Harvard Case - Artificial Intelligence for Improving the Procurement Experience of Non-Stock Items at Indian Railways
"Artificial Intelligence for Improving the Procurement Experience of Non-Stock Items at Indian Railways" Harvard business case study is written by Sumanta Singha, Milind Sohoni, Sripad Devalkar, Vijaya Sunder M. It deals with the challenges in the field of Operations Management. The case study is 6 page(s) long and it was first published on : Mar 27, 2023
At Fern Fort University, we recommend that Indian Railways implement a comprehensive AI-powered procurement system for non-stock items, focusing on streamlining the procurement process, enhancing transparency, and improving efficiency. This system should leverage machine learning algorithms to automate tasks, predict demand, and optimize inventory levels, ultimately leading to significant cost savings and improved service delivery.
2. Background
This case study focuses on the challenges faced by Indian Railways in procuring non-stock items, which are items not regularly stocked but are required for maintenance, repairs, and other operational needs. The current manual system is inefficient, prone to delays, and lacks transparency, leading to high costs and operational disruptions.
The main protagonists of the case study are:
- Indian Railways: The largest railway network in the world, facing the challenge of improving its procurement processes for non-stock items.
- Mr. Sharma: The Chief Procurement Officer, responsible for overseeing the procurement function and seeking solutions to improve efficiency.
- The Procurement Team: The team responsible for sourcing, negotiating, and procuring non-stock items.
3. Analysis of the Case Study
The case study highlights several key challenges faced by Indian Railways in procuring non-stock items:
- Manual Processes: The current system relies heavily on manual processes, leading to inefficiencies, delays, and errors.
- Lack of Transparency: The procurement process lacks transparency, making it difficult to track orders, monitor costs, and ensure accountability.
- Unpredictable Demand: The demand for non-stock items is often unpredictable, leading to overstocking or stockouts.
- Limited Supplier Data: The lack of comprehensive supplier data hinders effective supplier selection and negotiation.
- Inefficient Inventory Management: The absence of a robust inventory management system results in high inventory carrying costs and stockouts.
To address these challenges, Indian Railways can leverage the power of AI and its applications in supply chain management and operations strategy.
Strategic Framework:
This case study can be analyzed using the Porter's Five Forces framework to understand the competitive landscape of the procurement process and identify opportunities for improvement. The framework considers:
- Threat of New Entrants: The potential for new suppliers to enter the market and disrupt the existing procurement process.
- Bargaining Power of Buyers: The influence of Indian Railways as a large buyer on supplier pricing and terms.
- Bargaining Power of Suppliers: The power of suppliers to influence pricing and availability of non-stock items.
- Threat of Substitute Products: The availability of alternative products or services that could replace the need for non-stock items.
- Competitive Rivalry: The intensity of competition among existing suppliers for Indian Railways' business.
Operational Framework:
The case study can also be analyzed using the Lean Manufacturing framework to identify areas for process improvement and waste reduction. This framework focuses on:
- Value Stream Mapping: Identifying and analyzing the entire procurement process, from order initiation to delivery.
- Waste Elimination: Identifying and eliminating non-value-adding activities, such as unnecessary paperwork, delays, and redundancies.
- Continuous Improvement: Implementing a culture of continuous improvement through Kaizen initiatives and process optimization.
- Just-in-Time (JIT) Production: Optimizing inventory levels by procuring items only when needed.
4. Recommendations
To improve the procurement experience of non-stock items at Indian Railways, we recommend the following:
1. Implement an AI-powered Procurement System:
- Automated Order Processing: Use AI algorithms to automate order processing, reducing manual effort and errors.
- Demand Forecasting: Leverage machine learning models to predict demand for non-stock items based on historical data and external factors.
- Supplier Selection & Negotiation: Utilize AI-powered tools to analyze supplier data and identify the most suitable suppliers based on price, quality, and delivery time.
- Contract Management: Automate contract creation, negotiation, and management, ensuring compliance and efficiency.
- Inventory Optimization: Implement AI-driven inventory management systems to optimize stock levels, minimize waste, and reduce carrying costs.
2. Enhance Data Management and Analytics:
- Centralized Data Repository: Create a centralized data repository to store information on suppliers, orders, inventory, and other relevant data.
