Executive Summary

PT Gendhis Multi Manis, the first sugarcane-based sugar factory built in Indonesia since 1982, began operations in mid-2014. The factory, with a sugarcane milling capacity of 4,000 to 6,000 TCD (Ton Cane Day), faced significant operational challenges in sales, inventory management, and demand forecasting. Additionally, outdated software systems for accounting and HR, and reliance on manual processes, hampered efficiency. This case study explores the comprehensive digital transformation project undertaken to address these issues, which included implementing a suite of integrated modules and a machine learning algorithm for sales demand forecasting.

Company Background

• Name: PT Gendhis Multi Manis
• Industry: Sugar Manufacturing
• Location: Indonesia
• Founded: 2014
• Capacity: 4,000 – 6,000 TCD

Challenges

1. Sales and Demand Forecasting:
   • Inability to accurately forecast sales demand.
   • Sales data was fragmented and lacked real-time insights.
2. Inventory Management:
   • Difficulty in tracking inventory levels.
   • Overproduction and underproduction issues due to poor demand forecasting.
3. Vendor Management:
   • Manual processes for managing vendors, tracking RFQs, and coordinating via emails.
   • Lack of a centralized system for vendor information.
4. Outdated Software Systems:
   • Legacy software for accounting and HR was inefficient and prone to errors.
   • Heavy reliance on Excel spreadsheets for data management across departments.
5. Operational Efficiency:
   • Manual tracking of key business processes leading to errors and delays.
   • Lack of integration between different departments.

Objectives

• Implement a Customer Relationship Management (CRM) system.
• Develop an integrated Sales and Inventory Management system.
• Establish a Vendor Management module.
• Overhaul the Manufacturing and Quality Assurance processes.
• Modernize Accounting and Human Resources (HR) systems.
• Utilize a Machine Learning (ML) algorithm for accurate sales demand forecasting.

Implementation

1. CRM and Sales Module

• CRM System: Implemented a robust CRM to centralize customer data, manage interactions, and track sales leads.
• Sales Management: Integrated sales module to streamline sales processes, track performance, and enhance customer relationships.
• Forecasting: Used historical sales data to train the ML algorithm for predicting future sales.

2. Inventory Management System

• Real-Time Tracking: Developed an inventory management system to monitor stock levels in real-time.
• Automated Reordering: Set up automated reorder points to maintain optimal inventory levels.
• Integration with Sales: Linked inventory system with sales data to adjust inventory based on forecasted demand.

3. Vendor Management Module

• Centralized Database: Created a centralized database for all vendor information, facilitating easy access and management.
• RFQ Automation: Automated the RFQ process to streamline vendor interactions and ensure timely procurement.
• Email Integration: Integrated email functionalities within the system for seamless communication with vendors.

4. Manufacturing and Quality Assurance

• Production Planning: Implemented a production planning module to optimize manufacturing schedules based on demand forecasts.
• Quality Control: Established a quality assurance module to monitor product quality throughout the manufacturing process.

5. Accounting and HR System

• Accounting Software: Replaced legacy accounting software with a modern, integrated solution for accurate financial tracking and reporting.
• HR Management: Deployed an HR module to manage employee data, payroll, and performance reviews efficiently.
• Excel Integration: Transitioned from Excel-based operations to a centralized, automated system to reduce errors and improve data integrity.

6. Machine Learning Algorithm

• Data Collection: Aggregated historical sales, market trends, and other relevant data to train the ML model.
• Demand Forecasting: Deployed the ML algorithm to provide accurate demand forecasts, enabling better production planning and inventory management.
• Continuous Learning: Set up the system for continuous learning and improvement based on new data inputs.

Results

• Improved Demand Forecasting: The ML algorithm significantly improved sales demand forecasting accuracy, reducing overproduction and stockouts.
• Enhanced Inventory Management: Real-time inventory tracking and automated reordering led to optimized stock levels and reduced holding costs.
• Streamlined Vendor Management: Centralized vendor data and automated RFQ processes enhanced procurement efficiency and vendor relationships.
• Operational Efficiency: The integration of CRM, sales, inventory, manufacturing, and quality modules streamlined operations, reducing manual efforts and errors.
• Modernized Systems: Upgraded accounting and HR systems improved data accuracy and streamlined financial and personnel management.
• Increased Sales and Customer Satisfaction: Better sales processes and customer relationship management contributed to increased sales and improved customer satisfaction.

Conclusion

The digital transformation project at PT Gendhis Multi Manis successfully addressed the company’s challenges through the implementation of integrated modules and advanced machine learning for demand forecasting. This comprehensive approach not only optimized operations but also positioned the company for sustained growth and competitive advantage in the sugar manufacturing industry.