Assessment of competition landscape for predictive maintenance
Company Size
1,000+
Country
- United States
- Worldwide
Product
- SAP Predictive Maintenance
- IBM Predictive Maintenance
- Oracle Predictive Maintenance
- Microsoft Predictive Maintenance
- GE Predictive Maintenance
Tech Stack
- Predictive Analytics
- Data Integration
- Data Visualization
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Digital Expertise
- Productivity Improvements
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Functions
- Maintenance
Use Cases
- Predictive Maintenance
Services
- Data Science Services
- System Integration
About The Customer
The customer is a leading devices and services company operating in the Information Technology industry. Recognizing the future growth potential of value-added services in the machine to machine (M2M) market and the Internet of Things (IoT), the client sought to understand the competitive dynamics and the impact of predictive maintenance on their business. The client is a key player in the IT market and uses market information to stay up-to-date with market events and align its strategies as required.
The Challenge
The client, a leading devices and services company in the IT industry, recognized the potential of predictive maintenance in transforming their strategic and operational environment. However, due to the industry’s infancy, the client faced challenges in understanding the competitive dynamics and the impact of predictive maintenance on their business. They wished to analyze the market landscape of predictive maintenance offerings and gain insights into key vendors, company backgrounds, details of offerings, launch timelines, use case stories, industry focus, and list of customers.
The Solution
Blueocean Market Intelligence adopted a 360-Degree Discovery Approach to address the client's needs. They identified the top vendors offering predictive maintenance solutions, including top-tier vendors such as SAP, IBM, and niche vendors such as Blueyonder, Actuate, Mtell, Predikto, etc. An overview of all vendors was shared in a single slide, and separate slides were created for each company, containing information on the company background, product overview, and a use case scenario for the company’s predictive maintenance solution. A separate report was created that brought out a comparison among all the key vendors in this space within different categories, such as provision for independent or add-on solutions, department focus, integration capability, deployment options, and pricing.
Operational Impact
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