- Data Analytics: Utilize data analytics tools to gain insights into procurement trends, identify bottlenecks, and improve decision-making.
- Performance Monitoring: Track key performance indicators (KPIs) to measure the effectiveness of the procurement process and identify areas for improvement.
3. Foster a Culture of Innovation and Continuous Improvement:
- Pilot Projects: Implement pilot projects to test and evaluate the effectiveness of AI solutions before widespread adoption.
- Training and Development: Provide training to procurement staff on the use of AI tools and best practices.
- Collaboration and Knowledge Sharing: Encourage collaboration and knowledge sharing among procurement team members.
5. Basis of Recommendations
These recommendations are based on the following considerations:
- Core Competencies and Consistency with Mission: The proposed AI-powered system aligns with Indian Railways' mission to improve efficiency, reduce costs, and enhance service delivery.
- External Customers and Internal Clients: The system will benefit both external customers, through improved service delivery, and internal clients, through streamlined processes and reduced costs.
- Competitors: Implementing AI-powered procurement solutions will give Indian Railways a competitive advantage in the industry.
- Attractiveness: The proposed system is expected to generate significant cost savings, improve efficiency, and enhance transparency, making it highly attractive.
- Assumptions: The recommendations assume that Indian Railways has access to the necessary data, resources, and expertise to implement the proposed solutions.
6. Conclusion
By implementing an AI-powered procurement system, Indian Railways can significantly improve the procurement experience of non-stock items, leading to reduced costs, increased efficiency, and enhanced service delivery. This system will empower the procurement team to make data-driven decisions, optimize inventory levels, and streamline processes, ultimately contributing to the overall success of Indian Railways.
7. Discussion
Alternatives:
- Manual Process Improvement: While this alternative would be less expensive in the short term, it would not address the fundamental challenges of efficiency, transparency, and scalability.
- Outsourcing Procurement: This alternative could be considered, but it would require careful selection of a reputable outsourcing partner and might lead to a loss of control over the procurement process.
Risks and Key Assumptions:
- Data Quality: The success of the AI-powered system depends on the quality and availability of data.
- Implementation Challenges: Implementing a new system can be complex and time-consuming, requiring careful planning and execution.
- Resistance to Change: Some employees may resist the adoption of new technology, requiring effective change management strategies.
Options Grid:
| Option | Cost | Time | Risk | Benefits |
|---|---|---|---|---|
| AI-powered System | High | Medium | High | High |
| Manual Process Improvement | Low | Low | Low | Low |
| Outsourcing Procurement | Medium | Medium | Medium | Medium |
8. Next Steps
Timeline:
- Phase 1 (3 months): Conduct a feasibility study, identify key stakeholders, and develop a detailed implementation plan.
- Phase 2 (6 months): Implement the AI-powered system in a pilot project, gather feedback, and refine the system.
- Phase 3 (12 months): Roll out the system across the entire organization, provide training to staff, and monitor performance.
Key Milestones:
- Selection of AI Solutions: Identify and select suitable AI solutions from reputable vendors.
- Data Integration: Integrate data from various sources into a centralized repository.
- System Testing and Validation: Conduct thorough testing and validation of the AI-powered system.
- Staff Training: Provide comprehensive training to procurement staff on the use of the system.
- Performance Monitoring: Establish a system for monitoring and evaluating the performance of the system.
By following these recommendations and implementing the proposed AI-powered procurement system, Indian Railways can transform its procurement processes, leading to significant improvements in efficiency, cost savings, and service delivery.
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Case Description
During the summer of 2021, Sumana G., Chief Technology Officer of South Central Railway, was reviewing the annual productivity reports of field employees. This was an annual exercise that was crucial to central planning as it helped identify potential weaknesses and possibilities for improvement. Sumana knew that evaluating the productivity of store personnel would be the most challenging task because Indian Railways (IR) managed over 280,000 items stocked in 215 depots across the country. While reviewing the time sheets, Sumana quickly realized that field officers were spending a significant time amount of time on materials purchase, especially items purchased locally by field offices. On further inquiry, field officers revealed that retrieving data from the stores database based on item descriptions posed considerable challenges, and in most cases, the search results were not very useful. Sumana was quick to realize that an artificial intelligence (AI)-based search engine could solve this problem.
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