Harvard Case - Borusan Cat: Monetizing Prediction in the Age of AI (A)
"Borusan Cat: Monetizing Prediction in the Age of AI (A)" Harvard business case study is written by Navid Mojir, Gamze Yucaoglu. It deals with the challenges in the field of Marketing. The case study is 21 page(s) long and it was first published on : Apr 12, 2021
At Fern Fort University, we recommend Borusan Cat implement a multi-pronged strategy to monetize its AI-powered predictive maintenance capabilities, focusing on customer-centric value propositions and leveraging its strong brand equity. This strategy will involve: (1) Expanding its service offerings to include predictive maintenance contracts with tailored pricing models, (2) Developing digital platforms for data sharing and insights with customers, (3) Investing in marketing and communication efforts to highlight the value of AI-driven solutions, and (4) Exploring strategic partnerships to expand its reach and expertise.
2. Background
Borusan Cat, a leading distributor of Caterpillar equipment in Turkey, faces the challenge of leveraging its investment in AI-powered predictive maintenance technology. While the technology accurately predicts equipment failures, Borusan Cat needs to develop a strategy to monetize this capability and create value for its customers. The case study explores the company's current market position, the potential of predictive maintenance, and the challenges of transitioning to a more service-oriented business model.
The main protagonists of the case study are:
- Borusan Cat: The company seeking to monetize its AI investment.
- Caterpillar: The parent company providing equipment and support.
- Borusan Holding: The parent company of Borusan Cat, providing strategic guidance and resources.
- Customers: The end-users of Caterpillar equipment who are the target of Borusan Cat's service offerings.
3. Analysis of the Case Study
Strategic Framework: We will use a combination of frameworks to analyze the case study:
- SWOT Analysis: To understand Borusan Cat's internal strengths and weaknesses and external opportunities and threats.
- Porter's Five Forces: To assess the competitive landscape and identify potential barriers to entry and competitive pressures.
- Value Chain Analysis: To identify key activities and processes within Borusan Cat's operations and how AI-powered predictive maintenance can create value.
- Customer Segmentation: To understand the different needs and preferences of Borusan Cat's customer base and tailor its offerings accordingly.
Key Findings:
- Strengths: Strong brand reputation, established customer base, expertise in Caterpillar equipment, access to advanced technology, and a dedicated team of engineers.
- Weaknesses: Limited experience in service-oriented business models, potential resistance from customers to adopting new technologies, and a need to develop effective communication strategies.
- Opportunities: Growing demand for predictive maintenance solutions, potential to differentiate through AI-driven services, and expansion into new markets.
- Threats: Competition from other distributors and service providers, technological advancements that may disrupt the market, and regulatory changes affecting the industry.
Porter's Five Forces:
- Threat of New Entrants: High due to the availability of technology and potential for new players to enter the market.
- Bargaining Power of Buyers: High due to the limited number of equipment suppliers and the potential for customers to switch providers.
- Bargaining Power of Suppliers: Moderate due to the reliance on Caterpillar for equipment and technology.
- Threat of Substitute Products: Moderate due to the availability of alternative equipment and maintenance services.
- Competitive Rivalry: High due to the presence of established competitors and the need to differentiate service offerings.
Value Chain Analysis:
- Primary Activities: Equipment distribution, maintenance services, parts supply, and customer support.
- Support Activities: Technology development, human resources, procurement, and infrastructure.
- AI-powered predictive maintenance: Provides valuable insights into equipment health, optimizes maintenance schedules, reduces downtime, and improves operational efficiency.
Customer Segmentation:
- Segment 1: Large enterprises with significant equipment fleets and a strong focus on operational efficiency.
- Segment 2: Small and medium-sized enterprises with limited resources and a need for affordable maintenance solutions.
- Segment 3: Government agencies and infrastructure projects with specific requirements and compliance regulations.
4. Recommendations
1. Develop Predictive Maintenance Contracts:
- Offer tiered contracts: Based on equipment type, usage, and customer needs.
- Tailored pricing models: Fixed fees, pay-per-use, or performance-based pricing.
- Value-added services: Remote monitoring, data analytics, and customized reports.
- Pilot programs: To test different contract models and gather customer feedback.
