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Machine-to-Machine (M2M) Business Case: Solutions, Business Models, and ROI by Industry Vertical

Machine-to-Machine (M2M) Business Case: Solutions, Business Models, and ROI by Industry Vertical

The Machine-to-Machine (M2M) market is seen as the future for wireless revenue growth with various reports touting market size to reach 50 billion connections by 2020. However, the current market is still under 100 million connections.

Since M2M is not the only service for telecom carriers, rather it is not their core service; strategies adopted for M2M cannot be studied on stand-alone basis. The strategies have to be evaluated in context with other strategic initiatives taken by the companies. Hence, over-all strategies have also been discussed in this write-up.

This research provides analysis of the business case for M2M from the industry vertical or market segment perspective including specific cases within Healthcare, Vending, Petroleum (Oil & Gas and Propane), and Fleet Management. The report includes business model analysis, technical solution evaluation, and ROI assessment.

The expansion of M2M beyond its roots in utilities and manufacturing industries into many different enterprise operations has opened a host of new opportunities across the entire ecosystem. Furthermore, various enterprise companies in new industries are making plans to leverage M2M in processes that represent a major shift beyond previous implementations.

The information in this report is very valuable to industries/segments address as they assess the M2M business case as well as the M2M service providers as they seek to optimize their respective M2M offerings.

Target Audience:

  • Mobile network operators
  • Systems integration companies
  • Enterprise companies of all types
  • Investment and M&A companies of all types
  • M2M equipment and service providers of all types


General Methodology

Mind Commerce Publishing's research methodology encompasses input from a wide variety of sources.

We rely heavily upon our Subject Matter Experts (SME) in terms of their market knowledge, unique perspective, and vision. We utilize SME industry contacts as well as previous customers and participants in our market surveys and interactive interviews.

In addition, we rely upon our extensive internal database, which contains modeling, qualitative analysis, and quantitative data. We review secondary sources and compare to our primary sources to update previous findings (for prior version reports) and/or compile baseline information for technology and market modeling.

We share preliminary models with industry contacts (select previous clients, experts, and thought leaders) to verify the veracity of initial modeling. Prior to final report production (analysis, findings, and conclusions), we engage in an internal review with internal SMEs as well as cross-expertise, senior staff members to challenge results.

We believe that forecasts should be prepared as part of an integrated process which involves both quantitative as well as qualitative factors. We follow the following 3-step process for forecasting.

Forecasting Methodology

Step 1 - Forecasts Input: The inputs for the present and historical revenues are derived from industry players. Financial and other quantitative data for individual sub-market categories are derived from original research and tested with interviews with major industry constituents.

Step 2 - Forecasting of Future Years: Mind Commerce extends forecasts based on a variety of factors including demand drivers as well as supply side data. Key success factors and assumptions are considered.

Step 3 - Validation of Data: The final step is to validate projections, which is accomplished in consultation with both internal and external industry experts, including both topic and regional experts. Adjustments are made to the forecasts based on factors identified throughout this process.


1.0 EXECUTIVE SUMMARY
2.0 M2M MARKET OVERVIEW
1.1 THE BUSINESS OF M2M
1.2 OPERATORS BUSINESS MODELS FOR SELLING M2M
1.3 M2M STANDARDS
1.3.1 OPEN GEOSPATIAL COMMUNICATIONS
1.3.2 INTERNET ENGINEERING TASK FORCE (IETF)
1.3.3 THIRD GENERATION PARTNERSHIP (3GPP)
1.3.4 TIA
1.3.5 OASIS MQTT (MESSAGE QUEUING TELEMETRY TRANSPORT)
1.3.6 TR50
1.3.7 CONSTRAINED DEVICE APPLICATION LAYER PROTOCOL (COAP)
3.0 THE M2M ECOSYSTEM
2.1 MOBILE OPERATORS AND MOBILE VIRTUAL NETWORK OPERATORS
2.2 DISTRIBUTOR
2.3 DESIGN LABS
2.4 APPLICATION ENABLEMENT PLATFORM (AEP) VENDORS
2.4.1 2KEY BENEFITS OF AEP
2.5 DEVICE VENDORS
2.5.1 CHIPSET VENDOR
2.5.2 DECLINING CHIPSET COSTS
2.6 SYSTEM INTEGRATORS
2.7 DEVICE MANAGEMENT
2.7.1 DEVICE MANAGEMENT VENDORS
3.0 VERTICAL SEGMENTS OVERVIEW
3.1 HEALTHCARE
3.2 AUTOMOTIVE
3.3 RETAIL
3.3.1 PAYMENTS
3.3.2 VENDING
3.4 TRANSPORTATION
3.5 GOVERNMENT
3.6 ASSET MANAGEMENT
3.6.1 FLEET MANAGEMENT
3.6.2 ASSET TRACKING
4.0 BUSINESS CASE
4.1 PRODUCE USE CASE
4.1.1 SOLUTIONS OVERVIEW
4.1.2 BUSINESS MODEL
4.1.3 ARCHITECTURE
4.1.4 ROI
4.1.5 SUMMARY
4.2 HEALTH CARE USE CASE
4.2.1 SOLUTION OVERVIEW
4.2.2 ECOSYSTEM
4.2.3 BUSINESS MODEL
4.2.4 ROI
4.2.5 SUMMARY
4.3 VENDING USE CASE
4.3.1 SOLUTION OVERVIEW
4.3.2 ECOSYSTEM
4.3.3 BUSINESS MODEL
4.3.4 VENDING REVENUE MODEL (VENDING COMPANY)
4.3.5 ROI
4.3.6 SUMMARY
4.4 OIL AND GAS INDUSTRY
4.4.1 SOLUTION OVERVIEW
4.4.2 ECOSYSTEM
4.4.3 BUSINESS MODEL
4.4.4 ROI
4.4.5 SUMMARY
4.5 PROPANE MONITORING
4.5.1 SOLUTION ECOSYSTEM
4.5.2 BUSINESS MODEL
4.5.3 ARCHITECTURE
4.5.4 ROI
4.5.5 SUMMARY
5.0 FLEET MANAGEMENT
5.1.1 OVERVIEW
5.1.2 SOLUTION ECOSYSTEM
5.1.3 BUSINESS MODEL
5.1.4 ARCHITECTURE
5.1.5 ROI
5.1.6 SUMMARY
List of Graphs
Figure 1: M2M Architecture
Figure 2: Text Message Notification
Figure 3: Sample Revenue Distribution between Operator and partner
Figure 4: Photo of Pepsico's Social Vending Machine
Figure 5: Diagram of UHF Architecture
List of Tables
Table 1: Jasper Wireless: Mobile Operators using Jasper’s M2M Platform
Table 2: Oil and Gas Use Case Financial Model
Table 3: Propane ROI

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