Degree

Master of Business Administration Executive

Faculty / School

School of Business Studies (SBS)

Year of Award

2025

Advisor/Supervisor

Mr. Adnan Ahmed, Visiting Faculty, Department of Marketing

Project Type

MBA Executive Research Project

Access Type

Restricted Access

Keywords

Supply Chain Management, Inventory Optimization, Lead Time Forecasting, Machine Learning, Supplier Diversification

Executive Summary

For resilient and cost-effective supply chain efficient inventory management is a crucial component. The purpose of this study is to explore the challenges in inventory management and supply chain risks encountered by Gatronova, one of the pioneer company in Pakistan’s polyester and polyethylene terephthalate (PET) resin industry. Since company’s production highly depend on imported raw materials, specifically Purified Terephthalic Acid (PTA) and Mono-Ethylene Glycol (MEG), the company faces challenges like fluctuations in lead times, supply disruptions, and inefficiencies in stock management. These barriers not only distort operational continuity but also increase financial risks such as excess holding costs, emergency procurement expenses, and loss of revenue due to delayed production cycles.

The study has identified four major issues leading to inefficiencies in inventory management of Gatronova:

  1. Variability in Lead Time – Volatility in supply chain, geopolitical issues, and interruptions in global trade routes (e.g., Strait of Hormuz and Malacca) causing irregular lead times, leading to either stock-outs or excessive inventory holding.
  2. Limited Supply Base & Geographic Concentration Risks – Gatronova depends heavily on some specific suppliers concentrated in specific regions, which make it vulnerable to disruptions if any conflict occurs in those areas.
  3. No Predictive Analytics in Management of Inventory– The company has conventional push-based inventory systems which fails to optimize procurement planning, eventually becomes reason for reactive decision-making rather than proactive risk mitigation strategies.
  4. Risk of Geographical Diversification – Because the company depends on limited trade routes and regional suppliers it increases the risk for exposure to supply chain disruptions caused by geo-political tensions, impacts due to climate change, and logistical barriers. A natural catastrophe or trade constraints in one region could completely halt production process in Gatronova thus highlighting a crucial point that it is essential to expand both supplier networks and trade routes.

To assess and provide solution for these challenges, the study uses historical data analysis, machine learning techniques, and scenario-based simulations to propose a predictive lead time forecasting model. The model is based on previous supply chain data, external macro-economic factors, and real-time tracking metrics to determine accurate lead time predictions for raw material shipments. As well as according to supplier diversification strategy it is also recommended to decrease dependency on limited geographical locations and increase supply chain resilience.

Major findings from this study highlight that data-driven inventory planning, with integrated predictive analytics and diversified procurement strategies significantly minimizes uncertainty in availability of raw material. In addition to this it lowers costs and improves overall supply chain efficiency. The recommended facilitators when implemented are expected to:

  • Decrease lead-time variability by 30-40% by increasing accuracy in demand forecasting.
  • Decrease emergency procurement costs by ensuring adequate safety stock levels.
  • Expand supplier diversification by identifying alternative procurement sites outside of existing high-risk regions.
  • Develop contingency plan and strategies for geo-political risks, to make sure business continuity in case of any trade route disruptions.

In conclusion, to optimize the process by real-time inventory tracking, supplier contract flexibility, and contingency planning for geopolitical disruptions there is a need to data-driven decision-making model, machine learning-based forecasting models, and proactive supplier management that will help in improving overall operational efficiency in a volatile global trade system.

Pages

xii, 105

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Available for download on Monday, April 22, 2030

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