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Title

Service Innovation Structure Analysis for
Recognizing Opportunities and Difficulties of M2M
Businesses

Author(s)Uchihira, Naoshi; Ishimatsu, Hirokazu; Kageyama,
Yoshiki; Kakutani, Yuji; Mizushima, Kazunori;
Naruse, Hiroshi; Sakurai, Shigeaki; Yoneda,
SusumuCitation

2014 Portland International Conference on
Management of Engineering & Technology (PICMET),
2014: 777‑783

Issue Date

2014‑07

Type

Conference Paper

Text version

author

URL

http://hdl.handle.net/10119/12357

RightsThis is the author's version of the work.
Copyright © 2014 IEEE. 2014 Portland
International Conference on Management ofEngineering & Technology (PICMET), 2014, 777‑783.
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Description

Japan Advanced Institute of Science and Technology

Service Innovation Structure Analysis for Recognizing Opportunities andDifficulties of M2M Businesses
Naoshi Uchihira1, Hirokazu Ishimatsu2, Yoshiki Kageyama3, Yuji Kakutani4,
Kazunori Mizushima4, Hiroshi Naruse5, Shigeaki Sakurai6, Susumu Yoneda7
1Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan
2Japan University of Economics, Shibuya, Tokyo, Japan
3Toshiba Corporation, Kawasaki, Kanagawa, Japan
4Hitachi, Ltd., Yokohama, Kanagawa, Japan
5NEC Corporation, Minato, Tokyo, Japan
6Toshiba Solutions Corporation, Fuchu, Tokyo, Japan
7Softbank Mobile Corporation, Minato, Tokyo, Japan

Abstract:
With the popularization of high-speed and high-capacity communication networks,
Machine-to-Machine (M2M) communication has received significant attention. However,even though the related technologies have been actively investigated, creating new
businesses based on M2M communication is not easy. This paper proposes a service
innovation structure that visualizes the opportunities and difficulties of M2M
businesses. In our proposal, opportunities are classified as two types of value creation(optimization

value

and

identification

value)

usingtheSharing-Connecting-Analyzing-Identifying (SCAI) model. In addition, difficulties are
discussed using a fishbone chart. The SCAI model pays particular attention to theidentification value, which tends to be ignored in other models. Opportunities anddifficulties are structured as a map according to backcasting from a desired future.
Using this backcasting map, we can discuss M2M businesses more clearly andstrategically by recognizing the opportunities and the difficulties with stakeholders.

I. Introduction
Recently, Machine-to-Machine (M2M) communication has attracted increasing
attention from business and innovation perspectives as well as from scientific andtechnological communities. However, a number of large-scale and innovative M2Mbusinesses are not as one would expect. This paper presents a structure analysis of
M2M businesses by determining the opportunities and difficulties of M2M businesses.
We visualize opportunities and difficulties of M2M businesses based on a survey ofcurrent M2M businesses. Two types of value creation by M2M communication are
discussed: “optimization value” by big data analysis and “identification value” by big
data search. We also visualize difficulties using a fishbone chart. Specifically, we focus
on fragmentation of solution (“silo” solutions addressing specific vertical applications
requirements) as a typical difficulty. These opportunities and difficulties are structured
as a map by backcasting from a desired future. From this backcasting map(opportunity-difficulty map), we can visualize and discuss a strategic path to innovationin M2M businesses by identifying the opportunities and overcoming the difficulties.
We address the following research questions in this paper.
Major Research Question: What is the structure of innovative M2M businesses?
Subsidiary Research Question 1: What are the opportunities of M2M businesses?

Subsidiary Research Question 2: What are the difficulties of M2M businesses?

Subsidiary Research Question 3: What are the possible paths to innovation that canbe found by identifying opportunities and overcoming difficulties?



Subsidiary Research Question 4: What strategies can be derived from a map ofpotential paths to innovation?

The remainder of the paper is organized as follows. Section II briefly reviews theliterature on M2M businesses. M2M business opportunities and difficulties aredescribed in section III and IV. We propose backcasting analysis and discuss them usinga smart home example in section V and VI, and present our conclusions in section VII.