2. Build Digital Platforms for Data Sharing:
- Secure cloud-based platform: To store and manage equipment data.
- Real-time dashboards: For customers to monitor equipment health and receive alerts.
- Data analytics tools: To identify trends, optimize maintenance schedules, and improve decision-making.
- Collaboration tools: For communication and knowledge sharing between Borusan Cat and its customers.
3. Invest in Marketing and Communication:
- Target marketing campaigns: To educate customers about the benefits of predictive maintenance.
- Content marketing: Blog posts, case studies, and webinars showcasing successful implementations.
- Social media presence: To engage with customers and build brand awareness.
- Partnerships with industry influencers: To promote the value of AI-driven solutions.
4. Explore Strategic Partnerships:
- Collaboration with technology providers: To enhance AI capabilities and develop new solutions.
- Joint ventures with other distributors: To expand reach and market share.
- Partnerships with industry associations: To gain access to new customers and market insights.
5. Basis of Recommendations
These recommendations align with Borusan Cat's core competencies in equipment distribution and customer service. They address the needs of external customers by providing valuable solutions that improve operational efficiency and reduce costs. The recommendations also consider the competitive landscape by leveraging AI technology to differentiate Borusan Cat's offerings and attract new customers.
The attractiveness of these recommendations is supported by:
- Increased customer retention: By providing superior service and value.
- Enhanced brand reputation: As a leader in AI-powered maintenance solutions.
- New revenue streams: Through predictive maintenance contracts and data-driven services.
- Improved operational efficiency: By leveraging data insights and optimizing maintenance processes.
6. Conclusion
Borusan Cat has a unique opportunity to capitalize on its investment in AI-powered predictive maintenance technology. By implementing the recommended strategies, the company can transform its business model, create new revenue streams, and solidify its position as a leading provider of innovative solutions in the equipment industry.
7. Discussion
Alternatives:
- Focus solely on internal efficiency: Using AI to optimize internal processes without offering services to customers.
- Licensing the technology: Selling the AI technology to other distributors or service providers.
Risks:
- Customer resistance: To adopting new technologies and changing maintenance practices.
- Technological advancements: That may render the current AI technology obsolete.
- Competition: From other companies offering similar solutions.
Key Assumptions:
- Customer demand for predictive maintenance: Will continue to grow in the future.
- AI technology will continue to improve: Providing more accurate predictions and insights.
- Borusan Cat can effectively communicate the value of its AI-driven solutions: To its customers.
8. Next Steps
Timeline:
- Year 1: Develop and launch predictive maintenance contracts, build digital platforms, and initiate marketing campaigns.
- Year 2: Expand service offerings, explore strategic partnerships, and monitor customer feedback.
- Year 3: Refine strategies based on market trends and customer needs, and expand into new markets.
Key Milestones:
- Q1 2024: Pilot launch of predictive maintenance contracts with select customers.
- Q2 2024: Launch of digital platform for data sharing and insights.
- Q3 2024: Initiate marketing campaigns to promote AI-driven solutions.
- Q4 2024: Evaluate pilot programs and gather customer feedback.
- Q1 2025: Expand predictive maintenance contracts to wider customer base.
- Q2 2025: Explore strategic partnerships with technology providers and distributors.
- Q3 2025: Develop new AI-powered solutions based on customer needs.
- Q4 2025: Launch marketing campaigns targeting new market segments.
By taking these steps, Borusan Cat can successfully monetize its AI investment and position itself for long-term growth and success.
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Case Description
Borusan Cat is an international distributor of Caterpillar heavy machines. Esra Durgun (Director of Strategy, Digitization, and Innovation) and Ozgur Gunaydin (CEO) seem to have bet their careers on developing Muneccim, a new predictive technology that is designed to reduce downtime of heavy construction machines that Borusan Cat sells. While they have been able to manage to develop the technology to a level that beats any human expert in predicting machine failures, they still have not been able to find a way to monetize the technology. After spending a few years and millions of dollars developing the technology, they are both under pressure from Borusan Group, Borusan Cat's holding company, to show monetary results. Sales have been declining due to the economic downturn in Turkey, and the Company has been losing market share to their strong domestic competitors. Gunaydin and Durgun must decide about their monetization strategy for Muneccim and pick which segment of the market they want to target with this technology.
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