton also relied on aggregate financial data. This project aims to use granular financial modeling for better corporate investment decisions.

3. Cross-functional Co-ordination: Investment decisions require a holistic view of costs and revenues at the product level, which requires data integration from Finance, Sales, and Supply Chain. The framework allows for this inter-departmental coordination.

Aligned SDGs

GOAL 12: Responsible Consumption and Production

Aligned SDGs Justification

By identifying loss-making SKUs, we helped Lipton rationalize its portfolio. The framework helps Lipton make smarter decisions about which products to continue, discontinue, or optimize, directly reducing resources spent on unprofitable products and production waste. These data-driven production decisions align directly with sustainable consumption and production patterns.

NDA

No

Abstract

Lipton Teas and Infusions is a giant in the global tea industry and has a wide range of products across several brands and various regions. However, revenues, costs, and profitability are evaluated only at the aggregate level. The main objective of this experimental learning project was to develop a detailed and robust model that allows SKU-wise Profit and Loss Analysis. The framework is needed to allow decision-making at the product level. To achieve this, the ELP team was divided into three departments so that we could each work on a different aspect of the model and aggregate our findings at the end. In Customer Development Finance, a multi-year P&L model was constructed that shows profitability from gross sales value through trade spend and down to net revenue and gross margin, with complete regional depth. In Supply Chain Finance, several bottom-up costing models were developed covering raw materials, packaging materials, factory production costs, logistics, and bought-in products, each independently computing what costs should be and cross-referencing those figures against what the system P&L was reporting. In Financial Planning and Analysis, a centralized data repository was built to consolidate business planning, price architecture, capital expenditure tracking, and ERP data processing into a single, scalable framework for management reporting and forecasting. The key recommendation arising from this project is that the cross-functional data-sharing established during this engagement should be maintained as an ongoing practice, given that SKU-level profitability analysis is inherently dependent on coordinated inputs from Finance, Supply Chain, and Commercial functions working in alignment.

Document Type

Restricted Access

Document Name for Citation

Experiential Learning Project

Notes

This report is the product of a hands-on project that involved real time development of models and use of company data. All information presented in the report contains factual data and practical workings, therefore it is requested that unauthorized use of it is avoided. 

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