II. Literature Review
Not a few studies have discussed the functional potential of M2M technologies.
Lawton discussed the opportunities that M2M technology creates [1], and Wu discussed
key M2M application requirements and technology gaps [2]. Niyato, Xiao, and Wang
pointed out the challenges of M2M technologies, including standardization, trafficcharacterization, protocol re-design, spectrum management, and optimal network
design [3]. However, technological possibilities are not always implemented insuccessful businesses. Recently, some studies have tackled M2M business analysis andhave proposed M2M business models. Laya and Markendahl compared typical M2M
cases, including smart cities, smart houses, e-home care, and smart energy systems,
and analyzed the key factors of success and failure of M2M businesses [4,5]. Goncalvesand Dobbelaere showed 11 roles and the value chain among these roles. They extracted
three M2M business scenarios, i.e., application stream, mobile stream, and CE(Consumer Electronics) device stream scenarios [6]. Leminen et al. proposed aframework for “Internet of Things” businesses, including M2M businesses. They
analyzed several concrete cases from the automotive industry [7]. Although thoseanalyses revealed some aspects of M2M businesses, they did not go beyond an analysis
of currently implemented businesses. Thus, major gaps remain between current M2M
businesses and future M2M businesses.
This paper visualizes opportunities and difficulties of M2M businesses and analyzes
opportunities and difficulties using a backcasting approach according to an ideal future.

III. M2M Business Opportunities
First, we analyze M2M business opportunities according to a case survey conducted bya research project of the Joint Forum for Strategic Software Research1 (SSR-M2M
project). Table 1 shows the created values of these M2M services, which are derived
from our literature survey, interviews and discussion in the SSR-M2M project.
Table 1: Created values of M2M services
Service Example

Created Value

Construction Machine Proactive maintenance by failure prognostic (1A)

Tracking System

 Location monitoring as a protection against theft (1B)

(Komatsu, Hitachi)

 Operational guidance based on operating record (1C)

Home Security System

 Sensor-based incident reporting and rushing to the site

(SECOM, ALSOK)

within the allotted time period (2A)
 Insurance against loss or damage based on risk calculation(2B)

Home Energy

 Monitoring and visualizing energy consumption (3A)Management System Demand response optimization to save cost and energy(Hitachi, NEC, Toshiba)

effectively (3B)

By carefully assessing values in these cases, we find that the value created by “big data”
analysis, including statistics, data mining, and operations research, which we refer toas “optimization value,” is not the only important value even though M2M business is atypical target of “big data” analysis. Another important value is “identification value,”
from which specific objects and states are searched and detected from exhaustive data.
Joint Forum for Strategic Software Research (SSR) is a private funding agency
established and managed by electrical industries, including Hitachi, NEC, Tome, andToshiba, and supervised by academics.
1

Fig. 1 shows that M2M values consist of the connection value, the optimization value
and the identification value, which can be expressed as a 2-dimensional map (covering
ratio X volume). The connection value is created by M2M connections, which includes
one-to-one device monitoring. The simple connection can be expanded toward two
dimensions of “big data” mentioned above. These values can be classified into “closed”
and “open” according to the target big data. For example, “closed identification vale” iscreated by searching closed big data, which is constructed by the company itself. On theother hand, “open identification vale” is created by searching open big data, which isconstructed by sharing and connecting various types of data (original data, affiliateddata, government data, and open free data). In the case of the construction machine
tracking system, a location monitoring service started only for the company’s machines(closed identification vale), then customers requested to monitor other companies’
machines in the same system (open identification vale). The structure of the createdvalues and the mechanisms for creating values are illustrated in Fig. 2. To create theoptimization and identification values, fundamental functions, i.e., data analysis, data
identification, data sharing, and data connecting, are required (Table 2). We refer to thisstructure as a “Sharing-Connecting-Analyzing-Identifying (SCAI) model,” which is
unique when featuring data identification, sharing, connection, and analysis. Table 3
shows the relationships among the created values and functions for the examples shownin Table 1. As can be seen, a few values are strongly linked to identifying rather thananalyzing. This table (i.e., the SCAI model) can be used to recognize the values createdby M2M businesses.

Covering ratio

Identification value
by big data searching

Connection
value

Optimization
value bybig data
analyzing
VolumeFigure 1. Two dimension of values created by M2M services

Optimization
Value

Value
Creation
Connection Value

Identification
Value

Data
Analyzing

Value Calculation

Data
Identifying

Data
SharingResource
Extension

Data
Connecting

Original
Data

Affiliated
Data

Government
Data

Open
Data

Data Resource
Figure 2. Sharing-Connecting-Analyzing-Identifying model
Table 2: Fundamental functions of SCAI model
Data Sharing

Utilize not only original data but also open or affiliated data in orderto create values.

Data

Connect and link related data from various resources in order toConnecting

create values.

Data Analyzing

Create optimization value by statistics, data mining, and operational
research.

Data

Create identification value by searching and detecting specific

Identifying

objects and states.

Table3: Created values of M2M services (S: Strong, W: Weak, ---: No relation)
Created Value Example

Optimiza

Identifi(Ref. Table 1)

tion

cation1A: Proactive maintenance by
failure prognostics.
1B: Location monitoring as aprotection against theft.
1C: Operational guidance basedon operating record.
2A: Sensor-based incident
reporting and rushing to the site.
2B: Insurance against loss ordamage based on risk estimation.
3A: Monitoring and visualizing
energy consumption.
3B: Demand response
optimization.

S

W

---

S

W

---

W

S

S

W

W

W

S

S

Required functions

Failure prognostic model
Global positioning system forall machines
Rule-based remote diagnosis
Comprehensive security
guard network
Risk estimation model
Energy consumption patternanalysis
Optimization engine forsatisfying the given limit

Fig. 3 shows a typical value evolution process of M2M services from a SCAI model
viewpoint. In an early stage, optimization and identification values are developed for
closed big data. Then, the open optimization and identification values are developed foropen big data in a later stage.

Closed connection value

Closed optimization
value
Open optimization
value

Closed Identification
value

Open identification
value

Figure 3. Value evolution of M2M services

IV. Fishbone Chart Representing M2M Business Difficulties
Until

now,

successful

M2M

businesses

have

been

primarily

limitedtobusiness-to-business operational optimization (close optimization value) including
monitoring services of machines and equipment (e.g., construction machine) and havenot spread to other services. There are many difficulties and challenges to overcome.
Here, we extract typical and critical difficulties from several studies [1, 2, 8] and ourown interviews. These difficulties are summarized in Table 4. The difficulties can beclassified into 4 categories (pure technological difficulties, technology policy andmanagement issue, difficulties in application and use cases, and business model issues).
We visualize them as a fishbone chart (Fig. 4). These difficulties have complexlyintertwined cause and effect relationships; thus, it is difficult to determine a path toovercome them using current perspectives. For example, “fragmentation of solutions” isa major difficulty for breaking through the current business bottleneck; however, it has
intertwined causal relationships with “numerous incomplete standards,” “national
regulations,” and “too many stakeholders.” Therefore, we introduce a backcasting
approach according to a desired future.

Table 4: Typical and critical difficulties of M2M businesses
Category

Difficulty

Explanation

Pure

Network scalability

Ultrascalable connectivity is required.

technologicalCommunication protocol

There are great technological gaps between the

difficulties

for M2M

traditional internet application and M2M
communication. These gaps should be filled.

Accuracy and computing

In M2M, data is extremely “big” and has 3V

time of data analysis

characteristics (volume, variety, and velocity).
Accuracy and computing time of data analysis are atechnological problem.

Device management

Cloud-based mass device management is required.

Device authentication

Device authentication method for the interconnected

methodproviders is required.

Technology

Assurance ofInteroperability is very important and somepolicy andinteroperability

assurance mechanism is required.

management

National regulations

Regulations differ in each country.

issue

Security and privacy

Security and privacy must be controlled properly.

Carrier portability

When providing M2M services in a wide area,
portability among related carriers is necessary.

Numerous incomplete

There are several standards adopted by many

standards

organizations. They should be unified.

Difficulties in

Fragmentation of

The solutions have been developed and implemented

applications andsolutionsfor specific vertical application requirement.

use cases

Network operator andSome M2M service companies require long termcompany mismatch

successive operations. There is a gap between thesecompanies and network operators who want to shift tonovel and better technologies.

Data quality

There is a wide range of variations of devices, andquality control of data is not easy.

Business model

Charging policy andA new charging policy and method are necessary formethodmass devices.

Accountability andAccountability and liability for failures of

liabilityinterconnected systems

Various application

Application management which are provided by third

management

parties on the M2M infrastructure.

Costs of devices andInitial costs of devices and communications are aissuecommunications

bottleneck to introduce them and start services.

Too many stakeholders

Too many stakeholders make business model
complicated.

Darwinian sea

There is a big gap (Darwinian sea) between an initialsmall business and a final big business.

Technology policy andmanagement issue

National
regulations

Pure technological difficulties

Assurance ofinteroperability
Device management

Network scalability

Security andprivacy

Authentication
methodCarrier portability

Numerous incomplete
standards

Costs of devices andcommunications
Too many
stakeholders
Darwinian sea
Business model issue

Charging policy
and method
Data quality

Communication
protocol for M2M
Accuracy and computing
time of data analysis
Difficulties ofM2M
businesses
Fragmentationof solutions

Accountability and liability
Various application
management

Network operator andcompany mismatch

Difficulties in application and use case

Figure 4. Fishbone chart of M2M business difficulties

V. Backcasting Analysis from the Future M2M WorldIn the backcasting approach [9, 10], we must clarify the desired future. Then, we
consider opportunities and difficulties from the future M2M world. Fig. 5 presents these
considerations as a map (opportunity-difficulty map).
Future M2M world
Some M2M infrastructure, which exhaustively covers various M2M devices, has been
established. This infrastructure is open and various service providers can use it atnegligible cost.
(1) Difficulties of the future M2M world
By considering the future M2M world as a watershed, we can classify the difficulties
mentioned in Section IV into two categories (“pre-difficulties” and “post-difficulties”).

Pre-difficulties are defined as difficulties which should be solved before establishing afuture M2M world, and post-difficulties are defined as difficulties which will be solved
after establishing the future M2M world. This categorization of pre-difficulties andpost-difficulties can untangle the intertwined causal relationships. This categorization
is motivated by Internet history, wherein many difficulties, including communication
quality and security, were solved after the popularization of the Internet.
(2) Opportunities and the future M2M world
As mentioned in Section III, M2M businesses have two types of opportunities(optimization value and identification value). In the future M2M world, which willappear by transformation from vertical service structure to horizontal service structure,
the emphasis on opportunities will shift from the closed optimization and identification
values to the open values. In a vertical service structure, business prediction andoptimization based on closed and accurate big data are important. On the other hand,
many long-tail services (i.e., services created by the third parties like smart home
applications) appear in the horizontal service structure, in which the open identification
value becomes more important for these long-tail services. To encourage long-tailservices, we believe that value-oriented standardization is more important thantechnology-oriented standardization. From the backcasting viewpoint, closed values
and open values are regarded as “pre-opportunities” and “post-opportunities”,
respectively.
Pre‐opportunities

Pre‐difficulties
Communication
protocol for M2M

Accuracy and computing
time of data analysis
Device management

Authentication
methodNetworkscalability

Costs of devices andcommunications

Assurance ofinteroperability

Carrier
portability

Connection value
Closed Identification
value
Closed optimization
value

Ｔｉｍｅ

Future M2M world
National
regulations

Too many
stakeholders

Securityand privacy

Charging policy and method
Numerous incomplete standards
Network operator andcompany mismatch

Fragmentationof solutions

Accountability
and liability

Various application management

Post‐difficulties

Data
quality

Darwinian sea

Open optimization
value
Open identification
value
Open connection
value

Post‐opportunities

Figure 5. Opportunity-difficulty map from the future M2M world

VI. Smart Home Example and Discussion
This chapter explains the backcasting analysis in detail using the smart home example.
Especially, we try to show usage and effectiveness of the opportunity-difficulty map.
Smart home is an intelligent home supported by information and communication
technologies which make a home life more convenient, safer, and more efficient (Fig.6).
Table 5 shows possible value propositions (an opportunity list) provided by the smart
home. Here, 5 vertical start home services (1: home product remote maintenance, 2:home security, 3: home energy management, 4: home healthcare management, and 5:
food delivery) are integrated on the M2M infrastructure. According to SCAI model,
possible value propositions can be classified into “open/closed optimization value” and“open/closed identification value”. The food monitoring and delivery services (5A, 5B)
are classified into open connection value since these services are provided by a third
party using the open shared M2M infrastructure. Due to service integration, one stop
management and operational cost reduction are expected as additional values in Table 5.
Table 6 shows difficulties of the smart home M2M services according to Table 4 andFig.4. Each service proposition has own difficulties to achieve its goal. Fig. 6 is an
opportunity-difficulty map of M2M smart home services, in which related difficulties
are marked with bold lines and related value propositions are assigned into
pre-opportunities or post-opportunities.

2: Ｈｏｍｅsecurity

4: Ｈｏｍｅ healthcare
management

Ｍ２Ｍ Ｉｎｆｒａｓｔｒｕｃｔｕｒｅ
1: Ｈｏｍｅ productremote maintenance
Figure 6. Smart home example

3: Ｈｏｍｅenergymanagement

5: food delivery

Table5: Created values of the smart home M2M services(S: Strong, W: Weak, ---: No relation)
Created value proposition
1A: Proactive maintenance of home
products by failure prognostic.
1B: Notification and management
for home product recall.
2A: Sensor-based incident reporting
and rushing to the home.
2B: Home insurance against loss ordamage based on risk calculation.
3A: Monitoring and visualizing
home energy consumption.
3B: Demand response optimization
for home energy management.
4A: Healthcare monitoring andabnormality detection.
4B: Accurate diagnosis based onhealth monitoring data.
5A: Food monitoring andmanagement in a refrigerator.
5B: Meal deliveries based on foodmonitoring data for elderly people.
6A: One stop and easy management
for various home services.
6B: Operational cost reduction.

Optimi

Identifi

StagezationcationS

W

Closed optimization value---

S

Open identification value

W

S

Closed identification value

S

W

Closed optimization value

W

W

Closed connection value

S

S

W

W

Closed connection value

S

W

Closed optimization value---

W

Open connection value

W

W

Open connection value

---

---

---

---

Open optimization andidentification value

Additional values due toservice integration
Additional values due toservice integration

Table 6: Difficulties of the smart home M2M services(#1-#6: ID of start home services)
Category

Difficulty

Pure

Network scalability

technologicalCommunication protocol for M2M

difficulties

Accuracy and computing time of data analysis

#1

#2

#3

#4

#5

#6

X

X

X

Device management
Device authentication method
Technology

Assurance of interoperability

policy andNational regulations

management

Security and privacy

issue

Carrier portability

X

X

Fragmentation of solutions

inNetwork operator and company mismatch

applications

Data quality

and useCharging policy and method

cases

Accountability and liability
Various application management

Business

Costs of devices and communications

model issue

Too many stakeholders
Darwinian sea

X
X

X

Numerous incomplete standards
Difficulties

X

X
X

X

X

X

X
X

X
X

X

X

X

X
X

X

Pre‐opportunities

Pre‐difficulties
Communication
protocol for M2M

Accuracy and computing
time of data analysis

Closed connection
value (3A,4A)

Device management

Authentication
methodNetworkscalability

Closed Identification
Value(2A)

Costs of devices andcommunications

Assurance ofinteroperability

Carrier
portability

Closed optimization
Value(1A,2B,4B)

Ｔｉｍｅ

Future M2M world
National
regulations

Too many
stakeholders

Securityand privacy

Charging policy and method
Numerous incomplete standards
Network operator andcompany mismatch

Data
quality

Fragmentationof solutions

Accountability
and liability

Various application management

Open optimization
Value(3B)
Open identification
Value(1B,3B,6A,6B)
Open connection
value(5A,5B)

Darwinian sea

Post‐difficulties

Post‐opportunities

Figure 7. Opportunity-difficulty map (Smart Home Example)
It is possible to discuss a M2M service strategy using this opportunity-difficulty map.
Although there could be numerous discussions depending on the actual businesssituation and background, we show a simple strategic discussion example in order toexplain usage of the opportunity-difficulty map.
Example discussion of M2M smart home business strategy
Strategy before establishing future M2M worldHome security service (2A) will gradually spread due to closed identification
value, then may take healthcare monitoring service (4A) in its service menu.

Home energy management (3A, 3B) will be promoted by the government. In
fact smart maters based on the home energy management system standard“ECHONET Lite” [11] will be installed in over 50 million homes in Japan.
Home product maintenance service (1A) can use this M2M infrastructure. Here,difficulty about accuracy and computing time of data analysis should be solved.

Interoperability should be assured when home security and energy
management services share M2M infrastructure in some level.

Strategy after establishing future M2M worldRemaining difficulties will be solved after some troubles happen in the future
M2M world. Solutions will come from assessment and analysis of difficulties
and conflicts that occur in real businesses. For example, “numerous incompletestandards” as a post-difficulty will be solved by some appearing de facto
standards. A smart meter infrastructure (ECHONET Lite) is candidate for a defacto standard in Japan.
This smart meter infrastructure can provide an application platform forvarious services in the smart home. It is important to acknowledge that major
services (1B, 3B) and many tiny long-tail services (5A, 5B) can take advantageof this infrastructure.

VII. Conclusion
This paper has presented an innovative structure for M2M businesses. First, we
discussed opportunities and difficulties of M2M businesses, wherein the SCAI model
was introduced (our subsidiary research questions 1 and 2). We then discussed paths toservice innovation based on a backcasting approach using an opportunity-difficulty map.
Usage and effectiveness of the opportunity-difficulty map is shown in a smart home
example (our subsidiary research questions 3 and 4). The SCAI model seems
academically unique and practically useful for understanding the fundamental valuesof M2M businesses. However, this work is an introductory analysis, and considerable
future work is necessary to establish a substantial innovative structure for M2M
businesses (our major research question).

Acknowledgement
This research project was supported by the Joint Forum for Strategic Software
Research, Japan. We thank all participants for their suggestions and advice.

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